Computer-Assisted Photo-Identification of Narwhals

Although the narwhal (Monodon monoceros) is economically and culturally important to northern residents, sound management of this species is impaired by large gaps in knowledge. Research on this species has been limited partly by the cost of the methods used, and partly because some of these methods are invasive and therefore condemned by Inuit communities. Photo-identification, a non-invasive, inexpensive, and easy-to-use method recently developed for narwhals, uses photographs of natural marks to identify individuals. Its main drawback is the extended time required to process photographs. We developed a computer program to accelerate the identification process and thus mitigate the main drawback of photo-identification. This program uses the locations of notches on the dorsal ridge to compare a new image to each individual in a catalogue and lists those individuals in decreasing order of similarity. We tested consistency in user assignment of dorsal ridge features and the accuracy of the program by comparing sets of known individuals. While assignment errors were common, the program ranked the true match within the first 10% of the catalogue 78% of the time. The program accelerates the matching process by 1.2 to 4.1 times for catalogues ranging in size from 40 to 500 individuals, and the degree of acceleration increases with the size of the catalogue. This program could also be applied to the beluga whale (Delphinapterus leucas), another important northern species.Bien que le narval (Monodon monoceros) soit une espèce exploitée d’importance économique et culturelle pour les résidents du Nord, la gestion efficace de cette espèce est affaiblie par des lacunes importantes en matière de connaissance de l’espèce. La quantité de recherche sur le narval est limitée par le coût des méthodes utilisées et par le fait que certaines de ces méthodes sont invasives, ce qui est désapprouvé par les communautés inuites. La photo-identification, soit une méthode non-invasive, peu coûteuse et facile d’utilisation, a été récemment mise au point pour le narval. Cette méthode utilise des photographies de marques naturelles pour identifier les individus. Toutefois, le plus grand défaut de cette méthode est qu’elle requiert beaucoup de temps pour comparer les photographies. Nous avons mis au point un programme informatique dans le but d’accélérer le processus d’identification et donc remédier au principal inconvénient de la photo-identification. Ce programme utilise l’emplacement des entailles dans la crête dorsale des narvals pour comparer une nouvelle image à celles d’un catalogue et les ordonne en ordre décroissant selon leur similarité. Nous avons testé la constance de l’utilisateur lorsqu’il attribue les caractéristiques de la crête dorsale et l’exactitude du programme en comparant des photographies d’individus précédemment identifiés. Bien que les erreurs de l’utilisateur soient fréquentes, le programme classe le bon individu parmi le premier 10 % des individus du catalogue, et ce 78 % du temps. Ce nouveau programme permet alors un meilleur rendement du processus d’identification de 1,2 à 4,1 fois plus rapide que sans l’assistance d’un programme pour un catalogue comprenant de 40 à 500 individus. Par ailleurs, plus le catalogue est grand, plus le degré d’accélération augmente. Ce programme informatique pourrait aussi être appliqué au béluga (Delphinapterus leucas), une autre espèce d’importance pour les résidents du Nord

State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (\textit{Ursus maritimus}) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results

Learning and animal movement

Authors acknowledge the following grants for supporting this research: NSERC Discovery (ML and MA-M), NSF DMS-1853465 (WF and EG), and Canada Research Chairs Program (ML and MA-M).Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.Publisher PDFPeer reviewe

A generalized residual technique for analysing complex movement models using earth mover's distance

Complex systems of moving and interacting objects are ubiquitous in the natural and social sciences. Predicting their behaviour often requires models that mimic these systems with sufficient accuracy, while accounting for their inherent stochasticity. Although tools exist to determine which of a set of candidate models is best relative to the others, there is currently no generic goodness-of-fit framework for testing how close the best model is to the real complex stochastic system. We propose such a framework, using a novel application of the Earth mover's distance, also known as the Wasserstein metric. It is applicable to any stochastic process where the probability of the model's state at time t is a function of the state at previous times. It generalizes the concept of a residual, often used to analyse 1D summary statistics, to situations where the complexity of the underlying model's probability distribution makes standard residual analysis too imprecise for practical use. We give a scheme for testing the hypothesis that a model is an accurate description of a data set. We demonstrate the tractability and usefulness of our approach by application to animal movement models in complex, heterogeneous environments. We detail methods for visualizing results and extracting a variety of information on a given model's quality, such as whether there is any inherent bias in the model, or in which situations, it is most accurate. We demonstrate our techniques by application to data on multispecies flocks of insectivore birds in the Amazon rain forest. This work provides a usable toolkit to assess the quality of generic movement models of complex systems, in an absolute rather than a relative sense

Local Passive Acoustic Monitoring of Narwhal Presence in the Canadian Arctic: A Pilot Project

Long-term community-based monitoring of narwhals (Monodon monoceros) is needed because narwhals are important to local Inuit and are facing changes in their environment. We examined the suitability of passive acoustic recording for monitoring narwhals, using data gathered in the Canadian Arctic from an autonomous acoustic recorder (Repulse Bay, 2006) and a hand-held digital recorder (Koluktoo Bay, 2006 – 08). We found a relationship between the number of narwhals observed passing a fixed point and the number of calls heard. In addition, we found that an automated call detector could isolate segments of recording containing narwhal vocalizations over long recording periods containing non-target sound, thus decreasing the time spent on the analysis. Collectively, these results suggest that combining passive acoustic sampling with an automated call detector offers a useful approach for local monitoring of the presence and relative abundance of narwhals.La nécessité d’avoir un programme communautaire de surveillance à long terme des narvals (Monodon monoceros) s’avère évidente étant donné que les narvals revêtent de l’importance aux yeux des Inuits de la région et que leur environ­nement est en pleine évolution. Nous explorons la pertinence d’un programme de surveillance par acoustique passive pour les populations de narvals à partir de données récoltées dans l’Arctique canadien à l’aide d’une enregistreuse autonome (Repulse Bay, 2006) et d’une enregistreuse portable (Koluktoo Bay, 2006 – 2008). Grâce à des enregistrements accompagnés d’obser­vations sur le terrain, nous avons trouvé une corrélation entre le nombre de vocalisations entendues et le nombre de narvals observés. L’utilisation d’un détecteur automatique de vocalisations de narvals a permis d’isoler des segments d’enregis­trements contenant des vocalisations de narvals sur de longues périodes d’enregistrement contenant des sons non-ciblés, et ainsi diminuer le temps d’analyse. Ces résultats suggèrent que la combinaison de surveillance acoustique passive avec l’utili­sation d’un détecteur automatique offre une approche utile pour la surveillance locale de la présence et de l’abondance relative des narvals

Modelling multi-scale state-switching functional data with hidden Markov models

Data sets comprised of sequences of curves sampled at high frequencies in time are increasingly common in practice, but they can exhibit complicated dependence structures that cannot be modelled using common methods of Functional Data Analysis (FDA). We detail a hierarchical approach which treats the curves as observations from a hidden Markov model (HMM). The distribution of each curve is then defined by another fine-scale model which may involve auto-regression and require data transformations using moving-window summary statistics or Fourier analysis. This approach is broadly applicable to sequences of curves exhibiting intricate dependence structures. As a case study, we use this framework to model the fine-scale kinematic movement of a northern resident killer whale (Orcinus orca) off the coast of British Columbia, Canada. Through simulations, we show that our model produces more interpretable state estimation and more accurate parameter estimates compared to existing methods.Comment: 23 pages, 8 figures, 2 tables. Supplementary material appended to submissio

A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks

Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state‐space model called the first‐Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, called the hidden Markov movement model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. The HMMM was compared to a modified version of the DCRWS for highly accurate tracks, the DCRWS [Formula: see text] , and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim

Diving efficiency at depth and pre-breeding foraging effort increase with haemoglobin levels in gentoo penguins

Individual differences in oxygen storage and carrying capacity have been associated with fitness-related traits and, for air-breathing aquatic animals, to diving ability and foraging success. In winter, many seabirds must replenish the energy reserves they have depleted during the breeding period. Thus, winter foraging efficiency can influence their upcoming breeding behaviour. Using gentoo penguins Pygoscelis papua as a study species, we investigated (1) if inter-individual variation in diving efficiency (proportion of time spent at the bottom) is associated with indices of oxygen storage and carrying capacity (haemoglobin, haematocrit, body mass), and (2) if measures of pre-breeding foraging effort (mean trip duration, total time at sea, and vertical distance travelled) are associated with these oxygen indices and breeding status. Haemoglobin was positively correlated with diving efficiency, particularly for deeper dives, and only penguins with high haemoglobin levels frequently dove to depths ≥140 m. Such differences could affect resource access. However, because reaching deep offshore waters likely requires travelling more than foraging nearshore, vertical distance travelled during pre-breeding increased with haemoglobin levels. The relationship with haematocrit was non-linear, suggesting that commonly used analyses may be inappropriate for this index. We found that early-laying penguins spent less time at sea prior to nesting than non-breeding penguins, suggesting that more efficient foragers lay earlier. Given that diving efficiency at depth is linked to aerobic capacity, anthropogenic activities taking place in either nearshore or offshore waters (e.g. shallow-water fisheries, offshore oil rigs) may have differing impacts on individuals. Further understanding these links could help the conservation of diving species