Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology

Abstract

Functional traits mediate species' responses to, and roles within, their environment and are constrained by evolutionary history. While we have a strong understanding of trait evolution for macrotaxa such as birds and mammals, our understanding of invertebrates is comparatively limited. Here, we address this gap in North American beetles with a sample of ground beetles (Carabidae), leveraging a large-scale collection and digitization effort by the National Ecological Observatory Network (NEON). For 154 ground beetle species, we measured seven morphological traits, which we placed into a recently developed effect-response framework that characterizes traits by how they predict species' effects on their ecosystems or responses to environmental stressors. We then used cytochrome oxidase 1 sequences from the same specimens to generate a phylogeny and tested the evolutionary tempo and mode of the traits. We found strong phylogenetic signal in, and correlations among, ground beetle morphological traits. These results indicate that, for these species, beetle body shape trait evolution is constrained, and phylogenetic inertia is a stronger driver of beetle traits than (recent) environmental responses. Strong correlations among effect and response traits suggest that future environmental drivers are likely to affect both ecological composition and functioning in these beetles.

A functional trait approach reveals the effects of landscape context on ecosystem services provided by urban birds

“…Strong correlations between response and effect traits are considered to render ecosystem services vulnerable to environmental change (Suding et al 2008, Stachewicz et al 2021). However, these correlations are crucial to managing habitat for ecosystem services.…”

“…As a result, due to their habitat conditions, green spaces may vary significantly in the amount of ecosystem services they provide (Gardiner et al 2013, Aronson et al 2017). While relationships between response and effect traits are predicted (Suding et al 2008, Stachewicz et al 2021), explorations of these relationships and their influence on effect trait variation in the context of ecosystem service provisioning in urban landscapes are scant. Further, it is unknown how these response-effect trait relationships vary for different types of ecosystem service.…”

Morphometric Features of the Beetle Acinopus (Acinopus) Laevigatus Menetries, 1832 (Coleoptera, Carabidae) in the Mountain Ecosystems of Uzbekistan

“…In low-mountain and high-mountain populations of the beetle Carabus odoratus in the conditions of the Barguzin Range, a difference in the morphometric structure of the population was revealed, which gives the authors reason to assume that there is no constant genetic exchange between populations (Ananina and Sukhodolskaya, 2019). In some works, it is noted that in many species of ground beetles, body length and length of the abdomen are the most variable, and the length of head is the least variable (Stachewicz et al, 2021). However, using the example of Machozetus lehmanni, it was found that the most variable character is the length of head, and the most stable character is the width of elytra.…”

Dispersal limitation and environmental filtering effects: The taxonomic and functional beta diversity of ground beetles along the altitudinal gradient in Chinese warm‐temperature forests

“…Beta diversity resulted from two processes inlcuding species replacement and species loss and gain. The former leads to changes in unique and shared species among communities, whereas the latter not only influences this but also causes variations in species richness among them (Stachewicz et al., 2021 ; Wang et al., 2010 ), and these differences in species richness reflect the characteristic of nestedness within the communities. Decomposing beta diversity into turnover and nestedness components aids in gaining deeper insights into the spatial patterns of biological communities and their underlying driving mechanisms (Heino et al., 2019 ; Legendre, 2014 ).…”