Factors affecting diet, habitat selection and breeding success of the African Crowned Eagle Stephanoaetus coronatus in a fragmented landscape

This study aimed to identify variables that affect habitat selection and nesting success of the African Crowned Eagle Stephanoaetus coronatus, the largest forest raptor, in north-eastern South Africa. A preference for nesting in the Northern Mistbelt Forest vegetation type was established and 82% of all nests were located in indigenous trees. Nest abandonment was less common when distances to the nearest neighbour were greater. The diet of this species was investigated by examination of prey remains beneath nests and verified by comparison with museum specimens. In total, 156 remains were found, representing a minimum of 75 prey individuals. The diet of African Crowned Eagles constituted almost entirely mammals (99%), which were predominantly antelopes (61%) and monkeys (25%). It was also found that the proportion of primates in the diet correlates with latitude: populations in equatorial latitudes have a higher proportion of primates in their diets, whereas further south antelopes are a much more common diet component

Robust ecological analysis of camera trap data labelled by a machine learning model

Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time-consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case-study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out-of-sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user-community with a multi-platform, multi-language graphical user interface that can be used to run our model offline

Not in wilderness: African vulture strongholds remain in areas with high human density.

Vultures constitute an important functional group in many ecosystems, providing crucial ecosystem services both in natural and humanized environments. These scavengers are facing massive declines worldwide, but in several African countries virtually nothing is known on populations' status and threats, hampering the development of adequate conservation strategies. In Guinea-Bissau, globally important populations of Hooded Necrosyrtes monachus and African white-backed vultures Gyps africanus were recently reported. Using the country as a study area, we aim to characterize human-vulture interactions in West Africa applying a multidisciplinary approach. We assessed the status and distribution of vulture populations using data from 1711 km of roadside transects, examined predictors of their distribution, and produced a nationwide population estimate for the Hooded Vulture, using an innovative method based on the relationship between the size of human population in settlements and vulture numbers. We conducted 47 stakeholder interviews to assess perceived roles played by vultures, and to investigate potential anthropogenic threats. Hooded vultures were strongly associated with high human population densities, whereas no relation was found between African white-backed and Rüppell's vultures and any of the tested predictors, which included cattle density, precipitation and Normalized Difference Vegetation Index, among others. We estimate a national population of 43347 Hooded vultures, the largest population reported in the species range. Respondents were generally aware of the services provided by vultures, especially waste and carcass removal, including in urban areas. Hunting for witchcraft and traditional medicine was the most frequently recognised threat, while poisoning was ranked as having the highest impact. We hypothesise that poisoning-related mortality may be affecting African white-backed and Rüppell's vultures' distribution and explain their scarcity in apparently highly suitable habitats. Our results suggest a mutualistic rather than a commensalistic relationship between vultures and humans, with important implications for designing and implementing conservation strategies

Conservation and ecology of African Raptors

Africa supports breeding populations of over 20% of all raptor species globally and over 20 regular Palearctic migratory raptors. Here, we discuss the importance of Africa in terms of the diversity of both resident and migrant species, the ecosystem services they provide, and the threats they face. We examine the state of knowledge of African raptors, including monitoring to determine trends, and describe ongoing research. African raptors provide important ecosystem services, by bringing in tourism revenues, functioning as bio-indicator species, and controlling the spread of pathogens and pest species. Many species are under pressure from growing human populations and associated habitat loss, persecution, and pollution. Most are declining, with some exceptions, some catastrophically so, such as vultures. Of 66 African species, 26% are currently on the IUCN Red List. For many species, there is a need for their conservation status to be re-evaluated, but rigorous monitoring for most of Africa is generally lacking. A systematic literature review showed considerable variation in the number of studies per species, 36% of 67 species having been relatively “well-studied” (12 or more studies), but 64% with less than 10 studies. There has been a general and consistent increase in the numbers of studies on African raptors, the majority from Southern Africa (n = 466, 62%). We found most studies focused on feeding ecology (n= 247) and distribution and abundance, with the least number of studies on behaviour and movement ecology. We list some ongoing studies and conclude that developing future leadership in research and conservation will be critical for successful raptor conservation in Africa