Effective conservation planning requires biotic data across an entire region. In data-poor ecosystems conservation planning is informed by using environmental surrogates (e.g. temperature) predominantly in two ways: to develop habitat classification schemes or develop species distribution models.
This study tested the utility of both approaches for conservation planning of marine ecosystems, and ranked environmental surrogates (e.g. depth and distance to the shore) according to their power to predict the distribution and abundance of biotic species and specifically fish.
The authors set conservation levels to 21% of each conservation feature, thus when running scenarios to protect fish species the study aimed to protect at least 21% of each species, and when running scenarios of habitat classes the study aimed to protect at least 21% of each habitat class.
It was found that when aiming to protect 21% of the chosen conservation targets, distribution models protected 21% of the predicted abundance/occurrence of all modelled species and functional groups, but did not protect most habitats. Contrastingly, it was found that when using a habitat classification scheme 21% of all habitat types was protected, as well as 34% of all species and functional groups, but required protecting three times more area.
The authors conclude that using only distribution models as targets in data poor ecosystems can be a risky conservation planning strategy and state that the best conservation outcomes were achieved by incorporating local knowledge to synthesize the conservation outcomes of both scenarios.
Ferrari et al. (2018) Integrating distribution models and habitat classification maps into marine protected area planning. Estuarine, Coastal and Shelf Science. 212:40-50.