Rugosity-based regional modeling of hard-bottom habitat

摘要: Systematic conservation planning is most often directed at the representation and protection of marine biodiversity. However, direct observation sampling biodiversity extremely time consuming expensive. Due to these constraints, planners have sought proxies for use in their models. Hard- bottom habitats support high levels are frequently used as a surrogate it spa- tial planning. Rugosity (i.e. roughness seafloor) an indicator hard-bottom habitat. In present study, we expand on previous analyses relationship between rugosity create first data-driven regional model predict We logistic regression empirical compare other pre-formulated definitions with receiver operator characteristic curves. Our per- formed better than all models was able correctly presence or absence hard- habitat ~70% accuracy. This offers fast inexpensive alternative more traditional survey methods, should be value fisheries managers initial predictor By testing this low-resolution (90 m) bathymetry data, demonstrate that type information may plans regions such devel- oping countries, where high-resolution data not cur- rently available. Further, our proxy diversity non-coastal areas, under- represented sector