Adding Biotic Interactions into Paleodistribution Models: A Host-Cleptoparasite Complex of Neotropical Orchid Bees

Abstract

Orchid bees compose an exclusive Neotropical pollinators group, with bright body coloration. Several of those species build their own nests, while others are reported as nest cleptoparasites. Here, the objective was to evaluate whether the inclusion of a strong biotic interaction, such as the presence of a host species, improved the ability of species distribution models (SDMs) to predict the geographic range of the cleptoparasite species. The target species were Aglae caerulea and its host species Eulaema nigrita. Additionally, since A. caerulea is more frequently found in the Amazon rather than the Cerrado areas, a secondary objective was to evaluate whether this species is increasing or decreasing its distribution given South American past and current climatic conditions. SDMs methods (Maxent and Bioclim), in addition with current and past South American climatic conditions, as well as the occurrences for A. caerulea and E. nigrita were used to generate the distribution models. The distribution of A. caerulea was generated with and without the inclusion of the distribution of E. nigrita as a predictor variable. The results indicate A. caerulea was barely affected by past climatic conditions and the populations from the Cerrado savanna could be at least 21,000 years old (the last glacial maximum), as well as the Amazonian ones. On the other hand, in this study, the inclusion of the host-cleptoparasite interaction complex did not statistically improve the quality of the produced models, which means that the geographic range of this cleptoparasite species is mainly constrained by climate and not by the presence of the host species. Nonetheless, this could also be caused by unknown complexes of other Euglossini hosts with A. caerulea, which still are still needed to be described by science.

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

“…, Silva et al . ). Here, we demonstrated the need for the inclusion of biotic variables when predicting species distributions at large scales, even for species that have diffuse positive interactions.…”

Climate change likely to reduce orchid bee abundance even in climatic suitable sites

“…The integration of our prediction with the species’ traits can reveal interactions that can improve or reduce the species threat, as the capacity of flight that can facilitate or prevent the migration to gained areas in the future (e.g. Ackerman, Mesler, Lu, & Montalvo, ; Eltz, Whitten, Roubik, & Linsenmair, ; Kroodsma, ; Nemésio et al., ; Pokorny et al., ; Raw, ; Silva, Vilela, De Marco, & Nemésio, ; Silva et al., ; Tonhasca et al., ; Wikelski et al., ).…”

“…The orchid bees are driven mainly by climate (temperature and precipitation variables) as showed by different aspects of the community and species patterns (López‐Uribe, Zamudio, Cardoso, & Danforth, ; Nemésio & Vasconcelos, ; Nemésio et al., ) and biotic interaction do not improve the modeling of species distribution (Silva et al., ). However, further studies are necessary to better understand the contribution of biotic and abiotic factors in the species distribution.…”

Effects of climate change and habitat loss on a forest‐dependent bee species in a tropical fragmented landscape

“…This species occurs in the warm and humid forests of the Atlantic Forest (eastern coast of Brazil), a transition zone between tropical and subtropical environments, with high humidity and temperature and precipitation seasonality. For these reasons, we decided to select five climatic variables: temperature seasonality, temperature for the driest quarter, temperature for the wettest quarter, precipitation for the driest quarter and precipitation for the wettest quarter, since they exert an important effect on the macroecology of orchid bees (Abrahamczyk et al ., ; Nemésio & Vasconcelos, ; Silva et al ., ). We just incorporated two precipitation variables, and we did not include precipitation for the driest month and precipitation seasonality, which show high variability between AOGCMs (Varela et al ., ,b).…”