Risky business: How an herbivore navigates spatiotemporal aspects of risk from competitors and predators

Holbrook, Joseph D.

doi:10.1002/eap.2648

Cited by 13 publications

(16 citation statements)

References 108 publications

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“…Although a 1‐km distance may be relatively coarse for identifying encounters with a stalk‐and‐ambush predator, we selected this distance in part not only due to sample size limitations but also so that we could test conventional methods of evaluating predator–prey encounters (e.g., Middleton et al, 2013). Encounters between two animals should be more likely to occur when both are active (Avgar et al, 2008; Scharf et al, 2006), and previous research in our study area indicated that activity curves for coyotes and mountain lions have a high degree of overlap (Huggler et al, in revision). Thus, while mountain lions use a more stationary hunting strategy than coyotes, they are unlikely to be entirely stationary in the time leading up to encounters.…”

Section: Methodssupporting

confidence: 53%

“…We used Random forest (RF) models (Breiman, 2001) to develop spatial predictions of probable mountain lion use (risk) and kill site occurrence (reward) across our study area (Huggler et al, in revision). We expanded on Huggler et al (in revision)’s models by including kill sites containing elk and pronghorn to characterize the full suite of ungulate kill sites. Random forest is a machine learning approach that combines many classification trees, does not rely on normality, and can readily incorporate interactions and variables that are correlated (Breiman, 2001).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Cats and dogs: A mesopredator navigating risk and reward provisioned by an apex predator

Brunet

Monteith

Huggler

et al. 2022

Ecology and Evolution

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Successfully perceiving risk and reward is fundamental to the fitness of an animal, and can be achieved through a variety of perception tactics. For example, mesopredators may “directly” perceive risk by visually observing apex predators, or may “indirectly” perceive risk by observing habitats used by predators. Direct assessments should more accurately characterize the arrangement of risk and reward; however, indirect assessments are used more frequently in studies concerning the response of GPS‐marked animals to spatiotemporally variable sources of risk and reward. We investigated the response of a mesopredator to the presence of risk and reward created by an apex predator, where risk and reward likely vary in relative perceptibility (i.e., degree of being perceptible). First, we tested whether coyotes (Canis latrans) use direct or indirect assessments to navigate the presence of mountain lions (Puma concolor; risk) and kills made by mountain lions (reward) in an area where coyotes were a common prey item for mountain lions. Second, we assessed the behavioral response of coyotes to direct encounters with mountain lions. Third, we evaluated spatiotemporal use of carrion by coyotes at kills made by mountain lions. Indirect assessments generally outperformed direct assessments when integrating analyses into a unified framework; nevertheless, our ability to detect direct perception in navigating to mountain lion kills was likely restricted by scale and sampling limitations (e.g., collar fix rates, unsampled kill sites). Rather than responding to the risk of direct encounters with mountain lions, coyotes facilitated encounters by increasing their movement rate, and engaged in risky behavior by scavenging at mountain lion kills. Coyotes appear to mitigate risk by using indirect perception to avoid mountain lions. Our predator–predator interactions and insights are nuanced and counter to the conventional predator–prey systems that have generated much of the predation risk literature.

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Section: Methodssupporting

confidence: 53%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Cats and dogs: A mesopredator navigating risk and reward provisioned by an apex predator

Brunet

Monteith

Huggler

et al. 2022

Ecology and Evolution

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“…We assessed coyote use in each year they were collared to ensure that the majority of their locations (i.e., >85%) occurred within the study area and removed remaining coyote ID‐years with less than 500 locations within the study area. We used methods adapted from Ridout and Linkie (2009) and the overlap R package (Ridout & Linkie, 2009) to fit a kernel density function to coyote movement rates (meters per hour) and estimate peaks in activity when coyotes were most likely hunting (Huggler et al, 2022). We defined peaks in activity based on the times in which activity estimates were within 50% of the maximum (16:00–8:45 MST) and constrained our habitat selection analysis to this active period.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal predictions of the alternative prey hypothesis: Predator habitat use during decreasing prey abundance

et al. 2023

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The alternative prey hypothesis supposes that predators supported by a primary prey species will shift to consume alternative prey during a decrease in primary prey abundance. The hypothesis implies that during declines of one prey species, a predator modifies their behavior to exploit a secondary, or alternative, species. Despite occurring in many systems, the behavioral mechanisms (e.g., habitat selection) allowing predators to shift toward alternative prey during declines in the abundance of their primary prey are poorly understood. We evaluated habitat selection and use by a generalist predator with respect to two prey species during a dramatic decrease in the abundance of primary prey. Further, we evaluated how spatial variation in access to primary prey affected habitat selection, and assessed similarity and overlap between habitats used by each prey species. Coyotes (Canis latrans) exhibited decreasing selection for cottontail rabbits (Sylvilagus spp.; primary prey) during population decreases but did not shift habitat selection toward neonate mule deer (Odocoileus hemionus; alternative prey). Use of rabbit habitat remained high even during historically low rabbit abundance, while mule deer habitat was used in proportion to its availability. Coyotes seemingly do not make large shifts in habitat selection toward alternative prey following spatial and temporal decreases in the abundance of primary prey, but instead, take advantage of habitat overlap to facilitate prey‐switching behavior. Our work extends previous research conducted under the alternative prey hypothesis by explicitly evaluating the influence of habitat overlap between prey species and variation in access to prey habitat as factors affecting prey‐switching behaviors in predators.

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“…This crucial limitation can be solved by integrated step-selection analysis (iSSA), which allows simultaneous modelling of movement and habitat selection decisions by animals (Avgar et al , 2016), building upon the earlier technique of step selection analysis (SSA) (Fortin et al , 2005; Rhodes et al , 2005; Forester et al , 2009; Thurfjell et al , 2014). Not only has this fundamental methodological advancement lead to an explosion of the use of SSA and iSSA in recent years (Viana et al , 2018; Huggler et al , 2022; Northrup et al , 2022), and methodological extensions (Munden et al , 2021; Klappstein et al , 2021), but researchers have increasingly shown how the movement kernels parameterised during SSA can be ‘scaled-up’ to predict broader-scale space use patterns (Potts et al , 2014b; Avgar et al , 2016; Signer et al , 2017; Potts & Schlägel, 2020; Fieberg et al , 2021). Even though this markedly increases the level of understanding and the quality of predictions which can be obtained from animal movement analyses, such upscaling of step selection analysis is seldom done by the many studies using SSA, perhaps due to a lack of knowledge or perceived methodological difficulties.…”

Section: Introductionmentioning

confidence: 99%

How to scale up from animal movement decisions to spatio-temporal patterns: an approach via step selection

Potts

Börger

2022

Preprint

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Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research.This study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well-appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propogated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals.Here, we provide a guide for understanding and using the various existing techniques for scaling-up step selection models to predict broad scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python.By pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of step selection analysis.

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Risky business: How an herbivore navigates spatiotemporal aspects of risk from competitors and predators

Cited by 13 publications

References 108 publications

Cats and dogs: A mesopredator navigating risk and reward provisioned by an apex predator

Cats and dogs: A mesopredator navigating risk and reward provisioned by an apex predator

Spatiotemporal predictions of the alternative prey hypothesis: Predator habitat use during decreasing prey abundance

How to scale up from animal movement decisions to spatio-temporal patterns: an approach via step selection

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