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Social animals make behavioural decisions based on local habitat and conspecifics, as well as memorized past experience (i.e. 'familiarity') with habitat and conspecifics. Here, we develop a conceptual and empirical understanding of how spatial and social familiarity fit within the spatial–social interface—a novel framework integrating the spatial and social components of animal behaviour. We conducted a multi-scale analysis of the movements of GPS-collared plains bison (Bison bison, n = 66) residing in and around Yellowstone National Park, USA. We found that both spatial and social familiarity mediate how individuals respond to their spatial and social environments. For instance, individuals with high spatial familiarity rely on their own knowledge as opposed to their conspecifics, and individuals with high social familiarity rely more strongly on the movement of conspecifics to guide their own movement. We also found that fine-scale spatial and social phenotypes often scale up to broad-scale phenotypes. For instance, bison that select more strongly to align with their nearest neighbour have larger home ranges. By integrating spatial and social familiarity into the spatial–social interface, we demonstrate the utility of the interface for testing hypotheses, while also highlighting the pervasive importance of cognitive mechanisms in animal behaviour. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

Animals simultaneously navigate spatial and social environments, and their decision-making with respect to those environments constitutes their spatial (e.g. habitat selection) and social (e.g. conspecific associations) phenotypes. The spatial–social interface is a recently introduced conceptual framework linking these components of spatial and social ecology. The spatial–social interface is inherently scale-dependent, yet it has not been integrated with the rich body of literature on ecological scale. Here, we develop a conceptual connection between the spatial–social interface and ecological scale. We propose three key innovations that incrementally build upon each other. First, the use–availability framework that underpins a large body of literature in behavioural ecology can be used in analogy to the phenotype–environment nomenclature and is transferable across the spatial and social realms. Second, both spatial and social phenotypes are hierarchical, with nested components that are linked via constraints—from the top down—or emergent properties—from the bottom up. Finally, in both the spatial and social realms, the definitions of environment and phenotype depend on the focal scale of inquiry. These conceptual innovations cast our understanding of the relationships between social and spatial dimensions of animal ecology in a new light, allowing a more holistic understanding and clearer hypothesis development for animal behaviour. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population‐level occurrence distributions and precluding the use of low‐frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2‐h data. We then subsampled the 2‐h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12‐h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species‐specific range (mule deer: 400–1200 m2; elk: 600–1600 m2) successfully delineated migration corridors similar to the 2‐h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1000 m2 (elk) decreased the inflation of the 12‐h corridors relative to the 2‐h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe. [ABSTRACT FROM AUTHOR]

The green wave hypothesis (GWH) states that migrating animals should track or 'surf' high-quality forage at the leading edge of spring green-up. To index such high-quality forage, recent work proposed the instantaneous rate of green-up (IRG), i.e. rate of change in the normalized difference vegetation index over time. Despite this important advancement, no study has tested the assumption that herbivores select habitat patches at peak IRG. We evaluated this assumption using step selection functions parametrized with movement data during the green-up period from two populations each of bighorn sheep, mule deer, elk, moose and bison, totalling 463 individuals monitored 1-3 years from 2004 to 2014. Accounting for variables that typically influence habitat selection for each species, we found seven of 10 populations selected patches exhibiting high IRG-supporting the GWH. Nonetheless, large herbivores selected for the leading edge, trailing edge and crest of the IRG wave, indicating that other mechanisms (e.g. ruminant physiology) or measurement error inherent with satellite data affect selection for IRG. Our evaluation indicates that IRG is a useful tool for linking herbivore movement with plant phenology, paving the way for significant advancements in understanding how animals track resource quality that varies both spatially and temporally.

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. [ABSTRACT FROM AUTHOR]

Conserving migratory ungulates relies on the analysis of GPS collar data and associated maps of migration corridors to inform management and policy actions. Current methods for identifying migratory corridors use complex statistical models designed to account for movement uncertainty rather than estimating the amount of space required by animals to migrate. Furthermore, such methods can complicate conservation efforts by producing highly variable corridor widths and non‐contiguous corridors that do not fully connect seasonal ranges.To remedy, we propose an intuitive line buffer approach for delineating individual migration corridors that is simple to implement and focuses on the functional corridor widths needed by migratory ungulates.By buffering a line that connects successive GPS locations, we can delineate individual migration corridors with consistent widths that are robust to variable parameters (GPS fix rate, travel speed, tortuosity) and provide contiguous connection between seasonal ranges. Using a combination of expert knowledge, simulation and 10‐min GPS collar data collected from mule deer (Odocoileus hemionus) and pronghorn (Antilocapra americana), we suggest 400–600 m are reasonable estimates of functional migration corridor widths for individuals of those species.Synthesis and applications. Our line buffer approach is intended to simplify migration corridor delineation, improve transparency and encourage a broader discussion of functional corridor widths. These considerations help advance efforts to conserve habitat within migration corridors and prioritize conservation efforts within a single corridor or across multiple corridors. [ABSTRACT FROM AUTHOR]

Abstract: Identifying mechanisms that promote coexistence of sympatric species is important for predicting ecological effects of anthropogenic change. Many caribou (Rangifer tarandus (L., 1758)) populations are declining, and it is [...]