Stranding-Based Demographic Inference in Marine Mammals: Best Practices for Extracting Vital Rates Despite Compound Sampling Bias

Strandings records provide the only demographic data source for many marine mammal species. Yet they may be heavily biased. Every carcass passes through sequential filtering: mortality cause, oceanographic drift, decomposition, detection, and sampling. Each stage distorts age-specific signals. This creates a fundamental paradox: strandings are essential yet appear unreliable for demographic inference. This review resolves the paradox through a systematic best practices. Strandings are formalized as a six-stage filtering cascade. Three complementary approaches extract reliable signals despite bias. Design-based protocols reduce sampling bias through stratified collection and standardized networks. Model-based temporal analyses detect relative demographic changes when detection remains constant. Integrated population models combine strandings with auxiliary data to correct bias. The review provides decision tools that formalize when and how demographic inference from strandings is defensible. Applications across harbor porpoises, common dolphins and manatees demonstrate that stranding-based monitoring reliably detects demographic changes. Three research priorities emerge from this review: quantifying age/stage-specific detection probabilities, incorporating spatial population structure, and parameterizing management strategy evaluation with stranding-derived demographic rates. When properly analyzed, strandings provide irreplaceable demographic surveillance for species inaccessible to other methods. DOI https://doi.org/10.32942/X2RH2C Subjects Life Sciences

conservation, demography, marine mammals, mortality, Population Dynamics, strandings, survival, vital rates Dates Published: 2025-11-03 14:54 Last Updated: 2025-11-03 14:54 License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: Not applicable Language: English