Data and code for: Veterinary Expert System for Outcome (VESOP) Prediction

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 the
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 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 (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=1.52e-05, 2-year r=0.94; p=0.001). While 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.