Decreasing body size is associated with reduced calving probability in critically endangered North Atlantic right whales.

Body size is key to many life-history processes, including reproduction. Across species, climate change and other stressors have caused reductions in the body size to which animals can grow, called asymptotic size, with consequences for demography. A reduction in mean asymptotic length was documented for critically endangered North Atlantic right whales, in parallel with declines in health and vital rates resulting from human activities and environmental changes. Here, we tested whether smaller body size was associated with lower reproductive output, using a state-space model for individual health, survival and reproduction that quantifies the mechanistic links between these processes. Body size (as represented by the cube of length) was strongly associated with a female's calving probability at each reproductive opportunity. This relationship explained 62% of the variation in calving among reproductive females, along with their decreasing health (20%). The effects of decreasing mean body size on reproductive performance are another concerning indication of the worsening prospects for this species and many others affected by environmental change, requiring a focus of conservation and management interventions on improving conditions that affect reproduction as well as reducing mortality.

Estimating uncertainty in density surface models.

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.

REVIEW: Assessing North Atlantic right whale health: threats, and development of tools critical for conservation of the species.

Whaling has decimated North Atlantic right whales Eubalaena glacialis (NARW) since the 11th century and southern right whales E. australis (SRW) since the 19th century. Today, NARWs are Critically Endangered and decreasing, whereas SRWs are recovering. We review NARW health assessment literature, NARW Consortium databases, and efforts and limitations to monitor individual and species health, survival, and fecundity. Photographs are used to track individual movement and external signs of health such as evidence of vessel and entanglement trauma. Post-mortem examinations establish cause of death and determine organ pathology. Photogrammetry is used to assess growth rates and body condition. Samples of blow, skin, blubber, baleen and feces quantify hormones that provide information on stress, reproduction, and nutrition, identify microbiome changes, and assess evidence of infection. We also discuss models of the population consequences of multiple stressors, including the connection between human activities (e.g. entanglement) and health. Lethal and sublethal vessel and entanglement trauma have been identified as major threats to the species. There is a clear and immediate need for expanding trauma reduction measures. Beyond these major concerns, further study is needed to evaluate the impact of other stressors, such as pathogens, microbiome changes, and algal and industrial toxins, on NARW reproductive success and health. Current and new health assessment tools should be developed and used to monitor the effectiveness of management measures and will help determine whether they are sufficient for a substantive species recovery.

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.

Mesoscale activity facilitates energy gain in a top predator.

How animal movement decisions interact with the distribution of resources to shape individual performance is a key question in ecology. However, links between spatial and behavioural ecology and fitness consequences are poorly understood because the outcomes of individual resource selection decisions, such as energy intake, are rarely measured. In the open ocean, mesoscale features (approx. 10-100 km) such as fronts and eddies can aggregate prey and thereby drive the distribution of foraging vertebrates through bottom-up biophysical coupling. These productive features are known to attract predators, yet their role in facilitating energy transfer to top-level consumers is opaque. We investigated the use of mesoscale features by migrating northern elephant seals and quantified the corresponding energetic gains from the seals' foraging patterns at a daily resolution. Migrating elephant seals modified their diving behaviour and selected for mesoscale features when foraging. Daily energy gain increased significantly with increasing mesoscale activity, indicating that the physical environment can influence predator fitness at fine temporal scales. Results show that areas of high mesoscale activity not only attract top predators as foraging hotspots, but also lead to increased energy transfer across trophic levels. Our study provides evidence that the physical environment is an important factor in controlling energy flow to top predators by setting the stage for variation in resource availability. Such understanding is critical for assessing how changes in the environment and resource distribution will affect individual fitness and food web dynamics.

MapMySmoke: feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting.

BACKGROUND: Approximately 11,000 people die in Scotland each year as a result of smoking-related causes. Quitting smoking is relatively easy; maintaining a quit attempt is a very difficult task with success rates for unaided quit attempts stubbornly remaining in the single digits. Pharmaceutical treatment can improve these rates by lowering the overall reward factor of nicotine. However, these and related nicotine replacement therapies do not operate on, or address, the spatial and contextual aspects of smoking behaviour. With the ubiquity of smartphones that can log spatial, quantitative and qualitative data related to smoking behaviour, there exists a person-centred clinical opportunity to support smokers attempting to quit by first understanding their smoking behaviour and subsequently sending them dynamic messages to encourage health behaviour change within a situational context. METHODS: We have built a smartphone app-MapMySmoke-that works on Android and iOS platforms. The deployment of this app within a clinical National Health Service (NHS) setting has two distinct phases: (1) a 2-week logging phase where pre-quit patients log all of their smoking and craving events; and (2) a post-quit phase where users receive dynamic support messages and can continue to log craving events, and should they occur, relapse events. Following the initial logging phase, patients consult with their general practitioner (GP) or healthcare provider to review their smoking patterns and to outline a precise, individualised quit attempt plan. Our feasibility study consists of assessment of an initial app version during and after use by eight patients recruited from an NHS Fife GP practice. In addition to evaluation of the app as a potential smoking cessation aid, we have assessed the user experience, technological requirements and security of the data flow. RESULTS: In an initial feasibility study, we have deployed the app for a small number of patients within one GP practice in NHS Fife. We recruited eight patients within one surgery, four of whom actively logged information about their smoking behaviour. Initial feedback was very positive, and users indicated a willingness to log their craving and smoking events. In addition, two out of three patients who completed follow-up interviews noted that the app helped them reduce the number of cigarettes they smoked per day, while the third indicated that it had helped them quit. The study highlighted the use of pushed notifications as a potential technology for maintaining quit attempts, and the security of collection of data was audited. These initial results influenced the design of a planned second larger study, comprised of 100 patients, the primary objectives of which are to use statistical modelling to identify times and places of probable switches into smoking states, and to target these times with dynamic health behaviour messaging. CONCLUSIONS: While the health benefits of quitting smoking are unequivocal, such behaviour change is very difficult to achieve. Many factors are likely to contribute to maintaining smoking behaviour, yet the precise role of cues derived from the spatial environment remains unclear. The rise of smartphones, therefore, allows clinicians the opportunity to better understand the spatial aspects of smoking behaviour and affords them the opportunity to push targeted individualised health support messages at vulnerable times and places. TRIAL REGISTRATION: ClinicalTrial.gov, NCT02932917.

An experiment on the impact of a neonicotinoid pesticide on honeybees: the value of a formal analysis of the data.

BACKGROUND: We assess the analysis of the data resulting from a field experiment conducted by Pilling et al. (PLoS ONE. doi: 10.1371/journal.pone.0077193, 5) on the potential effects of thiamethoxam on honeybees. The experiment had low levels of replication, so Pilling et al. concluded that formal statistical analysis would be misleading. This would be true if such an analysis merely comprised tests of statistical significance and if the investigators concluded that lack of significance meant little or no effect. However, an analysis that includes estimation of the size of any effects-with confidence limits-allows one to reach conclusions that are not misleading and that produce useful insights. MAIN BODY: For the data of Pilling et al., we use straightforward statistical analysis to show that the confidence limits are generally so wide that any effects of thiamethoxam could have been large without being statistically significant. Instead of formal analysis, Pilling et al. simply inspected the data and concluded that they provided no evidence of detrimental effects and from this that thiamethoxam poses a "low risk" to bees. CONCLUSIONS: Conclusions derived from the inspection of the data were not just misleading in this case but also are unacceptable in principle, for if data are inadequate for a formal analysis (or only good enough to provide estimates with wide confidence intervals), then they are bound to be inadequate as a basis for reaching any sound conclusions. Given that the data in this case are largely uninformative with respect to the treatment effect, any conclusions reached from such informal approaches can do little more than reflect the prior beliefs of those involved.

A Bioenergetics Approach to Understanding the Population Consequences of Disturbance: Elephant Seals as a Model System.

Using long-term empirical data, we developed a complete population consequences of acoustic disturbance (PCAD) model and application for northern elephant seals. We assumed that the animals would not successfully forage while in a 100-km-diameter disturbance region within their foraging and transit paths. The decrease in lipid gain due to exposure was then translated to changes in birth rate and pup survival. Given their large foraging range, elephant seals were resilient to such a disturbance, showing no population-level effects. However, similar track analysis showed that given their more coastal nature, California sea lions were within a 25-km-diameter region of disturbance more often.

Expert Elicitation of Population-Level Effects of Disturbance.

Expert elicitation is a rigorous method for synthesizing expert knowledge to inform decision making and is reliable and practical when field data are limited. We evaluated the feasibility of applying expert elicitation to estimate population-level effects of disturbance on marine mammals. Diverse experts estimated parameters related to mortality and sublethal injury of North Atlantic right whales (Eubalaena glacialis). We are now eliciting expert knowledge on the movement of right whales among geographic regions to parameterize a spatial model of health. Expert elicitation complements methods such as simulation models or extrapolations from other species, sometimes with greater accuracy and less uncertainty.

Understanding the Population Consequences of Acoustic Disturbance for Marine Mammals.

Loud anthropogenic underwater noise, such as that associated with sonar operations, pile driving, or seismic surveys, can cause behavioral and physiological disturbance to many animals that may affect their survival or ability to breed. However, no formal framework for assessing the population-level consequences of this disturbance is currently available. We describe an interim version of a framework developed by a working group on the population consequences of disturbance, funded by the US Office of Naval Research through the University of California, that can be used to assess the effects of offshore renewable energy developments on marine mammal populations.

Effects of Model Formulation on Estimates of Health in Individual Right Whales (Eubalaena glacialis).

Right whales are vulnerable to many sources of anthropogenic disturbance including ship strikes, entanglement with fishing gear, and anthropogenic noise. The effect of these factors on individual health is unclear. A statistical model using photographic evidence of health was recently built to infer the true or hidden health of individual right whales. However, two important prior assumptions about the role of missing data and unexplained variance on the estimates were not previously assessed. Here we tested these factors by varying prior assumptions and model formulation. We found sensitivity to each assumption and used the output to make guidelines on future model formulation.

Combined ecological momentary assessment and global positioning system tracking to assess smoking behavior: a proof of concept study.

OBJECTIVE: Ecological momentary assessment (EMA) methods have provided a rich assessment of the contextual factors associated with a wide range of behaviors including alcohol use, eating, physical activity, and smoking. Despite this rich database, this information has not been linked to specific locations in space. Such location information, which can now be easily acquired from global positioning system (GPS) tracking devices, could provide unique information regarding the space-time distribution of behaviors and new insights into their determinants. In a proof of concept study, we assessed the acceptability and feasibility of acquiring and combining EMA and GPS data from adult smokers with attention deficit hyperactivity disorder (ADHD). METHODS: Participants were adults with ADHD who were enrolled in a larger EMA study on smoking and psychiatric symptoms. Among those enrolled in the latter study who were approached to participate (N = 11), 10 consented, provided daily EMA entries, and carried a GPS device with them during a 7-day assessment period to assess aspects of their smoking behavior. RESULTS: The majority of those eligible to participate were willing to carry a GPS device and signed the consent (10 out of 11, 91%). Of the 10 who consented, 7 participants provided EMA entries and carried the GPS device with them daily for at least 70% of the sampling period. Data are presented on the spatial distribution of smoking episodes and ADHD symptoms on a subset of the sample to demonstrate applications of GPS data. CONCLUSIONS: We conclude by discussing how EMA and GPS might be used to study the ecology of smoking and make recommendations for future research and analysis.

Estimating resource acquisition and at-sea body condition of a marine predator.

1. Body condition plays a fundamental role in many ecological and evolutionary processes at a variety of scales and across a broad range of animal taxa. An understanding of how body condition changes at fine spatial and temporal scales as a result of interaction with the environment provides necessary information about how animals acquire resources. 2. However, comparatively little is known about intra- and interindividual variation of condition in marine systems. Where condition has been studied, changes typically are recorded at relatively coarse time-scales. By quantifying how fine-scale interaction with the environment influences condition, we can broaden our understanding of how animals acquire resources and allocate them to body stores. 3. Here we used a hierarchical Bayesian state-space model to estimate the body condition as measured by the size of an animal's lipid store in two closely related species of marine predator that occupy different hemispheres: northern elephant seals (Mirounga angustirostris) and southern elephant seals (Mirounga leonina). The observation model linked drift dives to lipid stores. The process model quantified daily changes in lipid stores as a function of the physiological condition of the seal (lipid:lean tissue ratio, departure lipid and departure mass), its foraging location, two measures of behaviour and environmental covariates. 4. We found that physiological condition significantly impacted lipid gain at two time-scales - daily and at departure from the colony - that foraging location was significantly associated with lipid gain in both species of elephant seals and that long-term behavioural phase was associated with positive lipid gain in northern and southern elephant seals. In northern elephant seals, the occurrence of short-term behavioural states assumed to represent foraging were correlated with lipid gain. Lipid gain was a function of covariates in both species. Southern elephant seals performed fewer drift dives than northern elephant seals and gained lipids at a lower rate. 5. We have demonstrated a new way to obtain time series of body condition estimates for a marine predator at fine spatial and temporal scales. This modelling approach accounts for uncertainty at many levels and has the potential to integrate physiological and movement ecology of top predators. The observation model we used was specific to elephant seals, but the process model can readily be applied to other species, providing an opportunity to understand how animals respond to their environment at a fine spatial scale.

Using hierarchical bayes to understand movement, health, and survival in the endangered north atlantic right whale.

Body condition is an indicator of health, and it plays a key role in many vital processes for mammalian species. While evidence of individual body condition can be obtained, these observations provide just brief glimpses into the health state of the animal. An analytical framework is needed for understanding how health of animals changes over space and time.Through knowledge of individual health we can better understand the status of populations. This is particularly important in endangered species, where the consequences of disruption of critical biological functions can push groups of animals rapidly toward extinction. Here we built a state-space model that provides estimates of movement, health, and survival. We assimilated 30+ years of photographic evidence of body condition and three additional visual health parameters in individual North Atlantic right whales, together with survey data, to infer the true health status as it changes over space and time. We also included the effect of reproductive status and entanglement status on health. At the population level, we estimated differential movement patterns in males and females. At the individual level, we estimated the likely animal locations each month. We estimated the relationship between observed and latent health status. Observations of body condition, skin condition, cyamid infestation on the blowholes, and rake marks all provided measures of the true underlying health. The resulting time series of individual health highlight both normal variations in health status and how anthropogenic stressors can affect the health and, ultimately, the survival of individuals. This modeling approach provides information for monitoring of health in right whales, as well as a framework for integrating observational data at the level of individuals up through the health status of the population. This framework can be broadly applied to a variety of systems - terrestrial and marine - where sporadic observations of individuals exist.

Graph models of habitat mosaics.

Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.

Understanding movement data and movement processes: current and emerging directions.

Animal movement has been the focus on much theoretical and empirical work in ecology over the last 25 years. By studying the causes and consequences of individual movement, ecologists have gained greater insight into the behavior of individuals and the spatial dynamics of populations at increasingly higher levels of organization. In particular, ecologists have focused on the interaction between individuals and their environment in an effort to understand future impacts from habitat loss and climate change. Tools to examine this interaction have included: fractal analysis, first passage time, Lévy flights, multi-behavioral analysis, hidden markov models, and state-space models. Concurrent with the development of movement models has been an increase in the sophistication and availability of hierarchical bayesian models. In this review we bring these two threads together by using hierarchical structures as a framework for reviewing individual models. We synthesize emerging themes in movement ecology, and propose a new hierarchical model for animal movement that builds on these emerging themes. This model moves away from traditional random walks, and instead focuses inference on how moving animals with complex behavior interact with their landscape and make choices about its suitability.