Rosenbaum Samuel W, May Samuel A, Shedd Kyle R, Cunningham Curry J, Peterson Randy L, Elliot Brian W, McPhee Megan V
College of Fisheries and Ocean Sciences University of Alaska Fairbanks Juneau Alaska USA.
Alaska Department of Fish & Game Anchorage Alaska USA.
Evol Appl. 2024 Feb 7;17(2):e13647. doi: 10.1111/eva.13647. eCollection 2024 Feb.
As Pacific salmon ( spp.) decline across much of their range, it is imperative to further develop minimally invasive tools to quantify population abundance. One such advancement, trans-generational genetic mark-recapture (tGMR), uses parentage analysis to estimate the size of wild populations. Our study examined the precision and accuracy of tGMR through a comparison to a traditional mark-recapture estimate for Chilkat River Chinook salmon () in Southeast Alaska. We examined how adult sampling location and timing impact tGMR by comparing estimates derived using samples collected in the lower river mainstem to those using samples obtained in upriver spawning tributaries. Results indicated that tGMR estimates using a representative sample of mainstem adults were most concordant with, and 3% more precise than, the traditional mark-recapture estimate for this stock. Importantly, the timing and location of adult sampling were found to impact abundance estimates, depending on what proportion of the population dies or moves to unsampled areas between downriver and upriver sampling events. Additionally, we identified potential sources of bias in tGMR arising from violations of key assumptions using a novel individual-based modeling framework, parameterized with empirical values from the Chilkat River. Simulations demonstrated that increased reproductive success and sampling selectivity of older, larger individuals, introduced negative bias into tGMR estimates. Our individual-based model offers a customizable and accessible method to identify and quantify these biases in tGMR applications (https://github.com/swrosenbaum/tGMR_simulations). We underscore the critical role of system-specific sampling design considerations in ensuring the precision and accuracy of tGMR projects. This study validates tGMR as a potentially useful tool for improved population enumeration in semelparous species.
随着太平洋鲑鱼（多种）在其大部分分布范围内数量减少，进一步开发微创工具以量化种群数量变得势在必行。其中一项进展是跨代遗传标记重捕法（tGMR），它利用亲子关系分析来估计野生种群的规模。我们的研究通过与阿拉斯加东南部奇尔卡特河奇努克鲑（）的传统标记重捕估计值进行比较，检验了tGMR的精度和准确性。我们通过比较使用在下游河道主河道采集的样本得出的估计值与使用在上游产卵支流采集的样本得出的估计值，研究了成年鱼采样位置和时间如何影响tGMR。结果表明，使用主河道成年鱼代表性样本的tGMR估计值与该种群的传统标记重捕估计值最为一致，且精度高出3%。重要的是，发现成年鱼采样的时间和位置会影响丰度估计，这取决于在下游和上游采样事件之间种群中有多少比例死亡或迁移到未采样区域。此外，我们使用一个新颖的基于个体的建模框架，通过用奇尔卡特河的经验值进行参数化，确定了因违反关键假设而在tGMR中产生偏差的潜在来源。模拟表明，繁殖成功率的提高以及对年龄较大、体型较大个体的采样选择性增加，会给tGMR估计值带来负偏差。我们基于个体的模型提供了一种可定制且易于使用的方法，用于识别和量化tGMR应用中的这些偏差（https://github.com/swrosenbaum/tGMR_simulations）。我们强调特定系统采样设计考虑在确保tGMR项目精度和准确性方面的关键作用。这项研究验证了tGMR作为一种潜在有用工具，可用于改进一次性繁殖物种的种群数量统计。
