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Summary 1.Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development. 2. In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner. 3. It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters. 4. To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna (Thunnus maccoyii). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.

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Conservation concerns exist for many sharks but robust estimates of abundance are often lacking. Improving population status is a performance measure for species under conservation or recovery plans, yet the lack of data permitting estimation of population size means the efficacy of management actions can be difficult to assess, and achieving the goal of removing species from conservation listing challenging. For potentially dangerous species, like the white shark, balancing conservation and public safety demands is politically and socially complex, often leading to vigorous debate about their population status. This increases the need for robust information to inform policy decisions. We developed a novel method for estimating the total abundance of white sharks in eastern Australia and New Zealand using the genetic-relatedness of juveniles and applying a close-kin mark-recapture framework and demographic model. Estimated numbers of adults are small (ca. 280-650), as is total population size (ca. 2,500-6,750). However, estimates of survival probability are high for adults (over 90%), and fairly high for juveniles (around 73%). This represents the first direct estimate of total white shark abundance and survival calculated from data across both the spatial and temporal life-history of the animal and provides a pathway to estimate population trend.Top-order predators retain a very visible presence in human society due to their size, power, dramatic interactions with prey and infrequent, but high profile, interactions with humans that sometimes result in tragic outcomes. The latter generates considerable public and political debate, particularly for protected species, requiring a delicate balance between maintaining public safety and population recovery 1 . The white shark (Carcharodon carcharias) is emblematic of this duality. It is globally distributed, long lived (>50 yrs), attains up to 6.5 m, and has low reproductive potential making populations vulnerable to decline from human impacts 2,3 . It has gained notoriety from attacks on humans and through its prominence in popular culture 4 . White sharks are listed under international conventions restricting global trade and coordinating conservation measures. They are listed as Vulnerable by the International Union for the Conservation of Nature (IUCN), on Appendix II of the Convention on International Trade in Endangered Species (CITES) and on both Appendices II and III of the Convention on the Conservation of Migratory Species (CMS) 5 . They are protected in the national waters of several countries due to documented or perceived population declines and vulnerabilities given its life history 2 . White sharks are protected in Australia, listed as both Vulnerable and Migratory under the Federal Environment Protection and Biodiversity Conservation Act 1999, protected under various State legislation and subject to a national recovery plan to arrest decline and improve population status 6 . Despite global progress on identifying movement patterns, habitat and populat...

Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models or mixed additive models. We propose a class of smoothers that are appropriate for smoothing over difficult regions of -super-2 which can be represented in terms of a low rank basis and one or two quadratic penalties. The key features of these smoothers are that they do not 'smooth across' boundary features, that their representation in terms of a basis and penalties allows straightforward incorporation as components of generalized additive models, mixed models and other non-standard models, that smoothness selection for these model components is straightforward to accomplish in a computationally efficient manner via generalized cross-validation, Akaike's information criterion or restricted maximum likelihood, for example, and that their low rank means that their use is computationally efficient. Copyright (c) 2008 Royal Statistical Society.

Southern bluefin tuna is a highly valuable, severely depleted species, whose abundance and productivity have been difficult to assess with conventional fishery data. Here we use large-scale genotyping to look for parent–offspring pairs among 14,000 tissue samples of juvenile and adult tuna collected from the fisheries, finding 45 pairs in total. Using a modified mark-recapture framework where ‘recaptures' are kin rather than individuals, we can estimate adult abundance and other demographic parameters such as survival, without needing to use contentious fishery catch or effort data. Our abundance estimates are substantially higher and more precise than previously thought, indicating a somewhat less-depleted and more productive stock. More broadly, this technique of ‘close-kin mark-recapture' has widespread utility in fisheries and wildlife conservation. It estimates a key parameter for management—the absolute abundance of adults—while avoiding the expense of independent surveys or tag-release programmes, and the interpretational problems of fishery catch rates.

Recent studies have applied state-space models to satellite telemetry data in order to remove noise from raw location estimates and infer the true tracks of animals. However, while the resulting tracks may appear plausible, it is difficult to determine the accuracy of the estimated positions, especially for position estimates interpolated to times between satellite locations. In this study, we use data from two gray seals (Halichoerus grypus) carrying tags that transmitted Fastloc GPS positions via Argos satellites. This combination of Service Argos data and highly accurate GPS data allowed examination of the accuracy of state-space position estimates and their uncertainty derived from satellite telemetry data. After applying a speed filter to remove aberrant satellite telemetry locations, we fit a continuous-time Kalman filter to estimate the parameters of a random walk, used Kalman smoothing to infer positions at the times of the GPS measurements, and then compared the filtered telemetry estimates with the actual GPS measurements. We investigated the effect of varying maximum speed thresholds in the speed-filtering algorithm on the root mean-square error (RMSE) estimates and used minimum RMSE as a criterion to guide the final choice of speed threshold. The optimal speed thresholds differed between the two animals (1.1 m/s and 2.5 m/s) and retained 50% and 65% of the data for each seal. However, using a speed filter of 1.1 m/s resulted in very similar RMSE for both animals. For the two seals, the RMSE of the Kalman-filtered estimates of location were 5.9 and 12.76 km, respectively, and 75% of the modeled positions had errors less than 6.25 km and 11.7 km for each seal. Confidence interval coverage was close to correct at typical levels (80-95%), although it tended to be overly generous at smaller sizes. The reliability of uncertainty estimates was also affected by the chosen speed threshold. The combination of speed and Kalman filtering allows for effective calculation of location and also indicates the limits of accuracy when correcting service Argos locations and linking satellite telemetry data to spatial covariate and habitat data.

Quantitative ecosystem indicators are needed to fulfill the mandate for ecosystem-based fisheries management. A variety of community metrics could potentially be used, but before reference levels for such indices can be established the sensitivity of candidate indices to fishing and other disturbances must be determined. One approach for obtaining such information is to test candidate indicators with models that mimic real ecosystems and can be manipulated experimentally. Here we construct a size-based multispecies model of a community of fish species that interact by predation. The model was parameterized for 21 fish species to obtain a predation-regulated community. Following an analysis of the sensitivity of the model to parameter uncertainty, we tested the sensitivity of community-level indicators to increasing levels of fishing mortality (F). Abundance and biomass spectra were sensitive to fishing mortality, with the slope decreasing with increasing F. Species diversity size spectra were also very sensitive to F, with diversity in the largest size classes declining rapidly. In contrast, k-dominance curves were less sensitive to fishing pressure. Importantly, however, although most community-level metrics showed clear trends in response to fishing, single-species declines in spawning stock biomass were the most sensitive indicators of fishing effects