Error and uncertainty in habitat models

By Simon C. Barry, Jane Elith. 2006

647 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9eb4d0c3014a1f5c09fb371a5ef3222eb4cffe6c

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Modelling invasion for a habitat generalist and a specialist plant species (2008, background): “Many studies have found that model performance greatly relies on the particular independent variables tested, spatial resolution, or inadequate field data (SuarezSeoane et al., 2002; Gibson et al., 2004; Barry & Elith, 2006).”
The application of predictive modelling of species distribution to biodiversity conservation (2007, background): “These new methodological approaches also offer testable predictive models that allow a more adequate representation of species distributions in a context of recognized but often unquantified uncertainty (Elith et al ., 2002; Barry & Elith, 2006).”
Overcoming limitations of modelling rare species by using ensembles of small models (2015, background): “Although it is well known that modelling rare species is often problematic in terms of model overfitting and inaccurate model predictions (Hernandez et al. 2006; Barry & Elith 2006; Wisz et al. 2008), few studies have paid sufficient attention to this problem.”
Predicting species distributions for conservation decisions (2013, background): “SDMs enable the quantification of some types of uncertainties in the spatial predictions of environmental suitability (Barry & Elith 2006), and these can be explicitly incorporated in conservation prioritisation processes (Moilanen et al. 2006).”
Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections (2010, background): “In many cases, this distributional information is limited to that held by natural history collections (NHC) (Araújo & Williams, 2000; Barry & Elith, 2006).”
Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses (2009, background): “…models SDMs are designed to compute the spatial distribution of the suitable habitat for a given species, using as inputs presence records and a set of predictor variables with influence on the distribution of the species, or correlated with other variables that do (Barry and Elith 2006).”
European Bison habitat in the Carpathian Mountains (2010, background): “analyses, we cannot fully rule out overprediction due to missing covariates (Barry and Elith, 2006).”
Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, background): “In addition to obtaining valid point estimates of occurrence probability or abundance, an honest assessment of the uncertainty associated with such predictions is an important, but often neglected part of the modelling of distribution and abundance (Barry & Elith, 2006).”
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time (2009, neutral): “Uncertainty in SDMs results both from data deficiencies (e.g., missing covariates, and samples of species occurrences that are small, biased, or lacking absences) and from errors in specification of the model (Barry & Elith 2006).”
Palaeoclimatic models help to understand current distribution of Caucasian forest species (2012, background): “In order to increase the predictive power of SDMs based on the analysis of abiotic environmental predictors, we should consider biotic interactions, metapopulation dynamics and dispersal limitations (Legendre, 1993; Gavashelishvili, 2004; Hampe, 2004; Svenning & Skov, 2004; Barry & Elith, 2006).”
A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice (2017, background): “In the context of SDMs the estimation of model error or uncertainty involves quantifying not only model mistakes and faults, but also the statistical concept of variation (Barry and Elith, 2006).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “No universally ̒ rightʼ complexity level exists, not even for a given modelled target in a given study area: the purpose of the DM study is a main determinant of which complexity level is the most appropriate (Barry & Elith 2006, Elith et al. 2010, Halvorsen 2012).”
Establishing the link between habitat selection and animal population dynamics (2015, background): “Instead, we should simply select a sufficiently expandable inferential framework, verify its robustness (Barry and Elith 2006), and begin fleshing it out with biological mechanisms (Austin 2002, Railsback et al. 2003, Guisan and Thuiller 2005, Moorcroft and Lewis 2006, McLoughlin et al. 2010,…”