Conn Paul B, Bravington Mark V, Baylis Shane, Ver Hoef Jay M
Marine Mammal Laboratory Alaska Fisheries Science Center NOAA National Marine Fisheries Service Seattle WA USA.
CSIRO Marine Lab Hobart TAS Australia.
Ecol Evol. 2020 May 5;10(12):5558-5569. doi: 10.1002/ece3.6296. eCollection 2020 Jun.
Close-kin mark-recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population-level inference relative to traditional monitoring programs.One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.We used individual-based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long-lived mammal species subject to lethal sampling.Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov-Smirnov tests.Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.
近亲标记重捕法（CKMR）是一种根据在基因样本中观察到的亲缘关系来估计种群数量和生命率的方法。CKMR推断只要求对动物进行一次采样（例如，致死性采样），相对于传统监测项目，这可能会扩大种群水平推断的范围。CKMR的一个假设是，在年龄等个体协变量的条件下，所有动物被采样的概率相等。然而，如果基因数据是机会性收集的（例如，通过猎人或渔民），则采样概率可能存在空间差异，这会使CKMR估计值产生偏差，特别是当基因相关个体近距离聚集时。我们使用基于个体的模拟来研究扩散限制和空间偏差采样对丰度、繁殖力和成年存活率的朴素（非空间）CKMR估计器性能的影响。种群动态近似于一个受致死性采样影响的长寿哺乳动物物种。当扩散不受限制（即完全混合）或采样是随机的或存在中等程度的空间变化时，朴素CKMR丰度估计器相对无偏差。当扩散受到限制时，空间采样概率的极端变化会使丰度估计产生负偏差。繁殖时间表和存活率估计良好，但当成年个体可以迁出采样区域时，存活率估计除外。使用柯尔莫哥洛夫-斯米尔诺夫检验很容易检测到不完全混合。尽管CKMR似乎有望利用机会性收集的基因数据来估计丰度和生命率，但当扩散限制与空间偏差采样相结合时需要谨慎。幸运的是，在样本量足够的情况下，不完全混合很容易检测到。原则上，可以设计并拟合空间明确的CKMR模型，以避免在扩散限制下产生偏差，但开发此类模型需要额外的复杂性（可能还需要额外的数据）。我们建议在实施CKMR项目之前，使用模拟研究来检验所提出的建模方法的潜在偏差和精度。
