Developing macroinvertebrate biological indicators of land use in Southern Ecuador

Developing macroinvertebrate biological indicators of land use in

Southern Ecuador Carrie Anderson

Team 2: Watershed Management of the Andean Paramo Ecology and Conservation Biology

University of Idaho

Supervising Faculty:

Dr. Frank Wilhelm Department of Fish and Wildlife Resources

University of Idaho

Carlos Iñiguez A.Instituto de Ecología

Universidad Técnica Particular de Loja

Overview

• Background• Goals and Objectives • Site Description • Methods• Data Analysis • Support and

Feasibility • Timeline

http://tripwow.tripadvisor.com/slideshow-photo/18-panoramic-view-from-podocarpus-national-park-by-travelpod-member-thymeoff-vilcabamba-ecuador.html?sid=13359132&fid=tp-15

Introduction

• Decline in surface water quality

• Causes of decline – Pollution– Sedimentation– Acidification– Flow regulation– Impoundment– Land use change (USEPA 2011, Pimentel et

al. 1997)

http://pubs.usgs.gov/circ/circ1211/major_findings.htm

http://jdurward.blogspot.com/2007_04_01_archive.html

http://blackwarriorriver.org/siltation-sedimentation.html

Indicators of Stream Quality

• Abiotic factors commonly assessed– Stream discharge– pH– Alkalinity– Electric Conductivity (EC)– Temperature– Dissolved Oxygen (DO)– Nutrient concentrations(Fleischbein et al. 2006, Bücker

et al. 2010)Photo by Anderson (2010)

Indicators of Stream Quality

• Biotic factors commonly assessed– Riparian and aquatic habitat assessment – Biological indicator frameworks(Bücker et al. 2010, USEPA 2011, Grafe et al. 2002)

https://www.e-education.psu.edu/geosc10/book/export/html/1733

Biological Indicators

• Predictable and consistent response

• Tolerance varies• Occurrence / abundance

correlated to changes in abiotic conditions

(Bücker et al. 2010, Cain et al. 1992, Cairns et al. 1993)

Photos by Fausto Fausto López


• Types of Indicators – Periphyton

http://tispaquin.blogspot.com/2010/05/severe-nutrient-loading-at-china-lake.html


• Types of Indicators – Periphyton– Macrophytes


http://www.sweetwaterwekivasprings.com/component/content/article/87-July-August-2010/61-native-aquatic-plants-plant-a-rainbow-along-your-shoreline


• Types of Indicators – Periphyton– Macrophytes– Fish



http://www.flickr.com/photos/87498048@N00/4188587349/


• Types of Indicators – Periphyton– Macrophytes– Fish– Benthic

macroinvertebrates

(Adams and Greeley 2000, USEPA 2011)



http://www.flickr.com/photos/87498048@N00/4188587349/

http://www.cs.umb.edu/~fstours/

Biological Indices

• Sensitive organisms organized into indices:

Photo by Fausto López

Biological Indices

• Sensitive organisms organized into indices:– Trend Biotic Index – TBI


Biological Indices

• Sensitive organisms organized into indices:– Trend Biotic Index – TBI– Family Biotic Index – FBI


Biological Indices

• Sensitive organisms organized into indices: – Trend Biotic Index – TBI– Family Biotic Index – FBI– Biological Monitoring

Working Party – BMWP


Biological Indices

• Sensitive organisms organized into indices:– Trend Biotic Index – TBI– Family Biotic Index – FBI– Biological Monitoring

Working Party – BMWP – Ephemeroptera

Plecoptera Trichoptera Index – EPT

(Bücker et al. 2010, Grafe et al. 2002) Photo by Fausto López

EPT Index

• Commonly used for stream quality assessment• Widely transferable• Evaluates number of distinct EPT taxa– Particularly sensitive to pollution– Changes in occurrence correlated with concentration of

pollutants (USEPA 2002, Johnson et al. 1993)

http://www.cals.ncsu.edu/course/ent525/water/aquatic/pages/03_jpg.htm

http://www.discoverlife.org/mp/20q?search=Perlodidae

http://www.entomology.umn.edu/midge/VSMIVP

%20Key/English/Hydropsychidae.htm

http://www.entomology.umn.edu/midge/VSMIVP%20Key/English/netspinnercaddisfly.htm

Background

• Indicator frameworks in developed countries

• Implementation in developing countries?

(Resh 1995, Bücker et al. 2010)


Background

• Stream Quality in Ecuador – Focus on high elevation

stream networks• Importance to the local

people• Headwaters in the

Paramo Ecosystem (Bücker et al. 2010, Buytaert et

al. 2006) http://valleyoflongevity.com/

Background






Background






Background

• Need for stream quality monitoring methods

• Bioindicator framework in Ecuador lacking

(Bücker et al. 2010)


Objectives

• Goal: To contribute to the assessment of stream quality for high elevation systems of southern Ecuador

Objectives


• Objectives:– Relate biotic and abiotic parameters and connect this

relationship to land-use and catchment characteristics

Objectives

– Determine if a group of aquatic invertebrates exist that are indicative of land use and differences in stream habitat and water quality




Objectives

– Examine if a bioindicator approach is applicable in the Ecuadorian Andes using end member systems along a disturbance gradient

– Determine if a group of aquatic invertebrates exist that are indicative of land use and differences in stream habitat and water quality




Hypotheses

• H0 1 :– There is no relationship between macroinvertebrates and

land use in high elevation montane tropical streams in the Andes Mountains of southern Ecuador

Hypotheses



• H0 2: – No difference in macroinvertebrate taxa occurrence, richness,

evenness, and diversity between disturbed and undisturbed

Hypotheses



• H0 3: – No difference in abundance of EPT taxa between disturbed

and undisturbed sites

• H0 2: – No difference in macroinvertebrate taxa occurrence, richness,

evenness, and diversity between disturbed and undisturbed

Site Description

http://www.compassion.com/sponsordonor/projectslideshows/ecuadorprojecturban.htm

http://checkpointzero.com/blog/2010/02/11/where-the-hecuador-is-ecuador-anyway/

Potential Study Sites

Pilot Study

• i) Optimize sampling time per sample• ii) Determine number of samples given

between-sample variance


Pilot Study

• i) Optimize sampling time – Triplicate samples for 15 sec, 30 sec, 1 min, 2 min – Total abundance determined in the field (Krebbs 1999, Gordon et al. 1992)

http://www.idoldesigners.com/

Pilot Study

• ii) Number of Samples per reach– Cumulative

variance plotted versus number of samples

(Krebb 1999, London and Gertz 1994) Number of samples

0 10 20 30

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aria

nce

0

2

4

6

8

10

12

14

16

Control Impact

Field Methods

• Optimal design • At least 2

catchments– Impact – Control

• 4 reaches sampled

Field Methods

• Reach Characteristics – Width– Depth– Velocity– Discharge

(Hauer and Lamberti 1996)Photo by Carlos Iñiguez A.

Field Methods

• Abiotic Characteristics– Temperature– pH– Dissolved Oxygen (DO)

(Hauer and Lamberti 1996)

• Sampled using a YSI 556 Multiprobe

http://www.instrumart.com/products/31754/ysi-556-multiparameter-system

Sampling Methods

• Hess sampler or D-Net– Quantitative known area

• Sample time determined in Pilot Study(Hauer and Lamberti 1996)

http://www.benmeadows.com/D-Frame-Aquatic-Dip-Net_s_224916/Nets_31224961/?isredirect=true http://new.freshwaterlife.org/how-to-sample

Photo by Carlos Iñiguez A.

Sampling Methods

• Number of samples per stream reach• Randomized Sampling Method • Preserve samples in field using 70% ethanol – Return to laboratory setting for sample analysis

(Hauer and Lamberti 1996, Grafe et al. 2002)

Laboratory Methods

• Randomized subsample from each field sample

• Classify to lowest taxonomic level

• Transport unprocessed samples back to US for further analysis– Permits acquired in

Ecuador (Hauer and Lamberti 1996,

Grafe et al. 2002, Gordon 1992)

Photo by Carlos Iñiguez A.

http://www.montegraphia.com/?m=200907&paged=3

Data Analysis

Biological Indices • Abundance

Data Analysis

Biological Indices • Abundance • Richness

Data Analysis

Biological Indices • Abundance • Richness• % EPT

%EPT = (E + P + T) x 100 total abundance

Data Analysis

Biological Indices • Abundance • Richness• % EPT • Diversity – Shannon – Wiener Index

Shannon – Wiener IndexH’ = - Σpi log pi


Data Analysis

Biological Indices • Abundance • Richness• % EPT • Diversity – Shannon – Wiener Index

• Evenness – Simpson Index

(Krebs 1999, Hauer and Lamberti 1996, Brooks et al. 2003)

Shannon – Wiener IndexH’ = - Σpi log pi

Simpson IndexC = Σipi

2


Data Analysis

Statistical Analysis• Basic descriptive statistics

– Mean – Standard Deviation – Variance (Gordon 1992, London and Gertz 1984)




Data Analysis



• Inferential Statistics – ANOVA – Regression – Correlation (Gordon 1992, London and Gertz 1984, O’Brien 1979)




Data Analysis



• Inferential Statistics – ANOVA – Regression – Correlation (Gordon 1992, London and Gertz 1984, O’Brien 1979)

• Multivariate Statistics – Canonical Correspondence Analysis (CCA)(Bücker et al. 2010, Braak and Verdonschot, 1995)




Support and Feasibility

• Grant Provided by the National Science Foundation (NSF)

• Dr. Frank Wilhelm – University of Idaho

• Carlos Iniguez A.– Doctoral Candidate at UTPL

• Adrian Leiva– Carlos’ associate/ invertebrate taxonomist

Ecuador Summer Research Program

University of IdahoUniversidad Tecnica Particular

de Loja

Project 1: Cultural perspectives on high Andean watersheds

Amy Forsgren

Project 3: Evaluating the jurisdictional map of authority

over watershed decisions Danie Merriman

Project 4: Evaluating the physical impact of land use

Emily Shimada

Project 5: Evaluating the biotic impacts of land use

Carrie Anderson

Team 2: Watershed management of the

Andean páramo

Timeline

May 17, 2011 Depart from Spokane, WA and arrive in Ecuador

May 17 - 23, 2011 Orientation in Ecuador

May 25 – July 17, 2011 Field sampling and laboratory analysis

July 21, 2011 Return to the United States

Aug. – Dec. 2011 Data analysis

Jan.– April 2012 Compose completion report and design poster

April 2012 Present Results

Summary

• Decline in surface water quality in Ecuador

• Current monitoring of aquatic systems

• Contribute to the development of biological indicator framework

http://www.art.com/products/p14376719-sa-i2875358/mark-jones-paramo-del-angel-freilejon-northern-andes-ecuador.htm

References • Beck, E., & Müller-Hohenstein, K. (2001). Analysis of undisturbed and disturbed tropical mountain forest

ecosystems in Southern Ecuador. Die Erde , vol. 132: 1-8.• Braak, C.J., Verdonschot, P.F.. 1995. Canonical correspondence analysis and related multivariate methods in

aquatic ecology. Aquatic Sciences. 57: 255-289. • Brooks et al. . (2003). Hydrology and the Management of Watersheds. Ames: Iowa State Press, a Blackwell

Publishing Company.• Bücker et al. . (2010). The influence of land-use on macroinvertebrate communities in montane tropical

streams - a case study from Ecuador. Fundam. Appl. Limnol. , Vol. 177/4, 267 - 282.• Cain, D., Luoma, S., Carter, J., & Fend, S. (1992). Aquatic Insects as Bioindicators of Trace ELement

Contamination in Cobble-Bottom Rivers and Streams . Can. J. Fish. Aquat. Sci. , 49: 2141-2154.• Digby, P.G., Kempton, R.A.. 1987. Multivariate Analysis of Ecological Communities. Chapman and Hall. London

and New York. • Fleischbein, K., Wilcke, W., Valerezo, C., Zech, W., & Knoblich, K. (2006). Water budgets of three small

catchments under montane forest in Ecuador: experimental and modelling approach. Hydrological Processes , vol 20. 2491-2507.

• Gordon, N.D., McMahon, T.A., Finlayson, B.L.. Stream Hydrology: An Introduction for Ecologists. 1992. Jon Wiley & Sons. Chichester, New York.

• Grafe et al. (2002). The Idaho department of Environmental Quality Water Body Assessment Guidance, Second Edition-Final. Boise: Idaho department of Environmental Quality.

• Hauer, H.F., Lamberti G.A., 1996. Methods in Stream Ecology. Academic Press. San Diego. • Hellawell, J.M. 1986. Biological Indicators of Freshwater Pollution and Enviornmental Management. Elsevier,

New York.

• Johnson, R.K., Wiederholm, T., Rosenberg, D. M.. 1993. Freshwater biomonitoring using individual organisms, populations, and species assemblages of benthic macroinvertebrates. Freshwater Biomonitoring and Benthic Macroinvertebrates. 40 – 158.

• Krebs, J.C.. 1999. Ecological Methodology. Addison- Welsey Educational Publishers, Inc. Menlo Park, California.

• London & Gertz (editors). 1984. Statistics in the Environmental Sciences. ASTM. Philadelphia. • Metcalfe, J.L.. 1989. Biological water quality assessment of running waters based on macroinvertebrate

communities: Hisotry and present states in Europe. Environmental Pollution 60: 101 – 139. • Phillips, D.J.. 1980. Qquantitative Aquatic Biological Indicators: Their Use to Monitor Trace Metal and

Organochlorine Pollution. Applied Science Publishers LTD. London. • Plafkin, J.L., Barbour, M.T., Porter, K.D., Gross, S.K., Hughes, R.M.. 1989. Rapid Bioassessment Protocols for

Use in streams and Rivers. Benthic Macroinvertebrates and Fish. EPA/444/4-89/0001. Office of Water Regulations and Standards, United States Environmental Protection Agency, Washington, DC.

• Resh, V.H.. 1995. Freshwater benthic macroinvertebrates and rapid assessment procedures for water quality monitoring in developing and newly industrialized countries. Pages 167 – 177 in W.S. Davis and T.P Simon. Biological Assessment and Criteria. Lewis Publishers, Boca Raton, Florida.

• US - EPA. (2011, January 31). Biological Indicators of Watershed Health. Retrieved February 20, 2001, from U.S. Enviornmental Protection Agency: http://www.epa.gov/bioiweb1/

• US - EPA. (2002). Water Quality Standards Attainment Decisions. US - EPA.• USEPA. (2010, October 13). Volunteer Monitoring. Retrieved April 15, 2011, from United States

Environmental Protection Agency: http://water.epa.gov/type/watersheds/monitoring/vol.cfm• Wetzel, R. (2001). Limnology: Lake and River Ecosystems. 3rd edition. New York: Academic Press.• Wilhelm. (2009). Pollution of Aquatic Ecosystems 1. Encyclopedia of Inland Waters , vol. 3: 110-119.

Questions

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Developing macroinvertebrate biological indicators of land use in Southern Ecuador

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