Quantifying the age structure of free-ranging delphinid populations: Testing the accuracy of Unoccupied Aerial System photogrammetry.

Understanding the population health status of long-lived and slow-reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population-level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age structure). We tested a novel and frequentist approach, using Unoccupied Aerial System (UAS) photogrammetry, to assess the population age structure of small delphinids. First, we measured the precision and accuracy of UAS photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (Tursiops truncatus). Using a log-transformed linear model, we estimated TL using the blowhole to dorsal fin distance (BHDF) for surfacing animals. To test the performance of UAS photogrammetry to age-classify individuals, we then used length measurements from a 35-year dataset from a free-ranging bottlenose dolphin community to simulate UAS estimates of BHDF and TL. We tested five age classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS-simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS-estimated BHDF. Our age classifiers performed best in predicting age-class when using broader and fewer (two and three) age-class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5%-93% of the individuals were correctly classified within 2 years of their actual age-class bin. Similar classification performances were obtained using both proxies. UAS photogrammetry is a non-invasive, inexpensive, and effective method to estimate TL and age-class of free-swimming dolphins. UAS photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.

Estimating energetic intake for marine mammal bioenergetic models.

Bioenergetics is the study of how animals achieve energetic balance. Energetic balance results from the energetic expenditure of an individual and the energy they extract from their environment. Ingested energy depends on several extrinsic (e.g prey species, nutritional value and composition, prey density and availability) and intrinsic factors (e.g. foraging effort, success at catching prey, digestive processes and associated energy losses, and digestive capacity). While the focus in bioenergetic modelling is often on the energetic costs an animal incurs, the robust estimation of an individual's energy intake is equally critical for producing meaningful predictions. Here, we review the components and processes that affect energy intake from ingested gross energy to biologically useful net energy (NE). The current state of knowledge of each parameter is reviewed, shedding light on research gaps to advance this field. The review highlighted that the foraging behaviour of many marine mammals is relatively well studied via biologging tags, with estimates of success rate typically assumed for most species. However, actual prey capture success rates are often only assumed, although we note studies that provide approaches for its estimation using current techniques. A comprehensive collation of the nutritional content of marine mammal prey species revealed a robust foundation from which prey quality (comprising prey species, size and energy density) can be assessed, though data remain unavailable for many prey species. Empirical information on various energy losses following ingestion of prey was unbalanced among marine mammal species, with considerably more literature available for pinnipeds. An increased understanding and accurate estimate of each of the components that comprise a species NE intake are an integral part of bioenergetics. Such models provide a key tool to investigate the effects of disturbance on marine mammals at an individual and population level and to support effective conservation and management.

A sampling, exposure and receptor framework for identifying factors that modulate behavioural responses to disturbance in cetaceans.

The assessment of behavioural disturbance in cetacean species (e.g. resulting from exposure to anthropogenic sources such as military sonar, seismic surveys, or pile driving) is important for effective conservation and management. Disturbance effects can be informed by Behavioural Response Studies (BRSs), involving either controlled exposure experiments (CEEs) where noise exposure conditions are presented deliberately to meet experimental objectives or in opportunistic contexts where ongoing activities are monitored in a strategic manner. In either context, animal-borne sensors or in situ observations can provide information on individual exposure and disturbance responses. The past 15 years of research have greatly expanded our understanding of behavioural responses to noise, including hundreds of experiments in nearly a dozen cetacean species. Many papers note limited sample sizes, required knowledge of baseline behaviour prior to exposure and the importance of contextual factors modulating behavioural responses, all of which in combination can lead to sampling biases, even for well-designed research programs. It is critical to understand these biases to robustly identify responses. This ensures outcomes of BRSs help inform predictions of how anthropogenic disturbance impacts individuals and populations. Our approach leverages concepts from the animal behaviour literature focused on helping to avoid sampling bias by considering what shapes an animal's response. These factors include social, experience, genetic and natural changes in responsiveness. We developed and applied a modified version of this framework to synthesise current knowledge on cetacean response in the context of effects observed across marine and terrestrial taxa. This new 'Sampling, Exposure, Receptor' framework (SERF) identifies 43 modulating factors, highlights potential biases, and assesses how these vary across selected focal species. In contrast to studies that identified variation in 'Exposure' factors as a key concern, our analysis indicated that factors relating to 'Sampling' (e.g. deploying tags on less evasive individuals, which biases selection of subjects), and 'Receptor' (e.g. health status or coping style) have the greatest potential for weakening the desired broad representativeness of BRSs. Our assessment also highlights how potential biases could be addressed with existing datasets or future developments.

Modeling population effects of the Deepwater Horizon oil spill on a long-lived species.

The 2010 Deepwater Horizon (DWH) oil spill exposed common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, Louisiana to heavy oiling that caused increased mortality and chronic disease and impaired reproduction in surviving dolphins. We conducted photographic surveys and veterinary assessments in the decade following the spill. We assigned a prognostic score (good, fair, guarded, poor, or grave) for each dolphin to provide a single integrated indicator of overall health, and we examined temporal trends in prognostic scores. We used expert elicitation to quantify the implications of trends for the proportion of the dolphins that would recover within their lifetime. We integrated expert elicitation, along with other new information, in a population dynamics model to predict the effects of observed health trends on demography. We compared the resulting population trajectory with that predicted under baseline (no spill) conditions. Disease conditions persisted and have recently worsened in dolphins that were presumably exposed to DWH oil: 78% of those assessed in 2018 had a guarded, poor, or grave prognosis. Dolphins born after the spill were in better health. We estimated that the population declined by 45% (95% CI 14-74) relative to baseline and will take 35 years (95% CI 18-67) to recover to 95% of baseline numbers. The sum of annual differences between baseline and injured population sizes (i.e., the lost cetacean years) was 30,993 (95% CI 6607-94,148). The population is currently at a minimum point in its recovery trajectory and is vulnerable to emerging threats, including planned ecosystem restoration efforts that are likely to be detrimental to the dolphins' survival. Our modeling framework demonstrates an approach for integrating different sources and types of data, highlights the utility of expert elicitation for indeterminable input parameters, and emphasizes the importance of considering and monitoring long-term health of long-lived species subject to environmental disasters. Article impact statement: Oil spills can have long-term consequences for the health of long-lived species; thus, effective restoration and monitoring are needed.

El derrame de petróleo Deepwater Horizon (DWH) en 2010 expuso gravemente a este hidrocarburo a los delfines (Tursiops truncatus) de la Bahía Barataria, Luisiana, causando un incremento en la mortalidad y en las enfermedades crónicas, y deteriorando la reproducción de los delfines sobrevivientes. Realizamos censos fotográficos y evaluaciones veterinarias durante la década posterior al derrame. Asignamos un puntaje pronóstico (bueno, favorable, moderado, malo, o grave) a cada delfín para proporcionar un indicador integrado único de la salud en general. También examinamos las tendencias temporales de estos puntajes. Usamos información de expertos para cuantificar las implicaciones de las tendencias para la proporción de delfines que se recuperaría dentro de su periodo de vida. Integramos esta información, junto con información nueva, a un modelo de dinámica poblacional para predecir los efectos sobre la demografía de las tendencias observadas en la salud. Comparamos la trayectoria poblacional resultante con aquella pronosticada bajo condiciones de línea base (sin derrame). Las condiciones de enfermedad persistieron y recientemente han empeorado en los delfines que supuestamente estuvieron expuestos al petróleo de DWH: 78% de aquellos evaluados en 2018 tuvieron un pronóstico moderado, malo o grave. Los delfines que nacieron después del derrame contaron con mejor salud. Estimamos que la población declinó en un 45% (95% CI 14-74) relativo a la línea base y tardará 35 años (95% CI 18-67) en recuperar el 95% de los números de línea base. La suma de las diferencias anuales entre el tamaño poblacional de línea base y el dañado (es decir, los años cetáceos perdidos) fue de 30,993 (95% CI 6,607-94,148). La población actualmente está en un punto mínimo de su trayectoria de recuperación y es vulnerable a las amenazas emergentes, incluyendo los esfuerzos de restauración ambiental planeada que probablemente sean nocivos para la supervivencia de los delfines. Nuestro marco de modelado demuestra una estrategia para la integración de diferentes fuentes y tipos de datos, resalta la utilidad de la información de expertos para los parámetros de aportación indeterminable, y enfatiza la importancia de la consideración y el monitoreo de la salud a largo plazo de las especies longevas sujetas a los desastres ambientales. Modelado de los Efectos Poblacionales del Derrame de Petróleo Deepwater Horizon sobre Especies Longevas.

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales.

Assessing the long-term consequences of sub-lethal anthropogenic disturbance on wildlife populations requires integrating data on fine-scale individual behavior and physiology into spatially and temporally broader, population-level inference. A typical behavioral response to disturbance is the cessation of foraging, which can be translated into a common metric of energetic cost. However, this necessitates detailed empirical information on baseline movements, activity budgets, feeding rates and energy intake, as well as the probability of an individual responding to the disturbance-inducing stressor within different exposure contexts. Here, we integrated data from blue whales (Balaenoptera musculus) experimentally exposed to military active sonar signals with fine-scale measurements of baseline behavior over multiple days or weeks obtained from accelerometry loggers, telemetry tracking and prey sampling. Specifically, we developed daily simulations of movement, feeding behavior and exposure to localized sonar events of increasing duration and intensity and predicted the effects of this disturbance source on the daily energy intake of an individual. Activity budgets and movements were highly variable in space and time and among individuals, resulting in large variability in predicted energetic intake and costs. In half of our simulations, an individual's energy intake was unaffected by the simulated source. However, some individuals lost their entire daily energy intake under brief or weak exposure scenarios. Given this large variation, population-level models will have to assess the consequences of the entire distribution of energetic costs, rather than only consider single summary statistics. The shape of the exposure-response functions also strongly influenced predictions, reinforcing the need for contextually explicit experiments and improved mechanistic understanding of the processes driving behavioral and physiological responses to disturbance. This study presents a robust approach for integrating different types of empirical information to assess the effects of disturbance at spatio-temporal and ecological scales that are relevant to management and conservation.

Understanding the population consequences of disturbance.

Managing the nonlethal effects of disturbance on wildlife populations has been a long-term goal for decision makers, managers, and ecologists, and assessment of these effects is currently required by European Union and United States legislation. However, robust assessment of these effects is challenging. The management of human activities that have nonlethal effects on wildlife is a specific example of a fundamental ecological problem: how to understand the population-level consequences of changes in the behavior or physiology of individual animals that are caused by external stressors. In this study, we review recent applications of a conceptual framework for assessing and predicting these consequences for marine mammal populations. We explore the range of models that can be used to formalize the approach and we identify critical research gaps. We also provide a decision tree that can be used to select the most appropriate model structure given the available data. Synthesis and applications: The implementation of this framework has moved the focus of discussion of the management of nonlethal disturbances on marine mammal populations away from a rhetorical debate about defining negligible impact and toward a quantitative understanding of long-term population-level effects. Here we demonstrate the framework's general applicability to other marine and terrestrial systems and show how it can support integrated modeling of the proximate and ultimate mechanisms that regulate trait-mediated, indirect interactions in ecological communities, that is, the nonconsumptive effects of a predator or stressor on a species' behavior, physiology, or life history.

Challenge of Using Passive Acoustic Monitoring in High-Energy Environments: UK Tidal Environments and Other Case Studies.

The use of passive acoustic monitoring (PAM) around marine developments is commonplace. A buffer-based PAM system (e.g., C-POD) is a cost-effective method for assessing cetacean acoustic presence. Devices have been deployed by Sea Mammal Research Unit (SMRU) Marine around the United Kingdom, allowing an examination of the performance of C-PODs with respect to background noise, tilt angle, and environmental factors. C-PODs were found to often only monitor for a few seconds of each minute, resulting in significant loss of monitoring time. Issues were likely driven by environmental and deployment factors. The practical limitations of buffer-based PAM systems in high-energy/noisy environments are indicated here.