Colors of night: climate–morphology relationships of geometrid moths along spatial gradients in southwestern China

Melanism and color saturation of butterfly assemblages: A comparison between a tropical rainforest and a xeric white forest

The study of butterfly coloration has helped to identify the ecological pressures involved in the evolution of animal coloration. However, almost all studies that addressed this issue have focused on species that inhabit more temperate environments, leaving the species and ecological factors of tropical regions mostly understudied. Here, our purpose was to evaluate whether butterfly assemblages from two distinct Neotropical biomes (i.e., tropical rainforest and xeric white forest) differ regarding their melanism and/or color saturation. Our hypotheses were that (1) tropical rainforest butterflies should be more melanic and color saturated, and that (2) butterflies from more open/arid tropical environments should be more melanic on their dorsal wing surfaces than on their ventral wings. Therefore, we quantified melanism and color saturation from dorsal and ventral surfaces of 121 different butterfly species. Comparisons show that rainforest butterflies, when contrasted to white forest butterflies, have more melanic dorsal wing surfaces, which might be seen as a form of protection against parasites. Our data also show that rainforest butterflies, but not white forest species, have darker dorsal wing surfaces, when compared to their own ventral surfaces, a trend that was also found for species inhabiting both biomes, which might be associated to thermoregulatory advantages. At last, our results also point that butterflies' dorsal wing sides present a higher variance between species (regardless of Biome), when compared to their own ventral wing side, an indication that some ecological factor (e.g., predation avoidance) might be exerting a strong homogenizing force on ventral wing coloration.

Climate change effects on animal ecology: butterflies and moths as a case study

Butterflies and moths (Lepidoptera) are one of the most studied, diverse, and widespread animal groups, making them an ideal model for climate change research. They are a particularly informative model for studying the effects of climate change on species ecology because they are ectotherms that thermoregulate with a suite of physiological, behavioural, and phenotypic traits. While some species have been negatively impacted by climatic disturbances, others have prospered, largely in accordance with their diversity in life-history traits. Here we take advantage of a large repertoire of studies on butterflies and moths to provide a review of the many ways in which climate change is impacting insects, animals, and ecosystems. By studying these climate-based impacts on ecological processes of Lepidoptera, we propose appropriate strategies for species conservation and habitat management broadly across animals.

Temperature-driven color lightness and body size variation scale to local assemblages of European Odonata but are modified by propensity for dispersal

Previous macrophysiological studies suggested that temperature-driven color lightness and body size variations strongly influence biogeographical patterns in ectotherms. However, these trait-environment relationships scale to local assemblages and the extent to which they can be modified by dispersal remains largely unexplored. We test whether the predictions of the thermal melanism hypothesis and the Bergmann's rule hold for local assemblages. We also assess whether these trait-environment relationships are more important for species adapted to less stable (lentic) habitats, due to their greater dispersal propensity compared to those adapted to stable (lotic) habitats.We quantified the color lightness and body volume of 99 European dragon- and damselflies (Odonata) and combined these trait information with survey data for 518 local assemblages across Europe. Based on this continent-wide yet spatially explicit dataset, we tested for effects temperature and precipitation on the color lightness and body volume of local assemblages and assessed differences in their relative importance and strength between lentic and lotic assemblages, while accounting for spatial and phylogenetic autocorrelation.The color lightness of assemblages of odonates increased, and body size decreased with increasing temperature. Trait-environment relationships in the average and phylogenetic predicted component were equally important for assemblages of both habitat types but were stronger in lentic assemblages when accounting for phylogenetic autocorrelation.Our results show that the mechanism underlying color lightness and body size variations scale to local assemblages, indicating their general importance. These mechanisms were of equal evolutionary significance for lentic and lotic species, but higher dispersal ability seems to enable lentic species to cope better with historical climatic changes. The documented differences between lentic and lotic assemblages also highlight the importance of integrating interactions of thermal adaptations with proxies of the dispersal ability of species into trait-based models, for improving our understanding of climate-driven biological responses.

Artificial intelligence reveals environmental constraints on colour diversity in insects

Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.