Moving beyond static species distribution models in support of conservation biogeography

By Janet Franklin. 2010

495 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/62a877aa7dd884a5d33ef7dbfb3a2dcf9583883f

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, background): “Species distribution models (SDMs) are increasingly being applied to assess impacts of environmental change on natural ecosystems and biodiversity (Elith & Leathwick, 2009; Franklin, 2010a).”
Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates (2020, unknown): “Furthermore, the application of SDM can be limited by; first, assuming there is a balance between environmental changes and spatial distribution of the species [2, 78, 79], second, the representability of distributional spectrum of the species in relation to the occurrence data used in modelling [78”
RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes (2014, background): “In response to recent calls for moving towards integrated dynamic modelling approaches (Franklin 2010; Huntley et al. 2010; Schurr et al. 2012; Thuiller et al. 2013), we developed RangeShifter, a novel dynamic modelling platform for investigating species’ spatial dynamics, which provides flexibility”
Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling (2016, methodology): “The use of correlative SDMs for quantifying species–environment relationships is a widely used method in ecology (Elith & Leathwick, 2009; Franklin, 2010), but these models are often a static ‘snapshot’ representation of the environmental space in which a species does or does not occur.”
Bioclimatic velocity: the pace of species exposure to climate change (2014, background): “…of SDMs to assess exposure in dynamic environments may be further limited by the static nature of the models and the scale of analysis (Franklin, 2010b), in addition to other widely discussed limitations (see Fitzpatrick & Hargrove, 2009; Franklin, 2010a; Keenan et al., 2011; Schwartz, 2012).”
Nice weather for bettongs: using weather events, not climate means, in species distribution models (2012, background): “Typically, SDMs are a static representation of suitable habitat for a species (Franklin 2010) and do not account for temporal variation in habitat suitability (but see Reside et al. 2010).”
Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, background): “…factors constraining those distributions; (ii) current climatic and geographic constraints that define a species distribution reflect its biophysical limits; and (iii) a model fitted to current data will project sensibly to new environmental conditions (Franklin, 2010; Peterson et al., 2011).”
Benthos distribution modelling and its relevance for marine ecosystem management (2015, methodology): “…and more explicit lists can be found in advanced subject-specific text books (e.g. Legendre and Legendre, 1998; Zuur et al., 2012), reviews, and objectspecific comparative research papers (e.g. Guisan and Zimmermann, 2000; Guisan and Thuiller, 2005; Franklin, 2010; Robinson et al., 2011).”
Big data for forecasting the impacts of global change on plant communities (2017, methodology): “A comprehensive discussion of geospatial data and analytics used in plant ecology is beyond the scope of this paper and reviewed elsewhere (Wilson & Gallant, 2000; Kerr & Ostrovsky, 2003; Franklin, 2010a; Skidmore et al. , 2011; Thrasher et al. , 2013).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, background): “Previously, descriptions of the merits of linking ENMs with population modelling have focused on the fundamental step of integrating dispersal and metapopulation dynamics into forecasts of species geographic range (Brook et al. 2009, Franklin 2010, Huntley et al. 2010).”
Will remote sensing shape the next generation of species distribution models? (2015, methodology): “Climate data have been commonly used to predict potential species distributions across broad spatial scales (e.g. Franklin 2010).”
Conservation biogeography – foundations, concepts and challenges (2010, background): “…in the special issue may be grouped into the following broad themes (with some belonging in more than one category): species distribution modelling (Franklin, 2010; Gallien et al., 2010; Scoble & Lowe, 2010); data requirements (Devictor et al., 2010; Robertson et al., 2010); approaches for…”
A case for incorporating phylogeography and landscape genetics into species distribution modelling approaches to improve climate adaptation and conservation planning (2010, background): “Species distribution modelling, based predominantly on climate parameters, continues to lead the way (Beaumont & Hughes, 2002; Pearson & Dawson, 2003; Franklin, 2010).”