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Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well‐suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase. [ABSTRACT FROM AUTHOR]

Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time‐consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest‐entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north‐eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest‐entrances along the cliff with the colony. For this, we used three CNN architectures, U‐Net, ResUnet, and DeepLabv3. The U‐Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest‐entrances across the colony, with a mean absolute error of 2.7 nest‐entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. We show that the strong changes observed in the distribution of nest‐entrances are a measurable effect of a long record of human‐induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation. [ABSTRACT FROM AUTHOR]

The 'landscape of fear' concept offers valuable insights into wildlife behaviour, yet its practical integration into habitat management for conservation remains underexplored. In this study, conducted in the subtropical monsoon grasslands of Bardia National Park, Nepal, we aimed to bridge this gap through a multi‐year, landscape‐scale experimental investigation in Bardia National Park, Nepal. The park has the highest density of tigers (with an estimated density of ~7 individuals per 100 km2) in Nepal, allowing us to understand the effect of habitat management on predation risk and resource availability especially for three cervid species: chital (Axis axis), swamp deer (Rucervus duvaucelii) and hog deer (Axis porcinus). We used plots with varying mowing frequency (0–4 times per year), size (ranging from small: 49 m2 to large: 3600 m2) and artificial fertilisation type (none, phosphorus, nitrogen) to assess the trade‐offs between probable predation risk and resources for these cervid species, which constitute primary prey for tigers in Nepal. Our results showed distinct responses of these deer to perceived predation risk within grassland habitats. Notably, these deer exhibited heightened use of larger plots, indicative of a perceived sense of safety, as evidenced by the higher occurrence of pellet groups in the larger plots (mean = 0.1 pellet groups m−2 in 3600 m2 plots vs. 0.07 in 400 m2 and 0.05 in 49 m2 plots). Furthermore, the level of use by the deer was significantly higher in larger plots that received mowing and fertilisation treatments compared to smaller plots subjected to similar treatments. Of particular interest is the observation that chital and swamp deer exhibited greater utilisation of the centre (core) areas within the larger plots (mean = 0.21 pellet groups m−2 at the centre vs. 0.13 at the edge) despite the edge (periphery) also provided attractive resources to these deer. In contrast, hog deer did not display any discernible reaction to the experimental treatments, suggesting potential species‐specific variations in response to perceived predation risk arising from management interventions. Our findings emphasise the importance of a sense of security as a primary determinant of habitat selection for medium‐sized deer within managed grassland environments. These insights carry practical implications for park managers, providing a nuanced understanding of integrating the 'landscape of fear' into habitat management strategies. This study emphasises that the 'landscape of fear' concept can and should be integrated into habitat management to maintain delicate predator–prey dynamics within ecosystems. [ABSTRACT FROM AUTHOR]

Only a few studies on the nocturnal behavior of African ungulates exist so far, with mostly small sample sizes. For a comprehensive understanding of nocturnal behavior, the data basis needs to be expanded. Results obtained by observing zoo animals can provide clues for the study of wild animals and furthermore contribute to a better understanding of animal welfare and better husbandry conditions in zoos. The current contribution reduces the lack of data in two ways. First, we present a stand‐alone open‐source software package based on deep learning techniques, named Behavioral Observations by Videos and Images using Deep‐Learning Software (BOVIDS). It can be used to identify ungulates in their enclosure and to determine the three behavioral poses "Standing," "Lying—head up," and "Lying—head down" on 11,411 h of video material with an accuracy of 99.4%. Second, BOVIDS is used to conduct a case study on 25 common elands (Tragelaphus oryx) out of 5 EAZA zoos with a total of 822 nights, yielding the first detailed description of the nightly behavior of common elands. Our results indicate that age and sex are influencing factors on the nocturnal activity budget, the length of behavioral phases as well as the number of phases per behavioral state during the night while the keeping zoo has no significant influence. It is found that males spend more time in REM sleep posture than females while young animals spend more time in this position than adult ones. Finally, the results suggest a rhythm between the Standing and Lying phases among common elands that opens future research directions. [ABSTRACT FROM AUTHOR]

The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time‐consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity‐nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning‐based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual‐based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi‐field, and field conditions. [ABSTRACT FROM AUTHOR]

The description and analysis of animal behavior over long periods of time is one of the most important challenges in ecology. However, most of these studies are limited due to the time and cost required by human observers. The collection of data via video recordings allows observation periods to be extended. However, their evaluation by human observers is very time‐consuming. Progress in automated evaluation, using suitable deep learning methods, seems to be a forward‐looking approach to analyze even large amounts of video data in an adequate time frame.In this study, we present a multistep convolutional neural network system for detecting three typical stances of African ungulates in zoo enclosures which works with high accuracy. An important aspect of our approach is the introduction of model averaging and postprocessing rules to make the system robust to outliers.Our trained system achieves an in‐domain classification accuracy of >0.92, which is improved to >0.96 by a postprocessing step. In addition, the whole system performs even well in an out‐of‐domain classification task with two unknown types, achieving an average accuracy of 0.93. We provide our system at https://github.com/Klimroth/Video‐Action‐Classifier‐for‐African‐Ungulates‐in‐Zoos/tree/main/mrcnn%5fbased so that interested users can train their own models to classify images and conduct behavioral studies of wildlife.The use of a multistep convolutional neural network for fast and accurate classification of wildlife behavior facilitates the evaluation of large amounts of image data in ecological studies and reduces the effort of manual analysis of images to a high degree. Our system also shows that postprocessing rules are a suitable way to make species‐specific adjustments and substantially increase the accuracy of the description of single behavioral phases (number, duration). The results in the out‐of‐domain classification strongly suggest that our system is robust and achieves a high degree of accuracy even for new species, so that other settings (e.g., field studies) can be considered. [ABSTRACT FROM AUTHOR]