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Statistical and climate models are frequently used for biodiversity projections under future climatic changes, but their predictive capacity for freshwater plankton may vary among different species and community metrics. Here, we used random forests to model plankton species and community metrics as a function of biological, climatic, physical, and chemical data from long‐term (2000–2017) monitoring data collected from Lake Müggelsee Berlin, Germany. We (1) compared the predictability of well‐known lake plankton metric types (biomass, abundance, taxonomic diversity, Shannon diversity, Simpson diversity, evenness, taxonomic distinctness, and taxonomic richness) and (2) assessed how the relative influence of different environmental drivers varies across lake plankton metric models. Overall, the metric predictability was highest for biomass and abundance followed by taxonomic richness. The biomass of dominant phytoplankton taxonomic groups such as cyanobacteria (adjusted‐R2 = 0.53) and the abundance of dominant zooplankton taxonomic groups such as rotifers (adjusted‐R2 = 0.59) and daphnids (adjusted‐R2 = 0.51) were more predictable than other metric types. The plankton metric predictability increased when grouping phytoplankton species according to their functional traits (adjusted‐R2 = 0.37 ± 0.14, mean ± SD, n = 36 functional groups) compared to higher taxonomic units (adjusted‐R2 = 0.25 ± 0.15, n = 22 taxonomic groups). Light, nutrients, water temperature, and seasonality for phytoplankton and food resources for zooplankton were the main drivers of both taxonomic and functional groups, giving confidence that our models captured the expected major environmental drivers. Our quantitative analyses highlight the multidimensionality of lake planktonic responses to environmental drivers and have implications for our capacity to select appropriate metrics for forecasting the future of lake ecosystems under global change scenarios. ; European Union's Horizon 2020 Research and Innovation Programme ; Belmont Forum ...

Dreissenid mussels (including the zebra mussel Dreissena polymorpha and the quagga mussel D. rostriformis) are among the world's most notorious invasive species, with large and widespread ecological and economic effects. However, their long-term population dynamics are poorly known, even though these dynamics are critical to determining impacts and effective management. We gathered and analyzed 67 long-term (>10 yr) data sets on dreissenid populations from lakes and rivers across Europe and North America. We addressed five questions: (1) How do Dreissena populations change through time? (2) Specifi- cally, do Dreissena populations decline substantially after an initial outbreak phase? (3) Do different measures of population performance (biomass or density of settled animals, veliger density, recruitment of young) follow the same patterns through time? (4) How do the numbers or biomass of zebra mussels or of both species combined change after the quagga mussel arrives? (5) How does body size change over time? We also considered whether current data on long-term dynamics of Dreissena populations are adequate for science and management. Individual Dreissena populations showed a wide range of temporal dynamics, but we could detect only two general patterns that applied across many populations: (1) Populations of both species increased rapidly in the first 1-2 yr after appearance, and (2) quagga mussels appeared later than zebra mussels and usually quickly caused large dedines in zebra mussel populations. We found little evidence that combined Dreissena populations declined over the long term. Different measures of population performance were not congruent; the temporal dynamics of one life stage or population attribute cannot generally be accurately inferred from the dynamics of another. We found no consistent patterns in the long-term dynamics of body size. The long-term dynamics of Dreissena populations probably are driven by the ecological characteristics (e.g., predation, nutrient inputs, water temperature) and their temporal changes at individual sites rather than following a generalized time course that applies across many sites. Existing long-term data sets on dreissenid populations, although dearly valuable, are inadequate to meet research and management needs. Data sets could be improved by standardizing sampling designs and methods, routinely collecting more variables, and increasing support. ; Deutsche Forschungsgemeinschaft (DFG) [JE 288/8-1]; G.E. Hutchinson Chair at the Cary Institute of Ecosystem Studies (DLS); NSF-LTREB grants [DEB-1556246]; NSF-OPUS grant [DEB-1456532]; DFG [JE 288/9-1, JE 288/9-2]; USGS [G14AC000263]; US EPA [GL00E01184]; Cornell Agricultural Experiment Station [NYC-0226747]; New York State Department of Environmental Conservation grants; NSF [1517823]; Belarusian Republican Foundation for Fundamental Research; Mercator Fellowship; [TaMOP-4.2.2.A-11/1/KONV-2012-0038]; [GINOP-2.3.2-15-2016-00019] ; This study is a contribution of the Invasion Dynamics Network (InDyNet), funded by the Deutsche Forschungsgemeinschaft (DFG; JE 288/8-1) including a Mercator Fellowship to DLS. Additional support came from the G.E. Hutchinson Chair at the Cary Institute of Ecosystem Studies (DLS), NSF-LTREB grants (most recently DEB-1556246), and NSF-OPUS grant DEB-1456532 to DLS; DFG projects JE 288/9-1 and JE 288/9-2 to JMJ; TaMOP-4.2.2.A-11/1/KONV-2012-0038 and the GINOP-2.3.2-15-2016-00019 to CSB and LG-T; USGS G14AC000263 and US EPA GL00E01184 to LEB and AYK; Cornell Agricultural Experiment Station NYC-0226747 and New York State Department of Environmental Conservation grants to LGR and ALH, and NSF grant 1517823 (ALH); and the Belarusian Republican Foundation for Fundamental Research to BA and OM. We thank Krzysztof Lewandowski for his help with data from Polish lakes; Mike Davis, the Minnesota Department of Natural Resources, and the United States Army Corps of Engineers for the Lake Pepin data; Kristen Holeck and Ed Mills for help with Oneida Lake data; the Onondaga County Department of Water Environment Protection; Ulrike Scharfenberger for her advice on the statistical analyses; Jaclyn McGuire for helping to gather information for the supplementary materials; Maggie Oudsema; and Juergen Geist, other InDyNet members, and Ladd Johnson, Alex Latzka, and Teresa Newton for helpful comments and suggestions. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions. ; Main financial support for EMU: European Union's Horizon 2020 research and innovation programme Under the Marie Skłodowska-Curie Action, Innovative Training Networks, European Joint Doctorates. ; Project name, acronym and grant number: Management of climatic extreme events in lakes and reservoirs for the protection of ecosystem services, MANTEL, grant agreement No 722518. ; Publication date and, if applicable, length of embargo period: 22.04.2020, no embargo period. ; Main financial support for EMU: European Union's Horizon 2020 research and innovation programme Under the Marie Skłodowska-Curie Action, Innovative Training Networks, European Joint Doctorates

The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required. (c) 2017 Published by Elsevier Ltd. ; Australian Research Council (ARC)Australian Research Council [DP130104078, LP130100756] ; GLM development and funding support for LCB, BDB, CB and MRH was provided by the Australian Research Council (ARC) (grants DP130104078 & LP130100756). Additional contributions from individuals and organisations as well as sources of data, provided from a variety of organisations are summarised in Appendix D. This study was made possible through the sharing of ideas, data and models across the AEMON and GLEON networks as well as discussions and working groups held during AEMON workshops and GLEON meetings. ; Public domain authored by a U.S. government employee

Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising similar to 6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. The global biodiversity decline might conceal complex local and group-specific trends. Here the authors report a quantitative synthesis of longterm biodiversity trends across Europe, showing how, despite overall increase in biodiversity metric and stability in abundance, trends differ between regions, ecosystem types, and taxa. ; Y We are grateful to the ILTER network and the eLTER PLUS project (Grand Agreement No. 871128) for financial support. We acknowledge the E-OBS dataset from the EUFP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).The evaluation of forest plant diversity was based on data collected by partners of the official UNECE ICP Forests Network (http://icp-forests.net/contributors); part of the data were co-financed by the European Commission, project LIFE 07 ENV/D/000218 "Further Development and Implementation of an EU-level Forest monitoring Systeme (FutMon)". Data on wintering water birds in Bulgaria were provided by the national Executive Environment Agency with the Ministry of Environment and Waters. Data from the Finnish moth monitoring scheme were supported by the Finnish Ministry of the Environment. Data from the Swedish ICP Integrated Monitoring sites were financed by the Swedish Environmental Protection Agency. Data collection at Esthwaite Water and a subset of UK ECN sites was supported by Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCaPE programme delivering National Capability. Sponsorship of other UK ECN sites contributing data was provided by Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Department of Environment Food and Rural Affairs, Natural Resources Wales, Defense Science Technology Laboratory, Environment Agency, Forestry Commission, Forest Research, the James Hutton Institute (The Rural & Environment Science & Analytical Services Division of the Scottish Government), Natural England, Rothamsted Research, Scottish Government, Scottish Natural Heritage and the Welsh Government. Data from the Mondego estuary (Portugal) were supported by the Centre for Functional Ecology Strategic Project (UID/BIA/04004/2019) within the PT2020 Partnership Agreement and COMPETE 2020, and by FEDER through the project ReNATURE (Centro 2020, Centro-01-765-0145-FEDER-000007). We would like to thank Limburgse Koepel voor Natuurstudie (LiKoNa) for the data related to the National Park Hoge Kempen (BE). We would like to acknowledge the support for the long-term monitoring program MONEOS in the Scheldt estuary (BE) by `De Vlaamse Waterweg' and `Maritieme Toegang' (Flemish government). We are grateful to the board of the National Park "De Hoge Veluwe" for the permission to conduct our research on their property. We thank Ian J. Winfield and Terje Bongard for contributing data for the sites: Bassenthwaite Lake, Derwent Water (UK) and Atna River (Norway, freshwater invertebrate time series). Open access funding provided by Umea University. ; Pilotto, F; Haase, P (corresponding author), Senckenberg Res Inst, Gelnhausen, Germany; Nat Hist Museum, Gelnhausen, Germany; Univ Duisburg Essen, Essen, Germany. francesca.pilotto@umu.se; francesca.pilotto@umu.se

Este artículo contiene 11 páginas, 2 tablas, 4 figuras. ; Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15–91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.e. temporal taxonomic turnover) in most biogeoregions of Northern and Eastern Europe. ; We are grateful to the ILTER network and the eLTER PLUS project (Grand Agreement No. 871128) for financial support. We acknowledge the E-OBS dataset from the EUFP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). The evaluation of forest plant diversity was based on data collected by partners of the official UNECE ICP Forests Network (http://icp-forests.net/contributors); part of the data were co-financed by the European Commission, project LIFE 07 ENV/D/000218 "Further Development and Implementation of an EU-level Forest monitoring Systeme (FutMon)". Data on wintering water birds in Bulgaria were provided by the national Executive Environment Agency with the Ministry of Environment and Waters. Data from the Finnish moth monitoring scheme were supported by the Finnish Ministry of the Environment. Data from the Swedish ICP Integrated Monitoring sites were financed by the Swedish Environmental Protection Agency. Data collection at Esthwaite Water and a subset of UK ECN sites was supported by Natural Environment Research Council award number NE/ R016429/1 as part of the UK-SCaPE programme delivering National Capability. Sponsorship of other UK ECN sites contributing data was provided by Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Department of Environment Food and Rural Affairs, Natural Resources Wales, Defense Science Technology Laboratory, Environment Agency, Forestry Commission, Forest Research, the James Hutton Institute (The Rural & Environment Science & Analytical Services Division of the Scottish Government), Natural England, Rothamsted Research, Scottish Government, Scottish Natural Heritage and the Welsh Government. Data from the Mondego estuary (Portugal) were supported by the Centre for Functional Ecology Strategic Project (UID/BIA/04004/2019) within the PT2020 Partnership Agreement and COMPETE 2020, and by FEDER through the project ReNATURE (Centro 2020, Centro-01-765-0145-FEDER-000007). We would like to thank Limburgse Koepel voor Natuurstudie (LiKoNa) for the data related to the National Park Hoge Kempen (BE). We would like to acknowledge the support for the long-term monitoring program MONEOS in the Scheldt estuary (BE) by 'De Vlaamse Waterweg' and 'Maritieme Toegang' (Flemish government). We are grateful to the board of the National Park "De Hoge Veluwe" for the permission to conduct our research on their property. We thank Ian J. Winfield and Terje Bongard for contributing data for the sites: Bassenthwaite Lake, Derwent Water (UK) and Atna River (Norway, freshwater invertebrate time series). Open access funding provided by Umeå University. ; Peer reviewed

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass. ; National Science Foundation (NSF DEB) [1431428, 1136637]; Washington State University; Russian Science Foundation [14-14-00400]; Ministry of education and science of Russia Gos-Zasanie project [1354-2014/51]; Natural Environment Research Council [NE/J00829X/1, 1230750, NE/G019622/1, NE/J010227/1] ; Funding was provided by the National Science Foundation (NSF DEB #1431428; NSF DEB #1136637) and Washington State University. M. Timofeyev and E. Silow were partially supported by Russian Science Foundation project No 14-14-00400 and Ministry of education and science of Russia Gos-Zasanie project No 1354-2014/51. We are grateful to Marianne Moore, Deniz Ozkundakci, Chris Polashenski and Paula Kankaala for discussions that greatly improved this work. We also gratefully acknowledge the following individuals for contributing to this project: John Anderson, Jill Baron, Rick Bourbonniere, Sandra Brovold, Lluis Camarero, Sudeep Chandra, Jim Cotner, Laura Forsstom, Guillaume Grosbois, Chris Harrod, Klaus D. Joehnk, T.Y. Kim, Daniel Langenhaun, Reet Laugaste, Suzanne McGowan, Virginia Panizzo, Giampaolo Rossetti, R.E.H. Smith, Sarah Spaulding, Helen Tammert, Steve Thackeray, Kyle Zimmer, Priit Zingel and two anonymous reviewers. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.