Abstract
Species distribution models (SDMs) are used to interpret and map fish distributions based on habitat variables and other drivers. Reef fish avoidance behavior has been shown to vary in the presence of divers and is primarily driven by spearfishing pressure. Diver avoidance behavior or fish wariness may spatially influence counts and other descriptive measures of fish assemblages. Because fish assemblage metrics are response variables for SDMs, measures of fish wariness may be useful as predictors in SDMs of fishes targeted by spearfishing. We used a diver operated stereo-video system to conduct fish surveys and record minimum approach distance (MAD) of targeted reef fishes inside and outside of two marine reserves on the island of Oʻahu in the main Hawaiian Islands. By comparing MAD between sites and management types we tested the assumption that it provides a proxy for fish wariness related to spearfishing pressure. We then compared the accuracy of SDMs which included MAD as a predictor with SDMs that did not. Individual measures of MAD differed between sites though not management types. When included as a predictor, MAD averaged at the transect level greatly improved the accuracy of SDMs of targeted fish biomass.
Highlights
A current focus in marine ecology has been to use species distribution models (SDMs) to understand and sometimes predict fish distributions based on habitat, environmental, and other drivers
We tested whether using a measure of targeted fish wariness (MAD) as a predictor of targeted fish biomass in SDMs spanning marine reserve boundaries, improved explanatory power and predictive accuracy
Our results show that including mean minimum approach distance (MAD) as a predictor in SDMs greatly improves model performance and accuracy compared to models using reserve status only
Summary
A current focus in marine ecology has been to use species distribution models (SDMs) to understand and sometimes predict fish distributions based on habitat, environmental, and other drivers. Development of SDMs begins with observations of species distributions (typically summarized in terms of biomass, biodiversity, or similar metrics), and the identification and compilation of environmental variables known or believed to influence habitat suitability and, species distributions (Franklin, 2010; Schmiing et al, 2013). SDMs are often applied for predictive mapping, producing spatial. Incorporating reef fish avoidance behavior improves accuracy of species distribution models.
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