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Spatial and temporal variation of ecosystem properties at macroscales.
Soranno, Patricia A; Wagner, Tyler; Collins, Sarah M; Lapierre, Jean-Francois; Lottig, Noah R; Oliver, Samantha K.
Afiliação
  • Soranno PA; Department of Fisheries and Wildlife, Michigan St. University, 480 Wilson Rd, East Lansing, MI, 48824, USA.
  • Wagner T; U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, 402 Forest Resources Building, University Park, PA, 16802, USA.
  • Collins SM; Department of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA.
  • Lapierre JF; Department of Biological Science, University of Montreal, Montreal, Quebec, Canada, H3C 3J7.
  • Lottig NR; Trout Lake Research Station, Univ. of Wisconsin, 3110 Trout Lake Station Drive, Boulder Junction, WI, 54512, USA.
  • Oliver SK; Upper Midwest Water Science Center, U.S. Geological Survey, 8505 Research Way, Middleton, WI, 53562, USA.
Ecol Lett ; 22(10): 1587-1598, 2019 Oct.
Article em En | MEDLINE | ID: mdl-31347258
Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Análise Espaço-Temporal / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ecossistema / Análise Espaço-Temporal / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article