Final Project Proposal

Evaluating the Potential Ecotoxicity of Upstream Land Cover on Benthic Macroinvertebrate Health in Canada

Bryant M. Serre

ENVB.530.W21.Bryant.Serre.Presentation.ProjectProposal

Figure 1: Overview presentation of proposal.

Figure 2: Ontario Benthic Biomonitoring Network Kick and Sweep Training Session, May 2019 (Photo Courtesy of Bryant Serre [per TRCA]).

1.0 Introduction

Benthic macroinvertebrates are bottom dwelling organisms in lotic and lentic ecosystems; the consortia of organisms includes small aquatic animals in addition to larval starts of insects (e.g., dragonflies or infraorder Anisoptera) (Brinkhurst 1975; Hutchinson 1961). As benthic macroorganisms are confined to their habitat, they are affected by physical, chemical and biological conditions of streams; in turn, the benthos are subject to short and long term stressors, and valuable indicators of aquatic health (Read et al. 1983; Reynoldson and Metcalfe-Smith 1992). In this ecotoxicological assessment, individual organism health and state is rarely measured (Fargasova 1999; Bettinetti et al. 2020). Instead, the relative abundance of benthic macroinvertebrates is used to elucidate the biological conditions of aquatic ecosystems. For example, the decreasing relative proportion of Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) (EPT) taxonomic groups, in relation to all other sampled taxa, is used to indicate increasing watercourse stress (USEPA 2021). The relative proportion of benthic organisms is a long-standing form of aquatic monitoring (2021), and in turn, the product of upstream water quality and land cover.

Urbanization in drainage basins represents a composite of stressors. Surface runoff and stormwater collection can mobilize heavy metals (notably mercury, lead, and arsenic), asphaltenes (polycyclic and polynucleic aromatic hydrocarbons or PAHs), macronutrients (e.g., runoff from farm fields promoting excessive algal development), and plastic pollution into watercourses and waterbodies to cause chronic (i.e., reproductive), lethal (i.e., mortality) and sublethal (i.e., behavioral) biological effects (Muller et al. 2020; Scholz 2015). Moreover, the heightened temperature of urban surfaces (alia Urban Heat Island Effect, Oke 1982) is often transferred to surface water flows, significantly raises temperatures of receiving watercourses and waterbodies, and concomitantly shifting the range of tolerance for benthic macroinvertebrates and driving ecotoxicological impact (Jones et al. 2012). In the context of sustainable development and fighting climate change, the relevance to assessing and continually monitoring of benthic macroinvertebrates is axiomatic.

Currently, benthic health assessments are localized to individual watersheds and jurisdictions. In Canada, Environment and Climate Change Canada’s (ECCC) Canadian Aquatic Biomonitoring Network (CABIN) has reported on benthic health in individual cases (e.g., Southwestern Hudson Bay drainage area, Great Slave Lake drainage area): there has not been a comparative analysis of benthic indices across the entire data collection. In part, this will test the validity as to whether benthic macroinvertebrate indices are suitable indicators of aquatic ecosystem health (personal communication, C. Metcalfe (CABIN) 9 March 2022).

1.1 Background of Benthic Macroinvertebrate Monitoring

In North America, there is support across levels of environmental governance to analyze the relative abundance of benthic macroinvertebrates as a means to discern biological health of lotic, coastal and transitional (e.g., river deltas, mangrove forests) ecosystems. In North America, the International Joint Commission (IJC) has used benthic macroinvertebrates as part of a suite of indicators for monitoring the status of the Laurentian Great Lakes since 1972, when the Great Lakes Water Quality Agreement (GLWQA) was signed into effect to Improve the water quality of watercourses and waterbodies (notably Lake Erie which exhibited severe eutrophication and poor biological quality). The United States Environmental Protection Agency’s (USEPA) National Aquatic Resource Surveys (NARS) uses indices of relative benthos populations, in conjunction with measuring river chemical (e.g., conductivity, dissolved oxygen, soil chemistry, macronutrient concentrations), physical (e.g., human disturbance, clarity/turbidity, riparian vegetation condition) and recreational (e.g., presence of filamentous blue-green algae microcystin aeruginosa, enterococci, and faecal coliforms) indicators, to assess the quality and condition of watercourses across the contiguous United States (USEPA 2020). Similarly, Environment and Climate Change Canada’s (ECCC) Canadian Aquatic Biomonitoring Network (CABIN) measures changes in benthic biological communities to assess the state of Canadian lotic systems (ECCC 2018). Regionally, Credit Valley Conservation (CVC) in Southern Ontario uses benthic indicators as part of their approach to discern whole watershed health, beyond the confines of the lotic ecosystem itself (CVC 2017); similarly, the largest conservation authority, Toronto and Region Conservation (TRCA), collects benthic macroinvertebrate samples frequently as part of their Regional Watershed Monitoring Program (TRCA 2020). While these instances exemplify multiplicitous assessments of water quality using benthos abundance, benthic indices have not been evaluated wholly across all of Canada as to allow relative comparisons of water quality and aquatic ecosystem stress.

1.2 Project Objectives and Questions

Given the foregoing findings, it is the goal of this project to evaluate the health of aquatic ecosystems across Canada, using indices of benthic macroinvertebrate populations. Specifically, it is the objective of this study to test the relationship of benthic macroinvertebrate indices to the watershed land cover for each point. In turn, this will help understand the potential ecotoxicological effects of urbanization on aquatic ecosystem health. This study is guided by the following question: how do benthic population assemblages respond to changes in urbanization within the drainage basin? This objective is to be accomplished through computing the land cover composition of upstream drainage basins for benthic macroinvertebrate sampling points from the CABIN dataset, computing relative abundance taxonomic indices, and testing the statistical relationships between basin land cover and population assemblages.

Figure 3: Example of Benthic Macroinvertebrates from a Ponar Sample, Utah State University Extension (2020). * Chronomids, Hyallela Azteca (bright orange crustacea), Mayflies, Caddisflies and Stoneflies can be seen in the sample.

2.0 Data

This analysis is to be carried out across all of Canada, using existing benthic macroinvertebrate survey data collected under the CABIN program. Under the CABIN program, eleven (11) major drainage areas were surveyed; including 1) the Maritime Provinces Drainage Area (DA), 2) the St. Lawrence DA, 3) the Northern Quebec and Labrador DA, 4) the Southwestern Hudson Bay DA, 5) the Nelson River DA, 6) the Western and Northern Hudson Bay DA, 7) the Great Slave Lake DA, 8) the Pacific DA, 9) the Yukon River DA, 10) the Arctic Drainage DA, and 11) the Mississippi River DA (ECCC 2018). Under the general confines of macroinvertebrate sampling, species assemblages are presumed to be in Panmixia (free mouvement, ability to be present) across all drainage areas (SERAS 2003: SOP 2054 0.0).

2.1 Data for Benthic Sampling

The macroinvertebrate surveys under the CABIN program are collected on-site, by researchers across Canada using Kick Net (98.69% of samples) and Ponar (selectively, 1.3% of samples) methods. Coordinates are provided for each survey, which allows this data to be utilized as a point (vector) dataset. Kick Net sampling occurs when a net is held downstream, and one uses their foot to dislodge sediment upstream, when incorporating sediment into the water column, benthos are suspended and flow downstream into a sampling net (ECCC 2018).

2.2 Data for Watershed Delineation

To delineate the boundary of upstream land area, the ArcGIS watershed analysis tool requires 1) a polyline layer for watercourses and 2) a digital elevation model. In this case, the National Hydrographic Network (HN H) file is most appropriate, given the resolution of detail to higher reaches of catchement (and respectively smaller streams), where samples were retrieved (i.e., 1^st and 2^nd Strahler orders) (Natural Resources Canada (NRCAN) 2019). Moreover, these layers include flow direction calculations which are necessary for basin delineation by pour point (Parmenter and Melcher 2012). The digital elevation model used for this project is the Canadian Digital Elevation Model (CDEM) (NRCAN 2015). This is a Digital Surface Model (DSM) raster layer with 2 m resolution which will be used for z-coordinates (elevation) as to delineate basin boundaries.

2.3 Data for Land Use/Land Cover

The Agriculture and Agri-food Canada (AAFC) Annual Crop Inventory land cover dataset was used for land use classifications. This layer was selected for it’s yearly time intervals, corresponding with the different time periods CABIN benthic samples were retrieved (2009-2022). This raster layer has a final spatial resolution of 30 meters, and is considered sufficiently accurate for non-agriculture land cover estimation (AAFC 2015); the raster has a thematic resolution of 72 categories/land cover classes which is believed sufficient to parse out developed from undeveloped (or naturalized) land.

3.0 Methodology

This project will require several steps to be able to compute iterative functions in ArcGIS model builder for each stream point in which basins are delineated, and land cover is summarized. The steps to achieve the project objective of the objective of this study to test the relationship of benthic macroinvertebrate indices to the watershed land cover for each point are as follows:

Step 1: Organize CABIN Dataset

In Microsoft Excel, non-viable sample points are removed. Benthic Indices, as defined per the USEPA Benthic Monitoring Framework (i.e., a list of indices), will be computed for all CABIN sampling points. From supplemental files, the spatial locations for each point will be appended to the sample IDs. The .xlsx file will be converted to a .csv and imported into ArcGIS.

Step 2: Plotting Sample Points

In ArcGIS, the .csv file will be converted into a point vector file using the Create Points from Table (or Display XY Data in ArcMap) function.

Step 3: Importing all Layers

All layers will be imported into the ArcGIS catalogue, including the AAFC Annual Crop Inventory and the CDEM.

Step 4: Importing and Creating a Depressionless DEM

In ArcGIS, the Watershed Geoprocessing tool will be used to create a depressionless DEM, and flow raster which is then used in the Watershed tool to determine a watershed boundary from a Pour Point.

Step 5: Iterating Watershed Delineation Over All Sample Points using Python and ModelBuilder

In this step, the ModelBuilder for creating a watershed from a pour point will be iterated over the entire set of points in the CABIN database, using a for loop function. In this instance, the watershed delineation process will be computed for all CABIN sample points.

Step 6: Iterating Land Cover Composition for Watershed Boundaries

Within the loop function, the land cover raster will be clipped for the watershed boundary associated with each pour point. To compute the percent coverage of different land cover, the ‘Summarize Raster Within’ tool (Raster Analysis Toolbox) will be put into the loop and modified to give the amount of cells corresponding to each category within the raster layer. Writing to text in python, these will then be printed along with the Sample IDs.

Step 7: Relationship Testing between Land Cover and Benthic Indices

Once exported, the land cover for each pour point (upstream basin of the sample points) will be tested against the benthic macroinvertebrate indices for each sample point. The relationships will then be reported.

4.0 Possible Problems

The foreseen issues within the proposed project are related to coding and dataset coverage. Firstly, in the creation of a loop function to summarize land cover composition in drainage basins, there will be a need to index and match sampling years with the according yearly land cover layer. This will likely be resolved using an if and then operator within the loop. One way this may be addressed is through separating out loops and the datasets for different years. In turn, this would only require parsing the point data in a preliminary stage into multiple multipoint layers (i.e., layer of CABIN sample sites for 2011, 2012, etc.). Another issue is hydrological network dataset coverage; in the case of gaps in coverage, localized datasets will be used in lieu from the pertinent open data portals for conservation agencies.

5.0 Conclusion

Overall, it is the goal of this project to assess the relationship between upstream land cover (as an indicator of the stress of human development) on downstream water quality, through multi-species benthic macroinvertebrate indices. Ideally, testing the relationship between human stressors and aquatic health will yield insights into the comparative health of watercourses and waterbodies across Canada.

6.0 References

Agriculture and Agri-Food Canada (AAFC) (2015) Annual SpaceBased Crop Inventory for Canada. Agroclimate, Geomatics and Earth Observation Division, Science and Technology Branch. Retrieved from https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9

Barnes K, Cartwright L, Portiss R , Midwood J ,Boston C, Granados M, Sciscione T, Gibson C & Obembe O (2020) Evaluating the effectiveness of aquatic habitat restoration implemented using the Toronto Aquatic Habitat Restoration Strategy. Retrieved from https://trcaca.s3.ca-central 1.amazonaws.com/app/uploads/2021/01/07074413/TWAHRS-assessment-FINAL-technical-document-Nov-2020.pdf

Bettinetti, R., Zaupa, S., Fontaneto, D., & Boggero, A. (2020). Biological, chemical, and ecotoxicological assessments using benthos provide different and complementary measures of lake ecological status. Water, 12(4), 1140.

Brinkhurst, R. O. (1975). The benthos of lakes. Macmillan International Higher Education.

Credit Valley Conservation (CVC) (2017) Annual Report 2016 Credit Valley Conservation Integrated Watershed Monitoring Program (IWMP). Retrieved from https://www.creditvalleyca.ca/wp-content/uploads/2017/11/IWMP_2016_Web.pdf

Environment and Climate Change Canada (2018) National Science Forum 2017. Retrieved from https://publications.gc.ca/collections/collection_2018/eccc/En4-296-2017-eng.pdf

Fargašová, A. (1999). Ecotoxicology of metals related to freshwater benthos. General Physiology and Biophysics, 48-53.

Hutchinson, G. E. (1961). The paradox of the plankton. The American Naturalist, 95(882), 137-145.

Jones, M. P., Hunt, W. F., & Winston, R. J. (2012). Effect of urban catchment composition on runoff temperature. Journal of Environmental Engineering, 138(12), 1231-1236.

Müller, A., Österlund, H., Marsalek, J., & Viklander, M. (2020). The pollution conveyed by urban runoff: A review of sources. Science of the Total Environment, 709, 136125.

National Resources Canada (NRCAN) (2015) Canadian Digital Elevation Model (CDEM) [data file]. Retrieved from https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333

National Resources Canada (NRCAN) (2019) National Hydrographic Network. Retriev from https://www.nrcan.gc.ca/science-and-data/science-and-research/earth-sciences/geography/topographic-information/geobase-surface-water-program-geeau/national-hydrographic-network/21361

Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24.

Parmenter B, Melcher J (2012) Watershed and Drainage Delineation by Pour Point in ArcMap 10. Retrieved from http://sites.tufts.edu/gis/files/2013/11/Watershed-and-Drainage-Delineation-by-Pour-Point.pdf

Read, P. A., Anderson, K. J., Matthews, J. E., Watson, P. G., Halliday, M. C., & Shiells, G. M. (1983). Effects of pollution on the benthos of the Firth of Forth. Marine Pollution Bulletin, 14(1), 12-16.

Reynoldson, T. B., & Metcalfe-Smith, J. L. (1992). An overview of the assessment of aquatic ecosystem health using benthic invertebrates. Journal of aquatic ecosystem health, 1(4), 295-308.

Scholz, M. (2015). Wetland systems to control urban runoff. Elsevier.

SERAS (2003). Standard Operating Procedures Benthic MacroInvertebrate Sampling. Retrieved from https://clu-in.org/download/ert/2054-r00.pdf

U.S. Environmental Protection Agency (USEPA) (2021) Indicators: Benthic Macroinvertebrates. Retrieved from https://www.epa.gov/national-aquatic-resource-surveys/indicators-benthic-macroinvertebrates

U.S. Environmental Protection Agency (USEPA) (2020) National Rivers and Streams Assessment 2013–2014: A Collaborative Survey. EPA 841-R-19-001. Retrieved from https://www.epa.gov/national-aquatic-resource-surveys/nr

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