Close-kin mark-recapture informs critically endangered terrestrial mammal status.

Reliable information on population size is fundamental to the management of threatened species. For wild species, mark-recapture methods are a cornerstone of abundance estimation. Here, we show the first application of the close-kin mark-recapture (CKMR) method to a terrestrial species of high conservation value; the Christmas Island flying-fox (CIFF). The CIFF is the island's last remaining native terrestrial mammal and was recently listed as critically endangered. CKMR is a powerful tool for estimating the demographic parameters central to CIFF management and circumvents the complications arising from the species' cryptic nature, mobility, and difficult-to-survey habitat. To this end, we used genetic data from 450 CIFFs captured between 2015 and 2019 to detect kin pairs. We implemented a novel CKMR model that estimates sex-specific abundance, trend, and mortality and accommodates observations from the kin-pair distribution of male reproductive skew and mate persistence. CKMR estimated CIFF total adult female abundance to be approximately 2050 individuals (95% CI (950, 4300)). We showed that on average only 23% of the adult male population contributed to annual reproduction and strong evidence for between-year mate fidelity, an observation not previously quantified for a Pteropus species in the wild. Critically, our population estimates provide the most robust understanding of the status of this critically endangered population, informing immediate and future conservation initiatives.

Low levels of sibship encourage use of larvae in western Atlantic bluefin tuna abundance estimation by close-kin mark-recapture.

Globally, tunas are among the most valuable fish stocks, but are also inherently difficult to monitor and assess. Samples of larvae of Western Atlantic bluefin tuna Thunnus thynnus (Linnaeus, 1758) from standardized annual surveys in the northern Gulf of Mexico provide a potential source of "offspring" for close-kin mark-recapture (CKMR) estimates of abundance. However, the spatial patchiness and highly skewed numbers of larvae per tow suggest sampled larvae may come from a small number of parents, compromising the precision of CKMR. We used high throughput genomic profiling to study sibship within and among larval tows from the 2016 standardized Gulf-wide survey compared to targeted sampling carried out in 2017. Full- and half-siblings were found within both years, with 12% of 156 samples in 2016 and 56% of 317 samples in 2017 having at least one sibling. There were also two pairs of cross cohort half-siblings. Targeted sampling increased the number of larvae collected per sampling event but resulted in a higher proportion of siblings. The combined effective sample size across both years was about 75% of the nominal size, indicating that Gulf of Mexico larval collections could be a suitable source of juveniles for CKMR in Western Atlantic bluefin tuna.

Robustness of close-kin mark-recapture estimators to dispersal limitation and spatially varying sampling probabilities.

Close-kin mark-recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population-level inference relative to traditional monitoring programs.One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.We used individual-based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long-lived mammal species subject to lethal sampling.Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov-Smirnov tests.Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.

Accounting for environmental and observer effects in estimating abundance of southern bluefin tuna from aerial survey data.

Southern bluefin tuna (SBT) is a valuable species that has been subject to high exploitation rates since the 1950s. In 2011, the spawning stock biomass was estimated to be at a historically low level, at only 5% of pre-fished biomass. A key component for managing and rebuilding the stock is having reliable, fishery-independent estimates of juvenile abundance. This paper describes how such estimates have been constructed from aerial surveys of juvenile (age 2-4) SBT conducted annually in the Great Australian Bight from 1993-2000 and 2005-2009. During these surveys, observers flew along pre-set transect lines searching for surface schools of SBT. Data were collected on the location and biomass of SBT sightings, and on the environmental conditions present during the survey. Sea surface temperature (SST) was found to correlate with the size (biomass) of schools, and several environmental variables, SST and wind speed in particular, were found to correlate with the number of sightings (presumably by affecting the ability of observers to see surface schools as well as whether fish were present at the surface). In addition, observers changed over time and differed in their aptitude for spotting tuna. Thus, generalized linear mixed models (GLMMs) were used to standardize the sightings and biomass data to a common set of observers and environmental conditions in order to produce an annual time series of relative abundance estimates. These estimates, which form one of two key inputs to the management procedure used by the international Commission for the Conservation of Southern Bluefin Tuna to set the global catch quota, suggest juvenile abundance was highest in the first years of the survey (1993-1996), after which it declined and fluctuated around a level about four times lower.

Robust estimates of a high Ne/N ratio in a top marine predator, southern bluefin tuna.

Genetic studies of several marine species with high fecundity have produced "tiny" estimates (≤10-3) of the ratio of effective population size (Ne) to adult census size (N), suggesting that even very large populations might be at genetic risk. A recent study using close-kin mark-recapture methods estimated adult abundance at N ≈ 2 × 106 for southern bluefin tuna (SBT), a highly fecund top predator that supports a lucrative (~$1 billion/year) fishery. We used the same genetic and life history data (almost 13,000 fish collected over 5 years) to generate genetic and demographic estimates of Ne per generation and Nb (effective number of breeders) per year and the Ne/N ratio. Demographic estimates, which accounted for age-specific vital rates, skip breeding, variation in fecundity at age, and persistent individual differences in reproductive success, suggest that Ne/N is >0.1 and perhaps about 0.5. The genetic estimates supported this conclusion. Simulations using true Ne = 5 × 105 (Ne/N = 0.25) produced results statistically consistent with the empirical genetic estimates, whereas simulations using Ne = 2 × 104 (Ne/N = 0.01) did not. Our results show that robust estimates of Ne and Ne/N can be obtained for large populations, provided sufficiently large numbers of individuals and genetic markers are used and temporal replication (here, 5 years of adult and juvenile samples) is sufficient to provide a distribution of estimates. The high estimated Ne/N ratio in SBT is encouraging and suggests that the species will not be compromised by a lack of genetic diversity in responding to environmental change and harvest.

Inferring contemporary and historical genetic connectivity from juveniles.

Measuring population connectivity is a critical task in conservation biology. While genetic markers can provide reliable long-term historical estimates of population connectivity, scientists are still limited in their ability to determine contemporary patterns of gene flow, the most practical time frame for management. Here, we tackled this issue by developing a new approach that only requires juvenile sampling at a single time period. To demonstrate the usefulness of our method, we used the Speartooth shark (Glyphis glyphis), a critically endangered species of river shark found only in tropical northern Australia and southern Papua New Guinea. Contemporary adult and juvenile shark movements, estimated with the spatial distribution of kin pairs across and within three river systems, was contrasted with historical long-term connectivity patterns, estimated from mitogenomes and genome-wide SNP data. We found strong support for river fidelity in juveniles with the within-cohort relationship analysis. Male breeding movements were highlighted with the cross-cohort relationship analysis, and female reproductive philopatry to the river systems was revealed by the mitogenomic analysis. We show that accounting for juvenile river fidelity and female philopatry is important in population structure analysis and that targeted sampling in nurseries and juvenile aggregations should be included in the genomic toolbox of threatened species management.

Absolute abundance of southern bluefin tuna estimated by close-kin mark-recapture.

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.

Designing an effective mark-recapture study of Antarctic blue whales.

To properly conserve and manage wild populations, it is important to have information on abundance and population dynamics. In the case of rare and cryptic species, especially in remote locations, surveys can be difficult and expensive, and run the risk of not producing sample sizes large enough to produce precise estimates. Therefore, it is crucial to conduct preliminary analysis to determine if the study will produce useable estimates. The focus of this paper is a proposed mark-recapture study of Antarctic blue whales (Balaenoptera musculus intermedia). Antarctic blue whales were hunted to near extinction up until the mid- 1960s, when commercial exploitation of this species ended. Current abundance estimates are a decade old. Furthermore, at present, there are no formal circumpolar-level cetacean surveys operating in Antarctic waters and, specifically, there is no strategy to monitor the potential recovery of Antarctic blue whales. Hence the work in this paper was motivated by the need to inform decisions on strategies for future monitoring of Antarctic blue whale population. The paper describes a model to predict the precision and bias of estimates from a proposed survey program. The analysis showed that mark-recapture is indeed a suitable method to provide a circumpolar abundance estimate of Antarctic blue whales, with precision of the abundance, at the midpoint of the program, predicted to be between 0.2 and 0.3. However, this was only if passive acoustic tracking was utilized to increase the encounter rate. The analysis also provided guidance on general design for an Antarctic blue whale program, showing that it requires a 12-year duration; although surveys do not necessarily need to be run every year if multiple vessels are available to clump effort. Mark-recapture is based on a number of assumptions; it was evident from the analysis that ongoing analysis and monitoring of the data would be required to check such assumptions hold (e.g., test for heterogeneity), with the modeling adjusted as needed.

Molecular biomarkers for chronological age in animal ecology.

The chronological age of an individual animal predicts many of its biological characteristics, and these in turn influence population-level ecological processes. Animal age information can therefore be valuable in ecological research, but many species have no external features that allow age to be reliably determined. Molecular age biomarkers provide a potential solution to this problem. Research in this area of molecular ecology has so far focused on a limited range of age biomarkers. The most commonly tested molecular age biomarker is change in average telomere length, which predicts age well in a small number of species and tissues, but performs poorly in many other situations. Epigenetic regulation of gene expression has recently been shown to cause age-related modifications to DNA and to cause changes in abundance of several RNA types throughout animal lifespans. Age biomarkers based on these epigenetic changes, and other new DNA-based assays, have already been applied to model organisms, humans and a limited number of wild animals. There is clear potential to apply these marker types more widely in ecological studies. For many species, these new approaches will produce age estimates where this was previously impractical. They will also enable age information to be gathered in cross-sectional studies and expand the range of demographic characteristics that can be quantified with molecular methods. We describe the range of molecular age biomarkers that have been investigated to date and suggest approaches for developing the newer marker types as age assays in nonmodel animal species.

Spawning Dynamics and Size Related Trends in Reproductive Parameters of Southern Bluefin Tuna, Thunnus maccoyii.

Knowledge of spawning behaviour and fecundity of fish is important for estimating the reproductive potential of a stock and for constructing appropriate statistical models for assessing sustainable catch levels. Estimates of length-based reproductive parameters are particularly important for determining potential annual fecundity as a function of fish size, but they are often difficult to estimate reliably. Here we provide new information on the reproductive dynamics of southern bluefin tuna (SBT) Thunnus maccoyii through the analysis of fish size and ovary histology collected on the spawning ground in 1993-1995 and 1999-2002. These are used to refine previous parameter estimates of spawning dynamics and investigate size related trends in these parameters. Our results suggest that the small SBT tend to arrive on the spawning ground slightly later and depart earlier in the spawning season relative to large fish. All females were mature and the majority were classed as spawning capable (actively spawning or non-spawning) with a very small proportion classed as regressing. The fraction of females spawning per day decreased with fish size, but once females start a spawning episode, they spawned daily irrespective of size. Mean batch fecundity was estimated directly at 6.5 million oocytes. Analysis of ovary histology and ovary weight data indicated that relative batch fecundity, and the duration of spawning and non-spawning episodes, increased with fish size. These reproductive parameter estimates could be used with estimates of residency time on the spawning ground as a function of fish size (if known) and demographic data for the spawning population to provide a time series of relative annual fecundity for SBT.

Accounting for location error in Kalman filters: integrating animal borne sensor data into assimilation schemes.

Data assimilation is a crucial aspect of modern oceanography. It allows the future forecasting and backward smoothing of ocean state from the noisy observations. Statistical methods are employed to perform these tasks and are often based on or related to the Kalman filter. Typically Kalman filters assumes that the locations associated with observations are known with certainty. This is reasonable for typical oceanographic measurement methods. Recently, however an alternative and abundant source of data comes from the deployment of ocean sensors on marine animals. This source of data has some attractive properties: unlike traditional oceanographic collection platforms, it is relatively cheap to collect, plentiful, has multiple scientific uses and users, and samples areas of the ocean that are often difficult of costly to sample. However, inherent uncertainty in the location of the observations is a barrier to full utilisation of animal-borne sensor data in data-assimilation schemes. In this article we examine this issue and suggest a simple approximation to explicitly incorporate the location uncertainty, while staying in the scope of Kalman-filter-like methods. The approximation stems from a Taylor-series approximation to elements of the updating equation.

Chilean blue whales as a case study to illustrate methods to estimate abundance and evaluate conservation status of rare species.

Often abundance of rare species cannot be estimated with conventional design-based methods, so we illustrate with a population of blue whales (Balaenoptera musculus) a spatial model-based method to estimate abundance. We analyzed data from line-transect surveys of blue whales off the coast of Chile, where the population was hunted to low levels. Field protocols allowed deviation from planned track lines to collect identification photographs and tissue samples for genetic analyses, which resulted in an ad hoc sampling design with increased effort in areas of higher densities. Thus, we used spatial modeling methods to estimate abundance. Spatial models are increasingly being used to analyze data from surveys of marine, aquatic, and terrestrial species, but estimation of uncertainty from such models is often problematic. We developed a new, broadly applicable variance estimator that showed there were likely 303 whales (95% CI 176-625) in the study area. The survey did not span the whales' entire range, so this is a minimum estimate. We estimated current minimum abundance relative to pre-exploitation abundance (i.e., status) with a population dynamics model that incorporated our minimum abundance estimate, likely population growth rates from a meta-analysis of rates of increase in large baleen whales, and two alternative assumptions about historic catches. From this model, we estimated that the population was at a minimum of 16.5% (95% CI 7.3-34.4%) of pre-exploitation levels in 1998 under one catch assumption and 12.4% (CI 5.4-26.3%) of pre-exploitation levels under the other. Thus, although Chilean blue whales are probably still at a small fraction of pre-exploitation abundance, even these minimum abundance estimates demonstrate that their status is better than that of Antarctic blue whales, which are still <1% of pre-exploitation population size. We anticipate our methods will be broadly applicable in aquatic and terrestrial surveys for rarely encountered species, especially when the surveys are intended to maximize encounter rates and estimate abundance.

Using GPS data to evaluate the accuracy of state-space methods for correction of Argos satellite telemetry error.

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.

Classifying movement behaviour in relation to environmental conditions using hidden Markov models.

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.

tossm: an R package for assessing performance of genetic analytical methods in a management context.

tossm (Testing of Spatial Structure Methods) is a package for testing the performance of genetic analytical methods in a management context. In the tossm package, any method developed to detect population genetic structure can be combined with a mechanism for creating management units (MUs) based on the genetic analysis. The resulting Boundary-Setting Algorithm (BSA) dictates harvest boundaries with a genetic basis. These BSAs can be evaluated with respect to how well the MUs they define meet management objectives.