Modelling the abundance and distribution of marine birds accounting for uncertain species identification. Issue 1 (28th November 2014)

• Record Type: Journal Article
• Authors: Johnston, Alison; Thaxter, Chris B.; Austin, Graham E.; Cook, Aonghais S.C.P.; Humphreys, Elizabeth M.; Still, David A.; Mackay, Alastair; Irvine, Ryan; Webb, Andy; Burton, Niall H.K.; Votier, Steve
• Is Part Of: Journal of applied ecology. Volume 52:Issue 1(2015:Feb.)
• Page Start: 150
• Page End: 160
• Publication Date: 2014-11-28
• Subjects: Agriculture -- Periodicals; Biology, Economic -- Periodicals; Agricultural ecology -- Periodicals; Applied ecology -- Periodicals; 577
• DOI: 10.1111/1365-2664.12364
• Languages: English
• ISSNs: 0021-8901

Abstract

<abstract abstract-type="main" id="jpe12364-abs-0001"> <title>Summary</title> <p> <list id="jpe12364-list-0001" list-type="order"> <list-item> <p>Many emerging methods for ecological monitoring use passive monitoring techniques, which cannot always be used to identify the observed species with certainty. Digital aerial surveys of birds in marine areas are one such example of passive observation and they are increasingly being used to quantify the abundance and distribution of marine birds to inform impact assessments for proposed offshore wind developments. However, the uncertainty in species identification presents a major hurdle to determining the abundance and distribution of individual species.</p> </list-item> <list-item> <p>Using a novel analytical approach, we combined data from two surveys in the same area: aerial digital imagery that identified only 23% of individuals to species level and boat survey records that identified 95% of individuals to species level. The data sets were analysed to estimate the effects of environmental covariates on species density and to produce species‐specific estimates of population size.</p> </list-item> <list-item> <p>For each digital aerial observation without certain species identification, randomized species assignments were generated using the observed species proportions from the boat surveys. For each species, we modelled several random realizations of species assignments and produced a density surface from the ensemble of models. The uncertainty from each stage of the process was propagated, so that final confidence limits accounted for all sources of uncertainty, including species identification.</p> </list-item> <list-item> <p>In the breeding season, several species had higher densities near colonies and this pattern was clearest for three auk species. Sandeel density was an important predictor of the density of several gull species.</p> </list-item> <list-item> <p> <italic>Synthesis and applications</italic>. This method shows it is possible to construct maps of species density in situations in which ecological observations cannot be identified to species level with certainty. The increasing use of passive detection methods is providing many more data sets with uncertain species identification and this method could be used with these data to produce species‐specific abundance estimates. We discuss the advantages of this approach for estimating the abundance and distribution of birds in marine areas, particularly for quantifying the impacts of offshore renewable developments by making the estimates derived from the older digital surveys more comparable to the recently improved surveys.</p> </list-item> </list> </p> </abstract>