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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 individuals 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.

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.

We developed dynamic bioenergetics models to investigate how behavioural responses to anthropogenic disturbance events might affect the population dynamics of three marine mammal species (harbour porpoise, grey seal and harbour seal) with contrasting life‐history traits (capital versus income breeders) and movement behaviour (resident versus nomadic). We used these models to analyse how individual vital rates were affected by differences in the probability of disturbance and the duration of any behavioural response, while taking account of uncertainty in the model parameters and heterogeneity in behaviour. The outputs of individual movement models and telemetry data were then used to determine how the probability of exposure might vary among species, individuals, and geographical locations. We then demonstrate how these estimated probabilities of exposure can be translated into probabilities of disturbance. For illustrative purposes, we modelled the potential effects of a temporary decrease in energy assimilation associated with a series of disturbance events that might realistically occur during the construction of an offshore windfarm. Offspring starvation mortality was the vital rate that was most affected by these disturbance events. Monitoring of rate should be considered as standard practice so that populations responses can be detected as early as possible. Predicted effects on individual vital rates depended on the species' movement behaviour and the likely density of animals where the modelled construction activity was assumed to take place. The magnitude of these effects also depended critically on the assumed duration of the reduction in energy assimilation. No direct estimates of this variable are currently available, but we suggest some ways in which it could be estimated. The described approach could be extended to other species and activities, given sufficient information to parameterise the component models. However, we emphasise the need to account for among‐individual heterogeneities and uncertainties in the values of the many model parameters. [ABSTRACT FROM AUTHOR]

Anthropogenic activities can lead to changes in animal behavior. Predicting population consequences of these behavioral changes requires integrating short-term individual responses into models that forecast population dynamics across multiple generations. This is especially challenging for long-lived animals, because of the different time scales involved. Beaked whales are a group of deep-diving odontocete whales that respond behaviorally when exposed to military mid-frequency active sonar (MFAS), but the effect of these nonlethal responses on beaked whale populations is unknown. Population consequences of aggregate exposure to MFAS was assessed for two beaked whale populations that are regularly present on U.S. Navy training ranges where MFAS is frequently used. Our approach integrates a wide range of data sources, including telemetry data, information on spatial variation in habitat quality, passive acoustic data on the temporal pattern of sonar use and its relationship to beaked whale foraging activity, into an individual-based model with a dynamic bioenergetic module that governs individual life history. The predicted effect of disturbance from MFAS on population abundance ranged between population extinction to a slight increase in population abundance. These effects were driven by the interaction between the temporal pattern of MFAS use, baseline movement patterns, the spatial distribution of prey, the nature of beaked whale behavioral response to MFAS and the top-down impact of whale foraging on prey abundance. Based on these findings, we provide recommendations for monitoring of marine mammal populations and highlight key uncertainties to help guide future directions for assessing population impacts of nonlethal disturbance for these and other long-lived animals. [ABSTRACT FROM AUTHOR]

NMR metabolomics provides a potent method for monitoring alterations in the metabolic signature within tissues and biofluids. In this study, NMR analysis was utilized to determine variation in metabolite levels of human DU145 prostate cancer and non-tumorigenic PNT1A prostate epithelial cells after treatment with selenomethionine (SeM) and Se-methylselenocysteine (SeMSC). Currently, these are the first [sup.1]H NMR spectroscopic data on selenium-treated prostate cell lines. Fluorescence microscopy of SeM-incubated PNT1A cells revealed morphological features characteristic of apoptosis. SeMSC-treated PNT1A and DU145 prostate cells indicated greater changes in cellular morphology and in metabolite levels than SeM-treated cells. NMR of prostate cells treated with selenium showed a decreasing trend in metabolite levels with the largest change exhibited by creatine. This is mainly due to disrupted energy metabolism, and probably due to loss of structural integrity combined with dissipation of metabolites. Lactate, choline-containing compounds, and glycine levels increased depending on the type of selenium and cell line used. No clear pattern of variation in metabolite concentration levels from [sup.1]H NMR spectroscopy to distinguish apoptotic versus non-apoptotic pathway was observed. Factor analysis (FA) indicated the change in the concentration levels of twelve metabolites was able to distinguish DU145 cells from PNT1A cells when treated using SeM. This study indicated that NMR of intact cells treated with selenium can provide information on the biochemical processes of tissues; thus metabolic fingerprints for compromised cells can be acquired. Keywords: NMR spectroscopy, metabolomics, selenomethionine, Se-methylselenocysteine, prostate cancer, apoptosis, INTRODUCTION Selenium is an essential nonmetallic trace element necessary for normal metabolic functions in humans and animals. It serves as a cofactor for the antioxidant enzymes glutathione peroxidase and thioredoxin [...]