@Article{FaleiroNeméLoyo:2018:ClChLi,
               author = "Faleiro, Frederico Valtuille and Nem{\'e}sio, Andr{\'e} and 
                         Loyola, Rafael",
          affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade 
                         Federal de Uberl{\^a}ndia} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Climate change likely to reduce orchid bee abundance even in 
                         climatic suitable sites",
              journal = "Global Change Biology",
                 year = "2018",
               volume = "24",
               number = "6",
                pages = "2272--2283",
                month = "June",
             keywords = "Atlantic rainforest, biodiversity loss, Euglossini, pollinators, 
                         species distribution models.",
             abstract = "Studies have tested whether model predictions based on species' 
                         occurrence can predict the spatial pattern of population 
                         abundance. The relationship between predicted environmental 
                         suitability and population abundance varies in shape, strength and 
                         predictive power. However, little attention has been paid to the 
                         congruence in predictions of different models fed with occurrence 
                         or abundance data, in particular when comparing metrics of climate 
                         change impact. Here, we used the ecological niche modeling fit 
                         with presence-absence and abundance data of orchid bees to predict 
                         the effect of climate change on species and assembly level 
                         distribution patterns. In addition, we assessed whether 
                         predictions of presence-absence models can be used as a proxy to 
                         abundance patterns. We obtained georeferenced abundance data of 
                         orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian 
                         Atlantic Forest. Sampling method consisted in attracting male 
                         orchid bees to baits of at least five different aromatic compounds 
                         and collecting the individuals with entomological nets or bait 
                         traps. We limited abundance data to those obtained by similar 
                         standard sampling protocol to avoid bias in abundance estimation. 
                         We used boosted regression trees to model ecological niches and 
                         project them into six climate models and two Representative 
                         Concentration Pathways. We found that models based on species 
                         occurrences worked as a proxy for changes in population abundance 
                         when the output of the models were continuous; results were very 
                         different when outputs were discretized to binary predictions. We 
                         found an overall trend of diminishing abundance in the future, but 
                         a clear retention of climatically suitable sites too. Furthermore, 
                         geographic distance to gained climatic suitable areas can be very 
                         short, although it embraces great variation. Changes in species 
                         richness and turnover would be concentrated in western and 
                         southern Atlantic Forest. Our findings offer support to the 
                         ongoing debate of suitability-abundance models and can be used to 
                         support spatial conservation prioritization schemes and species 
                         triage in Atlantic Forest.",
                  doi = "10.1111/gcb.14112",
                  url = "http://dx.doi.org/10.1111/gcb.14112",
                 issn = "1354-1013",
             language = "en",
           targetfile = "faleiro_climate.pdf",
        urlaccessdate = "24 abr. 2024"
}