Investigating local population dynamics of bottlenose dolphins in the northern Bahamas and the impact of hurricanes on survival

This study was made possible with financial support from Earthwatch Institute, Atlantis Blue Project Foundation, with permission to conduct research granted by the Bahamas Department of Marine Resources.Little Bahama Bank in the northern Bahamas supports several populations of bottlenose dolphins (Tursiops truncatus). We provide the first estimates of birth rate and age-class-specific apparent survival rates for the local South Abaco population using data from a long-term (1997–2014) photo-identification (photo-ID) study and use the estimated life history parameters in a population viability analysis (PVA) to predict future population trends. Hurricane events are predicted to become more intense due to climate change but knowledge of how hurricanes may impact cetacean populations is limited. Little Bahama Bank is subject to hurricane activity, so we also investigate the potential impact of hurricanes on calf, juvenile and adult survival. Photo-ID data confirmed the existence of a core adult population with relatively high site fidelity in South Abaco, but also evidence of transient animals. Estimated annual birth rate was 0.278 (95% CI: 0.241–0.337). We found strong support for a decline in apparent survival for all age-classes. Estimated survival declined by 9% in adults (0.941 in 1998, to 0.855 in 2013), 5% in juveniles (0.820 in 2000, to 0.767 in 2013) and 36% in calves (0.970 in 1997, to 0.606 in 2013). Evidence that survival was influenced by repeated hurricane activity leading to increased mortality and/or emigration was stronger for calves and juveniles than for adults. PVA simulations of an assumed isolated South Abaco population showed that declines would lead to extinction within decades, even under the most optimistic scenario. Future work should focus on establishing if South Abaco is part of natural source–sink metapopulation dynamics on Little Bahama Bank by assessing trends in abundance in local populations and establishing how they interact; this will be important for assessing their conservation status in a potentially increasingly changing environment.Publisher PDFPeer reviewe

Assessing the performance of open-source, semi-automated pattern recognition software for harbour seal (<i>P. v. vitulina</i>) photo ID

Photographic identification (photo ID) is a well-established, non-invasive, and relatively cost-effective technique to collect longitudinal data from species that can be individually recognised based on natural markings. This method has been improved by computer-assisted pattern recognition software which speed up the processing of large numbers of images. Freely available algorithms exist for a wide range of species, but the choice of software can have significant effects on the accuracy of individual capture histories and derived demographic parameter estimates. We tested the performance of three open source, semi-automated pattern recognition software algorithms for harbour seal (Phoca vitulina vitulina) photo ID: ExtractCompare, I3S Pattern and Wild-ID. Performance was measured as the ability of the software to successfully score matching images higher than non-matching images using the cumulative density function (CDF). The CDF for the top ranked potential match was highest for Wild-ID (CDF1 = 0.34–0.58), followed by ExtractCompare (CDF1 = 0.24–0.36) and I3S pattern (CDF1 = 0.02–0.3). This trend emerged regardless of how many potential matches were inspected. The highest performing aspects in ExtractCompare were left heads, whereas in I3S Pattern and Wild-ID these were front heads. Within each aspect, images collected using a camera and lens performed higher than images taken by a camera and scope. Data processing within ExtractCompare took  &gt; 4 × longer than Wild-ID, and  &gt; 3 × longer than I3S Pattern. We found that overall, Wild-ID outperformed both ExtractCompare and I3S Pattern under tested scenarios, and we therefore recommend its assistance in harbour seal photo ID

The east coast of Scotland bottlenose dolphin population : improving understanding of ecology outside the Moray Firth SAC

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Allo-suckling occurrence and its effect on lactation and nursing duration in harbour seals (<i>Phoca vitulina</i>) in Orkney, Scotland

Fostering and allo-suckling are widespread among pinnipeds, and several hypotheses have been formulated to explain their occurrence. Here, we describe the occurrence of allo-suckling in harbour seals from photo-identification data of females and pups in Orkney (Scotland) during the pupping seasons between 2016 and 2019. We used a generalised linear model framework to investigate the effect of allo-suckling on the duration of lactation (females) and of nursing period (pups). A generalised additive model framework was used to explore how the probability of allo-suckling varied throughout the pupping season, and with changes in mother-pup separation time. Allo-suckling was observed in 31 females, at higher rates (18–37% of lactating females and 18–47% of the pups every year) than those observed in other phocid populations, with 13 females allo-suckling in multiple years. The duration of the pups’ nursing period was not affected by allo-suckling occurrence. However, females in mother-pup pairs where both mother and pup allo-suckled had longer lactation duration than when only the pup allo-suckled, or than in pairs where no allo-suckling was observed. The probability of allo-suckling increased during the pupping season and with increased mother-pup separation time. However, the proximate causes and the consequences on future reproductive output and pup survival remain unknown

Allo-suckling occurrence and its effect on lactation and nursing duration in harbour seals (<i>Phoca vitulina</i>) in Orkney, Scotland

Fostering and allo-suckling are widespread among pinnipeds, and several hypotheses have been formulated to explain their occurrence. Here, we describe the occurrence of allo-suckling in harbour seals from photo-identification data of females and pups in Orkney (Scotland) during the pupping seasons between 2016 and 2019. We used a generalised linear model framework to investigate the effect of allo-suckling on the duration of lactation (females) and of nursing period (pups). A generalised additive model framework was used to explore how the probability of allo-suckling varied throughout the pupping season, and with changes in mother-pup separation time. Allo-suckling was observed in 31 females, at higher rates (18–37% of lactating females and 18–47% of the pups every year) than those observed in other phocid populations, with 13 females allo-suckling in multiple years. The duration of the pups’ nursing period was not affected by allo-suckling occurrence. However, females in mother-pup pairs where both mother and pup allo-suckled had longer lactation duration than when only the pup allo-suckled, or than in pairs where no allo-suckling was observed. The probability of allo-suckling increased during the pupping season and with increased mother-pup separation time. However, the proximate causes and the consequences on future reproductive output and pup survival remain unknown

Similarity learning networks uniquely identify individuals of four marine and terrestrial species

Funding: Emmanuel Kabuga is supported by a Doctoral Fellowship from the University of Cape Town.Estimating the size of animal populations plays an important role in evidence-based conservation and management. Some methods for estimating population size rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, animals with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually done manually, which is expensive, laborious, and requires considerable skill. An alternative is to develop computer vision methods that can support or replace the manual identification task. We developed an automated approach using deep learning to identify whether a pair of photographs is of the same individual or not. The core of the approach is a similarity learning network that uses paired convolutional neural networks with a triplet loss function to summarize image pairs and decide whether they are from the same individual. Prior to the main matching step, two additional convolutional neural networks perform image segmentation, cropping the animal object within the image, and orientation prediction, deciding which side of the animal was photographed. We applied the approach to four species, with images of the same individual often spanning several years: systematic surveys of bottlenose dolphins (Tursiops truncatus, 2008–2019) and harbor seals (Phoca vitulina, 2015–2019), a citizen science dataset of western leopard toads (Sclerophrys pantherina, unknown dates), and a publicly available repository of humpback whale images (Megaptera novaeangliae, unknown dates). For these species, our best-performing models were able to identify whether a pair of images were from the same individual or different individuals in 95.8%, 94.6%, 88.2%, and 83.8% of the cases, respectively. We found that triplet loss functions outperformed binary cross-entropy loss functions and that data augmentation and additional manual curation of training data provided small but consistent improvements in performance. These results demonstrate the potential of deep learning to replace or, more likely, support and facilitate manual individual identification efforts.Peer reviewe