Mapping Species Distributions: Spatial Inference and Prediction

By Janet Franklin, Jennifer A. Miller. 2010

2,058 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9fbaddd7f036b4da6ade52052890143dd4d395ce

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Citations in the corpus

Mechanistic and Correlative Models of Ecological Niches (2015, methodology): “Correlative methods (Guisan & Zimmermann 2000; Franklin 2010; Peterson et al. 2011) have a distinct suite of advantages and disadvantages.”
A statistical explanation of MaxEnt for ecologists (2011, background): “Species distribution models (SDMs) estimate the relationship between species records at sites and the environmental and/or spatial characteristics of those sites (Franklin, 2009).”
Roles of Raptors in a Changing World: From Flagships to Providers of Key Ecosystem Services (2016, background): “…range of applied uses, such as understanding the ecological requirements or biogeography of species, predicting species distributions, identifying reintroduction sites and priority conservation areas, and predicting the response of populations to climate change or habitat loss (Franklin, 2009).”
Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, background): “, 2009) ENM/SDM are increasingly used to predict potential distributions of species of concern (Franklin, 2009; Elith et al., 2010; Peterson et al., 2011) because resource managers need accurate maps of species distribution and abundance for risk analysis.”
A standard protocol for reporting species distribution models (2020, methodology): “However, by developing a ‘checklist’ of standard operating procedures, we hope to make it easier for authors to report, and for readers to understand, SDM data and methods, as ODD has done for ABM/IBM (Grimm et al. 2006, 2010).”
blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018, methodology): “In ecology, this usually involves splitting the data into subsets for training and testing (also known as calibration and validation, respectively) (Franklin, 2010; Radosavljevic & Anderson, 2014).”
ecospat: an R package to support spatial analyses and modeling of species niches and distributions (2017, background): “One of its overarching aims is to investigate what factors shape the distribution of species (Franklin 2009, Peterson et al. 2011) and, departing from these, to reconstruct communities and ecosystems (Levin 1992, Dungan et al. 2002, Guisan and Rahbek 2011, Hastings et al. 2011).”
Overcoming limitations of modelling rare species by using ensembles of small models (2015, background): “2013) and applications of SDMs to conservation are manifold (Guisan & Thuiller 2005; Franklin 2009; Peterson et al. 2011).”
Applications of species distribution modeling to paleobiology (2011, methodology): “We refer to the literature for comprehensive treatments and comparisons of modeling algorithms (e.g., Guisan and Zimmermann, 2000; Elith et al., 2006; Hijmans and Graham, 2006; Franklin, 2009).”
Using species distribution models to inform IUCN Red List assessments (2014, methodology): “When species had greater than 10 presence localities we fitted SDMs 10 times, each time with a random 20% reserved for model evaluation (Franklin 2009).”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, background): “Species distribution models (SDMs) are increasingly being applied to assess impacts of environmental change on natural ecosystems and biodiversity (Elith & Leathwick, 2009; Franklin, 2010a).”
Extinction risk of North American seed plants elevated by climate and land‐use change (2017, methodology): “2005, 2011), numerous uncertainties remain, such as the potential effects of species interactions and species-specific range shifts (Franklin 2009; Hille Ris Lambers 2015).”