A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

A test of the frost wave hypothesis in a temperate ungulate.

Growing evidence supports the hypothesis that temperate herbivores surf the green wave of emerging plants during spring migration. Despite the importance of autumn migration, few studies have conceptualized resource tracking of temperate herbivores during this critical season. We adapted the frost wave hypothesis (FWH), which posits that animals pace their autumn migration to reduce exposure to snow but increase acquisition of forage. We tested the FWH in a population of mule deer in Wyoming, USA by tracking the autumn migrations of n = 163 mule deer that moved 15-288 km from summer to winter range. Migrating deer experienced similar amounts of snow but 1.4-2.1 times more residual forage than if they had naïve knowledge of when or how fast to migrate. Importantly, deer balanced exposure to snow and forage in a spatial manner. At the fine scale, deer avoided snow near their mountainous summer ranges and became more risk prone to snow near winter range. Aligning with their higher tolerance of snow and lingering behavior to acquire residual forage, deer increased stopover use by 1 ± 1 day (95% CI) day for every 10% of their migration completed. Our findings support the prediction that mule deer pace their autumn migration with the onset of snow and residual forage, but refine the FWH to include movement behavior en route that is spatially dynamic.

Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates.

Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short-distance local movement state and a long-distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long-distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long-distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long-distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long-distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.

Migrating mule deer compensate en route for phenological mismatches.

Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. Using GPS movement data collected from 72 adult female deer over a 10-year duration, we study a population of mule deer (Odocoileus hemionus) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. We show that individual deer arrive at their summer range within an average 6-day window by adjusting movement speed and stopover use. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Our findings suggest that ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.

Wave-like Patterns of Plant Phenology Determine Ungulate Movement Tactics.

Animals exhibit a diversity of movement tactics [1]. Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called "green-wave surfing" [3-5]. Yet general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested an emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of green-up facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n = 1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine-tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology.