Combined effects of life-history traits and human impact on extinction risk of freshwater megafauna.

Megafauna species are intrinsically vulnerable to human impact. Freshwater megafauna (i.e., freshwater animals ≥30 kg, including fishes, mammals, reptiles, and amphibians) are subject to intensive and increasing threats. Thirty‐four species are listed as critically endangered on the International Union for Conservation of Nature (IUCN). Red List of Threatened Species, the assessments for which are an important basis for conservation actions but remain incomplete for 49 (24%) freshwater megafauna species. Consequently, the window of opportunity for protecting these species could be missed. Identifying the factors that predispose freshwater megafauna to extinction can help predict their extinction risk and facilitate more effective and proactive conservation actions. Thus, we collated 8 life‐history traits for 206 freshwater megafauna species. We used generalized linear mixed models to examine the relationships between extinction risk based on the IUCN Red List categories and the combined effect of multiple traits, as well as the effect of human impact on these relationships for 157 classified species. The most parsimonious model included human impact and traits related to species’ recovery potential including life span, age at maturity, and fecundity. Applying the most parsimonious model to 49 unclassified species predicted that 17 of them are threatened. Accounting for model predictions together with IUCN Red List assessments, 50% of all freshwater megafauna species are considered threatened. The Amazon and Yangtze basins emerged as global diversity hotspots of threatened freshwater megafauna, in addition to existing hotspots, including the Ganges‐Brahmaputra and Mekong basins and the Caspian Sea region. Assessment and monitoring of those species predicted to be threatened are needed, especially in the Amazon and Yangtze basins. Investigation of life‐history traits and trends in population and distribution, regulation of overexploitation, maintaining river connectivity, implementing protected areas focusing on freshwater ecosystems, and integrated basin management are required to protect threatened freshwater megafauna in diversity hotspots.This work was carried out within the SMART Joint Doctorate (Science for the MAnagement of Rivers and their Tidal systems), funded with the support of the Erasmus Mundus program of the European Union, and is a contribution to the Leibniz Competition project Freshwater Megafauna Futures. S.D.L was supported by the European Unio

Freshwater megafauna diversity: Patterns, status and threats

Aim: Freshwater megafauna remain underrepresented in research and conservation, despite a disproportionately high risk of extinction due to multiple human threats. Therefore, our aims are threefold; (i) identify global patterns of freshwater megafauna richness and endemism, (ii) assess the conservation status of freshwater megafauna and (iii) demonstrate spatial and temporal patterns of human pressure throughout their distribution ranges. Location: Global. Methods: We identified 207 extant freshwater megafauna species, based on a 30 kg weight threshold, and mapped their distributions using HydroBASINS subcatchments (level 8). Information on conservation status and population trends for each species was extracted from the IUCN Red List website. We investigated human impacts on freshwater megafauna in space and time by examining spatial congruence between their distributions and human pressures, described by the Incident Biodiversity Threat Index and Temporal Human Pressure Index. Results: Freshwater megafauna occur in 76% of the world s main river basins (level 3 HydroBASINS), with species richness peaking in the Amazon, Congo, Orinoco, Mekong and Ganges-Brahmaputra basins. Freshwater megafauna are more threatened than their smaller counterparts within the specific taxonomic groups (i.e., fishes, mammals, reptiles and amphibians). Out of the 93 freshwater megafauna species with known population trends, 71% are in decline. Meanwhile, IUCN Red List assessments reported insufficient or outdated data for 43% of all freshwater megafauna species. Since the early 1990s, human pressure has increased throughout 63% of their distribution ranges, with particularly intense impacts occurring in the Mekong and Ganges-Brahmaputra basins. Main conclusions: Freshwater megafauna species are threatened globally, with intense and increasing human pressures occurring in many of their biodiversity hotspots. We call for research and conservation actions for freshwater megafauna, as they are highly sensitive to present and future pressures including a massive boom in hydropower dam construction in their biodiversity hotspots. © 2018 John Wiley & Sons LtdBundesministerium für Bildung und Forschung, Grant/Award Number: “GLANCE” project (01 LN1320A); European Union’s Horizon 2020 Programme for Research, Technological ?evelopment and demonstration, Grant/Award Number: AQUACROSS (642317); Villum Fonden, Grant/Award Number: VKR023371; Education, Audiovisual and Culture Executive Agency (Erasmus Mundus Joint ?octorate programme “SMART”); EU Marie Sklodowska-Curie programme, Grant/Award Number: H2020-MSCA-IF-2015-706784, H2020-MSCA-IF-2016-748625; Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (Junior Professorship Program

Combining eight research areas to foster the uptake of ecosystem-based management in fresh waters

Freshwater ecosystems are under a constant risk of being irreversibly damaged by human pressures that threaten their biodiversity, the sustainability of ecosystem services (ESs), and human well-being. Despite the implementation of various environmental regulations, the challenges of safeguarding freshwater assets have so far not been tackled successfully. A promising way forward to stop the loss of freshwater biodiversity and to sustain freshwater-based ESs is by implementing ecosystem-based management (EBM), an environmental planning and adaptive management approach that jointly considers social and ecological needs. Responsible for considerable recent success in sustainably managing and conserving marine ecosystems, EBM has not yet been championed for fresh waters. A major reason for the delayed uptake of EBM in fresh waters is likely to be its complexity, requiring planners to be familiar with the latest developments in a range of different research areas. EBM would therefore benefit from becoming more tangible to receive attention on the ground. To facilitate uptake, eight core research areas for EBM and their innovations are introduced, and the way in which they feed into the workflow that guides the EBM planning stage is explained. The workflow links biodiversity distributions with ES supply-and-demand modelling and SMART (specific, measurable, attainable, relevant, and timely) target planning, including scenario- and cross-realm perspectives, the prioritization of management alternatives, spatial prioritization of biodiversity conservation and ES areas, and the quantification of uncertainties. Given the extensive resources, time, and technical capacity required to implement the full workflow, a light and an ultralight version of the workflow are also provided. Applied in concert, the eight well-known research areas allow for better planning and operationalizing, and eventually for implementing EBM in freshwater ecosystems. EBM has great potential to increase public acceptance by introducing the consideration of human needs and aspirations into typically biodiversity-driven conservation and management approaches. This will ultimately improve the integrity of freshwater ecosystems. © 2019 John Wiley & Sons, Ltd.German Federal Ministry of Education and Research, Grant/Award Number: 01 LN1320A; Horizon 2020 Framework Programme, Grant/Award Number: 642317; Marie Sklodowska‐Curie Global Fellowship, Grant/Award Number: 748625; Ramón y Cajal, Grant/Award Number: RYC‐2013‐1397

From topography to hydrology The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1 93.2 and 0.61 0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP. © 2020 The Authors. Ecology and Evolution published by John Wiley and Sons Ltd.This work was funded by the German Federal Ministry of Education and Research (BMBF) within the “GLANCE” project (Global Change Effects in River Ecosystems; 01 LN1320A). We further cknowledge funding by the European Union's Horizon 2020 Research and Innovation Programme grant number 642317. SDL has received funding from the European Union's Horizon 2020 Research and Innovation Programme Under the Marie Skłodowska-Curie Grant agreement No 748625. SD acknowledges funding by the Leibniz Association within the Leibniz Competition program (grant number J45/2018). We thank the EU projects Biofresh (Contract No 226874)

A comparative analysis of restoration measures and their effects on hydromorphology and benthic invertebrates in 26 central and southern European rivers.

1. Hydromorphological river restoration usually leads to habitat diversification, but the effects on benthic invertebrates, which are frequently used to assess river ecological status, are minor. We compared the effects of river restoration on morphology and benthic invertebrates by investigating 26 pairs of non-restored and restored sections of rivers in Austria, Czech Republic, Germany, Italy and the Netherlands. 2. Sites were grouped according to (1) region: central Europe vs. southern Europe; (2) river type: mountain vs. lowland rivers; (3) restoration approach: active vs. passive restoration and (4) a combination of these parameters. All sites were sampled according to the same field protocol comprising hydromorphological surveys of river and floodplain mesohabitats, microhabitats at the river bottom and habitat-specific sampling of benthic invertebrates. Restoration effects were compared using Shannon–Wiener Indices (SWIs) of mesohabitats, microhabitats and invertebrate communities. Differences in metric values between non-restored and restored sites were compared for 16 metrics that evaluated hydromorphology and the benthic invertebrate community. 3. Mean SWIs differed for both mesohabitats (1·1 non-restored, 1·7 restored) and microhabitats (1·0 non-restored, 1·3 restored), while SWIs for invertebrate communities were not significantly different (2·4 non-restored, 2·3 restored). Meso- and microhabitat metrics in the restored sections were usually higher compared with the non-restored sections, but the effects on invertebrate metrics were negligible. 4. Measures in southern Europe and mountainous regions yielded larger differences between non-restored and restored sections of rivers. Differences in the meso- and microhabitat metrics were largest for actively restored sections of central European mountain rivers and rivers from southern Europe, followed by passively restored mountain rivers in central Europe. The smallest differences were observed for lowland sites. There was no significant restoration effect on invertebrate metrics in any categories. 5. Synthesis and applications. Restoration measures addressing relatively short river sections (several hundred metres) are successful in terms of improving habitat diversity of the river and its floodplain. Active restoration measures are suitable if short-term changes in hydromorphology are desired. To realize changes in benthic invertebrate community composition, habitat restoration within a small stretch is generally not sufficient. We conclude that restoring habitat on a larger scale, using more comprehensive measures and tackling catchment-wide problems (e.g. water quality, source populations) are required for a recovery of the invertebrate community

Substratum associations of benthic invertebrates in lowland and mountain streams

The preferences of aquatic invertebrate species for specific substrata at the river bottom have been subject of many studies. Several authors classified the substratum preferences of species or higher taxonomic units. Most of these compilations, however, are based on literature analyses and expert knowledge as opposed to the analysis of original data. To enhance our knowledge of invertebrate substratum preferences, we applied a ‘Multi-level pattern’ analysis based on almost 1000 substrate-specific invertebrate samples. The samples were taken in 18 streams in Germany, the Netherlands and Austria, comprising a total of 40 sampling sites and equally covering lowland and mountain streams. The main objectives of our analysis were (I) to derive substratum preferences of taxa in lowland and mountain streams, (II) to compare the preferences with existing data and (III) to compare species substratum associations between lowland and mountain streams. Of the 290 taxa analyzed, 188 were associated significantly to specific substrata. Twenty-five taxa in lowland streams and 51 taxa in mountain streams prefer one or two substratum types (of nine substratum types considered in total). In contrast, 112 species (mountain streams n = 84, lowland streams n = 28) are associated significantly with a broader range of substrata. We compared the classifications derived from our data analysis with those provided in the freshwaterecology.info database (www.freshwaterecology.info). Our results support the existing classifications of substratum preferences in most cases (70%). For 25 species, substratum preferences for both lowland and mountain streams were derived, many of them indicating different substratum associations in the two stream groups. As substratum preferences differed between closely related species, preferences should always be given at the species level as opposed to coarser taxonomic unit

Social equity shapes zone-selection: Balancing aquatic biodiversity conservation and ecosystem services delivery in the transboundaryDanube River Basin

Freshwater biodiversity is declining, despite national and international efforts to manage and protect freshwater ecosystems. Ecosystem-based management (EBM) has been proposed as an approach that could more efficiently and adaptively balance ecological and societal needs. However, this raises the question of how social and ecological objectives can be included in an integrated management plan. Here, we present a generic model-coupling framework tailored to address this question for freshwater ecosystems, using three components: biodiversity, ecosystem services (ESS), and a spatial prioritisation that aims to balance the spatial representation of biodiversity and ESS supply and demand. We illustrate this model-coupling approach within the Danube River Basin using the spatially explicit, potential distribution of (i) 85 fish species as a surrogate for biodiversity as modelled using hierarchical Bayesian models, and (ii) four estimated ESS layers produced by the Artificial Intelligence for Ecosystem Services (ARIES) platform (with ESS supply defined as carbon storage and flood regulation, and demand specified as recreation and water use). These are then used for (iii) a joint spatial prioritisation of biodiversity and ESS employing Marxan with Zones, laying out the spatial representation of multiple management zones. Given the transboundary setting of the Danube River Basin, we also run comparative analyses including the country-level purchasing power parity (PPP)-adjusted gross domestic product (GDP) and each country's percent cover of the total basin area as potential cost factors, illustrating a scheme for balancing the share of establishing specific zones among countries. We demonstrate how emphasizing various biodiversity or ESS targets in an EBM model-coupling framework can be used to cost-effectively test various spatially explicit management options across a multi-national case study. We further discuss possible limitations, future developments, and requirements for effectively managing a balance between biodiversity and ESS supply and demand in freshwater ecosystems. © 2018 The AuthorsThis study was funded by the European Union's Horizon 2020 - Research and Innovation Framework Programme under grant agreement No. 642317 , granting funding to SD, SCJ, KK, SDL, JML, SB, FV, AF, TH, and FB. SDL has received additional funding from the European Union's Horizon 2020 - Research and Innovation Framework Programme under the Marie Skłodowska-Curie grant agreement No. 748625 , and SCJ from the German Federal Ministry of Education and Research (BMBF) for the “GLANCE” project (Global Change Effects in River Ecosystems; 01 LN1320A ). VH is funded by a Ramon y Cajal contract (RYC-2013-13979) supported by the Spanish Government. The U.S. Geological Survey Land Change Science Program under the Land Resources Mission Area supported KJB's contributions to this paper. We thank Van Bustic for constructive comments on an earlier draft of this manuscript