Suchergebnisse

E.L.J. was funded under Scottish Government grant MMSS001/11. Sperm whale data were collected with support from One World Wildlife, the Natural Environment Research Council (NER/I/S/2002/00632), Whale and Dolphin Conservation (WDC), and J.M. Brotons of the Balearic Government Office of Fisheries Management. L.R. was supported by the MASTS pooling initiative, funded by the Scottish Funding Council (HR09011) and contributing institutions and their support are gratefully acknowledged. ; Comparing geographically referenced maps has become an important aspect of spatial ecology (e.g. assessing change in distribution over time). Whilst humans are adept at recognising and extracting structure from maps (i.e. identifying spatial patterns), quantifying these structures can be difficult. Here, we show how the Structural Similarity (SSIM) index, a spatial comparison method adapted from techniques developed in computer science to determine the quality of image compression, can be used to extract additional information from spatial ecological data. We enhance the SSIM index to incorporate uncertainty from the underlyin g spatial models, and provide a software algorithm to correct for internal edge effects so that loss of spatial information from the map comparison is limited. The SSIM index uses a spatially-local window to calculate statistics based on local mean, variance, and covariance between the maps being compared. A number of statistics can be calculated using the SSIM index, ranging from a single summary statistic to quantify similarities between two maps, to maps of similarities in mean, variance, and covariance that can provide additional insight into underlying biological processes. We demonstrate the applicability of the SSIM approach using a case study of sperm whales in the Mediterranean Sea and identify areas where local-scale differences in space-use between groups and singleton whales occur. We show how novel insights into spatial structure can be extracted, which could not be obtained by visual inspection or cell-by-cell subtraction. As an approach, SSIM is applicable to a broad range of spatial ecological data, providing a novel, implementable tool for map comparison. ; Publisher PDF ; Peer reviewed

The work was funded through Scottish Government MSQ0174 contract CR/2014/11; CREEM, University of St Andrews; the National Capability fund from the Natural Environment Research Council to the Sea Mammal Research Unit (grant no. SMRU1001); and MASTS pooling initiative, which is funded by the Scottish Funding Council (grant reference HR09011). ; High-resolution species distribution maps are required for marine spatial planning, consenting, and licensing to assess interactions between anthropogenic activities and ecosystems. This approach can inform conservation measures for protected species and facilitate commercial developments needed for economic growth. A case study centred on Orkney, UK, is an area where concern for a declining harbour seal population has led to constraints being placed on tidal energy generation developments. Telemetry data from 54 animals tagged between 2003 and 2015 were combined with terrestrial counts from 2008 to 2015 to produce density estimation maps. Predictive habitat models using GAM-GEEs provided robust predictions in areas where telemetry data were absent, and were combined with density estimation maps. Harbour seal usage maps with confidence intervals were produced around Orkney and the North coast of Scotland. The selected habitat model showed that distance from haul out, proportion of sand in seabed sediment, and peak flow of tidal current were important predictors of space-use. Fine-scale usage maps can be used in consenting and licensing of anthropogenic developments to determine local abundance. When quantifying anthropogenic impacts through changes to species distributions, usage maps could be spatially explicitly linked to individual-based models to inform predicted movement and behaviour. ; Publisher PDF ; Peer reviewed