Multi-mode movement decisions across widely ranging behavioral processes.

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

First evidence of neuronal connections between specific parts of the periaqueductal gray (PAG) and the rest of the brain in sheep: placing the sheep PAG in the circuit of emotion.

The periaqueductal gray (PAG) is a mesencephalic brain structure organised in subdivisions with specific anatomical connections with the rest of the brain. These connections support the different PAG functions and especially its role in emotion. Mainly described in territorial and predatory mammals, examination of the PAG connections suggests an opposite role of the ventral and the dorsal/lateral PAG in passive and active coping style, respectively. In mammals, the organisation of PAG connections may reflect the coping style of each species. Based on this hypothesis, we investigated the anatomical connections of the PAG in sheep, a gregarious and prey species. Since emotional responses expressed by sheep are typical of active coping style, we focused our interest on the dorsal and lateral parts of the PAG. After injection of fluorogold and fluororuby, the most numerous connections occurred with the anterior cingulate gyrus, the anterior hypothalamic region, the ventromedial hypothalamic nucleus and the PAG itself. Our observations show that the sheep PAG belongs to the neuronal circuit of emotion and has specific parts as in other mammals. However, unlike other mammals, we observed very few connections between PAG and either the thalamic or the amygdalar nuclei. Interestingly, when comparing across species, the PAG connections of sheep were noticeably more like those previously described in other social species, rabbits and squirrel monkeys, than those in territorial species, rats or cats.

Forest productivity mitigates human disturbance effects on late-seral prey exposed to apparent competitors and predators.

Primary production can determine the outcome of management actions on ecosystem properties, thereby defining sustainable management. Yet human agencies commonly overlook spatio-temporal variations in productivity by recommending fixed resource extraction thresholds. We studied the influence of forest productivity on habitat disturbance levels that boreal caribou - a threatened, late-seral ungulate under top-down control - should be able to withstand. Based on 10 years of boreal caribou monitoring, we found that adult survival and recruitment to populations decreased with landscape disturbance, but increased with forest productivity. This benefit of productivity reflected the net outcome of an increase in resources for apparent competitors and predators of caribou, and a more rapid return to the safety of mature conifer forests. We estimated 3-fold differences in forest harvesting levels that caribou populations could withstand due to variations in forest productivity. The adjustment of ecosystem provisioning services to local forest productivity should provide strong conservation and socio-economic advantages.

Group or ungroup - moose behavioural response to recolonization of wolves.

BACKGROUND: Predation risk is a primary motivator for prey to congregate in larger groups. A large group can be beneficial to detect predators, share predation risk among individuals and cause confusion for an attacking predator. However, forming large groups also has disadvantages like higher detection and attack rates of predators or interspecific competition. With the current recolonization of wolves (Canis lupus) in Scandinavia, we studied whether moose (Alces alces) respond by changing grouping behaviour as an anti-predatory strategy and that this change should be related to the duration of wolf presence within the local moose population. In particular, as females with calves are most vulnerable to predation risk, they should be more likely to alter behaviour. METHODS: To study grouping behaviour, we used aerial observations of moose (n = 1335, where each observation included one or several moose) inside and outside wolf territories. RESULTS: Moose mostly stayed solitary or in small groups (82% of the observations consisted of less than three adult moose), and this behavior was independent of wolf presence. The results did not provide unequivocal support for our main hypothesis of an overall change in grouping behaviour in the moose population in response to wolf presence. Other variables such as moose density, snow depth and adult sex ratio of the group were overall more influential on grouping behaviour. However, the results showed a sex specific difference in social grouping in relation to wolf presence where males tended to form larger groups inside as compared to outside wolf territories. For male moose, population- and environmentally related variables were also important for the pattern of grouping. CONCLUSIONS: The results did not give support for that wolf recolonization has resulted in an overall change in moose grouping behaviour. If indeed wolf-induced effects do exist, they may be difficult to discern because the effects from moose population and environmental factors may be stronger than any change in anti-predator behaviour. Our results thereby suggest that caution should be taken as to generalize about the effects of returning predators on the grouping behaviour of their prey.

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

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.