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New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology. This study presents a deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine resolution satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types.

Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very‐high‐resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye‐1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state‐of‐the‐art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data – the highest resolution satellite imagery commercially available. We compare the performance accuracy of the CNN against human accuracy generating high comparable detection performance.

Rhinoceroses face severe threats from poaching, habitat fragmentation, and ongoing habitat degradation. Monitoring rhinoceros across the vast, often inaccessible landscapes they inhabit is challenging. In this study, we assess the feasibility of detecting white rhinoceroses using very high-resolution (33-36 cm) satellite imagery acquired over the world’s largest private rhinoceros reserve in South Africa using a YOLO-based object detection model (YOLOv12x). We test whether synthetic imagery enhances model performance, whether rhinoceroses can be reliably distinguished from elephants in satellite imagery, and whether synthetically generated rhinoceroses are visually distinguishable from real ones by human annotators. We achieve an average precision (AP) of 0.65 in detection accuracy with synthetic augmentation yielding a marginal improvement. This study provides a demonstration of monitoring rhinos using this approach and introduces an open-access dataset to support the development and testing of new models. The aim is to facilitate effective monitoring of rhinos across the vast landscapes they inhabit. Developing new detection techniques can strengthen conservation and recovery initiatives, including translocations, assessment of breeding program success, and evaluation of anti-poaching efforts.

Conflicts between the interests of biodiversity conservation and other human activities pose a major threat to natural ecosystems and human well‐being, yet few methods exist to quantify their intensity and model their dynamics. We develop a categorization of conflict intensity based on the curve of conflict, a model originally used to track the escalation and deescalation of armed conflicts. Our categorization assigns six intensity levels reflecting the discourse and actions of stakeholders involved in a given conflict, from coexistence or collaboration to physical violence. Using a range of case studies, we demonstrate the value of our approach in quantifying conflict trends, estimating transition probabilities between conflict stages, and modeling conflict intensity as a function of relevant covariates. By taking an evidence‐based approach to quantifying stakeholder behavior, the proposed framework allows for a better understanding of the drivers of conservation conflict development across a diverse range of socioecological scenarios.

Animals have played a vital role in every stage of space exploration, from early sub-orbital flights to contemporary missions. New physiological and psychological challenges arise with plans to venture deeper into the solar system. Advances in chimeric and knockout animal models, along with genetic modification techniques have enhanced our ability to study the effects of microgravity in greater detail. However, increased investment in the purposeful design of habitats and payloads, as well as in AI-enhanced behavioral monitoring in orbit can better support the ethical and effective use of animals in deep space research.

Background There is a rich body of literature addressing the topic of illegal hunting of wild terrestrial mammals. Studies on this topic have risen over the last decade as species are under increasing risk from anthropogenic threats. Sub-Saharan Africa contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered. However, the spatial distribution of illegal hunting incidences is not well documented. To address this knowledge gap, the systematic map presented here aims to answer three research questions: (1) What data are available on the spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa in relation to environmental and anthropogenic correlates i.e. proximity to roads, water bodies, human settlement areas, different land tenure arrangements and anti-poaching ranger patrol bases? (2) Which research methodologies have primarily been used to collect quantitative data and how comparable are these data? (3) Is there a bias in the research body toward particular taxa and geographical areas? Methods Systematic searches were carried out across eight bibliographic databases; articles were screened against pre-defined criteria. Only wild terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature (IUCN) whose geographical range falls in Sub-Saharan Africa and whose threat assessment includes hunting and trapping were included. To meet our criteria, studies were required to include quantitative, spatially explicit data. In total 14,325 articles were screened at the level of title and abstract and 206 articles were screened at full text. Forty-seven of these articles met the pre-defined inclusion criteria. Results Spatially explicit data on illegal hunting are available for 29 species in 19 of the 46 countries that constitute Sub-Saharan Africa. Data collection methods include GPS and radio tracking, bushmeat household and market surveys, data from anti-poaching patrols, hunting follows and first-hand monitoring of poaching signs via line transects, audio and aerial surveys. Most studies have been conducted in a single protected area exploring spatial patterns in illegal hunting with respect to the surrounding land. Several spatial biases were detected. Conclusions There is a considerable lack of systematically collected quantitative data showing the distribution of illegal hunting incidences and few comparative studies between different tenure areas. The majority of studies have been conducted in a single protected area looking at hunting on a gradient to surrounding village land. From the studies included in the map it is evident there are spatial patterns regarding environmental and anthropogenic correlates. For example, hunting increases in proximity to transport networks (roads and railway lines), to water sources, to the border of protected areas and to village land. The influence of these spatial features could be further investigated through meta-analysis. There is a diverse range of methods in use to collect data on illicit hunting mainly drawing on pre-existing law enforcement data or researcher led surveys detecting signs of poaching. There are few longitudinal studies with most studies representing just one season of data collection and there is a geographical research bias toward Tanzania and a lack of studies in Central Africa.

Background Over the last two decades there has been an increase in the demand for land in Sub Saharan Africa, particularly from foreign agribusiness investment to provide food for an increasing human population. The majority of land outside of protected areas in sub-Saharan Africa is under customary tenure. Due to poor land administration in the region, communities living in undocumented land areas tend to be at greater risk of eviction from increasing liberalisation of land markets. To prevent local displacement and disturbance to investment caused by land disputes tenure clarification is growing in importance on national and international agendas. Land conversion can fragment wildlife habitat while reducing the suitable range areas of terrestrial mammal populations on the continent. Simultaneously illegal hunting is on the rise for a wide variety of taxa driven by a demand for food and income from the sale of animal products. To enable a better understanding of how land tenure arrangements impact upon spatial variations in illegal hunting, this protocol sets out the parameters for an evidence map which will collate and analyse the spatially explicit quantitative evidence that exists showing the effect of land tenure on illegal hunting of endangered terrestrial mammals in sub-Saharan Africa. Sub-Saharan Africa is the region of focus as it contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature. Taking stock of what methods have been used to gather data and where evidence exists can guide future research in this area while informing conservation interventions. Methods This evidence map will compare: (1) data availability on the spatial distribution of illicit hunting of endangered terrestrial mammals across different land tenure regimes in sub-Saharan Africa; (2) research methodologies that have primarily been used to collect quantitative data on illegal hunting and comparability of existing data; (3) preferences in the research body toward particular taxa and geographical areas, (4) the evidence map will provide an analysis on the influence other environmental and anthropogenic determinants that influence the spatial distribution of illicit hunting incidences, e.g., proximity to roads, water bodies, range patrol bases etc. Eight academic databases and numerous organisation repositories will be searched for relevant studies by three authors. Double screening will be carried out on all articles to locate studies that meet the specified inclusion criteria, for inclusion studies must contain spatially explicit quantitative data on illegal hunting of endangered terrestrial mammals as defined by the International Union for the Conservation of Nature. Relevant information from studies will be extracted to a custom-made extraction form. The resulting map will consist of a narrative synthesis, descriptive statistics and a heat map in the form of a matrix. By providing an overview of the evidence base the resulting map can inform future meta-analyses by showing where there is sufficient comparable data while guiding conservation interventions by indicating geographical areas where species are most at risk.

The African savannah ecosystem is populated by nomadic pastoralists who herd livestock in the day and corral them at night in temporary enclosures, called bomas, to protect them. The number and distribution of bomas on the savannah is important from an ecological perspective and may have a significant impact on wildlife movement. However, no study has yet examined this relationship. Here, using very high‐resolution satellite imagery from two time periods, we quanitified changes in boma distribution and density across an area of 3377 km2 in the Laikipia‐Samburu ecosystem of northern Kenya between 2011 and 2019. To assess wildlife movement in relation to bomas, we used a GPS data set on African bush elephant Loxodonta africana movement from 27 collared matriarchs representing herds of 9–15, covering 112 467 hourly GPS fixes over 31 months between 2018 and 2020. Our results showed a more than 46% increase in the total number of human‐built structures between 2011 and 2019, the majority of which were bomas, representing a 21.9% increase in human‐modified land area. Elephants readily adjusted their foraging habits and itineraries in this habitat shared with humans, who were also nomadic in space and time. Assessing the night–day activity ratio, we found elephants move more nocturnally when in closer proximity to bomas, particularly during the dry season. This temporal separation means elephants avoid the times humans are active in and around bomas while still accessing required resources—water and forage. The temporal shift was stronger during the dry season when shared resources are scarce. Using daily travel distance as a metric, we show elephants moved further in closer proximity to bomas which was likely linked to the need to travel between forage patches. Given the rise in human settlements, understanding the consequences of animals' behavioral adjustments is critical to understand the long‐term population viability of elephant populations. The African savannah ecosystem is populated by nomadic pastoralists who corral livestock at night in temporary enclosures, called bomas, to protect them. The number and distribution of bomas in the savannah is important from an ecological perspective. However, no studies have yet examined the spatial‐temporal dynamics of bomas and their relationship to wildlife. This study quantified the number and distribution of bomas over a large landscape in the Laikipia‐Samburu ecosystem of northern Kenya and investigated their impact on elephant movement using satellite remote sensing techniques.

Historically, natural history museums have collected and preserved specimens to provide data on the occurrence and distribution of wildlife populations. Zoologists still track animals by recording footprints, collecting dung and spoor and observing, recording and quantifying behaviour from the ground. However, these traditional observational techniques allow only a few populations to be monitored at once at limited spatial scales and disturbance from the ground can disrupt observation of natural behaviour. We are now in a golden age of technological advances and are able to remotely monitor and track wildlife via a variety of electronic sensors. Significant questions remain about how best to methodologically apply these new technologies for the purposes of wildlife monitoring. In this thesis, I consider challenges of using Earth observation satellites and unmanned aerial vehicles (UAVs) to track wildlife and understand movement in relation to the expanding human footprint and anthropogenic risk. Specifically: (i) I collate and analyse spatially explicit data on the distribution of illegal hunting incidences via a systematic map. I show that hunting increases in proximity to roads, water bodies, and human settlement areas and there is a considerable lack of systematically collected quantitative data. (ii) I investigate acoustic disturbance to understand anthrophony from the species perspective. I create a mitigation method applied in the case of UAV noise using species weighted audiograms (iii) I test whether very high-resolution satellite imagery and machine learning can be used to automate the detection of African elephants in vast heterogeneous landscapes. This is achieved presenting a new method to monitor elephants (iv) I record the spatial relationship of African elephants in relation to the human footprint using GPS tracking data and satellite imagery. I show elephants readily adapt their foraging habits and itineraries, spatially and temporally in relation to human settlement. Accurate and up-to-date data is vital for effective wildlife conservation planning. Remote sensing technologies offer enhanced capabilities to understand the spatial relationship between wildlife and the increasing human footprint. This body of work contributes to the global wildlife conservation effort by devising methods that can enable more reliable data collection at larger spatial scales.

The Great Wildebeest Migrationin the Serengeti-Mara ecosystem is a globally iconic wildlife phenomenon that supports the health and biodiversity of the region by supporting predator populations, regulating herbivore densities, and driving nutrient cycling. This study presents the first AI-powered satellite survey, using two deep learning-based models (U-Net and YOLOv8) to detect and count wildebeest over more than 4,000 km² across two consecutive years in August 2022 and 2023 with F1 scores reaching 0.830 (Precision: 0.832, Recall: 0.838). The satellite-based results show fewer than 600,000 individuals—approximately half the widely cited estimate of 1.3 million wildebeest, which has remained largely unchanged since the 1970s. While some variation may arise from differences in spatial and temporal coverage between survey methods, the satellite approach employs rigorously validated AI models with demonstrated accuracy. Rather than undermining previous methods, this discrepancy underscores the importance of using independent and complementary monitoring tools to refine population estimates and improve our understanding of wildebeest movement dynamics.