DOI: 10.5061/dryad.g79cnp5nr
title: Datasets relating (i) A wetland fish multimetric index to variation in agricultural stress among Laurentian Great Lakes coastal wetlands, (ii) Cyanobacteria biomass to total phosphorus concentrations among Canadian lakes

description: We present two datasets of biological responses against environmental
 stresses. In the first dataset, the biological response and environmental
 stress variables are fish multimetric index of community
 health and agricultural stress, respectively, in watersheds
 draining to Laurentian Great Lakes. In the
 second dataset, the biological response and environmental stress
 variables are cyanobacterial biomass and total phosphorus,
 respectively, in Canadian Lakes.
description: <strong>Relating
 wetland fish multimetric index to variation in agricultural stress among
 Laurentian Great Lakes coastal wetlands:</strong> The first dataset relates to
 estimating threshold effects of a measure of agricultural activity in
 watersheds draining into the Laurentian Great Lakes on scores of a
 multimetric index of community composition of fishes in bordering coastal
 wetlands (Bhagat et al., 2007). Run-off associated with agriculture is a
 major source of human induced disturbance affecting natural habitat loss
 for fishes. Danz et al. (2005) derived a composite agricultural stress
 index (AG) to characterize the risk of degradation of natural habitat
 using GIS based data. The measure of biological condition is a wetland
 fish multimetric index (MMI), a measure representing the inferred health
 of the fish assemblage in an ecoregion or watershed. Uzarski et al. (2005)
 developed and Bhagat et al. (2007) validated the fish multimetric index by
 assessing fish assemblages in stands of bulrush (<i>Schoenoplectus,
 spp</i>) in 30 coastal wetlands distributed across the US Great
 Lakes coast. MMI scores vary from 0 to 100, with larger scores
 representing greater ecological health of the fish assemblage.
 Traditionally, MMI scores falling in the lowest and highest quintiles are
 classified as “degraded” and “excellent” conditions, respectively. Bhagat
 et al. (2007) observed a statistically significant negative linear
 association between fish MMI and AG scores, but suggested the presence of
 threshold responses. They did not quantitatively test for the presence of
 breakpoints. Tomal and Ciborowski (2020) derived ecological models to test
 for the presence of two environmental breakpoints. Dr. Jan Ciborowski
 provided the dataset (the top panel of Figure 3 - Bhagat et al., 2007) who
 was one of the coauthors and the principal investigator. Tomal and
 Ciborowski (2020) rescaled the AG (a PCA score) to a 0 to 1 range with
 larger numbers reflecting more extensive agricultural
 activities. <strong>Relating
 cyanobacteria biomass to total phosphorus concentrations among Canadian
 lakes</strong><strong>:</strong> The second dataset relates to identifying putative threshold effects of total phosphorus (TP) on the risk of development of harmful algal blooms (dominated by toxigenic Cyanobacteria) in Canadian lakes (Beaulieu et al., 2014). TP is a limiting nutrient whose loads to lakes and rivers reflect contributions of sewage from urban centres, agricultural runoff, and other manifestations of human activity. Cyanobacteria biomass (CB) per unit volume is a standard index of concentration, and often used as a proxy for the risk of toxicity of harmful algal blooms. Cyanobacteria blooms are manifestations of eutrophication whose prevalence is increasing globally. CB harbours compounds that can be acutely toxic, and that are linked to diseases such as carcinoma. Thus, CB is directly related to risks to human and animal health. Opinion on the shape of the relationship between TP and CB is varied. TP is arguably one of the top single predictors of CB (<i>Chlorophyll a</i>), and empirically derived linear models are widely used in lake management. However, sigmoidal relationships between TP and CB are also well documented. Using linear regression, nonlinear regression and mixed-effects models, Beaulieu et al., 2014 concluded that linear models better explained the data pattern than nonlinear approaches. Yet, scatterplots (the top-left panel of Figure 3 of Beaulieu et al., 2014) appear to indicate discontinuities in the TP-CB relationship. Tomal and Ciborowski (2020) used a piecewise-linear quantile regression model to estimate two environmental thresholds of TP on CB. The dataset is obtained from https://cdnsciencepub.com/doi/full/10.1139/cjfas-2014-0168, where the original data are collected by the Ministries of the Environment of Alberta (43 lakes), British Columbia (10 lakes) and Ontario (97 lakes) relating to CB and TP concentrations. Tomal and Ciborowski (2020) extracted, aggregated, and scaled (using 10-based log) the cyanobacterial biomass and total phosphorus variables. <strong>References:</strong> Beaulieu, M., F. Pick, M. Palmer, S. Watson, J. Winter, R. Zurawell, and I. Gregory-Eaves (2014). Comparing predictive cyanobacterial models from temperate regions. <i>Canadian Journal of Fisheries and Aquatic Sciences</i> 71 (12), 1830-1839. Bhagat, Y., J. Ciborowski, L. Johnson, D. Uzarski, T. Burton, S. Timmermans, and M. Cooper (2007). Testing a fish index of biotic integrity for responses to different stressors in great lakes coastal wetlands. <i>Journal of Great Lakes Research</i> 33, 224-235. Danz, N., R. Regal, G. Niemi, V. Brady, T. Hollenhorst, L. Johnson, G. Host, J. Hanowski, C. Johnston, T. Brown, J. Kingston, J. Kelly (2005). Environmentally stratified sampling design for the development of Great Lakes environmental indicators. <em>Environmental Monitoring and Assessment</em> 102 (1), 41-65. Tomal, J., and Ciborowski, J. (2020). Ecological models for estimating breakpoints and prediction intervals. <em>Ecology and Evolution</em>, In press. Uzarski, D., T. Burton, M. Cooper, J. Ingram, and S. Timmermans (2005). Fish habitat use within and across wetland classes in coastal wetlands of the five great lakes: Development of a fish-based index of biotic integrity. <i>Journal of Great Lakes Research</i> 31, 171-187.
description: Tomal and Ciborowski (2020)
 used the fish multimetric index versus agricultural stress dataset and the
 cyanobacterial biomass versus total phosphorus dataset to estimate two
 environmental thresholds using a piecewise-linear regression model and a
 piecewise-linear quantile regression model. These datasets can further be
 used to extract other forms of linear and non-linear relationships between
 the biological responses and environmental stress variables. Both of the
 datasets are provided in CSV format (DataS1.csv & DataS2.csv). The
 R-codes to read the datasets are provided in DataS1.R and DataS2.R files.
 The R-codes to run the models in Tomal and Ciborowski (2020) can be
 obtained by emailing the corresponding author Dr. Jabed Tomal at
 jtomal@tru.ca. DataS1.csv dataset contains three
 variables: (1) Fish_MMI - Fish multimetric index of community health; (2)
 AG - Agricultural stress; and (3) Type - U: Uzarski & GLEI: Great
 Lakes environmental indicators. DataS2.csv dataset contains
 four variables: (1) CB - Cyanobacterial biomass (µg/L); (2) TP - Total
 phosphorus (µg/L); (3) Lake - Name of lakes; and (4) Province - Name of
 provinces: AB: Alberta, BC: British Columbia, & ON:
 Ontario.