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Incorporating diverse data and realistic complexity into demographic estimation procedures for sea otters

机译：将多样化的数据和现实的复杂性纳入海獭的人口统计学估算程序

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摘要

Reliable information on historical and current population dynamics is central to understanding patterns of growth and decline in animal populations. We developed a maximum likelihood-based analysis to estimate spatial and temporal trends in age/sex-specific survival rates for the threatened southern sea otter (Enhydra lutris nereis), using annual population censuses and the age structure of salvaged carcass collections. We evaluated a wide range of possible spatial and temporal effects and used model averaging to incorporate model uncertainty into the resulting estimates of key vital rates and their variances. We compared these results to current demographic parameters estimated in a telemetry-based study conducted between 2001 and 2004. These results show that survival has decreased substantially from the early 1996s to the present and is generally lowest in the north-central portion of the population's range. The greatest temporal decrease in survival was for adult females, and variation in the survival of this age/sex class is primarily responsible for regulating population growth and driving population trends. Our results can be used to focus future research on southern sea otters by highlighting the life history stages and mortality factors most relevant to conservation. More broadly, we have illustrated how the powerful and relatively straightforward tools of information-theoretic-based model fitting can be used to sort through and parameterize quite complex demographic modeling frameworks.
机译：有关历史和当前种群动态的可靠信息对于了解动物种群的增长和下降模式至关重要。我们使用年度人口普查和打捞car体收集物的年龄结构，开发了基于最大似然的分析方法来估计受威胁的南部水獭（Enhydra lutris nereis）的年龄/性别特定生存率的时空趋势。我们评估了可能的时空影响范围，并使用模型平均将模型不确定性纳入关键生命率及其方差的最终估计中。我们将这些结果与2001年至2004年进行的基于遥测的研究中估计的当前人口统计学参数进行了比较。这些结果表明，从1996年代初期到现在，生存率已经大幅下降，并且通常在人口范围的中北部最低。存活率的最大时间下降是成年女性，该年龄/性别类别的存活率变化主要是调节人口增长和推动人口趋势的原因。我们的研究结果可通过突出显示与保护最相关的生命史阶段和死亡率因素，从而将未来的研究重点放在南方水獭上。更广泛地说，我们已经说明了如何使用功能强大且相对简单的基于信息理论的模型拟合工具来对非常复杂的人口统计建模框架进行分类和参数化。

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