Henrik von Wehrden

An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter‐annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p ‐values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.