Institution: | 1. Harting Biological Consulting, Bozeman, Montana;2. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, Honolulu, Hawaii
Contribution: Conceptualization, Data curation, Investigation, Methodology, Supervision, Validation, Writing - review & editing;3. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, Honolulu, Hawaii
Contribution: Conceptualization, Methodology, Writing - review & editing;4. Joint Institute for Marine and Atmospheric Research, University of Hawai'i at Manoa, Honolulu, Hawaii
Contribution: Data curation, Investigation, Methodology, Writing - review & editing;5. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, Honolulu, Hawaii
Contribution: Data curation, Supervision, Validation, Writing - review & editing;6. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, Honolulu, Hawaii;7. Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, Honolulu, Hawaii
Contribution: Conceptualization, Supervision, Writing - review & editing;8. Zoological Pathology Program, College of Veterinary Medicine, University of Illinois at Urbana-Brookfield, Urbana, Illinois
Contribution: Data curation, Investigation, Methodology, Resources, Writing - review & editing;9. Marine Mammal Pathology Services, Olney, Maryland
Contribution: Data curation, Investigation, Resources, Writing - review & editing |
Abstract: | Identifying, assessing, and ranking the impact of individual threats is fundamental to the conservation and recovery of rare and endangered species. In this analysis, we quantify not only the frequency of specific causes-of-death (CODs) among Main Hawaiian Island (MHI) monk seals, but also assess the impact of individual CODs on the intrinsic growth rate, λ, of the MHI population. We used gross necropsy results, histopathology, and other evidence to assign probabilities of 11 COD types to each mortality and then used Monte Carlo sampling to evaluate the influence of each COD on λ. By right censoring realizations involving specific CODs, we were able to estimate λ (and its associated uncertainty) when CODs were selectively removed from influencing survival. Applying the analysis to all known and inferred deaths believed to have occurred 2004–2019, the CODs with the largest influence on λ were anthropogenic trauma, anthropogenic drowning, and protozoal disease. In aggregate, anthropogenic CODs had a larger effect on the growth rate than either natural or disease CODs. Possible bias associated with differential carcass detection, recovery, and COD classification are discussed. |