Mittell Elizabeth A, Leblanc Camille A, Kristjánsson Bjarni K, Ferguson Moira M, Räsänen Katja, Morrissey Michael B
Department of Aquaculture and Fish Biology, Hólar University, Sauðárkrókur, Iceland.
School of Biology, University of St Andrews, St Andrews, UK.
R Soc Open Sci. 2025 Jan 29;12(1):241802. doi: 10.1098/rsos.241802. eCollection 2025 Jan.
As a key life-history trait, growth rates are often used to measure individual performance and to inform parameters in demographic models. Furthermore, intraspecific trait variation generates diversity in nature. Therefore, partitioning out and understanding drivers of spatiotemporal variation in growth rate is of fundamental interest in ecology and evolution. However, this has rarely been attempted owing to the amount of individual-level data required through both time and space, and issues with missing data in important covariates. Here, we implemented a Bayesian state-space model using individual-level data from 20 populations of Arctic charr () across 15 capture occasions, which allowed us to: (i) integrate over the uncertainty of missing recapture records; (ii) robustly estimate size-dependence; and (iii) include a covariate (water temperature) that contained missing data. Interestingly, although there was substantial spatial, temporal and spatiotemporal variation in growth rate, this was only weakly associated with variation in water temperature and almost entirely independent of size, suggesting that spatiotemporal variation in other environmental conditions affected individuals across sizes similarly. This fine-scale spatiotemporal variation emphasizes the importance of local conditions and highlights the potential for spatiotemporal variation in a size-dependent life-history trait, even when environmental conditions are apparently very similar.
作为一个关键的生活史特征，生长速率常被用于衡量个体表现，并为种群统计学模型中的参数提供信息。此外，种内性状变异在自然界中产生了多样性。因此，划分并理解生长速率时空变异的驱动因素是生态学和进化领域的基本研究兴趣所在。然而，由于需要跨越时间和空间的个体水平数据，以及重要协变量中存在数据缺失问题，很少有人尝试这样做。在这里，我们使用了来自20个北极红点鲑种群在15次捕获时机的个体水平数据，实施了一个贝叶斯状态空间模型，这使我们能够：（i）整合缺失重捕记录的不确定性；（ii）稳健地估计大小依赖性；（iii）纳入一个包含缺失数据的协变量（水温）。有趣的是，尽管生长速率存在显著的空间、时间和时空变异，但这仅与水温变异存在微弱关联，且几乎完全独立于大小，这表明其他环境条件的时空变异对不同大小的个体产生了类似影响。这种精细尺度的时空变异强调了局部条件的重要性，并突出了即使环境条件看似非常相似时，大小依赖的生活史特征中时空变异的可能性。
