Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

Assessing arthropod diversity metrics derived from stream environmental DNA: spatiotemporal variation and paired comparisons with manual sampling

Background

Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.

Methods

We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.

Results

Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

The Relation of Lotic Fish and Benthic Macroinvertebrate Condition Indices to Environmental Factors Across the Conterminous USA

National and regional ecological assessments are essential for making rational decisions concerning water body conservation and management at those spatial extents. We analyzed data from 4597 samples collected from 3420 different sites across the conterminous USA during the U.S. Environmental Protection Agency's 2008-2009 and 2013-2014 National Rivers and Streams Assessment. We evaluated the relationship between both fish and macroinvertebrate multimetric index (MMI) condition scores and 38 environmental factors to assess the relative importance of natural versus anthropogenic predictors, contrast site-scale versus watershed-scale predictors, and examine ecoregional and assemblage differences. We found that most of the environmental factors we examined were related to either fish and/or macroinvertebrate MMI scores in some fashion and that the factors involved, and strength of the relationship, varied by ecoregion and between assemblages. Factors more associated with natural conditions were usually less important in explaining MMI scores than factors more directly associated with anthropogenic disturbances. Local site-scale factors explained more variation than watershed-scale factors. Random forest and multiple regression models performed similarly, and the fish MMI-environment relationships were stronger than macroinvertebrate MMI-environment relationships. Among ecoregions, the strongest environmental relationships were observed in the Northern Appalachians and the weakest in the Southern Plains. The fish and macroinvertebrate MMIs were only weakly correlated with each other, and they generally responded more strongly to different groups of variables. These results support the use of multiple assemblages and the sampling of multiple environmental indicators in ecological assessments across large spatial extents.

Toward sustainability: a case study demonstrating transdisciplinary learning through the selection and use of indicators in a decision-making process

The purpose of this article is to use a case study example to demonstrate how a transparent, transdisciplinary approach to decision making allows the US Environmental Protection Agency Region III (USEPA Region III) to fulfill its decision-making responsibilities while taking critical steps toward engaging in sustainability discussions. The case study goals were to use information about environmental condition to inform staff and fiscal resource prioritization and allocation for the federal 2010 fiscal year. This article will use a select group of 3 indicators to show 1) that data are not the same as indicators, 2) the feasibility of using disparate data in the same analysis, and 3) specific discussions about indicators can lead to transdisciplinary learning, supporting more informed decision making. We show that, when used in a transdisciplinary learning process, these indicator lessons provide a stepping stone for organizations like USEPA Region III to consider sustainability as more than just a lofty, ethical concept. Instead, these kinds of organizations can more routinely and substantively address sustainability through a progression of individual decisions. We discuss how sustainability can be linked to decision making through a process that requires stakeholders to articulate and confront their values. In this process, selecting indicators and understanding what those choices imply regarding the issues that are highlighted and the population affected is part of the assessment of environmental condition, which is the focus of the case study.

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.

Conceptual models as hypotheses in monitoring urban landscapes

Many problems and challenges of ecosystem management currently are driven by the rapid pace and spatial extent of landscape change. Parks and reserves within areas of high human population density are especially challenged to meet the recreational needs of local populations and to preserve valued environmental resources. The complex problem of managing multiple objectives and multiple resources requires an enormous quantity of information, and conceptual models have been proposed as tools for organizing and interpreting this information. Academics generally prefer a bottom-up approach to model construction that emphasizes ecologic theory and process, whereas managers often use a top-down approach that takes advantage of existing information to address more pragmatic objectives. The authors propose a formal process for developing, applying, and testing conceptual models to be used in landscape monitoring that reconciles these seemingly opposing perspectives. The four-step process embraces the role of hypothesis testing in the development of models and evaluation of their utility. An example application of the process to a network of national parks in and around Washington, DC illustrates the ability of the approach to systematically identify monitoring data that would both advance ecologic theory and inform management decisions.