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. Quigley; Eric S. Zolman; Cynthia R. Smith

doi:10.1111/cobi.14073

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

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English
Article number	e14073
Number of pages	13
Journal	Conservation Biology
Volume	Early View
Early online date	8 Feb 2023
DOIs	https://doi.org/10.1111/cobi.14073
Publication status	E-pub ahead of print - 8 Feb 2023

Keywords

Biomarker
Dolphin
Health assessment
Survival
Vital rate
Wildlife monitoring

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Thomas_2023_CB_PopSurvivalfromHD-CCBY-VoR
© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Cite this

Schwacke, L. H., Thomas, L., Wells, R. S., Rowles, T. K., Bossart, G., Townsend, F., Mazzoil, M., Allen, J. B., Balmer, B. C., Barleycorn, A. A., Barratclough, A., Burt, M. L., De Guise, S., Fauquier, D., Gomez, F. M., Kellar, N. M., Schwacke, J. H., Speakman, T. R., Stolen, E., ... Smith, C. R. (2023). An expert-based system to predict population survival rate from health data. Conservation Biology, Early View, Article e14073. Advance online publication. https://doi.org/10.1111/cobi.14073

@article{0da7e81313dc40a4b984a8177c62c2f0,

title = "An expert-based system to predict population survival rate from health data",

abstract = "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.",

keywords = "Biomarker, Dolphin, Health assessment, Survival, Vital rate, Wildlife monitoring",

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

note = "Funding: This work was supported by the Office of Naval Research Marine Mammal Biology Program [grant number N00014-17-1-2868].",

year = "2023",

month = feb,

day = "8",

doi = "10.1111/cobi.14073",

language = "English",

volume = "Early View",

journal = "Conservation Biology",

issn = "0888-8892",

publisher = "John Wiley & Sons, Ltd",

}

Schwacke, LH, Thomas, L, Wells, RS, Rowles, TK, Bossart, G, Townsend, F, Mazzoil, M, Allen, JB, Balmer, BC, Barleycorn, AA, Barratclough, A, Burt, ML, De Guise, S, Fauquier, D, Gomez, FM, Kellar, NM, Schwacke, JH, Speakman, TR, Stolen, E, Quigley, BM, Zolman, ES & Smith, CR 2023, 'An expert-based system to predict population survival rate from health data', Conservation Biology, vol. Early View, e14073. https://doi.org/10.1111/cobi.14073

TY - JOUR

T1 - An expert-based system to predict population survival rate from health data

AU - Schwacke, Lori H.

AU - Thomas, Len

AU - Wells, Randall S.

AU - Rowles, Teresa K.

AU - Bossart, Greg

AU - Townsend, Forrest

AU - Mazzoil, Marilyn

AU - Allen, Jason B.

AU - Balmer, Brian C.

AU - Barleycorn, Aaron A.

AU - Barratclough, Ashley

AU - Burt, M. Louise

AU - De Guise, Sylvain

AU - Fauquier, Deborah

AU - Gomez, Forrest M.

AU - Kellar, Nicholas M.

AU - Schwacke, John H.

AU - Speakman, Todd R.

AU - Stolen, Eric

AU - Quigley, Brian M.

AU - Zolman, Eric S.

AU - Smith, Cynthia R.

N1 - Funding: This work was supported by the Office of Naval Research Marine Mammal Biology Program [grant number N00014-17-1-2868].

PY - 2023/2/8

Y1 - 2023/2/8

N2 - 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.

AB - 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.

KW - Biomarker

KW - Dolphin

KW - Health assessment

KW - Survival

KW - Vital rate

KW - Wildlife monitoring

U2 - 10.1111/cobi.14073

DO - 10.1111/cobi.14073

M3 - Article

SN - 0888-8892

VL - Early View

JO - Conservation Biology

JF - Conservation Biology

M1 - e14073

ER -

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

Abstract

Keywords

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