EEB 2244 F13 - magicicada.org

General Ecology

EEB 2244/2244W Department of Ecology and Evolutionary Biology The University of Connecticut Fall 2013 © 2013 The University of Connecticut

EEB 2244 Discussion Exercises Fall 2013

© 2013 The University of Connecticut 1

Contents © 2013 The University of Connecticut. For use only in Fall 2013 EEB 2244. Not to be reproduced or disseminated without written permission.



Table of Contents Table of Contents ...................................................................................................................................... 2 Course information and syllabus .............................................................................................................. 5

Lectures: ................................................................................................................................................ 5 Discussions: .......................................................................................................................................... 5 Website (HuskyCT) .............................................................................................................................. 5 Instructor: .............................................................................................................................................. 5 W Coordinator: ..................................................................................................................................... 5 Teaching Assistants: ............................................................................................................................. 5 Textbooks: ............................................................................................................................................. 5 Grading ................................................................................................................................................. 5 W course ............................................................................................................................................... 5 Discussions ........................................................................................................................................... 6 Lecture Topics/Course Schedule .......................................................................................................... 6 Notes ..................................................................................................................................................... 6 What you are responsible for ................................................................................................................ 6 Grading Appeals ................................................................................................................................... 6 Scheduling Conflicts and Make-up Work ............................................................................................. 6 Extra Credit Work ................................................................................................................................. 6 Special Needs ........................................................................................................................................ 7 Communication ..................................................................................................................................... 7 Academic Integrity ................................................................................................................................ 7

Lecture and Lab Schedule ......................................................................................................................... 8 Tree Identification Using a Dichotomous Key ......................................................................................... 9

Introduction ........................................................................................................................................... 9 References: .......................................................................................................................................... 10

Climate and Carbon ................................................................................................................................ 13 Introduction ......................................................................................................................................... 13 In class ................................................................................................................................................ 13 Some guidelines: ................................................................................................................................. 14 Preparation .......................................................................................................................................... 14 Takeaways ........................................................................................................................................... 14 Other Sources ...................................................................................................................................... 14 A note on units: ................................................................................................................................... 14 Expert Areas ........................................................................................................................................ 15

Expert Area 1: Oceans as carbon sinks ........................................................................................... 15 Expert Area 2: Tropical forests as carbon sinks ............................................................................. 15 Expert Area 3: Tundra as a carbon sink .......................................................................................... 16 Expert Area 4: Temperate forests as carbon sinks .......................................................................... 16

Leaf Form and Function .......................................................................................................................... 17 Introduction ......................................................................................................................................... 17 Effects of abiotic factors on leaf form ................................................................................................ 17 Effects of biotic factors on leaf form .................................................................................................. 19 B) Young leaf coloration and orientation ........................................................................................... 20 References ........................................................................................................................................... 22

Behavioral Ecology ................................................................................................................................. 23 Introduction ......................................................................................................................................... 23 Part 1: The prisoner’s dilemma ........................................................................................................... 24 Part 2: Hawk-dove or “chicken” game ............................................................................................... 28



References ........................................................................................................................................... 30 Using Excel ............................................................................................................................................. 31

Entering your data: .............................................................................................................................. 31 Summary Statistics: ............................................................................................................................ 31

Calculate an average: ...................................................................................................................... 31 Calculate other summary statistics: ................................................................................................ 31

Life Tables .............................................................................................................................................. 33 Background ......................................................................................................................................... 33 Section I: Field work ........................................................................................................................... 34

Age-specific probability of survival for Adults .............................................................................. 35 Age-specific probability of survival for Sub-adults ........................................................................ 36 Age-specific probability for Juveniles ............................................................................................ 36 Birth Rate ........................................................................................................................................ 36

Section II: Data Analysis .................................................................................................................... 37 Exercise 1: Mortality ....................................................................................................................... 37 Exercise 2: Natality ......................................................................................................................... 38 Exercise 3: Population Projection ................................................................................................... 39

References ........................................................................................................................................... 40 Population Modeling ............................................................................................................................... 41

Introduction ......................................................................................................................................... 41 I. Interaction-enhanced Models ...................................................................................................... 41

II. Modeling exponential population growth ...................................................................................... 41 III. Modeling logistic population growth ............................................................................................ 42 IV. Modeling predator-prey cycles ..................................................................................................... 44

Understanding Statistics .......................................................................................................................... 45 Introduction ......................................................................................................................................... 45 Scientific versus Statistical (Null) Hypotheses ................................................................................... 45 “P-values” and Types of Inferential Errors ......................................................................................... 46 Objectives of Statistical Tests ............................................................................................................. 46 The Exercise ........................................................................................................................................ 47 Questions: ........................................................................................................................................... 49

Associations and Distributions of Populations ....................................................................................... 51 Introduction ......................................................................................................................................... 51 Sampling the plant species .................................................................................................................. 51 Analysis of quadrat data ...................................................................................................................... 52 References ........................................................................................................................................... 54 Data Sheet ........................................................................................................................................... 54 Software tools to use ........................................................................................................................... 55 Climate Data ....................................................................................................................................... 55 Species Distribution Data ................................................................................................................... 56 Developing a hypothesis: Questions to consider ................................................................................ 57 Exploring your hypothesis .................................................................................................................. 57 Going Further ...................................................................................................................................... 58 Literature: ............................................................................................................................................ 58

Island Biogeography ............................................................................................................................... 59 Introduction ......................................................................................................................................... 59 Procedures ........................................................................................................................................... 59 Questions: ........................................................................................................................................... 61

Measuring Biodiversity ........................................................................................................................... 63 Introduction ......................................................................................................................................... 63



Sampling the community using randomly placed quadrats ................................................................ 64 Species Richness ................................................................................................................................. 64 Species accumulation curves .............................................................................................................. 65 Species diversity ................................................................................................................................. 66 References: .......................................................................................................................................... 66

Conservation Biology ............................................................................................................................. 67 To give a good talk ............................................................................................................................. 67

Acknowledgements ................................................................................................................................. 68



Course information and syllabus

Lectures: Tuesday, Thursday 11:00–12:15, TLS 154

Discussions: Torrey Life Sciences 311

Website (HuskyCT)

Instructor: John Cooley TLS 81;

W Coordinator: Eric Schultz PBB 205B;

Teaching Assistants: James Bernot TLS 478; Wen Chen BioPharm 204; Jeffrey Divino BioPharm 210A; Tim Moore TLS 363a;

Textbooks: Required:

• Ricklefs, R.E. 2001. The Economy of Nature, 6th ed. (W.H. Freeman). • Ecology Discussion Manual (available via course website or at the UConn Coop).

Suggested: • Gotelli, N. J. 2008. A Primer of Ecology, 4th Ed. (Sinauer).

Grading Grades will be based on the following scheme. Final grades may be adjusted based on relative performance, but students with a composite score equaling or exceeding 90%, 80%, or 70% can expect to receive a grade no lower than A-, B-, or C-, respectively. Students enrolled in the W course will have additional graded assignments (detailed under “W course” below).

Assignment Date Point Value Exam 1 19 Sept. 100 Exam 2 24 October 100 Final Exam TBA 200 Section Assignments Weekly 100 Total (excluding W) 500

W course If you are enrolled in EEB 2244W, the writing assignments constitute an additional 125 points (or 25% of the non-W grade). W students are also required to attend a library orientation session. By university regulation, students enrolled in EEB 2244W must receive a passing grade for their W assignments to pass the course.



Please do not attempt to disenroll from a W section without talking to the staff, since failure to disenroll correctly can disenroll you from the entire course!

Discussions Please come to discussions prepared to ask questions about the lecture material. Also come prepared for the weekly exercise. You must attend the discussion in which you are registered, and if you miss a discussion, you may not attend another discussion later in the week without the prior permission and mutual consent of the instructor and the TAs involved.

Discussions meet weekly and attendance is required. Missing more than 3 sections for any reason will result in failure of the course.

Lecture Topics/Course Schedule Lecture topics, readings, and course schedule are detailed in a separate section below. Be aware that the schedule may change over the course of the term.

Notes You are responsible for taking lecture notes. Important figures shown in the lecture will be drawn from the textbook. Although every effort will be made to have lecture notes or outlines posted on HuskyCT before lecture, this cannot be guaranteed.

What you are responsible for Many times in a class such as this, students ask what they are responsible for. The only concise answer that can be given is that you are responsible for demonstrating mastery of the subject matter. You can do this by reading the assigned readings, attending weekly discussions, participating in discussion activities, and by attending lectures and taking notes. Some material in the textbook will not be covered in the lectures or discussions. Some material in the lectures will not be covered by the textbook or discussions. Some material in the discussions will not be covered in the lecture or the textbook. Nevertheless, you are responsible for it all. That said, every attempt will be made to be fair and reasonable, and if you feel that something was unfair or unreasonable, please follow the “grading appeals” instructions below.

Grading Appeals If you wish to submit a grade appeal, you must do so in writing no later than one week after your graded work is returned to you. Please feel free to use (and cite) supporting details from the course readings or other legitimate sources in your appeal—it will help us work through your appeal quickly.

Scheduling Conflicts and Make-up Work Only a note from a physician, the dean, or your academic advisor in advance of a due date or exam will be accepted as a valid excuse. If you have a scheduling conflict that makes it difficult for you to complete an assignment or take an exam on time, please discuss it with us as soon as you become aware of the conflict.

Extra Credit Work Out of fairness to all, no extra credit assignments can be offered in this course.



Special Needs We will gladly attempt to accommodate any student with special needs or concerns. Any student needing accommodation should contact the instructor. For support services, please contact the Center for Students with Disabilities (http://www.csd.uconn.edu/).

Communication We encourage in-class participation, as well as participation on the class discussion group in HuskyCT. We also encourage the use of email for communication with your instructors and TAs. While we will attempt to answer electronic communications in a timely fashion, pressing questions emailed after business hours the day before an exam/assignment due date may or may not receive a response before the next class meeting. Likewise, emails sent after close of business on Friday may not receive a response until the following Monday. Furthermore, certain questions/concerns may be inappropriate or too complicated to answer by email. In such cases, we reserve the right to request that you make an appointment to discuss these matters with an instructor face-to-face. In all communications with instructors and peers, you are expected to exercise common courtesy.

Academic Integrity The University of Connecticut takes academic integrity seriously, as outlined in Appendix A of the Student Code adopted March 2008 (http://www.community.uconn.edu/student_code_appendixa.html). Academic misconduct includes any activity that tends to compromise the academic integrity of the University, or subvert the educational process. Examples of academic misconduct include (but are not limited to) plagiarism, collusion (unauthorized collaboration), copying the work of another student, and possession of unauthorized materials during an examination. Ignorance of the University’s Code of Student Conduct is never considered an excuse for academic misconduct. Academic misconduct will result in lowered grades, failure of the course, or other disciplinary actions.



Lecture and Lab Schedule (subject to change)

Date Lecture Topic Readings (Ricklefs) Lab Topic

1 No section meeting this week 8/27 Ecology Chs. 1, 6 8/29 Nutrients Ch. 2

2 Tree Identification 9/3 Energy Ch. 3 9/5 Climate Ch. 4

3 Climate and Carbon 9/10 Biomes Ch. 5 9/12 Guest Lecture

4 Leaf Form and Function 9/17 Life Histories Ch. 7 9/19 Exam 1 Chs. 1-6

5 Behavioral Ecology 9/24 Behavioral Ecology Chs. 8,9 9/26 Life Tables Ch. 11

6 Using Excel, Life Tables 10/1 Population Growth Ch. 11 10/3 Population Yield Ch. 12

7 Population Modeling 10/8 Stochastic Models Ch. 12 10/10 Species Interactions Ch. 14

8 Understanding Statistics 10/15 Predation Ch. 15 10/17 Optimal Foraging Ch. 8

9 Optimal Foraging 10/22 HBEF: A Case Study Lindenmayer&Likens 10/24 Exam II Chs. 7-9, 11, 12, 14

10 Population Associations 10/29 Competition Ch. 16 10/31 Coevolution Ch. 17

11 Species Distribution Models 11/5 Community Structure Ch. 18 11/7 Succession Ch. 19

12 Island Biogeography 11/12 Biodiversity Ch. 20 11/14 Landscape Ecology Ch. 25

13 Measuring Biodiversity 11/19 Metapopulations Chs. 10, 12, 21 11/21 Conservation Movement Ch. 26

14 Conservation Biology 12/3 Conservation Biology Ch. 26 12/5 Pathways Chs. 22, 23, 24

15 TBA Final Exam



Tree Identification Using a Dichotomous Key

This project is outdoors. Check the weather and dress appropriately.

Introduction Dichotomous keys are invaluable tools for field biologists. Dichotomous keys allow identification of a specimen to a specific taxonomic level, often to genus and species. “Dichotomous” simply means that at every step in the key there is a couplet of two alternate choices. In a key, couplets start with very general information, and subsequent couplets involve making more and more fine discriminations about the anatomy of the specimen until it is identified. For an example of the use of dichotomous keys in biology, see the Wikipedia entry: http://en.wikibooks.org/wiki/Dichotomous_Key However, dichotomous keys based upon morphology have many limitations. Individuals of any species may vary considerably according to geography, season, sex, or age. This may make identification of a specimen difficult or impossible by simply using the key. In many cases only specialists can identify species of animals and plants. Some species cannot be identified by anatomy alone and more sophisticated molecular techniques must be used. Today you will learn how to use a simple dichotomous key to identify local species of trees around campus based upon their winter twig morphology. As you use the key, keep the following questions in mind: 1) What characteristics of the twigs did you find to be the most important in identifying the trees? How did these compare to the characteristics that the key emphasized? 2) Did you always identify a specimen correctly by strictly adhering to the key? How could you improve the key so that correct identifications would occur more often? To use the key, you must first decide whether the leaves (leaf scars in winter) you are looking at are alternate or opposite on the branch (see the drawing below and on page 3). Then you will need to spend some time looking at woody twig characteristics. Use the drawing below (Figure 1) to help you. Many of the traits used to distinguish trees in all seasons are shown here.



We will use the trees and larger shrubs along North Eagleville Road and elsewhere in the vicinity of the Life Sciences buildings for this exercise. During the summer of 1999, a census of trees and shrubs in the central portion of campus was conducted. In the area covered, 3,585 plants were given a unique four-digit identification number, their genus and species were determined, and the information was compiled in a computer database. Approximately 1,000 plants were mapped using a GIS mapping system and many of these were drawn onto campus maps. In the areas surveyed, 377 different species of trees and shrubs were identified. Of those species, 96 (24%) are native to the U.S. Thus, the survey revealed that the majority of landscape plants on campus are non-native species. This is not surprising considering that the campus is a highly cultivated and managed landscape with very little natural vegetation or woodland in the central campus area, but why are non-native species used so often? The number of specimens representing each species was determined relative to the total number of woody plants in the census. This calculation revealed that 40% of the 377 species are relatively uncommon or rare on campus. Uncommon species can be considered sensitive, in that removal of just one or a few plants can result in the total loss of that species on campus. Maintaining the diversity of plant species is of considerable importance to the teaching programs in several departments.

References: Petrides, G. 1998. A Field Guide to Eastern Trees. Houghton Mifflin Co., New York, NY. Watts, M. T. 1998. Tree Finder. A Manual for the Identification of Trees by Their Leaves. Nature Study Guild, New York, NY. Watts M. T., and T. Watts. 1970. Winter Tree Finder. A Manual for Indentifying Deciduous Trees in Winter. Nature Study Guild, New York, NY. Nix, S. no date. A Beginning Guide to Winter Tree Identification. How to Recognize and Name Dormant Trees. About.com: Forestry. http://forestry.about.com/od/treeidentification/a/winter_tree_id.htm



Figure 1. Winter twig characteristics for deciduous tree species.





Climate and Carbon

Introduction

Humans put large amounts of CO2 into the atmosphere by burning fossil fuels and by clearing land. However, only about half of the CO2 emitted into the atmosphere actually stays there. The rest is absorbed by carbon “sinks” -- components of the biosphere that take in more carbon than they release. Two such sinks are thought to be particularly important in slowing the rise of atmospheric CO2. First, the oceans have an enormous capacity to dissolve CO2, slowly moving carbon to deeper waters. Second, photosynthesis consumes CO2, and so enhanced plant growth can pull CO2 out of the atmosphere, storing carbon in plant tissues. Yet when scientists total up their best estimates of carbon fluxes, the numbers do not balance. Instead, there is a substantial “missing carbon sink,” encompassing 2 Pg or more of carbon per year, roughly 15-30% of the amount produced by human activity. In this exercise, you will work in teams to explore what is known about the possible identity of the missing carbon sink and the possible future capacity of various sinks to absorb CO2. This topic is critically important to current ecological debates about global warming.

Understanding carbon is key to understanding global climate. Spurred in part by the 2006 release of the documentary “An Inconvenient Truth,” public interest in climate change has grown rapidly. However, views presented in the mainstream media and on a variety of internet sites vary widely, prompting spirited discussions among politicians, scientists, economists, and other commentators. On the one hand, a majority of climate scientists conclude that the average global surface temperatures have risen since the start of the industrial revolution, and that greenhouse gases (including CO2) produced by human activity are a primary cause. This can be called the “anthropogenic global warming” hypothesis. A major organization supporting this position is the Intergovernmental Panel on Climate Change, or the IPCC. On the other hand, skeptics do not agree with the IPCC position and argue either that global warming is unproven, it is a hoax, that recent climate change is primarily due to natural causes, or that human production of greenhouse gases does not contribute importantly to climate change.

In class

Prior to this exercise, you should read Tim Appenzeller’s article on “The Case of the Missing Carbon” available on the HuskyCT site.

During discussion, students will be assigned to expert areas to research, read and form answers to questions specified for their expert area (see below), based on Appenzeller’s article and reading in class of an additional paper, as well as on-line searches. Students will then meet in new teams composed of one person from each expert group and will explain the material from their area of expertise. Your new team will consider this proposition:

Carbon may be accounted for well enough that we can be confident that by increasing carbon dioxide emissions, humans contribute significantly to global warming.

Each team should strive to produce two or three answers to the following questions:

According to skeptics, what are the major lines of evidence against this hypothesis?

What responses do climate scientists offer to the arguments of skeptics?



You should be able to find quite a few arguments put forward by skeptics in the time available. Focus on the best of these arguments and the nature of the evidence for and against them. During the class session, your group will write a brief description of the strongest arguments that you find by skeptics and the main counter-arguments.

Some guidelines:

• Be respectful of opinions contrary to your own. Your knowledge of this topic will be improved by carefully considering alternative points of view.

• The best analyses are presented in scientific literature that is technical and difficult to evaluate. For this exercise, we will rely on summaries presented on-line to wider audiences. Many of these are produced by climate scientists or other groups for the purpose of public education or political advocacy.

• Because we are relying on sources other than the primary scientific literature, it may be helpful to consider who produces the internet sites. Can you identify who contributes to the web sites that you visit? Are they affiliated with a scientific or political organization? Does it matter?

• Consider the degree to which the statements that you find are supported by evidence, and the degree to which they rely on propaganda.

Preparation

Read the following article on-line, paying particular attention to the four areas of carbon sinks as well as to new vocabulary:

Appenzeller, T. 2004. The case of the missing carbon. National Geographic Magazine, February 2004. http://ngm.nationalgeographic.com/ngm/0402/feature5/online_extra.html

Takeaways

In lecture, we will examine climate early in the semester, and return to the question of carbon and climate change near the end of the term (so this lab will help you study for the final exam). How will your understanding of these topics change over the course of the semester?

Other Sources

The course website contains links to other sources of information offering different points of view. Inclusion on this list does not indicate endorsement by the course staff. You are welcome to consult other sources as well.

The current Wikipedia entry for “propaganda” lists a number of techniques that you may encounter.

A note on units: kilogram = 103 g megagram = 106 g = 1 metric tonne gigagram = 109 g teragram = 1012 g petagram (Pg) = 1015 g = 109 tons = 1 gigatonne (GT)



Expert Areas Expert Area 1: Oceans as carbon sinks (1) Appenzeller’s article indicates that half of the “missing carbon” has been absorbed by the oceans. If the concentration of CO2 in the atmosphere continues to rise, can we expect that the capacity of oceans as a carbon sink will increase? Why or why not? (2) What are the most important environmental problems caused by the absorption of CO2 by ocean waters? Supplemental article: Raven, J. et al. 2005. Ocean acidification due to increasing atmospheric carbon dioxide. Royal Society of London policy document 12/05. Expert Area 2: Tropical forests as carbon sinks

(1) What limits the capacity of tropical forests to act as a carbon sink?

(2) If the concentration of CO2 in the atmosphere continues to rise, can we expect that the net

absorption of CO2 by tropical forests will increase? Why or why not?

Supplemental article: Clark, D.A. 2004. Sources or sinks? The response of tropical forests to current and future climate and atmospheric composition. Philosophical Transactions of the Royal Society London Series B 359:477-491. Note: “TNC” refers to total nonstructural carbohydrate, quantifying carbohydrate available to the plant as an energy source.



Expert Area 3: Tundra as a carbon sink

(1) A long-term warming trend is expected to convert soil nutrients in tundra into forms that plants can more readily use. Increased nutrient availability, in turn, is hypothesized to facilitate increased plant growth, pulling CO2 from the atmosphere. How did Mack and co-authors (2004) test this hypothesis experimentally, and what did they find?

(2) How is the role of tundra in the carbon cycle likely to change as a result of global warming? Supplemental article: Mack, M., E.A.G. Schuur, M. S. Bret-Harte, G.R. Shaver and F.S. Chapin III. 2004. Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. Nature 431:440-443. Expert Area 4: Temperate forests as carbon sinks

(1) What did Oren and co-authors (2001) find limits the ability of forests to respond to increased CO2 levels?

(2) If the concentration of CO2 in the atmosphere continues to increase, can we expect that the net absorption of CO2 by mid-latitude forests will increase? Why or why not?

Supplemental article: Oren et al. 2001. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere. Nature 411:469-471.



Leaf Form and Function Note: We will gather in TLS 311 before heading to the TLS greenhouses. See http://titanarum.uconn.edu/ for an introduction and overview of UConn’s live plant collection, including a listing of all species in the collection.

Introduction Leaves are critical to life on this planet. They capture solar radiation and transform it into chemical energy in the form of organic compounds. Leaves are therefore the primary providers of the biologically available energy that all organisms need in order to function, grow, and reproduce. Within any particular plant community, we can find tremendous variation in leaf size, shape, thickness, surface characteristics, orientation, pigmentation, and optical properties. This variation occurs on many different levels: among taxonomic groups, among species, within species, and even within an individual. In this exercise we will explore a wide diversity of leaf forms and leaf adaptations through demonstrations in the greenhouse along with some direct measurements of leaves. Keep the following questions in mind as you examine the plant specimens at each station: Are particular types of leaves or leaf modifications associated with particular environments? Are they associated with particular biomes? Is there evidence of co-evolution of leaf shape with particular animals?

Effects of abiotic factors on leaf form 1) Leaf pattern and color Many plants have variegated or unusually colored leaves. Some species have variegated leaves with purple or red underfaces (e.g. some members of the family Maranthaceae). Other plants have leaves that appear to be iridescent (e.g. Selaginella, Strobilanthes). Why might these plants have such unusually patterned leaves? Think about the kind of physical environment that might select for these particular colors and patterns. Hint: this is related to one of a plant’s primary activities – photosynthesis. Where would you expect to find these types of plants? Be as specific as you can (for example, not just “forest”, but what type of forest and where in the forest). 2) Leaf modifications for extreme environments Plants can live in all types of environments. Coping with extreme environments, however, can require morphological, physiological, and structural adaptations.



A) The plants displayed here all live in extreme environments, and their leaves have been modified accordingly. Welwitschia mirabilis and Tillandsia purpurea both live in a very particular type of extreme environment. Welwitschia mirabilis has leaves with many stomata. In fact, the number of stomata found in its leaves is similar to the number found in rainforest plants. However, this plant does not live in a rainforest. What type of environment do you think would cause a non-rainforest plant like Welwitschia mirabilis to have a stomatal count similar to that of rainforest species? Hint: these two species are found in extreme environments respectively along the western coasts of Africa and South America. B) Several species of Lithops and Conophytum inhabit a different type of extreme environment of southwestern Africa and have developed interesting ways to cope with it. What constitutes a leaf in these plants? In the wild, these plants are found in gravelly habitat with the upper leaf surface flush with the ground. Why might these leaves be camouflaged in this way and have such a reduced surface area? C) What modifications allow these species of Hawthornia (from southern Africa) to minimize water loss and still maintain photosynthetic activity? The species of Lithops and Conophytum are similarly modified. Look closely at these plants: where is the photosynthetic tissue? How does light get there? D) Tillandsia and related species on display in this section of the greenhouse belong to the pineapple family, Bromeliaceae. Most bromeliads are epiphytes that live on top of branches and trunks of canopy trees in tropical rainforests. These are habitats that experience periodic water deficits. The roots of most epiphytic bromeliads serve mostly for anchorage and nutrient absorption. Why would the canopy layer of tropical rainforests experience periodic water deficits? How do you think these epiphytic bromeliads cope with periodic water deficit? Hint: look at the special arrangement of their leaves. E) Crassula and various cacti on display in this section of the greenhouse all have alternative photosynthetic pathways, at least during part of their life cycles. Crassula arborescens and several species of Peperomia (some of which are also tropical rainforest epiphytes) have thick, succulent leaves and CAM photosynthesis. Contrast this with the cacti; how do they differ? Clusia rosea, on the other hand, is a tropical rainforest hemiepiphyte that typically has C4 photosynthesis during its epiphytic stage, but switches to C3 photosynthesis once its roots reach forest soil. What are succulent leaves and CAM photosynthesis an adaptation to and why? Why do you think Clusia rosea has different photosynthetic pathways during different stages of its life cycle?



3) Animal-like plants Some plants have highly-modified and often strange looking leaves. The leaves may be clam-shaped, covered with sticky hairs, or fused in the shape of a pitcher. Observe the plants in this display (Sarracenia flava, Pinguicula caudata, Drosera capensis, Dionaea muscipula, Nepenthes alata) noting the various leaf modifications. While the plants differ from one another in appearance, the leaf modifications all serve the same purpose. Notice that Brocchinia reducta is a bromeliad (2D above). What is the purpose of these leaf modifications? What type of environment could select for plants with these types of leaf modifications? 4) Hairy leaves Observe the plants on display, Helichrysum petiolare (from South Africa), Stachys lanata (from the Mediterranean), and Kleinia tomentosa (from African deserts), as well as Rebutia muscula and Tillandsia xerographica. What do all these plants have in common? What function(s) might this common feature serve? Hint: think about the color of these plants and the environments in which they live.

Effects of biotic factors on leaf form 5) Prickles and spines The prickles and spines on the cacti and other plants on display are actually modified leaves (most cacti do not have photosynthetic leaves). Where does photosynthesis take place in most cacti? Why are most cacti covered with spines/prickles? Why is it important for these plants to have such spines/prickles? 6) Divergent leaf shapes in the genus Passiflora Observe the wide variation in leaf shape among different but closely related species of the genus Passiflora. Can you think of a single factor that could have produced this impressive variation? Studies by Rausher (1978) suggest that selection by ovipositing female butterflies may be responsible for divergence in leaf shape by host plant species that they use for oviposition. How can ovipositing female butterflies influence leaf shape of larval host plants? Why would leaf shape of host plants diverge? Look for leaves that have spots that mimic butterfly eggs. Why might these have evolved?



7) Leaf color A) Bright coloration in mature leaves Observe the plants on display and notice the bright coloration on bracts (modified leaves) of Holmskioldia sanguinea and Justicia brandegeana. What do you think might be the primary function of these brightly colored patches/bracts?

B) Young leaf coloration and orientation Young leaves of some species, particularly tropical rainforest species, are often red, soft, limp, and hang down as if they are wilted (see young leaves of Hibiscus tilaceus and Syzygium jambos). Kursar and Coley (1992) have shown that these leaves delay production of chlorophyll while other pigments such as anthocyanins are produced early. Anthocyanins, which produce red coloration, disappear from the leaf when the leaf stops expanding and the cuticle (a waxy layer surrounding the epidermis of the plant shoot) develops. Coley and Aide (1989) hypothesized that this may be an adaptation against herbivory. According to this hypothesis, what would be a likely function of anthocyanins? Suggest a reason why Coley and Aide’s hypothesis does/does not make sense. 8) Leaf parts that develop into new plants Take a close look at some of the leaves of the species on display: Tectaria gemmifera, two Kalanchoe species, and Begonia hispida. You will be able to see tiny new plants developing on or from the leaves of the adult plants. These are examples of asexual reproduction or vegetative propagation. How does asexual reproduction differ from sexual reproduction? Indicate one advantage and one disadvantage of each form of reproduction. 9) Leaf pouches Notice the strange-looking green pouch on Dischidia rafflesiana. What might be the function(s) of this unusual type of leaf modification? What are the stream-like structures inside, and why do you think they are there? 10) Leaves that provide special food/home for insects Insects, particularly leaf-cutter ants, are among the most important herbivores in the tropics. However, the leaves of several tropical plant species produce pre-packaged sugary food for several species of ants and wasps to eat. Notice the dot-like structures called extra-floral nectaries on the underside of leaves of passion-flower plants (station 5). Other tropical plant species such as Acacia hindsii produce



fat-rich food in the form of Beltian bodies on the tips of their leaflets. In addition they produce extra-floral nectar on cup-like structures on the rachis of leaves. These plants also have hollow thorns that serve as homes for specific species of ants. Similarly, Hydnophytum formicarum and Myrmecodia platyrea provide thick bases for ants to make their homes. Why do you think these plant species try to attract, reward, and even house insects? Hint: think about what the plants might get in return. How could you test if these plant strategies actually pay off? 11) Leaf movement Although plants generally remain in one position, leaves can often be observed in motion. In the case of the sensitive plant Mimosa pudica, leaves and leaflets immediately fold together tightly when they are touched. This movement is triggered by nerve-like electrical impulses. You can touch a leaf yourself to observe how quickly the leaflets fold together. It takes much longer for the leaflets to unfold, so be careful to touch only one leaf. Another plant, the Asian telegraph plant Codariocalyx motorium, has leaves that move irregularly through the day. The reason for this irregular movement is unknown. Can you think of a reason why the sensitive plant (Mimosa pudica) folds its leaflets when it is touched? (Think about their environment) 12) Specific Leaf Area Now that you have had an opportunity to look at wide variation in leaf characteristics found around the world, let’s look in closer detail at some of the morphological or physiological traits that may be important in the adaptation to local environments. As you have seen, leaves can vary tremendously in size, thickness and toughness for example. Leaf area relates directly to the surface available for light interception and thus photosynthesis. The larger the leaf area, the greater is the potential ability to capture light and thus to photosynthesize. The leaf area per unit weight (also known as Specific Leaf Area - SLA) provides a way to quantify the ability of the leaves of a particular species to acquire carbon (via photosynthesis) while maintaining water balance. Leaves of species with high SLA are able to acquire carbon more efficiently than leaves with low SLA, because they invested less carbon and nutrients in building the leaves; they are lighter than leaves with lower SLA. However, leaves with high SLA will lose water more quickly. Why is this the case? Hint: think of the implications of surface area to volume relationships. Under what conditions is this a disadvantage for the plant?



Leaves with lower SLA are typically thicker; they will usually lose less water per unit time; and they tend to be longer lived. But the cost of lower SLA is lower photosynthetic efficiency (unless one thinks about water-use efficiency). Under what conditions is this ‘cost’ worth it to the plant? Leaf toughness is also related to SLA; tougher leaves will usually have lower SLA. Tougher leaves may have the same thickness as leaves with higher SLA, the differences in leaf weight being driven by dense fibers in the tougher leaves. For plant species with tougher leaves, the leaves tend to live longer. When is having a tough leaf an advantage to a plant species trading off against less efficient photosynthesis?

References Coley, P. D. and T. M. Aide. 1989. Red coloration of tropical young leaves: a possible antifungal

defense? Journal of Tropical Ecology 5:293-300. Kursar, T. A. and P. D. Coley. 1992. Delayed greening in tropical leaves: an antiherbivore defense?

Biotropica 24:256-262. Prance, G. T. and K. B. Sandved. 1985. Leaves. Crown Publishers, New York. Rausher, M. C. 1978. Search image for leaf shape in a butterfly. Science 200:1071-1073. Wright, I.J. et al. 2004. The worldwide leaf economic spectrum. Nature 428:821-827.



Behavioral Ecology

Introduction Today we will explore how organisms make optimal behavioral decisions. Frequently, organisms are faced with a set of decisions on how to behave (‘a strategy’) when interacting with others of the same or different species. A strategy is optimal when it leads to higher long-term fitness for an individual using this strategy relative to individuals using other strategies. Many strategies are dependent on the frequencies and types of strategies employed by other individuals in the population. Altruism

Social animals often employ seemingly altruistic group behaviors like cooperative hunting or defense. One particularly striking cooperative behavior is “altruism” which costs the provider and benefits the receiver. Such altruistic acts are expected to be maladaptive because individuals that perform these behaviors should have lower long-term fitness and thus should be eliminated by natural selection in a population. Many apparent cases of altruism arise in groups of related organisms, where costly altruistic individual behavior supports the long-term viability of an individual’s alleles that happen to be shared by receiving kin. J.B.S. Haldane famously quipped “I would lay down my life for two brothers or eight first cousins.” Why would this make sense? However, kin selection does not explain all cases. Reciprocal altruism also occurs among organisms only distantly related and between species.

For example, vampire bats feed on mammal blood during the night, but do not always find a host to feed upon. When they arrive back in their social roosting group, successful feeders will often regurgitate part of their blood meal to unsuccessful feeders (Wilkinson, 1984). This action occurs for non-kin as well as kin members of the roosting group. Why would a bat give up its hard-won blood meal to another unrelated bat?

One way to understand what types of behaviors should evolve, such as in the case of the vampire bats, is to apply game theory. Game theory is a general applied solution to context-dependent behaviors applied to understand behavioral ecology, evolution, economic and political decisions. Game theory assumes a set of strategies, each with a set of costs and benefits that depend on the frequency of other strategies in the population. Information on costs and benefits can be depicted in the currency of fitness or money in a pay-off matrix: John Nash, the troubled genius in the movie “A beautiful mind,’ found a way to determine which set of strategies are optimal in game theory at the age of 22, which eventually won him the Nobel Prize in 1994.

Other individuals Strategy 1 Strategy 2 You Strategy 1 Benefit-cost Benefit-cost

Strategy 2 Benefit-cost Benefit-cost



Part 1: The prisoner’s dilemma One of the most famous games involves two partners in a crime. Each prisoner is independently questioned by the police. The police know that at least one of the prisoners performed the crime, but they can’t pin it totally on either partner without a confession. The prisoners can employ two strategies: Cooperate with the other member and remain silent about the crime or Defect and implicate the other person as the guilty party. If both players cooperate and remain silent they receive 2 years each because the police know that at least one of them did the crime. If one player defects and the other prisoner remains silent, then the defector goes free but the other prisoner gets 10 years. If both prisoners defect, then they both receive 8 years. One could also imagine this scenario in terms of the vampire bats. If two bats cooperate then they receive the benefits of cooperation. A defector that does not reciprocate with a blood meal to a bat that cooperates gets to both keep her blood meal and gets blood meals when it is unsuccessful. The payoff looks like this: 1. Initial game Play the game against a partner in your group. Each of you will have both a black playing card (clubs or spades) and a red card (hearts or diamonds). Your goal is to have the least number of years in prison at the end of the exercise. At each round, choose to either cooperate (red) or defect (black) by showing the appropriate card to your partner at the same time. Record your strategy, that of your partner, and the cost of the strategy based on the payoff matrix above. Then sum up the total cost of the set of strategies employed and answer the questions below. Round 1: Initial prisoner’s dilemma results Game Your strategy

(C or D) Your partner’s strategy (C or D)

Cost

1 2 3 4 5

Summed costs: 1. Who won your 5-round match? 2. What percentage of times did the winner cooperate? How about the loser?

Your partner Cooperate

(red) Defect (black)

You Cooperate (red) -2 -10

Defect (black) 0 -8



2. Display/observer game Now play the game again, but this time one player is chosen to be a displayer – he or she will show their strategy and the observer will then get to choose their strategy based on what they see the other player do. Round 2: Display/observer prisoner’s dilemma results Game Displayer strategy

(C or D) Observer strategy (C or D)

Cost

1 2 3 4 5

Summed costs: 3. The observer obviously should have won. What is the best strategy: defect or cooperate? 4. Why is this strategy best (hint: look at the payoff matrix)? 5. Does displaying change which strategy is optimal?

Watch the following video on the computer for a real-world example of game theory: http://www.youtube.com/watch?v=p3Uos2fzIJ0&feature=player_embedded The payoff matrix is slightly different in this case: 6. What is the best strategy in this case? Discuss with your group. 7. Do you think the woman in the film was right to steal the money? What if it was an organism making a similar decision and the cost was its life?

Your partner Split Steal You Split 1/2 0

Steal 1 0



3. Iterative prisoner’s dilemma You should have a good idea of what strategy to play now. Play the game in part A again without any displaying with the same partner for 20 rounds. Consider that the optimal strategy for a random encounter might not be the optimal strategy when one plays against the same individual time and again. Think about not only what individual strategy wins but what combination of strategies might win. A prize is available for whoever has the least negative score at the end in each group. Round 3: Iterative prisoner’s dilemma results Game Your strategy


Cost

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Summed costs: 8. Who won your 20-round match? 9. What was the percentage of cooperation by the winner? 10. Did the winner defect or cooperate more on a percentage basis than the winner in round 1? 11. Does it make sense to change the strategy when you interact with the same partner over time, why or why not?



4. Iterative prisoner’s dilemma results with a secret strategy Choose one partner to receive a secret strategy from the TA. The TA will show each person a paper listing a strategy to use against the uninformed player. The player holding the secret strategy should not reveal it to the other player until the end of the round. Record on your chart whether you or your partner is applying the secret strategy. Round 4: Iterative prisoner’s dilemma results with a secret strategy Game Your strategy


Cost

Secret strategy?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Summed costs: 12. Who won your 20-round match, the player with the secret or without? 13. Why does the secret strategy beat out others?



Part 2: Hawk-dove or “chicken” game Lastly, we will evaluate a game that involves aggression and submission. This game is commonly called the hawk-dove game, but perhaps it is best known by the game of “chicken.” In chicken, two people in cars drive toward each other at high speeds. The “chicken” swerves and the person who stays the course wins. If both swerve, then they both lose, but only respect. If both stay the course, then they crash and lose their cars and maybe their lives. Here’s the payoff matrix:

A similar situation has been applied to understand animal conflict. Animals of the same species often fight over shared resources like food or mates. However, these fights can result in serious injuries or even death. The Hawk-Dove game was meant to provide insights into why animals should engage in aggression despite the costs. Should you be a war-mongering Hawk or a peaceful Dove? 1. Basic Hawk/Dove game Play the game against a partner in your group. Play your red card if you want to be a hawk, play the black card if you want to be a Dove. Your goal is to have the largest score at the end of the exercise. At each round, choose to either attack (red) or submit (black) by showing the appropriate card to your partner at the same time. Record your strategy, that of your partner, and the cost of the strategy based on the payoff matrix below (This matrix shows the points you will gain from each interaction. Your partner will use the same matrix for scoring, but may receive a different payoff for each interaction). Then sum up the total cost of the set of strategies employed and answer the questions below. Round 1: Initial Hawk-Dove results Game Your strategy

(H or D) Your partner’s strategy (H or D)

Score

1 2 3 4 5

Summed costs:

Your partner Stay straight Swerve You Stay straight -100 1

Swerve -1 0

Your partner Hawk (red) Dove (black) You Hawk (red) (a) 0 (b) 10

Dove (black) (c) 4 (d) 6



1. Who won your 5-round match?

2. What percentage of times did the winner attack? How about the loser? 3. How would you coordinate the game so that you would each receive the most benefit? 2. Mixed strategies in hawk/dove game The Nash equilibrium for Hawk-Dove is either “anti-coordination” in which a player uses the opposite strategy as the other player or a mixed strategy, in which each player randomly plays a certain strategy some proportion of the time. The mixed Nash equilibrium can be calculated from the payoff matrix as (b - d)/(b - d+c – a), where the letters stand for the payoffs in the matrix. What is the optimal mixture in this Hawk-Dove game? This time play the Hawk-Dove game 10 times, but this time, each round, each player should choose their strategy based on a secret coin flip. Round 2: Initial Hawk-Dove with a mixed strategy Game Your strategy

(H or D) Your partner’s strategy (H or D)

Score

1 2 3 4 5 6 7 8 9 10

Summed costs: 4. Did the mixed strategy improve the scores for both partners relative to round 1? 5. How might animals employ a mixed strategy in practical terms (assuming they don’t have coins to flip)?



3. Sequential display and assessment module As we have seen, fighting or playing chicken can be quite costly. In such situations, animals often evolve behaviors of assessment and display. The idea here is to avoid a costly battle (act as a Dove) if you determine that your opponent will win based on an honest display of strength. Walnut flies commonly employ a series of motions to allow them to assess the other fly’s potential in a fight. Work through each of the modules on the web, courtesy of the University of Arizona’s Principles of Animal Behavior module on sequential assessment: http://eebweb.arizona.edu/Animal_Behavior/sequential/sequential1.htm Answer these questions:

1. In the first scenario, which strategy did you employ?

2. Which strategy makes the most sense if your probability of winning a fight is 50%?

3. What are some displays that you’ve seen animals employ during aggressive encounters?

4. Do humans or governments ever use the same tactic? Give an example.

5. What is an honest signal and why is it important? Don’t miss the videos of actual displays in walnut flies at the end! Your TAs will hand out additional questions that will deal with the activities of the walnut flies.

References Dawkins, R. 1989. The Selfish Gene. Oxford University Press, Oxford. Wilkinson, G. S. 1984. Reciprocal food sharing in the vampire bat. Nature. 308: 181-184.



Using Excel What is Excel? Well, it’s a little bit of everything. Although it can do many different things, it

does some things better than others. One thing that it’s really good for is entering your data, because even if the program you will use to analyze your data can’t read Excel files, Excel can export to a variety of formats. Although other similar programs exist, Excel is basically the standard.

Entering your data: 1. Open a spreadsheet, which consists of a grid of cells defined by columns and rows.

Columns have alphabetic headings, and rows have numeric labels. 2. Put a label in the first cell of the column in which you will enter your data. 3. Type each data point into a cell. At the top of the screen, you’ll see a formula bar, which

shows the contents of each cell as you type it. 4. Save your file. 5. That’s all there is to it.

Summary Statistics: Calculate an average:

Suppose your data occupy cells A2 through Ai, where A is the column heading and i is the row number of the last data point (remember that cell A1 is occupied by your label). In Excel terminology, the “(data_range)” is (A2:Ai). Select an empty cell—cell A(i+1) is as good as any—and type the following formula into the formula bar: =Average(A2:Ai). Press return—and the average of the selected cells will fill the cell.

Calculate other summary statistics: The Excel formulae for several functions are listed below:

o Minimum value: =Min(data_range) o Maximum value: =Max(data_range) o Standard Deviation: =StDev(data_range)

You can always ask Excel help for the formula for any other function you wish to perform. You can also type ordinary algebraic formulae into the cells.

Tip: you can copy formulae between cells—though beware of something called “relative”

references—if formulae are moved, they can change. For example, if you copy the formula “=Average(A2:Ai)” from column A to column B, it will change to “=Average(B2:Bi)”. If you move it down a row, but keep it in column A, it will change to “=Average(A3:A(i+1))”. This can be a helpful feature—or it can be annoying. Try moving the formula “=Average($A2:$Ai)” to a new row—does it change?





Life Tables

Background The field of conservation biology typically appeals to people who are interested in nature and saving species and ecosystems from extinction. It does not necessarily attract people who love mathematics and statistics. However, due to the inherent complexity of population and community dynamics, mathematical and statistical tools are necessary for those interested in making rational decisions about conservation issues. In fact, if you ask practicing conservation biologists what they actually do, more than likely they spend much of their scientific time (as opposed to political and judicial time) in front of a computer, analyzing data and working with models to decipher population processes and trends. In this lab you will play the role of a conservation biologist working on a species that is potentially threatened with extinction. You will gather and analyze life history data to determine important population parameters such as birth and survival rates and use a matrix model to project population growth rates and to analyze factors influencing the future of the population. The signature environmental issue of the 1980's in North America was the battle over forests, especially the few areas left in the northwestern U.S., which to that point in time had never been logged. These so-called "old growth" forests have gigantic trees, some as big as 15 meters around and 100 meters tall, often dripping with mosses and covering a bouquet of lichens and dark green shade-tolerant plants on the ground underneath them. A resurgent environmental movement rallied around saving these forests, clashing with the logging industry and loggers whose profits and job prospects depended on harvesting trees. Nobody in the debate denied the magical pull that old growth forests can have on people. Similarly, nobody denied the monetary value of such large trees. Indeed, European settlement of that region a century earlier was motivated by the abundant local natural resources, foremost of which were the giant trees. By the 1980's, a single old growth tree could be worth thousands of dollars. For a logging company to forego harvesting a stand of trees might mean giving up tens of millions of dollars in revenue and hundreds of good jobs for people living in areas where alternate work was scarce. To counter the strong financial pull of the logging companies, environmental groups turned to the courts. One of the primary environmental laws in the U.S. is the Endangered Species Act, which states that no one may perform actions that further endanger the survival of a species on the verge of extinction. Species in danger of extinction are put on a list administered by the Environmental Protection Agency, and are classified as either "threatened", or if they are about to disappear, as "endangered". A bird species called the Northern Spotted Owl lives in forests in the northwestern U.S. and is on this list of threatened species. Spotted Owls are fairly large owls, about 1/2 meter from tip to toe, with a dark brown coat, a barred tail, and spots of white on the head and chest. The Spotted Owl’s life cycle can be divided roughly into three stages: juveniles that have just left the nest in that year, sub-adults that are in their second year and haven't yet started reproducing, and adults that have reached maturity and are reproducing. Adult spotted owls build nests primarily in the tall, large trees found in old-growth forests and are rarely found in an area once it has been logged (Forsman et al., 1996). Advocates for preserving the remaining old growth forests argued in court that cutting down additional old-growth trees would be illegal because it would increase the extinction risk of a threatened species. In order for the environmentalist’s argument described above to be effective, it must be shown that spotted owls really are in trouble and that cutting down trees would increase their extinction risk. Typically, the criterion used to decide that a species is at risk is that its population is small and



decreasing in size. Furthermore, once the legal arguments are over, if the goal is to try to save spotted owls, a decision must be made about what to do in order to help them. For instance, if the goal is to increase the survival of owl eggs or fledglings, the actions taken might be different than if the goal is to increase the survival of adults. Because the resources available for owl management are limited, any tools that help focus efforts in the places where they will do the most good will be beneficial. Scientists studying populations often use life tables to organize information about the survival and birth rates of different life history stages of a species. In the case of the owls, the life table would include the number of eggs laid by each female per year, the fraction that hatch and survive through their first year, the survival rate of the 1 year old sub-adult birds, and the annual survival rate of adult owls. The transitions between these stages are summarized in this life-history diagram:

Eggs/juveniles Sub-adults Adults Gathering the data needed to quantify the survival and birth rates depicted by the arrows above requires a lot of work, especially for a species that lives at the tops of tall trees. However, the rates are important to know because they can be used to make predictions about the future of the population. The basic approach to using life history information to project future population sizes is to: (1) record the number of individuals in each stage at the beginning of the year; (2) multiply the number of individuals in each stage at the beginning of the year by the corresponding survival and/or birth rates from the life table to determine how many individuals are expected to be added to or subtracted from each stage over the course of the year; (3) calculate the expected number of individuals in each stage at the end of the year by adding or subtracting the numbers in (2) from (1). This provides an estimate of the number of individuals in each stage at the beginning of the following year. By repeating the process using the results in (3) as the starting point for the next round of calculations, one can project population sizes into the future, and importantly, can determine if the population is growing or shrinking. The ultimate goal of this lab is to develop and use a model to determine whether the owl population is growing or shrinking and to evaluate whether any of the life history stages should receive special attention. To achieve this goal, you will first need to collect the life table data needed for the model. Your TA will supply you with life history data you need for your owls. Remember that each bit of this information is gathered over long periods of time by dedicated field researchers who often lived in very primitive and dangerous conditions. In Section II you will enter your data into a spreadsheet to estimate the rate of population growth for the owls. You'll then do some simple experiments with the model to attempt to figure out which of the life history stages is most important to protect for the long-term survival of the spotted owls.

Section I: Field work

Real world researchers use a variety of techniques to determine population parameters for species that are not easily observed. We will familiarize ourselves with these owls using the following link to find photos of and calls of the species: http://www.owlpages.com/owls.php?genus=Strix&species=occidentalis



Age-specific probability of survival for Adults Your first challenge will be to determine the age specific probability of survival for adults.

1. You will ‘capture’ all the adult owls on your field site and record this information. Your TA will allow you to choose a random card which has a number on it. This is the number of original adults captured and recorded for that season.

2. In the space below, record the number of adult owls that are captured and marked. Number of marked adults:

3. In theory we release every adult as it is captured and then wait a year to go back to our field site

and capture the adults again. Take a second card from your TA to find how many of your original adults are still there one year later. Number of marked adults that survived:

4. To determine the annual survival rate for adults, divide the number of survivors by the number

of owls that were originally marked. Record this age specific probability of adult survival in the space below and on the board (round to 3 decimal places).

The survival rate you just calculated is based on one small sample from one patch of forest. Additional sampling will give you a better estimate. To do this, we will average the class data. Record the resulting average age specific probability of adult survival in the space below.

5. Using the age specific probability of survival rate you just estimated, you can begin to make

some important calculations. For example, let’s say you started with 1000 adult spotted owls, but for some reason none of them could reproduce. How long would it take for the population to be reduced to half of its original size? Start by calculating the number of adult birds in year 1, using the age specific probability of survival rate you determined in the last step. (fill in the brackets)

Adults in year 1 = Adults in year 0 x annual adult survival rate = 1000 x [ ]

= [ ]

6. Complete the following table to determine how long it would take for the population to be reduced to half of its original size. (Hint: your answer to the last question is the first entry in the table and the starting point for the next calculation.)

Year Number of Adults 0 1000 1 2 3 4 5 6 7 8

7. Based on the values you recorded in the above table, how many years would it take for the

population to be reduced to half of its original size?



Age-specific probability of survival for Sub-adults Next you need to estimate the age specific probability of survival for the sub-adults. You can do so using the same method used for adults, by marking a number of sub-adults in one year and seeing how many survive until the next year.

8. Follow steps 1-3 above to ‘capture’ and record subadults and calculate survival.

9. Record Number of sub-adults that you marked: Number of marked sub-adults that survived for one year: Age specific probability for sub-adults (write this number on the board):

The rate you just calculated is based on one small sample from one patch of forest. Additional sampling will give you a better estimate. To do this, we will average the class data. Record the resulting average sub-adult rate in the space below.

Age-specific probability for Juveniles There is one more stage for the owls, the juvenile stage. Then Repeat steps 1-3 from the adult method and record the juvenile probability (write this number on the board):

The rate you just calculated is based on one small sample from one patch of forest. Additional sampling will give you a better estimate. To do this, we will average the class data. Record the resulting average juvenile rate in the space below.

Birth Rate The final rate you need is the number of juveniles that each adult owl successfully fledges (raises until it can leave the nest) per year. To obtain this number you must look in the nests of spotted owls. The next few steps take you through that process. Take six fledgling cards from your TA (one for each nest). Record the number of fledglings found at each of your nests.

To obtain a reasonable estimate of fertility, we will pool the class data. Record the average number of fledglings per nest (Total offspring / total nests). This is your estimate of the birth rate for adult female owls. Yes, it is a very small number, these are wild birds.

Nest Number of Fledglings 1 2 3 4 5 6



Section II: Data Analysis Open the Excel file “Owls” from the Excel spreadsheet folder on the desktop. Look at the bottom of the spreadsheet and note that there are three tabs: Exercise 1, Exercise 2 and Exercise 3. Clicking on these tabs allows you to move to different worksheets within the workbook “Owls”. During these exercises, you will construct and use a life table based on the data generated in section 1. Life tables are often based only on female animals. Your TA will discuss this with you. To simplify the analysis, we will assume that your data concern only female owls. Exercise 1: Mortality

1. Enter the owls’ age-specific probability of survival and birth rate information in the appropriate cells at the top of the sheet. You will be able to change only the numbers that are in red in this table. All of the other columns are computed from these numbers. You can see how the other values are computed by clicking on a cell and looking at the formula that is calculating the value in the cell.

Survivors (nx) are the number of individuals who reach at least age x, in other words, the number left in the original cohort on the cohort’s x-th birthday.

Survivorship (lx) is the proportion of the original cohort that is still living at age x.

Deaths (Dx) is the number of individuals who die while at age x.

Mortality rate (dx) records the proportion of the original cohort that dies while at age x.

Age specific mortality (mx) is the proportion of x-year-olds that dies while they are at age x (that do not survive to age x+1).

Age specific probability of survival (sx) is the proportion of x-year-olds that survive to age x+1.

Use the formulas in the Excel cells to help answer these questions.

a. What is the difference between survivors and survivorship?

b. What is the difference between deaths and mortality rate?

c. What is the difference between mortality rate and age-specific mortality? d. Double the juvenile age specific probability of survival (sx). What happens to the values for

survivors? What happens to deaths and mortality rate?



e. What happens if you add the age-specific mortality at some age x to age-specific probability of survival for the same age x? Why is this the case? (In life tables, it is also possible to start with the cohort size and the number of deaths at each age, but in this table the number of deaths are computed from the number of survivors.)

Exercise 2: Natality

In Exercise 1, you learned how to set up a life table with information on deaths (mortality) listed by age. You learned that deaths could be expressed in several different ways (e.g., mortality rates, age-specific mortality, etc.), all based on the same information. The same is true for births. In Exercise 2, we will build on that life table, adding information on births.

1. Enter the owls’ age-specific probability of survival and birth rate information in the

appropriate cells at the top of the sheet. You will be able to change only the numbers that are in red in this table. All of the other columns are computed from these numbers. You can see how the other values are computed by clicking on a cell and looking at the formula that is calculating the value in the cell.

Births per surviving female (bx) records the number of young (baby owls) born to each female from the original cohort who survives to age x, while she is age x.

Cohort births (Bx) computes the total number of young born to all surviving females in the cohort while they are at age x.

Births per original female (lxbx) computes the average (expected) number of young born to females of age x, for the entire original cohort, taking into account females who have died before reaching age x. We will assume that females that survive to age x but die before reaching age x+1 reproduce before they die.

GRR is the Gross Reproductive Rate, or total number of young born to a female who survives through all reproductive ages.

Total births computes the total number of young produced by the cohort, taking maternal mortality into account.

NRR is the Net Reproductive Rate or replacement rate, symbolized R(0). This number represents the average (or expected) number of young born to each female in the original cohort, taking maternal mortality into account.

Use the formulas in Excel to help answer these questions.

a. How is cohort births computed, and how does it differ from births per surviving female?

How is births per original female computed, and how does it differ from cohort births?



b. Is this population growing, stable, or declining in size? How can you tell?

Why do you think the replacement rate is called that?

c. Double the births per surviving female.

What happens to total births and the replacement rate? Is this population growing, stable, or declining in size?

d. Divide the births per surviving female in half. Record the replacement rate. ___________ How do you explain the pattern of change in replacement rate from the changes in births you

made? Exercise 3: Population Projection In Exercise 1, you learned how to set up a life table with information on deaths (mortality). In Exercise 2, we added information on births (natality) and computed the replacement rate to estimate future change in population size. In this exercise, you will see how the basic information in the life table can be used to “project” the age structure and details of change in population size into the future. Life tables are essential to predicting future population structure and growth based on the current population size and age.

1. Enter the owls’ age-specific probability of survival and birth rate information in the appropriate cells at the top of the sheet. You will be able to change only the numbers that are in red in this table. All of the other columns are computed from these numbers. You can see how the other values are computed by clicking on a cell and looking at the formula that is calculating the value in the cell.

2. Starting with 1000 sub-adults, 0 adults, and 0 juveniles, project the owl population size for the

next 20 years.

3. Look at the population growth rates projected by your spreadsheet. With the rates of survival and reproduction that you determined from field data, will the owl population grow, shrink or remain stable? What is the supporting evidence for your answer?

4. Has the population reached a stable age distribution by the 12th year (Use the age structure graph to help with your answer)?



You can now use your model to explore how various population parameters influence population growth. This information can help in the prioritization of conservation efforts. For instance, if the juvenile probability of survival is the most important, you might try to protect nests from predation.

5. Before beginning to explore the importance of each life stage to the population growth rate, first predict which stage's survival will affect the growth rate the most, and which will affect it the least. Justify your predictions in the space below.

6. Can population decline be stopped by increasing the juvenile rate (i.e. cell B5)? If so, how much must the juvenile rate be increased to achieve population stability? Remember that rates cannot exceed 1.0.

7. Change the juvenile probability of survival back to its original value.

8. Can population decline be stopped by increasing the sub-adult rate (cell B6)? If so, how much

must the sub-adult rate increase to achieve population stability?

9. Change the sub-adult rate back to its original value.

10. Can population decline be stopped by increasing the adult rate (cell B7)? If so, how much must the adult rate increase to achieve population stability?

11. Finally, how much must the number of births per surviving female (cell C7) rise to achieve population stability?

What you've just done is known as conducting a “sensitivity analysis” on the life table. The way you did it here is a bit crude, but is similar to the more sophisticated approaches that are used by real-world conservation biologists.

12. Which aspect of survival or reproduction most affected the growth rate of the owls? Is this what you predicted? If not, provide an explanation for your result. 13. If you had to choose one aspect of demography as the focus for conservation efforts, which would it be, and why?

Finally, use the link on the website to read the a 2007 report on the plight of these beautiful birds.

References Forsman E D, DeStefano S, Raphael M G, Gutierrez R. J. eds. 1996. Demography of the Northern Spotted Owl. Cooper Ornithological Society, Camarillo, CA. 121 pp.



Population Modeling

Introduction Today you will use STELLA, a modeling program that is used by many ecologists and wildlife biologists. In STELLA, models are built graphically using icons to represent input parameters. The equations to simulate a model are then automatically generated. The outcome can then be viewed as graphs, tables, and animation. For more information on STELLA you can go to http://www.hps-inc.com/. I. Interaction-enhanced Models Familiarize yourself with some of the capabilities of STELLA by working through pages 3-11 in the “Getting Started with the STELLA Software” packet.

II. Modeling exponential population growth Now you are going to create a model that looks something like this:

Explanations of each of the building blocks of the model start on p. 15 of the “Getting Started with the STELLA Software” packet. 1) Close the “PopDynam” model from the previous exercise. Open a new model by choosing New from the File menu. 2) Click on the Stock building block (the rectangle) and put it on your modeling screen. Label it “population”. 3) Draw an inflow to the stock by clicking once on the Flow icon (arrow with a circle under it) at the top of the screen. Move the cursor to a starting position on the diagram. Click and hold. Begin dragging toward the Stock. When contact is made, the Stock will be highlighted. Release the mouse. Now the Flow will be highlighted, and you can enter a name. Enter “birth flow”. 4) Click on a red connector (red arrow) in the menu at the top of the screen. Your cursor will turn into an arrow. Click and hold in the Population Stock. Drag the cursor toward the birth flow regulator until it is highlighted. Now the flow of newborns will be connected to the population size.



5) Draw an outflow from the stock. Select “Flow” and position the cursor in the center of the Stock out of which the Flow will be drawn. Click and hold. Drag the flow out of the Stock. Release the mouse and enter a name. Enter “death flow”. 6) Click on a red connector in the menu at the top of the screen – your cursor will turn into an arrow. Click and hold in the Population Stock. Drag the cursor toward the death flow regulator until the flow regulator is highlighted. Now the flow of dead individuals will be connected to the population size. 7) Now we need to define two Converters that describe rates of births and deaths. Click on a converter in the menu (looks like a circle) and move the cursor (which now looks like a circle) near the birth flow. Click to insert the converter. Name the converter “birth rate”. Then select a Connector from the menu, move your cursor to the inside of the birth-rate Converter, click and hold, and then drag the cursor toward the birth flow regulator. When the connector makes contact with the regulator, the regulator will highlight. Create a second Converter called “death rate” and connect it to the deaths flow regulator using another Connector. 8) At this point it may help to look at the left-most part of the screen, about ¼ of the way to the top. Click on a small button on the frame that looks like a world so that it changes to X2. Now you are in modeling mode. Double click on each flow to define its components: Birth flow = birth rate * population Death flow = death rate * population 9) Define initial population size by double-clicking on the Population stock. Start with n = 25. 10) Define the Converters (birth rate and death rate) by double-clicking on them and entering a constant fraction for each. Begin by making birth rate = 0.1 and death rate = 0. 11) Select the pink Graph Pad from the main menu at the top of the screen, and click somewhere on your model screen to make a graph. Double click on the y-axis – you will get a menu that lets you select what to graph. Click on “Population” in the column on the left, and move it to the “Selected” column on the right by clicking on the >> button. Click OK. 12) Go to the “Run” menu at the top of the screen, choose “Time Specs”, and change “To” from 12 to 100. Also change DT to equal 1. Click OK. Run the model. Watch your population grow exponentially. 13) Save your model on the desktop to prevent any accidents. 14) Experiment with your model, modifying birth and death rates. Can you make population size stabilize by only modifying birth and death rates?

III. Modeling logistic population growth Now you are going to modify your model, adding in a couple more connectors. 1) Begin with the exponential growth model you just made.



2) There are several ways to build in density-dependent population growth. These involve using connectors to allow birth and death rate (or perhaps only one of these) to depend on population size rather than to be a constant fraction. Try this first with death rate. Draw a Connector from the population stock to the Death Rate converter. Now double-click on the converter. Define a linear function that links death rate with the population size. Try setting death rate = 0.001 * Population. When population size is low, death rate is low. When population is high, death rate is high.

3) Now save the model again on the desktop with a new name and run it in the Graph Pad. What shape curve do you see? 4) What if death rate is constant and birth rate is density-dependent? Can you get logistic growth? Create a Connector making birth rate depend on population size, and experiment with the model.

Entering an appropriate equation is a little more difficult than with death rates. Birth rate should drop with population size, but it cannot be negative. Instead of entering an equation to define the converter, you can draw a relationship. To do this, click on the Birth Rate converter. You get a menu allowing you to define birth rate. Click on “population” in the window at top left, and then click on the “Become Graph” button at the bottom left of the popup window. Change the y-scale maximum to 10, and use you cursor to sketch in a relationship between birth rate (on the y-axis) and population (on the x-axis). Be careful what shape you draw – should the line slope down to the right or up to the right? Once you have the shape you want, click “OK”. Run your model. What happens?



5) Finally, make both birth rates and death rates density dependent.

Can you get logistic growth? Do both birth rate and death rate need to be density dependent to produce logistic population growth?

IV. Modeling predator-prey cycles Start on pg. 13 of the “Getting Started with the STELLA Software” packet. This tutorial will take you through the steps of setting up a deer population and the population of plants on which the deer feed. It is up to you to add in the population of predators that will prey on the deer. Can you achieve “coexistence” for the three species such as a pattern of oscillations like in the graph on pg. 7 or stable population levels like in the graph on pg. 11?

A comparable exercise on Population Growth can be found using the program Populus, which you can download on your own from the web site: http://cbs.umn.edu/populus/ . Open Populus, under the “Model” tab, select “Single-Species Dynamics,” and then “Density-Independent Growth.” Select various input parameters, the output variables you want to display and then press the “view” button to show the plot. Then look at “Density-Dependent Growth” models.



Understanding Statistics

Introduction You are already familiar with basic descriptive statistics. When a teacher reports the frequency distribution of scores from an exam for a large class (plotted as a histogram), the median (middle score) and the mean (average) are both measures of where the middle of the distribution lies. Likewise, the range and the standard deviation are measures of the spread of the distribution of scores. These statistics are descriptive, but they do not, in themselves, allow us to evaluate hypotheses. To put a scientific hypothesis to the test, we must use inferential statistics—methods that allow us to infer how probable it may be that a hypothesis is correct. The subject of inferential statistics is enormous and complex, but you can easily learn a few key concepts and some simple but powerful methods in the course of this lab exercise. In this lab, we will use statistical inference to study two aspects of the “demography” of U. S. Lincoln cents (pennies): aging and migration. Coins are “born” (and bear their birth year imprinted on their face), they age and “die” (go out of circulation, for one reason or another), and they “migrate” from their birthplace (a particular mint) to other places through commerce. Lincoln cents have been made since 1909, the centennial of Lincoln's birth, and a new design will appear in 2009, to commemorate Lincoln's bicentennial. Until 1974, they were produced in the US Mints in Philadelphia (no mintmark below the date), Denver (a "D" below the date), and San Francisco (an “S” below the date). In 1975, the San Francisco mint stopped minting pennies for commerce (San Francisco still strikes “proof” pennies for collectors). All pennies in circulation with dates after 1974 were minted at either Philadelphia or Denver, in almost equal numbers. Because the pennies we will use in this lab were collected over the past three decades, many older dates are represented. Rest assured that none of the pennies we will use are rare enough to be valuable (any penny worth more than a dime has already been taken out of the pot!), so please do not “collect” any of them! Just return them to the pot when you are done with the lab.

Scientific versus Statistical (Null) Hypotheses A scientific hypothesis is an explanation for an observation that can be evaluated by scientific methods. For example, suppose you have a feeling that you get more junk email on Sundays than on Mondays. It could be that it just seems that way because you get less legitimate mail on Sundays, because you don't check your Sunday mail until Monday, or because more junk mail is actually sent by spammers on Sundays than on Mondays.

Your scientific hypothesis is that, based on the “Sent” dates, more junk messages were sent on Sundays than on Mondays. To evaluate this hypothesis, you laboriously count all junk emails with Sunday vs. Monday “Sent” dates for several weeks, and compare the totals. The corresponding statistical hypothesis is that the number of Sunday junk emails does not differ significantly from the number of Monday junk emails. This is called the null hypothesis, in this case the hypothesis of no difference. To evaluate the scientific hypothesis we test the null hypothesis. If the null hypothesis can be shown to be very unlikely to be correct, and the means differ in the predicted direction (there are more Sunday junk emails than Monday junk emails, not the reverse!), then we conclude that scientific hypothesis has merit.



“P-values” and Types of Inferential Errors There are two different ways to go wrong with this approach, which bear the highly uninformative names Type 1 Error and Type 2 Error. Suppose that the null hypothesis is actually true, and there is no real difference between the number of Sunday and Monday junk emails. If you reject a true null hypothesis (and claim support for the scientific hypothesis), you have made a Type 1 Error. Suppose instead that the null hypothesis is actually false—there really is a difference in the between the number of Sunday and Monday junk emails. If you conclude erroneously that the null hypothesis is correct (you accept a false null hypothesis), that there is no difference, you have committed a Type II Error. Balancing the probabilities of making these two opposite mistakes is an important goal of inferential statistics.

In ecology and many other fields, it is customary to consider the result of a statistical test to be significant if, in rejecting the null hypothesis, there is a 5% or smaller probability that the null hypothesis is actually true. In other words, we agree to reject the null hypothesis if the probability P of committing a Type 1 error is less than 0.05. Mathematical statisticians have produced tables and computational tools to evaluate these probabilities for each kind of statistical test.

Objectives of Statistical Tests In this exercise, we will explore four common statistical questions, using simple statistical tests that work in ways that will very likely make sense to you. Problem 1 asks a question about correlation: is there a positive relationship between the age of pennies and their degree of oxidation, or put another way, are older pennies more tarnished than younger ones?

Correlation questions examine relationships, positive or negative, between two variables measured on a continuous scale (e.g. mass or length) or on an ordinal scale (e.g. first, second, third, and fourth place winners in a race, or in Problems 1 and 2, pennies ranked by their degree of oxidation). Problem 2 asks a question about the difference between two groups: do the dates on a group of 10 highly oxidized (tarnished) pennies tend to be older than the dates on a group of 10 less oxidized pennies? Differences between groups of measurements can be obvious, or they may be subtle. Only by the application of appropriate statistical tests can the subtle cases be evaluated rigorously. The same kinds of methods can be extended to comparing more than two groups for one or more characteristics, using an approach call analysis of variance (ANOVA). Problem 3 asks a question about observed (sample) proportions in relation to expected proportions: do the proportions of Denver pennies and Philadelphia pennies in circulation in Connecticut differ from the proportions in circulation in the US as a whole (for coin dates 1970 to present)? In biology, a familiar example of this kind of question arises in simple genetic crosses with expected phenotypic proportions (e.g. 3:1 or 9:3:3:1, as in Mendel's peas). Working with proportions of objects (or organisms, including people) in different categories is a common requirement in the natural and social sciences. Another way to do this is demonstrated in Problem 4.

Problem 4 asks a question that compares two sets of observed (sample) proportions: is the proportion of Denver pennies among the total in circulation in Connecticut higher for older pennies than for newer pennies? The older ones have had more time to get here, but perhaps people travel more now than they used to. Comparing sets of observed proportions differs from Problem 3 in that both sets of proportions arise



from sampling.

The Exercise This section is entirely in the laboratory. An Excel spreadsheet (EEB 2244 PennyStatsLab) leads you through the exercise. Divide into groups of 2-3 and gather around a computer. Your TA will provide each group with a pot of pennies to work with.

Please note that Problems 1 and 2 are on the first worksheet and Problems 3 and 4 on a second worksheet in the same Excel file.





Optimal foraging

Does it pay an organism to consume anything and everything that it encounters? Or, do those organisms that are a little more selective in their consumption have higher fitness than indiscriminate consumers? Optimal Foraging Theory is an attempt to answer such questions.

The theoretical aspects of optimal foraging are covered in detail in the lecture notes. In this exercise, we will make use of simulation models to explore these questions.

We’ll start with an extremely simple simulation, in which a frog decides whether or not to capture flies based on their distance from the frog. All the flies are the same (think gross energy content)—they only differ in their distance from the frog. However, because flies that are further away take more effort to capture, the net energy that a frog can acquire from capturing flies differs—flies that are closer are “worth” more than flies that are farther. This simulation is extremely simple, but it helps you start thinking about why different food items have different payoffs to a forager—the payoff of any food item is a combination of its energy content and the effort required to consume it.

The simulation may be found here: http://www.sumanasinc.com/webcontent/animations/content/foraging.html The next simulation we’ll look at is far more complex and is found here: http://www.life.illinois.edu/slugcity/Cyberslug21.html

This simulation models a world in which sea slugs (Pleurobranchia californica) feed on other marine invertebrates. The different prey species have different energy contents, and they also have different levels of defense. For example, Hermissenda crassicornis (Green orbs) are nutritious and have no significant defenses, while Flabellina iodinea (Red Orbs) have strong and toxic chemical defenses and warning coloration. Complicating matters, the environment also contains “Faux-Flabellina” that have warning coloration, but not toxic defenses (what kind of mimic are they?). Each of these prey species leaves a different chemical odor trail, so Pleurobranchia can learn to hunt or avoid different species. You can read more detailed background information about each species’ biology by scrolling down on the simulation’s web page to the section marked “How Does It Work?”.

Questions: • What sorts of evolutionary interactions have led Flabellina iodinea to have chemical defenses? • What might the advantages of warning coloration be? • Read through the instructions on how to use the simulation (found on the same web page, in the

section “How To Use It?”). Start running simulations, paying attention to the points raised in “Things to Notice” and “Things to Try.” See if you can answer the following questions:

• At the end of your simulation, are the relative preferences for the different prey items always the same? Or, can you find values of odor strength and/or hunger levels that lead to unexpected preferences?

• Does the presence or absence of Batesian mimics change the outcome of your simulation? Does the frequency (proportion in population) of the mimics matter?

• Can you identify an optimal foraging strategy for Pleurobranchia? Or is the situation more complex? Is it at least possible to identify some rules-of-thumb?

• If you allow the Pleurobranchia to grow and mate, do those individuals with “more optimal” strategies grow larger/faster or leave more offspring?





Associations and Distributions of Populations

Introduction Today you will learn how to analyze the distributions of organisms using sample quadrat data. Each group will estimate the density and spatial pattern of two or three species of plants within a study community by randomly placing 20 quadrats. An Excel worksheet will then be used to determine whether there are any positive or negative associations between the species. We will focus on a community of plants that float on the surface of ponds. These communities are known as water-weed or duck-weed communities and can be comprise of one to several species of plants but also contain other species including algae and other protists, bacteria, plus a variety of aquatic vertebrates and invertebrates. Today we will focus on a self-contained duck-weed community that is found in our greenhouses: populations of Lemna minor, Wolffia columbiana and Azolla caroliniana (a fern with a symbiotic nitrogen-fixing cyanobacteria living within it) growing in small tanks. We will use an enlarged digital photograph of the population to sample with small quadrats; this is much easier than doing this with live plants floating on water.

Species A Lemna minor Species B Azolla caroliniana Species C Wolffia columbiana (Duckweed) Small, green (Azolla) Large green or red (Water meal) Very tiny green

Sampling the plant species Each group will be provided with a small, clear plastic quadrat. Your task is to locate 20 randomly selected places within this population where your group will place the quadrat and count the number of plants. Why is random placement of the quadrat frame important? 1) Divide into 4 groups. Each group will count plants in quadrats located at 5 locations. 2) Use the data sheet at the end of this exercise for tallying your samples. Fill in the X-coordinate and Y-coordinate columns (see below) with the locations of the sampling quadrats within the bounds of the population plot. This will insure that each quadrat is equally likely to be placed anywhere within the study plot. It must be far enough from the boundaries to allow the entire quadrat to sit within the plot. To generate a list of random coordinates: In Excel, click on an empty cell and type RAND BETWEEN (1,25) then click and hold on that cell and drag the mouse downward for 5 cells. These are your X-coordinates. Fill these numbers in your data sheet. Repeat the RAND BETWEEN function using (1,28) for your Y-coordinates and fill these in your data sheet. The remaining lines will be filled



with the other groups’ information as you go along. Once you have your five samples completed, fill in your information on the board. 3) Notice that there is an x and y scale laid out in centimeters along the sides of the “plot.” This will allow you to easily locate the x and y coordinates of your sample points. Place the quadrat so that the lower left corner is on the coordinate point (the x-y intersection). 4) Your TA will point out the plants species (see figure above as well), and population samples of each are also available in the lab. Each time you place the quadrat frame, count the number of each of these species present within the quadrat. One of the challenges for some species will be deciding what constitutes an “individual” plant to count. Count only those plants for which the plant is at least halfway inside the interior edge of the quadrat. Record the numbers for each species on the data sheet and on the board up front. Add your classmates’ information as they post it.

Analysis of quadrat data When you have recorded all of the data and combined the data for the class, open “Quadrat.xls” from the Excel spreadsheets folder on the desktop. In Box A, replace the numbers indicated in red with your data. The red cells are the only ones whose values can be changed. Variance-to-mean ratio and spatial pattern The Variance-to-Mean Ratio can be used as a simple index of spatial pattern. VMR = 1, Random VMR < 1, Regular (Uniform) VMR > 1, Clumped The formulae in Box B calculate the mean, variance, and variance-to-mean ratio for each species using your data. The worksheet also conducts a simple statistical test to determine if the deviation of the variance-to-mean ratio from 1 is sufficient to warrant a conclusion of clumped or regular spacing, or, instead, if the spatial pattern should be considered to be random. Look through the formulae and the if-then statements as you go. We will then discuss the general idea of the statistics. Statistical test of association between the two species The quadrat data can also be used to test whether there is any association between the local densities of the species. The worksheet first determines for each quadrat whether species A and species B are above or below their own average density. The results are tallied in Box C. A simple statistical test is performed, and the result is indicated in the last line of Box C. There are three possible outcomes. No association: It is possible that there is no evidence for an association between the two species. This means that the abundance of species A in quadrats of this size is independent of the abundance of species B (at least as far as we can tell from the quadrat samples collected). Positive association: It may be that species A and species B are positively associated. This is indicated if there is a significant association between the two species and if there is a high abundance



of quadrats in which both species are found in either above-average or below-average densities (see Box C). Negative association: It may be that species A and species B are negatively associated. This is indicated if there is a significant association and if there is an excess of quadrats in which species A is at above-average density and species B is at below-average density (or vice versa). You will need to highlight, clear and add data to and from the “Species” columns to answer the following questions. Examine the results for your data 1) What are the variance-to-mean ratios for each species? 2) Are these ratios consistent with random spacing, or is there evidence for clumped or regular patterns? 3) What is a P-value? 4) What does it mean to have a P-value < 0.05? 5) What is the pattern of association between the species A and B; A and C; and B and C? 6) What are possible causes for positive or negative associations between species? 7) What is the difference between “No association” and “Negative association”

8) Can you think of a natural example of each of the three association types other than what we just examined in this exercise? No Association: Positive Association: Negative Association:



References Cross, J.W. 2006. Experiments and projects with duckweeds. Missouri Botanical Garden. http://www.mobot.org/jwcross/duckweed/education.htm Dickinson, M.B and T.E. Miller. 1998. Competition among small, free-floating, aquatic plants. American Midland Naturalist. 140: 55-67. Fortin, S. 1994. Population growth of Lemna. http://www.sftext.com/ecology/lemnaazolla.html

Data Sheet Sample X-

coordinate Y-coordinate

Number of species A

Number of species B

Number of species C

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20



Modeling Species Distributions

What determines where a species lives? Answering this question is not simple, and discussions about “Hutchinsonian” vs. “Eltonian” niches, “fundamental” and “realized” niches etc. all reflect the difficulty of determining whether species distributions are determined by climatic or physiographic factors, by interactions with other species, or by some combination of the two.

The advent of inexpensive but powerful computers has given ecologists new tools to explore these questions. One such tool is “Ecological Niche Modeling” (ENM; see Page 204-206 of Ricklefs 2010); note that “Species Distribution Modeling” and other terms refer to similar techniques). These techniques map distributional data onto environmental datasets (usually climatic data) to construct a model of the general conditions a species prefers. These methods may also be used to predict where a species might theoretically be found, under present conditions or even under projected conditions. The literature surrounding these techniques is active and sometimes contentious, because interpretation of these models can be challenging: In one sense, such a model is an attempt to identify the “fundamental niche” of a species; yet the actual distributional data used to construct the model are more appropriately described as derived from a “realized niche,” since no species exists in a vacuum (Soberón and Peterson 2005; Araújo and Guisan 2006). Concerns aside, these techniques are one tool among many to explore data and frame testable ecological hypotheses.

Invasive species are of particular interest to ecologists, because they disrupt and modify ecological communities. Modeling techniques may be used to understand how far invasive species might spread, and to identify native species that might be affected by the spread of invasives.

Changing climates affect both native and invasive species, and climate change may give some species advantages over others. Modeling techniques may also be used to predict how species distributions might change under various climate change scenarios.

Your task is to obtain and explore publically-available data to look at a seemingly straightforward question—in places such as New England, how might native grapevines be affected by the spread of invasives such as Oriental bittersweet? How might different climate change scenarios alter your conclusions? To accomplish this task, you will examine species distribution and climate datasets to develop hypotheses, and then you will use modeling tools to explore your hypotheses.

Software tools to use

Google Earth allows to you make simple maps of data.

MS Excel allows you to quickly visualize data. It also has limited statistical abilities.

Lifemapper is a modeling tool that has a web interface. It may be used to build simple species distribution models.

Climate Data

A wealth of climatic data are freely available via the internet. One excellent starting point is the WORLDCLIM dataset (Hijmans et al. 2005), which consists of 19 predictor variables describing worldwide patterns of temperature and precipitation. For each of these datasets, the globe is divided



up into a grid, such that each sector represents are area of roughly 100km2 at our latitude1; each sector is assigned a value for the predictor variable of interest. To speed up our analyses, we will use only four predictor variables—one describing annual mean temperature (Worldclim_1), one describing total annual precipitation (Worldclim_12), one describing the minimum temperature of the coldest month (Worldclim_6), and one describing the precipitation of the driest month (Worldclim_14). Species distributions could be affected by average conditions as well as the harshest conditions in any given locality, so it is at least a fair starting point start with a pair of temperature and precipitation variables describing average conditions and a pair describing extremes. We have downloaded and prepared these climatic datasets for you using GIS (Geographic Information Systems) tools that are beyond the scope of this course.

Species Distribution Data

Biological collections are repositories for dried or preserved plant and animal specimens. Each specimen typically has information associated with it, such as the species name, who collected the specimen (collector), when the specimen was collected (collection date), and where the specimen was collected (collection location). Specimens can tell us not only where certain species occur, but also how their distribution might have changed over time. Important ecological issues, such as habitat loss, species invasions, and climate change, can be studied using biological collections (Suarez and Tsutsui 2004). The University of Connecticut maintains an active biological collections facility that can be explored here: http://www.biodiversity.uconn.edu/Collections/chp.html.

Today we will examine the relationship between climate and species distributions for two plant species in the context of species invasions, species interactions, and climate change.

Celastrus orbiculatus (Oriental bittersweet) is a deciduous woody vine native to East Asia and invasive or introduced in North America and Europe.

Vitis riparia (Riverbank grape) is a vine native to North America. It produces a fruit that is appealing to both animals and people.

Do an internet search to learn a bit about each species’ ecology. Informative and reputable sources include, but are not limited to the Early Detection and Distribution Mapping System (EDDMaps), the Invasive Plant Atlas of New England (IPANE), the USDA Plants Database, and the US Forest Service.

The Global Biodiversity Information Database (GBIF) serves as a clearinghouse for specimen data from biological collections facilities (including UConn)2.

Navigate to http://data.gbif.org/welcome.htm. In the search box, type Celastrus orbiculatus and click search. GBIF should return a list of species and varieties. Click the first entry, labeled “Species” (Celastrus orbiculatus (en: Asiatic Bittersweet)). This will take you to downloadable results.

On the resulting page, select “ Placemarks for Google Earth (limit 10,000)” and choose to open the file with Google Earth. Specimen locations should appear on the globe. You may need to zoom in to see

1 Because the Earth is roughly spherical, and the grid is described by lines of latitude and longitude, the sectors are not square, and the area of the sectors decreases with increasing latitude. 2 Another source of distribution information is the USDA Natural Resources and Conservation Service (http://plants.usda.gov/java/). The data available on this site are slightly less detailed and slightly more difficult to extract, but you may find it extremely helpful to compare the distributions reported from this database with the distributions reported in GBIF. Why might the distributions differ?



them. Please watch the Google Earth Navigation tutorial: http://www.google.com/earth/learn/beginner.html#tab=navigation to learn how.

Repeat the procedure for Vitis riparia.

Developing a hypothesis: Questions to consider In what habitat does each species tend to grow? How does each species spread? What is the native range of each species?

Explore the geographic distribution of C. orbiculatus. What area(s) of the world have the most specimens?

Explore the geographic distribution of V. riparia. What area(s) of the world have the most specimens?

How do the distributions of C. orbiculatus and V. riparia differ? Load the climate data provided on HuskyCT in the “SDM” folder to Google Earth using the “Open” function. Color legends are provided in the same folder. Explore the data. Develop a hypothesis about why the distributions of these species are similar or different based on your exploration.

Exploring your hypothesis Now that you have a hypothesis based on map observations, you should explore the data behind the maps. Please use the Excel file “Vines.xlsx” provided in the folder “SDM”. This file contains both species distribution and climatic data. In Excel, create scatterplots of temperature vs. precipitation variables for each species that might support your hypothesis. Keep in mind that all points on the plot represent places where the respective species has been observed. If you have not made a scatterplot in Excel before, watch the first 3 minutes of this tutorial: http://www.youtube.com/watch?v=rdccasSXE-w

Tip: Excel assumes that the leftmost column of those you select for a scatterplot is the X-axis of your scatterplot. Rather than spend time trying to figure out how to cause Excel to behave differently, or rather than make scatterplots that do not show what you think they show, just rearrange your spreadsheet. You’ll see that the spreadsheet included in this exercise is arranged to help you make the correct scatterplots.



Do the bioclimatic envelopes for each species overlap or not? What does it mean when the bioclimatic envelopes overlap? What does it mean when they do not?

Now we’ll explore some maps of predicted species distributions from statistical models. Distribution models may be constructed using different statistical methods, but regardless of method, they typically contain climate and sometimes habitat-related variables as predictor variables.

Navigate to LifeMapper (http://www.lifemapper.org/species/) and search for Celastrus orbiculatus. Click on the name in the search result. This will bring you to a map showing the occurrence points used in the model (in yellow) and the predicted distribution of the species under current climate based on the SDM. You can view the map in Google Earth by clicking the “Google Earth” link. The next three entries in the display list are the result of SDM predictions under future climate scenarios. Click these on one at a time. You can bring the climate change projection maps into Google Earth or stay on the LifeMapper site. How is the distribution of C. orbiculatus predicted to shift in North America under climate change? Do all climate scenarios lead to the same prediction? If not, why might that be?

Going Further

Both bittersweet and grape are dispersed by birds, but different bird species prefer each. Suppose the European Starlings are a common disperser of Celastrus orbiculatus (Fryer, 2011), while woodpeckers native to North America are common dispersers of Vitis riparia (Hammerson, 2004). Like bittersweet, starlings are invasive—and in North America, starlings have been shown to have negative effects on woodpeckers (Ingold 1989). How might each plant species’ relationship with different bird dispersers, and the bird species’ interactions with each other affect your hypotheses about how the two plant species will interact?

Literature: Araújo, M. B., and A. Guisan. 2006. Five (or so) challenges for species distribution modeling. J. Biogeogr. 33:1677-1688. Fryer, Janet L. 2011. Celastrus orbiculatus. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture,

Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Available: http://www.fs.fed.us/database/feis/.

Hammerson, Geoffrey A. 2004. Connecticut Wildlife: Biodiversity, Natural History, and Conservation. Lebanon, NH: University Press of New England, 435pp.

Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978.

Ingold Danny J. 1989. Nesting Phenology and Competition for Nest Sites among Red-Headed and Red-Bellied Woodpeckers and European Starlings The Auk, Vol. 106, No. 2 (Apr., 1989), pp. 209-217. Stable URL: http://www.jstor.org/stable/4087714.

Phillips, S. J., and M. Dudik. 2008. Modeling of species distributions with Maxent: New extensions and a comparative evaluation. Ecography 31:161-175.

Ricklefs, R. E. 2010. The Economy of Nature. W. H. Freeman and Company, New York. Soberón, J., and A. T. Peterson. 2005. Interpretation of models of fundamental ecological niches and species’ distributional

areas. Biodiversity Informatics 2:1-10. Suarez, A.V. and N.D. Tsutsui. 2004. The Value of Museum Collections for Research and Society. BioScience 54(1):66-

74.



Island Biogeography

Introduction Island biogeography theory suggests that there is a relationship between the size of an area and the number of species it can be expected to contain. Additionally, the number of species found in an area is a function of the rate at which new species can colonize the area and the rate at which they are lost from the area. The objective of this lab is to examine island biogeography theory in the context of nature reserves run by the US Park Service.

Biogeography is a part of ecology that tries to figure out what types of species live in different areas of the world and whether there is any reason why each species lives where it does. Among the interesting observations that biogeographers made is that there are an incredibly large number of species on continents, a really large number of species on big islands, a smaller number of species on medium-sized islands, and, in general, not all that many different species on really small islands. They also noticed that the farther away an island is from a continent or another big island, the fewer species you find on it. People have come up with several theories to explain these observations. It might be hard for some species that can’t fly or swim to cross the ocean and get to an island. It might also be harder for many species to coexist on a small island, perhaps for the reason that there are not enough different kinds of habitat on an island to support many species. All of these theories have some merit, and are no doubt partially responsible for there being fewer species on islands than you’d expect.

In the late 1960’s, Robert MacArthur and E.O. Wilson combined some of these ideas and came up with another hypothesis, which has been quite influential. They thought of things this way. If you start with an island without any life on it, pretty soon individuals of some species will start to reach it. Initially, every individual that reaches the island will be a new species, and so the rate of species colonization will be very high. However, as time goes by, new individuals coming to the island will probably be members of species that are already established on the island, so the species colonization rate will go down, until finally every species around will be represented on the island and the species colonization rate will be 0. At the same time, if we look at the rate of species extinction on the island, this rate will start out at 0, since on an empty island there are no species to go extinct. As the number of species on the island starts rising, the number of species going extinct on the island will also rise. At some point, these two curves will cross, and that is the equilibrium number of species for that island – that’s the number of species you’d expect to find on that island. Both the species colonization and extinction rates will be affected by the size of the island and its distance from the mainland. The farther an island is from the mainland, the harder it is for species to get there, and so the lower the species colonization rate will be.

Procedures

Open the desktop file “Island Biogeography EEB2244” and take a close look at the tables presented. You will spend some time, with your TA guiding you, changing some data back and forth to see the overall effect on immigration and emigration rates.



Next, we make use of an extensive database on the flora and fauna on lands of the US National Park Service. These lands include National Parks (NP), National Recreation Areas (NRA), National Monuments (NM), and National Historic Sites and Places (NHS and NHP). You can use this database to test two fundamental aspects of island biogeography theory:

a. Species richness should be a positive linear function of park size. In other words, if we plot the number of species in US Parks versus the parks area, we should be able to fit a straight line through the slope, and the line’s slope should be positive.

b. If we compare data from mainland and island parks, the relationships between area and species richness should be positive for both, but the intercepts should be different.

The database: NPFAUNA can be accessed at http://endeavor.des.ucdavis.edu/NPS/

When you have accessed the site, use your browser to select "search a database by park name". To gather the data you need, first click a mouse on the name of the park. You will see some cursory information on the park, including its area, and then you will see a series of taxon-oriented groups (amphibians, mammals, etc.). If you click on any of these buttons, you will generate a list of all the species in that taxonomic group that occur in the park. This is how you will obtain your two key data points: park area and species richness.

For this exercise, we will focus on the basic taxonomic groups (reptiles, mammals, birds, fishes, and plants) plus one subset group (order or family) of amphibians for our comparison of mainland and island parks.

The easiest way to record the data is to make a set of columns with the following headings: Park name, park area (record this in hectares: 1 ha = 100m X 100m), and the number of species recorded for each of the groups. Add column headings for total species, log area, and log species total. You should have a total of eleven columns. Then choose a set of mainland parks and island parks to compare. The parklands in the Pacific Ocean represent the most extensive group of island parks in the US system and probably are the best to use. Therefore, tally species for:

American NM Channel Islands NP Hawaii Volcanoes NP Haleakala NP Kalaupapa NHS NP of American Samoa

Click on each of these parks and then record the number of species present and the size of the park. Then do the same for mainland parks. I suggest the parks in the southeast US, which, although quite



different from the Pacific Islands, are nonetheless more ecologically similar to the Pacific Island parks than are those in the rest of the database, which are mostly in the arid western US. Quantify species for:

Big South Fork NR/NRA Colonial NHP Everglades NP George Washington Birthplace NM Great Smokey Mountains NP Mammoth Cave NP Shenandoah NP Valley Forge NHP

Now plot species richness by park area for mainland and island parks. Both species number and park area should be converted to log10-units prior to plotting to ensure that the relationship is linear. You will need a calculator to do this or just use excel’s functions. Remember that species richness is dependent on park area (it is the Y variable), and park area varies independently of species richness (it is the X variable).

The key attributes of the species-area relationship are the slope and the y-intercept of the line describing the relationship. Specifically, you will need to estimate the parameters “a” and “b” for the equation describing the line: Y = a +bX

The statistic “b” describes the slope of the relationship and tells you how much of a change in Y (number of species) exists for a unit change in X (area). The statistic “a” is the Y-intercept and is the value of Y at X = 0. These two statistics uniquely define a line.

Questions:

1. Is the general prediction of increasing species richness with park area borne out in both the island and mainland setting?

2. What factors do you think might underlie the basic pattern of increasing species richness with park area?

3. Why should the intercept be lower for the island sites versus the mainland sites?

4. What is the value of calculating a line through the data?



5. Surely you observe some noise in the relationships for both the island and the mainland plots. What might account for this noise?

6. Did the data on your subset of amphibians display the same relationship between species richness and area as did the amphibians as a whole? Was this surprising? Why?

7. Does a comparison of the broad taxa (fish, plants, etc.) show the expected relationship? Why might this be?

8. Consider the mainland data for amphibian richness. If the parklands lost, on average, 90% of their area (e.g. 10,000 ha to 1,000, or 1,000 to 100), according to your calculations, by what percent would species number change?

9. Given your results, do you think that scarce conservation funds that could be used to buy a finite amount of land would be better spent to enlarge a small park or a large park (if the goal was to protect as many species as possible)?

10. How might isolation effects differ among taxa (e.g. amphibians vs. plants)? Why? What is an isolation effect?



Measuring Biodiversity

Introduction Biodiversity has become somewhat of a buzzword in our society. Ecologists, environmental managers, politicians, and many others talk of “saving” or “maintaining” Biodiversity. Biodiversity is literally “the variability of life” and refers to the variety of living things and the ecosystems that they form. But how do you measure Biodiversity? In order to understand an ecosystem and its stability through time, we need to find a way to quantify Biodiversity. We can measure diversity at different spatial scales. Alpha (α) diversity is the diversity within a single location. Beta (ß) diversity is the diversity within a landscape or region. Gamma (γ) diversity is the diversity within an entire geographic area. At all of these scales, practical considerations (time, budget, access) limit our measurement ability and accuracy. At all of these scales, the diversity of many different things (species, alleles, habitats, communities, etc.) may be measured and all contribute to Biodiversity.

In today’s lab we will explore species biodiversity within a single tropical rainforest plot. You will work with two measures of diversity (species richness and Simpson’s Diversity index), and you will construct a species accumulation curve. You will also assess how the diversity changes across the plot.

The data come from a long-term study on the biodiversity of a 50 hectare rainforest plot on Barro Colorado Island (BCI) in the middle of the Panama Canal Zone in Panama. BCI is the field site of the Smithsonian Tropical Research Institute (STRI) and STRI is responsible for maintaining this and other plots as well as conducting re-censuses of the 50 ha plot every 5 years and sponsoring a variety of other research projects. See the video clip which provides an introduction to BCI: http://www.thewildclassroom.com/biomes/travelinfo/rainforestBCI.html. The 50 ha plot (~124 acres) is laid out as 1000 x 500 m. It contains something over 300 tree species and about 200,000 living tree stems over 10cm in diameter at breast (dbh: ~4.5 feet) height. Each of these stems has been mapped and its status (growth, death, reproduction) followed every 5 years since 1981. The BCI plot is one of a network of similar plots established elsewhere around the world. The total number of tree species found in Panama is about 1200; Panama is 5 times the size of Connecticut, 40% the size of New England, but less than 1% the size of the entire United States.

How many species of trees occur in a similar 50ha plot in New England?

How many tree species are found in all of Connecticut?

How many tree species are found in New England?

How many tree species are found in The United States?

How many tree species are found in North America?

How does this compare with the estimated number of tree species for all of the Neotropics?



How is it that Panama has so many tree species per unit area? Compare the species per unit area of New England to that of Panama. What does this comparison tell you about the biodiversity of these areas? Can you list a few reasons why some areas are more biodiverse than others?

Sampling the community using randomly placed quadrats 1) The class will work in 4 groups. Given the full BCI 50-ha plot data set, there are simply too many individual trees and species to handle given the time and effort we can devote to sampling. Indeed looking at a plot of the full data set, you simply can’t see the individual trees for the forest. So we have “thinned” the data set randomly to a much smaller number of species. Each species is identified by a unique symbol and color, and identified by species name (cross-referenced by a unique number and abbreviated name). You will sample the 50-ha plot with a series of 6 randomly placed quadrats. 2) Use the data sheet below for tallying your samples. In Excel, click on an empty cell and type what is inside these quotes “=randbetween(1,990)”. Now press enter and a random number between 1 and 990 should show up. Copy and paste or click and drag to create a column of six random numbers. These are your x-coordinates. Repeat this using (1,490) for your y-coordinates. These numbers will allow you to locate your sampling quadrats within the bounds of the population plot. This will insure that each quadrat is equally likely to be placed anywhere within the study plot. 3) Notice that there is an x and y scale in meters along the sides of the “plot.” This will allow you to easily locate the x and y coordinates of your sample points. Place the quadrat so that the lower left corner is at your sample point (the x,y). 4) For each quadrat, list the species you found (use the unique number for each species) and for each species encountered, list the number of individuals found in the quadrat. Fill in the table below. Repeat this until you have sampled all 6 plots. 5) The data will then be tabulated on the board for the entire class for subsequent analysis.

Species Richness In the course of your sampling, you have unknowingly measured the species richness of your sample. Species richness is the total number of species in a given sample, area, or entire community. Your set of plots is your sample of the community. Located on the board is a species richness table. This table represents the species richness of each group’s samples. Fill in the table using a “1” if you found that species in that sample and a “0” if you did not find that species in the sample. Look at the other groups’ data. Did each group find the same species in each sample?



Did each group find the same number of species in each sample? Why or why not?

Species accumulation curves As you have probably seen by now, species richness was not the same for each sample or for each group even though the communities sampled were almost the same. Why might this be? We’ll use a species accumulation curve to look at this problem. This is simply a plot of the cumulative number of species found against the number of samples taken. The tricky part is that you need to keep track of every new species found. For example, say you had a total of six species found in your first sample and six species in your second sample. The important question is whether or not the species in the second sample are the same as the species in the first sample. Your species total for the first sample is six. The total for the second sample is the number of species in the first sample (six) plus any new species found in the second sample that were not found in the first sample. By using the accumulated number of species, you can create a species accumulation curve. The table on the board that has species presence and absence recorded (see the previous section) also has a row called “Accumulation”. Here you can total up the accumulated number of species for the entire class. Using this data, fill in the graph on the board and on the graph on the next page. On the x-axis you will have “Samples” and on the y-axis you will have “Cumulative Number of Species”. Each point represents a sample number and its associated cumulative number of species. What information does the completed graph give you? In particular what does it tell you about your measure of total species richness (total cumulative number of species) in relation to sample size? What does it tell you about sample effort and about the order in which successive samples are tallied? Have you captured most of the species diversity of the BCI plot in your samples? Have all of the species been captured by the class as a whole?



Species diversity Now we will look at another way to measure species diversity. On the board is a table called Species Diversity. Enter the abundance of each species in each of your samples in this table. After every group has filled in their data on this table, we can use the table to calculate two different forms of Simpson’s Diversity Index for the composite sample for the class as a whole. First, we will hand calculate Simpson’s Index (Ds) for the class as a whole. Use the following equation: Ds = ∑pi2 , where pi is the total number of organisms of a particular species divided by the total number of organisms of all species. The value of Ds ranges between 0 and 1 where 0 is high diversity and 1 is low diversity. We will do this for the first few samples. Now that you understand how to calculate diversity indices, we will use the computer to calculate the remaining values and to assess the diversity of bryophytes in the woodlot. Open up the Excel worksheet on the desktop labeled BCIdata. In the top row you will see a separate column heading for each of the species in our community. In the first column you will also see group and sample number and a series of numbers in red. Replace these red numbers with the data from the Species Diversity Table on the board. After entering the data for your class, scroll down until you can see a box with values of Simpson’s Index for the class. Click on the values of Simpson’s Ds. Take your time to examine how this index is calculated using this website: http://www.countrysideinfo.co.uk/simpsons.htm What range of values can Simpson’s Ds assume? What is the biological interpretation of this index? Another way of looking at Simpson’s Ds is to use the Simpson’s Reciprocal Index (1/Ds). The value of this index starts with 1 as the lowest possible figure (i.e. a community containing only 1 species and therefore not diverse). The maximum value is the number of species in the sample. Compare species diversity between the different samples of each group. Are some samples higher in species diversity than others? Why? Are different species found in different places in the plot? What environmental or biological factors might explain this? For information on environmental variation (specifically soil characteristics) across the plot see the paper by John et al. 2007. But some of the variation in species distribution may reflect species dispersal characteristics rather than response to environmental factors; see Condit et al. 2000, for an example of this.

References: Condit, R, SP Hubbell, JV Lafrankie, R Sukumar, N Manokaran, RB Foster and PS Ashton. 1996. Species-area and specie-

individual relationships for tropical trees: A comparison of three 50-ha plots. Journal of Ecology 84: 549-562. Condit, R. et al. 2000. Spatial patterns in the distribution of tropical trees. Science. 288: 1414-1418. John, R. et al. 2007. Soil nutrients influence spatial distribution of tropical trees. Proceedings of the National Academy of

Science 104: 864-869. Plotkin, JB et al. 2000. Predicting species diversity in tropical forest. Proceedings of the National Academy of Sciences. 97:

10850-10854. Smithsonian Institution. No date. Barro Colorado Island.

http://www.stri.org/english/research/facilities/terrestrial/barro_colorado/index.php http://ctfs.si.edu/datasets/bci/ (video clip): http://www.thewildclassroom.com/biomes/travelinfo/rainforestBCI.html Weigand, T, CVS Gunatilleke, IAUN Gunatilleke and A Huth. 2007. How individual species structure diversity in tropical

forests. Proceedings of the National Academy of Sciences 104: 19029-19033. Wright, SJ. 2002. Plant diversity in tropical forests: a review of mechanisms of species coexistence. Oecologia 130: 1-14.



Conservation Biology

You will research a current topic in conservation biology, conducting library research. At the end of the term, you will give a short presentation of your findings.

You must submit your topic and one primary journal article to your TA not less than one week prior to your presentation.

You may use PowerPoint or other means to present your results (check with your TA).

To give a good talk

• Be Dynamic • Be enthusiastic (talk about something that interests you) • Have a beginning, middle and end. • Summarize scope of the talk. • Adjust content to audience knowledge. • Proofread your slides. • Provide something of value, new, important information. • Use appropriate graphics to make your points. • Eye contact. • Use just a few key words per slide to cue you. • Use some mild humor. • Ask for questions. • Summarize your key points. • Answer questions. • Thank the audience.

Note: Students taking EEB 2244W may find it useful to develop term paper topics that are also appropriate for this presentation.



Acknowledgements This manual is the work of many different people. Eldridge Adams, Rob Colwell, John Cooley, Susan Herrick, John Silander, Peter Turchin, and Mark Urban contributed material used in this manual. All contents of this manual © 2013 The Department of Ecology and Evolutionary Biology, The University of Connecticut.

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