Local and Landscape‐Level Environmental Conditions Drive Habitat Selection Across Terrestrial Mammal Species - Björn H. Franke; Aafke M. Schipper; Tal Avgar; Luca Börger; Nilanjan Chatterjee; Thomas Müller; Brian J. Smith; Briana Abrahms; Abdullahi H. Ali; Nina Attias; Hattie L. A. Bartlam‐Brooks; Floris M. van Beest; et al. (Global Ecology and Biogeography, 2026)
Aim Animal movements are a fundamental process affecting communities and ecosystems. Quantifying habitat selection across species and habitats is key for understanding how animals respond to environmental change. Currently, we lack comparative studies that examine how habitat selection varies across species traits and landscapes. We aim to quantify global patterns of habitat selection to help understand the fundamental drivers of movement behaviour. Location Global. Time Period Contemporary. Major Taxa Studied Terrestrial mammals. Methods We estimated selection coefficients for terrain ruggedness, vegetation productivity, human population density and distance to roads of individual terrestrial mammals through step‐selection analysis of 1344 GPS tracks across 48 species. We quantified intra‐ and interspecific variation and tested whether selection coefficients were associated with species traits and habitat availability. Results We observe an overall avoidance of roads and areas of high human population density as well as rugged terrain, with a large proportion of individuals selecting for areas of intermediate NDVI. However, we also found large intraspecific variation in habitat selection and show that this variation is predicted by local and landscape‐level environmental conditions rather than species traits. Individuals in more remote areas exhibited weaker functional responses to human presence than those in more disturbed areas. Avoidance of rugged terrain is also context‐dependent, with stronger avoidance when local ruggedness is high. The only exception to the observed intraspecific variability is consistent species‐level responses to road proximity. Main Conclusions Our findings contribute to the understanding of habitat selection by terrestrial mammals, showing that selection is largely shaped by environmental conditions and that animals exhibit high plasticity in their responses. Our results also provide further evidence for the significant impact of roads on animal movement. These insights can help us to understand the potential effects of environmental change on the behaviour of mammal species around the world.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Plasticity in elk migration timing is a response to changing environmental conditions - Gregory J. M. Rickbeil; Jerod A. Merkle; Greg Anderson; M. Paul Atwood; Jon P. Beckmann; Eric K. Cole; Alyson B. Courtemanch; Sarah Dewey; David D. Gustine; Matthew J. Kauffman; Douglas E. McWhirter; Tony Mong; et al. (Global Change Biology, 2019)
Migration is an effective behavioral strategy for prolonging access to seasonal resources and may be a resilient strategy for ungulates experiencing changing climatic conditions. In the Greater Yellowstone Ecosystem ( GYE ), elk are the primary ungulate, with approximately 20,000 individuals migrating to exploit seasonal gradients in forage while also avoiding energetically costly snow conditions. How climate‐induced changes in plant phenology and snow accumulation are influencing elk migration timing is unknown. We present the most complete record of elk migration across the GYE , spanning 9 herds and 414 individuals from 2001 to 2017, to evaluate the drivers of migration timing and test for temporal shifts. The timing of elk departure from winter range involved a trade‐off between current and anticipated forage conditions, while snow melt governed summer range arrival date. Timing of elk departure from summer range and arrival on winter range were both influenced by snow accumulation and exposure to hunting. At the GYE scale, spring and fall migration timing changed through time, most notably with winter range arrival dates becoming almost 50 days later since 2001. Predicted herd‐level changes in migration timing largely agreed with observed GYE ‐wide changes—except for predicted winter range arrival dates which did not reflect the magnitude of change detected in the elk telemetry data. Snow melt, snow accumulation, and spring green‐up dates all changed through time, with different herds experiencing different rates and directions of change. We conclude that elk migration is plastic, is a direct response to environmental cues, and that these environmental cues are not changing in a consistent manner across the GYE . The impacts of changing elk migration timing on predator–prey dynamics, carnivore–livestock conflict, disease ecology, and harvest management across the GYE are likely to be significant and complex.

An evaluation of density‐dependent and density‐independent influences on population growth rates in Weddell seals - Jay J. Rotella; William A. Link; James D. Nichols; Gillian L. Hadley; Robert A. Garrott; Kelly M. Proffitt (Ecology, 2009)
Much of the existing literature that evaluates the roles of density‐dependent and density‐independent factors on population dynamics has been called into question in recent years because measurement errors were not properly dealt with in analyses. Using state–space models to account for measurement errors, we evaluated a set of competing models for a 22‐year time series of mark–resight estimates of abundance for a breeding population of female Weddell seals ( Leptonychotes weddellii ) studied in Erebus Bay, Antarctica. We tested for evidence of direct density dependence in growth rates and evaluated whether equilibrium population size was related to seasonal sea‐ice extent and the Southern Oscillation Index (SOI). We found strong evidence of negative density dependence in annual growth rates for a population whose estimated size ranged from 438 to 623 females during the study. Based on Bayes factors, a density‐dependence‐only model was favored over models that also included environmental covariates. According to the favored model, the population had a stationary distribution with a mean of 497 females (SD = 60.5), an expected growth rate of 1.10 (95% credible interval = 1.08–1.15) when population size was 441 females, and a rate of 0.90 (95% credible interval = 0.87–0.93) for a population of 553 females. A model including effects of SOI did receive some support and indicated a positive relationship between SOI and population size. However, effects of SOI were not large, and including the effect did not greatly reduce our estimate of process variation. We speculate that direct density dependence occurred because rates of adult survival, breeding, and temporary emigration were affected by limitations on per capita food resources and space for parturition and pup‐rearing. To improve understanding of the relative roles of various demographic components and their associated vital rates to population growth rate, mark–recapture methods can be applied that incorporate both environmental covariates and the seal abundance estimates that were developed here. An improved understanding of why vital rates change with changing population abundance will only come as we develop a better understanding of the processes affecting marine food resources in the Southern Ocean.

Variation in offspring sex ratio among individual Weddell seal (Leptonychotes weddellii) females of different quality - Kelly M. Proffitt; Robert A. Garrott; Jay J. Rotella (Behavioral Ecology and Sociobiology, 2008)

Long‐term evaluation of body mass at weaning and postweaning survival rates of Weddell seals in Erebus Bay, Antarctica - Kelly M. Proffitt; Robert A. Garrott; Jay J. Rotella (Marine Mammal Science, 2008)
Variability in juvenile survival rate is expected to be an important component of the dynamics of long‐lived animal populations. Across a range of species, individual variation in juvenile body mass has been shown to be an important cause of variation in fates of juveniles. Our goal in this paper was to estimate age‐specific apparent survival rates for Weddell seals ( Leptonychotes weddellii ) in Erebus Bay, Antarctica, and to investigate hypotheses about relationships between body mass at weaning and apparent survival rate for juveniles. Mark–resighting models found average apparent juvenile survival rate (survival from weaning to age 3) was similar between males and females, and revealed positive relationships between body mass at weaning and apparent juvenile survival rate. The effects of body mass on juvenile survival rate differed between the sexes and the relationship between body mass and survival rate was stronger in males. These results indicate that the magnitude of energy transferred from mother to pup during lactation likely has important consequences on offspring survival rate and maternal fitness.

Using form analysis techniques to improve photogrammetric mass‐estimation methods - Kelly M. Proffitt; Robert A. Garrott; Jay J. Rotella; Subhash Lele (Marine Mammal Science, 2008)
Numerical characterization of animal body forms using elliptical Fourier decomposition may be a useful analytic technique in a variety of marine mammal investigations. Using data collected from the Weddell seal ( Leptonychotes weddellii ), we describe the method of body form characterization using elliptical Fourier analysis and demonstrated usefulness of the technique in photogrammetric mass‐estimation modeling. We compared photogrammetric mass‐estimation models developed from (1) standard morphometric measurement covariates, (2) elliptical Fourier coefficient covariates, and (3) a combination of morphometric and Fourier coefficient covariates and found that mass‐estimation models employing a combination of morphometric measurements and Fourier coefficients outperformed models containing only one covariate type. Inclusion of Fourier coefficients in photogrammetric mass‐estimation models employing standard morphometric measurements reduced the width of the prediction interval by 24.4%. Increased precision of photogrammetric mass‐estimation models employing Fourier coefficients as model covariates may expand the range of ecological questions that can be addressed with estimated mass measurements.

Exploring Linkages between Abiotic Oceanographic Processes and a Top-trophic Predator in an Antarctic Ecosystem - Kelly M. Proffitt; Robert A. Garrott; Jay J. Rotella; Donald B. Siniff; J. Ward Testa (Ecosystems, 2007)

THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES - Kelly M. Proffitt; Robert A. Garrott; Jay J. Rotella; Jeff Banfield (Marine Mammal Science, 2007)
Development and application of photogrammetric mass‐estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression statistics and decreases model explanatory power. Thus, it is important to understand and account for prediction variance when addressing ecological questions that require use of estimated mass values. In a simulation study based on data collected from Weddell seals, we developed regression models of pup weaning mass as a function of maternal postparturition mass where maternal mass was directly measured and second where maternal mass was photogrammetrically estimated. We demonstrate that when estimated mass was used, the regression coefficient was biased toward zero and the coefficient of determination was 30% less than the value obtained when using maternal postparturition mass obtained from direct measurement. After applying bias correction procedures, however, the regression coefficient and coefficient of determination were within 2% of their true values. To effectively use photogrammetrically estimated masses, prediction variance should be understood and accounted for in all analyses. The methods presented in this paper are effective and simple techniques to explore and account for prediction variance.