Individuals of large or dark-colored ectothermic species often have a higher reproduction and activity than small or light-colored ones. However, investments into body size or darker colors should negatively affect the fitness of individuals as they increase their growth and maintenance costs. Thus, it is unlikely that morphological traits directly affect species’ distribution and abundance. Yet, this simplification is frequently made in trait-based ecological analyses. Here, we integrated the energy allocation strategies of species into an ecophysiological framework to explore the mechanisms that link species’ morphological traits and population dynamics. We hypothesized that the effects of morphological traits on species’ distribution and abundance are not direct but mediated by components of the energy budget and that species can allocate more energy towards dispersal and reproduction if they compensate their energetic costs by reducing mobility costs or increasing energy uptake. To classify species’ energy allocation strategies, we used easily measured proxies for the mobility costs and energy uptake of butterflies that can be also applied to other taxa. We demonstrated that contrasting effects of morphological traits on distribution and abundance of butterfly species offset each other when species’ energy allocation strategies are not taken into account. Larger and darker butterfly species had wider distributions and were more abundant if they compensated the investment into body size and color darkness (i.e. melanin) by reducing their mobility costs or increasing energy uptake. Adults of darker species were more mobile and foraged less compared to lighter colored ones, if an investment into melanin was indirectly compensated via a size-dependent reduction of mobility costs or increase of energy uptake. Our results indicate that differences in the energy allocations strategies of species account for a considerable part of the variation in species’ distribution and abundance that is left unexplained by morphological traits alone and that ignoring these differences can lead to false mechanistic conclusions. Therefore, our findings highlight the potential of integrating proxies for species’ energy allocation strategies into trait-based models not only for understanding the physiological mechanisms underlying variation in species’ distribution and abundance, but also for improving predictions of the population dynamics of species.

Proxies for mobility costs and energy uptake

As a proxy for the energetic costs of mobility, we measured the wingbeat frequency of 316 individuals of 102 butterfly species using high-speed camera footage taken during the years 2013 to 2017 at different sites in Central Europe (a total of 793,896 frames or 2,646 s). Wingbeat frequencies of individuals in Hz were calculated as wingbeat counts of each scene divided by its length (in s). Subsequently, for each species, we averaged wingbeat frequencies across individuals (median: 3 individuals, min: 1 individual, max: 9 individuals). To integrate across the peak and normal mobility costs of a species, we averaged wingbeat frequencies during in situ and escape flight (i.e. normal/peak flight, DATASET: energy_budget_butterflies-intra_specific_data.csv). When only normal or peak wingbeat frequencies were available for a species (i.e. for 1 and 43 species, respectively), we used values that were predicted based on the relationship between these two variables (mean_flight). Furthermore, while filming, we also recorded the ambient temperature to evaluate whether the wingbeat frequency of species was temperature dependent. However, the correlation between these two variables was not significant (temperature.C).

To obtain a proxy for the energy uptake of adult butterflies, we counted how often individuals were observed collecting nectar on flowers based on the results of a Google Images search (accessed on May 15, 2017). To avoid potential bias of the access point, which could result from Google’s search algorithms, we used the international homepage (i.e. google.com) and searched for the scientific name of a butterfly species. Of the first 100 hits, only images of clearly identifiable and living adult individuals were used for further analyses (DATASET: energy_budget_butterflies-links_google_image_search.xlsx). We assigned each image a value of 1 or 0 depending on whether the individual was observed foraging or not (i.e. whether the proboscis was inside the flower or not), and a value of 0.5 if it sat on a flower but the proboscis was not visible. Hence, to avoid potential observer biases (e.g. the preference of the photographers for taking pictures of butterflies on flowers), butterflies that clearly only sat on flowers were not considered as foraging. Finally, we averaged these values for each species (nectar_foraging_google). A rarefaction analysis showed that standard deviations calculated for an increasing number of randomly sampled images of species remained constant at 0.04 for sample sizes above 32 images. This suggests that our results are not affected by differences among locations and conditions of these observations and, although we used all images sampled for further analyses, it indicates that a relatively small number of images is already sufficient to provide a robust estimate for the propensity of nectar foraging of a species. The reliability of our approach was further confirmed by a positive relationship between image-based estimates and expert classifications of the nectar-foraging propensity of species (P < 0.001, rho = 0.31, n = 436; DATASET: energy_budget_butterflies-expert_nectar_foraging_classification.csv).

Morphological traits

Estimates of the color darkness, body size and wing size of a species were calculated based on scanned dorsal drawings of European butterfly species. In our study, we considered only data for females. Specifically, we used the inverted average RGB (i.e. color lightness) of pixels of the basal third of the wings and the body as an estimate of the color darkness of a species (color_lightness_8bit, DATASET: energy_budget_butterflies-species_level_data.csv). We considered only the basal third of the wings because their distal part is less relevant for thermoregulation in butterflies. As an estimate of the body size of a species, we used the sum of volumes of each pixel row of images of the body surface [π × (½ length of pixel row)² × pixel edge length in mm; body_volume.mm3]. In addition, we calculated the wing size of images as the number of pixels of the four wings × pixel area in cm²(wing_area.cm2).

Distribution and abundance of species

Regional distributions (i.e. occupancy; OccuEU) were estimated based on gridded distribution data of species across Europe [in a grid of cells with a size of 50 km × 50 km, CGRS]. For each species, regional distributions were calculated by dividing the number of grid cells in which it was present by the total number of grid cells (1,720 grid cells). To calculate the local distribution and abundance of species (i.e. local occupancy and population density; OccuCH, AbundCH), we used survey data for butterfly species assessed as part of the Biodiversity Monitoring Switzerland during the years 2003–2016 (www.biodiversitymonitoring.ch, accessed on October 4, 2017). The monitoring scheme involved the counting of butterflies at 520 regularly placed sites (in a grid of cells with a size of 5 km × 5 km) along transects of 2.5 km length. Transects were visited four to seven times each year during comparable weather conditions. Species abundances were calculated as the average number of individuals per occupied transect and year. Note that this abundance measure is not correlated with the number of generations per year (nbr_generations).

Habitat availability

To account for the potential effect of habitat availability on the distribution and abundance of species, we used gridded distribution information on all 473 larval host plants of butterflies in Switzerland for the years 2003–2016 from the Info Flora Database (accessed on October 18, 2017; a grid of cells with a size of 5 km × 5 km). We considered only larval host plants of the butterfly species because adult butterflies are mainly generalist nectarivores. Based on these data, we then calculated the habitat availability for each butterfly species as the number of grid cells occupied by host plants divided by the total number of grid cells across Switzerland (i.e. occupancy of host plants; OccuCH_hostplants_logit).

Data transformation

To normalize the data, nectar-foraging propensity, habitat availability, local distribution and regional distribution were logit transformed, and wingbeat frequencies, body volume, color darkness, wing area, egg number and local abundance were log_e transformed.

For a more detailed description, including references and data sources, please see the referenced article (Pinkert et al. 2020, ECY19-1196).