Quantifying marine mammal hotspots in British Columbia, Canada

Abstract

Abstract. 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 à 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.

A comparative analysis of common methods to identify waterbird hotspots

“…Kernel density and G i * are useful in assessing large areas where waterbirds have been present (with high densities) on at least one occasion. Yet kernel density and G i * can have difficulty identifying individual grid cells as hotspots, especially at small spatial scales and with heavily skewed data (Harris, ; Songchitruska & Zeng, ). Both kernel density estimation and G i * demonstrated high similarity to the mean effort‐corrected count data for almost all species groups.…”

Cetacean biodiversity, spatial and temporal trends based on stranding records (1920-2016), Victoria, Australia

“…Beaked whales contributed over 7% of stranding events in Victoria across nine species, however many of these species have never been sighted alive within state waters and are difficult to identify at sea [52]. Collecting demographic information is particularly important for these species in which the life cycle and distribution is not accurately known, as it can help identify areas of significance and habitat use [53, 54]. Mesoplodon layardii and M .…”

Spring distribution of ringed seals (Pusa hispida) in Eclipse Sound and Milne Inlet, Nunavut: implications for potential ice-breaking activities

“…A z-score above 1.96 (red) is significant at the α = 0.05 level and indicates a hotspot for ringed seals. To conceptualize the spatial relationship, we used the more conservative polygon contiguity conceptualization (i.e., contiguity edges corners in ArcGIS)a common approach when equal area grid units are used within areal data sets (Harvey et al 2017). We calculated the spatial and percentage overlap (km 2 and % area, respectively) of ringed seal hotspots between 2016 and 2017 using the Union geoprocessing tool in ArcGIS.…”