Search

Wildlife tagging provides critical insights into animal movement ecology, physiology, and behavior amid global ecosystem changes. However, the stress induced by capture, handling, and tagging can impact post-release locomotion and activity and, consequently, the interpretation of study results. Here, we analyze post-tagging effects on 1585 individuals of 42 terrestrial mammal species using collar-collected GPS and accelerometer data. Species-specific displacements and overall dynamic body acceleration, as a proxy for activity, were assessed over 20 days post-release to quantify disturbance intensity, recovery duration, and speed. Differences were evaluated, considering species-specific traits and the human footprint of the study region. Over 70% of the analyzed species exhibited significant behavioral changes following collaring events. Herbivores traveled farther with variable activity reactions, while omnivores and carnivores were initially less active and mobile. Recovery duration proved brief, with alterations diminishing within 4-7 tracking days for most species. Herbivores, particularly males, showed quicker displacement recovery (4 days) but slower activity recovery (7 days). Individuals in high human footprint areas displayed faster recovery, indicating adaptation to human disturbance. Our findings emphasize the necessity of extending tracking periods beyond 1 week and particular caution in remote study areas or herbivore-focused research, specifically in smaller mammals.

Given climate change threats to ecosystems, it is critical to understand the responses of species to warming. This is especially important in the case of apex predators since they exhibit relatively high extinction risk, and changes to their distribution could impact predator-prey interactions that can initiate trophic cascades. Here we used a combined analysis of animal tracking, remotely sensed environmental data, habitat modeling, and capture data to evaluate the effects of climate variability and change on the distributional range and migratory phenology of an ectothermic apex predator, the tiger shark (Galeocerdo cuvier). Tiger sharks satellite tracked in the western North Atlantic between 2010 and 2019 revealed significant annual variability in the geographic extent and timing of their migrations to northern latitudes from ocean warming. Specifically, tiger shark migrations have extended farther poleward and arrival times to northern latitudes have occurred earlier in the year during periods with anomalously high sea-surface temperatures. A complementary analysis of nearly 40 years of tiger shark captures in the region revealed decadal-scale changes in the distribution and timing of shark captures in parallel with long-term ocean warming. Specifically, areas of highest catch densities have progressively increased poleward and catches have occurred earlier in the year off the North American shelf. During periods of anomalously high sea-surface temperatures, movements of tracked sharks shifted beyond spatial management zones that had been affording them protection from commercial fishing and bycatch. Taken together, these study results have implications for fisheries management, human-wildlife conflict, and ecosystem functioning.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost‐ and time‐efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone‐based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent‐based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems. [ABSTRACT FROM AUTHOR]

Untangling the abiotic and biotic feedback mechanisms driving animal movements and ranges is a core question in ecology, yet progress is hampered by inadequate modelling procedures. Here we show how a recently developed process‐based modelling approach, combining step‐selection functions and individual‐based models, enables a flexible method to infer movement drivers and multi‐scale emergent space use patterns. As a case study, we examine the movement behaviours of a highly invasive social generalist (wild pigs, Sus scrofa) in relation to conspecific space use and multiple land cover types in a complex agricultural landscape, showing that social interactions are predominantly more important to this species than selection for land cover. Thus, animal movement studies should not neglect the effects of sociality when inferring resource driven movements and, crucially, use multi‐scale techniques that incorporate movement processes to untangle drivers of animal space use. [ABSTRACT FROM AUTHOR]

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations. [ABSTRACT FROM AUTHOR]

Animals determine their daily movement trajectories in response to a network of ecological processes, including interactions with other organisms, their memories of previous events, and the changing environment. These combine to cause the emergent space use patterns observed over longer periods of time, such as a whole season. Understanding which processes cause these patterns to emerge, and how, requires a process‐based modelling approach. Individual‐based decisions can be described as a system of partial‐differential equations (PDEs) to produce a dynamic description of space use built from the underlying movement process. Here we combine PDE‐based models with step‐selection analysis to investigate the combined effects of three established ecological processes that partially shape movement and space use: 1) a heterogeneous environment; 2) the environmental markings of moving conspecifics; and 3) the memory of direct interactions with conspecifics. We apply this framework to a large GPS‐based dataset of white‐tailed deer Odocoileus virginianus in the southeastern US. We fit models at the population level to provide predictive models, then tailor these to fit individual deer. We specifically incorporate relationships between each possible pair of deer and define each animal's responses to their unique local environments using separate integrated step‐selection analyses. We show how individual movements and decisions yield emergent patterns in animal distributions, and we provide a full generalised description of the framework so that it may be applied to any species simultaneously responding to multiple potentially interacting stimuli (e.g. sociality, morphology, etc.). We found that the population of bucks had highly varied preferences for vegetation, but were shaping their space use in response to conspecific interactions, dependent on the individual relationships between two deer. We advocate for increased consideration of individual‐based movement rules as determinants of realized animal space use, and particularly how these affect emergent distributions of entire species. [ABSTRACT FROM AUTHOR]

Context. Predation risk, whether from large carnivores or humans, is a primary driver of movement and habitat selection for ungulates. Throughout much of the geographical range of white-tailed deer, predation risk during autumn and winter is driven by hunters; however, information about how deer respond to hunting pressure during a long hunting season is lacking. Aims. We aimed to determine how differences in daily temporal exposure to hunting risk affected habitat selection of adult male white-tailed deer. Methods. We deployed GPS collars on 42 adult male white-tailed deer during the 2017–2018 and 2018–2019 firearm hunting seasons in Mississippi, USA. GPS collars provided fine-scale spatial and temporal information regarding habitat use of our study area. Key results. We showed a mismatch between what hunters and deer selected; landscape characteristics selected most by hunters were least selected by deer during the day, which we believe has never been documented. Fine-scale temporal analysis showed that deer selected for some areas (food plots) as much as five times more often during the night, when no hunting risk was present, than during the day, when risk was present. Conclusions. Habitat selection by deer varies depending on the amount of risk that is present. Even in a high-risk heavily hunted area, adult male white-tailed deer can recognise external cues on the landscape, and shift habitat selection both spatially and temporally to favour areas that present less risk. Implications. This study, to our knowledge, provides the first evidence that white-tailed deer actively selected for habitat characteristics that hunters were not utilising. To minimise the effectiveness ofrisk-avoidance behaviours used by deer, we suggest hunters moderate temporal hunting effort across time to decrease the risk to which deer are exposed, thereby minimising the response by deer. [ABSTRACT FROM AUTHOR]

We provide a 5E structured-inquiry lesson so that students can learn more of the mathematics behind the logistic model of population biology. By using models and mathematics, students understand how population dynamics can be influenced by relatively simple changes in the environment.

Site-specific variation in relative habitat abundance and disturbance regimes may produce differences in habitat preferences of associated populations. An evaluation of the predictive power of habitat selection models across space would benefit our understanding of the reliability of models of selection and space use in predicting animal occurrence. We used presence-absence data from winter surveys of moose (Alces alces (L., 1758)) to estimate resource selection functions (RSFs) across two study sites using Far North Land Cover updated with recent disturbance from fire and timber harvest. Moose selected foraging habitat (e.g., deciduous land cover) and for increasing deciduous foliage cover (ΔNDVI, i.e., the difference in the normalized difference vegetation index). Snow depth negatively influenced habitat selection, likely due to increased predation risk and reduced movement and foraging efficiency. Models lost little predictive power when applied to another site based on comparison of receiver operating characteristic (ROC) curves. Our results corroborated the current body of knowledge concerning moose habitat selection, i.e., moose preferentially use forest stands dominated by deciduous species, but suggested that moose strongly avoided very recently disturbed areas. Minimal site-specific variation and ROC comparison suggests that RSFs may be extended into novel systems, given adequate consideration for habitat quality and abundance, thereby simplifying management needs of this important species. Key words: Alces alces, disturbance, habitat selection, logistic regression, management, moose, remote sensing, receiver operating characteristic, resource selection function. Les variations propres aux sites des regimes relatifs d'abondance et de perturbation de l'habitat peuvent produire differentes preferences en matiere d'habitat chez les populations associees. Une evaluation de la puissance predictive de modeles de selection d'habitat dans l'espace serait utile pour mieux comprendre la fiabilite des modeles de selection et d'utilisation de l'espace pour ce qui est de predire la presence d'animaux. Nous avons utilise des donnees de presence -absence tirees d'enquetes hivernales sur les orignaux (Alces alces (L., 1758)) pour estimer les fonctions de selection des ressources (FSR) dans deux sites d'etude en utilisant l'ensemble de donnees Couverture du territoire du Grand Nord mis a jour pour y inclure les perturbations recentes dues au feu et a la recolte du bois. Les choix des orignaux etaient axes sur des habitats d'approvisionnement (p. ex. couverture terrestre de feuillus) et une plus grande couverture de feuillage caduque (ΔNDVI, c.-a-d., la variation de l'indice de vegetation par difference normalisee). La profondeur de la neige avait une influence negative sur la selection d'habitat, probablement en raison du risque de predation accru et de la reduction de l'efficacite des deplacements et de l'approvisionnement. Les modeles perdaient peu de leur puissance predictive quand ils etaient appliques a un autre site sur la base de la comparaison de courbes de fonction d'efficacite du recepteur (FER). Nos resultats corroborent les connaissances actuelles sur la selection d'habitat par les orignaux, a savoir qu'ils utilisent preferentiellement des peuplements forestiers oU dominent des essences a feuilles caduques, mais indiqueraient qu'ils evitent fortement les zones recemment perturbees. Des variations propres aux sites tres limitees et la comparaison des FER donnent a penser que les FSR pourraient etre elargies a de nouveaux systemes en tenant adequatement compte de la qualite et de l'abondance des habitats, ce qui simplifierait les besoins de gestion pour cette importante espece. [Traduit par la Redaction] Mots-cles: Alces alces, perturbation, selection de l'habitat, regression logistique, gestion, orignal, teledetection, fonction d'efficacite du recepteur, fonction de selection des ressources., Introduction The foraging ecology of moose (Alces alces (L., 1758)) is well documented, and they spend much of their active time foraging across various habitat types (Belovsky 1981). While aquatic [...]