Direct habitat descriptors improve the understanding of the organization of fish and macroinvertebrate communities across a large catchment.

In large-scale aquatic ecological studies, direct habitat descriptors (e.g. water temperature, hydraulics in river reaches) are often approximated by coarse-grain surrogates (e.g. air temperature, discharge respectively) since they are easier to measure or model. However, as biological variability can be very strong at the habitat scale, surrogate variables may have a limited ability to capture all of this variability, which may lead to a lesser understanding of the ecological processes or patterns of interest. In this study, we aimed to compare the capacity of direct habitat descriptors vs. surrogate environmental variables to explain the organization of fish and macroinvertebrate communities across the Loire catchment in France (105 km2). For this purpose, we relied on high-resolution environmental data, extensive biological monitoring data (>1000 sampling stations) and multivariate analyses. Fish and macroinvertebrate abundance datasets were considered both separately and combined to assess the value of a cross-taxa approach. We found that fish and macroinvertebrate communities exhibited weak concordance in their organization and responded differently to the main ecological gradients. Such variations are probably due to fundamental differences in their life-history traits and mobility. Regardless of the biological group considered, direct habitat descriptors (water temperature and local hydraulic variables) consistently explained the organization of fish and macroinvertebrate communities better than surrogate descriptors (air temperature and river discharge). Furthermore, the organization of fish and macroinvertebrate communities was slightly better explained by the combination of direct or surrogate environmental variables when the two biological groups were considered together than when considered separately. Tied together, these results emphasize the importance of using a cross-taxa approach in association with high-resolution direct habitat variables to more accurately explain the organization of aquatic communities.

Riverscape approaches in practice: perspectives and applications.

Landscape perspectives in riverine ecology have been undertaken increasingly in the last 30 years, leading aquatic ecologists to develop a diverse set of approaches for conceptualizing, mapping and understanding 'riverscapes'. Spatiotemporally explicit perspectives of rivers and their biota nested within the socio-ecological landscape now provide guiding principles and approaches in inland fisheries and watershed management. During the last two decades, scientific literature on riverscapes has increased rapidly, indicating that the term and associated approaches are serving an important purpose in freshwater science and management. We trace the origins and theoretical foundations of riverscape perspectives and approaches and examine trends in the published literature to assess the state of the science and demonstrate how they are being applied to address recent challenges in the management of riverine ecosystems. We focus on approaches for studying and visualizing rivers and streams with remote sensing, modelling and sampling designs that enable pattern detection as seen from above (e.g. river channel, floodplain, and riparian areas) but also into the water itself (e.g. aquatic organisms and the aqueous environment). Key concepts from landscape ecology that are central to riverscape approaches are heterogeneity, scale (resolution, extent and scope) and connectivity (structural and functional), which underpin spatial and temporal aspects of study design, data collection and analysis. Mapping of physical and biological characteristics of rivers and floodplains with high-resolution, spatially intensive techniques improves understanding of the causes and ecological consequences of spatial patterns at multiple scales. This information is crucial for managing river ecosystems, especially for the successful implementation of conservation, restoration and monitoring programs. Recent advances in remote sensing, field-sampling approaches and geospatial technology are making it increasingly feasible to collect high-resolution data over larger scales in space and time. We highlight challenges and opportunities and discuss future avenues of research with emerging tools that can potentially help to overcome obstacles to collecting, analysing and displaying these data. This synthesis is intended to help researchers and resource managers understand and apply these concepts and approaches to address real-world problems in freshwater management.

Global prevalence of non-perennial rivers and streams.

Flowing waters have a unique role in supporting global biodiversity, biogeochemical cycles and human societies1-5. Although the importance of permanent watercourses is well recognized, the prevalence, value and fate of non-perennial rivers and streams that periodically cease to flow tend to be overlooked, if not ignored6-8. This oversight contributes to the degradation of the main source of water and livelihood for millions of people5. Here we predict that water ceases to flow for at least one day per year along 51-60 per cent of the world's rivers by length, demonstrating that non-perennial rivers and streams are the rule rather than the exception on Earth. Leveraging global information on the hydrology, climate, geology and surrounding land cover of the Earth's river network, we show that non-perennial rivers occur within all climates and biomes, and on every continent. Our findings challenge the assumptions underpinning foundational river concepts across scientific disciplines9. To understand and adequately manage the world's flowing waters, their biodiversity and functional integrity, a paradigm shift is needed towards a new conceptual model of rivers that includes flow intermittence. By mapping the distribution of non-perennial rivers and streams, we provide a stepping-stone towards addressing this grand challenge in freshwater science.

A high-resolution life cycle impact assessment model for continental freshwater habitat change due to water consumption.

Global value chains and climate change have a significant impact on water resources and increasingly threaten freshwater ecosystems. Recent methodological proposals for life cycle impact assessment (LCIA), evaluate water use impacts on freshwater habitats based on river hydraulic parameters alterations. However, they are limited to French rivers due to lack of global data and models. On this basis, this article proposes an approach to compute regionalized characterization factors for modeling river habitat change potential (HCP) induced by water consumption, potentially applicable worldwide. A simplified model is developed for fish guilds and invertebrates. Based on French datasets, it establishes a relationship between HCP and river hydraulic parameters. A methodology to derive discharge and hydraulic geometry at the reach scale is proposed and applied to European and Middle Eastern rivers below 60°N latitude. Regionalized HCPs are calculated at the river reach scale and aggregated at watershed. Then, the impact of agricultural water use in contrasted European and Middle Eastern countries is evaluated comparing the outcomes from the HCP and the Available Water Remaining (AWARE) models at the national scale, considering water supply mix data. The same analysis is carried out on selected river basins. Finally, result consistency, uncertainty and global applicability of the overall approach are discussed. The study demonstrates the reproducibility of the impact model developed for French rivers on any hydrographic network where comparable ecological, hydrological and hydraulic conditions are met. Furthermore, it highlights the need to characterize impacts at a higher spatial resolution in areas where HCP is higher. Large scale quantification of HCP opens the way to the operationalization of mechanistic LCIA models in which the habitat preferences of freshwater species are taken into account to assess the impacts of water consumption on biodiversity.

Spatialized freshwater ecosystem life cycle impact assessment of water consumption based on instream habitat change modeling.

In this article a new characterization model and factors are proposed for the life cycle impact assessment (LCIA) of water consumption on instream freshwater ecosystems. Impact pathways of freshwater consumption leading to ecosystem damage are described and the alteration of instream physical habitat is identified as a critical midpoint for ecosystem quality. The LCIA characterization model aims to assess the change in habitat quantity due to consumptive water use. It is based on statistical, physical habitat simulation for benthic invertebrates, fish species and their size classes, and guilds of fish sharing common habitat preferences. A habitat change potential (HCP) midpoint, mechanistic indicator, is developed and computed on the French river network at the river reach scale (the river segment with variable length between the upstream and downstream nodes in the hydrographic network), for median annual discharges and dry seasons. Aggregated, multi-species HCPs at a river reach are proposed using various aggregation approaches. Subsequently, the characterization factors are spatially aggregated at watershed and sub-watershed scales. HCP is highly correlated with median and low flow discharges, which determine hydraulic characteristics of reaches. Aggregation of individual HCPs at reach scale is driven by the species most sensitive to water consumption. In spatially aggregated HCPs, consistently with their reduced smaller average discharge rate, small stream habitats determine the overall watershed characterization. The study is aimed primarily at life cycle assessment (LCA) practitioners and LCIA modelers. However, since it is the result of a productive cross-fertilization between the ecohydrology and LCA domains, it could be potentially useful for watershed management and risk assessment as well. At the moment, the proposed model is applicable in France. For a broader implementation, the development of global, high resolution river databases or the generalization of the model are needed. Our new factor represents nevertheless an advancement in freshwater ecosystems LCIA laying the basis for new metrics for biodiversity assessment.

What is socio-ecological research delivering? A literature survey across 25 international LTSER platforms.

With an overarching goal of addressing global and regional sustainability challenges, Long Term Socio-Ecological Research Platforms (LTSER) aim to conduct place-based research, to collect and synthesize both environmental and socio-economic data, and to involve a broader stakeholder pool to set the research agenda. To date there have been few studies examining the output from LTSER platforms. In this study we enquire if the socio-ecological research from 25 self-selected LTSER platforms of the International Long-Term Ecological Research (ILTER) network has produced research products which fulfil the aims and ambitions of the paradigm shift from ecological to socio-ecological research envisaged at the turn of the century. In total we assessed 4983 publically available publications, of which 1112 were deemed relevant to the socio-ecological objectives of the platform. A series of 22 questions were scored for each publication, assessing relevance of responses in terms of the disciplinary focus of research, consideration of human health and well-being, degree of stakeholder engagement, and other relevant variables. The results reflected the diverse origins of the individual platforms and revealed a wide range in foci, temporal periods and quantity of output from participating platforms, supporting the premise that there is a growing trend in socio-ecological research at long-term monitoring platforms. Our review highlights the challenges of realizing the top-down goal to harmonize international network activities and objectives and the need for bottom-up, self-definition for research platforms. This provides support for increasing the consistency of LTSER research while preserving the diversity of regional experiences.

Fish habitat selection in a large hydropeaking river: Strong individual and temporal variations revealed by telemetry.

Modeling individual fish habitat selection in highly variable environments such as hydropeaking rivers is required for guiding efficient management decisions. We analyzed fish microhabitat selection in the heterogeneous hydraulic and thermal conditions (modeled in two-dimensions) of a reach of the large hydropeaking Rhône River locally warmed by the cooling system of a nuclear power plant. We used modern fixed acoustic telemetry techniques to survey 18 fish individuals (five barbels, six catfishes, seven chubs) signaling their position every 3s over a three-month period. Fish habitat selection depended on combinations of current microhabitat hydraulics (e.g. velocity, depth), past microhabitat hydraulics (e.g. dewatering risk or maximum velocities during the past 15days) and to a lesser extent substrate and temperature. Mixed-effects habitat selection models indicated that individual effects were often stronger than specific effects. In the Rhône, fish individuals appear to memorize spatial and temporal environmental changes and to adopt a "least constraining" habitat selection. Avoiding fast-flowing midstream habitats, fish generally live along the banks in areas where the dewatering risk is high. When discharge decreases, however, they select higher velocities but avoid both dewatering areas and very fast-flowing midstream habitats. Although consistent with the available knowledge on static fish habitat selection, our quantitative results demonstrate temporal variations in habitat selection, depending on individual behavior and environmental history. Their generality could be further tested using comparative experiments in different environmental configurations.

How is success or failure in river restoration projects evaluated? Feedback from French restoration projects.

Since the 1990s, French operational managers and scientists have been involved in the environmental restoration of rivers. The European Water Framework Directive (2000) highlights the need for feedback from restoration projects and for evidence-based evaluation of success. Based on 44 French pilot projects that included such an evaluation, the present study includes: 1) an introduction to restoration projects based on their general characteristics 2) a description of evaluation strategies and authorities in charge of their implementation, and 3) a focus on the evaluation of results and the links between these results and evaluation strategies. The results show that: 1) the quality of an evaluation strategy often remains too poor to understand well the link between a restoration project and ecological changes; 2) in many cases, the conclusions drawn are contradictory, making it difficult to determine the success or failure of a restoration project; and 3) the projects with the poorest evaluation strategies generally have the most positive conclusions about the effects of restoration. Recommendations are that evaluation strategies should be designed early in the project planning process and be based on clearly-defined objectives.

Effect of classification procedure on the performance of numerically defined ecological regions.

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a "training dataset" representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected "training" cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.

Strong influence of variable treatment on the performance of numerically defined ecological regions.

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale "sub-domains" defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.

Definition procedures have little effect on performance of environmental classifications of streams and rivers.

Mapped environmental classifications are defined using various procedures, but there has been little evaluation of the differences in their ability to discriminate variation in independent ecological characteristics. We tested the performance of environmental classifications of the streams and rivers of France that had been defined from the same environmental data using geographic regionalization and numerical classification of individual river valley segments. Test data comprised invertebrate assemblages, water chemistry, and hydrological indexes obtained from sites throughout France. Classification performance was measured by analysis of similarity (ANOSIM). Geometric regions defined by a regular grid and without regard to environmental variables and a posteriori classifications based on clustering the test datasets defined lower and upper bounds of performance for a given number of classes. Differences in classification performances were generally small. The ANOSIM statistics for the a posteriori classifications were around twice that of all environmental classifications, including geometrically defined regions. The hydro-ecoregions performed slightly better for the invertebrate data and the network classification performed slightly better for the chemistry and hydrological data. Our results indicate that environmental classifications that are defined using different procedures can be comparable in terms of their ability to discriminate variation of ecological characteristics and that alleged differences in performance arising from different classification procedures can be small relative to unexplained variation. We conclude that definition procedures might have little effect on the performance of large-scale environmental classifications and decisions over which procedures to use should be based primarily on pragmatic considerations.