Sensitivity of predictive species distribution models to change in grain size

By Antoine Guisan, Catherine H. Graham, Jane Elith, Falk Huettmann. 2007

574 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d40c060b5cd4035b9a140cad2d7edc4c3e42f240

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Cross-scale variation in species richness–environment associations (2011, background): “…the degree to which species–environment associations are pertinent to smaller grains is of major relevance for delineating the range of applicability of climatic niche models, global change predictions and macroecological processes (Araújo et al., 2005; Guisan et al., 2007; Willis & Bhagwat, 2009).”
Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling (2009, unknown): “…versus realized distributions/niches, and we refer readers to these excellent resources (e.g. Soberón & Peterson, 2004, 2005; Araújo & Guisan, 2006; Pearson et al., 2006; Peterson, 2006; Araújo & Luoto, 2007; Guisan et al., 2007; Jiménez-Valverde et al., 2008; Phillips & Dudı́k, 2008).”
Empirical Evidence for the Scale Dependence of Biotic Interactions (2015, result): “This is in contrast to several studies that found strong grain-dependence in the strength of environment as a predictor of species distribution and richness (Guisan et al., 2007; Randin et al., 2009; Belmaker & Jetz, 2011).”
The data void in modeling current and future distributions of tropical species (2011, background): “Most niche models are based on presence of species per climate/habitat grid cell and thus the number of occurrences depends on grid cell size or resolution (Graham et al., 2004; Guisan et al., 2007).”
European Bison habitat in the Carpathian Mountains (2010, methodology): “While we cannot rule out that finer scale data would have resulted in better predictions, we note that maps based on finer-scale (30 m) models were highly similar to those based on coarser grain data (100 m), suggesting the grain change did not impair Maxent performance (Guisan et al., 2007).”
Modelling and predicting fungal distribution patterns using herbarium data (2008, background): “Bakkestuen et al. (2008) find the same main environmental gradients for grain sizes 1, 5 and 10 km, and Guisan et al. (2007) have demonstrated that a 10 times increase in grain size does not seriously alter predictions; 5 km thus seems to be an appropriate and robust scale for regionalextent…”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, result): “Our results corroborated previous studies highlighting that ENMs are generally insensitive to the effects of changes in predictor grain size (e.g., Guisan et al., 2007), although our spatial-filtering procedure (thinning to 50-km resolution) also likely ensured that few occurrence points would fall…”
Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data (2017, result): “This result concords with previous analyses of the effects of spatial resolution in other modelling applications (Guisan et al. 2007; Redfern et al. 2008; Becker et al. 2010), but see (Storch 2002; Gottschalk et al.”
Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications (2019, unknown): “In contrast, SDMs can be constructed with minimal investment of resources, using freely available data and software (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005; Hijmans, Phillips, Leathwick, & Elith, 2012; Kriticos et al., 2012; Phillips, Anderson, & Schapire, 2006; Thuiller, Lafourcade, Engler,”
Overcoming limitations of modelling rare species by using ensembles of small models (2015, background): “Accordingly, many studies show increasing model accuracy with number of occurrences (e.g. Hernandez et al. 2006; Guisan et al. 2007; Wisz et al. 2008; see also references mentioned there) and below about 30 occurrences model accuracy is often low and variability of model accuracy increases across…”
Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation (2009, methodology): “…(2007) USA Species conservation Non reported Invertebrates P-A Guisan et al. (2005) Switzerland Methodological Prospective sampling Plants P Guisan et al. (2007) Australia, Canada, New Zealand, Switzerland and South America Methodological Prospective sampling Birds, Mammals, Plants,…”
Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, background): “Performance of these models may also depend on species characteristics, spatial resolution and extent of the study area, and choice of predictor variables (Guisan et al. 2007a, b).”
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? (2007, background): “…performance may result from a lack in spatial matching between species observations—specifically of sessile organisms like trees—and their associated environments (Guisan and Thuiller 2005) and/or from limited/reduced precision of environmental predictors at the coarser grain (Guisan et al. 2007).”