Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions

By J. Andrew Royle, R. Chandler, C. Yackulic, James D. Nichols. Methods in Ecology and Evolution, 2012

433 citations recorded by the source corpus.

1. Understanding the factors affecting species occurrence is a pre‐eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so‐called presence‐only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map species distribution using presence‐only data.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/aa3e5755e9bf73099f0a045615fe5b2c5aa9392c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Climate change, genetic markers and species distribution modelling (2015, neutral): “lated geographical range, but under-estimates it within the range (Royle et al., 2012).”
Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, methodology): “…no information about the presence or absence of the species) or background data (a sample of locations representing the environmental variation of the study area) are often used together with presence data for model training and evaluation (see Peterson et al., 2011; but see Royle et al., 2012).”
Prevalence, thresholds and the performance of presence–absence models (2014, background): “The first property we investigate is whether the metric is sensitive to the proportion of locations that are observed to be occupied, known as prevalence (Royle et al. 2012).”
The importance of correcting for sampling bias in MaxEnt species distribution models (2013, background): “One fundamental assumption of SDMs in general and hence also MaxEnt models is that the entire area of interest has been systematically or randomly sampled (Phillips et al., 2009; Royle et al., 2012).”
A climate of uncertainty: accounting for error in climate variables for species distribution models (2015, methodology): “P R E S E N C E / A B S E N C E D A T A F O R T H E C A R O L I N A W R E N Similar to Royle et al. (2012), we obtained presence/absence data collected on the Carolina wren (Thryothorus ludovicianus ) from the North American Breeding Bird Survey (BBS).”
MaxEnt versus MaxLike: empirical comparisons with ant species distributions (2013, methodology): “4 in Royle et al. 2012), but we obtained similar results for more typical small data sets of dozens or hundreds of v www.esajournals.org 7 May 2013 v Volume 4(5) v Article 55 v www.esajournals.org 8 May 2013 v Volume 4(5) v Article 55 v www.esajournals.org 9 May 2013 v Volume 4(5) v Article 55 v…”
Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates (2020, background): “1) are a general class models that predict count data (i.e. the number of individuals; Warton and Shepherd 2010, Aarts et al. 2012, Fithian and Hastie 2012, Renner and Warton 2012, Royle et al. 2012).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, methodology): “These were maxlike (Royle et al., 2012); generalised linear models ( glm ) (McCullagh and Nelder, 1989); classification and regression trees ( cart ) (Breiman et al., 1984); support vector machine ( svm ) (Vapnik, 1995); boosted regression trees ( brt ) (Friedman, 2001); maximum entropy ( MaxEnt )…”