Modelling Critically Endangered marine species: Bias-corrected citizen science data inform habitat suitability for the angelshark (Squatina squatina)
As an increasingly important resource in ecological research, citizen scientists have proven dynamic and cost-effective in the supply of data for use within habitat suitability models. With predictions critical to the provision of effective conservation measures in cryptic marine species, this study delivers baseline ecological data for the Critically Endangered angelshark (Squatina squatina), exploring: (i) seasonal, sex-differentiated distributions; (ii) environmental distribution predictors; and (iii) examining bias-corrected, imperfect citizen science data for use in coastal habitat suitability models with cryptic species.
Citizen science presence data, comprising over 60,000 hours of sampling effort, were used alongside carefully selected open-source predictor variables, with maxent generating seasonal male and female habitat suitability models for angelsharks in the Canary Islands. A biased prior method was used, alongside two model validation measures to ensure reliability.
Citizen science data used within maxent suggest that angelshark habitat suitability is low in coastal areas during warmer months, with fewer occurrences despite a negligible change in sampling effort. The prime importance of bathymetry may indicate the importance of depth for reproductive activity and possible diel vertical migration, whereas aspect may act as a proxy for sheltered habitats away from open ocean. Substrate as a predictor of female habitats in spring and summer could imply that soft sediment is sought for birthing areas, assisting in the identification of areas critical to reproductive activity and thus locations that may benefit from spatial protections.
Model outputs to inform recovery plan development and ecotourism are identified as plausible safeguards of population recovery, whereas the comparison of biased and bias-corrected models highlights some variance between methodologies, with bias-corrected models producing greater areas of habitat suitability. Accordingly, an adaptive framework is provided for the implementation of citizen science data within the modelling of cryptic coastal species distribution.