The ecological impact of artificial light at night (ALAN) on phenological events such as reproductive timing is increasingly recognized. In birds, previous experiments under controlled conditions showed that ALAN strongly advances gonadal growth, but effects on egg-laying date are less clear. In particular, effects of ALAN on timing of egg-laying are found to be year-dependent, suggesting an interaction with climatic conditions such as spring temperature, which is known have strong effects on the phenology of avian breeding. Thus, we hypothesized that ALAN and temperature interact to regulate timing of reproduction in wild birds. Field studies have suggested that sources of ALAN rich in short wavelengths can lead to stronger advances in egg-laying date. We therefore tested this hypothesis in the great tit (Parus major), using a replicated experimental setup where eight previously unlit forest transects were illuminated with either white, green, or red LED light, or left dark as controls. We measured timing of egg-laying for 619 breeding events spread over six consecutive years and obtained temperature data for all sites and years. We detected overall significantly earlier egg-laying dates in the white and green light versus the dark treatment, and similar trends for red light. However, there was a strong inter-annual variability in mean egg-laying dates in all treatments, which was explained by spring temperature. We did not detect any fitness consequence of the changed timing of egg-laying due to ALAN, which suggests that advancing reproduction in response to ALAN might be adaptive.

The dataset was collected in the field by monitor Great tits breeding events over 6 consecutive years and in 8 different forest sites across the Netherlands. Temperature data was obtained from a published dataset (see paper Methods per relevant information).

We upload three main scripts with all relevant datasets used within them. The three scripts allow to:

1) run statistical models and produce figures 1, 2 and 4 - "script for main models (figure 1-2-4).R"

2) run statistical models and produce figure 3 - "script for models testing temperature and lay dates (figure 3).R"

3) run the climwin analysis - "script for climwin sliding window.R"