Predicting life history traits for data-poor ray species

Fisheries are the main global threat to elasmobranchs (sharks and rays). Elasmobranchs commonly display conservative life history traits (slow growth, long life, late sexual maturity and low fecundity), making them extremely vulnerable to non-natural mortalities. However, there is significant variation, with some species exhibiting life history traits more resilient to exploitation. In order to assess species status and fisheries sustainability, species-specific life history data are needed, yet these data are severely lacking. Novel approaches to utilise available data to determine life history for data-poor species is crucial in assessing their resilience to fisheries pressure and therefore the management and conservation actions required. This project aims to utilise Bayesian and machine learning statistical models to estimate the maximum intrinsic rate of population increase (rmax) for data-sparse ray species using widely available life history, phylogenetic, and environmental temperature data. This will allow wider assessment of status and relative resilience of different ray species that can be used to inform global fisheries management.

Faculty Supervisor:

Nicholas Dulvy

Student:

Partner:

Newcastle University

Discipline:

Life Sciences

Sector:

Aquaculture and Fishing; Sustainability & the Environment; Life Sciences (not health)

University:

Simon Fraser University

Program: