Correlation and studies of habitat selection: problem, red herring or opportunity?

By J. Fieberg, Jason Matthiopoulos, Mark Hebblewhite, M. Boyce, Jacqui Frair. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010

326 citations recorded by the source corpus.

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.

Subfields: Ecological Modeling, Ecology

View Paper: https://www.semanticscholar.org/paper/9a1efe565687ff4954e6c0acf1ddc5ead1972c95

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Citations in the corpus

Multi-level functional responses for wildlife conservation: the case of threatened caribou in managed boreal forests (2012, background): “Such mixed-effects models allow RSF inference at animal-specific or at population-averaged levels (Fieberg et al. 2010).”
The pitfalls of ignoring behaviour when quantifying habitat selection (2014, background): “Ultimately, models that combine both movement modelling and habitat selection in a single framework are likely to offer the greatest inferential power because these two processes are not independent (Beyer et al., 2010; Fieberg et al., 2010).”
Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges (2010, methodology): “On one side, these opportunities and challenges should promote the adoption of analytical methods that: (i) model correlation explicitly as an intrinsic and relevant property of data (Wittemyer et al. 2008; Boyce et al. 2010; Fieberg et al. 2010), with particular emphasis on individual-level variabi”
Integrated step selection analysis: bridging the gap between resource selection and animal movement (2015, methodology): “Our aim here was to use the simplest, and hence most general, mechanistic process imaginable, leading us to choose a stepping-stone movement process as our pattern-generating process.”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, methodology): “…species occurrence, decreasing predictive power both for interpolation as well as extrapolation (predictions made for dissimilar environmental conditions or different region as data used for model fitting; Diniz-Filho et al. 2003, Diggle and Ribeiro 2007, Fieberg et al. 2010, Thibaud et al. 2014).”
Wolves, white-tailed deer, and beaver: implications of seasonal prey switching for woodland caribou declines (2013, methodology): “the two-stage modelling approach described by Fieberg et al. (2010) and Northrup et al.”
The home-range concept: are traditional estimators still relevant with modern telemetry technology? (2010, methodology): “Mechanistic movement and home-range models can also inform analyses of habitat selection (Fieberg et al. 2010).”
Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses (2018, methodology): “3 Fieberg et al. , 2010).”
Practical guidance on characterizing availability in resource selection functions under a use-availability design. (2013, background): “With the exception of Johnson et al. (2008), the issue of autocorrelation in habitat selection studies only has been addressed in terms of model assumptions (i.e., independence of errors [Fieberg et al. 2010]).”
Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology (2010, background): “…datasets, come increased statistical headaches such as the quantification of GPS bias (Frair et al. 2010), appropriate statistical modelling of highly correlated data (Fieberg et al. 2010) and debates over the appropriate movement modelling approaches (Bartumeus et al. 2002; Smouse et al. 2010).”
Flexible habitat selection by cougars in response to anthropogenic development (2014, methodology): “A simple solution to this shortfall is to apply model selection at the individual level, and then to obtain a population model by averaging coefficients among individuals (i.e., a 2-step modeling approach; Fieberg et al., 2010).”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, methodology): “multilevelmodels that account for animal-to-animal variability in habitat preference studies (Fieberg et al. 2010).”