The common hippopotamus (Hippopotamus amphibius L.) is part of the animal species endangered because of multiple human pressures. Monitoring of species for conservation is then essential, and the development of census protocols has to be chased. UAV technology is considering as one of the new perspectives for wildlife survey. Indeed, this technique has many advantages but its main drawback is the generation of a huge amount of data to handle. This study aims at developing an algorithm for automatic count of hippos, by exploiting thermal infrared aerial images acquired from UAV. This attempt is the first known for automatic detection of this species. Images taken at several flight heights can be used as inputs of the algorithm, ranging from 38 to 155 meters above ground level. A Graphical User Interface has been created in order to facilitate the use of the application. Three categories of animals have been defined following their position in water. The mean error of automatic counts compared with manual delineations is +2.3% and shows that the estimation is unbiased. Those results show great perspectives for the use of the algorithm in populations monitoring after some technical improvements and the elaboration of statistically robust inventories protocols.

The common hippopotamus Hippopotamus amphibius L. is a vulnerable species that requires efficient methods to monitor its populations for conservation purposes. Rapid evolution of civil drones provides new opportunities but survey protocols still need development. This study aims to determine the optimal flight parameters for accurate population estimates. A second objective is to evaluate the effects of three environmental factors: wind speed, sun reflection and cloud cover. We estimated the population of two main hippo schools (Dungu and Wilibadi II) located in Garamba National Park in Democratic republic of Congo. Eight observers reviewed 252 photos taken over the Dungu school, representing a total of 2016 experimental units. A detection rate and a level of certainty were associated with each experimental unit, and five parameters were related to each count: flight height, three environmental parameters (sun reflection on water surface, cloud cover, and wind speed), and observers’ experience. Flight height reduced the observers’ confidence in their detection ability, rather than the detection itself. For accurate counts of large groups an average height of 150 m was shown to be a good compromise between animal detection without zooming in and the area covered in one frame. Wind speed had little influence on the counts, but it affected the performance of the UAS. Sun reflection reduced the detection rate of hippos and increased level of certainty, while cloud cover reduced detection rates slightly. Therefore, we recommend flying when the sun is still low on the horizon and when there is little cloud, or when cloud cover is light and even. This last point reinforces our recommendation for flights early in the day. The counts also showed large differences between groups of inexperienced and experienced observers. Experienced observers achieved better detection rates and were generally more confident in their detection. Experienced observers detected 86.5% of the hippos on average (confidence interval = ±0.76%). When applied to data from the second site, the detection was 84.3% (confidence interval = ±1.84%). Two correction factors were then calculated, as the inverse of the detection rate, based on the estimated number of hippos present during one flight (Factor 1) or in the general population respectively (Factor 2). Factor 2 especially was consistent with previous studies using traditional aerial counts (1.22 vs 1.25). Factor 2 was found to be appropriate for use by experienced observers. These results confirm the use of correction factor 2 for hippo surveys, regardless of the study site, as it accounts for hippo behavior. Optimum counting and cost efficiency were achieved with two trained observers counting 7 pictures. This study is a promising approach for routine surveys of the hippopotamus which is a species usually ignored in wildlife counts. Drone technology is expected to improve rapidly; therefore UAS could become a very useful and affordable survey tool for other species requiring specific monitoring.

Camera traps (CTs) have been increasingly used for wildlife monitoring worldwide. In the tropics, most CT inventories target wildlife‐friendly sites, and CTs are commonly placed towards wildlife trails. However, it has been argued that this placement strategy potentially provides biased results in comparison to more systematic or randomized approaches. Here, we investigated the impact of CT placement on the remotely sensed mammal diversity in a tropical forest in Gabon by comparing pairs of systematically placed and wildlife‐trail‐oriented CTs. Our survey protocol consisted of 15–17 sampling points arranged on a 2 km² grid and left for one month in the field. This protocol was replicated sequentially in four areas. Each sampling point comprised a CT pair: the ‘systematic CT’, installed at the theoretical point and systematically oriented towards the most uncluttered view; and the ‘trail CT’, placed within a 20‐m radius and facing a wildlife trail. For the vast majority of species, the detection probabilities were comparable between placements. Species average capture rates were slightly higher for trail‐based CTs, though this trend was not significant for any species. Therefore, the species richness and composition of the overall community, such as the spatial distribution patterns (from evenly spread to site‐restricted) of individual species, were similarly depicted by both placements. Opting for a systematic orientation ensures that pathways used preferentially by some species—and avoided by others—will be sampled proportionally to their density in the forest undergrowth. However, trail‐based placement is routinely used, already producing standardised data within large‐scale monitoring programmes. Here, both placements provided a comparable picture of the mammal community, though it might not be necessarily true in depauperate areas. Both types of CT data can nevertheless be combined in multi‐site analyses, since methods now allow accounting for differences in study design and detection bias in original CT data.

In order to improve sustainability and design adequate management strategies in threatened tropical forests, integrated assessments of the use of ecosystem services are needed, combining biophysical, social, and economic approaches. In particular, no integrated ecosystem services (ES) assessment has been conducted in Central Africa, where rural communities deeply depend on forests in a high-poverty context. Here, we aimed to quantify the use of ES provided by tropical forests to local populations in the Dja area (Cameroon), identify its determinants and evaluate its sustainability. We conducted various interviews and field surveys with 133 households in three villages, focusing on three provisioning services (bushmeat, firewood, and timber), and five cultural services (cultural heritage, inspiration, spiritual experience, recreation, and education). Local populations consumed a mean of 56 kg of bushmeat/person/year (hunting zones covering on average 213 km²), 1.17 m³ of firewood/person/year (collection zones covering on average 4 km²), and 0.03 m³ of timber/person/year. Between 25% and 86% of respondents considered cultural services as important. The use of ES was mainly influenced by population size, deforestation rate, and forest allocations, whereas the influence of socio-demographic characteristics of households remained limited to slight differences between Baka and Bantu people. We conclude that the consumption of firewood and timber is sustainable, whereas high hunting pressure has resulted in severe defaunation in the area due to the large decline in the abundance and biomass of forest mammals hunted for bushmeat by local populations.