Stacking species distribution models and adjusting bias by linking them to macroecological models

By Justin M. Calabrese, G. Certain, C. Kraan, Carsten F. Dormann. 2014

349 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/74738ce5aa9d4805401afef8cdc981507a5f49d6

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “One way of predicting community-level properties is simply to add predictions of single-species models into ‘stacked’ species distribution models (Guisan & Rahbek 2011; Calabrese et al. 2014).”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, methodology): “Previous studies that use presence-only data and unclipped SDMs to estimate community composition should be expected to overpredict richness, despite being correlated with observed species numbers (Calabrese et al., 2013; Pineda & Lobo, 2009).”
Testing species assemblage predictions from stacked and joint species distribution models (2019, methodology): “Rather, it has been suggested that probabilistic stacking, for which species presences are obtained from repeated Bernoulli trials (Dubuis et al., 2011; Pottier et al., 2013; Calabrese et al., 2014) should be preferred over binary stacking (Calabrese et al.”
Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change (2015, background): “In macroecological modelling, richness is estimated directly using variables thought to influence the number of coexisting species (Whittaker et al., 2001; Gotelli et al., 2009; Guisan & Rahbek, 2011; Calabrese et al., 2014).”
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes (2017, background): “As mentioned earlier, individual level models when aggregated tend to overestimate richness – the number of distinct species at a plot (Clark et al., 2016; Calabrese et al., 2014).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, background): “…appropriate thresholds for converting predictions into binary maps, S-SDMs can provide accurate, low-error model predictions (Benito et al., 2013; Calabrese et al., 2014) which have been shown to perform better than direct community modelling methods (Ko et al., 2016) due to their ability to…”
Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models (2015, methodology): “A correction for the probability values based on the true species richness has been recently proposed by Calabrese et al. (2014).”
Joint species distribution modelling with the r‐package Hmsc (2020, background): “…species distribution models (SSDMs) model each species separately and then combine (‘stack’) their predictions to assess community-level patterns (Calabrese, Certain, Kraan, & Dormann, 2014; Guisan & Rahbek, 2011), whereas joint species distribution models (JSDMs) explicitly acknowledge the…”