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 20days 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.

Natural selection favors individuals with the highest inclusive fitness (i.e., total number of descendants). In cases where one sex is more productive, one or both parents may maximize their inclusive fitness by investing in the offspring of the more prolific sex. Such preferential production can lead to skewed sex ratios at various life history stages, including at birth, resulting in secondary sex ratio bias. Several competing hypotheses have been proposed to explain observed variation in secondary sex ratios including Fisher's frequency dependence and two hypotheses related to maternal condition: Trivers-Willard and the local resource hypotheses. Although it has been shown that maternal condition can influence the number of offspring produced in white-tailed deer, there is no consensus as to which of the hypotheses drives sex ratio bias in wild populations. Using a spatiotemporally extensive dataset of pregnant white-tailed deer from Mississippi, USA, we examined fetal sex ratio in relation to the Fisherian frequency-dependence hypothesis and hypotheses related to maternal condition. While there was a male-sex ratio bias in pregnant females that reduced in intensity with the number of offspring, there was no support for condition-related hypotheses. Instead, secondary sex ratios for white-tailed deer in Mississippi were nearly consistent with Fisherian frequency dependence. Our findings add to the body of literature on secondary sex biases in white-tailed deer and help inform sex bias ratios for a southern population of a cervid of management importance in the US.

Habitat conservation planning for wintering waterfowl in North America uses estimates of waterfowl energy requirements to assess the ability of regional landscapes to support populations. However, because the spatial and temporal configuration of resources can influence an individual animal's use of the landscape, there may be benefits to considering the availability of energy from food within individual home ranges on developing conservation planning. This possibility was investigated for female Mallard Anas platyrhynchos wintering in the Mississippi Alluvial Valley using: (1) published energy values for different landcover types (quantified as duck energy days; DEDs/ha), (2) maps of landcover and water availability, and (3) winter home range estimates created using location data from 128 birds fitted with VHF radio transmitters. Following current methods used for regional assessments of food resources available for birds, landcover types were first transformed into their corresponding DED values, and the amount of energy considered potentially accessible to Mallard within their home ranges was then limited by accounting for water availability and DED decay rates. Relatively energy-rich landcover types, such as moist-soil wetlands and croplands, were less likely to be accessible given water coverage. Moreover, a large proportion of Mallard locations were in areas that provided no apparent energy value. Most (> 90%), but not all, Mallard home ranges surpassed the birds' minimum winter energy needs (i.e. 123 DEDs). We suggest that waterfowl habitat conservation planning should consider an individual bird, home-range approach for DED assessments, and use those assessments to: (1) begin examining potential gaps in landscape water coverage, (2) ensure that multiple resource patches are accessible to individual birds, and (3) provide the mixture of landcover types (including those used for other life history needs), required by focal waterfowl species.

Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence. Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to identify the unique signal characteristics associated with each behavior. In contrast, unsupervised approaches aggregate unlabeled signals into groups based purely on signal similarity but, without additional information, do not identify specific behaviors. In this paper, we propose a probabilistic framework for interpreting uncertainty in machine learning techniques-the probability profile-and demonstrate how to post hoc identify behaviors within signal groups. We assess model performance using a matrix-based measure of dissimilarity. We used a Random Forest (RF) and a clustered self-organizing map (CSOM) for comparison and demonstrate the use of a behavioral profile for each using a data set of high-frequency accelerometer and magnetometer data collected from 7 captive wild pigs (Sus scrofa) moving in a 1 ha outdoor enclosure. We found that the RF had more discrimination than the CSOM which had fewer clusters associated with high probabilities of a single behavior (>50%). The leave-p-out cross validation statistic of the probability matrix (<(L-1)over bar>) indicated that there was an average maximum dissimilarity of 20% and 65% between the training and test data sets for the RF and CSOM methods, respectively. Using a probability profile to describe groups predicted from machine learning allows the variation and error inherent in behavioral prediction to be incorporated directly into the model to better reflect the nuances of behavior derived from accelerometer and/or magnetometer signals. We discuss the data requirements of this framework, demonstrate its application to field data, highlight critical assumptions and caveats, and examine how it may be used to generate new ecological inference.

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.

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.

The United States, particularly the southern portion, has recently suffered drastic population expansion of wild pigs causing destruction of prime farmland. An associated concern, which has been understudied, is the potential transfer of nutrients and pathogens to surface water. This study aimed to identify the abiotic and biotic impacts of captive wild pigs on water quality, including nutrients, fecal indicator and pathogenic bacteria, and antimicrobial resistance. Overall, the study demonstrated that wild pigs harbored Salmonella spp., Campylobacter spp., Escherichia coli, and Clostridium perfringens, which were found in water runoff collected directly beneath the hog paddock, often 2 log10 greater than above-paddock levels. However, the impacts to downstream water quality were limited, perhaps because of a relatively large riparian buffer between the paddock and surface water. A higher rate of ammonium concentration changes over time was detected in the runoff water below the paddock; additionally, microbial releases detected in runoff were also time dependent, possibly associated with increasing pig numbers. Antibiotic resistance was generally not associated with the wild pigs. Antibiotic resistance genes were found in upstream as well as downstream surface water, suggesting that nonpoint sources of microbial contamination were present. Interestingly, intI1 levels were greater in below-paddock runoff by nearly 2 log10 . Overall, it appears that wild pigs potentially pose a threat to water quality but only if they have direct access to the water. Pathogen, fecal indicator bacteria, and some nutrient release were significantly associated with wild pigs, but riparian buffers limited water quality impairment.

The Journal of Wildlife ManagementVolume 83, Issue 6 p. 1474-1474 Book Review Animal Locomotion (second edition). Andrew A. Biewener and Sheila N. Patek. 2018. Oxford University Press, Oxford, U.K. 240 pp. $95.00 hardcover. ISBN: 978-019874316. Jane E. Dentinger, Jane E. Dentinger Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS, 39762 USASearch for more papers by this authorGarrett M. Street, Garrett M. Street Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS, 39762 USASearch for more papers by this author Jane E. Dentinger, Jane E. Dentinger Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS, 39762 USASearch for more papers by this authorGarrett M. Street, Garrett M. Street Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS, 39762 USASearch for more papers by this author First published: 18 July 2019 https://doi.org/10.1002/jwmg.21713Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume83, Issue6August 2019Pages 1474-1474 RelatedInformation

Although increasing atmospheric nitrous oxide (N2O) has been linked to nitrogen loading, predicting emissions remains difficult, in part due to challenges in disentangling diverse N2O production pathways. As coastal ecosystems are especially impacted by elevated nitrogen, we investigated controls on N2O production mechanisms in intertidal sediments using novel isotopic approaches and microsensors in flow-through incubations. Here we show that during incubations with elevated nitrate, increased N2O fluxes are not mediated by direct bacterial activity, but instead are largely catalysed by fungal denitrification and/or abiotic reactions (e.g., chemodenitrification). Results of these incubations shed new light on nitrogen cycling complexity and possible factors underlying variability of N2O fluxes, driven in part by fungal respiration and/or iron redox cycling. As both processes exhibit N2O yields typically far greater than direct bacterial production, these results emphasize their possibly substantial, yet widely overlooked, role in N2O fluxes, especially in redox-dynamic sediments of coastal ecosystems.