Future large hydropower dams impact global freshwater megafauna

Dam construction comes with severe social, economic and ecological impacts. From an ecological point of view, habitat types are altered and biodiversity is lost. Thus, to identify areas that deserve major attention for conservation, existing and planned locations for (hydropower) dams were overlapped, at global extent, with the contemporary distribution of freshwater megafauna species with consideration of their respective threat status. Hydropower development will disproportionately impact areas of high freshwater megafauna richness in South America, South and East Asia, and the Balkan region. Sub-catchments with a high share of threatened species are considered to be most vulnerable; these are located in Central America, Southeast Asia and in the regions of the Black and Caspian Sea. Based on this approach, planned dam locations are classified according to their potential impact on freshwater megafauna species at different spatial scales, attention to potential conflicts between climate mitigation and biodiversity conservation are highlighted, and priorities for freshwater management are recommended

An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales

The global number of dam constructions has increased dramatically over the past six decades and is forecast to continue to rise, particularly in less industrialized regions. Identifying development pathways that can deliver the benefits of new infrastructure while also maintaining healthy and productive river systems is a great challenge that requires understanding the multifaceted impacts of dams at a range of scales. New approaches and advanced methodologies are needed to improve predictions of how future dam construction will affect biodiversity, ecosystem functioning, and fluvial geomorphology worldwide, helping to frame a global strategy to achieve sustainable dam development. Here, we respond to this need by applying a graph-based river routing model to simultaneously assess flow regulation and fragmentation by dams at multiple scales using data at high spatial resolution. We calculated the cumulative impact of a set of 6374 large existing dams and 3377 planned or proposed dams on river connectivity and river flow at basin and subbasin scales by fusing two novel indicators to create a holistic dam impact matrix for the period 1930–2030. Static network descriptors such as basin area or channel length are of limited use in hierarchically nested and dynamic river systems, so we developed the river fragmentation index and the river regulation index, which are based on river volume. These indicators are less sensitive to the effects of network configuration, offering increased comparability among studies with disparate hydrographies as well as across scales. Our results indicate that, on a global basis, 48% of river volume is moderately to severely impacted by either flow regulation, fragmentation, or both. Assuming completion of all dams planned and under construction in our future scenario, this number would nearly double to 93%, largely due to major dam construction in the Amazon Basin. We provide evidence for the importance of considering small to medium sized dams and for the need to include waterfalls to establish a baseline of natural fragmentation. Our versatile framework can serve as a component of river fragmentation and connectivity assessments; as a standardized, easily replicable monitoring framework at global and basin scales; and as part of regional dam planning and management strategies

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

Pole-to-Pole Connections : Similarities between Arctic and Antarctic Microbiomes and Their Vulnerability to Environmental Change

Acknowledgments JK acknowledges the Carl Zeiss foundation for PhD funding, the Marie-Curie COFUND-BEIPD PostDoc fellowship for PostDoc funding, FNRS travel funding and the logistical and financial support by UNIS. JK and FK acknowledge the Natural Environment Research Council (NERC) Antarctic Funding Initiative AFI-CGS-70 (collaborative gearing scheme) and logistic support from the British Antarctic Survey (BAS) for field work in Antarctica. JK and CZ acknowledge the Excellence Initiative at the University of Tübingen funded by the German Federal Ministry of Education and Research and the German Research Foundation (DFG). FH, AV, and PB received funding from MetaHIT (HEALTH-F4-2007-201052), Microbios (ERC-AdG-502 669830) and the European Molecular Biology Laboratory (EMBL). We thank members of the Bork group at EMBL for helpful discussions. We acknowledge the EMBL Genomics Core Facility for sequencing support and Y. P. Yuan and the EMBL Information Technology Core Facility for support with high-performance computing and EMBL for financial support. PC is supported by NERC core funding to the BAS “Biodiversity, Evolution and Adaptation” Team. MB was funded by Helge Ax:son Johnsons Stiftelse and PUT1317. DRD acknowledges the DFG funded project DI698/18-1 Dietrich and the Marie Curie International Research Staff Exchange Scheme Fellowship (PIRSES-GA-2011-295223). Operations in the Canadian High Arctic were supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), ArcticNet and the Polar Continental Shelf Program (PCSP). We are also grateful to the TOTAL Foundation (Paris) and the UK NERC (WP 4.3 of Oceans 2025 core funding to FCK at the Scottish Association for Marine Science) for funding the expedition to Baffin Island and within this context Olivier Dargent and Dr. Pieter van West for sample collection, and the Spanish Ministry of Science and Technology through project LIMNOPOLAR (POL200606635 and CGL2005-06549-C02-01/ANT to AQ as well as CGL2005-06549-C02-02/ANT to AC, the last of these co-financed by European FEDER funds). We are grateful for funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland), funded by the Scottish Funding Council (HR09011) and contributing institutions. Supplementary Material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2017.00137/full#supplementary-materialPeer reviewedPublisher PD

Competitive hierarchies in bryozoan assemblages mitigate network instability by keeping short and long feedback loops weak

Competitive hierarchies in diverse ecological communities have long been thought to lead to instability and prevent coexistence. However, system stability has never been tested, and the relation between hierarchy and instability has never been explained in complex competition networks parameterised with data from direct observation. Here we test model stability of 30 multispecies bryozoan assemblages, using estimates of energy loss from observed interference competition to parameterise both the inter- and intraspecific interactions in the competition networks. We find that all competition networks are unstable. However, instability is mitigated considerably by asymmetries in the energy loss rates brought about by hierarchies of strong and weak competitors. This asymmetric organisation results in asymmetries in the interaction strengths, which reduces instability by keeping the weight of short (positive) and longer (positive and negative) feedback loops low. Our results support the idea that interference competition leads to instability and exclusion but demonstrate that this is not because of, but despite, competitive hierarchy

Inductive biases in deep learning models for weather prediction

Deep learning has recently gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction (NWP) models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and how they contribute to model performance remains unclear. Here, we review and analyse the inductive biases of six state-of-the-art DLWP models, involving a deeper look at five key design elements: input data, forecasting objective, loss components, layered design of the deep learning architectures, and optimisation methods. We show how the design choices made in each of the five design elements relate to structural assumptions. Given recent developments in the broader DL community, we anticipate that the future of DLWP will likely see a wider use of foundation models -- large models pre-trained on big databases with self-supervised learning -- combined with explicit physics-informed inductive biases that allow the models to provide competitive forecasts even at the more challenging subseasonal-to-seasonal scales

Global Dam Watch: curated data and tools for management and decision making

Dams, reservoirs, and other water management infrastructure provide benefits, but can also have negative impacts. Dam construction and removal affects progress toward the UN sustainable development goals at local to global scales. Yet, globally-consistent information on the location and characteristics of these structures are lacking, with information often highly localised, fragmented, or inaccessible. A freely available, curated, consistent, and regularly updated global database of existing dams and other instream infrastructure is needed along with open access tools to support research, decision-making and management needs. Here we introduce the Global Dam Watch (GDW) initiative (www.globaldamwatch.org ) whose objectives are: (a) advancing recent efforts to develop a single, globally consistent dam and instream barrier data product for global-scale analyses (the GDW database); (b) bringing together the increasingly numerous global, regional and local dam and instream barrier datasets in a directory of databases (the GDW directory); (c) building tools for the visualisation of dam and instream barrier data and for analyses in support of policy and decision making (the GDW knowledge-base) and (d) advancing earth observation and geographical information system techniques to map a wider range of instream structures and their properties. Our focus is on all types of anthropogenic instream barriers, though we have started by prioritizing major reservoir dams and run-of-river barriers, for which more information is available. Our goal is to facilitate national-scale, basin-scale and global-scale mapping, analyses and understanding of all instream barriers, their impacts and their role in sustainable development through the provision of publicly accessible information and tools. We invite input and partnerships across sectors to strengthen GDW’s utility and relevance for all, help define database content and knowledge-base tools, and generally expand the reach of GDW as a global hub of impartial academic expertise and policy information regarding dams and other instream barriers

Rethinking megafauna

Concern for megafauna is increasing among scientists and non-scientists. Many studies have emphasized that megafauna play prominent ecological roles and provide important ecosystem services to humanity. But, what precisely are “megafauna”? Here we critically assess the concept of megafauna and propose a goal-oriented framework for megafaunal research. First, we review definitions of megafauna and analyze associated terminology in the scientific literature. Second, we conduct a survey among ecologists and paleontologists to assess the species traits used to identify and define megafauna. Our review indicates that definitions are highly dependent on the study ecosystem and research question, and primarily rely on ad hoc size-related criteria. Our survey suggests that body size is crucial, but not necessarily sufficient, for addressing the different applications of the term megafauna. Thus, after discussing the pros and cons of existing definitions, we propose an additional approach by defining two function-oriented megafaunal concepts: “keystone megafauna” and “functional megafauna”, with its variant “apex megafauna”. Assessing megafauna from a functional perspective could challenge the perception that there may not be a unifying definition of megafauna that can be applied to all eco-evolutionary narratives. In addition, using functional definitions of megafauna could be especially conducive to cross-disciplinary understanding and cooperation, improvement of conservation policy and practice, and strengthening of public perception. As megafaunal research advances, we encourage scientists to unambiguously define how they use the term “megafauna” and to present the logic underpinning their definition

Simulating rewetting events in intermittent rivers and ephemeral streams: a global analysis of leached nutrients and organic matter

Climate change and human pressures are changing the global distribution and extent of intermittent rivers and ephemeral streams (IRES), which comprise half of the global river network area. IRES are characterized by periods of flow cessation, during which channel substrates accumulate and undergo physico‐chemical changes (preconditioning), and periods of flow resumption, when these substrates are rewetted and release pulses of dissolved nutrients and organic matter (OM). However, there are no estimates of the amounts and quality of leached substances, nor is there information on the underlying environmental constraints operating at the global scale. We experimentally simulated, under standard laboratory conditions, rewetting of leaves, riverbed sediments, and epilithic biofilms collected during the dry phase across 205 IRES from five major climate zones. We determined the amounts and qualitative characteristics of the leached nutrients and OM, and estimated their areal fluxes from riverbeds. In addition, we evaluated the variance in leachate characteristics in relation to selected environmental variables and substrate characteristics. We found that sediments, due to their large quantities within riverbeds, contribute most to the overall flux of dissolved substances during rewetting events (56‐98%), and that flux rates distinctly differ among climate zones. Dissolved organic carbon, phenolics, and nitrate contributed most to the areal fluxes. The largest amounts of leached substances were found in the continental climate zone, coinciding with the lowest potential bioavailability of the leached organic matter. The opposite pattern was found in the arid zone. Environmental variables expected to be modified under climate change (i.e. potential evapotranspiration, aridity, dry period duration, land use) were correlated with the amount of leached substances, with the strongest relationship found for sediments. These results show that the role of IRES should be accounted for in global biogeochemical cycles, especially because prevalence of IRES will increase due to increasing severity of drying events