Abstract Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems. EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher). Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research.

AbstractHuman–elephant conflict is growing in Africa as human populations and development increases, creating disturbance to elephant habitats. Beehive fences have been trialed as a coexistence tool with some success but all studies have looked at small sample sizes over a short time period. Our study analyses the behavior of African elephants (Loxodonta africana) that approached a network of beehive fence protected farms in two conflict villages over 9 years next to Tsavo East National Park. We compare differences in elephant raids and beehive occupation rates annually, during a drought, and during peak crop production seasons. Out of 3999 elephants approaching our study farms 1007 elephants broke the beehive fence and entered the protected farm areas (25.18%). This was significantly less than the 2649 encounters where elephants remained either outside the farm boundary or broke into the control farms (66.24%). A further 343 elephants entered the farm by walking through a gap at the end of a fence (8.56%). The annual beehive fence break‐through rates averaged 23.96% (±SE 3.15) resulting in a mean of 76.04% elephants deterred from beehive fences protected farm plots. Over six peak crop growing seasons the beehive fences kept between 78.3% and 86.3% of elephants out of the farms and crops. The beehive fences produced one ton of honey sold for $2250; however, a drought caused a 75% reduction in hive occupation rates and honey production for 3 years after negatively impacting honey profits and the effectiveness of the fences. Beehive fences are very effective at reducing up to 86.3% of elephant crop‐raids during peak crop seasons after good rainfall, but any increase in elephant habitat disturbance or the frequency and duration of droughts could reduce their effectiveness as a successful coexistence tool.

Translocation of elephants is used to mitigate human-elephant conflict in Asia and Africa. However, few studies investigate how translocations affect the movements and social behaviour of individuals following their release, which may have important implications for whether translocated animals survive and succeed. Using GPS-tracking data, we explored movements of five translocated bull elephants (Loxodonta africana) moved to Tsavo, Kenya, and compared them with five resident bull elephants. Position data was collected hourly for 1 year (March 2018-March 2019), and analysed to investigate home range, displacement rates, problematic behaviour and group size. Of the five translocated elephants, three were illegally killed and one continued to break fences and raid crops. Only one elephant stayed away from human settlement. We found group size and composition to be significantly different, with translocated elephants observed in smaller groups with no female elephant interactions. All elephants showed variation in home ranges and displacement rates, but differences were not significant between resident and translocated elephant groups. For future translocations, we recommend careful consideration of elephant social systems, elephant age, timing, release site and proximity to human settlements that might create human-elephant conflict. This will improve chance for success of such high-stake and expensive translocations.

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localized and dispersed among data owners and databases. Here, we synthesized data from several research groups and conservation NGOs and present the first release of the Landscape Dynamics (landDX) spatial-temporal database, covering ~30,000 km2 of southern Kenya. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions, to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management, and for local and governmental spatial planning.

The value of art in Science is undisputed. From DaVinci’s drawings came a plethora of inventions, most notable is arguably his “aerial screw” which is highly suggestive of the helicopter we know today (Da Vinci, 1894), and the night skies of Charles Mezzier who, through his drawings documented countless formerly undocumented celestial bodies (Messier, 1781). In these and many other cases great art has undoubtedly gone on to aid science. Within the field of conservation the benefits of art are more nuanced. Recently art in its modern forms has been proven to be impactful in terms of attitudes to nature with multiple nature documentaries being empirically proven to have a range of impacts (Silk et al., 2021, Jones et al., 2019), from knowledge gains not translating into reduction in plastic usage following the viewing of Blue Planet II (Dunn et al., 2020) to Blackfish causing a decrease in the market value of Seaworld (Boissat et al., 2021). Animal imagery including photos and drawings as well as movies have also been shown to generally improve attitudes to nature (Thomas-Walters et al., 2020). But does art really have a place in modern day conservation where more species than ever before are on the brink of extinction due to human activities (Barnosky et al., 2011)?

The ability to locate essential resources is a critical step for wildlife translocated into novel environments. Understanding this process of exploration is highly desirable for management that seeks to resettle wildlife, particularly as translocation projects tend to be expensive and have a high potential for failure. African savannah elephants ( Loxodonta africana ) are very mobile and rely on large areas especially in arid environments, and are translocated for differing management and conservation objectives. Thus, research into how translocated elephants use the landscape when released may both guide elephant managers and be useful for translocations of other species that adjust their movement to social and ecological conditions. In this study, we investigated the movement of eight GPS tracked calves (translocated in three cohorts) following their soft release into a 107 km 2 fenced wildlife sanctuary in northern Kenya and compared their movement with that of five tracked wild elephants in the sanctuary. We describe their exploration of the sanctuary, discovery of water points, and activity budgets during the first seven, 14, and 20 months after release. We explored how patterns are affected by time since release, ecological conditions, and social factors. We found that calves visited new areas of the sanctuary and water points during greener periods and earlier post-release. Social context was associated with exploration, with later release and association with wild elephants predictive of visits to new areas. Wild elephants tended to use a greater number of sites per 14-day period than the released calves. Activity budgets determined from hidden Markov models (including the states directed walk, encamped, and meandering) suggested that released calves differed from wild elephants. The first two cohorts of calves spent a significantly greater proportion of time in the directed walk state and a significantly lower proportion of time in the encamped state relative to the wild elephants. Our results represent a step forward in describing the movements of elephant orphan calves released to the wild following a period of profound social disruption when they lost their natal family and were rehabilitated with other orphan calves under human care. We discuss the implications of the elephant behavior we observed for improving release procedures and for defining success benchmarks for translocation projects.

This field note is to invite our colleagues to peer review and test a new illustrated Human-Elephant Coexistence (HEC) Toolbox that is being developed in Kenya by Save the Elephants (STE) under the organizations’ mission to secure a future for elephants and to develop a tolerant relationship between humans and elephants. Through presenting the first edition here (Fig. 1), we are inviting our elephant colleagues and community leaders from across the African savannah elephant range States to provide feedback, or any corrections, on the tools, as well as sharing content for additional methods not yet represented. By publishing our process and methods for how we are compiling this encyclopaedia of HEC tools and this novel approach to the peer review process, we hope to provide a transparent process to gauging the validity of the methods presented. This is particularly important because some of the technical advice around the conflict reduction tools presented are not published formally in the scientific literature.

This management piece documents the outcome of an elephant translocation from Isiolo to Tsavo East National Park, Kenya in November 2021. The translocation aimed to reduce human-elephant conflict in the area and to prevent any retaliation toward the elephants. This was achieved by removing the ‘problem elephant group’, however the aim of resettling the elephants in Tsavo was not achieved, as the group fragmented and some swiftly moved far outside release site. Two elephants, which we were able to monitor through satellite collars, exhibited homing behaviour and both left Tsavo East National Park within 1-7 weeks of being released. If translocation continues to be the method of choice for problem elephants, there is a need for thorough planning and sound science to inform future operations, which should include collaring of each individual. Trained personnel and substantial budgeting for post release monitoring, and any potential conflict-reduction interventions, are therefore key management considerations for ensuring the health and wellbeing of translocated elephants in the future. In the long-term, focusing mitigation management on a larger number of habitual crop-raiders will have more impact and be a more effective approach for elephant managers. This could involve better spatial land-use planning, maintenance of corridors between protected areas, negative conditioning tactics and maintenance and upgrading of barriers. Ce document relatif à la gestion des éléphants rend compte du bilan de la translocation de plusieurs sujets depuis Isiolo jusqu’au parc national de Tsavo Est au Kenya en novembre 2021. L’objectif était de réduire les conflits humains-éléphants dans la zone et d’éviter toute forme de représailles de la part des habitants. Les « éléphants problématiques » ont donc été délocalisés, mais l’ambition initiale de les établir dans Tsavo Est n’a pu être finalisée du fait de la fragmentation du groupe après la remise en liberté et de certains éléments s’étant rapidement déplacés loin du site de lâcher. Deux sujets, que nous avons pu suivre grâce à leur collier GPS, ont montré un comportement instinctif de retour vers leur habitat précédent et tous deux ont quitté le parc national de Tsavo Est dans les sept semaines suivant leur introduction. Si la méthode de la translocation continue d’être privilégiée pour les éléphants problématiques, il sera nécessaire de s’appuyer sur une planification rigoureuse et des données scientifiques solides pour les prochaines opérations, ainsi que sur la mise en place de colliers émetteurs sur chacun des individus. Du personnel formé et un budget substantiel, pour la post-introduction des animaux et les interventions potentielles de réduction des conflits, sont donc les clefs pour une gestion de qualité et pour assurer le bien-être et la bonne santé des éléphants transférés à l’avenir. À long terme, il convient d’accentuer les interventions d’atténuation envers un plus grand nombre d’éléphants habitués à piller les cultures, afin d’avoir un réel impact et une approche plus efficace pour les personnes chargées de leur gestion. Cela peut se traduire par une meilleure planification de l’usage des terres, l’entretien des couloirs biologiques entre les zones protégées, des tactiques de conditionnement négatif et la maintenance ou l’amélioration des barrières.

This paper addresses the problem of identifying individual animals in images based on extracting and matching contours, focusing in particular on the trailing edges of humpback whale flukes and the outline of the ears of African savanna elephants. A coarse-grained FCNN is learned to isolate the contour in an image, and a fine-grained FCNN is learned to provide more precise boundary information. The latter is trained by generating synthetic boundaries from coarse, easily-extracted training data, avoiding tedious manual effort. An A* algorithm extracts the final contour, which is converted to set of digital curvature descriptors and matched against a database of descriptors using local-naive Bayes nearest neighbors. We show that using the learned fine-grained FCNN produces more accurate contours than using image gradients for fine localization, especially for elephant ears where the boundaries are primarily texture. Matching using contours extracted using the fine-grained FCNN improves top-1 accuracy from 80% to 85% for flukes and 78% to 84% for ears.

In aerial wildlife counts, human observers often fail to detect animals. We conducted a multi-species sample-count in Tsavo National Park, Kenya, with traditional rear-seat-observers (RSOs) and an automated ‘oblique-camera-count’ (OCC) imaging system to compare estimates of 23 wildlife species derived from these two survey methods. An aerial Total Count of elephant, buffalo and giraffe, conducted a month previously, provided a further comparison. In the Tsavo Core (9560km2), which harbours 80% of Tsavo’s elephants, the OCC system acquired 81 000 images for interpretation, of which 67 000 were obtained in parallel with RSO-counting along 3004km of flight line. The Tsavo outer blocks (24 171km2) were surveyed using the OCC system without RSOs to acquire a further 84 000 images. A random sample of 11 553 images were re-interpreted to derive species-specific probabilities of detection and correction factors. Using ‘Jolly II’, non-parametric and Bayesian analyses, and applying correction factors, we demonstrate that the RSOs did not detect 14% of elephants, 60% of giraffe, 48% of zebra and 66% of the large antelopes. For comparison, the Total Count observers did not detect 27% of elephant, 33% of buffalo, 57% of giraffe and 85% of carcasses. The OCC method raises the elephant population estimate to 16 681±4047 (95% cl) from the 12 722 counted in the Total Count (Z=1.917, p=.0276). These results suggest that RSO-based methods have significantly undercounted wildlife populations. To align with improved counting methods, previous results need to be re-calibrated.