Does landscape connectivity shape local and global social network structure in white-tailed deer?

Individual-level patterns of resource selection do not predict hotspots of contact

Contact among animals is crucial for various ecological processes, including social behaviors, disease transmission, and predator-prey interactions. However, the distribution of contact events across time and space is heterogeneous, influenced by environmental factors and biological purposes. Previous studies have assumed that areas with abundant resources and preferred habitats attract more individuals and, therefore, lead to more contact. To examine the accuracy of this assumption, we used a use-available framework to compare landscape factors influencing the location of contacts between wild pigs (Sus scrofa) in two study areas in Florida and Texas (USA) from those influencing non-contact space use. We employed a contact-resource selection function (RSF) model, where contact locations were defined as used points and locations without contact as available points. By comparing outputs from this contact RSF with a general, population-level RSF, we assessed the factors driving both habitat selection and contact. We found that the landscape predictors (e.g., wetland, linear features, and food resources) played different roles in habitat selection from contact processes for wild pigs in both study areas. This indicated that pigs interacted with their landscapes differently when choosing habitats compared to when they encountered other individuals. Consequently, relying solely on the spatial overlap of individual or population-level RSF models may lead to a misleading understanding of contact-related ecology. Our findings challenge prevailing assumptions about contact and introduce innovative approaches to better understand the ecological drivers of spatially explicit contact. By accurately predicting the spatial distribution of contact events, we can enhance our understanding of contact based ecological processes and their spatial dynamics.

Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus)

BACKGROUND

Dispersal is a fundamental process to animal population dynamics and gene flow. In white-tailed deer (WTD; Odocoileus virginianus), dispersal also presents an increasingly relevant risk for the spread of infectious diseases. Across their wide range, WTD dispersal is believed to be driven by a suite of landscape and host behavioral factors, but these can vary by region, season, and sex. Our objectives were to (1) identify dispersal events in Wisconsin WTD and determine drivers of dispersal rates and distances, and (2) determine how landscape features (e.g., rivers, roads) structure deer dispersal paths.

METHODS

We developed an algorithmic approach to detect dispersal events from GPS collar data for 590 juvenile, yearling, and adult WTD. We used statistical models to identify host and landscape drivers of dispersal rates and distances, including the role of agricultural land use, the traversability of the landscape, and potential interactions between deer. We then performed a step selection analysis to determine how landscape features such as agricultural land use, elevation, rivers, and roads affected deer dispersal paths.

RESULTS

Dispersal predominantly occurred in juvenile males, of which 64.2% dispersed, with dispersal events uncommon in other sex and age classes. Juvenile male dispersal probability was positively associated with the proportion of the natal range that was classified as agricultural land use, but only during the spring. Dispersal distances were typically short (median 5.77 km, range: 1.3-68.3 km), especially in the fall. Further, dispersal distances were positively associated with agricultural land use in potential dispersal paths but negatively associated with the number of proximate deer in the natal range. Lastly, we found that, during dispersal, juvenile males typically avoided agricultural land use but selected for areas near rivers and streams.

CONCLUSION

Land use-particularly agricultural-was a key driver of dispersal rates, distances, and paths in Wisconsin WTD. In addition, our results support the importance of deer social environments in shaping dispersal behavior. Our findings reinforce knowledge of dispersal ecology in WTD and how landscape factors-including major rivers, roads, and land-use patterns-structure host gene flow and potential pathogen transmission.

Context-dependent group size: effects of population density, habitat, and season

Group size can vary in relation to population density, habitat, and season. Habitat and season may also interact with population density and affect group size through varying foraging benefits of social aggregation in different ecological contexts. We tested the hypothesis that group size varies across ecological contexts, including population density, habitat type, and season, for woodland caribou (Rangifer tarandus) in ten herds over 25 years in Newfoundland, Canada. We predicted that group size would increase as a function of population density. Based on the foraging benefits of social aggregation, we predicted larger groups as habitat openness increased because open areas tend to have higher quality foraging resources. We predicted larger groups during winter when foraging resources are covered in snow because caribou and other social animals exploit social information about the location of foraging resources. In contrast to our prediction, group size decreased as a function of population density. In support of our prediction, group size was larger in winter than calving and summer, and we found that group size increased with habitat openness in some, but not all, cases. Patterns of animal grouping are context-dependent and the additive effect of different ecological contexts on variation in group size informs our understanding of the implicit trade-offs between competition, predation risk, and profitability of forage.

Circuitscape in Julia: Empowering Dynamic Approaches to Connectivity Assessment

The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the capacity of their analytic tools to handle these datasets. Technology developments in software and high-performance computing are rapidly emerging in many fields, but uptake within conservation may lag, as our tools or our choice of computing language can constrain our ability to keep pace. We recently updated Circuitscape, a widely used connectivity analysis tool developed by Brad McRae and Viral Shah, by implementing it in Julia, a high-performance computing language. In this initial re-code (Circuitscape 5.0) and later updates, we improved computational efficiency and parallelism, achieving major speed improvements, and enabling assessments across larger extents or with higher resolution data. Here, we reflect on the benefits to conservation of strengthening collaborations with computer scientists, and extract examples from a collection of 572 Circuitscape applications to illustrate how through a decade of repeated investment in the software, applications have been many, varied, and increasingly dynamic. Beyond empowering continued innovations in dynamic connectivity, we expect that faster run times will play an important role in facilitating co-production of connectivity assessments with stakeholders, increasing the likelihood that connectivity science will be incorporated in land use decisions.

Variability in the estimation of ungulate group sizes complicates ecological inference

Foundational work has examined adaptive social behavior in animals in relation to the costs and benefits of group living. Within this context, a "group" of animals represents an organizational unit that is integral to the study of animal ecology and evolution.Definitions of animal group sizes are often subjective with considerable variability within and across species. However, investigations of both the extent and implications of such variability in the estimation of animal group sizes are currently lacking.Selecting ungulates as a case study, we conducted a literature review to assess prevailing practices used to determine group sizes among terrestrial Cetartiodactyla and Perissodactyla. Via this process, we examined group size definitions for 61 species across 171 peer-reviewed studies published between 1962 and 2018.These studies quantified group sizes via estimation of ungulate aggregations in space and time. Spatial estimates included a nearest neighbor distance ranging from 1.4 m to 1,000 m, and this variation was partially explained by a weak positive correlation (|r| = .4, p < .003) with the body size of the ungulate research subjects. The temporal extent over which group size was estimated was even broader, ranging from three minutes to 24 hr.The considerable variability in ungulate group size estimation that we observed complicates efforts to not only compare and replicate studies but also to evaluate underlying theories of group living. We recommend that researchers: (a) clearly describe the spatiotemporal extents over which they define ungulate group sizes, (b) highlight foundational empirical and ecological rationale for these extents, and (c) seek to align such extents among individual species so as to facilitate cross-system comparisons of ungulate group size dynamics. We believe an integrative approach to ungulate group size estimation would readily facilitate replication, comparability, and evaluation of competing hypotheses examining the tradeoffs of animal sociality.

Transmission on empirical dynamic contact networks is influenced by data processing decisions

Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network. In this study, we examine how four processing decisions, including temporal sampling window (TSW), spatial threshold of contact (SpTh), minimum contact duration (MCD), and temporal aggregation (daily or hourly) influence the information content of contact data (indicated by changes in entropy) as well as disease transmission model dynamics. We found that changes made to information content by processing decisions translated to significant impacts to the transmission dynamics of disease models using the contact data. In particular, we found that SpTh had the largest independent influence on information content, and that some output metrics (R₀, time to peak infection) were more sensitive to changes in information than others (epidemic extent). These findings suggest that insights gained from transmission modeling using dynamic contact data can be influenced by processing decisions alone, emphasizing the need to carefully consideration them prior to using contact-based models to conduct analyses, compare different datasets, or inform policy decisions.

Social networks: a tool for assessing the impact of perturbations on wildlife behaviour and implications for pathogen transmission

Wildlife are increasingly subject to perturbations, which can impact pathogen transmission and lead to disease emergence. While a myriad of factors influence disease dynamics in wildlife, behaviour is emerging as a major influence. In this review, we examine how perturbations alter the behaviour of individuals and how, in turn, disease transmission may be impacted, with a focus on the use of network models as a powerful tool. There are emerging hypotheses as to how networks respond to different types of perturbations. The broad effects of perturbations make predicting potential outcomes and identifying mitigation opportunities for disease emergence critical; yet, the current paucity of data makes identification of underlying trends difficult. Social network analysis facilitates a mechanistic approach to how perturbation-induced behavioural changes result in shifts in pathogen transmission. However, the field is still developing, and future work should strive to address current deficits. There is particular need for empirical data to support modelling predictions and increased inclusion of pathogen monitoring in network studies.