Environmental conditions shape the chemical signal design of lizards

Abstract

The signals that animals use to communicate often differ considerably among species. Part of this variation in signal design may derive from differential natural selection on signal efficacy; the ability of the signal to travel efficiently through the environment and attract the receiver's attention. For the visual and acoustic modalities, the effect of the physical environment on signal efficacy is a well‐studied selective force. Still, very little is known on its impact on chemical signals. Here, we took a broad, phylogenetic comparative approach to test for a relationship between animals' signal chemistry and properties of their natural environment. Our study focused on lizards from the Lacertidae family. We sampled 64 species across three continents and determined the lipophilic composition of their glandular signalling secretions using gas chromatography–mass spectrometry. For each species, an array of environmental variables of high temporal and spatial resolution was obtained from climate databases. Species varied considerably in the overall richness (number of constituents) of their secretions, as well as in the relative contribution of the major chemical compound classes. Signal richness and the relative contribution of the respective compounds exhibited little evidence of phylogenetic relatedness, suggesting that chemical signals may change very rapidly. Neither insularity nor substrate use affected chemical signal composition, however, we found a strong statistical relationship between the chemistry of the lizards' secretions and aspects of the thermal and hydric environment they inhabit. Species from ‘xeric’ milieus contained high proportions of stable fatty acid esters and high molecular weight alcohols in their glandular secretions, which likely increase the persistence of secretion scent‐marks. In contrast, species inhabiting ‘mesic’ environments produced secretions of a high chemical richness comprising high levels of aldehydes and low molecular weight alcohols. This chemical mix probably creates a volatile‐rich signal that can be used for long‐distance airborne communication. We argue that the observed variation in signal design results from differential natural selection, optimizing signal efficacy under contrasting environmental conditions. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12984/suppinfo is available for this article.

How to tackle chemical communication? Relative proportions versus semiquantitative determination of compounds in lizard chemical secretions

“…Thus, we consider that although the TIC procedure is easier and faster to implement than the SQ procedure, it embodies some statistical limitations that must be taken into account. The TIC procedure can be useful to characterize and compare overall chemical profiles (Baeckens, García‐Roa, Martín, & Van Damme, ; Baeckens, Martín, García‐Roa, & van Damme, ; Baeckens, Martín, García‐Roa, Pafilis, et al., ; García‐Roa, Jara, López, Martín, & Pincheira‐Donoso, ), probably reflecting the “chemosensory perception” that a lizard may have of a secretion. Nevertheless, when the target is one or more particular compounds, it is advisable to ensure independence of these by means of including an internal standard, as has been performed in the SQ procedure (García‐Roa, Sáiz, et al., ).…”

Rapid and repeated divergence of animal chemical signals in an island introduction experiment

“…Details on the chemical analyses can be found in the Supporting Information. After the chemical content of the samples was determined, we estimated the complexity of the chemical composition of each individual secretion sample by calculating the chemical ‘richness’ and ‘diversity’, following Baeckens, Martín, García‐Roa, Pafilis, et al (2018), Baeckens, Martín, García‐Roa, and Van Damme (2018). The total number of different lipophilic compounds found in each lizard secretion was considered the chemical richness.…”

Sexual selection and the chemical signal design of lacertid lizards

“…We used a tree described by Baeckens et al (2017b) to analyse our data in a phylogenetic setting. The tree was constructed with information on sequences from three mitochondrial and two nuclear gene regions.…”