Large mammal behavioral defenses induced by the cues of human predation

Large mammals respond to human hunting via proactive and reactive responses, which can induce subsequent nonconsumptive effects (NCEs). Thus, there is evidence that large mammals exhibit considerable behavioral plasticity in response to human hunting risk. Currently, however, it is unclear which cues of human hunting large mammals may be responding to. We conducted a literature review to quantify the large mammal behavioral responses induced by the cues of human hunting. We detected 106 studies published between 1978 and 2022 of which 34 (32%) included at least one measure of cue, typically visual (n = 26 of 106, 25%) or auditory (n = 11 of 106, 10%). Space use (n = 37 of 106, 35%) and flight (n = 31 of 106, 29%) were the most common behavioral responses studied. Among the 34 studies that assessed at least one cue, six (18%) measured large mammal behavioral responses in relation to proxies of human hunting (e.g. hunting site or season). Only 14% (n = 15 of 106) of the studies quantified an NCE associated with an animal's response to human hunting. Moreover, the association between cues measured and antipredator behaviors is unclear due to a consistent lack of controls. Thus, while human hunting can shape animal populations via consumptive effects, the cues triggering these responses are poorly understood. There hence remains a need to link cues, responses, NCEs, and the dynamics of large mammal populations. Human activities can then be adjusted accordingly to prevent both overexploitation and unintended NCEs in animal populations.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles

BACKGROUND

Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements.

METHODS

We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic.

RESULTS

Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence.

CONCLUSIONS

Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Wind-energy development alters pronghorn migration at multiple scales

Migration is a critical behavioral strategy necessary for population persistence and ecosystem functioning, but migration routes have been increasingly disrupted by anthropogenic activities, including energy development. Wind energy is the world's fastest growing source of electricity and represents an important alternative to hydrocarbon extraction, but its effects on migratory species beyond birds and bats are not well understood. We evaluated the effects of wind-energy development on pronghorn migration, including behavior and habitat selection, to assess potential effects on connectivity and other functional benefits including stopovers. We monitored GPS-collared female pronghorn from 2010 to 2012 and 2018 to 2020 in south-central Wyoming, USA, an area with multiple wind-energy facilities in various stages of development and operation. Across all time periods, we collected 286 migration sequences from 117 individuals, including 121 spring migrations, 123 fall migrations, and 42 facultative winter migrations. While individuals continued to migrate through wind-energy facilities, pronghorn made important behavioral adjustments relative to turbines during migration. These included avoiding turbines when selecting stopover sites in spring and winter, selecting areas farther from turbines at a small scale in spring and winter, moving more quickly near turbines in spring (although pronghorn moved more slowly near turbines in the fall), and reducing fidelity to migration routes relative to wind turbines under construction in both spring and fall. For example, an increase in distance to turbine from 0 to 1 km translated to a 33% and 300% increase in the relative probability of selection for stopover sites in spring and winter, respectively. The behavioral adjustments pronghorn made relative to wind turbines could reduce the functional benefits of their migration, such as foraging success or the availability of specific routes, over the long term.

Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States

Vegetation phenology and productivity play a crucial role in surface energy balance, plant and animal distribution, and animal movement and habitat use and can be measured with remote sensing metrics including start of season (SOS), peak instantaneous rate of green-up date (PIRGd), peak of season (POS), end of season (EOS), and integrated vegetation indices. However, for most metrics, we do not yet understand the agreement of remotely sensed data products with near-surface observations. We also need summaries of changes over time, spatial distribution, variability, and consistency in remote sensing dataset metrics for vegetation timing and quality. We compare metrics from 10 leading remote sensing datasets against a network of PhenoCam near-surface cameras throughout the western United States from 2002 to 2014. Most phenology metrics representing a date (SOS, PIRGd, POS, and EOS), rather than a duration (length of spring, length of growing season), better agreed with near-surface metrics but results varied by dataset, metric, and land cover, with absolute value of mean bias ranging from 0.38 (PIRGd) to 37.92 days (EOS). Datasets had higher agreement with PhenoCam metrics in shrublands, grasslands, and deciduous forests than in evergreen forests. Phenology metrics had higher agreement than productivity metrics, aside from a few datasets in deciduous forests. Using two datasets covering the period 1982–2016 that best agreed with PhenoCam metrics, we analyzed changes over time to growing seasons. Both datasets exhibited substantial spatial heterogeneity in the direction of phenology trends. Variability of metrics increased over time in some areas, particularly in the Southwest. Approximately 60% of pixels had consistent trend direction between datasets for SOS, POS, and EOS, with the direction varying by location. In all ecoregions except Mediterranean California, EOS has become later. This study comprehensively compares remote sensing datasets across multiple growing season metrics and discusses considerations for applied users to inform their data choices.