Density‐dependent functional responses in habitat selection by two hosts of the raccoon rabies virus variant

Tardy, Olivia; Massé, Ariane; Pelletier, Fanie; Mainguy, Julien; Fortin, Daniel

doi:10.1890/es14-00197.1

Cited by 38 publications

(37 citation statements)

References 72 publications

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“…, Tardy et al. ). This is an interesting approach to assess trade‐off situations, but seemingly makes the inherent assumption that resource A and B are inversely correlated.…”

Section: Discussionmentioning

confidence: 99%

“…, Tardy et al. , Mason and Fortin ). If there is no evidence of selection or avoidance (i.e., β 1 and

θ_{1}

= 0), the resulting predictions reduce to exp (0) and w ( x ) = 1.…”

Section: Methodsmentioning

confidence: 98%

“…, Tardy et al. ), which is different than the two‐step process in approach 3. This modeling approach was presented in Tardy et al.…”

Section: Methodsmentioning

confidence: 99%

“…This modeling approach was presented in Tardy et al. () where they used a logistic estimating function to generate regression coefficients, and plotted the marginal functional response across the gradient in

{\bar{m}}^{A} false(x_{1} false)

using the log‐linear RSF (Boyce et al. ):

w false(x false) = exp ({normalβ}_{1} false(x_{1 i} false) + {normalθ}_{1} false(x_{1 i} \times {\overset{m}{true}}^{A} (x_{1}) false))

where w ( x ) is the relative probability of selection, β 1 is the marginal RSF coefficient for resource x 1 , x 1 is the vector of resource x 1 across individuals at used ( i = 1) and available units ( i = 0), and

{\bar{m}}^{A} false(x_{1} false)

is a vector (across individuals) of mean values for resource x 1 at available units.…”

Section: Methodsmentioning

confidence: 99%

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Functional responses in habitat selection: clarifying hypotheses and interpretations

Holbrook

Olson

DeCesare

et al. 2019

Ecological Applications

113

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A fundamental challenge in habitat ecology and management is understanding the mechanisms generating animal distributions. Studies of habitat selection provide a lens into such mechanisms, but are often limited by unrealistic assumptions. For example, most studies assume that habitat selection is constant with respect to the availability of resources, such that habitat use remains proportional to availability. To the contrary, a growing body of work has shown the fallacy of this assumption, indicating that animals modify their behavior depending on the context at broader scales. This has been termed a functional response in habitat selection. Furthermore, a diversity of methods is employed to model functional responses in habitat selection, with little attention to how methodology might affect scientific and conservation conclusions. Here, we first review the conceptual and statistical foundations of methods currently used to model functional responses and clarify the ecological tests evaluated within each approach. We then use a combination of simulated and empirical data sets to evaluate the similarities and differences among approaches. Importantly, we identified multiple statistical issues with the most widely applied approaches to understand functional responses, including: (1) a complex and important role of random‐ or individual‐level intercepts in adjusting individual‐level regression coefficients as resource availability changes and (2) a sensitivity of results to poorly informed individual‐level coefficients estimated for animals with low availability of a given resource. Consequently, we provide guidance on applying approaches that are insensitive to these issues with the goal of advancing our understanding of animal habitat ecology and management. Finally, we characterize the management implications of assuming similarity between the current approaches to model functional responses with two empirical data sets of federally threatened species: Canada lynx (Lynx canadensis) in the United States and woodland caribou (Rangifer tarandus caribou) in Canada. Collectively, our assessment helps clarify the similarities and differences among current approaches and, therefore, assists the integration of functional responses into the mainstream of habitat ecology and management.

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“…, Tardy et al. ). This is an interesting approach to assess trade‐off situations, but seemingly makes the inherent assumption that resource A and B are inversely correlated.…”

Section: Discussionmentioning

confidence: 99%

“…, Tardy et al. , Mason and Fortin ). If there is no evidence of selection or avoidance (i.e., β 1 and

θ_{1}

= 0), the resulting predictions reduce to exp (0) and w ( x ) = 1.…”

Section: Methodsmentioning

confidence: 98%

“…, Tardy et al. ), which is different than the two‐step process in approach 3. This modeling approach was presented in Tardy et al.…”

Section: Methodsmentioning

confidence: 99%

{\bar{m}}^{A} false(x_{1} false)

using the log‐linear RSF (Boyce et al. ):

w false(x false) = exp ({normalβ}_{1} false(x_{1 i} false) + {normalθ}_{1} false(x_{1 i} \times {\overset{m}{true}}^{A} (x_{1}) false))

{\bar{m}}^{A} false(x_{1} false)

is a vector (across individuals) of mean values for resource x 1 at available units.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Functional responses in habitat selection: clarifying hypotheses and interpretations

Holbrook

Olson

DeCesare

et al. 2019

Ecological Applications

113

View full text Add to dashboard Cite

show abstract

“…The enhanced performance of Global Positioning Systems in recent years has increased the relocation frequency of individuals in habitat selection and movement studies [13], such that individuals are commonly relocated 24 or more times a day [9,14,15]. To provide robust inference (i.e., robust estimates of the variance of the regression coefficients), conditional logistic regression has to account for temporal autocorrelation that is inherent to such rich longitudinal datasets.…”

Section: Introductionmentioning

confidence: 99%

Robust Inference from Conditional Logistic Regression Applied to Movement and Habitat Selection Analysis

2017

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Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters.The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14–450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.

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Conceptual and methodological advances in habitat‐selection modeling: guidelines for ecology and evolution

Northrup

Wal

Bonar

et al. 2021

Ecological Applications

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Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat‐selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat‐selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat‐selection process. This review is purposefully non‐technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat‐selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

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Density‐dependent functional responses in habitat selection by two hosts of the raccoon rabies virus variant

Cited by 38 publications

References 72 publications

Functional responses in habitat selection: clarifying hypotheses and interpretations

Functional responses in habitat selection: clarifying hypotheses and interpretations

Robust Inference from Conditional Logistic Regression Applied to Movement and Habitat Selection Analysis

Conceptual and methodological advances in habitat‐selection modeling: guidelines for ecology and evolution

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