WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS?

By Antoine Guisan, N. E. Zimmermann, Jane Elith, Catherine H. Graham, Steven J. Phillips, A. Townsend Peterson. 2007

419 citations recorded by the source corpus.

Subfields: Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1b2d899bd1ffab9cfbc7b7fb322230b025c211db

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In Ecological Modeling

In Nature and Landscape Conservation

Citations in the corpus

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. (2009, background): “Data for three regions (NSW, NZ, SWI) had more direct links to species’ ecology at the local scale than the climate-dominated variables from AWT, CAN, and SA (Elith et al. 2006, Guisan et al. 2007).”
SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages (2011, methodology): “successional status), modelling techniques, and data properties such as sample size, location error, or scale (Austin, 2007; Guisan et al., 2007; Elith & Leathwick, 2009; Franklin, 2010).”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, background): “The efficacy of algorithms designed to estimate the ecological niche also responds to numerous factors, which has been the subject of large numbers of analyses (e.g. Stockwell & Peterson 2002; Graham et al. 2007; Guisan et al. 2007a,b; Elith & Graham 2009).”
Optimizing ensembles of small models for predicting the distribution of species with few occurrences (2018, methodology): “Here, we used AUC to tune predictive performance of SDMs but it might be also very useful to test model tuning using other metrics, like the Boyce index or partial AUC.”
Biotic and abiotic variables show little redundancy in explaining tree species distributions (2010, methodology): “…trees at the regional scale are also suggested by earlysuccessional species being more difficult to model than dominant late-successional species, most likely due to the realized niche of the former depending on the degree of range filling of the latter (Guisan et al. 2007, Pearman et al. 2008).”
Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors (2009, background): “The recent SDM literature has emphasized comparison of these different model types to better understand their relative differences in performance (Bio et al. 1998, Franklin 1998, Moisen and Frescino 2002, Segurado and Araújo 2004, Elith et al. 2006, Maggini et al. 2006, Guisan et al. 2007).”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “Many studies have highlighted that geographical and ecological species’ characteristics affect SDM accuracy (Berg et al., 2004; Segurado & Ara ujo, 2004; Guisan et al., 2007).”
Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? (2011, background): “Species distribution models are empirical models relating known occurrences of species to environmental predictor variables using statistically derived response curves that aim to best reflect the species’ ecological tolerances (Guisan et al. 2007).”