2017
DOI: 10.1016/j.gecco.2017.11.002
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Predicting species distribution combining multi-scale drivers

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Cited by 101 publications
(85 citation statements)
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“…Distribution models (DMs) are commonly used to provide insights into the environmental drivers that control species or habitat distributions (e.g. Fournier, Barbet‐Massin, Rome, & Courchamp, ; Stirling, Scott, & Wright, ). Depth and slope have been cited among the main environmental predictors associated with the zonation of benthic communities on seamounts (De la Torriente et al, ; Du Preez, Curtis, & Clarke, ; McClain & Lundsten, ; Serrano et al, ).…”
Section: Discussionmentioning
confidence: 99%
“…Distribution models (DMs) are commonly used to provide insights into the environmental drivers that control species or habitat distributions (e.g. Fournier, Barbet‐Massin, Rome, & Courchamp, ; Stirling, Scott, & Wright, ). Depth and slope have been cited among the main environmental predictors associated with the zonation of benthic communities on seamounts (De la Torriente et al, ; Du Preez, Curtis, & Clarke, ; McClain & Lundsten, ; Serrano et al, ).…”
Section: Discussionmentioning
confidence: 99%
“…Since its inception, the SDM technique has advanced from simple correlative ecological niche models (Araújo and Peterson, 2012) to more complex mechanistic models, incorporating range (Fordham et al, 2013a;Zurell et al, 2016) and population dynamics, dispersal abilities (Fordham et al, 2013b) and community dynamics (Singer et al, 2016). However, ecologists and conservation practitioners are still faced with problems of spatial and temporal scale variations in the datasets used in operating SDMs.There is no consensus about the most suitable predictor spatial grain that can improve the accuracy of SDM prediction (Raven, 2002;Bradter et al, 2013;Fournier et al, 2017) and there is a lack of full understanding of the effects of building SDMs at multiple predictor spatial grains. This has an impact on management planning and conservation decision making (Porfirio et al, 2014;Yates et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Nevertheless, we are aware that beyond temperature, local factors such as water chemistry and habitat structure are critical to narrow down the water bodies most likely to be invaded (Gallardo and Aldridge 2013c). For this reason, recent studies advocate for the integration of large-scale climate and regional-scale habitat conditions in a two-step modelling approach that makes use of all the available information to investigate the potential distribution of invasive species (Fournier et al 2017, Gallardo and Aldridge 2013c, Gallien et al 2012). Following this multi-scale approach, first, we use continental SDMs to locate broad areas suitable for the establishment of invasive species in the study area.…”
Section: Methodsmentioning
confidence: 99%