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Can environmental complexity predict functional trait composition of ground-dwelling ant assemblages? A test across the Amazon Basin

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Guilherme, Diego Rodrigues
Souza, Jorge Luiz Pereira
Franklin, E.
Pequeno, Pedro Aurélio Costa Lima
Chagas, Andreia Conceição Das
Baccaro, Fabricio Beggiato

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Abstract:

Environmental gradients may influence species distributions by filtering their functional traits, resulting in a correspondence between community functional composition and local environmental conditions. We used a vegetation gradient as an indicator for environmental complexity to test whether it predicted the morphological composition of ground-dwelling ant assemblages across geographic extents. The sampling design covered 126 plots distributed across eight sampling sites along a broad environmental gradient in the Amazon Basin. Plots covered different phytophysiognomies that have a strong relation with forest biomass and, consequently, litter production. We selected six morphological traits related to ant foraging strategies and behavior. Generalized linear mixed models were used to predict how environmental complexity affects trait composition of ground-dwelling ant assemblages. Structurally less complex environments (eg. Amazonian savannah) harboured more species of smaller ants, with relatively smaller mandibles and relatively larger eyes. In more complex environments (eg. dense ombrophylous forest), there were more ant species of larger size, with relatively larger mandibles and relatively smaller eyes. No relationship was detected between relative femur length and the environmental gradient investigated. The functional approach focused on individual traits may illuminate which ant foraging strategies are best adapted to a particular habitat. Our data reveal that the morphological composition of ground-dewelling ant assemblages responds clearly to environmental complexity suggesting that certain ant characteristics offer ecological advantages to particular species in particular habitats. © 2019 Elsevier Masson SAS

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