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
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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
Biological Indicators
• Types of Indicators – Periphyton
http://tispaquin.blogspot.com/2010/05/severe-nutrient-loading-at-china-lake.html
Biological Indicators
• Types of Indicators – Periphyton– Macrophytes
http://tispaquin.blogspot.com/2010/05/severe-nutrient-loading-at-china-lake.html
http://www.sweetwaterwekivasprings.com/component/content/article/87-July-August-2010/61-native-aquatic-plants-plant-a-rainbow-along-your-shoreline
Biological Indicators
• Types of Indicators – Periphyton– Macrophytes– Fish
http://tispaquin.blogspot.com/2010/05/severe-nutrient-loading-at-china-lake.html
http://www.sweetwaterwekivasprings.com/component/content/article/87-July-August-2010/61-native-aquatic-plants-plant-a-rainbow-along-your-shoreline
http://www.flickr.com/photos/87498048@N00/4188587349/
Biological Indicators
• Types of Indicators – Periphyton– Macrophytes– Fish– Benthic
macroinvertebrates
(Adams and Greeley 2000, USEPA 2011)
http://tispaquin.blogspot.com/2010/05/severe-nutrient-loading-at-china-lake.html
http://www.sweetwaterwekivasprings.com/component/content/article/87-July-August-2010/61-native-aquatic-plants-plant-a-rainbow-along-your-shoreline
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
Photo by Fausto López
Biological Indices
• Sensitive organisms organized into indices:– Trend Biotic Index – TBI– Family Biotic Index – FBI
Photo by Fausto López
Biological Indices
• Sensitive organisms organized into indices: – Trend Biotic Index – TBI– Family Biotic Index – FBI– Biological Monitoring
Working Party – BMWP
Photo by Fausto López
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
Background
• Indicator frameworks in developed countries
• Implementation in developing countries?
(Resh 1995, Bücker et al. 2010)
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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
• 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
• 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
• Need for stream quality monitoring methods
• Bioindicator framework in Ecuador lacking
(Bücker et al. 2010)
Photo by Fausto López
Objectives
• Goal: To contribute to the assessment of stream quality for high elevation systems of southern Ecuador
Objectives
• Goal: To contribute to the assessment of stream quality for high elevation systems of southern Ecuador
• 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
• Goal: To contribute to the assessment of stream quality for high elevation systems of southern Ecuador
• Objectives:– Relate biotic and abiotic parameters and connect this
relationship to land-use and catchment characteristics
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
• Goal: To contribute to the assessment of stream quality for high elevation systems of southern Ecuador
• Objectives:– Relate biotic and abiotic parameters and connect this
relationship to land-use and catchment characteristics
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 1 :– There is no relationship between macroinvertebrates and
land use in high elevation montane tropical streams in the Andes Mountains of southern Ecuador
• H0 2: – No difference in macroinvertebrate taxa occurrence, richness,
evenness, and diversity between disturbed and undisturbed
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
• 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
Photo by Fausto López
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
Cu
mu
lati
ve v
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
%EPT = (E + P + T) x 100 total abundance
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
%EPT = (E + P + T) x 100 total abundance
Data Analysis
Statistical Analysis• Basic descriptive statistics
– Mean – Standard Deviation – Variance (Gordon 1992, London and Gertz 1984)
Photo by Fausto López
Photo by Fausto López
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
Data Analysis
Statistical Analysis• Basic descriptive statistics
– Mean – Standard Deviation – Variance (Gordon 1992, London and Gertz 1984)
• Inferential Statistics – ANOVA – Regression – Correlation (Gordon 1992, London and Gertz 1984, O’Brien 1979)
Photo by Fausto López
Photo by Fausto López
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Data Analysis
Statistical Analysis• Basic descriptive statistics
– Mean – Standard Deviation – Variance (Gordon 1992, London and Gertz 1984)
• 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)
Photo by Fausto López
Photo by Fausto López
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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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