DOI: 10.5061/dryad.h18931zqt
title: Data and code for: Veterinary Expert System for Outcome (VESOP) Prediction

description: 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.
description: We collated data from seven bottlenose dolphin
 (<em>Tursiops</em> <em>truncatus</em>) health
 assessment studies 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. VESOP was implemented using
 logistic regression within a Bayesian analysis framework. Data include
 morphometrics, hematologic and serum biochemical results, pulmonary
 ultrasound scores, hormone measurements, and one- and two-year survival
 outcomes. 
description: All code was implement in R and Rjags.
description: # Veterinary Expert System for Outcome Prediction (VESOP) data and code We
 collated health 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. VESOP was implemented using
 logistic regression within a Bayesian analysis framework. Data include
 morphometrics, hematologic and serum biochemical results, pulmonary
 ultrasound scores, hormone measurements, and one- and two-year survival
 outcomes. ## Description of the Data and file structure The following
 files were used to implement the VESOP model for the manuscript and
 associated supplemental information: Schwacke, LH, L Thomas, RS Wells, TK
 Rowles, G Bossart, F Townsend, M Mazzoil, JB Allen, BC Balmer, AA
 Barleycorn, A Barratclough, ML Burt, S De Guise, D Fauquier, FM Gomez, NM
 Kellar, JH Schwacke, TR Speakman, E Stolen, BM Quigley, ES Zolman, and CR
 Smith. An expert-based system to predict population survival rate from
 health data. Data input files: VESOPdata\_1yr.csv, VESOPdata\_2yr.csv-
 data from the 7 dolphin sampling locations used to fit the VESOP model and
 generate graphs for 1- and 2-year survival, respectively; each row is a
 separate dolphin health record and includes: * Site- site where dolphin
 was sampled * Year- year dolphin was sampled * panelclass- age
 classification for the dolphin * sex- sex of the dolphin * Neutrophils
 (thousand/µL), Albumin (g/dL), Globulin (g/dL), Alkaline.Phosphatase
 (µ/L), Hemoglobin (g/dL), weight (kg), maximum.girth (cm), length (cm),
 Cholesterol (mg/dL), TotalT3 (ng/dL), Glucose (mg/dL), Lung (1 indicating
 moderate-severe; 0 indicating normal-mild), Cortisol (µg/dL), health
 measurements made on the dolphin * refSiteLung, refSiteCort- indicates if
 site was an oiled site (1) or unoiled site (2) * Survival- binary variable
 indicating if the dolphin survived 1 (or 2) years * S.beta\_par1,
 S.beta\_par2- beta distribution parameters if CMR survival probability was
 calculated * CMRSurvival- estimated CMR survival probability
 phi\_est\_1Yr\_bysite\_CMR.Rds, phi\_est\_2Yr\_bysite\_CMR.Rds data in R
 format (can be read in with readRDS) giving 1000 replicate predictions of
 survival per site. Sites are the rows and replicate predictions are the
 columns. Rownames give the site names. R code files: * applyPanelsVESOP.R
 code that reads in VESOPdata file and adds health index flags, calling
 functions in calcFlags.R to estimate appropriate threshold values. Creates
 a temp.csv file that is input for run VESOP model.R * calcFlags.R -
 functions that estimate threshold values using either non-linear quantile
 regression or calling R quantile function with stratified data * run VESOP
 model.R - reads in data file (temp.csv), sets up model health factors and
 outcome fields, runs the rjags model (VESOPModel.R), and processes output
 to create graphs. * VESOPModel.txt - rjags model code * printSurvs.R -
 various functions that use VESOP model outputs to calculate population
 survival statistics and plot results. ## Sharing/access Information Links
 to other publicly accessible locations of the data: Was data derived from
 another source? No If yes, list source(s):