Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods

By Adam Algar, H. Kharouba, Eric Young, Jeremy T. Kerr. 2009

184 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/29fef872af03cbab796ddc4b1845ffe6f06f9bf7

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, methodology): “Algar et al. (2009) showed that S-SDM tended to overpredict butterfly SR in species-rich areas.”
Space can substitute for time in predicting climate-change effects on biodiversity (2013, background): “Nevertheless, this study complements studies that have focused on modeling changes in species richness (16, 18) and adds support to the prospect of projecting impacts to biodiversity as a result of climate change.”
The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling (2012, background): “Moreover, novel communities may emerge from individualistic range dynamics (Williams & Jackson, 2007; Algar et al., 2009; Stralberg et al., 2009; Schweiger et al., 2010).”
Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models (2015, background): “It has been shown that bS-SDMs tend, on average, to overpredict species richness per unit area (Algar et al., 2009; Dubuis et al., 2011; Mateo et al., 2012), whereas pS-SDMs do not (Dubuis et al., 2011; Calabrese et al., 2014).”
Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change (2015, background): “In most cases, species richness is estimated by stacking individual species distribution models (Algar et al., 2009; Mateo et al., 2012).”
SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages (2011, methodology): “For the integrated modelling framework to be valid, the S-SDM-based predictions should, on average, overpredict species richness compared with observations (as found in Algar et al., 2009; Pineda & Lobo, 2009) and significantly exceed the MEM-based predictions of species richness.”
The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts (2014, background): “1303 *These authors contributed equally to this paper.”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, methodology): “MEMs have contributed substantially to our understanding of large-scale ecology and biodiversity, and have been valuable research tools (Brown, 1995; Gaston, 2000; Gaston & Blackburn, 2000; Hawkins & Diniz-Filho, 2004; Kerr et al., 2007; Algar et al., 2009).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, background): “Modelling species richness by stacking the habitat suitability maps for individual species is an important conservation tool (Benito et al., 2013; Distler et al., 2015), despite its tendency to overestimate species richness (Algar et al., 2009).”
The impact of modelling choices in the predictive performance of richness maps derived from species‐distribution models: guidelines to build better diversity models (2013, background): “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).”