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

Tritrophic phenological match-mismatch in space and time

Increasing temperatures associated with climate change may generate phenological mismatches that disrupt previously synchronous trophic interactions. Most work on mismatch has focused on temporal trends, whereas spatial variation in the degree of trophic synchrony has largely been neglected, even though the degree to which mismatch varies in space has implications for meso-scale population dynamics and evolution. Here we quantify latitudinal trends in phenological mismatch, using phenological data on an oak–caterpillar–bird system from across the UK. Increasing latitude delays phenology of all species, but more so for oak, resulting in a shorter interval between leaf emergence and peak caterpillar biomass at northern locations. Asynchrony found between peak caterpillar biomass and peak nestling demand of blue tits, great tits and pied flycatchers increases in earlier (warm) springs. There is no evidence of spatial variation in the timing of peak nestling demand relative to peak caterpillar biomass for any species. Phenological mismatch alone is thus unlikely to explain spatial variation in population trends. Given projections of continued spring warming, we predict that temperate forest birds will become increasingly mismatched with peak caterpillar timing. Latitudinal invariance in the direction of mismatch may act as a double-edged sword that presents no opportunities for spatial buffering from the effects of mismatch on population size, but generates spatially consistent directional selection on timing, which could facilitate rapid evolutionary change

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

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