An expert-based system to predict population survival rate from health data

Lori H. Schwacke*, Len Thomas, Randall S. Wells, Teresa K. Rowles, Greg Bossart, Forrest Townsend, Marilyn Mazzoil, Jason B. Allen, Brian C. Balmer, Aaron A. Barleycorn, Ashley Barratclough, M. Louise Burt, Sylvain De Guise, Deborah Fauquier, Forrest M. Gomez, Nicholas M. Kellar, John H. Schwacke, Todd R. Speakman, Eric Stolen, Brian M. QuigleyEric S. Zolman, Cynthia R. Smith

*Corresponding author for this work

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Timely detection and understanding of causes for population decline are essential for effective wildlife management and conservation. Assessing trends in population size has been the standard approach but we propose that monitoring population health could prove more effective. We collated data from seven bottlenose dolphin (Tursiops truncatus) populations in southeastern U.S. to develop the Veterinary Expert System for Outcome Prediction (VESOP), which estimates survival probability using a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. VESOP was implemented using logistic regression within a Bayesian analysis framework, and parameters were fit using records from five of the sites that had a robust stranding network and frequent photographic identification (photo-ID) surveys to document definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photo-ID data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. VESOP analyses found multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk one year following health assessment related to low alkaline phosphatase, with an odds ratio of 10.2 (95% CI 3.41-26.8), while 2-year mortality was most influenced by elevated globulin (OR=9.60; 95% CI 3.88-22.4); both are markers of inflammation. The VESOP model predicted population-level survival rates that correlated with estimated survival rates from CMR analyses for the same populations (1-year Pearson's r = 0.99; p = 1.52 × 10-5; 2-year r = 0.94; p = 0.001). Although our proposed approach will not detect acute mortality threats that are largely independent of animal health, such as harmful algal blooms, it is applicable for detecting chronic health conditions that increase mortality risk. Random sampling of the population is important and advancement in remote sampling methods could facilitate more random selection of subjects, obtainment of larger sample sizes, and extension of the approach to other wildlife species.
Original languageEnglish
Article numbere14073
Number of pages13
JournalConservation Biology
VolumeEarly View
Early online date8 Feb 2023
Publication statusE-pub ahead of print - 8 Feb 2023


  • Biomarker
  • Dolphin
  • Health assessment
  • Survival
  • Vital rate
  • Wildlife monitoring


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