Risk effects arise when prey alter their behavior in response to predators, and these responses carry costs. Empirical studies have found that risk effects can be large. Nonetheless, studies of predation in vertebrate conservation and management usually consider only direct predation. Given the ubiquity and strength of behavioral responses to predators by vertebrate prey, it is not safe to assume that risk effects on dynamics can be ignored. Risk effects can be larger than direct effects. Risk effects can exist even when the direct rate of predation is zero. Risk effects and direct effects do not necessarily change in parallel. When risk effects reduce reproduction rather than survival, they are easily mistaken for limitation by food supply.

Relationships between direct predation and risk effects

https://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/25603/RippleWilliam.Forestry.TrophicCascadesYellowstone.pdf?sequence=3

Trophic cascades in Yellowstone: The first 15 years after wolf reintroduction

Predation risk\" and \"fear\" are concepts well established in animal behavior literature. We expand these concepts to develop the model of the \"landscape of fear\". The landscape of fear represents relative levels of predation risk as peaks and valleys that reflect the level of fear of predation a prey experiences in different parts of its area of use. We provide observations in support of this model regarding changes in predation risk with respect to habitat types, and terrain characteristics. We postulate that animals have the ability to learn and can respond to differing levels of predation risk. We propose that the landscape of fear can be quantified with the use of well documented existing methods such as giving- up densities, vigilance observations, and foraging surveys of plants. We conclude that the landscape of fear is a useful visual model and has the potential to become a unifying ecological concept.

/pdf/the-landscape-of-fear-ecological-implications-of-being-3av2mhurxm.pdf

The Landscape of Fear: Ecological Implications of Being Afraid~!2009-09-09~!2009-11-16~!2010-02-02~!

In the past decade, ecologists have witnessed vast improvements in our ability to collect animal movement data through animal-borne technology, such as through GPS or ARGOS systems. However, more data does not necessarily yield greater knowledge in understanding animal ecology and conservation. In this paper, we provide a review of the major benefits, problems and potential misuses of GPS/Argos technology to animal ecology and conservation. Benefits are obvious, and include the ability to collect fine-scale spatio-temporal location data on many previously impossible to study animals, such as ocean-going fish, migratory songbirds and long-distance migratory mammals. These benefits come with significant problems, however, imposed by frequent collar failures and high cost, which often results in weaker study design, reduced sample sizes and poorer statistical inference. In addition, we see the divorcing of biologists from a field-based understanding of animal ecology to be a growing problem. Despite these difficulties, GPS devices have provided significant benefits, particularly in the conservation and ecology of wide-ranging species. We conclude by offering suggestions for ecologists on which kinds of ecological questions would currently benefit the most from GPS/Argos technology, and where the technology has been potentially misused. Significant conceptual challenges remain, however, including the links between movement and behaviour, and movement and population dynamics.

/pdf/distinguishing-technology-from-biology-a-critical-review-of-2r7z5y61vz.pdf

Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology

Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information-theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k-fold cross validation.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1366-9516.2006.00243.x

Scale for resource selection functions

A trophic cascade recently has been reported among wolves, elk, and aspen on the northern winter range of Yellowstone National Park, Wyoming, USA, but the mechanisms of indirect interactions within this food chain have yet to be established. We investigated whether the observed trophic cascade might have a behavioral basis by exploring environmental factors influencing the movements of 13 female elk equipped with GPS radio collars. We developed a simple statistical approach that can unveil the concurrent influence of several environmental features on animal movements. Paths of elk traveling on their winter range were broken down into steps, which correspond to the straight-line segment between successive locations at 5-hour intervals. Each observed step was paired with 200 random steps having the same starting point, but differing in length and/or direction. Comparisons between the characteristics of observed and random steps using conditional logistic regression were used to model environmental features influencing movement patterns. We found that elk movements were influenced by multiple factors, such as the distance from roads, the presence of a steep slope along the step, and the cover type in which they ended. The influence of cover type on elk movements depended on the spatial distribution of wolves across the northern winter range of the park. In low wolf-use areas, the relative preference for end point locations of steps followed: aspen stands > open areas > conifer forests. As the risks of wolf encounter increased, the preference of elk for aspen stands gradually decreased, and selection became strongest for steps ending in conifer forests in high wolf-use areas. Our study clarifies the behavioral mechanisms involved in the trophic cascade of Yellowstone's wolf-elk-aspen system: elk respond to wolves on their winter range by a shift in habitat selection, which leads to local reductions in the use of aspen by elk.

/pdf/wolves-influence-elk-movements-behavior-shapes-a-trophic-1yetpsj168.pdf

Douglas W. Smith

Papers

Wolves influence elk movements: behavior shapes a trophic cascade in yellowstone national park