TY - JOUR A1 - Vanneste, Thomas A1 - Valdes, Alicia A1 - Verheyen, Kris A1 - Perring, Michael P. A1 - Bernhardt-Roemermann, Markus A1 - Andrieu, Emilie A1 - Brunet, Jorg A1 - Cousins, Sara A. O. A1 - Deconchat, Marc A1 - De Smedt, Pallieter A1 - Diekmann, Martin A1 - Ehrmann, Steffen A1 - Heinken, Thilo A1 - Hermy, Martin A1 - Kolb, Annette A1 - Lenoir, Jonathan A1 - Liira, Jaan A1 - Naaf, Tobias A1 - Paal, Taavi A1 - Wulf, Monika A1 - Decocq, Guillaume A1 - De Frenne, Pieter T1 - Functional trait variation of forest understorey plant communities across Europe JF - Basic and applied ecology : Journal of the Gesellschaft für Ökologie N2 - Global environmental changes are expected to alter the functional characteristics of understorey herb-layer communities, potentially affecting forest ecosystem functioning. However, little is known about what drives the variability of functional traits in forest understories. Here, we assessed the role of different environmental drivers in shaping the functional trait distribution of understorey herbs in fragmented forests across three spatial scales. We focused on 708 small, deciduous forest patches located in 16 agricultural landscape windows, spanning a 2500-km macroclimatic gradient across the temperate forest biome in Europe. We estimated the relative effect of patch-scale, landscape-scale and macroclimatic variables on the community mean and variation of plant height, specific leaf area and seed mass. Macroclimatic variables (monthly temperature and precipitation extremes) explained the largest proportion of variation in community trait means (on average 77% of the explained variation). In contrast, patch-scale factors dominated in explaining community trait variation (on average 68% of the explained variation). Notably, patch age, size and internal heterogeneity had a positive effect on the community-level variability. Landscape-scale variables explained only a minor part of the variation in both trait distribution properties. The variation explained by shared combinations of the variable groups was generally negligible. These findings highlight the importance of considering multiple spatial scales in predictions of environmental-change effects on the functionality of forest understories. We propose that forest management sustainability could benefit from conserving larger, historically continuous and internally heterogeneous forest patches to maximise ecosystem service diversity in rural landscapes. (C) 2018 Gesellschaft fur Okologie. Published by Elsevier GmbH. All rights reserved. KW - Agricultural landscapes KW - Biogeography KW - Community ecology KW - Forest understorey KW - Functional trait diversity KW - Fragmentation KW - Global environmental change KW - Landscape connectivity KW - Macroclimatic gradient KW - Multi-scale analysis Y1 - 2018 U6 - https://doi.org/10.1016/j.baae.2018.09.004 SN - 1439-1791 SN - 1618-0089 VL - 34 SP - 1 EP - 14 PB - Elsevier GmbH CY - München ER - TY - JOUR A1 - Kissling, W. D. A1 - Dormann, Carsten F. A1 - Groeneveld, Juergen A1 - Hickler, Thomas A1 - Kühn, Ingolf A1 - McInerny, Greg J. A1 - Montoya, Jose M. A1 - Römermann, Christine A1 - Schiffers, Katja A1 - Schurr, Frank Martin A1 - Singer, Alexander A1 - Svenning, Jens-Christian A1 - Zimmermann, Niklaus E. A1 - O'Hara, Robert B. T1 - Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents JF - Journal of biogeography N2 - Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities. KW - Community ecology KW - ecological networks KW - global change KW - guild assembly KW - multidimensional complexity KW - niche theory KW - prediction KW - species distribution model KW - species interactions KW - trait-based community modules Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02663.x SN - 0305-0270 VL - 39 IS - 12 SP - 2163 EP - 2178 PB - Wiley-Blackwell CY - Hoboken ER -