Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

By Michael P. Austin. 2002

1,956 citations recorded by the source corpus.

Subfields: Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/b70c51cbceb9e868c47d954d6d9a88fd66bec4a5

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In Ecological Modeling

In Nature and Landscape Conservation

Citations in the corpus

A statistical explanation of MaxEnt for ecologists (2011, background): “Since species’ responses to these tend to be complex, it is usually desirable to fit nonlinear functions (Austin, 2002).”
Bunching up the background betters bias in species distribution models (2019, methodology): “We used an ecological model (sensu Austin 2002) grounded in concepts from gradient analysis of vegetation (Whittaker 1967) to underpin distribution models of Sitka spruce.”
Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challenges (2009, methodology): “Other more subjective methods can also be used to evaluate models, including expert interpretation of the model to check its consistency with knowledge of the species (Austin 2002).”
Improving generalized regression analysis for the spatial prediction of forest communities (2006, background): “This is considered by some authors as an important progress since species only rarely present bell-shaped or linear response curves along environmental gradients (Austin & Smith, 1989; Austin, 2002).”
Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China (2007, background): “Elevation and slope, despite having no direct biological effects on plants (Austin 2002), can influence crofton weed invasion through their correlation with temperature and precipitation.”
Temporal variation in bird assemblages: How representative is a one-year snapshot? (2005, background): “…to be highly temporally variable, accurate prediction of the effects of various management actions and habitat changes on bird populations will benefit from an improved understanding of the underlying mechanisms that influence patterns in bird distribution and abundance (Wiens 1994; Austin 2002).”
Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice (2011, background): “…and accounting for the resultant biases, the solution we introduced here addresses only one of several major problems in SDM analysis (e.g. Austin 2002, 2007; Gaston 2003; Guisan & Thuiller 2005; Araújo &Guisan 2006; Araújo &Rahbek 2006; Heikkinen et al. 2006; Soberón 2007; Elith…”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, background): “, 1998); effects of factors not considered in the modelling process (Guisan & Zimmermann, 2000; Austin, 2002); or effects of individuals actively aggregating in space (Keitt et al.”
Non‐stationarity and local approaches to modelling the distributions of wildlife (2007, background): “Progress has been made in laying the ecological foundation for predictive distribution modelling (Austin, 2002) and models have been built to understand niche requirements, for nature conservation, to predict the impacts of land use or environmental change on a species, and to assess the risks of…”
Modelling the influence of biotic factors on species distribution patterns (2016, neutral): “Relationships between the response variable and individual covariates were restricted to functional forms with simple shapes (Austin, 2002) and the flexibility of these forms was part of the modelling approach.”
Predicting distributions of invasive species (2013, background): “1), but it is most closely related to the realised niche (Austin, 2002; Austin et al., 1990; Colwell & Rangel, 2009; Jiménez-Valverde et al., 2008; Soberon & Nakamura, 2009).”
An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data (2004, methodology): “When two or more predictors had a correlation coefficient greater than 0·5, only the most proximal (in the sense of Austin 2002 ) was kept for the ENFA.”
Living on the edge - modelling habitat suitability for species at the edge of their fundamental niche (2008, background): “…habitats close to a source population (Pulliam, 1988; James et al., 1984), or dispersal limitations that can prevent the colonisation of suitable habitats (Pulliam, 2000), environmental changes and habitat deterioration can force a species to relocate to suboptimal habitats (Austin, 2002).”
Testing the predictive performance of distribution models (2013, neutral): “…as possible, we included as many species as possible and used general environmental variables, doubtlessly sacrificing some explanatory power that could have been achieved by building individual models for each of the 79 bird species based on a detailed review of their ecology (Austin 2002).”
Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. (2012, background): “For example, they could build models that incorporate known mechanisms (Kearney and Porter 2009, Monahan 2009), use a-priori relevant predictor variables (Austin 2002), and use alternative model selection criteria such as AIC (Warren and Seifert 2011).”
Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods (2006, methodology): “Spatial prediction of species distributions using GAMs is explored by Austin (2002), while a GAM application tool for ecological analyses and spatial prediction has been built by Lehmann et al. (2002).”
GRASP: generalized regression analysis and spatial prediction (2002, methodology): “This allows exploration of shapes of species response curves to environmental gradients, and allows the fitting of statistical models in better agreement with ecological theory (Austin, 1999b, 2002).”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “As a general rule of thumb, a good strategy is to select variables that a) are ecologically relevant, b) are feasible to collect data on and c) are closer to the mechanism (in the sequence resource-direct-indirect-proxy variables: Austin 2002, Harrell 190 2001).”