Prediction of stream fish assemblages from land use characteristics: implications for cost-effective design of monitoring programmes

A general approach for evaluating of the coverage, resolution, and representation of streamflow monitoring networks

Streamflow monitoring networks provide information for a wide range of public interests in river and streams. A general approach to evaluate monitoring for different interests is developed to support network planning and design. The approach defines three theoretically distinct information metrics (coverage, resolution, and representation) based on the spatial distribution of a variable of interest. Coverage is the fraction of information that a network can provide about a variable when some areas are not monitored. Resolution is the information available from the network relative to the maximum information possible given the number of sites in the network. Representation is the information that a network provides about a benchmark distribution of a variable. Information is defined using Shannon entropy where the spatial discretization of a variable among spatial elements of a landscape or sites in a network indicates the uncertainty in the spatial distribution of the variable. This approach supports the design of networks for monitoring of variables with heterogeneous spatial distributions ("hot spots" and patches) that might otherwise be unmonitored because they occupy insignificant portions of the landscape. Areas where monitoring will maintain or improve the metrics serve as objective priorities for public interests in network design. The approach is demonstrated for the streamflow monitoring network operated by the United States Geological Survey during water year 2020 indicating gaps in the coverage of coastal rivers and the resolution of low flows.

Testing Three Species Distribution Modelling Strategies to Define Fish Assemblage Reference Conditions for Stream Bioassessment and Related Applications

Species distribution models are widely used for stream bioassessment, estimating changes in habitat suitability and identifying conservation priorities. We tested the accuracy of three modelling strategies (single species ensemble, multi-species response and community classification models) to predict fish assemblages at reference stream segments in coastal subtropical Australia. We aimed to evaluate each modelling strategy for consistency of predictor variable selection; determine which strategy is most suitable for stream bioassessment using fish indicators; and appraise which strategies best match other stream management applications. Five models, one single species ensemble, two multi-species response and two community classification models, were calibrated using fish species presence-absence data from 103 reference sites. Models were evaluated for generality and transferability through space and time using four external reference site datasets. Elevation and catchment slope were consistently identified as key correlates of fish assemblage composition among models. The community classification models had high omission error rates and contributed fewer taxa to the ‘expected’ component of the taxonomic completeness (O/E₅₀) index than the other strategies. This potentially decreases the model sensitivity for site impact assessment. The ensemble model accurately and precisely modelled O/E₅₀ for the training data, but produced biased predictions for the external datasets. The multi-species response models afforded relatively high accuracy and precision coupled with low bias across external datasets and had lower taxa omission rates than the community classification models. They inherently included rare, but predictable species while excluding species that were poorly modelled among all strategies. We suggest that the multi-species response modelling strategy is most suited to bioassessment using freshwater fish assemblages in our study area. At the species level, the ensemble model exhibited high sensitivity without reductions in specificity, relative to the other models. We suggest that this strategy is well suited to other non-bioassessment stream management applications, e.g., identifying priority areas for species conservation.

Predicting fine-scale distributions of peripheral aquatic species in headwater streams

Headwater species and peripheral populations that occupy habitat at the edge of a species range may hold an increased conservation value to managers due to their potential to maximize intraspecies diversity and species' adaptive capabilities in the context of rapid environmental change. The southern Appalachian Mountains are the southern extent of the geographic range of native Salvelinus fontinalis and naturalized Oncorhynchus mykiss and Salmo trutta in eastern North America. We predicted distributions of these peripheral, headwater wild trout populations at a fine scale to serve as a planning and management tool for resource managers to maximize resistance and resilience of these populations in the face of anthropogenic stressors. We developed correlative logistic regression models to predict occurrence of brook trout, rainbow trout, and brown trout for every interconfluence stream reach in the study area. A stream network was generated to capture a more consistent representation of headwater streams. Each of the final models had four significant metrics in common: stream order, fragmentation, precipitation, and land cover. Strahler stream order was found to be the most influential variable in two of the three final models and the second most influential variable in the other model. Greater than 70% presence accuracy was achieved for all three models. The underrepresentation of headwater streams in commonly used hydrography datasets is an important consideration that warrants close examination when forecasting headwater species distributions and range estimates. Additionally, it appears that a relative watershed position metric (e.g., stream order) is an important surrogate variable (even when elevation is included) for biotic interactions across the landscape in areas where headwater species distributions are influenced by topographical gradients.

Assessing the ecological status in the context of the European Water Framework Directive: where do we go now?

The Water Framework Directive (WFD) is now well established as the key management imperative in river basins across Europe. However, there remain significant concerns with the way WFD is implemented and there is now a need for water managers and scientists to communicate better in order to find solutions to these concerns. To address this, a Science-Policy Interface (SPI) activity was launched in 2010 led by Directorate-General for Research and Innovation and Onema (the French national agency for water and aquatic ecosystems), which provided an interactive forum to connect scientists and WFD end-users. One major aim of the SPI activity was to establish a list of the most crucial research and development needs for enhancing WFD implementation. This paper synthesises the recommendations from this event highlighting 10 priority issues relating to ecological status. For lakes, temporary streams and transitional and coastal waters, WFD implementation still suffers from a lack of WFD-compliant bioassessment methods. For rivers, special attention is required to assess the ecological impacts of hydromorphological alterations on biological communities, notably those affecting river continuity and riparian covering. Spatial extrapolation tools are needed in order to evaluate ecological status for water bodies for which no data are available. The need for more functional bioassessment tools as complements to usual WFD-compliant tools, and to connect clearly good ecological state, biodiversity and ecosystem services when implementing WFD were also identified as crucial issues.