Diel patterns in foraging activity are dictated by a combination of abiotic, biotic, and endogenous limits. Understanding these limits is important for insects because ectotherm taxa will respond more pronouncedly to ongoing climatic change, potentially affecting crucial ecosystem services. We leverage an experimental macrocosm, the Montreal Insectarium Grand Vivarium, to test the importance of endogenous mechanisms in determining temporal patterns in foraging activity of butterflies. Specifically, we assessed the degree of temporal niche partitioning among 24 butterfly species originating from the Earth’s tropics within controlled environmental conditions. We found strong niche overlap, with the frequency of foraging events peaking around solar noon for 96% of the species assessed. Our models suggest that this result was not due to the extent of cloud cover, which affects radiational heating and thus limits body temperature in butterflies. Together, these findings suggest that an endogenous mechanism evolved to regulate the timing of butterfly foraging activity within suitable environmental conditions. Understanding similar mechanisms will be crucial to forecast the effects of climate change on insects, and thus on the many ecosystem services they provide.

Design overview

We sampled four sites in the Montreal Insectarium Grand Vivarium (MIGV) between April 6^th and April 19^th, 2022. Sites were selected initially to be similar in conditions but spread across the greenhouse to minimize the risk of pseudoreplication. We limited our sample to two weeks because we aimed to reduce as much as possible (i) variation in daylight conditions throughout the sampling period (Table S1) and (ii) the difference in butterfly age between samples. We performed twenty sampling cycles within five days, each cycle consisting of a two-minute point count per site (n = 80 total point counts). During each point count, we recorded foraging events observed at a site within an area of 10 m² (n = 275 foraging events from 24 butterfly species). One observer kept track of butterflies within the point count area, with his colleague taking notes. Both observers paid attention to avoid double-counting of the same individuals during the two-minute point count. Position of the four sampling sites inside the MIGV was constant through the sampling period. There were two sampling days without visitors in the MIGV and three with visitors; we did not observe any effect of visitors on the activity of butterflies.

Sampling timing

Preliminary observations suggested that most point count samples would sample zero butterflies before 7 AM and after 5 PM. Therefore, we spread between three and five samples every sampling day, aiming to get at least one sample in the morning after 7 AM, one around noon, and one in the afternoon before 5 PM. This pattern is consistent with butterflies’ characteristic diel activity, which has only a few exceptions not represented in our study (e.g., in the Families Hesperiidae and Hedylidae [1]). Sampling hours were staggered across days so that we could better estimate species-specific flight curves, in no particular order (Fig. S1).

Sampling nectaring behavior

A foraging event consisted of a butterfly entering the point count area, landing on a flower, and extending its proboscis to reach the nectar within that flower. We did not count butterflies that were already foraging at the beginning of the point count and avoided double-counts. While avoiding double-counts is difficult in the field, we found it reasonably easy within the controlled conditions of the greenhouse and given the duration of our point counts (two minutes). Note that we did not mark butterflies to avoid affecting their behavior, and because this was not allowed within the MIGV. We note that our sample characteristics (always ≤10 individuals per point count, typically <5; Fig. S1) in relation to the large numbers of butterflies in the greenhouse (i.e., hundreds), suggest limited effect of pseudoreplication due to double-sampling of the same butterfly in different point counts. However, our results should be interpreted cautiously, keeping in mind that some individuals might have been indeed sampled more than once.

Sampling environmental conditions

During sampling we recorded time of the day, temperature, and cloud cover. Time of the day was recorded to the closest 30-minute interval (e.g., 7.17 AM was associated to 7.30 AM, 4.01 PM to 4.00 PM and so on).

Cloud cover was visually assessed as proportion of the sky covered in clouds. We did not measure light intensity or related information (e.g., UV radiation) because these aspects are not considered primary determinants of butterfly activity in the literature (van Swaay et al. 2015, Riva et al. 2020).

Ambient temperature was recorded throughout the point counts with a thermometer that was left on for the 2-min sampling event duration, at a constant height of 1.5 m. The temperature value was recorded once the instrument stabilized on a temperature that we deemed representative of the sampling point. Because we were within a greenhouse, the temperature value typically was stable, so a single value was sufficient to capture changes in the microclimate in 2 minutes.

Substrate temperature was measured each time a butterfly landed using an infrared thermometer. Both temperatures were recorded with a precision of 0.1°C. Because the ambient temperature thermometer did not work on some occasions, we used the substrate temperature in our models.