Integrating population- and individual-level information in a movement model of Yellowstone bison

Spatial-social familiarity complements the spatial-social interface: evidence from Yellowstone bison

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'.

Parasitic infection increases risk-taking in a social, intermediate host carnivore

Toxoplasma gondii is a protozoan parasite capable of infecting any warm-blooded species and can increase risk-taking in intermediate hosts. Despite extensive laboratory research on the effects of T. gondii infection on behaviour, little is understood about the effects of toxoplasmosis on wild intermediate host behavior. Yellowstone National Park, Wyoming, USA, has a diverse carnivore community including gray wolves (Canis lupus) and cougars (Puma concolor), intermediate and definitive hosts of T. gondii, respectively. Here, we used 26 years of wolf behavioural, spatial, and serological data to show that wolf territory overlap with areas of high cougar density was an important predictor of infection. In addition, seropositive wolves were more likely to make high-risk decisions such as dispersing and becoming a pack leader, both factors critical to individual fitness and wolf vital rates. Due to the social hierarchy within a wolf pack, we hypothesize that the behavioural effects of toxoplasmosis may create a feedback loop that increases spatial overlap and disease transmission between wolves and cougars. These findings demonstrate that parasites have important implications for intermediate hosts, beyond acute infections, through behavioural impacts. Particularly in a social species, these impacts can surge beyond individuals to affect groups, populations, and even ecosystem processes.

Predation strongly limits demography of a keystone migratory herbivore in a recovering transfrontier ecosystem

Large herbivore migrations are imperiled globally; however the factors limiting a population across its migratory range are typically poorly understood. Zambia's Greater Liuwa Ecosystem (GLE) contains one of the largest remaining blue wildebeest (Connochaetes taurinus taurinus) migrations, yet the population structure, vital rates, and limiting factors are virtually unknown. We conducted a long-term demographic study of GLE wildebeest from 2012 to 2019 of 107 collared adult females and their calves, 7352 herd observations, 12 aerial population surveys, and concurrent carnivore studies. We applied methods of vital rate estimation and survival analysis within a Bayesian estimation framework. From herd composition observations, we estimated rates of fecundity, first-year survival, and recruitment as 68%, 56%, and 38% respectively, with pronounced interannual variation. Similar rates were estimated from calf-detections with collared cows. Adult survival rates declined steadily from 91% at age 2 years to 61% at age 10 years thereafter dropping more sharply to 2% at age 16 years. Predation, particularly by spotted hyena, was the predominant cause of death for all wildebeest ages and focused on older animals. Starvation only accounted for 0.8% of all unbiased known natural causes of death. Mortality risk differed substantially between wet and dry season ranges, reflecting strong spatio-temporal differences in habitat and predator densities. There was substantial evidence that mortality risk to adults was 27% higher in the wet season, and strong evidence that it was 45% higher in the migratory range where predator density was highest. The estimated vital rates were internally consistent, predicting a stable population trajectory consistent with aerial estimates. From essentially zero knowledge of GLE wildebeest dynamics, this work provides vital rates, age structure, limiting factors, and a plausible mechanism for the migratory tendency, and a robust model-based foundation to evaluate the effects of potential restrictions in migratory range, climate change, predator-prey dynamics, and poaching.

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1-7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

Migrating bison engineer the green wave

Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world's most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone's bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.

Functional attributes of ungulate migration: landscape features facilitate movement and access to forage

Long-distance migration by terrestrial mammals is a phenomenon critical to the persistence of populations, but such migrations are declining globally because of over-harvest, habitat loss, and movement barriers. Increasingly, there is a need to improve existing routes, mitigate route segments affected by anthropogenic disturbance, and in some instances, determine whether alternative routes are available. Using a hypothesis-driven approach, we identified landscape features associated with the primary functional attributes, stopovers and movement corridors, of spring migratory routes for mule deer in two study areas using resource selection functions. Patterns of selection for landscape attributes of movement corridors and stopovers mostly were similar; however, landscape features associated with movement corridors aligned better with areas that facilitated movement, whereas selection of stopovers was consistent with sites offering early access to spring forage. For movement corridors, deer selected for dry sites, low elevation, and low anthropogenic disturbance. For stopovers, deer selected for dry sites, with consistently early green-up across years, south-southwesterly aspects, low elevation, and low anthropogenic disturbance. Stopovers and movement corridors of a migratory route presumably promote different functions, but for a terrestrial migrant, patterns of habitat selection indicate that the same general habitat attributes may facilitate both movement and foraging in spring. Our findings emphasize the roles of topographical wetness, vegetation phenology, and anthropogenic disturbance in shaping use of the landscape during migration for this large herbivore. Avoiding human disturbance and tracking ephemeral forage resources appear to be a consistent pattern during migration, which reinforces the notion that movement during migration has a nutritional underpinning and disturbance potentially alters the net benefits of migration.

Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties

Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m³ ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m³ ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

Mitochondrial Genome Analysis Reveals Historical Lineages in Yellowstone Bison

Yellowstone National Park is home to one of the only plains bison populations that have continuously existed on their present landscape since prehistoric times without evidence of domestic cattle introgression. Previous studies characterized the relatively high levels of nuclear genetic diversity in these bison, but little is known about their mitochondrial haplotype diversity. This study assessed mitochondrial genomes from 25 randomly selected Yellowstone bison and found 10 different mitochondrial haplotypes with a haplotype diversity of 0.78 (± 0.06). Spatial analysis of these mitochondrial DNA (mtDNA) haplotypes did not detect geographic population subdivision (F_ST = -0.06, p = 0.76). However, we identified two independent and historically important lineages in Yellowstone bison by combining data from 65 bison (defined by 120 polymorphic sites) from across North America representing a total of 30 different mitochondrial DNA haplotypes. Mitochondrial DNA haplotypes from one of the Yellowstone lineages represent descendants of the 22 indigenous bison remaining in central Yellowstone in 1902. The other mitochondrial DNA lineage represents descendants of the 18 females introduced from northern Montana in 1902 to supplement the indigenous bison population and develop a new breeding herd in the northern region of the park. Comparing modern and historical mitochondrial DNA diversity in Yellowstone bison helps uncover a historical context of park restoration efforts during the early 1900s, provides evidence against a hypothesized mitochondrial disease in bison, and reveals the signature of recent hybridization between American plains bison (Bison bison bison) and Canadian wood bison (B. b. athabascae). Our study demonstrates how mitochondrial DNA can be applied to delineate the history of wildlife species and inform future conservation actions.

Disentangling the effects of climate, density dependence, and harvest on an iconic large herbivore's population dynamics

Understanding the relative effects of climate, harvest, and density dependence on population dynamics is critical for guiding sound population management, especially for ungulates in arid and semiarid environments experiencing climate change. To address these issues for bison in southern Utah, USA, we applied a Bayesian state-space model to a 72-yr time series of abundance counts. While accounting for known harvest (as well as live removal) from the population, we found that the bison population in southern Utah exhibited a strong potential to grow from low density (β0 = 0.26; Bayesian credible interval based on 95% of the highest posterior density [BCI] = 0.19-0.33), and weak but statistically significant density dependence (β1 = -0.02, BCI = -0.04 to -0.004). Early spring temperatures also had strong positive effects on population growth (Pfat1 = 0.09, BCI = 0.04-0.14), much more so than precipitation and other temperature-related variables (model weight > three times more than that for other climate variables). Although we hypothesized that harvest is the primary driving force of bison population dynamics in southern Utah, our elasticity analysis indicated that changes in early spring temperature could have a greater relative effect on equilibrium abundance than either harvest or. the strength of density dependence. Our findings highlight the utility of incorporating elasticity analyses into state-space population models, and the need to include climatic processes in wildlife management policies and planning.

Inferring behavioral states of grazing livestock from high-frequency position data alone

Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positions of grazing cows were collected at intervals of 20 seconds in six upland areas in Switzerland along with visual observations of animal behavior for comparison. A total of 87 linear and cumulative distance metrics and 15 turning angle metrics across multiple time steps were used to classify position data into the behavioral states of walking, grazing, and resting. Five random forest classification models, a linear discriminant analysis, a support vector machine, and a state-space model were evaluated. The most accurate classification of the observed behavioral states in an independent validation dataset was 83%, obtained using random forest with all available movement metrics. However, the state-specific accuracy was highly unequal (walking: 36%, grazing: 95%, resting: 58%). Random undersampling led to a prediction accuracy of 77%, with more balanced state-specific accuracies (walking: 68%, grazing: 82%, resting: 68%). The other evaluated machine-learning approaches had lower classification accuracies. The state-space model, based on distance to the preceding position and turning angle, produced a relatively low accuracy of 64%, slightly lower than a random forest model with the same predictor variables. Given the successful classification of behavioral states, our study promotes the more frequent use of global positioning data alone for animal behavior studies under the condition that data is collected at high frequency and complemented by context-specific behavioral observations. Machine-learning algorithms, notably random forest, were found very useful for classification and easy to implement. Moreover, the use of measures across multiple time steps is clearly necessary for a satisfactory classification.