A spatial Bayesian state-space framework for estimating species abundance trends: global case studies in marine megafauna

Monitoring abundance trends for species is essential for conservation reporting under frameworks like the Global Biodiversity Framework, yet existing methods are reliant on ecological time series data which often contain gaps for populations across space and for long time periods. We addressed these data limitations by introducing a novel Bayesian multi-population autoregressive state-space (MARSS) modelling framework that estimates total abundance trends of species, along with uncertainty due to population deviations and sampling methods. We incorporated spatial synchrony in the modeling framework to estimate trends during unsampled time periods for populations using information from nearby sampled populations, informed by migration networks. Simulation experiments revealed that spatial synchrony improved population trend estimates, but only marginally, and could only be reliably estimated for species with high numbers of monitored populations. We applied this modeling framework to two broad case studies on pinniped species and sea turtles because of their high conservation concern and diverse trajectories after widespread industrial exploitation. We created a database of 555 time series of population abundances for 29 of 34 extant pinniped species and 304 time series for 7 of 7 extant sea turtle species. On average, pinnipeds and sea turtles had strong increases in abundance between 1980 and 2010, followed by a slowing of population growth rate to near zero from 2010 to 2020, though there is considerable variability across species and conspecific populations. We identified prominent spatial synchrony in species with comprehensive data coverage (e.g., California sea lion, green sea turtle), emphasizing the benefits of strengthening monitoring efforts for species with declining or uncertain trends. Observation errors of sampling methods (e.g., aerial, beach, boat surveys) varied widely, with implications for optimizing monitoring efforts given precision-cost tradeoffs. Our spatial Bayesian MARSS framework is broadly applicable to species with spatially structured populations and improves species assessments by leveraging spatial dynamics, identifying indicator populations, and better quantifying process and observation uncertainty for conservation status determination.