National Marine Mammal Foundation, San Diego, California, USA.
Centre for Research into Ecological and Environmental Modelling, University of St Andrews, The Observatory, St Andrews, UK.
Conserv Biol. 2024 Feb;38(1):e14073. doi: 10.1111/cobi.14073. Epub 2023 Apr 10.
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 7 bottlenose dolphin (Tursiops truncatus) populations in the southeastern United States to develop a method for estimating survival probability based on a suite of health measures identified by experts as indices for inflammatory, metabolic, pulmonary, and neuroendocrine systems. We used logistic regression to implement the veterinary expert system for outcome prediction (VESOP) within a Bayesian analysis framework. We fitted parameters with records from 5 of the sites that had a robust network of responders to marine mammal strandings and frequent photographic identification surveys that documented definitive survival outcomes. We also conducted capture-mark-recapture (CMR) analyses of photographic identification data to obtain separate estimates of population survival rates for comparison with VESOP survival estimates. The VESOP analyses showed that multiple measures of health, particularly markers of inflammation, were predictive of 1- and 2-year individual survival. The highest mortality risk 1 year following health assessment related to low alkaline phosphatase (odds ratio [OR] = 10.2 [95% CI: 3.41-26.8]), whereas 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 ; 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 can be used to detect 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.
及时发现和了解种群减少的原因对于有效的野生动物管理和保护至关重要。评估种群规模趋势一直是标准方法，但我们提出，监测种群健康可能更为有效。我们整理了美国东南部 7 个宽吻海豚（Tursiops truncatus）种群的数据，以开发一种方法，根据专家确定的一套健康指标来估计生存概率，这些指标被确定为炎症、代谢、肺部和神经内分泌系统的指标。我们使用逻辑回归在贝叶斯分析框架内实施兽医专家系统进行结果预测（VESOP）。我们使用来自 5 个地点的记录拟合参数，这些地点有一个强大的海洋哺乳动物搁浅响应网络和频繁的照片识别调查，记录了明确的生存结果。我们还对照片识别数据进行了捕获-标记-重捕（CMR）分析，以获得与 VESOP 生存估计值进行比较的单独种群生存率估计值。VESOP 分析表明，多项健康指标，特别是炎症指标，可预测个体 1 年和 2 年的生存情况。健康评估后 1 年死亡率最高的风险与碱性磷酸酶水平低有关（优势比[OR] = 10.2 [95% CI：3.41-26.8]），而 2 年死亡率最受球蛋白升高的影响（OR = 9.60 [95% CI：3.88-22.4]）；这两者都是炎症的标志物。VESOP 模型预测的种群生存率与同一种群的 CMR 分析估计的生存率相关（1 年 Pearson r = 0.99，p = 1.52 × 10 -5 ；2 年 r = 0.94，p = 0.001）。尽管我们提出的方法无法检测到与动物健康基本无关的急性死亡威胁，例如有害藻类大量繁殖，但它可用于检测增加死亡率风险的慢性健康状况。对种群进行随机抽样很重要，远程抽样方法的进步可以促进对研究对象的更随机选择、获得更大的样本量，并将该方法扩展到其他野生动物物种。
