Accurately predicting rare and poorly detectable species habitat for spatial protection.
Abstract
With the loss of biodiversity worldwide, understanding species distribution is essential for species management, but modelling the distribution of rare and poorly detectable species can be challenging because of data gaps and observer biases. Over- or under-predictions are frequent, leading to uncertainty in spatial management measures, particularly for highly mobile and data-poor species. Here we developed a 'Combined Model for Accurate Prediction' to accurately predict the distribution of data-poor and rare species. This modelling framework aims to improve the accuracy of both predicted 'core' and 'unsuitable' habitats, to help support managers with spatial protection measures. We tested the combined modelling approach on 11 data-poor and rare diadromous fish during their at-sea life history phase and used the combined model to analyse the adequacy of existing marine protected areas (MPAs) for these fish. The combined modelling approach modelled both 'core' and 'unsuitable' habitats with high accuracy. Of the seven diadromous species modelled, most MPAs designated to protect diadromous fish are outside their core habitats. Furthermore, when their core habitat was within an MPA, only 50% of this area was designated to protect them. These results highlight inadequate protection of the existing networks of MPAs for protected and threatened species. Synthesis and applications. Being able to accurately model species distribution is critical to reliable and transparent biodiversity and conservation assessments. By modelling accurate 'core' and 'unsuitable' habitats with models that minimise omission and commission rates respectively, conservation measures could be targeted in specific spatial areas that maximise the protection of rare and poorly detected species. This method therefore helps minimise impacts on stakeholders, while providing managers with increased confidence in the model predictions.