The dual nature of metacommunity variability
Type | Article | ||||||||
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Date | 2021-12 | ||||||||
Language | English | ||||||||
Author(s) | Lamy Thomas1, 2, Wisnoski Nathan I.3, 4, Andrade Riley5, 6, Castorani Max C. N.7, Compagnoni Aldo8, 9, Lany Nina10, 11, Marazzi Luca12, Record Sydne13, Swan Christopher M.14, Tonkin Jonathan D.15, 16, Voelker Nicole14, Wang Shaopeng17, Zarnetske Phoebe L.11, 18, Sokol Eric R.19, 20 | ||||||||
Affiliation(s) | 1 : Marine Science Inst., Univ. of California Santa Barbara CA ,USA 2 : MARBEC, Univ. of Montpellier, CNRS, Ifremer, IRD Sète, France 3 : Dept of Biology, Indiana Univ. Bloomington IN ,USA 4 : WyGISC, Univ. of Wyoming Laramie WY, USA 5 : School of Geographical Sciences and Urban Planning, Arizona State Univ. Tempe AZ, USA 6 : Dept of Natural Resources and Environmental Sciences, Univ. of Illinois at Urbana – Champaign Urbana IL, USA 7 : Dept of Environmental Sciences, Univ. of Virginia Charlottesville VA, USA 8 : Martin Luther Univ. Halle‐Wittenberg Halle (Saale), Germany 9 : German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig, Germany 10 : Dept of Forestry, Michigan State Univ. East Lansing MI, USA 11 : Ecology, Evolution and Behavior Program, Michigan State Univ. East Lansing MI ,USA 12 : Dept of Biology, Bryn Mawr College Bryn Mawr PA ,USA 13 : Dept of Geography and Environmental Systems, Univ. of Maryland, Baltimore County Baltimore MD ,USA 14 : Dept of Integrative Biology, Oregon State Univ. OR ,USA 15 : School of Biological Sciences, Univ. of Canterbury Christchurch ,New Zealand 16 : Key Laboratory for Earth Surface Processes of the Ministry of Education, Inst. of Ecology, College of Urban and Environmental Sciences, Peking Univ. Beijing China 17 : Dept of Integrative Biology, Michigan State Univ. East Lansing MI, USA 18 : Inst. of Environment, Florida International Univ. Miami FL ,USA 19 : Inst. of Arctic and Alpine Research (INSTAAR), Univ. of Colorado Boulder Boulder CO ,USA 20 : Battelle, National Ecological Observatory Network (NEON) Boulder CO, USA |
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Source | Oikos (0030-1299) (Wiley), 2021-12 , Vol. 130 , N. 12 , P. 2078-2092 | ||||||||
DOI | 10.1111/oik.08517 | ||||||||
WOS© Times Cited | 12 | ||||||||
Keyword(s) | biodiversity, long-term ecological research, metacommunity, scale, stability, variability | ||||||||
Abstract | There is increasing interest in measuring ecological stability to understand how communities and ecosystems respond to broad-scale global changes. One of the most common approaches is to quantify the variation through time in community or ecosystem aggregate attributes (e.g. total biomass), referred to as aggregate variability. It is now widely recognized that aggregate variability represents only one aspect of communities and ecosystems, and compositional variability, the changes in the relative frequency of species in an assemblage, is equally important. Recent contributions have also begun to explore ecological stability at regional spatial scales, where interconnected local communities form metacommunities, a key concept in managing complex landscapes. However, the conceptual frameworks and measures of ecological stability in space have only focused on aggregate variability, leaving a conceptual gap. Here, we address this gap with a novel framework for quantifying the aggregate and compositional variability of communities and ecosystems through space and time. We demonstrate that the compositional variability of a metacommunity depends on the degree of spatial synchrony in compositional trajectories among local communities. We then provide a conceptual framework in which compositional variability of 1) the metacommunity through time and 2) among local communities combine into four archetype scenarios: spatial stasis (low/low), spatial synchrony (high/low), spatial asynchrony (high/high) and spatial compensation (low/high). We illustrate this framework based on numerical examples and a case study of a macroalgal metacommunity in which low spatial synchrony reduced variability in aggregate biomass at the metacommunity scale, while masking high spatial synchrony in compositional trajectories among local communities. Finally, we discuss the role of dispersal, environmental heterogeneity, species interactions and suggest future avenues. We believe this framework will be helpful for considering both aspects of variability simultaneously, which is important to better understand ecological stability in natural and complex landscapes in response to environmental changes. |
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