Does morphology predict trophic position and habitat use of ant species and assemblages?

A functional traits-based theory of organismal communities is critical for understanding the principles underlying community assembly, and predicting responses to environmental change. This is particularly true for terrestrial arthropods, of which only 20% are described. Using epigaeic ant assemblages, we asked: (1) can we use morphological variation among species to predict trophic position or preferred microhabitat; (2) does the strength of morphological associations suggest recent trait divergence; (3) do environmental variables at site scale predict trait sets for whole assemblages? We pitfall-trapped ants from a revegetation chronosequence and measured their morphology, trophic position [using C:N stoichiometry and stable isotope ratios (δ)] and characteristics of microhabitat and macrohabitat. We found strong associations between high trophic position (low C:N and high δ(15)N) in body tissue and morphological traits: predators were larger, had more laterally positioned eyes, more physical protection and tended to be monomorphic. In addition, morphological traits were associated with certain microhabitat features, e.g. smaller heads were associated with the bare ground microhabitat. Trait-microhabitat relationships were more pronounced when phylogenetic adjustments were used, indicating a strong influence of recent trait divergences. At the assemblage level, our fourth corner analysis revealed associations between the prevalence of traits and macrohabitat, although these associations were not the same as those based on microhabitat associations. This study shows direct links between species-level traits and both diet and habitat preference. Trait-based prediction of ecological roles and community structure is thus achievable when integrating stoichiometry, morphology and phylogeny, but scale is an important consideration in such predictions.

Robust tests for one or more allometric lines.

In allometry, the study of how size variables scale against each other, it is often of interest to fit lines to bivariate data and test hypotheses about slope and elevation about one or several lines. The nature of the problem suggests that bivariate techniques related to principal component analysis are more appropriate than linear regression. Inference methods have been developed for this problem and are in widespread use, however, we demonstrate that such methods are not robust to bivariate contamination, and propose alternative approaches which are. The new approaches use Huber's M-estimator via a plug-in approach, where robust test procedures have the same form as classical ones, but where we plug in robust estimators of parameters and standard errors in place of classical estimators. Simulations demonstrate that these new procedures are robust against bivariate contamination, and can make accurate inferences even from small samples.