Sample size requirements for genetic studies on yellowfin tuna.

In population genetics, the amount of information for an analytical task is governed by the number of individuals sampled and the amount of genetic information measured on each of those individuals. In this work, we assessed the numbers of individual yellowfin tuna (Thunnus albacares) and genetic markers required for ocean-basin scale inferences. We assessed this for three distinct data analysis tasks that are often employed: testing for differences between genetic profiles; stock delineation, and; assignment of individuals to stocks. For all analytical tasks, we used real (not simulated) data from four sampling locations that span the tropical Pacific Ocean. Whilst spatially separated, the genetic differences between the sampling sites were not substantial, a maximum of approximately Fst = 0.02, which is quite typical of large pelagic fish. We repeatedly sub-sampled the data, mimicking a new survey, and performed the analyses. False positive rates were also assessed by re-sampling and randomly assigning fish to groups. Varying the sample sizes indicated that some analytical tasks, namely profile testing, required relatively few individuals per sampling location (n ≳ 10) and single nucleotide polymorphisms (SNPs, m ≳ 256). Stock delineation required more individuals per sampling location (n ≳ 25). Assignment of fish to sampling locations required substantially more individuals, more in fact than we had available (n > 50), although this sample size could be reduced to n ≳ 30 when individual fish were assumed to belong to one of the groups sampled. With these results, designers of molecular ecological surveys for yellowfin tuna, and users of information from them, can assess whether the information content is adequate for the required inferential task.

Predicting the spread and persistence of genetically modified dominant sterile male mosquitoes.

BACKGROUND: Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains. METHODS: In this study, we: (i) describe a diffusion-advection-reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains. RESULTS: If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location. CONCLUSIONS: Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes.

Effects of ignoring survey design information for data reuse.

Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating data sets with progressively more uneven inclusion probabilities and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g., inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.

Monitoring the resilience of a no-take marine reserve to a range extending species using benthic imagery.

Global climate change is driving the redistribution of marine species and thereby potentially restructuring endemic communities. Understanding how localised conservation measures such as protection from additional human pressures can confer resilience to ecosystems is therefore an important area of research. Here, we examine the resilience of a no-take marine reserve (NTR) to the establishment of urchin barrens habitat. The barrens habitat is created through overgrazing of kelp by an invading urchin species that is expanding its range within a hotspot of rapid climate change. In our study region, a multi-year monitoring program provides a unique time-series of benthic imagery collected by an Autonomous Underwater Vehicle (AUV) within an NTR and nearby reference areas. We use a Bayesian hierarchical spatio-temporal modelling approach to estimate whether the NTR is associated with reduced formation of urchin barrens, and thereby enhances local resilience. Our approach controls for the important environmental covariates of depth and habitat complexity (quantified as rugosity derived from multibeam sonar mapping), as well as spatial and temporal dependence. We find evidence for the NTR conferring resilience with a strong reserve effect that suggests improved resistance to the establishment of barrens. However, we find a concerning and consistent trajectory of increasing barrens cover in both the reference areas and the NTR, with the odds of barrens increasing by approximately 32% per year. Thus, whereas the reserve is demonstrating resilience to the initial establishment of barrens, there is currently no evidence of recovery once barrens are established. We also find that depth and rugosity covariates derived from multibeam mapping provide useful predictors for barrens occurrence. These results have important management implications as they demonstrate: (i) the importance of monitoring programs to inform adaptive management; (ii) that NTRs provide a potential local conservation management tool under climate change impacts, and (iii) that technologies such as AUVs and multibeam mapping can be harnessed to inform regional decision-making. Continuation of the current monitoring program is required to assess whether the NTR can provide long term protection from a phase shift that replaces kelp with urchin barrens.

Global patterns of change and variation in sea surface temperature and chlorophyll a.

Changes over the scale of decades in oceanic environments present a range of challenges for management and utilisation of ocean resources. Here we investigate sources of global temporal variation in Sea Surface Temperature (SST) and Ocean Colour (Chl-a) and their co-variation, over a 14 year period using statistical methodologies that partition sources of variation into inter-annual and annual components and explicitly account for daily auto-correlation. The variation in SST shows bands of increasing variability with increasing latitude, while the analysis of annual variability in Chl-a shows mostly mid-latitude high variability bands. Covariation patterns of SST and Chl-a suggests several different mechanisms impacting Chl-a change and variance. Our high spatial resolution analysis indicates these are likely to be operating at relatively small spatial scales. There are large regions showing warming and rising of Chl-a, contrasting with regions that show warming and decreasing Chl-a. The covariation pattern in annual variation in SST and Chl-a reveals broad latitudinal bands. On smaller scales there are significant regional anomalies where upwellings are known to occur. Over decadal time scales both trend and variation in SST, Chl-a and their covariance is highly spatially heterogeneous, indicating that monitoring and resource management must be regionally appropriate.

Reliably discriminating stock structure with genetic markers: Mixture models with robust and fast computation.

Delineating naturally occurring and self-sustaining subpopulations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borrowed from superficially similar analytical tasks in human genetics even though models specifically for stock identification have been previously developed. Unfortunately, the analytical tasks in resource management and human genetics are not identical-questions about humans are typically aimed at inferring ancestry (often referred to as "admixture") rather than breeding stocks. In this article, we argue, and show through simulation experiments and an analysis of yellowfin tuna data, that ancestral analysis methods are not always appropriate for stock delineation. In this work, we advocate a variant of a previously introduced and simpler model that identifies stocks directly. We also highlight that the computational aspects of the analysis, irrespective of the model, are difficult. We introduce some alternative computational methods and quantitatively compare these methods to each other and to established methods. We also present a method for quantifying uncertainty in model parameters and in assignment probabilities. In doing so, we demonstrate that point estimates can be misleading. One of the computational strategies presented here, based on an expectation-maximization algorithm with judiciously chosen starting values, is robust and has a modest computational cost.

Do communities exist? Complex patterns of overlapping marine species distributions.

Understanding the way in which species are associated in communities is a fundamental question in ecology. Yet there remains a tension between communities as highly structured units or as coincidental collections of individualistic species. We explored these ideas using a new statistical approach that clusters species based on their environmental response: a species archetype, rather than clustering sites based on their species composition. We found groups of species that are consistently highly correlated, but that these groups are not unique to any set of locations and overlap spatially. The species present at a single site are a realization of species from the (multiple) archetype groups that are likely to be present at that location based on their response to the environment.

Twenty years of high-resolution sea surface temperature imagery around Australia: inter-annual and annual variability.

The physical climate defines a significant portion of the habitats in which biological communities and species reside. It is important to quantify these environmental conditions, and how they have changed, as this will inform future efforts to study many natural systems. In this article, we present the results of a statistical summary of the variability in sea surface temperature (SST) time-series data for the waters surrounding Australia, from 1993 to 2013. We partition variation in the SST series into annual trends, inter-annual trends, and a number of components of random variation. We utilise satellite data and validate the statistical summary from these data to summaries of data from long-term monitoring stations and from the global drifter program. The spatially dense results, available as maps from the Australian Oceanographic Data Network's data portal (http://www.cmar.csiro.au/geonetwork/srv/en/metadata.show?id=51805), show clear trends that associate with oceanographic features. Noteworthy oceanographic features include: average warming was greatest off southern West Australia and off eastern Tasmania, where the warming was around 0.6°C per decade for a twenty year study period, and insubstantial warming in areas dominated by the East Australian Current, but this area did exhibit high levels of inter-annual variability (long-term trend increases and decreases but does not increase on average). The results of the analyses can be directly incorporated into (biogeographic) models that explain variation in biological data where both biological and environmental data are on a fine scale.

To mix or not to mix: comparing the predictive performance of mixture models vs. separate species distribution models.

Species distribution models (SDMs) are an important tool for studying the patterns of species across environmental and geographic space. For community data, a common approach involves fitting an SDM to each species separately, although the large number of models makes interpretation difficult and fails to exploit any similarities between individual species responses. A recently proposed alternative that can potentially overcome these difficulties is species archetype models (SAMs), a model-based approach that clusters species based on their environmental response. In this paper, we compare the predictive performance of SAMs against separate SDMs using a number of multi-species data sets. Results show that SAMs improve model accuracy and discriminatory capacity compared to separate SDMs. This is achieved by borrowing strength from common species having higher information content. Moreover, the improvement increases as the species become rarer.

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.

The analysis of biodiversity using rank abundance distributions.

Biodiversity is an important topic of ecological research. A common form of data collected to investigate patterns of biodiversity is the number of individuals of each species at a series of locations. These data contain information on the number of individuals (abundance), the number of species (richness), and the relative proportion of each species within the sampled assemblage (evenness). If there are enough sampled locations across an environmental gradient then the data should contain information on how these three attributes of biodiversity change over gradients. We show that the rank abundance distribution (RAD) representation of the data provides a convenient method for quantifying these three attributes constituting biodiversity. We present a statistical framework for modeling RADs and allow their multivariate distribution to vary according to environmental gradients. The method relies on three models: a negative binomial model, a truncated negative binomial model, and a novel model based on a modified Dirichlet-multinomial that allows for a particular type of heterogeneity observed in RAD data. The method is motivated by, and applied to, a large-scale marine survey off the coast of Western Australia, Australia. It provides a rich description of biodiversity and how it changes with environmental conditions.