Improving the forecast for biodiversity under climate change

Acknowledgments: This paper originates from the “Ecological Interactions and Range Evolution Under Environmental Change” and “RangeShifter” working groups, supported by the Synthesis Centre of the German Centre for Integrative Biodiversity Research (DFG-FZT-118), DIVERSITAS, and its core projects bioDISCOVERY and bioGENESIS. Supported by the Canada Research Chair, Natural Sciences and Engineering Research Council of Canada, and Quebec Centre for Biodiversity Science (A.G.); the University of Florida Foundation (R.D.H.); KU Leuven Research Fund grant PF/2010/07, ERA-Net BiodivERsA TIPPINGPOND, and Belspo IAP SPEEDY (L.D.M.); European Union Biodiversity Observation Network grant EU-BON-FP7-308454 (J.-B.M. and G.P.); KU Leuven Research Fund (J.P.); and NSF grants DEB-1119877 and PLR-1417754 and the McDonnell Foundation (M.C.U.).Peer reviewedPostprin

Traditional Ecological Knowledge: Wisdom for Sustainable Development. Edited by Nancy M. Williams and Graham Baines, 1993. Canberra: Centre for Resource and Environmental Studies, Australian National University

Traditional Ecological Knowledge: Wisdom for Sustainable Development. Edited by Nancy M. Williams and Graham Baines, 1993. Canberra: Centre for Resource and Environmental Studies, Australian National University. Reviewed by John Cordell, Faculty of Environmental Sciences, Griffith University, Queensland 4111, Australia

Linking changes in species composition and biomass in a globally distributed grassland experiment

Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonisation and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e. local extinctions) were balanced by gains (i.e. colonisation). Under fertilisation, the decline in species richness resulted from increased species loss and decreases in species gained. Biomass increase under fertilisation resulted mostly from species that persist and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained or persisting.EEA Santa CruzFil: Ladouceur, Emma. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Ladouceur, Emma. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Ladouceur, Emma. University of Leipzig. Department of Biology; AlemaniaFil: Ladouceur, Emma. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Blowes, Shane A. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Blowes, Shane A. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Chase, Jonathan M. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Chase, Jonathan M. Martin Luther University Halle-Wittenberg. Institute of Computer Science; AlemaniaFil: Clark, Adam T. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Clark, Adam T. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Clark, Adam T. Karl-Franzens University of Graz. Institute of Biology; Austria.Fil: Garbowski, Magda. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Garbowski, Magda. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaFil: Alberti, Juan. Universidad Nacional de Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Laboratorio de Ecología. Mar del Plata; Argentina.Fil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Arnillas, Carlos Alberto. University of Toronto. Department of Physical and Environmental Sciences; Canadá.Fil: Bakker, Jonathan D. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Barrio, Isabel C. Agricultural University of Iceland. Faculty of Environmental and Forest Sciences; IslandiaFil: Bharath, Siddharth. Atria University; India.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Harpole, Stanley. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Harpole, Stanley. Helmholtz Centre for Environmental Research – UFZ. Department of Physiological Diversity; AlemaniaMartin Luther University Halle-Wittenberg. Institute of Computer Science; Alemani

BioTIME: A database of biodiversity time series for the Anthropocene.

MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL

Comparison of systems of biological indication approved during the course of the joint Anglo-Soviet investigations held under the auspices of the Institute of Hydrobiology of the Academy of Sciences of the Ukrainian SSR

In accordance with the plan for joint Anglo-Soviet scientific and technical collaboration on environmental problems, the comparative evaluation of systems of hydrobiological analysis of the surface water quality started in 1977 at the Regional Laboratory of the Severn-Trent Water Authority in Nottingham were continued in the spring of 1978. The investigations were carried out under the auspices of the Institute of Hydrobiology of the Academy of Sciences of the Ukrainian SSR. Hydrobiological and hydrochemical samples were collected by Soviet and British specialists from the Kiev reservoir and the rivers Dnieper, Sozh, Desna and Snov. The samples were processed on the expedition ships and in the Laboratory for the Hydrobiology of Small Water Bodies of the Institute of Hydrobiology of the Academy of Sciences of the Ukrainian SSR. The possible approved methods to be adopted were evaluated from the samples using the phytoperiphyton, phytoplankton, zooplankton and zoobenthos against a background of hydrochemical characteristics. The study concludes that weather conditions complicated the work on testing the systems of biological indication of water quality and made it inadvisable to use those methods of comparison which were used when similar work was carried out in Nottingham

Potential of LCA for designing technological innovations – the case of organic eggs

Ecological sustainability in agriculture is a concept that contains various environmental problems, which are caused by emission of pollutants and unsustainable use of limited resources, during different processes along the food chain. Technological innovations may help to improve ecological sustainability of food products. Preceding to the development of ecological sustainable technological innovations three questions need to be answered; 1) how ecological sustainable is the current production process, 2) which processes in the chain causes the highest ecological impact and 3) which production parameters significantly affect the ecological impact of these processes? The aim of this research is to demonstrate Life Cycle Assessment to the designers of technological innovations Life Cycle Assessment as a method to answer these questions, by means of a case study of the organic egg. In this study the LCA of organic eggs was calculated and compared to equivalent egg products. Ecological hotspots within the production chain were identified and the effectiveness of production parameters from the laying hen farm were identified on the LCA using sensitivity analysis. This LCA case study showed that organic eggs score worse than equivalent eggs on acidification, eutrophication and land use. Technological innovators should focus on ammonia emission from the laying hen farm to reduce the impact of acidification. Another focus should be nitrate leaching during concentrate production to reduce eutrophication. Innovative organic laying hen farmers may focus on a high feed conversion to improve the LCA of organic eggs in a broader sense. A shift from single tiered housing of laying hens to multi tiered housing with manure drying on manure belts, can reduce acidification 53% and eutrophication with 18%, almost enough to level out the 60% higher acidification and the 25% higher eutrophication of organic eggs compared to equivalent egg products

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Resilience trinity: safeguarding ecosystem functioning and services across three different time horizons and decision contexts

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi‐faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time‐horizons: 1) reactive, when there is an imminent threat to ES resilience and a high pressure to act, 2) adjustive, when the threat is known in general but there is still time to adapt management and 3) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology and engineering are often implicitly focussing on provident, adjustive or reactive resilience, respectively, but these different notions of resilience and their corresponding social, ecological and economic tradeoffs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer‐term management actions are not missed while urgent threats to ES are given priority