Optimising the automated recognition of individual animals to support population monitoring

Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages. In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I3S-Pattern, and WildID. As a case study, we consider the African wild dog Lycaon pictus, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts

No detectable effect of geolocator deployment on the short‐ or long‐term apparent survival of a tropical seabird

A wide range of biologging devices are now commonly deployed to study the movement ecology of birds, but deployment of these devices is not without its potential risks and negative impacts on the welfare, behaviour and fitness of tagged individuals. However, empirical evidence for the effects of tags is equivocal. Global location sensing (GLS) loggers are small, light level recording devices that are well suited to studying the large-scale migratory movements of many birds. However, few published studies have examined their impact on adult survival, a key demographic rate for long-lived species, such as seabirds. To address this, we collate a long-term mark-recapture data set in conjunction with a 10-year GLS tagging programme and examine the impact of tarsus-mounted GLS loggers on the adult apparent survival probabilities of a medium-sized tropical gadfly petrel. We found no evidence to indicate that deployment of GLS loggers affected apparent adult survival probabilities either in the short-term, i.e., during deployment, or in the long-term, i.e., from carrying a device at some point in the past. Annual adult apparent survival was estimated at 0.965 (CIs 0.962, 0.968) during 1993-2018. Our findings suggest that using GLS loggers to document the movements of medium-sized gadfly petrels over multiple years is a viable technique without negatively impacting adult survival. This result has potential relevance to movement ecology studies of other ecologically and morphologically similar seabirds through GLS logger deployments

Comparison of driving avoidance and self-regulatory patterns in younger and older drivers.

This study sought to ascertain whether both the avoidance of difficult driving situations and self-regulation (i.e., avoidance matched to one's cognitive abilities) are typical of older drivers. Older and younger drivers (mean ages 71 and 30 years, respectively) self-rated their avoidance of ten specific driving situations (e.g., driving at night, in fog). Both groups also self-evaluated their physical and mental health, while we administered general (Mini-Mental State Examination) and specific (Digit Symbol Substitution Test) cognitive assessments. The older drivers reported greater avoidance of all ten situations than the younger drivers did, although the effect size remained small. There were also more correlations between self-reported driving avoidance and both health-related perceptions and objective indicators of cognitive function among older drivers, suggesting that self-regulation is a strategy that is typical of this group. Results also showed that, with the exception of the cognitive function indicators, the factors under investigation (i.e., age, driving experience, health-related perceptions) underpinned the self-regulatory patterns in different ways, depending on the drivers' age group. Hypotheses regarding the underlying mechanisms, further factors of interest (including relevant neuropsychological tests), and alternative ways of measuring self-regulation are put forward