Abstract
Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood.
Highlights
The study of species- environment relationships can provide important insight into the processes underlying a species’ habitat use and distribution
When survey effort data are available, pseudo-absences generated from surveyed areas can be used along with occurrence data in presence-absence models such as generalized linear models (GLMs), generalized additive models (GAMs), or Classification and Regression Trees (CARTs) [4]
Our model results indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat
Summary
The study of species- environment relationships can provide important insight into the processes underlying a species’ habitat use and distribution. SDMs are useful to conservation as they can be used to predict locations where species are likely to occur in areas that have not been surveyed or have been poorly surveyed. When survey effort data are available, pseudo-absences generated from surveyed areas can be used along with occurrence data in presence-absence models such as generalized linear models (GLMs), generalized additive models (GAMs), or Classification and Regression Trees (CARTs) [4]. The use of pseudo-absences presents limitations; while species presences can be confirmed, species absences can be difficult to document with certainty, for mobile species, and increased sampling effort must be performed in order to ensure the reliability of absence data [12]. Available data for many species of conservation concern have been collected opportunistically and/or from a variety of platforms, and datasets derived from systematic surveys are often limited or incomplete
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