Open AccessQuantifying marine mammal hotspots in British Columbia, Canada
Author(s) -
Harvey Gillian K. A.,
Nelson Trisalyn A.,
Fox Caroline H.,
Paquet Paul C.
Publication year - 2017
Publication title -
ecosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.255
H-Index - 57
ISSN - 2150-8925
DOI - 10.1002/ecs2.1884
Subject(s) - geography , marine mammal , hotspot (geology) , marine protected area , marine conservation , ecology , biodiversity hotspot , biodiversity , wildlife , spatial ecology , ecosystem , environmental resource management , habitat , environmental science , biology , geophysics , geology
Global biodiversity is undergoing rapid decline due to direct and indirect anthropogenic impacts to species and ecosystems. Marine species, in particular, are experiencing accelerated population declines leading to many species being considered at risk by regional, national, and international standards. As one conservation approach, decisions made using spatially explicit information on marine wildlife populations have the potential to facilitate recovery and contribute to national and international commitments toward conservation targets. Delineating areas of intense use by species at risk can inform future marine spatial planning and conservation efforts, including the identification of marine protected areas. Methods for detecting hotspots (e.g., areas with high density and/or abundance) enable categorical mapping of the most intensely used areas. Yet, many of the current methods for delineating hotspots, such as the top 5% threshold, are subjective and fail to account for spatial patterns. Our goal was to map spatially continuous distributions of marine mammal densities and employ quantitative statistical methods to extract hotspot locations on the northern coast of British Columbia. We integrated systematically surveyed species information with environmental variables using generalized additive models to predict marine mammal distribution and density. Hotspots were identified from the density surfaces using two approaches: aspatial top 5% method and spatially local G i * statistic using three neighborhood definitions. Heterogeneous density patterns were observed for all species, and high‐density regions were generally clustered in areas exhibiting oceanographic characteristics that may promote concentrated food resources. Combining species density surfaces and extracting hotspot locations identified regions important to multiple species and present candidate locations for future conservation efforts. Contributions from this research provide robust statistical methods to objectively map hotspot locations and generate GIS data products for informing coastal conservation decisions.