Avoiding misleading estimates of among‐individual variance caused by non‐random sampling of individuals in a changeable environment

Abstract Animal ecologists frequently quantify variance in hierarchically structured traits in wild populations. Importantly, phenotypic plasticity within the period of measurement can modify the trait of interest in response to various unmeasured, temporally or spatially changeable, environmental conditions. Non‐random sampling among units of the random effect (e.g. individuals) regarding the environment at issue may lead to estimates of the variance among () or within () such units that conflate several types of processes. This mixing of underlying biology can affect interpretations of the random effect variance. Here, we explore the conditions leading to this situation and assess potential solutions when relevant information is missing. We simulated a trait's phenotypic values that depended on the environmental variable, and individuals that differed in their deviation to the mean population phenotype (random intercepts). We also simulated different types of variation in an environmental variable that was either shared or specific to each individual. We then varied the repeatability in the timing of sampling () and analysed simulated datasets using linear mixed‐effect models with different fixed‐ and random‐effect structures. In the presence of unmeasured environmental factors, the estimated among‐individual variance () contained a larger signature of the current environment as the strength of the temporal autocorrelation and the repeatability in the timing of sampling () increased. For low to moderate values of (e.g. <60% of the total variance in our simulations) the risk of pre‐study and within‐study effects conflating estimates of variance components was low and could easily be corrected with a model including period or individual‐period combination as random effects. Higher led to an increase in conflating effects that were difficult to correct. Our study shows the importance of limiting the variance among individuals in the timing structure of sampling (). We recommend researchers estimate and report it in papers. Finally, can be limited by sampling all individuals in the same period, or sensitivity analyses could be conducted by removing extreme sampling dates at the analysis stage to reduce .