A method to estimate prey density from single-camera images: A case study with chinstrap penguins and Antarctic krill

Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap penguin (Pygoscelis antarcticus) from Cape Shirreff, Livingston Island, Antarctica to develop a novel approach for estimating the density of Antarctic krill (Euphausia superba) encountered during foraging activities. Using the open-source Video and Image Analytics for a Marine Environment (VIAME), we trained a neural network model to identify video frames containing krill. Our image classifier has an overall accuracy of 73%, with a positive predictive value of 83% for prediction of frames containing krill. We then developed a method to estimate the volume of water imaged, thus the density (N·m-3) of krill, in the 2-dimensional images. The method is based on the maximum range from the camera where krill remain visibly resolvable and assumes that mean krill length is known, and that the distribution of orientation angles of krill is uniform. From 1,932 images identified as containing krill, we manually identified a subset of 124 images from across the video record that contained resolvable and unresolvable krill necessary to estimate the resolvable range and imaged volume for the video sensor. Krill swarm density encountered by the penguins ranged from 2 to 307 krill·m-3 and mean density of krill was 48 krill·m-3 (sd = 61 krill·m-3). Mean krill biomass density was 25 g·m-3. Our frame-level image classifier model and krill density estimation method provide a new approach to efficiently process video-logger data and estimate krill density from 2D imagery, providing key information on prey aggregations that may affect predator foraging performance. The approach should be directly applicable to other marine predators feeding on aggregations of prey.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

Individual variability in diving behavior of the Black-vented Shearwater in an ever-changing habitat

Oceanic mesoscale systems are characterized by inherent variability. Climatic change adds entropy to this system, making it a highly variable environment in which marine species live. Being at the higher levels of the food chain, predators maximize their performance through plastic foraging strategies. Individual variability within a population and the possible repeatability across time and space may provide stability in a population facing environmental changes. Therefore, variability and repeatability of behaviors, particularly diving behavior, could play an important role in understanding the adaptation pathway of a species. This study focuses on characterizing the frequency and timing of different dives (termed simple and complex) and how these are influenced by individual and environmental characteristics (sea surface temperature, chlorophyll a concentration, bathymetry, salinity, and Ekman transport). This study is based on GPS and accelerometer-recorded information from a breeding group of 59 Black-vented Shearwater and examine consistency in diving behavior at both individual and sex levels across four different breeding seasons. The species was found to be the best performing free diver in the Puffinus genus with a maximum dive duration of 88 s. Among the environmental variables assessed, a relationship was found with active upwelling conditions enhancing low energetic cost diving, on the contrary, reduced upwelling and warmer superficial waters induce more energetically demanding diving affecting diving performance and ultimately body conditions. The body conditions of Black-vented Shearwaters in 2016 were worse than in subsequent years, in 2016, deepest and longest complex dives were recorded, while simple dives were longer in 2017-2019. Nevertheless, the species' plasticity allows at least part of the population to breed and feed during warmer events. While carry-over effects have already been reported, the effect of more frequent warm events is still unknown.

A community genomics approach to natural hybridization

Hybridization is a complicated, oft-misunderstood process. Once deemed unnatural and uncommon, hybridization is now recognized as ubiquitous among species. But hybridization rates within and among communities are poorly understood despite the relevance to ecology, evolution and conservation. To clarify, we examined hybridization across 75 freshwater fish communities within the Ozarks of the North American Interior Highlands (USA) by single nucleotide polymorphism (SNP) genotyping 33 species (N = 2865 individuals; double-digest restriction site-associated DNA sequencing (ddRAD)). We found evidence of hybridization (70 putative hybrids; 2.4% of individuals) among 18 species-pairs involving 73% (24/33) of study species, with the majority being concentrated within one family (Leuciscidae/minnows; 15 species; 66 hybrids). Interspecific genetic exchange-or introgression-was evident from 24 backcrossed individuals (10/18 species-pairs). Hybrids occurred within 42 of 75 communities (56%). Four selected environmental variables (species richness, protected area extent, precipitation (May and annually)) exhibited 73-78% accuracy in predicting hybrid occurrence via random forest classification. Our community-level assessment identified hybridization as spatially widespread and environmentally dependent (albeit predominantly within one diverse, omnipresent family). Our approach provides a more holistic survey of natural hybridization by testing a wide range of species-pairs, thus contrasting with more conventional evaluations.

High-resolution recording of foraging behaviour over multiple annual cycles shows decline in old Adélie penguins' performance

Age-related variation in foraging performance can result from both within-individual change and selection processes. These mechanisms can only be disentangled by using logistically challenging long-term, longitudinal studies. Coupling a long-term demographic data set with high-temporal-resolution tracking of 18 Adélie penguins (Pygoscelis adeliae, age 4-15 yrs old) over three consecutive annual cycles, we examined how foraging behaviour changed within individuals of different age classes. Evidence indicated within-individual improvement in young and middle-age classes, but a significant decrease in foraging dive frequency within old individuals, associated with a decrease in the dive descent rate. Decreases in foraging performance occurred at a later age (from 12-15 yrs old to 15-18 yrs old) than the onset of senescence predicted for this species (9-11 yrs old). Foraging dive frequency was most affected by the interaction between breeding status and annual life-cycle periods, with frequency being highest during returning migration and breeding season and was highest overall for successful breeders during the chick-rearing period. Females performed more foraging dives per hour than males. This longitudinal, full annual cycle study allowed us to shed light on the changes in foraging performance occurring among individuals of different age classes and highlighted the complex interactions among drivers of individual foraging behaviour.

Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras

Direct observation of foraging behavior is not always possible, especially for marine species that hunt underwater. However, biologging and tracking devices have provided detailed information about how various species use their habitat. From these indirect observations, researchers have inferred behaviors to address a variety of research questions, including the definition of ecological niches. In this study, we deployed video cameras with GPS and time-depth recorders on 16 chinstrap penguins (Pygoscelis antarcticus) during the brood phase of the 2018–2019 breeding season on Signy (South Orkney Islands). More than 57 h of footage covering 770 dives were scrutinized by two observers. The outcome of each dive was classified as either no krill encounter, individual krill or krill swarm encounter and the number of prey items caught per dive was estimated. Other variables derived from the logging devices or from the environment were used to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable. We also demonstrate that it was possible to accurately predict the outcome of each dive from dive and horizontal movement variables in a manner that has not been used for penguins previously. For example, our models show that a fast dive ascent rate and a high density of dives are good indicators of krill and especially of swarm encounter. Finally, we discuss how video footage can help build accurate habitat models to provide wider knowledge about predator behavior or prey distribution.