Global reconstruction of life-history strategies: A case study using tunas
Global reconstruction of life-history strategies: A case study using tunas
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DOI：
10.1111/1365-2664.13327
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发表时间：
2019-04-01
影响因子：
5.7
通讯作者：
Matthiopoulos, Jason
中科院分区：
文献类型：
--
作者：
Horswill, Cat;Kindsvater, Holly K.;Matthiopoulos, Jason
测量受剥削人口的人口参数对于预测其脆弱性和灭绝风险至关重要。然而，目前的种群下降和物种丧失速度远远超出了我们凭经验监测所有潜在威胁种群的能力。这一问题的规模无法仅通过额外的数据收集来解决，因此，根据从相似物种收集的替代数据进行种群评估是一种常见的做法。然而，这种方法引入了难以量化的偏差和不精确性。分层建模的最新发展使得能够根据可用的生活史数据之间的相关性来重建缺失值，并根据系统发育和环境条件将相似的物种联系起来。然而，这些方法无法解决空间或分类上紧密排列的种群或物种之间的生活史变异性。在这里，与生活史理论相一致的理论上激励的约束为解决这个问题提供了新的途径。我们描述了一种贝叶斯分层方法，它将碎片化、多物种和多种群数据与已建立的生活史理论相结合，以便根据模型拟合获得的性状相关性（生活史权衡）客观地确定种群之间的相似性。我们重建了 23 个金枪鱼种群的 59 个未观察到的生活史参数，这些种群维持着世界上一些最有价值的渔业。通过不同场景的交叉验证进行的测试表明，当同一物种的其他种群的信息可用时，可以准确地重建生活史。尽管可信区间显着增加，但对于单个种群代表的物种来说，几个性状的重建也是准确的。合成和应用。所描述的贝叶斯分层方法提供了难以直接测量的生活史特征，并重建了缺失的生活史信息，可用于评估直接或间接受人类开发自然资源影响的种群和物种。该方法对于检查空间或分类学上相似的种群特别有用，并且为主要市场金枪鱼描述的重建生活史策略可立即应用于这些渔业的全球管理。所描述的贝叶斯分层方法提供了难以直接测量的生活史特征，并重建了缺失的生活史信息，可用于评估直接或间接受人类开发自然资源影响的种群和物种。该方法对于检查空间或分类上相似的种群特别有用，并且为主要市场金枪鱼描述的重建生活史策略可立即应用于这些渔业的全球管理。
Measuring the demographic parameters of exploited populations is central to predicting their vulnerability and extinction risk. However, current rates of population decline and species loss greatly outpace our ability to empirically monitor all populations that are potentially threatened. The scale of this problem cannot be addressed through additional data collection alone, and therefore it is a common practice to conduct population assessments based on surrogate data collected from similar species. However, this approach introduces biases and imprecisions that are difficult to quantify. Recent developments in hierarchical modelling have enabled missing values to be reconstructed based on the correlations between available life-history data, linking similar species based on phylogeny and environmental conditions. However, these methods cannot resolve life-history variability among populations or species that are closely placed spatially or taxonomically. Here, theoretically motivated constraints that align with life-history theory offer a new avenue for addressing this problem. We describe a Bayesian hierarchical approach that combines fragmented, multispecies and multi-population data with established life-history theory, in order to objectively determine similarity between populations based on trait correlations (life-history trade-offs) obtained from model fitting. We reconstruct 59 unobserved life-history parameters for 23 populations of tuna that sustain some of the world's most valuable fisheries. Testing by cross-validation across different scenarios indicated that life-histories were accurately reconstructed when information was available for other populations of the same species. The reconstruction of several traits was also accurate for species represented by a single population, although credible intervals increased dramatically.Synthesis and applications. The described Bayesian hierarchical method provides access to life-history traits that are difficult to measure directly and reconstructs missing life-history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life-history strategies described for the principal market tunas have immediate application to the world-wide management of these fisheries.The described Bayesian hierarchical method provides access to life-history traits that are difficult to measure directly and reconstructs missing life-history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life-history strategies described for the principal market tunas have immediate application to the world-wide management of these fisheries.