Climate influences the response of community functional traits to local conditions in bromeliad invertebrate communities

• Autor(a) principal: Guzman, Laura Melissa
• Data de Publicação: 2021
• Outros Autores: Trzcinski, M. Kurtis, Barberis, Ignacio M., Céréghino, Régis, Srivastava, Diane S., Gilbert, Benjamin, Pillar, Valério D., de Omena, Paula M., MacDonald, A. Andrew M., Corbara, Bruno, Leroy, Céline, Ospina Bautista, Fabiola, Romero, Gustavo Q., Kratina, Pavel, Debastiani, Vanderlei J., Gonįalves, Ana Z., Marino, Nicholas A. C., Farjalla, Vinicius F., Richardson, Barbara A., Richardson, Michael J., Dézerald, Olivier, Piccoli, Gustavo C. O., Jocqué, Merlijn, Montero, Guillermo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1111/ecog.05437; http://hdl.handle.net/11449/221624
• topic: bromeliads; climatic variation; functional traits; local conditions; macroinvertebrates

Functional traits determine an organism's performance in a given environment and as such determine which organisms will be found where. Species respond to local conditions, but also to larger scale gradients, such as climate. Trait ecology links these responses of species to community composition and species distributions. Yet, we often do not know which environmental gradients are most important in determining community trait composition at either local or biogeographical scales, or their interaction. Here we quantify the relative contribution of local and climatic conditions to the structure and composition of functional traits found within bromeliad invertebrate communities. We conclude that climate explains more variation in invertebrate trait composition within bromeliads than does local conditions. Importantly, climate mediated the response of traits to local conditions; for example, invertebrates with benthic life-history traits increased with bromeliad water volume only under certain precipitation regimes. Our ability to detect this and other patterns hinged on the compilation of multiple fine-grained datasets, allowing us to contrast the effect of climate versus local conditions. We suggest that, in addition to sampling communities at local scales, we need to aggregate studies that span large ranges in climate variation in order to fully understand trait filtering at local, regional and global scales.