Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Abstract

BackgroundThe movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.MethodsWe obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.ResultsWe found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.ConclusionsThe analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-017-0105-1) contains supplementary material, which is available to authorized users.

Ecological drivers of African swine fever virus persistence in wild boar populations: Insight for control

“…We assumed that individuals could potentially contact other individuals in all directions each day, with a contact probability that decayed with distance. However, in reality, wild boar movements are biased toward habitat features and related individuals (Kay et al, ; Podgórski, Lusseau, et al, ). Although these movements would lead to temporal variation in the distance‐transmission probability relationship as we assumed, it may be that the particular locations of the movements (e.g., biased toward particular resources or conspecifics) are important to capture, rather than just the overall distance‐variation structure.…”

“…Males dispersed independently and females dispersed with their sisters. Although the dispersal of young wild boar leaving their maternal groups is the main source of long‐distance movement, the majority of individuals disperse in relatively short distances (1–3 km diameter) from an average home range (<5 km 2 ) and longer dispersals (5–30 km) are less common (Kay et al, ; Keuling, Lauterbach, Stier, & Roth, ; Podgórski, Scandura, et al, ; Prévot & Licoppe, ; Truvé & Lemel, ).…”

“…Each parameter had a uniform prior distribution as follows: frequency of introduction ϕ ~Unif(0,60), β d ~Unif(0.0001,1), β c ~Unif(0.0001,0.99), ρ h ~Unif(0.0005,0.1), ρ c ~Unif(0.0005,0.8), π ~Unif(0.1,1.5), θ ~Unif(0.5,6), λ ~Unif(0.1, 2.5), β wd ~Unif(0.01,1), and β wc ~Unif(0.001,1). Prior distribution ranges were informed by movement and contact data (Kay et al, ; Pepin et al, ; Podgórski et al, , ). As part of the parameter generation process we implemented the following constraints for each parameter set: β d > β c , β wd > β d , β wc > β c ; to further inform prior distributions with biologically realistic knowledge.…”

Exposure of a population of invasive wild pigs to simulated toxic bait containing biomarker: implications for population reduction

“…We laid 0.75 km 2 grids over each of the three areas. We used this grid size so that any wild pig living within the grids should encounter one or more bait sites per 0.75 km 2 , based on previous studies . Initially we overlaid 12–15 grid cells in each of the three baiting areas of Camp Bullis, and pre‐baited two or three sites within each grid cell using 11.3 kg of whole‐kernel corn at each site ( n = 80 bait sites total; Table ).…”

Potential effects of incorporating fertility control into typical culling regimes in wild pig populations

“…Territoriality of groups and solitary males could act to regulate density on the landscape to some degree [38–40]. Monthly and overall home range sizes are small (mean = 3.4 km 2 , standard deviation = 4.6; mean = 6.1 km 2 , standard deviation = 7.8), and average daily movement is rarely beyond 0.35 km (standard deviation = 0.34) [41]. Natal dispersal occurs primarily in males [38].…”