SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages

By Antoine Guisan, C. Rahbek. 2011

459 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/e44fc9c298476267bb72dd4fc298afb5832d267a

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Citations in the corpus

Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change (2015, background): “Forecasting the effects of climate change on biodiversity within an integrated framework of species distribution modelling and macroecological modelling could greatly improve our estimates of future biodiversity (Guisan & Rahbek, 2011).”
Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, background): “…distribution using the selected threshold can be post-processed with other ancillary information (regarding species dispersal, biotic interaction and human modification of the environment) through a series of steps (e.g. Phillips et al., 2006; Guisan & Rahbek, 2011; Boulangeat et al., 2012).”
Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate (2020, background): “This has been an origin to develop species climatic niche based on the current distribution of the species and project it to new areas or time slices to find suitable habitats for the presence of the species (Guisan and Rahbek, 2011; Wiens et al., 2009).”
Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co‐occurrence (2018, methodology): “For instance, SDMs have been integrated in a recently proposed modelling framework aimed at reconstructing the community assembly process (SESAM framework, Guisan and Rahbek 2011).”
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).”
Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects (2017, background): “Considering the importance of these issues, calls for unifying theories and including essential mechanisms in macroecology and biogeography have proliferated (McGill et al. 2007, Gotelli et al. 2009, Kearney et al. 2010, Loreau 2010, Guisan and Rahbek 2011).”
Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, methodology): “…relies on the existence of macroecological controls on community assembly, whereas S-SDM relies on a Gleasonian overlay of species and therefore inherits assumptions typically associated with SDM, such as equilibrium and niche stability (see Guisan & Rahbek (2011) for more detailed explanations).”
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): “SDMs have also been used to predict species richness and composition based on the assemblage of single predicted species ranges, following the ‘predict first, assemble later’ principle proposed by Ferrier & Guisan (2006).”
What ' s on the horizon for macroecology? (2012, methodology): “Integrating all parameters that potentially aff ect community assembly into one macroecological framework might be achieved by combining existing methods, such as recently proposed by Guisan and Rahbek (2011).”
The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling (2012, methodology): “…idea that species richness in a given unit should be limited and determined by macroecological factors (energy, heterogeneity, stability, etc.), a recent approach (SESAM) combines the use of macroecological models of species richness with stacked species distribution models (Guisan & Rahbek, 2011).”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “These descriptions can be refined by including species interactions, nonclimatic predictors or spatial autocorrelation terms (Araújo and Luoto, 2007; Dormann et al., 2007; DeMarco et al., 2008; Guisan and Rahbek, 2011).”
Improving species distribution models: the value of data on abundance (2014, methodology): “SDMs are now widely used and frequently refined (Guisan & Rahbek 2011; Higgins, O’Hara & R€omermann 2012).”
The importance of snow in species distribution models of arctic vegetation (2018, background): “2016) to construct the final predicted species assemblages (Guisan and Rahbek 2011).”
Defining and evaluating predictions of joint species distribution models (2020, background): “Yet, it has been suggested that, because they do not account for species interactions, SSDMs tend to overpredict species richness (Calabrese et al., 2014; Guisan & Rahbek, 2011; Pineda & Lobo, 2009; Thuiller et al., 2015).”