捕获标记恢复分析中缺少发布数据:推断的后果

IF 0.9 4区 环境科学与生态学 Q4 BIODIVERSITY CONSERVATION
R. W. Brook, Joshua L. Dooley, Glen S. Brown, K. Abraham, R. Rockwell
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引用次数: 0

摘要

人口统计概率,如年生存率和收获概率,是研究和监测野生动物种群健康和收获可持续性的关键指标。对于水禽种群,标记恢复分析用于利用协调带状操作的数据得出这些概率的年度估计值。用于分析收获物种的标记恢复数据的最常用的参数化是Brownie模型。然而,如果带状水禽在多年带状手术的一年中没有被放生,那么根据死亡恢复模型估计年度存活和恢复概率是一个挑战。由于新冠肺炎,包括年度水禽分级计划在内的许多野生动物监测工作在2020年和2021年被取消或减少,这突出表明管理者需要更好地了解分析和监管决策数据缺失的后果。我们总结了模型参数化的方法,以及在缺少一年发布数据时使用替代方法来探索人口统计参数估计的行为。将约束固定效应模型(数据缺失年份的参数设置为等于发布数据年份的参数)与随机效应模型进行比较,我们发现1)使用随机效应模型时,缺失发布数据年份估计的偏差较小,2)偏差的方向是不可预测的,但偏差的预期范围通常是已知的,与生存和恢复概率的潜在可变性相称,以及3)如果缺失的释放年份发生在时间序列的最后一年,则潜在偏差最大。我们得出的结论是,在某些情况下,各种建模方法可以在一年的释放数据缺失期间提供合理的估计,特别是当潜在的人口统计参数或模型中约束的参数随时间变化很小时(例如,长寿物种的成体存活率),这将导致其他估计参数(例如年度恢复概率)中相对较小的偏差。我们还建议,当发布数据缺失时,使用其他分析技术,如随机效应模型,可以改进对感兴趣的人口统计参数的估计。随机效应模型还允许估计参数,例如失踪释放数据年份的青少年恢复概率,这些参数使用标准建模技术是无法识别的。在准确和精确的参数估计对于做出收获管理决策很重要的情况下,无论模型类型或使用的数据如何,都无法对缺失的释放数据进行分析替代。我们建议从业者通过使用经验数据和具有已知人口统计概率的模拟数据,通过模拟来确定丢失数据的潜在后果,以确定在发布数据丢失时分析其捕获恢复数据的最佳行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Missing release data in capture-mark-recovery analyses: consequences for inference
Demographic probabilities such as annual survival and harvest probability are key metrics used in research and for monitoring the health of wildlife populations and sustainability of harvest. For waterfowl populations, mark-recovery analysis is used to derive annual estimates of these probabilities using data from coordinated banding operations. The most commonly used parameterization for analyzing mark-recovery data from harvested species is the Brownie model. However, if banded waterfowl are not released during a year of a multi-year banding operation, then estimating annual survival and recovery probabilities from a dead recovery model is a challenge. Due to COVID-19, many wildlife monitoring efforts, including annual waterfowl banding programs, were canceled or reduced during 2020 and 2021, highlighting the need for managers to better understand the consequences of missing data on analyses and regulatory decisions. We summarized methods of model parameterization and use of alternative methods to explore the behavior of demographic parameter estimates when a year of release data was missing. Comparing constrained fixed-effect models (parameters during the missing year of data were set equal to parameters for years with release data) with random-effect models, we found that 1) bias of estimates during a year of missing release data were smaller when using a random-effect model, 2) the direction of the bias was unpredictable but the expected range in bias could be generally known, commensurate to the underlying variability in survival and recovery probabilities, and 3) potential bias was greatest if the missing year of releases occurred during the final year of a time series. We conclude that in some circumstances, various modeling approaches can provide reasonable estimates during a year of missing release data, particularly when  underlying demographic parameters, or the parameter constrained in a model, vary little over time (e.g., adult survival in long-lived species), which would result in relatively little bias in the other estimated parameter (e.g., annual recovery probability). We also suggest that using alternative analytical techniques, such as random-effect models, may improve estimates for the demographic parameters of interest when release data are missing. Random-effect models also allowed for estimation of parameters, such as juvenile recovery probabilities during the year of missing release data, which are not identifiable using standard modeling techniques. Where accurate and precise parameter estimation is important for making harvest management decisions and regardless of the model type or the data used, there is no analytical replacement for missing release data. We suggest that practitioners determine the potential consequences for missing data through simulation by using empirical data and simulated data with known demographic probabilities to determine the best actions to take for analyzing their capture-recovery data when release data are missing.
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来源期刊
Journal of Fish and Wildlife Management
Journal of Fish and Wildlife Management BIODIVERSITY CONSERVATION-ECOLOGY
CiteScore
1.60
自引率
0.00%
发文量
43
审稿时长
>12 weeks
期刊介绍: Journal of Fish and Wildlife Management encourages submission of original, high quality, English-language scientific papers on the practical application and integration of science to conservation and management of native North American fish, wildlife, plants and their habitats in the following categories: Articles, Notes, Surveys and Issues and Perspectives. Papers that do not relate directly to native North American fish, wildlife plants or their habitats may be considered if they highlight species that are closely related to, or conservation issues that are germane to, those in North America.
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