Ryan A. Hill

The reference condition: predicting benchmarks for ecological and water-quality assessments

Abstract Benchmarks provide context and are a critical element of all ecological assessments. Over the last 25 y, hundreds of papers have been published on various aspects of ecological assessments, and most of the analyses described in these papers depend on specifying an ecological benchmark for context. Freshwater scientists and managers usually use reference sites (typically sites in natural or least-disturbed condition) to assess the ecological conditions at other sites. Accurate and precise assessments require that assessed sites be matched with appropriate reference conditions. Two general types of approaches have been proposed to predict reference conditions: classifications based on natural environmental settings and models that use continuously variable environmental attributes as inputs. Two types of classifications have been examined: geographic-dependent regionalizations based on general landscape features and geographic-independent typologies that are typically based on combinations of regional and channel features. We examined >1000 papers that addressed some aspect of predicting the reference condition in freshwater ecosystems. We focused on 5 types of benchmarks: ecological, thermal, hydrologic, geomorphic, and chemical. Our review showed that over the last 25 y, researchers have developed increasingly sophisticated methods that can be used to predict reference conditions. Most disciplines have increasingly moved toward site-specific modeling approaches as a way to improve both accuracy and precision of predictions, although typological approaches dominate geomorphic characterizations. Papers published in J-NABS have been especially important in advancing and refining methods for predicting ecological benchmarks. Much of the progress made in the science of ecological assessment emerged from research that advanced our understanding of how the spatial and temporal distributions of freshwater biota are related to naturally occurring environmental features and how those relationships can be most accurately and precisely described and predicted. Thus, the performance of ecological assessments is critically linked to how well we characterize freshwater environments, and research in the watershed sciences that addresses predicting thermal, hydrologic, geomorphic, and chemical attributes of freshwater ecosystems has paralleled research focused on predicting biota. We anticipate that knowledge produced from future collaborations between ecologists and watershed scientists coupled with the application of modern modeling techniques will largely determine progress in characterizing and predicting biota–environment relationships and, thus, the accuracy and precision of future ecological assessments.

Predicting thermal reference conditions for USA streams and rivers

Abstract. Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r2  =  0.84–0.94, residual mean square error [RMSE]  =  1.2–2.0°C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r2  =  0.87–0.95, RMSE  =  1.1–1.9°C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Linking land use, in-stream stressors, and biological condition to infer causes of regional ecological impairment in streams

Abstract.  We used field-derived data from streams in Nevada, USA, to quantify relationships between stream biological condition, in-stream stressors, and potential sources of stress (land use). We used 2 freshwater macroinvertebrate-based indices to measure biological condition: a multimetric index (MMI) and an observed to expected (O/E) index of taxonomic completeness. We considered 4 categories of potential stressors: dissolved metals, total dissolved solids, nutrients, and flow alteration. For physicochemical factors that varied predictably across natural environmental gradients, we quantified potential stress as the site-specific difference between observed (O) and expected (E) levels of each factor (O–Estress). We then used 2 sets of Random Forest models to quantify relationships between: 1) biological condition and potential stressors, and 2) stressor values and land uses. The 2 indices of biological condition were differentially responsive to stressors, indicating that no single measure of biological condition could fully characterize assemblage response to stress. Total dissolved solids (as measured by electrical conductivity [EC]) and metal contamination were the stressors most strongly associated with biological degradation. The most likely sources of these stressors were agriculture, urban development, and mining. Our findings highlight the need to develop EC criteria for streams. Measures of biological condition and stress that account for natural variability should reduce errors of inference and increase confidence in causal analyses. This approach will require development of robust models capable of predicting physical and chemical reference conditions. Causal analyses for individual sites require appropriate hypotheses about which stressors and what levels of stress can cause biological degradation. Our study demonstrates the usefulness of field data collected from multiple sites within a region for developing these hypotheses.

Revising the index of watershed integrity national maps

Watersheds provide a range of services valued by society, incorporating biotic and abiotic functions within their boundaries. Recently, an operational definition of watershed integrity was applied and indices of watershed integrity (IWI) and catchment integrity (ICI) were developed and mapped for the conterminous United States. However, these indices were originally derived using equally-weighted first-order approximations of relationships between anthropogenic stressors (obtained from the U.S. EPAs StreamCat dataset) and six watershed functions. In addition, the original calculations of the IWI and ICI did not standardize metrics across these differing scales, resulting in IWI and ICI values that are not directly comparable. We provide an example of how to iteratively update the stressor-watershed function relationships using random forest models and a nationwide response metric representative of one of the six watershed functions. Specifically, we focused on the chemical regulation function (CHEM) of IWI and ICI by relating a composite metric of chemical water quality from 1914 samples to land use metrics explicit to CHEM to refine the nature of these relationships (e.g., non-linear versus linear). The rate of nitrogen fertilizer, agricultural land use, and urban land use were found to be the three most important stressors predicting the national water quality response metric. Revision of CHEM values improved the prediction of several regional- to national-scale water quality indicators. In all cases, exponential decay curves replaced the original negative linear relationship for CHEM. Therefore, the original IWI and ICI values are probably over-estimates of the actual integrity of the Nations watersheds and catchments. With these revisions, we provide updated national maps of IWI and ICI. The methods outlined here can be implemented iteratively as more and better data become available for all six of the watershed functions to elevate the accuracy and applicability of these indices to various land management issues.