Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

By Anne Dubuis, J. Pottier, Vanessa Rion, Loïc Pellissier, J. Theurillat, Antoine Guisan. 2011

232 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/824c281ca110b0de82633a1caa5d2555f5ad1086

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Citations in the corpus

Species distribution models in conservation biogeography: developments and challenges (2013, background): “, 2006; Kearney & Porter, 2009), as well as community properties such as taxonomic or phylogenetic diversity, effectively links ecological theory to conservation biogeography (e.g. Slik et al., 2009; Dubuis et al., 2011; Syphard et al., 2013).”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, background): “…above and in Appendix S2 demonstrate why it is not surprising that such differences exist, but the two case studies of Aranda & Lobo (2011) and Dubuis et al. (2011), coupled with our four examples (Appendix S3), provide clear evidence that the direction of the biases will tend towards…”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, background): “these estimates lack the ability to predict community composition and are therefore limited in their usefulness when a list is needed (e.g., when the presence of a particular assemblage of rare or threatened species is sought; Calabrese et al., 2013; Dubuis et al., 2011).”
Testing species assemblage predictions from stacked and joint species distribution models (2019, background): “…to transform continuous species occurrence probabilities to binary maps prior to stacking, often lead to overprediction of species richness (D'Amen, Dubuis, et al., 2015; Dubuis et al., 2011), but not always (D'Amen, Pradervand, & Guisan, 2015; Zurell, Zimmermann, Sattler, Nobis, & Schröder, 2016).”
Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change (2015, methodology): “The congruence in our study of projections using species distribution models and those using macroecological models is generally consistent with findings from other studies (Algar et al., 2009; Dubuis et al., 2011), and suggests that an understanding of species-specific niche processes may not be ne”
Big data for forecasting the impacts of global change on plant communities (2017, background): “For example, range maps with erroneous records, or diversity maps based on stacked species models, overestimate richness (Dubuis et al. , 2011).”
The impact of modelling choices in the predictive performance of richness maps derived from species‐distribution models: guidelines to build better diversity models (2013, methodology): “Consequently, a significant level of overprediction error in species richness should be expected (Gioia & Pigott 2000; Algar et al. 2009; Newbold et al. 2009; Dubuis et al. 2011; Mateo et al. 2012; but see Lehmann, Leathwick & Overton 2002).”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, unknown): “We classified 23 of the 33 SDM variants as stacked species distribution models (SSDM; Dubuis et al. 2011, Guisan and Rahbek 2011), since they essentially model species individually and then stack the model predictions together to build up a compound prediction at the community level (Ferrier and…”
Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models (2015, methodology): “One widely used method is to predict the distributions of individual species with niche-based species distribution models (SDMs; also called ecological niche models, ENMs; see Guisan et al., 2013), and then to stack them to predict species assemblages (stacked-SDM, S-SDM; Dubuis et al., 2011).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “8). species richness (Wohlgemuth et al. 2008, Aranda & Lobo 2011, Dubuis et al. 2011), nature types (Dobrowski et al. 2008, Danz et al. 2011), landforms (Hjort & Marmion 2009), and ecological processes such as wildfire (Parisien & Moritz 2009) and abandonment of agricultural practices (Gellrich &…”