The effect of snow depth on movement rates of GPS-collared moose

During deep snow conditions, wildlife must balance between minimizing movements to conserve energy while seeking high amounts of browse to gain the energy. Knowledge of how snow begins to hinder their movements is therefore vital when predicting their wintertime behavior. We assessed the phenomenon with moose. Movement data from 122 GPS-collared moose were integrated with snow depth data from designated measurement stations. The effects of increasing snow depths on moose movement rates were then modeled with spline regression. The study was conducted in Finland, between 2009 and 2011. The moose were known for their sex and for the presence of calf at heel. On average, the movement rates decreased sharply until snow depths of ca. 30–40 cm, after which further significant decreases were not seen. The movement rates decreased from several kilometers per day to less than 500 m per day. Moose in the northernmost study area with the deepest snow covers moved as much as the moose in the other areas with less snow. Although we saw differences in the movement rates between males and females, differences between individuals were markedly higher than those caused by sex or a calf at heel. Moose are keystone species whose heavy browsing, especially during winter, can have profound effects on vegetation and forest regeneration. As snow covers in large parts of the boreal zone are predicted to decrease due to warming climate, the wintertime movements of moose and how they affect the local vegetation will remain relevant questions

Foraging Decisions in Risk-Uniform Landscapes

Behaviour is shaped by evolution as to maximise fitness by balancing gains and risks. Models on decision making in biology, psychology or economy have investigated choices among options which differ in gain and/or risk. Meanwhile, there are decision contexts with uniform risk distributions where options are not differing in risk while the overall risk level may be high. Adequate predictions for the emerging investment patterns in risk uniformity are missing. Here we use foraging behaviour as a model for decision making. While foraging, animals often titrate food and safety from predation and prefer safer foraging options over riskier ones. Risk uniformity can occur when habitat structures are uniform, when predators are omnipresent or when predators are ideal-free distributed in relation to prey availability. However, models and empirical investigations on optimal foraging have mainly investigated choices among options with different predation risks. Based on the existing models on local decision making in risk-heterogeneity we test predictions extrapolated to a landscape level with uniform risk distribution. We compare among landscapes with different risk levels. If the uniform risk is low, local decisions on the marginal value of an option should lead to an equal distribution of foraging effort. If the uniform risk is high, foraging should be concentrated on few options, due to a landscape-wide reduction of the value of missed opportunity costs of activities other than foraging. We provide experimental support for these predictions using foraging small mammals in artificial, risk uniform landscapes. In high risk uniform landscapes animals invested their foraging time in fewer options and accepted lower total returns, compared to their behaviour in low risk-uniform landscapes. The observed trade off between gain and risk, demonstrated here for food reduction and safety increase, may possibly apply also to other contexts of economic decision making

Density‐habitat relationships of white‐tailed deer (Odocoileus virginianus) in Finland

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods

The Many Faces of Fear: Comparing the Pathways and Impacts of Nonconsumptive Predator Effects on Prey Populations

Background: Most ecological models assume that predator and prey populations interact solely through consumption: predators reduce prey densities by killing and consuming individual prey. However, predators can also reduce prey densities by forcing prey to adopt costly defensive strategies. Methodology/Principal Findings: We build on a simple Lotka-Volterra predator-prey model to provide a heuristic tool for distinguishing between the demographic effects of consumption (consumptive effects) and of anti-predator defenses (nonconsumptive effects), and for distinguishing among the multiple mechanisms by which anti-predator defenses might reduce prey population growth rates. We illustrate these alternative pathways for nonconsumptive effects with selected empirical examples, and use a meta-analysis of published literature to estimate the mean effect size of each pathway. Overall, predation risk tends to have a much larger impact on prey foraging behavior than measures of growth, survivorship, or fecundity. Conclusions/Significance: While our model provides a concise framework for understanding the many potential NCE pathways and their relationships to each other, our results confirm empirical research showing that prey are able to partially compensate for changes in energy income, mitigating the fitness effects of defensive changes in time budgets. Distinguishing the many facets of nonconsumptive effects raises some novel questions, and will help guide both empirica

Contrast in Edge Vegetation Structure Modifies the Predation Risk of Natural Ground Nests in an Agricultural Landscape

Nest predation risk generally increases nearer forest-field edges in agricultural landscapes. However, few studies test whether differences in edge contrast (i.e. hard versus soft edges based on vegetation structure and height) affect edge-related predation patterns and if such patterns are related to changes in nest conspicuousness between incubation and nestling feeding. Using data on 923 nesting attempts we analyse factors influencing nest predation risk at different edge types in an agricultural landscape of a ground-cavity breeding bird species, the Northern Wheatear (Oenanthe oenanthe). As for many other bird species, nest predation is a major determinant of reproductive success in this migratory passerine. Nest predation risk was higher closer to woodland and crop field edges, but only when these were hard edges in terms of ground vegetation structure (clear contrast between tall vs short ground vegetation). No such edge effect was observed at soft edges where adjacent habitats had tall ground vegetation (crop, ungrazed grassland). This edge effect on nest predation risk was evident during the incubation stage but not the nestling feeding stage. Since wheatear nests are depredated by ground-living animals our results demonstrate: (i) that edge effects depend on edge contrast, (ii) that edge-related nest predation patterns vary across the breeding period probably resulting from changes in parental activity at the nest between the incubation and nestling feeding stage. Edge effects should be put in the context of the nest predator community as illustrated by the elevated nest predation risk at hard but not soft habitat edges when an edge is defined in terms of ground vegetation. These results thus can potentially explain previously observed variations in edge-related nest predation risk