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.

Summary: Changes in natural patterns of animal behaviour and physiology resulting from anthropogenic disturbance may alter the conservation status of a population if they affect the ability of individuals to survive, breed or grow. However, information to forecast population‐level consequences of such changes is often lacking. We developed an interim framework to assess the population consequences of disturbance when empirical information is sparse. We show how daily effects of disturbance, which are often straightforward to estimate, can be scaled to the disturbance duration and to multiple sources of disturbance. We used expert elicitation to estimate parameters that define how changes in individual behaviour or physiology affect vital rates and incorporated them into a stochastic population model. Model outputs can be used to evaluate cumulative impacts of disturbance over space and time. As an example, we forecast the potential effects of disturbance from offshore wind farm construction on theNorthSea harbour porpoise (Phocoena phocoena) population. Synthesis and applications. The interim framework can be used to forecast the effects of disturbances from human activities on animal populations, to assess the effectiveness of mitigation measures and to identify priority areas for research that reduces uncertainty in population forecasts. The last two applications are likely to be important in situations where there is a risk of unacceptable change in a species' conservation status. The framework should, however, be augmented with empirical data as soon as these are available.

Assessing the non-lethal effects of disturbance and their population-level consequences is a significant ecological and conservation challenge, because it requires extensive baseline knowledge of behavioral patterns, life-history and demography. However, for many marine mammal populations, this knowledge is currently lacking and it may take decades to fill the gaps. During this time, undetected population declines may occur. In this study we identify methods that can be used to monitor populations subject to disturbance and provide insights into the processes through which disturbance may affect them. To identify and address the knowledge gaps highlighted above, we reviewed the literature to identify suitable response variables and methods for monitoring these variables. We also used existing models of the population consequences of disturbance (PCoD) to identify demographic characteristics (e.g., the proportion of immature animals in the population, or the ratio of calves/pups to mature females) that may be strongly correlated with population status and therefore provide early warnings of future changes in abundance. These demographic characteristics can be monitored using established methods such as visual surveys combined with photogrammetry, and capture-recapture analysis. Individual health and physiological variables can also inform PCoD assessment and can be monitored using photogrammetry, remote tissue sampling, hands-on assessment and individual tracking. We then conducted a workshop to establish the relative utility and feasibility of all these approaches for different groups of marine mammal species. We describe how future marine mammal monitoring programs can be designed to inform population-level analysis.

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.