The BioCore Lab 1st Edition

LB 144 (Sect 1-6)

Fall 2014

Drs. Cheruvelil and Luckie

Water lilies (Nymphaea odorata) on cover courtesy of Angela De Palma-Dow. Taken August 8, 2013 in Lake Desor, Isle Royal, MI.

Table of Contents Page

Preface……………………………………………………………………………………...v Doing Biological Research ..................................................................................................1 I. An Introduction to Studying Ecology…………………………………………………..2 II. The Scientific Methods: How to Conduct Biological Research ....................................3 The Hypothesis Score Card…………………………………………………………….9 Exercise 1 – Seminar Evaluation ...................................................................................13 Exercise 2 –Termite Trail Behavior ...............................................................................19 Exercise 3 – Observing Patterns in Nature ....................................................................23 Exercise 4 – Characterizing Communities .....................................................................29 III. Effective Teamwork and Leadership ...........................................................................31 Exercise 5 – Moon Landing ...........................................................................................33 Exercise 6 – What makes an Effective Team? ...............................................................39 Exercise 7 – Conflict Management ................................................................................41 Exercise 8 – Team Ground Rules Contract Form ..........................................................43 Exercise 9 – Teamwork Reflection ................................................................................45 IV. Inside Scientific Literature ...........................................................................................47 Exercise 10 – Navigating Electronic Resources ............................................................49 Guidelines for Reading Scientific Articles/Book Chapters ...........................................53 Exercise 11 – Dissection of a Scientific Article ............................................................55 Exercise 12 – Writing a Scientific Abstract ...................................................................61 V. Ecology Research Experiments and Field Studies……………………………………63

Exercise 13 – Formulating and Revising Questions & Hypotheses ..............................65 Exercise 14 – Conducting a Lit Review & Annotated Bibliography .............................69 Exercise 15 –Devising a Research Plan……………………….……………….………71 Exercise 16 – Writing a Research Proposal ...................................................................73 Exercise 17 – Making a Scientific Poster ......................................................................75 VI. Statistical Analysis of Data ...........................................................................................81 Exercise 18 – Statistical Analysis of Data .....................................................................95 Case Study for Exercise 10a-b .......................................................................................99 Exercise 19 – Choosing Among Statistical Tests #1 ....................................................101 Exercise 20 Practicing Chi-square Tests .......................................................................107 Exercise 21 – Choosing Among Statistical Tests #2 ....................................................109 Appendix A: Microscopy …………………………………………………………………111 Appendix B: MS Excel and Mac Computers………………………………………..…...117 Appendix A: Instructions to Authors………...…………………………………………..119 References………………………………………………………………………………..129 Smith 2007 a-b Teamwork Readings……………………………………………………131

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Preface

About BioCore: BioCore I (lecture and lab combined) is formally an introductory biology course, but in reality is a wondrous exploration of life at all levels. It examines the interplay of genes, cells, and chemistry allowing organisms to live, survive, and interact with each other and the environment, all within a scientific framework. In BioCore I labs, you are junior scientists in training. Therefore, labs are aimed at giving you hands-on experiences ‘doing biology research’ so that you gain the skills listed below in order to confidently ‘think and work like scientists’.

Why integrate the sciences (including cell/molecular and organismal biology)? There are two main reasons we should be integrative in this course. First, over the past 50 years, research in biology has become more quantitative and interdisciplinary, relying more heavily on other sciences. To understand large, rapidly changing ecosystems, or to make sense of massive amounts of data from the Human Genome Project, today's biologists must be able to use modern mathematical, statistical, computational, and technological tools. Second, biology instruction has not kept pace with research into how people learn. Studies on learning reveal that: students learn best if they are actively engaged working both individually and in groups constructing their own knowledge [this is also how scientists work]. The textbook Integrating Concepts in Biology and this BioCore Lab course pack take advantage of these insights and enable you to better achieve your full learning potential by directly involving you in your own learning.

You will do a variety of activities in this lab that will ask you to construct your own knowledge. For example, you will analyze and interpret published data, you will learn and practice how to read text and scientific figures, you will complete a case study that provides a context within which you can connect new statistical information, and you will design, implement, and present your own research. As you gain knowledge, you will find you can learn more and retain new information more easily as well as make connections between knowledge and skills.

Learning Goals. During 2011-2012, the LBC Biology faculty members (Drs. Cheruvelil, Fata-Hartley, Luckie, Murphy, Smith, Urquhart) developed a set of conceptual and skills learning goals for the LBC biology students. Although the content learning goals depend on the course, the skills learning goals are what we would like our students to learn by the end of the LBC144-145 biology sequence. Therefore, both classes are geared toward advancing these goals, such that by the end of the sequence, all students should excel at:

1. Science process skills, such as: observation, hypothesis formation and testing, inference, prediction, interpretation, and experimentation.

2. Effective and cooperative teamwork, for example, team building, communication and leadership. 3. Communication aimed at a variety of audiences important for scientists:

a) Speaking: practice speaking and listening to others in large and small groups. b) Reading: practice careful and critical reading of text, identification of important points

and ideas, as well as slow and deliberate reading and interpretation of figures and graphs. c) Writing: practice composition of text, writing hypotheses, building figures and graphs. d) Thinking: practice identifying data and using data in evidence-based arguments.

Acknowledgements. This book's content comes from a desire to create a laboratory experience that is "inquiry-based", where students learn how to "do biology" instead of just watching it. The original "Teams and Streams" concept was developed by Drs. Doug Luckie, Joe Maleszewski, and John Wilterding for LBS145 over a decade ago (Wilterding and Luckie 2002). The LB Biology Staff see this book as a "work in progress" because we are always revising it with the goal of developing the best possible laboratory experiences for LB 144 students. Many people have contributed to this book over the years. So many, in fact, that it has become impossible to list them all. We wish to thank the LBC administration, faculty (honors biology too!), staff, graduate students teaching assistants, and undergraduate learning assistants for their help and encouragement.

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DOING BIOLOGICAL RESEARCH

Objectives Generally upon completion of this semester's laboratory, you should excel at:

1. Science process skills, such as: observation, hypothesis formation and testing, inference, prediction, interpretation, and experimentation.

2. Effective and cooperative teamwork, for example, team building, communication and leadership.

3. Communication aimed at a variety of audiences important for scientists: a. Speaking: practice speaking and listening to others in large and small groups. b. Reading: practice careful and critical reading of text, identification of important

points and ideas, as well as slow and deliberate reading and interpretation of figures and graphs.

c. Writing: practice composition of text, writing hypotheses, building figures and graphs.

d. Thinking: practice identifying data and using data in evidence-based arguments. Specifically as a result of conducting an independent biological research project, you should be able to: 1. Generate explanatory hypotheses based on your own observations and prior knowledge. 2. Apply critical reading, thinking, and writing skills when evaluating and synthesizing

scientific literature. 3. Apply appropriate experimental or field sampling techniques to study biological phenomena,

answer scientific questions, and test hypotheses. 4. Determine what statistical tests are most appropriate for different biological datasets, perform

those statistical tests, and interpret them correctly, all within the context of the biological question(s) being asked.

5. Demonstrate the ability to communicate research findings to others orally and visually through a poster presentation setting.

6. Demonstrate that your team (collectively, and each individual) works effectively, including communicating, meeting mutual goals, and keeping all individuals accountable and working cooperatively.

Associated Reading (found at the end of this course pack) Smith, K.A. 2007a. Teamwork. Ch 2 in: Teamwork and Project Management. K.A. Smith and

P.K. Imbrie, eds., 3rd ed. McGraw Hill, Boston, MA. Smith, K.A. 2007b. Teamwork skills and problem solving. Ch 3 in: Teamwork and Project

Management. K.A. Smith and P.K. Imbrie, eds., 3rd ed. McGraw Hill, Boston, MA. Overview A major objective of the LB 144 lab is for students to be introduced to, practice, and become familiar with the process of investigative science. Throughout this course, students will work as members of a team (the most common way for science to advance!) to learn some of the ways biologists answer scientific questions. These approaches include reading and synthesizing the scientific literature, making observations, forming hypotheses and predictions, testing

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hypotheses, collecting and analyzing data, and presenting results. As part of this lab, you and your team will first learn how to conduct research, how to perform as part of an effective team, how to effectively use the scientific literature, and how (and why) to analyze data with statistics. Then, you will apply all of this knowledge to answer questions pertaining to organisms and how their lives have been (and are being) shaped by the environment in which they live (ecology). To do so, your team will write a research proposal (the first step in any scientific endeavor), develop hypotheses and predictions, design an experiment or field study to test these hypotheses, collect and analyze data, and present results visually and orally during a formal poster session. I. An Introduction to Studying Ecology Ecology is one of the sub-disciplines of biology. It is a very interdisciplinary field, pulling from the other biological sub-disciplines of physiology, genetics, behavior, and evolution. Ecology is the study of living organisms (individuals, populations, and/or communities) and how they interact with the environment and other organisms (individuals, populations, and/or communities). The aim of most ecologists is to understand how the distribution and abundance of organisms (individuals, populations, and/or communities) change through space and time (pattern) and why (process). Ecology can be divided into five main levels. Individual or Behavioral Ecology (Animal Behavior). Each organism interacts with the environment in which it lives. The pattern of these interactions determines the survival and “success” of the organism – its fitness. A question asked by an ecologist studying individuals is, “How does a tree seedling become established in this ecosystem?” Population Ecology. In ecological terms, a population is defined as a group of individuals of the same species living in the same area or region at the same time. These individuals share various life history characteristics, compete among each other for resources, and mate with each other. Population ecology is the study of how populations change through time and what environmental factors may be responsible for these changes. An example of a question that a population ecologist may ask is ‘At what life stage are green turtles most vulnerable to mortality?’ The answer to this question might help managers determine when and where to direct conservation efforts. Community Ecology. Populations are almost never isolated units. Populations of one species invariably interact with populations of other species. Community ecology is the study of how species interact with each other and how the environment shapes these interactions. A community ecologist may study predation, parasitism, herbivory, competition, mutualism, commensalism or succession. An application of community ecology is its use for aquatic environmental assessment, such as examining how the diversity, or ‘balance’ of any given river’s fauna changes along a gradient of human impact. Ecosystem Ecology. There are no completely closed systems on earth. Nutrients travel through the abiotic environment until they are picked up by plants or microbes and introduced into a food web. Energy obtained with the help of these nutrients then flows through the food web as animal biomass until it is again decomposed and reintroduced into the soil or water. Ecosystem ecology is concerned with how species interact with their physical environment with special

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emphasis on energy flow and nutrient cycling. Ecosystem ecologists may look at how terrestrial inputs of nutrients are incorporated into aquatic food webs and how different types of inputs may have community and population level effects on the flora and fauna. When an ecosystem ecologist focuses on individuals and the environment, they may be called physiological ecologists. Landscape Ecology. This ecological sub-discipline is a relatively new field of study that asks questions about spatial pattern or spatial structure. Spatial pattern or structure refers to the spatial heterogeneity we see across the landscape. Landscape ecologists study the distribution patterns of communities and ecosystems, the ecological processes that affect those patterns, and changes in pattern and process over time. These scientists are often working at large spatial scales or extents and often use maps and GIS to understand the interactions of organisms and their environment at these large spatial scales. Ecology and environmental science are distinct, but tightly related fields. While ecologists study interactions between organisms and their environment, environmental scientists specifically study the interactions between humans and the environment. Analyzing environmental issues such as depletion of natural resources, loss of species, or global climate change, and planning for better practices begins with ecological understanding. II. The Scientific Methods: How to Conduct Biological Research In science, the process of constructing knowledge relies, in large part, on the scientific methods. The scientific method we will practice in LB144 consists of several steps, beginning with a description of a phenomenon followed by posing a question or questions about that phenomenon. A scientist then formulates hypotheses that could explain the observations, and then devises fair (unbiased) tests of these hypotheses that attempt to answer the question. After carrying out the fair tests, or experiments, the scientist analyzes his/her data and decides whether or not the results support or do not support a particular hypothesis. Observing Patterns and Asking Questions in Biology The first step in tackling a scientific problem is asking questions about the world around us. We have been doing this for as long as we can remember: observing patterns around us and asking questions about them. It may seem obvious, but without questions that need answering, there can be no experiment. What is a Hypothesis? A research or explanatory hypothesis is a possible answer to a question from which specific predictions can be made and tested. There can be multiple hypotheses used to answer a single question and for each hypothesis, multiple predictions can be made. Scientists often talk in terms of the null hypothesis (H0) and alternative hypotheses (H1, H2, H3, etc…). Null and alternative hypothesis are different from a research or explanatory hypothesis in their form and function. Specifically, the null and alternative hypotheses connect the measured data to the testable predictions that were made from the research hypothesis. The null hypothesis is most often a hypothesis of no difference, but it can also be described as the hypothesis of what is expected. Most of the time in nature, you would expect to see no difference, but occasionally you will expect a difference.

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An example of a research hypothesis for an experiment could be: If some types of trees are more tolerant to acidic soil conditions than others, then we will observe different types of trees growing in areas with lower pH than higher pH soil. An example of a null hypothesis for this research hypothesis might be:

H0: There is no difference between the pH of the soil in a deciduous forest and the pH of the soil in a coniferous forest.

Two alternative hypotheses might be: H1: The pH of the soil in a deciduous forest is higher than the pH of the soil in a coniferous

forest; and H2: The pH of the soil in a deciduous forest is lower than the pH of the soil in a coniferous

forest. “Proof” in Science It is important to remember that the scientific method never proves anything; science progresses by disproving hypotheses. When an experiment fails to disprove a hypothesis, it simply means that we may infer that our experiment supports the hypothesis. If you fail to disprove or reject your null hypothesis, your experiment is over. In other words, if we can disprove the null hypothesis, we are closer to inferring that our experiment supports one of our alternative hypotheses. Literature Review We may not be the first scientist to ask a particular question about an observed pattern, so before we go to the trouble to design an experiment, we must first do a literature review. A literature review consists of using key words to search for previous research conducted on the topic of interest to us. Besides finding out whether or not what you are interested in has already been investigated, a literature review can help with ideas for the design of your own experiment. When conducting a literature review, we are mainly interested in the primary literature, which describes scientific peer-reviewed journal articles that report the findings of specific experiments or descriptive studies (NOT review articles or books that summarize what is and is not known about a particular topic or general texts that summarize what has been reported in review articles). More information about scientific literature is in Section IV and Appendix C. Formulating, Evaluating, and Testing Hypotheses: Techniques of Experimental Design Once we have determined our unique angle on the biological topic of interest by completing a literature review, we can begin to formulate, evaluate, and test our hypotheses. First, some definitions:

Population. A population is a group of similar kinds of things (e.g., organisms of the same species) occupying the same area at the same time.

Community. In ecology, a community is defined as an assemblage or group of organisms (of more than one species) occupying a specific geographical place during a particular time.

Parameters = variables = metrics. These are all words that describe aspects of a population or community that we can identify, quantify or estimate.

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Discrete or Categorical Data – each item is a separate, whole unit. These can either be nominal, as in the sorting of a population into named groups (e.g., male or female; juvenile or adult), or they can be ordinal (ranking), as when named categories are organized in terms of some relationship they have to each other (e.g., classifying lake nutrient levels as low, moderate, or high).

Continuous Data – items are along a scale that, at least theoretically, could be subdivided (e.g., time, weight, or temperature).

Treatments. A treatment may be a category for a sampling method, such as in an experiment in which plants near the river represent treatment 1, and plants farther away from the river represent treatment 2, or it may be the application of an experimental condition, such as when one set of poinsettias (Euphorbia pulcherrima) is grown under a 12 hour photoperiod to represent treatment 1, and another set of poinsettias is grown under a 16 hour photoperiod to represent treatment 2. Most experiments have multiple treatments.

Controls. A control is what we compare our treatments to (Is there a difference between each/any treatment and the control?). It allows us to rule out variables other than the ones we are testing. For example, to measure the effect of nitrogen fertilizer on duckweed (Lemna minor) growth, in one beaker we would add nitrogen fertilizer to our growing duckweed, while in another beaker we would grow the duckweed without fertilizer but with all other conditions the same as the treatment with fertilizer. Then, we can compare the growth between the treatment and the control (the beaker without fertilizer).

Randomization. To minimize inherent human bias in selecting experimental units (or members of a population we wish to sample), we randomize our treatments or measurement of experimental units (defined as: lacking any definite plan or order or purpose; governed by or depending on chance). Randomization reduces the potential impact of investigator bias during the sampling procedure.

Replication. Replication reduces the impact of variability among experimental units, random measurement error, and random, uncontrollable influences. In a controlled experiment, this means that we would want to have at least three replications of each treatment (and of the control). For example, in the above experiment including duckweed treatments, we would have three beakers with fertilizer and three beakers without fertilizer. In an observational experiment, replication usually involves taking multiple samples distributed throughout the observational area (e.g., three samples of plants near the river and three samples of plants further away from the river).

Sample Size (n). Larger sample sizes reduce the effects of natural variation within a population. The magic number, as far as statisticians are concerned, is n = 30, but this may not always be practical.

Sampling. We can account for all the members of a population or community by performing a census, but usually it is much simpler to sample within a population. Sampling means that we use a subset of a population or community to make inferences

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about the entire population or community. We wish to describe patterns, associations, and interactions among organisms in a population or community in nature in a measured way but without intervention. Therefore, a sample is not the entire population in the defined universe, but represents some subset of the entire population in that universe. In order to make sure samples are representative of the entire population or community, replicate samples are usually taken in some sort of randomized or systematic way. Traditionally, random sampling plans were preferred over systematic sampling plans because random sampling helped to avoid subjective selection of sample locations. However, systematic sampling has no elements of subjectivity if sample location is selected prior to examining the study area. For example, there are no subjective decisions if in a study of herbivory by Colorado potato beetles (Leptinotarsa decemlineata), we sample every tenth potato plant and count Colorado potato beetles. Below, we describe the two general kinds of sampling.

Qualitative Sampling. Qualitative sampling tells you what is there, but gives you no information regarding population density. For example, if you're trying to determine what kinds of birds are present in a forest, you might record birdcalls and make visual observations while you walk through that forest. If you hear songbirds and raptors (birds of prey), you would conclude that both types of birds are present in the forest. In aquatic systems, qualitative sampling may be done with a d-frame net (timing sampling effort makes these samples semi-quantitative). The data obtained from such sampling may be used in inventory studies (e.g., what species reside in a given environment) or when quantitative sampling is too difficult (e.g., some riverine habitats are difficult to sample quantitatively).

Quantitative Sampling. Quantitative sampling not only tells you what is there, but also gives information regarding population density (# per meter2). In both aquatic and terrestrial environments, there are samplers available that sample a given area or volume. Some Quantitative Sampling Methods: If the organisms you are sampling are stationary, you can sample the population by using quadrats or plotless distance methods to obtain the density (number of individuals) or % cover (space being occupied in a given area) of all individuals of a particular species. These methods are useful for quantifying a specific species (bracken fern, Pteridium aquilinum) or type of species (herbs) that may be found in irregular clumped patterns in the areas of a forest understory. Two basic techniques are used to gain density data in this situation (Barbour et al. 1999): (1) Quadrats. The number and species of individuals are recorded within randomly

located plots within a particular area, which can be determined randomly or systematically. The plot shape may be rectangular to take in more of the variable landscape or circular to minimize edge effect, while the plot size is determined by the type of individuals measured; if trees are the subject, then the plot size must be larger than for counting ants. The number of quadrats required can be determined by selecting the number where the number of individuals plotted against the number of quadrat starts to dampen. The standard deviation and mean number of individuals per

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quadrat can be calculated and converted to a larger unit area basis (e.g., number per hectare (10000m2)).

(2) Distance Methods. A number of variants from the nearest individual method have

been developed to estimate the density of a population (e.g., point-centered quarter, nearest neighbor, random pairs), but Engeman et al. (1994) showed that a slightly modified nearest individual method using up to three individuals is most accurate, efficient, and computationally straightforward. The base nearest individual method uses the distance from randomly selected points to the nearest individual to estimate population density. For example, points could be randomly selected from transects or coordinates laid over a map of the area, then the distance from each point to the nearest bracken fern is measured, given the problem above. Take only one distance measurement per point. After many points have been used, calculate density per hectare using the average distance in the following calculation:

Density = 5000 / (average distance in meters)2

However, if you want to characterize a population or community structure, below are some additional methods. For populations: Characterizing a population (1 species) of interest includes calculating metrics such as density, frequency, and the area covered by that species. These can be calculated as follows: (1) Density = (Total number of individuals) (Total area sampled) (2) Frequency = (Total number of plots in which species occurs) (Total number of

groups) (3) Coverage = (Total area covered by species) (Total area sampled) The table below shows how to characterize population structure based on a system developed by Daubenmire.

Table A2. Coverage by individual species is often grouped using Daubenmire’s cover class system (Barbour et al. 1999)

Cover class

Range of cover (%)

Median coverage (%)

1 0-5 2.5 2 5-25 15.0 3 25-50 37.5 4 50-75 62.5 5 75-95 85.0 6 95-100 97.5

Note: The total coverage of your plot may exceed 100% due to vertical stratification of canopies.

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For communities (Adapted from Baxter, CSUS Spring 2014 Lab Manual): Characterizing a community (more than 1 species) of interest includes characterizing biodiversity, which often includes calculating metrics such as species richness and various measures of diversity. Confusion often arises when discussing biodiversity of a given area. Biodiversity is defined as the number, or abundance, of different species living within a particular area. Scientists sometimes refer to the biodiversity of an ecosystem, a natural area made up of a community of plants, animals, and other living things in a particular physical and chemical environment. Three concepts that are associated with the concept of biodiversity are Species Richness, Simpson’s diversity index and Shannon Weaver Diversity. Each of these metrics is described below. (1) Species Richness – Species Richness is simply defined as the number of species in a

given area. This concept does not take into account rarity of species, however. If scientists sampled a natural area and found 2 individuals of species A, 8 individuals of species B, and 3 individuals of species C, the Species Richness of the area would be 3 regardless of the number of each species was found.

(2) Simpson’s diversity index – Simpson’s diversity index (D) is based on the probability that two individuals chosen randomly from the same community belong to the same species. The index is calculated as follows:

where pi is the proportion of individuals of the i

th species to the total number of individuals in the community: ni/N (ni = the number of individuals of species i; N = the total number of individuals of all species) and s is the total number of species in the community. Simpson’s index is increased by having additional unique species (increasing species richness) and/or by having greater species evenness; it ranges from 1 to s.

(3) Shannon Weaver Diversity Index (H’) – This is an ecological model that uses the proportion of each species within a given area and yields a number indicating the diversity of that area. To calculate the index, use the proportion of each species encountered, the total number of individuals encountered, and the species richness of the area. If an area is dominated by one or two species, H’ will be a small number, but the more evenly spread species proportions are, the higher H’ will be. The larger the H, the more diverse that area is. The formula for the Shannon Weaver Diversity Index is as follows:

S

i

ii

N

n

N

nH

1

ln*'

Where: ni – Number of individuals in species i N – Total number of individuals of all species S – Number of species (Species Richness) pi – Relative abundance of each species

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Now that we’ve got those definitions under our belts, here is some more information to help you formulate, evaluate, and test hypotheses. When forming and evaluating hypotheses, it is often easy to come up with a statement that you think would explain your phenomena of interest, however, often that statement is not actually an explanatory hypothesis because:

1) It is an inconsistent/nonsense statement that is not grounded in science (uses words such as "likes" "prefers"), testable, or meaningful.

2) It is missing something. For example, it offers a mechanism, but doesn't include variables.

3) It asks a question. "What happens if we..? Why does..?" 4) It describes an experiment (a way to test an explanation). 5) It is a restatement of the question asked or background information provided, as either

a question or a statement (restatement of question does not = an explanation). 6) It is not specific enough to be testable, because the mechanism is too broad or vague to

test. 7) The statement attempts to answer the question, but misaddresses the variable(s). 8) It is a testable, but only a partial explanation of the phenomenon that either addresses

the question asked but is incomplete in addressing all variables or addresses the phenomenon, variables, and their outcome, but is not specific in relating the cause and effect to a variable.

9) It is a testable explanation regarding the question asked, it includes all variables (addressed and described) BUT an explanation of how/why is absent.

An explanatory hypothesis is a statement that is a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation (The American Heritage Dictionary). A hypothesis is a possible answer to a question, from which predictions can be made and tested. There can be multiple hypotheses used to answer a single question and for each hypothesis, multiple predictions can usually be made. The foundation for high quality, biological research is a good hypothesis. A good hypothesis is more than just an educated guess. When developing hypotheses, you should apply the following hypothesis score card to be sure that they are of high quality. THE HYPOTHESIS SCORE CARD [by Dr. Cori Fata-Hartley, MSU College of Natural Sciences] A good hypothesis must:

1.) explain how or why: provide a mechanism 2.) be compatible with and based upon the existing body of evidence. 3.) link an effect to a variable. 4.) state the expected effect. 5.) be testable. 6.) have at least two outcomes. 7.) have the potential to be refuted.

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Hypotheses can be scored based on each of these seven elements. For example, when considering a hypothesis, you might give one point for each of the elements. An accomplished hypothesis would have a score of 7. An incomplete or developing hypothesis would have a score of 5-6. A score below 5 would represent an attempted hypothesis or not a hypothesis. You should use this scoring procedure when developing your own hypotheses or when evaluating hypotheses of others.

Analyzing Data Part of designing an experiment is choosing how you will summarize and analyze the data once it is collected. Summarizing data (often called ‘descriptive statistics’, for example calculating means and standard deviations) is the first step to discovering patterns in a data set, followed by conducting statistical tests to determine whether we have a statistically significant reason to reject our null hypothesis. Before choosing a statistical test, we need to determine whether we will be looking at an association between variables or at differences between variable distributions, means, or variances. In this class, we will be using different statistical tests at different times. See section VI for more info. We will use MS Excel that is installed on the lab Macs, along with websites that have useful online calculators for this course. If you would like a refresher on how to use MS Excel or are used to a PC, see Appendix B for some tips.

Writing a Proposal Most researchers require funding before they may conduct research to answer the questions in which they are interested, so the next step after designing an experiment is often to write a research proposal. The proposal is written in future tense (since the research has yet to be conducted) and usually begins with an Introduction or Statement of Purpose that includes background information obtained from a literature review, justification for why the research should be conducted, and the specific hypotheses to be tested (along with the associated predictions). The introduction is usually followed by a Methods section to describe in detail how the experiment will be carried out and a Project Timeline indicating when all the elements of the experiment will be completed and by whom. As a part of a research team in LB 144, you will be writing a research proposal for ecology research that you will conduct. Getting the Word Out: Presenting the Results of an Experiment Scientists have an important role in society to inform (not dictate) decisions. We must discover how systems work, document changes occurring to systems (e.g., climate change) and ways we can use these systems (e.g., stem cells), understand the consequences of those changes and uses, and develop and evaluate options for dealing with those changes and uses. For all of this to occur, science has to be relevant, usable, credible, and understandable.

To meet all of these goals, scientists must not stop short at completing experiments, analyzing data, and drawing conclusions. We have to get the word out by reporting our results to various audiences using a combination of approaches. Scientists communicate with many types of audiences, including other scientists (peers), policy-makers, ecosystem managers, advocacy groups (e.g., conservation groups or cancer societies), the general adult public, and children. There are also a number of ways scientists present their findings, often depending on the audience we are trying to reach. To communicate with other scientists, we often publish articles in scientific journals or give oral or poster presentations at meetings in our field of research. We often give seminars or talks that can be adapted to any audience, as you will experience when

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you attend an on-campus seminar this semester, and we can publish information on the web or in the popular press. Please see Appendix C for guidelines for scientific writing.

For science to inform decisions, scientists must communicate effectively with a diversity of audiences. It is always important to understand your audience before presenting and to get feedback on your presentation from a representative of that audience before your presentation. For example, if your audience is the general public and you are publishing your results on the www, ask a non-science friend to view your www pages and describe them to you. If they cannot fully describe your pages, then you will not reach your audience. To practice your oral and written communication skills, you will share the results of your experiments visually with a poster and orally during a poster session.

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Exercise 1 Seminar Evaluation

Purpose: To experience one way scientists communicate what they are doing to one another. All across campus various departments and organizations host seminar series and special seminars on a wide range of topics. Your assignment is to attend one pre-approved science-related seminar of your choice. Take notes at the seminar and then write a 1.5–2 page (double-spaced) summary and analysis of its content. There will be a list of appropriate seminars posted on the course website. If you find another seminar that you wish to attend that is not on the list, you MUST approve it with YOUR lab instructor before using it for your seminar evaluation. Directions: Formatting Specifications: must be 1.5-2 pages, typed using Times New Roman 12 pt font, double-spaced, with 1 inch margins all around. Required Elements:

1. Your name and section number. 2. The name of the speaker or presentor. 3. The title of the seminar. 4. The name of the seminar series or department hosting the speaker. 5. The date on which the seminar occurred. 6. 1.5 – 2 pages (double-spaced) in which you:

Briefly summarize the seminar Answer the following questions:

i. What was the speaker’s “take-home message” or most important point? ii. What did you like about the seminar? Why?

iii. What did you dislike about the seminar? Why? iv. What is one new thing that you learned? v. What was the most intersting question asked of the speaker? Why?

7. Turn in your hand-written notes with the typed summary NOTE: You will be representing LBC when you attend this seminar, and we expect you to act accordingly (arrive on time, turn off your cell phone, refrain from speaking or sleeping during the seminar, stay until the end of the seminar = after the question and answer period).

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II. The Scientific Methods: How to Conduct Biological Research (Continued) Applying the Scientific Method: A Study Examining Birch Beetle (Agrilus anxions) Behavior. Below, the scientific method is broken down into steps that are illustrated with an example from a study that looked at the behavior of the birch beetle. [Adapted from Platt (1964)] 1. Observation and description of a phenomenon leading to question(s)

I seem to find adult birch beetles on birch leaves, but not on other leaves. How come? Do adult birch beetles eat birch leaves, but not other leaves? Or, maybe adult birch beetles are only found on birch leaves because that is where they lay their eggs?

2. Explanatory hypothesis formation (explanation of the phenomenon)

If birch beetles utilize birch leaves for eating or laying eggs, then I should find birch leaves in the gut of birch beetles and birch beetle eggs on birch leaves.

3. Good hypotheses lead to testable predictions. Predictions are short statements that

describe what specifically will be observed and/or measured. Predictions that result from the hypothesis above could be:

P1: If adult birch beetles primarily consume birch leaves, then I should find birch

leaves are the major food found in birch beetle guts. P2: If adult birch beetles lay eggs on birch leaves, then I should be able to find birch

beetles laying eggs on birch leaves, and/or birch beetle eggs on birch leaves. 4. Tests of hypothesis and predictions using experimental design techniques:

In this experiment, I will carry out behavioral observations to determine if birch beetles eat birch leaves and/or if they lay eggs on birch leaves. Adult birch beetles will be collected in the field and dissected. Birch beetle gut contents will be characterized to determine if birch leaves or any other leaves have been eaten. Birch leaves, as well as the soil directly under the trees, will be examined in the field for the presence of birch beetle eggs.

5. Forming null and alternative hypotheses.

For the first part of the observation and research hypothesis (Prediction 1), an example null and alternative hypothesis might be:

Ho: When analyzing beetle gut contents, there is no difference in the amount of birch leaves and other leaf types found; or When analyzing beetle gut contents, the amounts of each leaf types found are the same.

H1: When analyzing beetle gut contents, there is a larger amount of birch leaves as compared to other leaf types.

H2: When analyzing beetle gut contents, there is a smaller amount of birch leaves as compared to other leaf types.

Based on the information provided above, now see if you can come up with the Null and Alternative Hypotheses for the Second prediction of this observation and research hypothesis.

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What is the second prediction in this example? Ho: H1: H2:

6. Data visualization. After conducting your experiment, you need to summarize your

data using Figures and Tables. What might your predicted results look like in graphical form? What are your independent and dependent measures? How would you display your data and summarize them in order to assess your hypotheses? Draw some example figures (or tables) below using pretend data and remember to label all axes and column headers. Note that in science, we use statistics in order to assess whether differences that are visually apparent are statistically significant (meaning that they are not likely due to chance alone). We will learn more about statistics in the coming weeks, including how to choose the correct statistical test for hypotheses like these.

7. Conclusions: Refer to Figure 1-1 through 1-3 on the next page. Which (if

any) of your hypotheses and predictions are supported by your data?

Do you reject or fail to reject your Null hypothesis? How does this support your research/ explanatory hypothesis?

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Mea

n #

Egg

s pe

r S

ampl

e (S

E)

Per

cent

Sto

mac

h C

onte

nts

(SE

) P

erce

nt B

irch

Bee

tles

Obs

erve

d (S

E)

100

80

60

40

20

0

Feeding Ovipositing

Behavior

Figure 1-1. Mean percentage of birch beetles feeding and laying eggs on birch leaves (+/- SE)

120

100

80

60

40

20

0

Birch Non-Birch Other

Food Type

Figure 1-2. Mean percent of birch leaves, non- birch leaves, and other food found in birch beetles.

50

40

30

20

10

0

Birch Leaves Soil

Location

Figure 1-3. Mean # eggs per sample from birch leaves and soil (+/- SE)

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Checking your answers: We see in Figure 1-1 that 80% of the birch beetles were observed feeding on birch leaves, while only about 1-3% were observed ovipositing (laying eggs) on them. This confirms our observation that birch beetles eat birch leaves. Figure 1-2 shows us that birch leaves make up almost 100% of birch beetles ‟gut contents”. This also strongly supports prediction 1 that birch leaves are the primary gut contents of birch beetles. Both of these results support the first part of our hypothesis that birch leaves are the primary food source for birch beetles. As for the second part of our hypothesis, Figure 1-1 does not support prediction 2. Indeed, hardly any birch beetles were observed ovipositing on birch leaves. If we look at Figure 1-3, we see that a much larger number of eggs were actually found in the soil under the birch trees than on the leaves themselves, which is contrary to prediction 2. Therefore, we would reject our second null hypothesis (which should be something along the lines that there is no difference in the amount of birch beetle eggs found on birch leaves and other places). How does this tie back into our original research question? It appears that our birch beetles feed almost exclusively on birch leaves, but lay their eggs in the soil. These results are fairly straightforward. Means differ by a large margin, and error is relatively low. However, as discussed earlier, an accepted statistical analysis must be used to make sure these results are not due to chance. This requires a certain level of replication (multiple beetles are observed, on multiple leaves, and multiple soil samples are taken) and careful randomized measurements (to minimize human error). The results of one study often raise questions for another, especially in the fields of ecology and animal behavior. What questions do these results raise for you? For example, how would the time of year in which sampling occurs affect these results? Do birch beetles lay their eggs exclusively under birch trees? What might influence how or when birch beetles travel from their foraging location (the leaves) to their ovipositing sites in the soil (on the ground)?

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Exercise 2

Termite Trail Behavior Adapted from S. Deal & J. Pierson

http://www.woodrow.org/teachers/biology/institutes/1996/piedea.html Purpose: To use the scientific method to investigate termite trail following behavior. You are using the eastern subterranean termite, Reticulitermes flavipes, found commonly in the southern United States.

Directions: 1. Read the directions and assign roles.

You are assigned the following roles: 1. Person with the birthday latest in the year is task manager and should make sure that

each step is done. The task manager should read this handout aloud to the rest of the team.

2. Person with the birthday earliest in the year is timekeeper and must keep the team on schedule.

3. Person with second earliest birthday is reporter who will keep notes and report out to the class.

4. If you have a team of four, the remaining person is moderator and should make sure that everyone gets a chance to speak in turn. If you have fewer than 4 people in your team, it is each person’s responsibility to be sure that all team members are contributing.

2. On a clean sheet of paper, draw lines using a variety of pens and pencils (e.g., solid and

dashed lines, shapes, letters). Place a termite carefully onto the paper using a paintbrush.

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3. Record your observations on the termite behavior below. Do the termites seem to be attracted to some lines more than others? Which?

4. Based on your observations, formulate a research hypothesis (i.e., a testable statement) about

how the termite will behave with respect to the various lines. If your hypothesis was supported, what prediction(s) would you make regarding termite response to various lines?

Hypothesis: Prediction(s): 5. Evaluate your hypothesis using the score card on page 5. Revise your hypothesis in order to

achieve an explanatory hypothesis. Based on these revisions, also revise your prediction(s). Hypothesis: Prediction(s):

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6. How would you further test this hypothesis? Describe an experiment you would design: 7. After returning the termite to its container, discuss the observations your group recorded and

the experiment that you designed to test your hypothesis. Propose an explanation for the termite behavior (i.e., what might be the mechanism behind its behavior?).

Explanation: By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 3a

Observing Patterns in Nature - Part 1 Purpose: To 1) begin to see how observations we make about the natural world can be turned into questions that may be investigated experimentally, 2) to gain experience with field ecology, 3) to practice formulating hypotheses, and 4) to practice manipulating and visualizing data in Excel. Directions: Part 1 (individual, to be completed prior to lab) – Take a stroll in Sanford Woodlot. Use this sheet to record observations you make as you wander through the Woodlot. Think about how you might characterize some of the biotic (living) and abiotic (non-living) components you may find: What types of trees predominate? How could we characterize understory diversity? What types of aquatic organisms reside in the Red Cedar River? In the wetlands? What types of birds and mammals are present? If you are unfamiliar with Sanford woodlot, you can use Google maps in satellite view (https://www.google.com/maps) or download google earth (https://www.google.com/earth/) and get a bird’s eye view of this urban forest. This might help you to answer questions such as: Where is it warmest and coolest? Dry and moist? Dark and light? What areas are more disturbed over others? You are encouraged to write freely about what you see. In particular, be sure to notice where you see herbs, shrubs, and trees, and the environmental characteristics that seem to be associated with each of these three types of primary producer.

Leave No/Little Trace in LB 144: Because of the large number of LB 144 students, it is extremely important that we minimize our impact to campus natural areas. When possible, stay on/near trails, and when sampling, try to take non-destructive samples. In other words, record and tabulate biological sample information in the field rather than taking samples back to the lab. Always minimize your impact so the area is in good shape for your fellow LB 144 students as well as the entire MSU community.

a. For the next 20 minutes as you wander through the Sanford Natural Area, use the area

below to record your observations and ask questions about the patterns you see.

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Exercise 3b

Observing Patterns in Nature - Part 2 The Research Teams in LB144 will usually consist of 4 individuals. You are expected to help out in all tasks but you will have one specialty (your primary job or role in the team). Below, we describe the roles. If your team consists of 3 individuals, eliminate the “Primary Investigator” and divide those responsibilities amongst the team members. Before embarking on your first assignment as a team in LB144, choose who will take on each role below and record names where indicated. * Primary Investigator (PI) - Plan! NAME ________________________ The primary investigator will be responsible for organizing meeting times, overall project planning, as well as implementing troubleshooting techniques throughout the investigations. In addition to sharing the final grade for each group assignment, the PI will be assigned and graded for writing specific sections of the proposal and poster as well as editorial duties on all sections of both. * Protocol Expert (PE) - Protocols! NAME ________________________ This individual is responsible for overseeing the creation of scientific protocols for each week’s investigations (written experimental descriptions and steps you plan to do). In addition to sharing the grade for each group assignment, the PE will be assigned and graded for writing sections of the proposal and poster as well as editorial duties on all sections of both. * Data Recorder/Documentarian (DRD) -Notebooks! NAME ___________ The data recorded is responsible for recording and organizing the results and taking many pictures to document the team’s efforts. In addition to sharing the grade for each group assignment, the DRD will be assigned and graded for writing sections of the proposal and poster as well as editorial duties on all sections of both. * Laboratory Technician (LT) -Hardware! NAME ___________________ This individual is responsible for learning the many experimental procedures and becoming an expert on how to use the various pieces of equipment needed. In addition to sharing the grade for each group assignment, the LT will be assigned and graded for writing sections of the proposal and poster as well as editorial duties on all sections of both.

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Now that you have your team roles, we will continue thinking about the organisms in Sanford Woodlot. In order to decide on a sampling approach for any project, we must understand the organism of interest and the question we would like to answer. In this case, the organisms of interest are herbs, shrubs, and trees, and our question is “How are herbs, shrubs, and trees distributed in Sanford Woodlot?”. For example, we might imagine that one of the three groups are located in different places (not all found together in equal amounts) because of many reasons (water sources, disturbance, light, etc…). Because herbs, shrubs, and trees are relatively small and immobile, we can use a sampling approach such as the quadrat or the transect method. Because our question asks about the Sanford Woodlot, we need to sample throughout the Woodlot in order to characterize the natural variability in herbs, shrubs, and trees present in the Woodlot.

Set up: We will be collecting samples today in the Sanford Woodlot using the transect method. Each team will sample herbs, shrubs, and trees along 1 of 3 transects (A-C in the map below) that run North-South in the Sanford Woodlot. The LB144 teaching team has already set up these transects. To decide on the location of those transects, we used a random number generator to choose 3 starting points within the Woodlot behind Holmes Hall (indicated by the 3 lines in the map below). From these starting points, a 100 ft tape measure was laid out in a Southward direction toward Holmes Hall. We then used a random number generator in Excel to choose 10 random sample points along each transect (indicated by the crosses in the map below). The way that this works is if the number 55 was generated by Excel, then a sample point was marked with an orange flag at the 55 ft spot along the transect (see map below, transects and sample locations not to scale).

Transect & Sample Locations 

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Based on your individual observations in the Woodlot, formulate a team hypothesis (i.e., a testable statement) about how one of the three groups (herbs, shrubs, or trees) are distributed in Sanford Woodlot. If your hypothesis is supported by data that you will collect, what prediction(s) would you make? Hypothesis: Prediction(s): Evaluate your hypothesis using the score card. Revise your hypothesis in order to achieve an explanatory hypothesis. Based on these revisions, also revise your prediction(s). Hypothesis: Prediction(s): Field preparation: Based on your hypothesis and predictions (above), what data do you think your team should collect at each sample point? On a separate piece of paper, create a data table to characterize the setting and record your observations. At a minimum, be sure to include a-h below. However, depending on your hypotheses and predictions (above), you may also want to record environmental variables such as soil or air temperature, light, moisture, etc…

a. Collectors’ names b. Date/time c. Weather conditions d. Transect (A-C) e. Sample site number (1-10) f. Number of herbs found at the sample site (if none, record that too!) g. Number of shrubs found at the sample site (if none, record that too!) h. Number of trees found at the sample site (if none, record that too!)

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Field Exercise: 1. Each team will sample herbs, shrubs, and trees from 5 locations marked along 1 of 3

transects. 2. Before sampling, each team should get a 1 ft2 quadrat from their instructor. 3. Locate your sample transect (A, B, or C) and your 5 sample points (1-5 or 6-10). 4. At each sample point, put your 1 ft2 quadrat on the ground and record how many herbs,

shrubs, and trees there are within the quadrat, as well as the environmental variables that you decide to measure.

5. At each sample location, record the data in your data sheet. 6. Bring completed data sheet and all lab supplies back to the lab.

Back in the lab:

1. Enter your data into the spreadsheet located at the front of the classroom. 2. Discuss and then formulate written answers to the following questions (a-d) about this

sampling approach with your team. Use succinct answers; we are not looking for essay answers.

a. Why did we (as a lab) take samples at pre-determined random locations along each transect as opposed to choosing locations once we got out to the transects?

b. Why did we (as a lab) take samples from 3 transects as opposed to 1 transect? c. Why did we take 6 sets of samples (as a class – one set taken per lab section) from 3

transects as opposed to 1 set? d. Using your data, what evidence do you have that your team’s hypothesis regarding

how herbs, shrubs, or trees are distributed in Sanford Woodlot was supported or not?

Completed before leaving lab (as a team): Data entered into spreadsheet, completed datasheet(s), and your answers to questions a-d turned in. By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 4 Characterizing Communities

Purpose: To a) practice using data, b) understand how diversity is measured, c) gain exposure to some metrics commonly used in ecological research that can help you think about your own LB144 lab research. Directions: You need to describe and compare the diversity present for two insect communities living in the Red Cedar River and Grand River in Ingham county, MI. To measure diversity, you will use the following quantitative metrics: species richness, Simpson's Index, and Shannon Weaver Diversity Index. If these metrics don't sound familiar to you, take a look backwards in the course pack for where they are described. Table 1. Aquatic macroinvertebrate sample counts from Red Cedar River and Grand River in Ingham County, MI, USA. Samples are total number of individuals sampled over the 14 year period from randomly selected sites. Data collected from Michigan Clean Water Corps Stream Citizen monitoring program http://www.micorps.net/ August 2014.

Red Cedar River, Ingham Co. 2000-2014

Scientific Name (Order)

Common name # of

individuals# sites

sampled Frequency

Diptera flies (crane flies,

midge flies) 62 25 Coleoptera beetles 20 25

Odonata dragon and damsel

flies 22 25 Tricoptera caddisflies 24 25

Ephemeroptera mayflies 14 25 Plecoptera stoneflies 7 25

Species Richness Grand River, Ingham Co. 2000-2014

Scientific Name (Order)

Common name # of

individuals# sites

sampled Frequency

Diptera flies (crane flies,

midge flies) 10 5 Coleoptera beetles 4 5

Odonata dragon and damsel

flies 7 5 Tricoptera caddisflies 5 5

Ephemeroptera mayflies 4 5 Plecoptera stoneflies 1 5

Species Richness

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Using the data in Table 1, answer the following questions. 1. What is your research question? 2. Calculate the frequency and species richness for each River and fill these in on Table 1. 3. How is frequency different than richness? Why is frequency an important metric to measure

(in addition to richness)? 3. Using the online diversity calculator located at the Maryland Sea Grant Biodiversity

Calculator (http://ww2.mdsg.umd.edu/interactive_lessons/biofilm/diverse.htm) calculate Simpson and Shannon diversity indices for both sites and complete table 2.

Table 2. Diversity summary table for Red Cedar and Grand River sampling sites 2000-2014.

Site Simpson D Shannon H

Red Cedar River Grand River

4. On the Maryland Sea Grant website, above the biodiversity calculator, there are some

examples of community types (i.e. where all species are the same, one dominates or there is only one species recorded). Compare your results to some of those example communities. Using these examples as a guide, how would describe the diversity in the Red Cedar and Grand rivers?

5. Which river contains a more diverse aquatic insect community? What specific evidence do

you use to justify your answer?

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III. Effective Teamwork and Leadership [Inspiration:Dr. Karl Smith, Purdue University] We define a team as “a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable” (Smith 2007). One of the goals for this course is for you to learn to work effectively as part of a team. This goal exists because scientific innovation and discovery happen by teams. For example, in Figure 1-4 below, you can see that for the two most premiere scientific journals (Science and Nature), very few research articles are published with a single or two authors. In fact, an overwhelming majority of science published in these journals is conducted by large, collaborative teams. A somewhat silly, but demonstrative, example of this fact is shown by the following article citation: Merchant, S.S., S.E. Prochnik, O. Vallon, E.H. Harris, S.J. Karpowicz, G.B. Witman, A. Terry,

A. Salamov, L.K. Fritz-Laylin, L. Maréchal-Drouard, W.F. Marshall, L. Hu Qu, D.R. Nelson, A.A. Sanderfoot, M.H. Spalding, V.V. Kapitonov, Q. Ren, P. Ferris, E. Lindquist, H. Shapiro, S.M. Lucas, J. Grimwood, J. Schmutz, P. Cardol, H. Cerutti, G. Chanfreau, C. Chen, V. Cognat, M.T. Croft, R. Dent, S. Dutcher, E. Fernández, H. Fukuzawa, D. González-Ballester, D. González-Halphen, A. Hallmann, M. Hanikenne, M. Hippler, W. Inwood, K. Jabbari, M. Kalanon, R. Kuras, P.A. Lefebvre, S.D. Lemaire, A.V. Lobanov, M. Lohr, A. Manuell, I. Meier, L. Mets, M. Mittag, T. Mittelmeier, J.V. Moroney, J. Moseley, C. Napoli, A.M. Nedelcu, K. Niyogi, S.V. Novoselov, I.T. Paulsen, G. Pazour, S. Purton, J. Ral, D.M. Riaño-Pachón, W. Riekhof, L. Rymarquis, M. Schroda, D. Stern, J. Umen, R. Willows, N. Wilson, S.L. Zimmer, J. Allmer, J. Balk, K. Bisova, C. Chen, M. Elias, K. Gendler, C. Hauser, M.R. Lamb, H. Ledford, J.C. Long, J. Minagawa, M.D. Page, J. Pan, W. Pootakham, S. Roje, A. Rose, E. Stahlberg, A.M. Terauchi, P. Yang, S. Ball, C. Bowler, C.L. Dieckmann, V.N. Gladyshev, P. Green, R. Jorgensen, S. Mayfield, B. Mueller-Roeber, S. Rajamani, R.T. Sayre, P. Brokstein, I. Dubchak, D. Goodstein, L. Hornick, Y.W. Huang, J. Jhaveri, Y. Luo, D. Martínez, W.C.A. Ngau, B. Otillar, A. Poliakov, A. Porter, L. Szajkowski, G. Werner, K. Zhou, I.V. Grigoriev, D.S. Rokhsar, and A.R. Grossman. 2007. The Chlamydomonas Genome Reveals the Evolution of Key Animal and Plant Functions. Science 318: 245-250.

Figure 1-4. The value of teamwork for premiere scientific publishing. Number of research articles in Science and Nature with 1, 2, and multiple authors for the time period of August-December 2007.

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Exercise 5

Moon Landing Adapted from activities on:

http://www.nasa.gov/audience/foreducators/topnav/materials/listbysubject/Moon_landingpage.html

Purpose: To demonstrate that group collaboration usually leads to a better outcome than individual work, practice resolving differences and influencing others to maximize teamwork and group processes, and practice being a team player.

You are a member of a space crew scheduled to rendezvous with a mother ship on the lighted surface of the moon. However, due to mechanical difficulties, your own ship was forced to land at a spot 320 km (200 miles) from the rendezvous point. During re-entry and landing, much of the equipment aboard was damaged and, since survival depends on reaching the mother ship, the most critical items available must be chosen for the 320 km trip. 15 items are listed as being intact and undamaged after landing. Your task is to rank them in order of their importance for allowing your crew to reach the rendezvous point.

Directions: 1) Using the ‘STEP 1’ column of the table on the next page, and with no consultation with your

teammates (individually), place the number 1 by the most important item, the number 2 by the second most important, and so on through to number 15 for the least important item to help the crew reach the rendezvous site. You should make the following assumptions: the number in the crew is the same as the number on your team, you are the actual people in the situation, the team has agreed to stick together, and all 15 items are in good condition. Do not discuss the situation or the task until each of your team members has finished the individual ranking.

2) Once all individuals are completed with Step 1, come together as a team and assign the following roles: a) Person with the birthday latest in the year is task manager and should make sure that

each step is done. b) Person with the birthday earliest in the year is timekeeper and must keep the team on

schedule. c) Person with second earliest birthday is reporter who will keep notes and report out to the

class on the process and outcomes of the activity. d) If you have a team of four, the remaining person is moderator and should make sure that

everyone gets a chance to speak in turn and that consensus is reached. If you have fewer than four people in your team, it is each person’s responsibility to be sure that all team members are contributing.

3) Step 2 is to rank order the 15 items as a team (place numbers in the ‘STEP 2’ column of the table). Once discussion begins, don’t change your individual ranking. Avoid taking votes and work toward a consensus.

4) Once your team has completed Step 2, skip down to Steps 7-8 to individually complete the team process check and come up with ways to answer the debrief questions, then discuss your ideas for Steps 7-8 with your teammates.

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STEP 1

Individual Ranking

STEP 2 Team Ranking

STEP 3 Planning Experts’ Ranking

STEP 4 Difference between Steps 1 and 3

STEP 5 Difference between Steps 2 and 3

Box of matches Food concentrate 20 meters of nylon rope Parachute silk Portable heating unit Two .45 caliber pistols One case of dehydrated milk Two 50 kg tanks of oxygen Stellar map (of the moon’s constellations)

Life raft Magnetic compass 25 liters of water

Signal flares First aid kit with hypodermic needle

Solar-powered FM receiver/ transmitter

TOTAL Your score

Team Score

STEP 6 Total the absolute difference of Steps 4 and 5 (the lower the score the better): STEP 7 Team Process Check: Individually circle the number that you feel best describes how your team worked together.

Disagree Agree All team members participated 1 2 3 4 5 The members with roles did their jobs 1 2 3 4 5 The group stayed focused on task 1 2 3 4 5 The group decisions were consensus 1 2 3 4 5 Overall, the team functioned well while performing this task

1 2 3 4 5

Notes:

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STEP 8 Team and Class Debrief: Each team will be asked to report back what they found out about problem solving, decision making, and teamwork and group dynamics during this exercise. To prepare for this class discussion, how would your team answer the following questions: a) How did your team come to decisions? Did this work well for everyone?

b) How well did your team share ideas and listen to each other? Was there an issue with anyone

dominating the discussion? Were all ideas heard? c) What made it difficult or easy to collaborate?

d) On a scale of 1 (dissatisfied) to 10 (very satisfied) how do you feel about the processes used

to come to consensus? Are you satisfied with the outcome (the team ranking)? e) What could be improved for future team activities?

By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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III. Effective Teamwork and Leadership (continued) Although we know that teamwork is important for science, many of us have experienced frustrating situations when working in teams. For example, maybe you can think of a time when you were part of a team that got the job done (wrote the report, finished a project, completed a lab assignment) but that ended up with members hating each other so intensely that they never wanted to see each other again. Or, maybe you’ve been part of a team whose members really enjoyed one another’s company and had a great time socially, but in the end hadn’t finished the project (or did poorly on it). These situations are frustrating for all involved (teachers and students). Therefore, we aim to provide you with tools and training to help your LB 144 teams work effectively, such as communication skills, setting realistic, achievable and mutually acceptable goals, and keeping all individuals accountable and integrated into the team. At this point, please read the two chapters from Smith (2007) that can be found at the end of this Doing Biology Stream. Although these chapters are from a book about engineering teams, most of the material is highly relevant to LB 144 teams. In fact, these chapters will help you learn about the different types of teams that exist (we are aiming for Cooperative Learning Groups by the end of the semester), characteristics of successful teams, how to increase team performance, stages of team development, team task roles and behaviors, how to establish team norms, teamwork and leadership skills such as communication and decision-making skills, and how to manage team conflict. Read these chapters thoughtfully and engage in the material before you attend lab. Take notes on the chapters and reflect on what you learn. For example, here are some questions (certainly not all!) that you should be able to answer after having completed this reading:

a) What are some examples of how you listen in all three ways listed on page 40 of the reading?

b) What are some of the behaviors in Tables 3.1-3.2 (p. 36) that you’re particular practiced at or haven’t really tried before?

c) What sorts of teamwork skills (forming, functioning, formulating, and fermenting; pp. 39-40) are you most interested to learn and practice (and how do you plan to do so?)?

d) What approaches to decision-making are you most comfortable with and why (pp. 45-48)?

e) What are your feelings about conflict and how do you tend to deal with conflicts that arise (pp. 48-52)?

f) What are some of the most common challenges and problems you’ve had when working in teams? Make a list. After completing this reading, how would you start to resolve these problems and challenges?

We will be forming our semester-long base groups this week. Therefore, you will make a lasting first impression on your teammates and begin forging a relationship with them in lab this week. Coming to lab unprepared to work effectively with your team (by not completing your reading) is not likely to set a good precedent or be greeted with pleasure by your teammates.

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39

Exercise 6

What Makes an Effective Team? Adapted from K. Smith (2007)

Purpose: To determine characteristics of an effective team and get to know your teammates. Directions: Individually, and with no discussion with your teammates, think about a really effective team you’ve been a member of, a team that accomplished extraordinary things. Start by thinking about teams in an academic, professional, or work setting. If no examples come to mind, then think about social or community-based teams. If still unsuccessful, think about sports teams. Still no luck? Imagine yourself as a member of a really effective team. Picture the team in your mind and try to identify the specific characteristics of that team that made it so effective. Make a list of characteristics that would make your team effective, and be specific (Example: rather than listing “good listening”, list what it takes to be a good listener). Now, recall a team that you were on that you considered to be highly ineffective. What characteristics do you believe made that team ineffective? Make a list of the attributes that you think describes a really ineffective team, and be specific. When told by your instructor, discuss these scenarios and lists with your teammates. Compile a master list of the characteristics that make a team effective. Your team will report back to the class about this list. Scenario 1: Effective team Scenario 2: Ineffective team Characteristics of scenario 1’s team: Characteristics of scenario 2’s team:

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Master list of characteristics of an effective team from all teams in lab: By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 7

Conflict Management

Adapted from Smith (2007) Purpose: To assess how you and your teammates typically act in conflict situations and help you develop a set of skills and procedures for guiding conflict along a more constructive path. Directions: Individually, take a few minutes to complete Ex 2, the “How I Act in Conflict” questionnaire, on page 54 of Smith (2007). Try to use professional conflicts and not personal conflicts as your point of reference. Next, complete Ex 3 by reading the Ralph Springer case study on page 55 of Smith (2007) and completing the ranking form at the end. As a team, share and discuss the results of Ex 2 with your teammates. Discuss each of the possible ways to resolve the conflict. Then, compare your individual responses from Ex 2 to your rankings in Ex 3. Note that each of the alternatives listed in Ex 3 represents ones of the strategies listed on the scoring form in Ex 2. Match the alternatives to the strategies that they represent. Discuss similarities and differences in the order in which each team member would have used the strategies and the relative effectiveness of each.

By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 8

Team Ground Rules Contract Form Adapted from forms developed by Dr. Deborah Allen, University

of Delaware and Dr. Rique Campa, Michigan State University Purpose: To establish team norms in order to promote more constructive and productive teamwork. Directions: Project groups are an effective aid to learning, but to work best they require that all team members clearly understand their responsibilities to one another. These team ground rules describe the general responsibilities of every member to the team. You can adopt additional ground rules if your group believes they are needed. Your signature on this contract form signifies your commitment to adhere to these rules and expectations. Some questions to discuss when thinking about these rules: 1) What are your professional goals (i.e., what would you like to do following graduation)? How

will working in a team help you achieve some of your professional goals? 2) Besides class time, when are you available to work with your team members (exchange your

class and work schedules)? 3) What is the best method(s) and time for your team members to contact you? Share the

necessary phone number(s), e-mail addresses, etc... NOTE: This contact information is private, so should not be shared with others outside of your team, and should only be used for class-related communication.

All group members agree to:

1. Come to class and team meetings on time. 2. Come to class and team meetings with assignments and other necessary preparations

correctly and thoughtfully completed. Additional ground rules (add as many as you like; see examples on pp 36-38 of Smith (2007)):

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If a member of the team repeatedly fails to meet these ground rules, other members of the group are expected to take the actions below. When filling in the “If not resolved” sections, think about how, when, and who will communicate dissatisfaction to offending team members. Reflect back on what you’ve learned thus far about your teammates in terms of what previous team experiences they have had, how you have worked together as a group thus far, and how each member tends to feel and deal with conflict. Step 1: If not resolved, what will your team do? How? When?: Step 2: Meet as a team with your lab instructor. If not resolved, what will your team do? How? When?: Step 3: The quit or fire clause: If the steps above have been completed without resolving the problem, any team member may quit the team. Alternatively, if all other team members are in agreement, the offending team member may be fired from the team. In either case, the individual no longer working as part of a team is required to complete the remaining class activities and assignments individually. The LB 144 teaching team reserves the right to make final decisions to resolve difficulties that arise within a team. Before this becomes necessary, the team should try to find a fair and equitable solution to the problem. Group Name:______________ Member’s Names (printed), Signatures, and date: 1.____________________________ 2.____________________________ 3.____________________________ 4.____________________________

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Exercise 9 Teamwork Reflection

Developing good teamwork skills is essential for any professional scientist, regardless of career choice or discipline. These skills take time to develop and also require exposure to a number of different people and different working styles. Not only is it useful for you to evaluate your team’s overall performance, it is also useful to reflect on your own performance relative to the group and how you can modify your own behavior to help your team become more effective. You recently completed a CATME survey that allows you to rate your team’s performance as a whole as well as provide feedback to the other members of your team. Your instructor has released this feedback to you (link provided via email). Now it is time to reflect on the feedback provided by your teammates. Scientists perform these types of personal performance assessments all the time, and you may find this exercise helpful for your professional school applications or curriculum vitae. The most important aspect of this assessment is for you to identify an area that you need improve on, and then document how you plan to accomplish this improvement. To do so, read and reflect on the feedback provided by the CATME survey and then answer the following questions (use one page total to answer the four questions below). Individual HW Questions: 1. How does your working style compare/contrast to that of your teammates (similarities and

differences)? 2. After reading and reflecting on your teammates' evaluation of your performance, in what

ways were you surprised by their assessments of you? 3. For any feedback that was negative, how can you improve? 4. Regardless of feedback, what do you think that YOU can do to make your team work more

efficiently and productively for the rest of the semester? Team assignment: Discuss all of the things that you and your teammates came up with for the questions above. Choose 3 specific things that you will do to improve your team effectiveness during the rest of the semester. Share this list with your instructor and get approval of your team's plan for improvement. You will revisit this plan later in the semester to evaluate your team's progress. 1. 2. 3. Instructor Signature _________________________

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IV. Inside Scientific Literature In science, writing is the most important means of communicating research findings. Major scientific findings are never kept secret. Instead, scientists share their ideas and results with other scientists, encouraging critical review and alternate interpretations from colleagues and the entire scientific community. In most cases, scientists report the results of their research activities in scientific journals in a standard written format that is not APA or MLA, but is science- and science journal-specific (see Appendix C for more info on format). In this course, you will practice finding, reading, understanding, and synthesizing the peer-reviewed primary scientific research. Types of Literature The vast collection of scientific literature can be generally divided into three categories based on how ‘close’ they are to the original experiments and descriptions of scientific phenomena.

1) Primary literature: The bulk of scientific journal articles are primary, meaning that they report the findings of specific experiments or descriptive studies. These papers are peer-reviewed.

2) Secondary literature: From time-to-time investigators write review articles or books that summarize what is and is not known about a particular topic. Rather than conducting new experiments, these authors rely heavily on the primary literature, therefore these review articles and books are considered a part of the secondary literature. These papers are peer-reviewed.

3) Tertiary literature: More general texts that summarize what has been reported in review articles comprise the tertiary literature (e.g., your textbook). These texts are often not peer-reviewed.

Most new research relies heavily on previous work reported in primary literature. However, review articles can be extremely helpful in understanding how your research project fits into the larger scope of scientific investigation, and can be used as a source to locate primary literature references for the topic of interest. Note that websites were not included in the above description of scientific literature sources. This is because they are not refereed — that is, just about anyone can publish something on the web without some impartial reader reviewing it beforehand. Web pages are often wonderful sources of information, but they can just as often be replete with bad information. At this point, it is very difficult to determine the reliability of web sources and, in general, they should generally only be used as a starting point about a particular topic.

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Exercise 10 Navigating the Web of Science and MSU Electronic Journals

Purpose: To become familiar with using online reference databases (Web of Science) and with the electronic resources available through the MSU Library, a skill that will be used to search for background information for research projects throughout the semester. Science builds on science, and this happens through the peer-reviewed scientific literature. Before setting out on a research project, it is important to gather support for your hypotheses and predictions based on currently accepted scientific theory. Also as a scientist, you want to make sure you are not simply repeating someone else’s study (unless of course you are interested in falsifying others’ claims). This exercise will help you become familiar with the Web of Science citation database and with the electronic resources available through the MSU Library. Newer articles, published within the last few years, are frequently available as PDFs for downloading and printing – allowing you to skip the process of actually going to the library (although, going to the library is a good thing), finding the paper journal and then photocopying the article you need. MSU subscribes to many online databases for use by students. The Web of Science is a general-subject database in which many journals are indexed and cited and through which you can use one article to find many others about your topic of interest. Directions: A. Getting to know the ISI Web of Science Database

1. Go to the Electronic Resources page of the MSU Libraries (http://er.lib.msu.edu/). Under the Commonly Used E-Resources header, click on the “Web of Science” shortcut. If you are off campus, you will be prompted to login with your NetID and Password. This will bring you to the Web of Science Search page. You are ready to go!

2. You can search for articles by Topic, Author, or the Title of the article. Using a title search, type in a keyword that would be in a title of interest to you and hit ‘Search’. In the list of results, you can use the “Full Text” button to see whether an electronic version (.pdf) of the journal and article of interest is available through MSU.

Complete the following questions that test your ability to navigate the ISI Web of Science.

3. A researcher whom you are assisting is interested in studying more about Japanese macaques and their mating habits. a. If you were to do a topic search, what key words might you use to search for

scientific journal articles on this subject? List at least three.

b. Using the Web of Science database to do a topic search, how many articles came up when you used your first key word?

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c. Write out the full citation for one of the articles that you found by searching using your first key word (refer to Appendix C in The Lab Book for proper formatting!):

4. Now start a new search. Narrow the Web of Science search parameters by choosing to search within journals published in 2003. You can do so by going to 'Timespan,' clicking on second option, and entering into the From and to boxes '2003' and '2003'. Once you've done that, search for articles that contain the word ‘axolotl’ in the title, keywords, or abstract. How many articles did you find?

5. You may also use Web of Science to follow one article to many others about your topic

of interest. You may explore all of the articles that your paper of interest cited AND you may explore all of the articles that have cited your paper of interest (and so on). To see how this works, conduct a "Basic Search". Click on “Add Another Field,” make sure to select 'Title' for both drop down menus, and then enter the key words biodiversity and tropics into the Title line. Be sure that your Timespan is unlimited (i.e., don't limit it to only 2003 like you did for the last question).

a. How many articles are found?

b. Sort by times cited and find an article that has been cited at least 5 times by other articles. Write out the full citation for the article.

c. Follow the link (click on the article title) and then click on the number next to ‘Times Cited’ under Citation Network to see the articles that have cited your original article (in b). How many of those articles have also been cited? Hint: You will need to organize the results (sort by times cited) in order to answer this question.

d. Choose any of these articles and click on the title to see the abstract. Write out the full citation for the article.

e. How many articles were cited in this paper’s bibliography? Click on the '# Cited Reference’ link under Citation Network menu for a link to this article and report how many of these citations were relatively recent (post 1990). If the references section for this article is very long (more than 2 pages of results, you may count only the first 2 pages of post-1990 references for this question.

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f. Choose any of the Cited References and click on the title. Notice that you could keep tracing back articles of interest through those that were referenced by and those that referenced your original article of interest. This is sometimes referred to as the Branching Tree Method of literature searching.

B. Getting to know electronic journals

Go back to the electronic resources page of the MSU library webpage. Under Find Electronic Journals, enter the journal title Nature into the search box (other journals available online through the MSU library will work similarly), change the dropdown box to ‘Title equals’, and then click on the ‘Search’ button. Click on ‘Nature Journals Online’ – this brings you to the Archive page, from which you can browse any issue. Click on a few issues to explore the online version of the journal Nature. Answer the following questions that test your ability to navigate the online version of the journal Nature.

1. What is the topic discussed in the feature article on the cover of the current issue of Nature (most recent issue listed in archives)?

2. What is the date and volume of this article?

3. Using the archives, what is the title of the first article in the second issue of 2006?

Answer the following questions that test your ability to locate pertinent journals.

4. You will be conducting research this semester that is either ecological in nature or related to animal behavior. Under Find Electronic Journals, enter the word ecology into the search box. Of the journal titles returned to you, which do you think might be helpful when you are conducting your literature review?

5. Now, do the same by entering the word behavior into the Find Electronic Journals search

box. Of the journal titles returned to you, which do you think might be helpful when you are conducting your literature review?

6. What other key words (besides ecology and behavior) might you search with to look for

pertinent journals?

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IV. Inside Scientific Literature (Continued) Guidelines for Reading Scientific Articles/Book Chapters (primary/secondary literature) [adapted from a similar document developed by Dr. Patricia Soranno, MSU Dept of Fisheries and Wildlife] Words don't communicate until they are strung together in an orderly sequence (sentence, paragraph, etc.). To read in an effective way, you must begin by searching for this organization. Without an understanding of the overall structure of the article, chapter, or paper that you are reading, it will be difficult to remember and understand what you have read. Below are some general guidelines that might help you read more effectively. Although it may seem like these steps take a long time, they really don’t, and they will help you get the most important information from a reading more quickly and effectively. Also, and very importantly, when reading the scientific literature, you are likely to come across words that are unknown to you as well as scientific names of organisms that you have not before heard of. Do not just skip over those words and names; rather, use a dictionary or encyclopedia (online is fine) to look them up so that you understand the article. Preliminary: 1. Carefully read the title, identify whom the authors are and where they are from (their

affiliation), and determine what year the article was written. All of these things may help you remember the article.

2. Determine how long the article is. Determine overall organization: 3. Read all of the major subheadings of the article first. Write them down on a piece of paper to

help you remember later. If there are no subheadings then find the 'topic sentences' in each paragraph and jot them down to get the main ideas of the paper.

4. If there is an 'abstract', also read this to get a general idea of what the article is about Concentrate on content: 5. Identify what the main objectives or research questions of the article are and write them

down. There are usually only 1-3 main objectives in any single writing. These should be easy to find in all works of writing, but often they are not. In general, you can often find these at the end of an introduction, or sometimes at the very beginning of the introduction. If you cannot find what the main objectives of the article are in the introduction, then jump to the end of the article and try to find what the main conclusions are, and whether they restate the main objectives of the article.

6. After you have figured out what the main 'questions' or objectives are of the article, then skip

to the end and identify what the main conclusions are, and write these down. Again, these are not always easy to find and you may have to read more than just the last paragraph.

7. NOW, you are ready to read the article - you should already have a general idea what the

main questions/issues are, you have a general idea of what the main conclusions are, and now you can concentrate on the intricacies of the author's logic and reasoning. You should never

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read an article or book chapter from beginning to end like a novel - you do NOT want to be surprised by the ending. You should know the ending, and concentrate on the arguments and logical reasoning of the writer as you are reading the article.

8. Be sure to read and understand ALL figures, tables and graphs. Most often, this is where the

most important information is found! 9. You should never finish reading unless you can answer the following questions: 'What

was the main point of this article/chapter/paper?', 'What were their main lines of reasoning and the evidence that they used?', and 'What were their main conclusions?'. If you cannot answer these questions, then re-do the above steps until you can.

10. While doing the above activities, you should also keep track of what you think the major

strengths and major weaknesses of the article are. Summarize them after you have addressed all of the above issues.

Usually, if you can address all of the above issues, then you have effectively read an article.

Reading Worksheet for Scientific Articles/Book Chapters (primary/secondary literature) You can use the general format below to make an electronic worksheet to fill in when reading the scientific literature. You should USE YOUR OWN WORDS when filling out this worksheet. Preliminary Title: Year published: Authors: Affiliation of authors: Length of article: Overall Organization Major subheadings or topic sentences of paragraphs: Content

What are the main objectives and/or the main research questions of this article? What are the main conclusions of this article? What are the main lines of evidence that the authors use to support their conclusions?

You should refer to figures and tables AND other articles cited by the authors here! What are the major strengths of this article? What are the major weaknesses of this article? Example: could the study itself have been

more thorough or better designed? From memory, state the ‘take home messages’ from this article. If you cannot do this at

this point, you should retrace the above steps until you can.

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Exercise 11 Dissection of a Scientific Article

Purpose: To a) practice actively and critically reading and understanding a peer-reviewed, primary scientific research article and b) learn more about the system you will study in-depth this semester. Actively and critically read the article that you were assigned by your lab instructor using the article reading guide above, and then answer the following questions. Notes: We don’t expect you to totally understand the statistical methods yet (since we’ll do statistics later), but you should be able to understand the methods and results generally and be able to understand the paper’s conclusions. Also, and very importantly, when reading this paper, you are likely to come across words that are unknown to you as well as scientific names of organisms that you have not before heard of. Do not just skip over those words and names; rather, use a dictionary or encyclopedia (online is fine) to look them up so that you understand the article. 1. What is the full article citation (refer to Appendix C for proper formatting)? 2. What do the authors claim as the justification for the study? In other words, why did they

conduct their research? 3. List the authors’ hypothesis(es) and briefly state the associated prediction(s) (Note that

predictions are not always laid out for you; sometimes you have to formulate those on your own.):

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4. Briefly describe the methods the authors used for both the observational and the experimental parts of their study. For the experimental part of the study, be sure to list treatment(s) and the control.

5. What statistical method or tool did the authors use? We don’t expect you to totally

understand the statistical methods yet (since we’ll do statistics later), but you should be able to list the approach(es)/tool(s) and explain it(them) generally.

6. What major conclusions do the authors make? Are their hypotheses supported or refuted by

their results? What limitations to their research or sources of error did the authors discuss? 7. What do the authors say about how their results fit into the ‘big picture’ of their field of

study? 8. What questions do the authors raise for possible future studies? If they don't provide any,

what do you think could be studied next on this topic?

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IV. Inside Scientific Literature (Continued) The Scientific Abstract Adapted from: Van Dyke, F.A. 2003. Developing Critical Writing Skills. Ch1 in A Workbook in Conservation Biology. Van Dyke, F.A. ed. McGraw Hill, Boston MA. In LB144, and throughout your science education, you will be called on to read and understand scientific abstracts. In most scientific journals, each paper begins with an abstract, or summary, of the content of the article. An important purpose of abstracts is to enable scientists to sift more quickly through the overwhelming volume of professional scientific literature and identify articles of particular interest and importance to their own study. However, you will also be expected to write good abstracts, which is even more important than reading and understanding them. Writing an original abstract of a scientific paper requires more than familiarity with the paper’s content. Rather, a writer must be able to mentally recognize the paper to answer several specific questions:

1) What question, exactly, are the investigators trying to answer? Why would their audience care about this question, and how is the specific question of the study connected to broader questions that may be of more general interest?

2) What results speak most directly to the question the research addresses? How can these results be arranged in a logical sequence that leads the reader to see the interdependence of different results? What conclusions or inferences should be included that inform the reader what the results mean?

3) Why are the results important and how should the information be used?

Thus, producing an original, well-written abstract from a scientific paper demonstrates that one has carefully and critically read and understood the content of the paper. Sections of an Abstract Identifying and Expressing the Problem Studied In general, the abstract begins by telling readers what questions the authors are attempting to answer. However, the best abstracts don’t begin with a direct statement of the problem such as, “We studied the growth rates of wombats from birth to weaning in relation to the energetic cost of lactation in their mothers.” There is nothing wrong with this sentence in itself, but statements of this form are usually preceded by statements of context that relate to the problem investigated in the individual study to some question or issue of broader or more general interest. For example, consider these opening statements from an abstract about elk and oil wells:

Environmental disturbance can affect use of home range by large, free-ranging ungulates, but quantitative assessments of such effects are rare. We compared seasonal and annual use of elk (Cervus elaphus) at Line Creek in south central Montana, 1988-1991, before, during, and after installation of an oil well” (Van Dyke and Klein 1996).

What does the opening sentence accomplish that the second sentence (the actual statement of the problem) does not? By answering this question, you can appreciate the goals of a scientist for the beginning of an abstract (and the beginning of a scientific paper, more generally):

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1) Engage the reader’s interest and attention by posing a problem of general interest but

insufficient resolution (1st sentence). 2) State the problem studied in such a way, or in such a sequence, that readers perceive a

clear connection between the problem studied and the area of more general interest that first engaged their attention.

Summarizing the Approach Employed This is usually, and appropriately, the shortest portion of the abstract. However, a brief statement of the methodological approach should be provided to give the reader a basic idea of how the investigators attacked the problem. Armed with this info, discerning readers will be able to determine if the methodology was appropriate to the type of problem investigated. Summarizing the Most Important Results A full-length scientific article includes numerous results and conclusions - far too many to include in the abstract. To produce a well-written abstract, the writer must make three important decisions about what to include and how to arrange it:

1. Choice of results. A well-written abstract presents the results that speak most directly to the question being asked by the study.

2. Sequence of results. In a well-written abstract, results are arranged sequentially so that their order, as well as their content, conveys a meaningful message to the reader.

3. Conclusions. A well-written abstract is one in which quantitative, objective statements of results are complemented by specific conclusions that are logically inferred directly from the results. In other words, skillful writers don't just summarize the results, they tell the reader what the results mean.

A well-written abstract is distinguished as much by what the author intentionally omits as by what the author intentionally includes. As an example, consider again the abstract on elk and oil wells and its summary of results:

Use of range by elk during the post-drilling period in autumn was different from use during drilling and pre-drilling periods, but use of range also changed during the same periods in another local population of elk not subjected to disturbance from oil drilling. Use of range grid cells containing or adjacent to the well site declined during the post-drilling period, but seasonal and annual sizes in range and boundaries for the population were similar in all periods. Distances between individually marked elk did not differ across periods, suggesting that drilling did not affect the social stability of elk. Use of forest habitats in autumn increased after initiation of drilling.

Here the authors (Van Dyke and Klein 1996) selectively present facts that address the primary question, "Does oil drilling cause elk to change their use of range and habitat?"; they also place the following statements in sequence to lead the reader through a logical progression of ideas and levels of investigation (landscape level, site level, individual animal distances, habitat patches); and, where appropriate, they offer brief explanations of what particular results mean or suggest.

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Summarizing the Most Important Conclusions and Their Significance or Application In a well-written abstract, the final two or three sentences describe the most important conclusions, why the study is important, and what ought to be done as a result. "Application" of the results does not necessarily mean management or conservation application. Arguably, all well-designed studies have applications, which may be applications of action (changes in management or policy informed by scientific results) or applications of thought (new ways of thinking about existing scientific theories or concepts). For example, consider a contrast between a statement of conceptual application from the study of elk and oil wells, and an action application from a study of the effects of prairie fragmentation on nest predation. Conceptual application: Suggests a general way of thinking about how animals may respond to site-specific environmental disturbance.

Results suggested that elk compensated for site-specific environmental disturbance by shifts in use of range, centers of activity, and use of habitat rather than abandonment of range.

Action application: Suggests a management action, procedure, or policy informed by the results of the study.

The potential effects of prairie size and woody vegetation on success of ground-nesting birds should be considered in decisions regarding acquisition and management of prairie habitats (Burger et al. 1994).

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Exercise 12 Writing a Scientific Abstract Adapted from Van Dyke (2003)

Purpose: To practice actively and critically reading and understanding a peer-reviewed, primary scientific research article, and to practice writing an original scientific abstract. Directions: 1) Using the MSU Library webpage and the literature search skills you’ve now developed, find

the following article: Frederickson, M.E., M.J. Green, and D.M. Gordon. 2005. ‘Devil’s gardens’ bedeviled by

ants. Nature 437(7058): 495-496. 2) Actively and critically read the article using the article reading guide found earlier in this

stream. 3) This paper has no abstract, and it is your team’s job to write one that is limited to 250 words.

How you work together as a team to complete this assignment is up to your team, but one approach could be for each individual to first read the article, take notes, and answer the questions below and then for the team to write and revise the abstract together.

The abstract should state: 1) the problem studied, 2) the approach employed in the study of the problem, 3) the most important results, and 4) their significance and application. Stating the format in this way makes the act of writing an abstract sound simple. In fact, writing an abstract requires considerable skill. In particular, if you are writing an abstract for a paper written by someone else (as is the case here), it requires careful and discerning reading. Notes: When reading this paper, you are likely to come across words that are unknown to you as well as scientific names of organisms that you have not before heard of. Do not just skip over those words and names; rather, use a dictionary or encyclopedia (online is fine) to look them up so that you understand the article. When your team has a draft of the abstract, see how well you can answer each of these questions:

1. Statement of the problem. Have we clearly identified and stated the problem studied, and have we placed the problem in a context of more general interest that will engage the reader's attention?

2. Summary of approach: Have we provided enough information for the reader to

understand the approach employed in the study of the problem, but not the details of the study?

3. Summary of results. Have we selected and arranged results in a way that speaks directly to the question posed by the research, and arranged the results in a way that moves the reader through a logical progression of ideas? 4. Significance and application. Did we tell the reader exactly what the most important conclusions are, why they are important, and how they should be used?

Your team’s abstract is to be turned in to your lab section’s course dropbox.

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V. Ecology Research Experiments and Field Studies A. Overview

During the past year, LBC has increased biology student research capacity by building two "research systems" that you will use for your BioCore lab research (LB144 and LB145). We have built one terrestrial and one aquatic system. Each lab section will work with one or the other system and we describe the two research systems below.

(1) Freshwater benthic system: We have built an aquaria system that resides in C3 and will allow student teams to study a wide variety of single- and multiple-species ecological questions in separate small experimental tanks. This system includes:

a) A complete freshwater ecosystem that will serve as inspiration when thinking about your lab research project. This tank is connected to a Neptune System that monitors and records water quality parameters (e.g., temperature, pH, water levels). This tank is also being monitored by a video camera for behavioral observations.

b) A series of organismal culture tanks (phytoplankton, zooplankton, mussels or snails, and crayfish). Each tank will provide student teams with the organisms (plants and invertebrates) needed to conduct their experiments in separate small experimental tanks.

c) A series of experimental tanks that allow for the manipulation of various parameters (e.g., light, temperature).

(2) Terrestrial bird feeding system: We have installed a system outside of Holmes Hall that will allow students to study bird feeding behaviors and the factors that affect those behaviors. This ecosystem includes a weather station, 2 bird-feeding stations (one in an area that is relatively undisturbed and one that is relatively disturbed) that include many individual feeders each, and cameras that will record bird visits and behavior. We anticipate that this system can also be linked with LBC's participation in the Worldwide PhenoCam Project (http://phenocam.sr.unh.edu/webcam/)

Your team will use one of these two new systems to: (1) formulate hypotheses and predictions related to either pond/lake ecosystems or bird feeding behavior (depending on lab section), (2) design an experiment to test your hypotheses, (3) write and revise a research proposal, (4) collect, analyze, and interpret data, and (5) visually and orally present the research results during a poster session.

A Note About Lab Experiments in LB144: No vertebrates can be used in experimental studies. If you use a chemical (e.g., caffeine, acid) as a factor, your team must conduct research to determine the physiologically and environmentally relevant concentration of the chemical(s). Also, keep in mind that you will have a VERY limited budget for your research projects (almost nothing) and plan accordingly.

B. Research Tasks Below is a brief description of the general activities your team will participate in, as are the tasks that your research team needs to accomplish. Your team will need to work out a detailed timeline for these events prior to beginning the project.

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Research Team Task #1: Questions and hypotheses Based on the scientific papers already read and your previous understanding and observations of lake/ponds or birds, formulate some related questions that are interesting to you. You might also do some web searching to help you brainstorm ideas. For example, think about which environments are most diverse, when organisms act particular ways, and whether there might be a correlation between any given physical parameter and such diversity or behavior (or any other biological attribute)? You will then formulate a testable explanatory hypothesis (refer back to prior information on formulating hypotheses, including the hypothesis score card), conduct a literature review and annotated bibliography to determine what is known on the subject using the primary literature (see Appendix C), and revise your hypotheses based on the results of that literature review. Research Team Task #2: Research proposal After revising your hypotheses and finishing your literature review and annotated bibliography, your team will write a research proposal. You will decide on an appropriate approach and experimental design to test your hypothesis/es (and associated timeline), as well as the appropriate sampling method to be employed, and the analytical method (see next section on statistical methods) that will be used in your data analysis. You will go through a review process with your research proposal so that you can practice your scientific writing and receive feedback on your experimental design before you embark on your data collection. Research Team Task #3: Experimental set up and data collection After you have received the go-ahead from your lab instructor, you will set up your experiments and begin collecting data, recording everything in your lab notebooks. We anticipate that this phase of the research will last 2-4 weeks. During this data collection phase of your project, formal labs will be run somewhat independently, with each team collecting data on their own either in the field or lab. Students will be required to devote some time outside of lab/recitation to finish data collection.

Research Team Task #4: Data analysis/interpretation After all data collection and processing (e.g., all biological sample identification and counting) is finished, data in lab notebooks should be entered into an Excel spreadsheet. You will then conduct the statistical tests described in your proposal and interpret the results (see the next section of this course pack and your teaching team for help). You may need to complete data analysis and interpretation outside of lab/recitation. Research Team Task #5: Making a research poster Your team will make a research poster to share your project with the rest of the class. You are encouraged to bring a complete digital draft of your poster to lab, open lab hours, and/or office hours to get feedback from your instructors so that your team has the opportunity to make revisions before printing the poster. You will need to complete this poster, get it printed, and prepare for orally presenting it at the poster session outside of lab/recitation. Research Team Task #6: Poster Presentation & Peer Review During the poster session, date/time to be announced, your team will orally present your research project to an audience as well as review other student team posters.

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Exercise 13a

Formulating and Revising Questions and Hypotheses - Part 1

Purpose: To formulate questions and hypotheses that can be experimentally answered/tested by your team. One of the keys to producing good science is that science will answer questions, but in turn, many new questions spring up. As such, for any given topic there are many different questions that can be asked and researched. Often the most difficult part of conducting science, however, is refining a question and developing a testable hypothesis. Directions: Throughout this course, you have been exposed to an array of natural and published ecosystems and experiments, all of which can evoke questions about the world around us. For this exercise, think about the system that has been assigned to you (Freshwater benthic system or Terrestrial bird feeding system, described previously) and come up with three different, specific questions that you would like to explore more in detail. After you come up with these questions, develop a testable hypothesis for each. Remember to evaluate your hypothesis using the hypothesis score card below. Some general questions that may help you in this process are:

1. How does ecosystem quality or animal behavior vary by location or species? 2. What natural environmental factors affect plant growth/abundance or animal behavior? 3. How do human disturbances affect plant growth/abundance or animal behavior? 4. What are the effects of different environmental factors (or varying levels of one factor)

on plant growth/abundance or animal behavior? 5. What is the effect of one individual on another individual’s behavior (all same species)? 6. What is the effect of one species on another species’ behavior, or density?

THE HYPOTHESIS SCORE CARD [by Dr. Cori Fata-Hartley, MSU College of Natural Sciences] A good hypothesis must:

1.) explain how or why: provide a mechanism

2.) be compatible with and based upon the existing body of evidence.

3.) link an effect to a variable.

4.) state the expected effect.

5.) be testable.

6.) have at least two outcomes.

7.) have the potential to be refuted.

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Question 1:

Testable Hypothesis: Question 2: Testable Hypothesis: Question 3: Testable Hypothesis:

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Exercise 13b

Formulating and Revising Questions and Hypotheses - Part 2

Based on your discussions of part 1 of this exercise, and using consensus, your team will now decide on the specific question that is most interesting, ecologically relevant, and logistically feasible to study in more detail during the rest of the semester. Remember to re-evaluate your hypothesis using the hypothesis score card! Question:

Hypothesis(es): Prediction(s): By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 14

Conducting a Literature Review and Annotated Bibliography

After deciding on your question, hypotheses, and predictions, the next step is to conduct a literature review and an annotated bibliography to see what is known about your question (and to share that knowledge with your lab instructor). Using all that you have already learned in this class, find and read the primary literature about your topic. Do not feel constrained by your question/hypothesis/prediction – as you read, you may decide to revise these slightly based on what is in the literature. Also, remember to re-evaluate your hypothesis using the hypothesis score card. Literature Review and Annotated Bibliography You are required to locate, critically and actively read, and understand 3-4 (1 per team member) articles from the primary literature that are related to your study (see earlier in this manual for more info regarding the literature and for ways to effectively read scientific articles). These articles will provide you with background information specific to your topic of interest, help you refine your question/hypothesis/prediction, as well as design your study. For each paper, record the full citation (referring to Appendix C for correct citation format), along with a short summary of each paper (less than ½ page each) that demonstrates its relevance to your study and provides evidence to justify your proposed research. Photocopy or print off the first page of each of your journal articles and include those sheets with the annotated bibliography when you turn it in to your instructor. By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 15 Devising a Research Plan

Directions: This exercise is geared toward helping your team figure out its plan for accomplishing your research project. Submit typed answers to the questions/tasks below to your lab instructor for feedback. They will assess the quality of your research questions, hypotheses, predictions, determine whether you have conducted a thorough enough literature review, assess whether your project is both ecologically relevant and feasible (e.g., the gear needed to conduct your study is available), and whether the timeline/action plan for study set-up and data collection is appropriately laid out (e.g., is detailed enough). Note about microscopes: Many organisms in lakes/ponds are microscopic. If your project includes such organisms, each member of your group will need to demonstrate the ability to properly use and care for a microscope. Go to and complete appendix A and then see your instructor for a 1-on-1 skills assessment. Questions/tasks to answer/complete:

1. What is the research question?

2. What is/are the hypothesis/es and prediction(s)?

3. How will you address these research question (e.g., what is your experimental design)? Use a diagram if appropriate.

4. Sketch a graph of the data that would match your prediction. This will help to ensure that the data you plan to collect will address your research question.

5. List all of the equipment, supplies, and organisms required for your project, as well as

how much of each you will need. If you need something ordered, please suggest a source. Be very specific.

6. How much time and space will your group need to complete your project? Does your project have any specific requirements?

7. Timeline, responsibility, and communication: What is your detailed timeline for study set-up, data collection, analysis, and interpretation? For each specific task in that timeline, who will be in charge? How (format and frequency) will you communicate with each other that tasks have been completed, when and what problems arise, how problems have been addressed, and share project results and drafts?

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Exercise 16

Writing a Research Proposal Purpose: To practice the scientific method and communicating science in a written proposal format. Background: As we mentioned early in this c-pack, most researchers require funding before they may conduct research to answer the questions in which they are interested. More importantly, scientists need to know the current state of knowledge about their topic of interest before they embark on new discovery. Therefore, the first step of scientific study is to write a research proposal. Therefore, you will practice scientific writing by writing and revising portions of a research proposal over the course of the next few weeks. A research proposal is set up similarly to a scientific article, although it is written in the future tense (since the research has yet to be conducted!). It begins with an Introduction that includes background information obtained from a literature review, justification for why the research should be conducted, the specific questions/hypotheses/predictions to be tested, and the general approach to be taken. The introduction is followed by a Methods section that describes in detail how the experiment will be carried out and how the resulting data will be analyzed along with a Project Timeline indicating when all the elements of the experiment will be completed and by whom. Finally, you will include a References section that includes all of the peer-reviewed literature that you cite in your proposal. As a part of a research team in LB 144, you will provide enough information about each of these sections for us to evaluate the project that you will then conduct during this stream. Instructions: Use the exercise above and this template as a guide to help your research team organize your ideas and activities for conducting your ecology study. With a computer, type these sections: Title, Introduction, Methods, and References (see Appendix C for more details and remember that a proposal is written in the future tense (since you have yet to conduct the research)).

1) TITLE

2) INTRODUCTION

a. What are the observation(s) and question(s)? b. Rationale: Why is this interesting (from an Ecological standpoint)?

c. Relevant background information from the primary literature: What do other

researchers have to say about the topic of your proposed study (use your sources and summaries from the Literature Review/Annotated Bibliography exercise)?

d. Hypotheses and Predictions: What are your hypotheses (including the null

hypothesis) for your observed pattern (Remember to evaluate your hypothesis using the hypothesis score card)? What predictions follow from each hypothesis?

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e. Approach: What general approach (controlled lab experiment, field study) will you take to test your hypotheses?

3) METHODS

a. Methods: What is your experimental design? What organisms are you studying? What sampling methods, including number of replications and specific sampling areas, do you anticipate using?

b. Analysis: What analytical tool(s) (i.e., statistical tests) will you use to understand

your data? See the next section of the c-pack for more information on statistics.

c. Predicted results: What do you anticipate your results will look like (use graphs if appropriate)?

4) References: Include each publication that you cite in your proposal, and follow the

format described in Appendix C.

By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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Exercise 17 Making a Scientific Poster

Introduction Earlier in the semester, we discussed the various ways scientists communicate their work to one another and the community. You will now present your team's research project using a poster and a formal poster session to which all at LBC will be invited. A poster is similar to a scientific paper in that it has the same general sections and these sections contain the same general information (see Appendix C). However, a scientific poster is more concise. This format uses less text and more figures, pictures, and tables to relay information to the audience. Often, authors will use clear bulleted lists rather than complete sentences and paragraphs to get their message across. A formal poster presentation happens when the author(s) of a poster stand next to that poster and answer questions from an audience about the project. This is what you will do at the LB 144 Poster Session! Poster Requirements The following items and sections are required elements for your poster. Please read through each item carefully.

1. TITLE: should be concise and catchy – besides the overall design of your poster, it is the first thing that viewers usually look for to decide whether they should bother reading your poster.

2. AUTHORS: first name followed by last name, in alphabetical order.

3. Your poster NUMBER: each team will be given a number that needs to be included in

the upper right-hand corner of the poster – this number is for identification purposes. 4. Your AFFILIATION (aka contact information):

Lyman Briggs College, Michigan State University

LB 144 Introductory Organismal Biology, Section __

5. ABSTRACT: Your poster will include an abstract, like you practiced during the Doing Biology Stream. Prior to the symposium, each team must electronically submit their abstract in order to receive feedback. Include the following information in this submission: 1) the authors, 2) the poster title, and 3) a brief (<250 words) abstract of the poster. Here is an example abstract:

Murkey, L., N. Seymour, and A. Sylvester. Behavioral differences between male and female Anas platyrhynchos. We observed individual and group behavior patterns of Mallard ducks (Anas platyrhynchos) to study tendencies and relationships between males and females. We tested the hypothesis that male Mallard ducks will spend more time eating and interacting with other ducks and less time grooming as compared to female Mallard

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ducks that will spend more time grooming and less time eating and interacting. Nine treatments were conducted that observed the ducks in the Red Cedar River by the Michigan State University football stadium. The treatments spanned a three-day period during which both male and female ducks were studied separately for twenty minutes each. For all treatments, we recorded time spent eating, grooming, and interacting. Our study found that male Mallard ducks spent more time interacting (mean = 12.3+1.3 as compared to 8.9+2.2 min, p = 0.03) and females spent more time eating (22.7+4.1 as compared to 12.2+3.6 min, p = 0.003) while the time spent grooming was the same for both males and females (32+7 and 30.5+5 min, p = 0.09). This research adds support to the idea that sex plays a role in determining duck behavior.

6. INTRODUCTION: this is where you briefly summarize what is known or has already been done with regards to the subject and set up the hypotheses and prediction you are investigating with your research (don’t forget to check the hypothesis score card!), as well as the general approach that you took. Don’t forget to cite your references, and you must also state why the study is important in an ecological sense (i.e. put your project into a larger context). Encouraged: photos of the study site or organism(s) or phenomenon under study.

7. METHODS: this must be a brief description of your methods including data collection and analytical tools used (i.e., statistical tests) and must include a description of any special equipment that was used. Encouraged: pictures of the methods in action.

8. RESULTS: must state what was found, including statistical values associated with the analysis; must also include a Figure or Figures summarizing the results in the form of graphs (DO NOT include tables of RAW data (tables that summarize raw data are good though) – if you do not know what this means, ask your teaching team).

9. DISCUSSION & CONCLUSION: This is where you must summarize and interpret

your results and discuss them in terms of your original questions/hypotheses and expectations/predictions (i.e., How do your results support or not support your hypotheses?) and make some inferences about why this might be the case. You should also include sources of error or bias that might explain results that do not support your hypotheses, mention further opportunities for study in this area, and finally, wrap it all up by placing your study into the broader context of the field of study (i.e., revisiting some of the text from the introduction, including the primary literature you cited).

10. REFERENCES: Use Appendix C to format your full citations, however, you may

shorten them for the poster by: a. within the text, using a numbering system that corresponds to your references

section. b. in the reference section, omit the article title.

Poster Formatting Requirements

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Your team will need to make its poster in PowerPoint (changing the size of the layout in Page Setup to custom with size = 36 horizontal by 48 inches vertical or 48 horizontal by 36 vertical) and then go to the MSU library and print your poster (~$25 for full color poster). Use of a white background, short bullet points, large fonts, colorful graphics and pictures that visually explain your project will make for a better poster. Generally speaking, font size should be as large as there is space for and nothing should be smaller than 20pt (usually 20pt is reserved only for the references and Table or Figure legends – everything else should be much larger; see below), or else it will be too difficult to read. Below are font ranges to shoot for, everything you will notice is relative in size to other things, so keep that in mind when you are making adjustments. The main point is to be consistent in your sizing. As far as font style is concerned, pick fonts that are easy to read. Font Sizes:

Title: between 60-72 pt Authors: between 50-60 pt (smaller than title) Affiliation: between 30-40 pt (smaller than authors) Section headings: between 30-40 pt (same size or larger than the affiliation) Body text: between 24-32 pt (smaller than section headings) Figure legends: no smaller than 20 pt References: no smaller than 20 pt

By listing all members of your team on the assignment, you are indicating that all team members actively participated in this assignment and are responsible for its contents (quality and originality).

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V. Ecology Research Experiments and Field Studies (Continued)

The Poster Session You may begin to set up your poster before the poster session is scheduled to start, at which time you will receive instructions on where you will be displaying your poster. All team members must be present at the session and at least one member of your group must remain with the poster at all times to orally present your team’s research project and answer questions from viewers. Stand near the poster but not in front of it as to block anyone’s view. Be available to give a summary of the project and answer questions (when asked). You should ask a viewer whether everything is clear, whether they would like an overview, or whether they have any questions, but do not be overbearing and do give them time to read your work. Your goal in presenting a poster is to get some feedback about your question, your approach to answering it, the methods you used, the results you obtained, and your conclusions. Good posters will generate this kind of helpful input from your peers, and good poster sessions encourage relaxed, thoughtful discussions. A poster session can be an important tool in gaining insight from viewers, so do as much listening as talking.

Poster Session Assignment (handed out at the session) Just prior to the session, each student will be given a Poster Peer Review Sheet on which to score other team posters. The sheet will include criteria (e.g., clarity, completeness, etc.) on which to make numerical judgments and provide necessary constructive (positive and negative) written feedback. As you read through each poster, make note of the title, authors, and any features that make the poster appealing to you. Look carefully at the poster layout. Does the poster “tell a story” that is easy to follow? Or is the poster cluttered and confusing? Do the hypotheses score high using the Hypothesis Score Card? Also comment on how well the presenter engages you during your visit.

Poster Session Timeline - date/time to be announced Poster Set-up: during this time, at least one of your team members is responsible for

setting up your poster in the location to be announced. When you arrive, an instructor will be on hand to show you where to set up your poster.

Poster Session: during this time, at least one of your team members needs to be standing by your poster (take turns) to present the project and answer questions. Each student will be given a Poster Peer Review Assignment. You will need to evaluate another team’s poster from a different section. When you are finished with the assignment, hand it in to one of the instructors.

Poster Take-down: at this time, at least one team member must be present to take the poster to the lab for additional grading.

POSTER EXAMPLE: Following is an example of all of the required components for your poster. Refer to the information provided above for exact requirements and see Appendix C for more info on what information goes into each section. Key elements for each component are listed. Their sizes relative to one another are not necessarily to scale, nor does the layout have to be exactly like this, but it should have a logical flow. Fill up the entire poster as much as possible by using LARGE font sizes as described above.

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Abstract Mini-paper/poster that briefly (<250 words) summarizes your study.

Methods Should be written in past tense and narrative format (bullet lists are fine!) and should indicate how you randomized and replicated your experiment, and what statistical tool you used to analyze your data.

Figure(s) and Table(s) Each figure should be numbered, referred to in the text of the results and have a descriptive title located just beneath it. Each table should be numbered, referred to in the text of the results and have a descriptive title located just above it. No raw data!

Results Be sure to refer to your Figures here and save any interpretations of your results for the Discussion section. Your statistical results with corresponding value(s) in parentheses should appear here also.

Conclusion & Discussion Summarize and interpret your results, briefly discuss what you might have done differently if your results are not what you expected, or how your results correspond with previous research (cited) if they are what you expected, and wrap it all up by referring back to what you wrote in the introduction (cited).

Photo(s) of Organism and/or Study Site (optional)

Each photo or map of the study site should have its own caption underneath it.

TITLE Authors Affiliation

References Properly formatted references for the citations in the Intro/Disc go here.

Photo(s) of Methods in Action (optional)

Each photo should have its own caption underneath it.

Introduction Be sure to state your question, your hypothesis, predictions, and general approach. Supporting literature with proper citations should appear here as well.

Poster #

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VI. Statistical Analysis of Data Part of conducting biological research is designing an experiment. A big step in experimental design is choosing how you will summarize and analyze the data once it is collected. Summarizing data is the first step to discovering patterns in a data set. Some definitions and terms used to summarize data follow (these are often called ‘descriptive statistics’).

Distribution. An arrangement of values of a variable showing their observed or theoretical frequency of occurrence. Normal Distribution of a Dataset. If the value of your mean, median and mode (below) are all the same, then your data set is perfectly normal. In reality, however, a perfectly normal data set is theoretical and for statistical purposes, a data set is considered normal when the mean, median and mode do not differ significantly from the theoretical normal. Further statistical tests (e.g., t-tests, ANOVA, regression; see below) assume that data are approximately normal, therefore, if the data are not normal, then the results of your statistical tests will not be correct. Mean. The numerical average of a data set. It is the simplest measurement to make and reports the central tendency of the data. This value is calculated by summing (���all of the values of each observation (Y) and diving by the total number of observations (n),

which is indicated by: YY

n

Median. The middle-most value of a data set and another measurement of central tendency.

Mode. The most frequently occurring value in a data set.

Range. The distance between the lowest and highest data points, calculated by subtracting the lowest observation from the highest observation.

Variance (s2). A measurement of dispersion or spread in a data set, it measures how many of the data points are close to the mean and how many are widespread. The variance is calculated by taking each observation (X), subtracting the mean, and then squaring that difference, summing all of these differences, and then dividing by the total

number of observations minus 1 (n-1), which is indicated by:

sX X

n2

2

1

. The

variance is also the square of the standard deviation (below). Standard Deviation (s). One standard deviation is the distance from the mean on a normal data curve, to the point where the curve changes from convex to concave. The standard deviation is calculated by the square root of the variance (above).

It is extremely important to examine your data visually as well. Here are some kinds of graphical plots you might use. Be sure that you think carefully about what you are trying to convey with your graph or plot. Simplest is often best, and 3D graphics are almost always unnecessary and confusing.

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Histogram or Bar Graph. Used to look at the frequency with which particular values appear in a data set, or to compare treatment averages, or to group data by categories. Scatter Plot. Used to compare one parameter to another to look for patterns or relationships. Can be used to obtain a correlation coefficient (= R2-value) in Microsoft Excel. Pie Chart. Used to look at the proportion or frequency of individuals in a sample population. Line Graphs. Often used to show trends through time. After you have visually inspected your data and calculated descriptive statistics, the next step is to conduct an appropriate statistical test. Statistical tests are used to examine different kinds of data (sometimes collected by different methods) to determine whether we have a statistically significant reason to reject our null hypothesis. For example, suppose that we have two populations of ocean birds, and one has experienced an oil spill in their breeding area. We would like to know whether the spill has affected the birds’ breeding success. A first step toward determining whether there was an effect of the spill might be to see if there is a difference in the average number of offspring per female between the two populations. Say that we count offspring per female for these two populations over the course of a breeding season and come up with averages of 2.1 for the spill population and 4.2 for the non-spill area. Without conducting any statistics, this result seems to indicate a large difference between the two populations (note that although this result indicates a difference in breeding success between the two populations, we can’t say that this difference is caused by the oil spill yet!). However, what if the two averages we arrived at were 2.1 and 2.5 offspring/female (spill and non-spill, respectively)? Is a difference of 0.4 offspring/female a large enough difference that it isn’t due to chance alone? This is where statistics come in – we conduct statistical analyses to determine if a difference found is ‘statistically significant’, meaning that it is not likely to be due to chance. When conducting statistical test, the way that we determine whether something is statistically significant is by using something called a ‘p-value’. Scientists often use the cutoff p-value of 0.05 to determine statistical significance: p < 0.05 indicates that the result is statistically significant (i.e., there is less than a 5% chance that it is due to chance alone) and p > 0.05 indicates that the result is not statistically significant (i.e., there is greater than a 5% chance that the result is due to chance alone). Note, however, that this cutoff of 0.05 is somewhat arbitrary, and a result of p = 0.06 or 0.07 may actually indicate a biologically relevant result, even if only marginally statistically significant using that cutoff. Before choosing a statistical test, we need to determine the type of data that we are collecting (e.g., continuous or categorical), how many and what type of variables we are interested in, and whether we will be looking at an association between variables or at differences between variable distributions, means, or variances. In this class, we will be using different statistical tests at different times, and one of our goals is for you to understand when different statistical approaches are most appropriate to use. The following is a short list and brief definitions of the tests you can expect to use in this class.

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Correlation. Examination of two population parameters to see whether they are associated in some way to one another (positively or negatively). The result of this test is the correlation coefficient or r-value and a P-value (see above for description).

Regression. Examination of two population parameters to see whether they are related to each other or depend on one another in some way (positively or negatively). In an experiment, one of the population parameters is manipulated in some way to see whether the other parameter is dependent on the manipulated parameter. The result of this test is the coefficient of determination, or r2-value, and a P-value.

The t-test. Used to test the difference between means of two independent samples. The result of this test is a t statistic and a P-value.

ANOVA. Acronym for “Analysis of Variance”. Used to test for differences in the variance among multiple independent treatments or sample populations (t-test is for 2 sample population, ANOVA is for more than 2). The result of this test is an F statistic and a P-value.

Chi-square (X2) one-sample test for goodness of fit. Used to test the difference between the distribution of a data set and a theoretical distribution. The result of this test is a X2 statistic and a P-value.

Chi-square (X2) test of independence between two or more samples. Used to test for independence between two or more frequency distributions of nominal (categorical) data. The result of this test is a X2 statistic and a P-value.

There are many excellent web resources for basic statistics. One that students have found helpful in the past is written by Jim Deacon of the University of Edinburgh, called The Really Easy Statistics Site: http://www.biology.ed.ac.uk/research/groups/jdeacon/statistics/tress1.html last accessed (8/9/2011) Data analysis example Suppose we measured the lengths of 100 Daphnia (microscopic aquatic animals) in tenths of mm. Now what? These DATA have to be listed, summarized or visualized somehow to make it easier to comprehend the important length features of the sample, and hence population. Not all animals will be the same length. Some will be older and hence larger. Even if two animals were the same age and sex they could be different lengths. There is always NATURAL VARIATION in any population caused by genetic differences, nutritional differences, etc…

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Here are the raw measurement data Daphnia lengths (mm)

3.0 1.1 1.2 1.2 1.2 1.3 2.8 2.7 2.6 2.6 2.6 2.4 2.5 2.5 2.5 2.4 2.5 2.3 2.4 2.4 2.4 1.3 1.3 1.3 1.4 1.4 1.4 1.4 1.4 2.3 2.3 2.3 2.3 2.3 1.5 1.5 1.5 1.5 1.5 1.5 1.5 2.2 2.2 2.2 2.2 2.2 1.6 1.6 1.6 1.6 1.6 2.2 2.2 2.2 2.2 1.6 1.6 1.6 1.6 1.6 2.0 2.0 2.0 2.0 2.0 2.0 1.7 1.7 1.7 1.7 1.8 1.8 2.0 2.0 2.1 2.1 2.1 2.1 1.8 1.8 1.8 1.9 1.9 1.9 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.7 2.7 2.7 2.7 2.7 2.3 2.3 2.3

Are these data continuous or categorical/discrete? How do you know?

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A good way to analyze the data would be to first, create a FREQUENCY TABLE of Daphnia lengths:

Length interval (mm) Mid-point (mm) Frequency 1.05 – 1.15 1.1 1 1.15 – 1.25 1.2 3 1.25 – 1.35 1.3 4 1.35 – 1.45 1.4 5 1.45 – 1.55 1.5 7 1.55 – 1.65 1.6 10 1.65 – 1.75 1.7 4 1.75 – 1.85 1.8 5 1.85 – 1.95 1.8 3 1.95 – 2.05 2.0 8 2.05 – 2.15 2.1 12 2.15 – 2.25 2.2 9 2.25 – 2.35 2.3 9 2.35 – 2.45 2.4 5 2.45 – 2.55 2.5 4 2.55 – 2.65 2.6 3 2.65 – 2.75 2.7 6 2.75 – 2.85 2.8 1 2.85 – 2.95 2.9 0 2.95 – 3.05 3.0 1 Total 100

then create a graph, called a FREQUENCY HISTOGRAM to look at the distribution of data:

0

2

4

6

8

10

12

14

1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9

Length (mm)

Fre

qu

ency

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then, calculate STATISTICS of LOCATION and DISPERSION (as seen in figure below):

Use the data above and try these on your own to be sure that you get the same answers: Statistics of location: mean, median, mode Mean = 1.98 mm Median = 2.05 mm Mode = bimodal 2.05-2.15 and 1.55 - 1.65 (note that you figure out that there are actually 2 modes by looking at the figure above, in addition to using the raw data) Statistics of dispersion: range, variance, standard deviation (s.d.) Range = 3.0 - 1.1 = 1.9 mm variance = 0.19 mm2

s.d. = 0.44 mm From the frequency histogram, you can also get a good idea of the SHAPE OF THE DISTRIBUTION of the data. Are these data NORMALLY distributed? How do you know? Common shapes of distributions:

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Most statistical tests assume the data are normally distributed:

If, at any time during this course, the data that you collect are not normally distributed, see your lab instructor for ways to transform your data before conducting your statistical analyses. T-test One of the most common experimental designs consists of comparing two groups. Often one group is a control group and the other a treatment group – or the two groups might simply represent two geographic locations, or two different species. For instance, one group of cattle might be fed a standard diet while a second group is fed an enhanced diet designed to improve growth. The mean growth rate of individual cows in the two groups could be compared to test whether the improved diet increased growth. A second example that is depicted in the table below might be comparing the average age (days; a continuous variable) at the beginning of reproduction (age at maturity) for two different genotypes of Daphnia. Each age observation is the mean age (days) at maturity for a clone of females (a clone consists of genetically identical asexual females).

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Mean Age (days) of Daphnia Genotypes

Clone I II A 7.2 8.8 B 7.1 7.5 C 7.7 7.7 D 7.2 7.6 E 7.3 7.4 F 7.2 6.7 G 7.5 7.2

Mean 7.31 7.56 Variance 0.044 0.4096

We want to test the null hypothesis that there are no differences between the mean age at first reproduction for the two genotypes (I and II). Therefore, the most relevant test statistic is the difference between means, and the critical question is: What is the probability of getting the observed difference between means if the null hypothesis is true? If the differences between means are roughly normally distributed, a T-TEST and the t-distribution can be used to determine these probabilities. The hypotheses are set up as follows:

H0: mean(I) = mean(II) HA: mean(I) mean(II)

The t-value is computed using the following formula (see next page for details on degrees of freedom):

t

Difference Difference

Standard error with (n n degrees of freedom

observed expected

difference1 2 2)

And using the values from the table above to get the observed difference between the two genotypes and an expected difference of 0 (i.e., the two means are equal):

98.0

256.0

056.731.7

t with (7 + 7 – 2) = 12 degrees of freedom

We would then look this value up in a t-table, and use the two-tailed probability (because the alternate hypothesis is rather than < or >), which is p > 0.20. This result indicates that the observed difference is a likely occurrence if the null hypothesis of no difference is true. The null hypothesis should thus be accepted, and you would write:

t = -0.98, (p > 0.20)

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Degrees of Freedom Degrees of freedom (or df) is a measure of the number of independent pieces of information on which a parameter estimate (e.g., mean, t-statistic, F-statistic) is based. It is a measure of how much precision an estimate of variability has. The degrees of freedom for an estimate is equal to the number of observations (values) minus the number of additional parameters estimated for that calculation. For example, in order to estimate the t-statistic above we need to estimate two separate means, and so we must subtract 2 from the total n. As we estimate more parameters, the degrees of freedom available decreases. Degrees of freedom can also be thought of as the number of observations (values) which are freely available to vary given the additional parameters estimated. The reasoning behind degrees of freedom is often difficult for people to understand. Therefore, another way to think about degrees of freedom is to consider the following example:

If you knew the sum of a three numbers must equal 16, and you randomly chose 2 numbers, the third number would be fixed (e.g., If you randomly chose 3 and 10, then the remaining number must equal 3 for the sum to equal 16). In this situation, 2 numbers can vary, but the third number is fixed, so there are only 2 degrees of freedom.

ANOVA: Single-Factor Analysis of Variance for Multi-Sample Hypotheses When we talked about the t-test, we were looking for differences between two samples. Suppose you wish to do a similar thing, but now have many samples. For example, say we want to compare the effectiveness of four different feed types on pig weight (measured in kg; a continuous variable). Nineteen pigs of similar size and age are randomly placed into four groups. Feed types are randomly assigned to the groups, the pigs fed under standardized conditions, and the final weight of each pig measured.

Pig weights (kg) Feed 1 Feed 2 Feed 3 Feed 4 60.8 68.7 102.6 87.9 57.0 67.7 102.1 84.2 65.0 74.0 100.2 83.1 58.6 66.3 96.5 85.7 61.7 69.8 90.3

Mean 60.6 69.3 100.4 86.2 Variance 9.4 8.6 7.7 8.4

The null hypothesis of interest is that feed types do not differ in their effects on weight. The alternative hypothesis is that feed types differ in their effects on weight. You might be inclined to do t-tests on all possible pairs of means, but this would inflate the chance of making a mistake. To avoid these problems, the correct test of equality of feed types is an Analysis of Variance, or ANOVA. In this experiment, there is a single treatment type (feed) and four treatment LEVELS. Hence this is called a SINGLE-FACTOR ANOVA. The logic of the test is as follows. If there is truly no effect of feed type, then all four experimental groups of pigs can be considered to come from a single statistical population. Thus the means of the groups are all equal, as are the variances. A good estimate of the common population variance would be the weighted mean variance of all four groups. However, there is another way to estimate this

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common population variance: by multiplying the variance of the means by the sample size of the groups. Now, the important point is that each of these independent estimates of the common population variance will be the same if there is no effect of treatments. If there is a treatment effect, it is likely that this effect will change the means and thus increase the variability of the means without changing the variances of the individual groups. In the above table it certainly looks as if the means are different from one another, but the variances of the groups are broadly similar. All that remains is to compute these two variance estimates and determine their ratio. Long-hand calculations are tedious, so it is best to use available statistical packages. There is an easy and convenient ANOVA package available at the following website: http://faculty.vassar.edu/lowry/anova1u.html The statistic for the ANOVA is known as an F-statistic. If the F-statistic is close to one, you would fail to reject your null-hypothesis. If the F-statistic is significantly greater than 1 (p <0.05), you would reject your null hypothesis, suggesting that there is a difference between treatments. You can obtain a p-value from an ANOVA table to find the probability of what you observe happening by chance (or from the above website). The F-statistic only tells you that there is a difference between at least two of the treatments; it does not tell you where the statistically significant difference lies. If you want to determine which treatment is different from the others, you would follow up with a post-hoc test (test performed after the initial test). One useful post-hoc test is a Tukey’s HSD test, which is very similar to a t-test in that it compares each treatment to each other treatment to find if the means are significantly different from one another. These post-hoc tests can also be conducted at the website listed above. Practice: Using the website listed above, enter the pig data from the table above and assume that you have four independent samples (or treatments) with 4 or 5 replicates for each sample (or treatment). Test the following hypothesis: H0: there is no effect of feed type on pig weight H1: there is an effect of feed type on pig weight What would your predictions for Ho and H1 be? Would you accept or reject your null hypothesis? Explain why. If there is a difference between treatments, which treatment(s) is/are different from the others? How do you know?

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Linear Regression The experimental designs studied thus far involve only one variable (for example, the age at first reproduction of two genotypes of Daphnia). There are many biological experiments, however, in which more than one variable is measured. A very common example involves the manipulation of one variable (say temperature) followed by observations of the response to that manipulation on a second variable (say growth rate). The goal is to quantify the relationship between the two variables so that a mathematical 'model' of the relationship can be derived. This model could be used to predict growth rate from a known temperature, or to gain insight into the nature of temperature-controlled growth. LINEAR REGRESSION is one example of such an approach.

Consider the following experiment designed to investigate the relationship between relative humidity of the air and weight loss in flour beetles. Nine groups of 25 beetles were each weighed at the beginning of the experiment. Then each group was exposed to a different relative humidity in an experimental chamber, weighed again after six days, and the weight loss of each group was computed as the difference in mean weights. This protocol generated nine paired observations of weight loss (mg) and % relative humidity:

Relative Humidity (%) Mean Weight Loss (mg)

0 8.98 12 8.14

29.5 6.67 43 6.08 53 5.90

62.5 5.83 75.5 4.68 85 4.20 93 3.72

The TWO variables in this experiment are relative humidity and weight loss. Weight loss is called the Dependent Variable because its values depend on or are caused by relative humidity. It is typical to designate the dependent variable as Y. By contrast, relative humidity is called the Independent Variable because it is independent of weight loss. Humidity values are FIXED by the experimenter. It is typical to designate the independent variable X. These data can be plotted on a graph called a SCATTERPLOT so the relationship between the variables is clearer.

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Two things can be learned from the graph - the relationship is negative or inverse (weight loss decreases as humidity increases), and except for minor variations, the relationship looks like a straight line. If we could figure out the best straight line to describe the data, we could use the equation to predict future weight loss even for levels of humidity that were not measured in the experiment. Furthermore, a linear relationship would indicate that there is a constant change in weight loss for a given change in humidity no matter what the humidity range. How can we decide on the best straight line? Since one goal of the study is to predict weight loss from humidity, it makes intuitive sense to choose a straight line that goes through the data so that there are as many points above as below the line. A 'first guess' at the line is shown below:

The short vertical line at the point corresponding to the 85% humidity data point is a measure of the distance between the observed (or measured) value of Y and Y

, which is the value predicted

by the equation for the straight line. The idea is to adjust the position of the straight line to make all of these vertical distances as small as possible. This distance is YY

and the sum of these

0123456789

10

0 20 40 60 80 100

X - % relative humidity

Y -

we

igh

t lo

ss (

mg

)

0123456789

10

0 20 40 60 80 100

X - % relative humidity

Y -

we

igh

t lo

ss (

mg

)

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differences would be an overall measure of how well the straight line fits the data. Because

positive and negative differences cancel each other out, it is better to use 2

YY

as an overall

measure of fit. The best straight line will be the one for which this sum of squared differences is minimized - hence this straight is known as the Least Squares Linear Regression.

An important metric used to discuss linear regressions is the r2 value – which is also known as the Coefficient of Determination, or the percentage of the variance in Y that is accounted for by changes in X. The higher the r2 value the more variance in Y is accounted for by changes in X. A p-value can also be calculated for the relationship between these two variables, humidity and weight loss, and as with the T-test above, a p-value of less than 0.05 indicates that the relationship between these two variables is statistically significant (i.e., is not due to chance alone). Correlation Although also used for experiments with TWO variables, Correlation is a little different from regression. It makes no assumptions as to whether one variable is dependent on the other(s) and is not concerned with the relationship between variables. In fact, correlation analysis tests for interdependence of the variables, and it gives us an estimate of the degree of association between the variables. The main result of a correlation is called the Correlation Coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are correlated. If r is close to 0, it means there is no correlation between the variables. If r is positive, it means that as one variable gets larger, the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation). A p-value can also be calculated for the association between these variables, and as above, a p-value of less than 0.05 indicates that the correlation between these variables is statistically significant (i.e., is not due to chance alone), and thus the null hypothesis of no association can be rejected.

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Exercise 18

Statistical Analysis of Data Purpose: To a) practice formulating hypotheses, b) practice summarizing and statistically analyzing data, and c) learn how to perform and interpret the results of a correlation, regression, t-test, and/or ANOVA. Approach: Read a case study, perform data analysis about asthma (see the pages following this assignment), and complete the exercise below.

1) Describe the problem of interest/research question in your own words: 2) Describe the dataset available to you (Hint: look at each table of data - what information

does the data describe?): 3) Within that dataset, describe the data that you will use:

For 4a-9a, you will formulate hypotheses and predictions (be sure that they get good marks using the hypothesis score card!) related to your first research question (10a in the case study) and answer related questions. Then, during the second half of the case study you will do the same for for 4b-9b using your second research question (10b in the case study).

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4a) What is the first research question (10a in the case study) and the associated hypotheses and predictions?

Question:

Null Hypotheses (H0): Prediction (for H0; hint what will the data look like if Ho is true?):

Alternative Hypotheses (H1):

Prediction (for H1; hint what will the data look like if H1 is true?):

5a) Describe the statistical test(s) you will use to answer this question, including the data that will be used in each test:

6a) Describe the results of your statistical tests.

7a) Conclusions: Do your results support your alternative hypothesis and prediction?

8a) What evidence did you use to make these conclusions?

9a) What additional evidence and/or further investigation would make you feel more confident about your conclusions?

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4b) What is your second research question (10b in the case study) and associated hypotheses and predictions?

Question: Null Hypotheses (H0): Prediction (for H0): Alternative Hypotheses (H1):

Prediction (for H1):

5b) Describe the statistical test(s) you will use, including the data that will be used in each test:

6b) Describe the results of your statistical tests.

7b) Conclusions: Do your results support your alternative hypothesis and prediction?

8b) What evidence did you use to make these conclusions?

9b) What additional evidence and/or further investigation would make you feel more confident about your conclusions?

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Case Study: Asthma Prevalence in the United States Purpose: To a) practice formulating hypotheses, b) practice summarizing and statistically analyzing data, and c) learn how to perform and interpret the results of a correlation, regression, t-test, and/or ANOVA. Directions

1. Read the article that starts on the next page, paying particular attention to the bolded section.

2. Consider the scenario below when completing the exercise:

A researcher at Montana State University is concerned about the number of diagnosed cases of asthma. He is interested in the prevalence of asthma in different regions in the United States. As suggested in the article, asthma symptoms may be worsened by air pollution. However, the researcher wants to know if air pollution may also be linked with asthma prevalence in general in the US. In addition, he wants to know if asthma prevalence may be related to other social and/or economic factors in these regions.

This researcher approaches your team to answer the following questions:

10a) Does asthma prevalence differ among the Great Lakes, Southeast, and Northeast regions? 10b) Are rates of asthma related to air pollution or other social or economic attributes in these regions?

3. To answer the questions above, you should use the following statistical analyses:

correlation, regression, t-test, and/or ANOVA. NOTE: you do not have to use all of these statistical tests! Just use those tests that apply to your question of interest. Methods for calculating these statistics are found in the folder that corresponds to your case study.

4. The data set has been provided in the asthma folder. You should use these data in your analyses. However, you do NOT have to use all of the data provided (use just the data that you need to use to answer your question). You are also free to collect additional data sets online if you’d like to.

5. Use this exercise as a report of your findings and recommendations to the University of California, Davis researcher. Place all statistical calculations and graphs in your section's course dropbox.

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Poor Children Drive City’s Asthma Rate Wed, July 17, 2012 By Sumathi Reddy and Jie Jenny Zou

One in eight New York City children has been diagnosed with asthma, with poor children nearly twice as likely to suffer from the respiratory disease, according to a report to be posted by the city health officials on Wednesday.

The report was based on a 2009 survey and is the first time the city Department of Health and Mental Hygiene has estimated the number of children with asthma. The survey of parents found that 177,000 children 12 years and younger—or 13% of children in that age group—had received an asthma diagnosis at some point in their lives.

"New York City's rate on average is higher, but then within the city we know the rate varies dramatically," said Thomas Matte, assistant commissioner for environmental surveillance and policy. "Our rate is really pulled up by the high rates" in poor neighborhoods, he said.

For instance, children in East Harlem are almost 13 times more likely than those on the Upper East Side to visit an emergency room because of asthma, the report said. Eighteen percent of Hispanic children and 17% of black children have been diagnosed with asthma, compared with 5% of white children.

Nationwide, the number of people with asthma continues to grow, with about one in 10 children, or 10%, having asthma in 2009, according to the Centers for Disease Control and Prevention. The CDC classifies children as between the ages of 1 and 18.

Asthma has been a persistent problem in urban centers with high poverty rates.

Dr. Joshua Needleman, a pediatric pulmonologist at New York-Presbyterian/Weill Cornell Medical Center, said the report confirms what doctors know. "You find asthma all over the place, but the kids in the poor areas is where you see the most burden," said Dr. Needleman, who has worked in Baltimore.

Mr. Matte noted that poor households are more likely to have potential triggers, which include pest infestations, mold and secondhand smoke.

He said having an action plan—a set of written instruction for parents and children—is key to managing asthma. But the survey found that only one in three children taking medication for asthma had such a plan.

Child asthma hospitalization rates, which city officials have been tracking for some time, have shown decreases across the board. Still, disparities persist. Rates in the Bronx were two to three times higher than in the city's other boroughs.

http://online.wsj.com/article/SB10001424052702303754904577533361469050818.html

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Exercise 19 Choosing Among Statistical Tests - Part I

Now that you’ve learned about and worked through examples associated with four different statistical tests (t-test, ANOVA, regression and correlation), it is important to pull it all together into a synthetic framework so that you can decide how to analyze data in the future. For example, if you were given a dataset and a research question, how would you decide which statistical test to use in order to answer that question? Below, create a dichotomous key or flow chart for the statistical tests we have learned thus far this semester: t-test, ANOVA, regression, correlation. Wikipedia has a good description of basic flow charts if you’d like some help: http://en.wikipedia.org/wiki/Flowchart. You might ask questions in your chart like: Are the data categorical or continuous? How many variables were measured? How many populations are there? Are there dependent or independent variables? Was an experiment conducted? Etc…

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VII. Statistical Analysis of Data (Continued) Chi-Square (X2) Tests Studies often collect data on categorical variables that can be summarized as a series of counts and can be analyzed with Chi-square tests. The Chi-square test is a useful statistical test for determining whether distributions of categorical data are different (either from each other or from a hypothetical distribution). There are two different types of chi square tests which each are used to address a different question. The Chi-square (X2) one-sample test for goodness of fit is used to test the difference between the distribution of a data set and a theoretical distribution. The Chi-square (X2) test of independence between two or more samples is used to test for independence between two or more frequency distributions of nominal data. For both tests, we need to be able to categorize the data into some finite number of categories, and we need a null hypothesis prediction for how many ought to be in each category. Then we calculate:

E

EO 22

where O are our observed (sampled) values per category and E are our expected or predicted values. This equation has degrees of freedom one fewer than the number of categories.

Both of these tests result in a X2 statistic and a P-value. The X2 statistic (or value) is used to find the p-value on a X2 table such as the one in the table below. The X2 value represents the probability that the observed data deviated this much from the expected data by chance alone. If there is a 5% probability (or less) that this occurred, we say that there is a significant difference between the observed and the expected data (X2goodness of fit) or between the two (or more) distributions (X2 test of independence).

Chi-square (X2) values for one-tailed tests. * df** 0.99 0.95 0.90 0.80 0.50 0.20 0.10 0.05 0.01

1 0.002 0.004 0.016 0.064 0.455 1.642 2.706 3.841 6.6352 0.020 0.103 0.211 0.446 1.386 3.219 4.605 5.991 9.2103 0.115 0.352 0.584 1.005 2.366 4.642 6.251 7.815 11.3454 0.297 0.711 1.064 1.649 3.357 5.989 7.779 9.488 13.2775 0.554 1.145 1.610 2.343 4.351 7.289 9.236 11.070 15.0866 0.872 1.635 2.204 3.070 5.348 8.558 10.645 12.592 16.8127 1.239 2.167 2.833 3.822 6.346 9.803 12.017 14.067 18.4758 1.646 2.733 3.490 4.594 7.344 11.030 13.362 15.507 20.0909 2.088 3.325 4.168 5.380 8.343 12.242 14.684 16.919 21.666

10 2.558 3.940 4.865 6.179 9.342 13.442 15.987 18.307 23.209* The chi-squared value represents the probability that the observed data deviated this much from the expected data by chance alone. If there was a 5% probability (or less) that this occurred, we say that there is a significant difference between the observed and the expected data. In these cases, we must look elsewhere for the source of the observed difference. ** df = degrees of freedom, which is calculated by taking the number of categories and subtracting by one.

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An example in the use Chi-square for goodness-of-fit test This statistical procedure is used to test the difference between the distribution of a data set and a theoretical distribution. As an example, let us say we watch bees on three species of flowers. We have 10 asters, 20 daisies, and 20 dandelions on which to make our observations. We count the number of bees that visit each species, giving us three categories (n = 3) in which to put our bee data. Say we observed 1000 bees. Now, how do we develop our expected frequencies (E)? As a null hypothesis, say bees visit each flower species with equal probability. Then, the number of bees we see on a flower should be proportional to the abundance of that flower in the environment. Therefore, we expect 20% of 1000 on asters, 40% on daisies, and 40% on dandelions, or 200, 400, and 400. These are the numbers we use for E in the formula above. See Table 2 for an example of how to set up and calculate the Chi-square value. Table 2. Example of the calculations made to conduct a Chi-square test for goodness of fit for frequency of bees visiting flowers.

Categories (n) Observed number of

bee visits (O)

Expected frequency of

bee visits based on null

Expected number of bee visits (E)

(O-E)2/E

Asters 186 0.20 200 0.98 Daisies 449 0.40 400 6.00 Dandelions 365 0.40 400 3.06 Total 1000 1000 X2 = 10.04

df = n-1 = 2; p-value (based on Table B1) = p < 0.01

2 needs to be compared to a table of Chi-square critical values. Values larger than the critical values are significant. Based on the example above, we would have to reject the null hypothesis (that the bees visit each kind of flower with equal frequency) because the 2 value obtained is larger than the critical value (in this case 5.991 in Table 1). In other words, bees do not appear to visit the different flowers with the same frequency.

An example in the use of the Chi-square test for independence In this type of problem, the observations are classified by two characteristics, and we wish to examine the hypothesis that the two characteristics occur independently of each other. That is, the distribution of one characteristic should be the same regardless of the distribution of the other characteristics. For example, if eye color and hair color are independent of one another, then the proportion of blue-eyed people having light-color hair should be the same as the proportion of brown-eyed people having light-colored hair, and so on (these theoretical distributions or proportions are for the population, not for the sample). Our procedure is to examine the sample and to decide whether the proportions of observations in the various categories are significantly different from each other. If they are different, we reject the null hypothesis that the two characteristics are independent or different.

Again, we use the Chi-square to test our hypothesis, but this time it is the Chi-square test for independence. And, in this case the expected frequencies must be calculated from the data (they

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are not given as in the previous problem). To do this we would create a table like the one shown in Table 3, which is often called a contingency table.

Table 3. Contingency table of observed values from hair and eye color example.

Eye color Hair Color

Total Light Dark

Blue 32 12 44 Brown 14 22 36 other 6 9 15 Total 52 43 95

Examine the totals for each characteristic. We note that 52 people out of 95 had light hair. If the characteristics are independent, we should expect to find the same proportion of blue-eyed people with light hair. Or, since we observe 44 out of 95 people with blue eyes, we expect to find (44 x 52)/95 people with blue eyes and light hair, or 24.1. By the same reasoning, (44 x 43)/95 = 19.9 people should have blue eyes and dark hair. In short, to calculate the expected values, multiply the row total by column total and divide by the grand total. Now we would create another table as shown in Table 4 with the expected frequencies or values.

Table 4. Contingency table of expected values from hair and eye color example.

Eye color Hair color

Total Light Dark

Blue 24.1 19.9 44 Brown 19.7 16.3 36 Other 8.2 6.8 15 Total 52 43 95

We figure degrees of freedom differently in this type of problem. Let r denote the number of rows in the table and c the number of columns. Then the degrees of freedom are obtained by (r-1) (c-1). For our problem the degrees of freedom are (3-1) (2-1) = 2. Table 1 gives a X2 critical value for 2 degrees of freedom (at the 0.05 level) of 5.991. As you can see from the calculations made below in Table 5, we would reject the null hypothesis that the two characteristics are independent (p-value < 0.01).

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Table 5. Example of the calculations made to conduct a Chi-square test for frequency of hair and eye color.

Eye/Hair Combinations Observed number of

individuals (O) Expected number of

individuals (E) (O-E)2/E

Blue eyes, light hair 32 24.1 2.59 Blue eyes, dark hair 12 19.9 3.14 Brown eyes, light hair 14 19.7 1.65 Brown eyes, dark hair 22 16.3 1.99 Other eyes, light hair 6 8.2 0.59 Other eyes, dark hair 9 6.8 0.71

Total: 95 95 X2 = 10.67 df = (r-1)(c-1) = (3-1)(2-1) = 2; p-value (based on Table B1) = p < 0.01

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Exercise 20

Practicing Chi-Square Tests This exercise will be handed out in lab

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Exercise 21 Choosing Among Statistical Tests - Part II

Now that you’ve learned about and worked through examples associated with six different statistical tests (t-test, ANOVA, regression, correlation, Chi-square goodness of fit, and Chi-square test of independence), it is important to pull it all together into a synthetic framework so that you can decide how to analyze data in the future. For example, if you were given a dataset and a research question, how would you decide which statistical test to use in order to answer that question? Below, adapt your dichotomous key or flow chart from exercise 19 to include Chi-square goodness of fit, and Chi-square test of independence. You might ask questions in your chart like: Am I comparing a distribution of data to a hypothetical one? Am I comparing two data distributions?

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Appendix A: Microscopy About microscopes: Many organisms in lakes/ponds are microscopic. If your project includes such organisms, each member of your group will need to demonstrate the ability to properly use and care for a microscope. Read and answer the questions on the next few pages and then see your instructor for a 1-on-1 skills assessment. Microscopy Orientation and Knowledge Quiz. In order to operate the microscopes in the lab, you need to complete the activity below, complete the self-quiz on the following pages, and get both checked by your instructor at the beginning of class. As part of our exploration of different statistical approaches (and later during the semester), we will be using both dissection and compound microscopes. It is important for everyone to demonstrate proficiency with the microscopes used in LB 144 before embarking on these exercises. Therefore, next you will review microscope components and their function and practice your microscope skills.

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Know Your Light Microscope Preliminary Lab Task

Directions: Label the parts of the compound microscope pictured below.

1

2

3

4

5

6

7

8

9

J. Tuell

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Directions: 1. Turn compound microscope light on. 2. Place the slide into the slide holder and move the specimen over the light beam. 3. Verify that the 3.2x objective is clicked into place over the slide. 4. Move the stage all the way up towards the 3.2x objective by turning the coarse knob towards

you (counter-clockwise). 5. Careful! Don’t look through the objective at this time. Monitor the distance between the

stage and slide to assure that the stage does NOT come into contact with the lens of the objective.

6. Move the specimen into the center of the view and bring it into focus by slowly turning the coarse knob away from you (clockwise). Notice that the coarse knob will lower the microscope stage.

7. Once the specimen is in focus, survey the slide by slowly moving the slide on the stage. 8. Change the position of the condenser.

How does this impact your ability to view the specimen? _____________________________________________________________________

9. Open and close the iris of the microscope. How does the increase and decrease in iris diameter influence your ability to observe the specimen? ___________________________________________________________

10. Move the 10x objective into place above your specimen. Adjust the focus of the object in view by slowly turning the fine adjustment knob. ! If you accidentally turn the coarse knob, go back to step three of this exercise and give

it another try once you have re-focused the specimen under 3.2x. 11. Repeat step 10. and move from the 10x to the 40x objective. 12. Demonstrate your microscopy skills to your lab instructor BEFORE you move on using the

100x objective. 13. If your lab instructor is satisfied with your ability, you are ready to use the 100x lens to view

your specimen (per instructors direction): Turn the 40x objective away from the slide, place a drop of oil on top of the microscope slide, click the 100x lens into place above the slide and use the fine adjustment knob to focus on the specimen. If you accidentally turned the coarse knob, wipe the oil OFF the microscope slide, go

back to step three of this exercise, and give it another try once you have re-focus the specimen under 10x.

Note: Once you have applied oil to your slide you CANNOT turn back to view the specimen under lower magnification UNLESS you remove the oil from the slide (use lens paper) or replace the oily cover slip with a clean one.

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A Summary of Microscopy DOs and DON’Ts

The DOs The DON’Ts Use two hands when carrying a microscope; one hand under the base and one on the arm. HANDLE WITH CARE!

Don’t drop the microscope!

Always BEGIN focusing with the LOW power objective (10x).

Don’t start focusing on a specimen using HIGHER magnification than 10x.

Focus AWAY from specimen. Never move the stage TOWARDS the objectives.

Use FINE knob to make image clear for the 40x and 100x objectives.

Do NOT use the coarse knob when viewing a specimen under 40x or 100x objectives.

Apply one drop of immersion oil on top of your specimen before you switch to 100x.

Don’t SWITCH BACK to a lower objective once you placed immersion oil on your slide unless you remove the oil from your slide.

Use a lens paper to wipe off oil from 100x objective.

Don’t leave the oil on the objective as it will be difficult to remove once it has ‘caked-up’ the lens.

Use lens paper when you clean any glass surface on the microscope.

Don’t use paper other than lens paper to clean off any glass component on the microscope.

Use Kimwipes to clean off or dry microscope slides.

Don’t use brown paper towels to clean or dry microscope slides.

Read the DOs and DON’Ts list carefully! Do NOT ignore the ‘DOs and DON’Ts’ list.

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Microscope Self-Quiz (Individual Assignment)

You can use notes and outside sources to help you answer these questions. Microscopes are expensive pieces of equipment and it’s important to use them properly when in the laboratory so that your data is gathered accurately and so that future classes can enjoy the microscopes. Before you can use any microscope in the lab, please complete this self-quiz to demonstrate your proficiency in using laboratory microscopes. 1. What is the difference between a dissecting scope and a compound microscope? 2. Which of the following regulates the amount of light passing through the slide specimen on the microscope stage?

a) nosepiece b) objective lens c) iris diaphragm lever d) fine focus knob

3. What is the total magnification produced by a microscope, using a 10X ocular lens and a 10X objective lens?

a) 10X b) 20X c) 100X d) 1000X

4. Working distance is the:

a) distance from your house to your job. b) distance the microscope nosepiece travels using the coarse focus knob. c) distance from the bottom of the objective lens to the specimen. d) distance the specimen can travel across the microscope stage.

5. Which of the following describes proper microscope care and technique? a) Be sure to carry the microscope upright, with one hand on the arm and the other under the base. b) To protect the optics of the microscope, place it down gently and don't drag it across the tabletop. c) Always begin focusing with the lowest power lens available. d) Only use the coarse focus knob with the scanning and low power lenses. e) All the above are examples of correct microscope care and technique.

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Appendix B: MS Excel and Mac Computers How to Excel when using Microsoft Excel Basics: Listed here are some useful functions – you can either enter the formula in cells (as listed below) or go to the function menu (either on the toolbar *or* under “Insert”/”function”)

Average (mean) =AVERAGE(select cell range)

Median =MEDIAN(select cell range)

Mode =MODE(select cell range)

Variance =VAR(select cell range) Standard Deviation =STDEV(select cell range) A little less basic: T-tests

=TTEST(select group1, select group 2, tails, type) in general, for this class, “tails” = 2 and “type” = 3.

Correlation =CORREL(array1,array2) Regression

- make an X-Y scatterplot (chart wizard) - click on data series, pick “add trendline” - can place equation, R^2 value, and line on chart

Random numbers =RAND() * Returns an evenly distributed random number greater than or equal to 0 and less than 1. A new random number is returned every time the worksheet is calculated. * To generate a random real number between a and b, use: RAND()*(b-a)+a * If you want to use RAND to generate a random number but don't want the numbers to change every time the cell is calculated, you can enter =RAND() in the formula bar, and then press F9 to change the formula to a random number.

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=RANDBETWEEN(0,20) * Will return a number between the two values specified. You may or may not be able to do this (depending on how equipped your version of Excel is…) AutoFill (see the Help menu w/in Excel for more detail if you need more…) • Filling in values automatically You can use AutoFill to automatically enter a series of data that repeats a pattern: for example, North, South, East, West or 1, 2, 3, 4. You select the cells that contain the beginning of the series, and then hold down CONTROL and drag the fill handle until the series is comp Filling cells in general e.g. if you have fern height

and you want fern height fern height fern height fern height

fill right – control+r fill down – control + d … for these and other options (up, left) – Edit menu, “fill” (pick from “fill” menu) Cut / copy / paste / et al

- all can be found in the Edit menu, or control/apple + C = copy control/apple + Y = redo control/apple + X = cut control/apple + Z = undo control/apple + V = paste control/apple + F = find

A crash course on Macs

- if you’re a macro person (keyboard shortcuts instead of using pull-down menus), almost everything is consistent – just use the “apple” key instead of “control”

- if you’re looking for the internet – it’s Safari (compass icon on toolbar) - right-click (on a one-button mouse) – hit “control” and click simultaneously - in general, Macs can read PC and Mac files… but PCs have issues reading Mac files

(beware creating PowerPoints on Macs and trying to run them on PCs… pictures etc might not transfer well…)

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Appendix C: Instructions to Authors [Adapted from LBC145 coursepacks written by Drs. Luckie and Fata-Hartley, using McMillian (2001)]

One of the learning objectives of your research project in the course is to develop your scientific writing skills. In science, writing is the most important means of communicating research findings. Major scientific findings are never kept secret. Instead, scientists share their ideas and results with other scientists, encouraging critical review and alternate interpretations from colleagues and the entire scientific community. In most cases, scientists report the results of their research activities in scientific journals in a standard written format. In this course, you will practice writing using standard scientific format and style. Throughout this LB144 laboratory, you will practice scientific research and writing by writing a research proposal and a poster associated with your research project. Below, we provide you with information to help with writing your proposal and poster. However, be sure to very closely follow the instructions provided in the exercises about these two assignments. Your scientific writing will be reviewed by the lab instructor, LAs, and your peers in order to point out your areas of weakness and make suggestions for future improvements. At the end of this appendix, we provide you with an example of a rubric used for a research proposal. This is only an example and is NOT the exact rubric that will be used for your assignment. If you are not certain about the level of independence and what constitutes plagiarism in this program, ask your instructor to clarify the class policy. Plagiarism will not be taken lightly.

Types of Literature:

The vast collection of scientific literature can be generally divided into three categories based on how ‘close’ they are to the original experiments and descriptions of scientific phenomena. 1) Primary literature: The bulk of scientific journal articles are primary, meaning that they report the findings of specific experiments or descriptive studies. These articles are peer-reviewed. 2) Secondary literature: From time-to-time investigators write review articles or books that summarize what is and is not known about a particular topic. Rather than conducting new experiments, these authors summarize the primary literature. Although still peer-reviewed, these review articles and books are considered a part of the secondary literature. 3) Tertiary literature: More general texts that summarize what has been reported in review articles comprise the tertiary literature (e.g. your textbook). These texts are either not peer-reviewed or not held to the same level of review. Most new research relies heavily on previous work reported in primary literature. However, review articles can be extremely helpful in understanding how your research project fits into the larger scope of scientific investigation, and can be used as a source to locate primary literature references for the topic of interest. Note that websites were not included in the above description of scientific literature sources. This is because they are not refereed — that is, just about anyone can publish something on the web without some impartial reader reviewing it beforehand. Web pages are often wonderful sources of information, but they can just as often be replete with bad information. At this point,

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it is very difficult to determine the reliability of web sources and, in general, they should generally only be used as a starting point about a particular topic. IMPORTANT things to remember when writing scientifically:

The word data is the plural form of datum. In general, effect is a noun and affect is a verb. The word it’s = it is, there is no possessive. Avoid choppy sentences. When writing a research proposal, using future tense. You are reporting what you intend

to do. When writing a poster, using past tense. You are reporting what you did. Use correct paragraph structure: Each paragraph should have an introductory and

conclusion sentence, and each paragraph should flow from the one previous to the next using transition sentences.

It is not appropriate to use contractions in scientific writing – they are very informal. Remember that species names are latinized words therefore they must be italicized or

underlined. The first letter of the genus name is capitalized, whereas the first letter of the specific epithet is not capitalized (e.g., Drosophila melanogaster).

The RESEARCH PROPOSAL: Scientists need to know the current state of knowledge about their topic of interest before they embark on new discovery. Therefore, the first step of scientific study is to write a research proposal. A scientific proposal usually includes the following parts: a title, an introduction, a methods section, and a references/literature cited section. The SCIENTIFIC POSTER: After conducting research, one way to get the word out about the results it to present your research visually and orally through a scientific poster. A poster can be thought of as a mini-scientific paper that is visually appealing. Therefore, it usually includes the following parts: a title, an abstract, an introduction, a methods section, a results section, a discussion/conclusion section, and references/literature cited. Below, we describe more about each section of a proposal (written in future tense) and a poster (written in past tense). However, please be sure to follow all detailed instructions in the exercises related to writing your research proposal and poster. Title The title should be as short as possible and as long as necessary to communicate to the reader the highlights of the project. The best titles will succinctly communicate to the reader the question being addressed in the paper, including the specimen being studied and the methods used, as well as the main findings. Abstract See pages 55-57 of this course pack for very detailed information about the abstract, which is essentially a ‘mini’ paper. The reader should be able to determine the questions, findings, and significance without reading the entire poster if pressed for time. The Abstract should not be longer than 250 words.

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Introduction The introduction can be thought of as an inverted funnel - with the beginning being wide, setting the context for the study, and the end being narrow, being specific to your research. Therefore, you begin by reviewing the literature and putting your research into historical, geographical, and biological context. Review the background information that will allow the reader to understand the objective and significance of the project. The background information also provides evidence to support the hypothesis posed. A hypothesis is an educated guess; the Introduction should provide the “education”. Include only information that directly prepares the reader to understand the question investigated. Most of this information will come from outside sources, such as scientific journals or books dealing with the topic you are investigating. Next, the introduction describes the question(s) and hypothesis(es) investigated. The American Heritage Dictionary defines hypothesis as “a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation”. A hypothesis is a possible answer to a question, from which predictions can be made and tested. There can be multiple hypotheses used to answer a single question and for each hypothesis, multiple predictions can usually be made. The foundation for high quality, biological research is a good hypothesis. A good hypothesis is more than just an educated guess. When developing your project and writing your paper, you should apply the following hypothesis score card to be sure that your hypothesis is of high quality. THE HYPOTHESIS SCORE CARD [By Dr. Cori Fata-Hartley, MSU College of Natural Sciences] A good hypothesis must:

8.) explain how or why: provide a mechanism 9.) be compatible with and based upon the existing body of evidence. 10.) link an effect to a variable. 11.) state the expected effect. 12.) be testable. 13.) have at least two outcomes. 14.) have the potential to be refuted.

Finally, provide the general approach chosen to investigate the hypothesis. Explain how this approach will address the question and describe the predicted outcomes.

All sources of information must be referenced and included in the References/Literature Cited section, but the introduction must be in your own words. Generally, no quotations are permitted in scientific writing. Refer to the references when appropriate. As you describe your investigation, include only the question and hypothesis that you finally investigated. It is a good idea to write down each item (question, hypothesis, supporting evidence, prediction) before you begin to write your introduction. Write the introduction in past tense when referring to your experimental investigation; if you are writing a research proposal, you should refer to your experimental investigation using the future

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tense. In both cases, when relating background information, use present tense when referring to another investigator’s published work. Methods The methods section describes and summarizes your experiment in such a way that it may be understood and repeated. The trick is to be descriptive but concise. Do not write command style sentences, and stay away from numbered lists (although, there is some lee-way for posters). Be sure to include details such as: what species you studied, where they were obtained, what the treatments and controls were, how many replicates of each were used, and the statistical tests used. It is often useful to provide subheadings for organizational purposes and for ease of reading. Do not include failed attempts unless other investigators may try the technique used. Do not try to justify your procedures in this section of the report. You must cite the source of the protocol for each experiment. For a research proposal, you will need to include even more details in this section in order for the reviewers to determine the likelihood of success of your work. Results This section consists of at least three components: (1) description of the findings, (2) reference to figures (graphs, figures, pictures), and (3) reference to tables. Remember to number figures and tables consecutively in the order that they are mentioned in this section. Refer to figures and tables within the paragraph as you describe your results, using the word Figure or Table in parentheses, followed by its number, for example, (Figure 1). Avoid citing a figure with a full sentence or statement such as, “please see figure 1 for graphed data points” or even just “please see table 2.” For your research proposal, do not place each figure or table at the end of each paragraph in which it is cited. Rather, place figures and tables after the References section. If you have performed a statistical analysis of your data, such as Chi-squared, include the results here and refer the reader to a table with these data. For your poster, you may include the figures and tables near the text that describes the results. Report your data as accurately and specifically as possible. Do not make over-simplified generalizations (i.e., “cell growth decreased”). Do not report what you expected to happen in the experiment. Do not discuss the meaning of your results in this section. Do use a topic sentence at the beginning of each paragraph that introduces the reader to the topic and use transitions between paragraphs. Discussion/Conclusion The discussion/conclusion can be thought of as an inverted funnel. Therefore, the material specific to your project goes first and the material that is more general and puts your research into context goes later. In this section, you interpret the results of your experiments, discuss any limitations or biases of your research, discuss future work that should be done, and link back to the literature review that you discussed in the introduction to put your results into a broader context. Write as clearly and succinctly as possible. A good discussion/conclusion will do the following:

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1.) Briefly remind the reader about the question and hypothesis being addressed. 2.) Briefly summarize the results of the experiments. Do not include details regarding

experimental procedures or repeat the results section - synthesize it. 3.) Interpret the results. Explain how the results answer the questions posed. State whether

your results support or refute your hypothesis. Do not use the word prove in your conclusions. Your results will support, verify, or confirm your hypothesis. They may negate, refute, or contradict your hypothesis. The word prove is not appropriate in scientific writing.

4.) Discuss how your results and interpretations relate with previously published research. This will require you to cite outside references. Some may come from the Introduction, while you will also find new references that specifically relate to your findings. You can speculate and propose theoretical implications of your work.

5.) Describe weaknesses in experimental design or technical difficulties that arose during the research. Explain how these problems specifically affected the outcome of the research.

6.) Discuss experiments that would be performed if the research were to be continued. Explain how those experiments would contribute to answering the questions addressed by the research.

Figures/Tables All figures should be computer generated. The format of the figure will depend on the type of data collected. Photographs and graphs must be done in a professional manner and include computer generated labels when appropriate. Under each photograph or graph, there must be a legend. The legend will include the Figure number, a title, and a one-sentence description of what was done in the experiment. A reader must be able to understand the general concept of the experiment performed without reading the Methods section. Examples of photograph and graph style figures are shown below. While you may not be able to understand the subject matter of the figures, note the style. Each legend begins with the figure number and is followed by a title. In the photograph, the samples are clearly labeled and the labels are referred to in the legend. In the graph, each axis is clearly labeled and the experiment is briefly described in the legend.

Figure 1. Preparation of corn root and corn stalk samples. Corn samples were cut into units no greater than 4 mm3 using a surgical scalpel. Three stalk samples and three root samples were prepared. Root 1 (R1), Root 2 (R2), Root 3 (R3), Stalk 1 (S1), Stalk 2 (S2), and Stalk 3 (S3).

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Figure 2. DPM and Virus Yield. HeLa cell monolayers were infected with Mengovirus at a multiplicity of 50 pfu/cell. DPM+ samples had 80 μM DPM (in ethanol) added to the media at the time of infection. DPM- samples were dosed with an equivalent volume of ethanol. Medium from DPM+ cultures was exchanged with drug-free medium at the indicated times. Virus was harvested at 8 hrs PI and the titer determined by plaque assay.

Tables should only be used when the data being presented cannot be reported in a simple and comprehensible manner in the Results section. Tables should not be used to present raw data. In other words, do not report everything you have recorded in your lab manual in the form of tables. The format for a table is quite different than that of a figure. The title appears at the top of the table. There is no legend. A footnote may be necessary to clarify an important point in the table. Two examples of tables are displayed on the next page.

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Table 2. Mengovirus plaque phenotypes in the presence of DPM.

a Values represent the average of two experiments each done in triplicate.

b Plaques in the absence of DPM averaged about 2mm in diameter.

Reference Citation Formatting

In-text Citations

Single author: Herbivory benefits plants (Belsky, 1986). Two authors: Seed-eating rodents were more successful than ants (Brown and Davidson, 1977). Multiple authors: Simple models of mutual interference are not complete (Free et al., 1977) *NOTE: et al. is an abbreviation for the Latin term et alii meaning and others. Since it is an abbreviation, it requires a period.*

References Cited Section Format

Journal articles:

Single Author: Belsky, A. J. 1986. Does herbivory benefit plants? A review of the evidence. American Naturalist 127: 870–892.

Two Authors:

Brown, J. H. and D. W. Davidson. 1977. Competition between seed-eating rodents and ants in desert ecosystems. Science 196: 880–882.

Concentration, µM DPM

Plaque Reduction (%)a

Relative Plaque Sizeb

80 100 N/A

60 98 minute

40 93 +

20 68 ++

10 25 ++

0 0 ++++

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Multiple Authors: Free, C. A., J. R. Beddington, and J. H. Lawton. 1977. On the inadequacy of simple models of mutual interference for parasitism and predation. Journal of Animal Ecology 46: 543–554. *NOTE: DO NOT change the order in which the authors are listed in a specific journal article.*

If the source was published in an online ONLY journal: Ricca, M. A., D. H. Van Vuren, F. W. Weckerly, J. C. Williams, A. K. Miles. 2014. Irruptive dynamics of introduced caribou on Adak Island, Alaska: an evaluation of Riney-Caughley model predictions. Ecosphere 5: 1-24. http://www.esajournals.org/doi/pdf/10.1890/ES13-00338.1 Books: Chapter within a book: Goldberg, D. E. 1990. Components of resource competition in plant communities. Pp. 27–50 in

J. B. Grace and D. Tilman, eds., Perspectives on Plant Competition. Academic Press, San Diego.

An entire book: Hynes, H. B. N. 1970. The Ecology of Running Waters. University of Toronto Press, Toronto.

Theses:

Watson, D. 1987. Aspects of the population ecology of Senecio vulgaris L. Ph.D. thesis, University of Liverpool.

A textbook:

Freeman S. 2005. Biological Science – 2nd ed., Chapter 13 “Mendel and the Gene”. Pearson Prentice-Hall, NJ. A Web Site: Anonymous. 2002. Wisconson Fast Plants Web Site. http://www.fastplants.org/Introduction/Introduction.htm, last accessed 7/10/02

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An Example Scientific Proposal Rubric (points TBA) Title _____ Does the title succinctly communicate to the reader the question being addressed in the

proposal including the specimen being studied and the approach used? Comments: Introduction _____Introduction complete _____observations and questions _____rationale for project _____background info, based on lit review _____hypothesis (null and alternative) clearly stated and adhering to score card _____predicted results for null and alternative hypotheses clearly stated _____ Does the intro clearly/correctly cite background literature and credit others for their ideas

and words? _____ Is the intro well organized so that the audience can follow its points and examples? _____ Does the introduction employ clear and precise language? Comments: Methods _____ Is the approach clearly states and appropriate to test the stated hypotheses? _____ Are the sampling methods clearly defined (review the different elements of good study

design!)? ______Is the description of the supplies and equipment to be used described specifically without

ambiguity? ______Is the analytical tool (statistical test) described and appropriate for the data to be

collected? ______Is the timeline present, clearly written with every group member contributing Comments: References _____ Is the reference section included with the correct number of primary journal articles? _____ Does the reference section use the correct format? _____ Are the chosen papers appropriate for the project? Comments: General content/ writing _____ Proposal writing in future tense _____Spelling and grammar sufficient _____Answered topic/questions in full and thoroughly _____Is the proposal presented in a way that is engaging to the audience? _____Does the final version of the proposal incorporate instructor suggestions from the draft? Comments:

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References [cited in this lab manual]

Barbour, M. G., J. H. Burk, and W. D. Pitts. 1999. Terrestrial plant ecology. 3rd ed. Benjamin/Cummings Publishing Company, Menlo Park, CA.

Engeman, R. M., Sugihara, R. T., Pank, L. F., Dusenberry, W. E. 1994. A comparison of plotless

density estimators using Monte Carlo simulation. Ecology 75: 1769–1779. McMillian, V. E. 2001. Writing Papers in the Biological Sciences, 3rd ed. St. Martin’s Press,

Inc., New York. Wilterding, J. H. and D. B. Luckie. 2002. Increasing Student-Initiated Active Learning with

Investigative 'Streams:' A Molecular Biology Example. Journal of College Science Teaching Vol 31(5): 303-307.

Platt, J. R.1964. Strong inference. Science 146: 347-353. Smith, K. A. 2007a. Teamwork. Ch 2 in: Teamwork and Project Management. K. A. Smith and

P. K. Imbrie, eds., 3rd ed. McGraw Hill, Boston, MA. Smith, K. A. 2007b. Teamwork skills and problem solving. Ch 3 in: Teamwork and Project

Management. K. A. Smith and P. K. Imbrie, eds., 3rd ed. McGraw Hill, Boston, MA. Van Dyke, F. A. 2003. Developing Critical Writing Skills. Ch1 in A Workbook in Conservation Biology.

Van Dyke, F. A. ed. McGraw Hill, Boston MA.

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