Accurately predicting species’ distributions is critical for the management and conservation of fish and wildlife populations. Joint species distribution models (JSDMs) account for dependencies between species often ignored by traditional species distribution models. We evaluated how a JSDM approach could improve predictive strength for stream fish communities within large watersheds (the Chesapeake Bay Watershed, USA), using a cross-validation study of JSDMs fit to data from over 50 species. Our results suggest that conditional predictions from JSDMs have the potential to make large improvements in predictive accuracy for many species, particularly for more generalist species where single species models may not perform well. For some species there was no added explanatory effect from conditional information, most of which already exhibited strong marginal predictive ability. For several rare species there were significant improvements in occurrence predictions, while the results for two invasive species considered did not show the same improvements. Overall, the optimal number of species to condition upon, as well as the effects of conditioning upon an increasing number of species, varied widely among species.

Many management and conservation contexts can benefit from understanding relationships between species abundances, which can be used to improve predictions of species occurrence and abundance. We present conditional prediction as a tool to capture information about species abundances via residual covariance between species. From a fitted joint species distribution model, this framework produces a species coefficient matrix that contains relationships between species abundances. The species coefficients allow co-observed species to be treated as a second set of predictors supplementing covariates in the model to improve prediction. We use simulations to demonstrate the potential benefits and limitations of conditional prediction across data types and species covariance before applying conditional prediction to two management contexts with real data. Simulations demonstrate that conditional prediction provides the largest benefits to continuous data and when there is residual covariance between many species. In our first application, we show that conditioning on other species improves in-sample and out-of-sample predictions of fish and invertebrate species, including Atlantic cod. In our second application, we show that the species coefficient matrix can be used to identify bird species at risk of nest parasitism by Brown-headed Cowbirds. Synthesis and applications. We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts. We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts.image

First posted February 21, 2023 For additional information, contact: Director, Virginia and West Virginia Water Science CenterU.S. Geological Survey1730 East Parham RoadRichmond, Virginia 23228Contact Pubs Warehouse “The Bay Connects us, the Bay reflects us” writes Tom Horton in the book “Turning the Tide—Saving the Chesapeake Bay”. The Chesapeake Bay watershed contains the largest estuary in the United States. The watershed stretches north to Cooperstown, New York, south to Lynchburg and Virginia Beach, Virginia, west to Pendleton County, West Virginia, and east to Seaford, Delaware, and Scranton, Pennsylvania. The watershed is more than 64,000 square miles that contain 150 major rivers and streams, hereafter referred to collectively as streams, that total more than 100,000 miles in length. The watershed contains thousands of smaller creeks and tributaries, large numbers of plants and animals, and, in 2020, more than 18.4 million people. As changes occur in population, land use, and climate within the watershed, so too do the diversity and health of the Bays ecosystems.

Climate and land-use/land-cover change ("global change") are restructuring biodiversity, globally. Broadly, environmental conditions are expected to become warmer, potentially drier (particularly in arid regions), and more anthropogenically developed in the future, with spatiotemporally complex effects on ecological communities. We used functional traits to inform Chesapeake Bay Watershed fish responses to future climate and land-use scenarios (2030, 2060, and 2090). We modeled the future habitat suitability of focal species representative of key trait axes (substrate, flow, temperature, reproduction, and trophic) and used functional and phylogenetic metrics to assess variable assemblage responses across physiographic regions and habitat sizes (headwaters through large rivers). Our focal species analysis projected future habitat suitability gains for carnivorous species with preferences for warm water, pool habitats, and fine or vegetated substrates. At the assemblage level, models projected decreasing habitat suitability for cold-water, rheophilic, and lithophilic individuals but increasing suitability for carnivores in the future across all regions. Projected responses of functional and phylogenetic diversity and redundancy differed among regions. Lowland regions were projected to become less functionally and phylogenetically diverse and more redundant while upland regions (and smaller habitat sizes) were projected to become more diverse and less redundant. Next, we assessed how these model-projected assemblage changes 2005-2030 related to observed time-series trends (1999-2016). Halfway through the initial projecting period (2005-2030), we found observed trends broadly followed modeled patterns of increasing proportions of carnivorous and lithophilic individuals in lowland regions but showed opposing patterns for functional and phylogenetic metrics. Leveraging observed and predicted analyses simultaneously helps elucidate the instances and causes of discrepancies between model predictions and ongoing observed changes. Collectively, results highlight the complexity of global change impacts across broad landscapes that likely relate to differences in assemblages' intrinsic sensitivities and external exposure to stressors.

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.

Stream ecosystems are complex networks of interacting terrestrial and aquatic drivers. To untangle these ecological networks, efforts evaluating the direct and indirect effects of landscape, climate, and instream predictors on biological condition through time are needed. We used structural equation modeling and leveraged a stream survey program to identify and compare important predictors driving condition of benthic macroinvertebrate and fish assemblages. We used data resampled 14 years apart at 252 locations across Maryland, USA. Sample locations covered a wide range of conditions that varied spatiotemporally. Overall, the relationship directions were consistent between sample periods, but their relative strength varied temporally. For benthic macroinvertebrates, we found that the total effect of natural landscape (e.g., elevation, longitude, latitude, geology) and land use (i.e., forest, development, agriculture) predictors was 1.4 and 1.5 times greater in the late 2010s compared to the 2000s. Moreover, the total effect of water quality (e.g., total nitrogen and conductivity) and habitat (e.g., embeddedness, riffle quality) was 1.2 and 4.8 times lower in the 2010s, respectively. For fish assemblage condition, the total effect of land use-land cover predictors was 2.3 times greater in the 2010s compared to the 2000s, while the total effect of local habitat was 1.4 times lower in the 2010s, respectively. As expected, we found biological assemblages in catchments with more agriculture and urban development were generally comprised of tolerant, generalist species, while assemblages in catchments with greater forest cover had more-specialized, less-tolerant species (e.g., Ephemeroptera, Plecoptera, and Trichoptera taxa, clingers, benthic and lithophilic spawning fishes). Changes in the relative importance of landscape and land-use predictors suggest other correlated, yet unmeasured, proximal factors became more important over time. By untangling these ecological networks, stakeholders can gain a better understanding of the spatiotemporal relationships driving biological condition to implement management practices aimed at improving stream condition.

Land-use and climate change are significantly affecting stream ecosystems, yet understanding of their long-term impacts is hindered by the few studies that have simultaneously investigated their interaction and high variability among future projections. We modeled possible effects of a suite of 2030, 2060, and 2090 land-use and climate scenarios on the condition of 70,772 small streams in the Chesapeake Bay watershed, United States. The Chesapeake Basin-wide Index of Biotic Integrity, a benthic macroinvertebrate multimetric index, was used to represent stream condition. Land-use scenarios included four Special Report on Emissions Scenarios (A1B, A2, B1, and B2) representing a range of potential landscape futures. Future climate scenarios included quartiles of future climate changes from downscaled Coupled Model Intercomparison Project - Phase 5 (CMIP5) and a watershed-wide uniform scenario (Lynch2016). We employed random forests analysis to model individual and combined effects of land-use and climate change on stream conditions. Individual scenarios suggest that by 2090, watershed-wide conditions may exhibit anywhere from large degradations (e.g., scenarios A1B, A2, and the CMIP5 25th percentile) to small degradations (e.g., scenarios B1, B2, and Lynch2016). Combined land-use and climate change scenarios highlighted their interaction and predicted, by 2090, watershed-wide degradation in 16.2% (A2 CMIP5 25th percentile) to 1.0% (B2 Lynch2016) of stream kilometers. A goal for the Chesapeake Bay watershed is to restore 10% of stream kilometers over a 2008 baseline; our results suggest meeting and sustaining this goal until 2090 may require improvement in 11.0%-26.2% of stream kilometers, dependent on land-use and climate scenario. These results highlight inherent variability among scenarios and the resultant uncertainty of predicted conditions, which reinforces the need to incorporate multiple scenarios of both land-use (e.g., development, agriculture, etc.) and climate change in future studies to encapsulate the range of potential future conditions.

Ongoing changes in climate are expected to alter current species’ habitat and potentially result in shifts in species distributions. While climatic conditions are important to a species’ ability to persist in an area, for many taxa, other environmental factors, such as geology, land cover, and topography, are also important for providing suitable habitat. Furthermore, aquatic species experience changes in climatic conditions through the effect precipitation and air temperature have on streamflow regimes and water temperature. In this study, species distribution models (SDMs) for ten stream-dwelling crayfish species were generated using a maximum entropy approach across the Mobile River Basin in the southeastern United States. SDMs were developed using model-generated contemporary estimates of streamflow and water temperature as well as geologic, topographic, and land cover data. Future distributions were then projected using global climate model (GCM) projections of streamflow and water temperature. Geology, topography, and streamflow appear to be more important predictors of suitable habitat than water temperature for crayfish species within the Mobile River Basin. Species distributions regulated by limited influences from stream flow and water temperature displayed relatively small changes in projected future habitat distributions based on various GCM scenarios. When shifts in species distributions were projected into the future, these shifts did not appear to follow a northward retreat or expansion, likely due to the limited impact of water temperature on the modeled distributions of suitable habitat for these species. Furthermore, species’ habitat distribution responses among future climate scenarios were variable within and among species and did not vary unidirectionally with increased severity of climate change as realized through increased warming patterns.

While ecotoxicology has long recognised the importance of identifying levels at which contaminants pose threats to biota, most estimates of species responses to toxicants are derived from controlled laboratory studies and may hold limited relevance to natural systems. However, designing appropriate field-based studies investigating contaminant induced changes in assemblages has been challenging, partially due to the difficulty in identifying comparable uncontaminated reference sites. The aim of this study is to characterise the effects of heavy metal contamination on natural fish assemblages using an ecologically relevant catchment-scale design. We hypothesise that environmental variables, including discharge, sediment, and landscape variables, can be used to characterise differences in fish species richness and abundances between sites contaminated with heavy metals and uncontaminated reference sites. We apply a geographic information systems approach that uses assemblage-environment relationships developed using hydrologic model outputs, land cover, and topographic data from uncontaminated reference sites to predict expected fish species richness and abundance at sites contaminated with heavy-metals within the Big River catchment in south-eastern Missouri, U.S.A. These predicted levels of richness and abundance are then compared to observed assemblages at contaminated sites to estimate the potential impacts of historical lead mining activities on freshwater taxa. We developed models that characterised variation in Centrarchidae (bass and sunfish) richness and abundance, Cyprinidae (minnows) abundance, and Percidae (darters) richness using variables including streamflow regime, suspended sediment concentration, and land cover at uncontaminated sites. Using these relationships, we predicted expected fish species richness and abundance at heavy metal contaminated sites across the Big River catchment and found a significant reduction in centrarchid abundance from field-collected data compared to predicted estimates. Our results suggest that centrarchids, which tend to occupy a higher trophic level than cyprinids and percids, have lower abundances at sites contaminated with heavy metals than predicted by assemblage-environment relationships. These decreases in abundance are not associated with decreases in centrarchid species richness, cyprinid abundance, or percid richness. This geographic information systems-based approach provides a useful and ecologically relevant framework for understanding the response of taxa to the presence of contaminants without assuming habitat equivalence across sites. Our findings also suggest the need for further research regarding how heavy metals impact fishes of varying trophic levels in natural settings.