Disentangling the potential effects of land-use and climate change on stream conditions

Physical habitat is more than a sediment issue: A multi-dimensional habitat assessment indicates new approaches for river management

Degraded physical habitat is a common stressor affecting river ecosystems and typically addressed in the United States (US) through a regulatory focus on sediment. However, a narrow regulatory focus on sediment may overlook other aspects of physical habitat and the processes for its creation, maintenance, and degradation. In addition, there exist few "ready-to-use" regional assessments of the multiple dimensions of physical habitat to better understand continuous patterns of condition and prioritize management efforts across a large spatial scale. In this study, we use rapid habitat monitoring data to train a machine-learning (i.e., random forest) model to predict twelve physical habitat metrics for nearly 120,000 km of nontidal rivers and streams across the Chesapeake Bay watershed, US. We capture a range of habitat conditions driven by both natural variables and anthropogenic pressures. Covariation among habitat metrics indicated two major dimensions of habitat variation: 1) coarse bed substrate and hydromorphic heterogeneity and 2) bank stability and riparian condition. The model predicted localized changes from 2001 to 2019, and the predicted areas of deterioration roughly balanced improvements across the watershed, indicating little progress towards long-term watershed management goals. To evaluate connections to regulatory and management endpoints, we compared our physical habitat predictions to paired estimates of sediment and flow alteration across the region. Sediment concentrations were greater in reaches with less bank stability and lower riparian quality; however, the relation was weak for coarse bed condition metrics, including embeddedness, which is frequently used for establishing regulatory sediment restrictions. For flow alteration, most habitat metrics had lower scores with altered flow metrics, but metrics of instream habitat heterogeneity and coarse substrate condition were most strongly affected. Increased flashy, high flows negatively affected most metrics, but coarse substrate metrics were also negatively affected by greater low flow severity. This study highlights a potential disconnect between a narrow focus on regulatory sediment targets given the multiple dimensions and responses of physical habitat. A more holistic approach to physical habitat in management interventions - one that considers hydromorphic processes, diversity and variability in microhabitats, and explicit consideration of alterations to both low and high flows - may be warranted. By providing direct estimates of multiple aspects of physical habitat, this model can help support managers in the Chesapeake Bay watershed to better understand the range of habitat conditions, identify high-quality reaches for conservation, and target potential management actions tailored to localized conditions.

Observed and projected functional reorganization of riverine fish assemblages from global change

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.

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

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.

Pharmaceuticals in water and sediment of small streams under the pressure of urbanization: Concentrations, interactions, and risks

Small streams are crucial but vulnerable elements of ecological networks. To better understand the occurrence of pharmaceutically active compounds (PhACs) in streams, this study focused on the occurrence, distribution, and environmental risk of 111 PhACs and 7 trace elements based on a total of 141 water and sediment samples from small streams located in the urbanizing region of Budapest, Hungary. Eighty-one PhACs were detected in the aqueous phase, whereas sixty-two compounds were detected in the sediment. Carbamazepine (CBZ) was the most frequently identified PhAC in water, and was found in 91.5% of all samples. However, the highest concentrations were measured for lamotrigine (344.8 μg·L^-1) and caffeine (221.4 μg·L^-1). Lidocaine was the most frequently occurring PhAC in sediment (73.8%), but the maximum concentrations were detected for CBZ (395.9 ng·g^-1) and tiapride (187.7 ng·g^-1). In both water and sediment, more PhACs were found downstream of the wastewater treatment plants (WWTPs) than in the samples not affected by treated wastewater, even though no relationship was observed between the total amount of treated wastewater and the number of detected PhACs. The PhAC concentrations were also independent of the distance from the WWTP effluents. PhAC-polluted samples were detected upstream of the WWTPs, thereby suggesting the relevance of diffuse emissions in addition to WWTP outlets. The most frequently detected PhACs in the sediment were usually also present in the water samples collected at the same place and time. The varying concentrations of PhACs and the fluctuating water-sediment properties resulted in a lack of correlation between the general chemical properties and the concentrations of PhACs, which makes it difficult to predict PhAC contamination and risks in urbanized small streams. The environmental risk assessment indicated that diclofenac had the highest risk in the sampling area.

Linking Altered Flow Regimes to Biological Condition: an Example Using Benthic Macroinvertebrates in Small Streams of the Chesapeake Bay Watershed

Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8-4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7-10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.

The impact of climate change induced alterations of streamflow and stream temperature on the distribution of riparian species

Distributions of riparian species will likely shift due to climate change induced alterations in temperature and rainfall patterns, which alter stream habitat. Spatial forecasting of suitable habitat in projected climatic conditions will inform management interventions that support wildlife. Challenges in developing forecasts include the need to consider the large number of riparian species that might respond differently to changing conditions and the need to evaluate the many different characteristics of streamflow and stream temperature that drive species-specific habitat suitability. In particular, in dynamic environments like streams, the short-term temporal resolution of species occurrence and streamflow need to be considered to identify the types of conditions that support various species. To address these challenges, we cluster species based on habitat characteristics to select habitat representatives and we evaluate regional changes in habitat suitability using short-term, temporally explicit metrics that describe the streamflow and stream temperature regime. We use stream-specific environmental predictors rather than climatic variables. Unlike other studies, the stream-specific environmental predictors are generated from the time that species were observed in a particular reach, in addition to long term trends, to evaluate habitat preferences. With species occurrence data from local monitoring surveys and streamflow and stream temperature modeled from downscaled Coupled Model Intercomparison Project - Phase 5 (CMIP5) climate projections, we predict change in habitat suitability at the end-of-century. The relative importance of hydrology and stream temperature varied by cluster. High altitudinal, cold water species' distributions contracted, while lower elevation, warm water species distributions expanded. Modeling with short-term temporally explicit environmental metrics did produce different end-of-century projections than using long-term averages for some of the representative species. These findings can help wildlife managers prioritize conservation efforts, manage streamflow, initiate monitoring of species in vulnerable clusters, and address stressors, such as passage barriers, in areas projected to be suitable in future climate conditions.