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