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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]

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.

Energy budgets in grazing cattle are notoriously difficult to study given complex environmental and physiological interactions between plants and animals, each operating according to individual grazing budgets. However, these synergistic energy pathways can be modeled using a series of mechanistic relationships describing forage resource distribution and animal behavior, each which can be observed with high levels of accuracy and precision using biometric sensors and remote sensing technology. The objective of this study is to demonstrate the coordination of data inputs into a coherent framework utilizing biometric and remote sensing technology. Crossbred steers (n = 10) were fitted with GPS and video tracking collars (Vectronic) and accelerometers (Wildbytes technologies), then managed for 286 days as part of a broader research grazing ecology project on pasture (~11.54 hectare) comprised of bermudagrass (Cynodon dactylon) and tall fescue (Festuca arundinacea) and equipped with an automated walk-over-weigh (TruTest) scale system. Distribution of forage quality was sampled monthly with a ½ m² quadrat using a 20 x 20-meter grid (n = 297) and remotely assessed via an unmanned aerial vehicle (Matrice 100) mounted hyperspectral camera (MicaSense) at 8 cm resolution. Animal behavior was predicted utilizing a trained randomforest prediction algorithm, and linked to geo-location and forage quality at the spatial-temporal scale in Program R. We then constructed animal ethogram, distribution of forage quality, and spatial-temporal distribution of animal behavior across the grazing landscape for a test period. We found steers allocated the greatest proportion of time to grazing and resting behaviors, 30 and 60% respectively, with the remainder dedicated to ruminating and walking behaviors. A diurnal, circadian rhythm in animal behavior demonstrates that steers grazed between 0900 and 1000 in the morning, and again between 1300 and 2000 in the evening, while resting behavior dominated the hours between 0300 and 0900, with a slight increase in grazing behavior noted at 0600, or approximately sunrise. Animal behavior linked forage availability showed steers selectively grazed areas with higher NDVI signatures, while seeking sources of water and cover to practice resting behavior. Given the need to develop robust models predicting forage intake and energy in grazing animals to improve sustainability and environmental metrics, animal tracking and biometric technologies offer a vital solution to improving management of extensively managed grazing livestock. [ABSTRACT FROM AUTHOR]

‘Species distribution modeling’ was recently ranked as one of the top five ‘research fronts’ in ecology and the environmental sciences by ISI's Essential Science Indicators, reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non‐linearity, and multicollinearity. Identifying habitat characteristics that are not well‐predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations. [ABSTRACT FROM AUTHOR]

Animal behavior plays a crucial role as an indicator of animal health and nutritional status and serves as an indicator of animal growth. The objective of this study was to build an ethogram describing behavior in grazing cattle. We collected video and accelerometer data from crossbred steers (n = 10) used as part of a larger grazing study on the HH Leveck Animal Research Center, Mississippi State, MS. Daily Diary accelerometers (Wildbyte® technologies, Swansea) were programmed to collect magnetometer and accelerometer data at 40 Hz and attached to the GPS collars fitted on the animals prior to their release into a 10-hectare pasture of Tall Fescue and Bermudagrass, overseeded with Annual Ryegrass. Automated camera traps (Bushnell Essential®) were synced with UTC time and programmed to record 30-second video clips when triggered. Approximately 387,000 accelerometer signals representing 161 minutes of behavior from 10 animals were recorded, and behavior classified according to 1 of 5 categories: traveling, foraging, resting, ruminating, and grooming (Kilgour et al., 2012). Categorized accelerometer data was used to train a random forest model (Liaw and Weiner, 2002) in Program R (R Core Team, 2020), which resulted in a model sensitivity of 0.97, 0.93, 0.90, 0.87, and 0.80 for Traveling, Foraging, Resting, Ruminating, and Grooming, respectively, and an overall model accuracy of 0.95. Behaviors were aggregated into behavior bouts, and a daily ethogram was calculated for March 2019. This revealed that the steers spent the most amount of time traveling, an average of 1,026 minutes per day. This behavior was followed, in the average length of time, by foraging and resting for 205 ± 52.8 minutes and 31.8 ± 28.2 minutes per day, respectively. These results indicate the ability to accurately build a behavioral ethogram for grazing cattle and warrant further study in future research and livestock management. [ABSTRACT FROM AUTHOR]

Body weight (BW) is a critical component for monitoring animal weight gain, body condition, nutritional status. Remote animal weighing systems facilitate frequent collection of animal BW, however, datasets often contain spurious data. The objective of this study was to describe the utility of using a remote Walk-over-Weigh system and subsequent methods for data cleaning. Beef steers (n = 10) were tagged with Electronic RFID tags (EID) in an improved pasture (~12.1 hectares) containing Bermuda and Tall Fescue and inter-seeded with Annual Ryegrass and grazed from Feb. - Dec. 2020. Static chute weights (n = 80) were collected monthly, and a WOW system placed by the water to remotely collect BW (n = 5,466). Data were first loaded into Program R and scanned for spurious data using each of 2 primary approaches, 1) the whole herd and individual means ± 1 standard deviation (SD) calculated daily or over the entire trial and 2) each of 3 data smoothing algorithms, which included a quadratic growth model, cubic splines, and polynomial regression. Then, data with spurious observations removed were paired with static chute weights and fitted to a linear model to measure accuracy (mean bias) and precision (R2) of WOW data. Whole herd mean ± 1SD and individual daily mean ± 1SD identified 1,204 and 1,516 spurious data, with mean bias of -12.46 and -15.37 KG and R2 of 0.90 and 0.68, respectively. Smoothing functions identified 1,707, 4,684, and 4,776 spurious points, with a mean bias of 13.61, -19.78, and 12.58 KG, and R2 of 0.94, 0.70, and 0.87 for quadratic growth models, cubic splines, and polynomial regression, respectively. These results indicate the utility of using a simple WOW system to collect data for measuring growth curves and using weight data in a real-time fashion to make management and marketing decisions. [ABSTRACT FROM AUTHOR]

Previous research in feedlot studies has demonstrated that cattle feeding behavior is driven by internal metabolic processes and external environmental stimuli and serves as an indicator of animal health, nutritional status, and growth and feed quality and availability. However, technology has only recently allowed measurement of foraging behavior in grazing cattle. Objectives of this study were to measure frequency and duration of foraging bouts, meals, and total distance traveled during meals in grazing steers. The study was conducted as part of a larger grazing study on a 10-hectare Bermudagrass and Tall Fescue pasture, overseeded with Annual Ryegrass, located at the HH Leveck Animal Research Center, Mississippi State, MS. Using tri-axial accelerometers and GPS information from 10 crossbred steers, we examined foraging and meal bout frequency and duration and distance and speed traveled per meal for the period of March 2019. Observed animal behavior was used to train a randomforest model to predict foraging behavior, with model accuracy and sensitivity of 0.95 and 0.93, respectively. We found individual foraging bouts occurred on average 2,849 bouts per day and took on average 5.0 ± 1.8 min (range: 3-9 min), and that steers fed on average 205 ± 52.8 min/day (range: 120-270 min/day). Steers had an average of 9.5 ± 2.9 meals/day, that took on average, 89.3 ± 93.9 min/meal (range: 0.5-938.5 min/meal). Steers traveled an average of 412.4 ± 93.9 meters per meal, with an average foraging speed between 0 and 0.63 m/s. Traveling distance while foraging was positively correlated with meal length (0.83, P < 0.01) and foraging speed (0.70, P < 0.01). These results show that cattle grazing behavior can be accurately quantified in grazing cattle and warrants further research to examine associations between animal efficiency and performance, forage quality, and pasture management. [ABSTRACT FROM AUTHOR]