Browsing by Author "Fewster RM"

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    Estimating abundance of a small population of Bryde's whales: a comparison between aerial surveys and boat-based platforms of opportunity
    (John Wiley & Sons Ltd., on behalf of Zoological Society of London, 2024-08-01) Hamilton ONP; Fewster RM; Low P; Johnson F; Lea C; Stockin KA; van der Linde K; Constantine R; Boersch-Supan P; Williams R
    Accurate abundance estimates are essential for the development of effective conservation management strategies, yet they are difficult to produce for small populations that are elusive and sparsely distributed throughout their range. For such populations it is challenging to collect a representative dataset sufficient for robust estimation of detectability and abundance. Over a one-year study, we used two methods to estimate abundance of a Nationally Critical, widely dispersed Bryde's whale population in the Hauraki Gulf, Aotearoa/New Zealand; (i) distance sampling from systematic line-transect aerial surveys (n = 22 surveys, 9,944 km, total sightings 21–24 whales), and (ii) mark-recapture (MR) using photo-identification images collected from a platform-of-opportunity and small-boat surveys (218 sampling occasions, 27 whales). From the aerial surveys, we estimated an average of 15 whales (95% CI = 6, 30; CV = 37%) at the sea-surface at any time. For the boat-based surveys, we developed a custom MR model to address seasonal and individual heterogeneity in capture probabilities and obtained an estimate of 72 distinct whales (95% CI = 38, 106; CV = 24%) in the population. These two approaches provide different perspectives on the abundance and dynamics of Bryde's whales. The aerial surveys estimate the average number of individuals present at any one time, whereas the MR model estimates the total number of animals that used the Gulf during the study. Although neither sampling method is optimal for estimating the abundance of this small, dispersed population, the use of two complementary approaches informs conservation managers about patterns of abundance and distribution over different temporal and spatial scales. It is common to have limited resources for marine research where model assumptions cannot be met. Here, we highlight pragmatic strategies showing how models can be customized to the population of interest to assist with monitoring species of conservation concern.
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    Genogeographic clustering to identify cross-species concordance of spatial genetic patterns
    (John Wiley and Sons, Ltd, 2022-04) Arranz V; Fewster RM; Lavery SD; Burridge C
    Aim: While in recent years, there have been considerable advances in discerning spatial genetic patterns within species, the task of identifying common patterns across species is still challenging. Approaches using new data from co-sampled species permit rigorous statistical analysis but are often limited to a small number of species. Meta-analyses of published data can encompass a much broader range of species, but are usually restricted by uneven data properties. There is a need for new approaches that bring greater statistical rigour to meta-analyses and are also able to discern more than a single spatial pattern among species. We propose a new approach for comparative multi-species meta-analyses of published population genetic data that address many existing limitations. Innovation: The proposed “genogeographic clustering” technique takes a three-stage approach: (i) use common genetic metrics to gain location-specific measures across the sampled range of each species; (ii) for each species, determine the spatial genetic pattern by fitting a smooth “genogeographic” trend curve to the genetic data; and (iii) quantitatively cluster species according to their similarity in spatial pattern. We apply this technique to 21 species of intertidal invertebrates from the New Zealand coastline, to resolve common spatial patterns from disparate profiles of genetic diversity. Main conclusions: The genogeographic curves are shown to successfully capture the known spatial patterns within each intertidal species and readily permit statistical comparison of those patterns, regardless of sampling and marker inconsistencies. The species clustering technique is shown to discern groups of species that clearly share spatial patterns within groups but differ significantly among groups. Genogeographic species clustering provides a novel approach to discerning multiple common spatial patterns of diversity among a large number of species. It will permit more rigorous comparative studies from diverse published data and can be easily extended to a wide variety of alternative measures of genetic diversity or divergence.