How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates

By Steve Sinclair, Matt D. White, G. Newell. 2010

372 citations recorded by the source corpus.

Climate change presents unprecedented challenges for biological conservation. Agencies are increasingly looking to modeled projections of species' distributions under future climates to inform management strategies. As government scientists with a responsibility to communicate the best available science to our policy colleagues, we question whether current modeling approaches and outputs are practically useful. Here, we synthesize conceptual problems with species distribution models (SDMs) associated with interspecific interactions, dispersal, ecological equilibria and time lags, evolution, and the sampling of niche space. Although projected SDMs have undoubtedly been critical in alerting us to the magnitude of climate change impacts, we conclude that until they offer insights that are more precise than what we can derive from basic ecological theory, we question their utility in deciding how to allocate scarce funds to large-scale conservation projects.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/8593c85eb452a469287c6a1ebd30fdb028ea1bf9

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Citations in the corpus

Establishing the link between habitat selection and animal population dynamics (2015, background): “…analyst (Austin 1999, Beyer et al. 2010) and instability in changing environments (Randin et al. 2006, Zurell et al. 2009, McLoughlin et al. 2010, Sinclair et al. 2010, Matthiopoulos et al. 2011, Wenger and Olden 2012) has alerted practitioners to the dangers of their widespread and unvalidated…”
Modelling climate-change-induced shifts in the distribution of the koala (2011, background): “2009) and the capacity of a species to adapt, evolve or emigrate must always be considered when using species distribution models to inform conservation planning decisions (Sinclair et al. 2010).”
Impacts of climate change on the future of biodiversity (2012, background): “Despite a number of limitations (lack of biological process as well as methodological and theoretical issues, Soberon & Nakamura 2009), species distribution models still constitute the bulk of studies in this domain (Sinclair et al. 2010).”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, background): “They can also assess the potential expansion of introduced species in newly colonized areas [17,18], estimate the future range of a species under climate change [18,19] or assist in reserve planning [20].”
Biotic interactions influence the projected distribution of a specialist mammal under climate change (2012, background): “It is often argued that biotic interactions are implicit in models that describe the current distributions of species (Guisan et al., 2002; Kearney & Porter, 2004; Sinclair et al., 2010; but see Bateman et al., 2012b), because those distributions reflect interactions with other species as well as th”
Uses and misuses of bioclimatic envelope modeling. (2012, background): “…bioclimatic envelope models estimate potential distributions (i.e., the geographic projection of the estimated realized niches of species), rather than the occupied distributional area of the species (Arau´jo and Guisan 2006, Sobero´n 2007, 2010, Hirzel and Lay 2008, Sobero´n and Nakamura 2009).”
Benthos distribution modelling and its relevance for marine ecosystem management (2015, background): “(2 01 0) 302 H. Reiss et al. D ow nloaded from https://academ ic.oup.com /icesjm s/article-abstract/72/2/297/674105 by AZTI FU N D AC IO N user on 24 Septem ber 2018 assumption will probably not hold when making dynamic predictions (Dormann, 2007; Sinclair et al., 2010).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, methodology): “Widespread use of BEMs has been accompanied by discussions of the biological (e.g. Pearson & Daw-son, 2003; Arau´jo & Pearson, 2005; Sinclair et al. , 2007) and methodological (e.g. Heikkinen et al. , 2006; Beau-mont et al. , 2008) uncertainties that surround the outputs.”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, methodology): “Hence, we reviewed the methods section of each of the studies that had distributional prediction as a main objective to assess the degree to which they handled the distinction between actual and potential distributions appropriately.”
Potential of remote sensing to predict species invasions (2015, background): “…risks of policy or management failures associated with heavy reliance on correlative approaches (Barve et al., 2011; Dawson et al., 2011; Guisan et al., 2012, 2014; Petitpierre et al., 2012; Roubicek et al., 2010; Sinclair et al., 2010; Veloz et al., 2012; Webber et al., 2012; Wiens et al., 2009).”
Climate Change Impacts on Seagrass Meadows and Macroalgal Forests: An Integrative Perspective on Acclimation and Adaptation Potential (2018, background): “Although the need for an integrative approach is well recognized (Guisan and Thuiller, 2005; Guisan et al., 2006; Lavergne et al., 2010; Sinclair et al., 2010; Franco et al., 2018), implementation is largely unexplored and lacks consensus.”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “…past changes in species distributions quantitatively, the adequacy of SDMs to attain certain research questions and the accuracy and reliability of their results are currently a matter of discussion (e.g. Jimenez-Valverde et al., 2008; Colwell and Rangel, 2009; Godsoe, 2010; Sinclair et al., 2010).”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “…is exacerbated when making predictions under novel or no-analog conditions (Williams and Jackson 2007, Dietze et al. 2018) where machine learning methods favor model complexity motivated by model fit over transferability and ecological theory (Sinclair et al. 2010, Bell and Schlaepfer 2016).”
Using species distribution models to infer potential climate change-induced range shifts of freshwater fish in south-eastern Australia (2011, background): “…shifts, and, in particular, whether correlative species distribution models (SDMs) are appropriate for predicting range shifts, or whether more mechanistic approaches are required (Heikkinen et al. 2006; Thuiller 2007; Kearney and Porter 2009; Elith et al. 2010; Sinclair et al. 2010).”