Individual differences in habitat selection mediate landscape level predictions of a functional response

Newediuk, Levi; Prokopenko, Christina M.; Wal, Eric Vander

doi:10.1007/s00442-021-05098-0

Cited by 8 publications

(8 citation statements)

References 55 publications

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“…For instance, given 20 times more wetlands available on the non-breeding grounds than on the breeding grounds (Figure 1), pelicans might increase the strength of their selection of wetlands after arriving at the breeding grounds. Therefore, finescale habitat selection by pelicans may be contextually dependent, varying with differences in resource availability and physiological conditions between animal individuals [12,58].…”

Section: Discussionmentioning

confidence: 99%

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Seasonal Habitat Selection by American White Pelicans

2022

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Resource utilization strategies of avian migrants are a major concern for conservation and management. Understanding seasonal habitat selection by migratory birds helps us explain the ongoing continental declines of migratory bird populations. Our objective was to compare the second-order and third-order habitat selection by the American White Pelican (Pelecanus erythrorhynchos; hereafter pelican) between the breeding and non-breeding grounds. We tested the Lack hypothesis that habitat selection by migratory birds is stronger on the breeding grounds than on the non-breeding grounds. We used random-effect Dirichlet-multinomial models to estimate the second-order habitat selection between the seasons with the GPS locations of 32 tracked pelicans. We used Gaussian Markov random field models to estimate the third-order habitat selection by pelicans at the breeding and non-breeding grounds, accounting for spatial autocorrelation. Pelicans strongly selected waterbodies and wetlands at both non-breeding and breeding grounds, tracking their foraging habitats between the seasons at the home range level. However, pelicans exhibited seasonal differences in the strength of the third-order selection of wetlands and waterbodies with foraging habitat selection being stronger at the breeding grounds than at the non-breeding grounds, supporting the Lack hypothesis.

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Section: Discussionmentioning

confidence: 99%

“…Individual variation in habitat selection has been found in many mammals and birds [58]. For instance, breeding and non-breeding white storks differ in both the secondorder and third-order habitat selection [55].…”

Section: Discussionmentioning

confidence: 99%

Seasonal Habitat Selection by American White Pelicans

2022

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“…This may (at least in part) explain why a growing number of studies have documented surprising levels of apparent variation in habitat selection within species and even populations (e.g. Leclerc et al, 2016; Montgomery et al, 2018; Newediuk et al, 2022). Individual animals undoubtedly vary somewhat in their habitat preferences because of differences in behavioural traits (Bastille‐Rousseau & Wittemyer, 2019; Leclerc et al, 2016; Stuber et al, 2022), population density (Avgar et al, 2020; Matthiopoulos et al, 2015; van Beest et al, 2014) and habitat availability (Aarts et al, 2013; Godvik et al, 2009; Mysterud & Ims, 1998).…”

Section: Introductionmentioning

confidence: 99%

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting

et al. 2022

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Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale‐dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs. In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non‐independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

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“…Because RSFs assume independent and identically distributed (IID) data, autocorrelation can lead to incorrectly narrow confidence intervals, and therefore random variations being incorrectly identified as significant effects. This may (at least in part) explain why a growing number of studies have documented surprising levels of apparent variation in habitat selection within species and even populations (e.g., Leclerc et al ., 2016, Montgomery et al ., 2018, Newediuk et al ., 2022). While individual animals undoubtedly vary somewhat in their habitat preferences, this pattern of extreme variation in selection parameters can easily arise when pseudoreplication from autocorrelated data causes unimportant covariates to be (misleadingly) found to be significant.…”

Section: Introductionmentioning

confidence: 99%

“…Such pseudoreplication—and the incorrectly narrow confidence intervals that arise from it—can cause random variations to be incorrectly identified as significant effects. This may (at least in part) explain why a growing number of studies have documented surprising levels of apparent variation in habitat selection within species and even populations (e.g., Leclerc et al ., 2016, Montgomery et al ., 2018, Newediuk et al ., 2022). Individual animals undoubtedly vary somewhat in their habitat preferences because of differences in behavioral traits (Bastille-Rousseau & Wittemyer, 2019, Leclerc et al ., 2016, Stuber et al ., 2022), population density (Avgar et al ., 2020, Matthiopoulos et al ., 2015, van Beest et al ., 2014), and habitat availability (Aarts et al ., 2013, Godvik et al ., 2009, Mysterud & Ims, 1998).…”

Section: Introductionmentioning

confidence: 99%

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting

Alston

Fleming

Kays

et al. 2022

Preprint

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Resource selection functions are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because resource selection functions assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations, and step selection functions have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data, and (in the case of step selection functions) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on resource selection functions. This method weights each observed location in an animal’s movement track according to its level of autocorrelation, expanding confidence intervals to match an objective target (i.e., the effective sample size for Autocorrelated Gaussian Density Estimation) and accounting for bias that can arise when there are gaps in the movement track. In this study, we describe the mathematical principles underlying our method, demonstrate its practical advantages versus conventional approaches using simulations and empirical data on a water mongoose (Atilax paludinosus), a caracal (Caracal caracal), and a serval (Leptailurus serval), and discuss pathways for further development of our method. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

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Individual differences in habitat selection mediate landscape level predictions of a functional response

Abstract: Highlighted Student Paper statement: Changing habitat availability affects habitat selection, but individuals vary. We found this variation is best modelled by not assuming all individuals modify selection with availability.

Cited by 8 publications

References 55 publications

Seasonal Habitat Selection by American White Pelicans

Seasonal Habitat Selection by American White Pelicans

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting

Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting

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