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Stacked species distribution and macroecological models provide incongruent predictions of species richness for Drosophilidae in the Brazilian savanna
Author(s) -
Mata Renata Alves,
Tidon Rosana,
Oliveira Guilherme,
Vilela Bruno,
DinizFilho José Alexandre Felizola,
Rangel Thiago Fernando,
Terribile Levi Carina
Publication year - 2017
Publication title -
insect conservation and diversity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.061
H-Index - 39
eISSN - 1752-4598
pISSN - 1752-458X
DOI - 10.1111/icad.12240
Subject(s) - species richness , body size and species richness , biome , ecology , macroecology , species distribution , biodiversity , geography , biology , habitat , ecosystem
We tested the adequacy of two richness‐modelling approaches within the ‘spatially explicit species assemblage modelling’ ( SESAM ) framework for drosophilid flies in a tropical biome. The pattern of drosophilid species richness throughout the Brazilian savanna was investigated by comparing richness estimates from macroecological models ( MEM ) and stacked species distribution models (S‐ SDM ). We used occurrence records for macroecological modelling and to generate geographic ranges by modelling species’ niches, which were stacked to generate SDM richness. Richness predictions were compared between models and with empirical data from well‐sampled areas. The spatial variation in drosophilid richness for both estimates revealed more species in the central and south‐eastern regions of the biome. Nonetheless, MEM generated a more fragmented pattern than S‐ SDM , with scattered patches of high richness. S‐ SDM produced richness estimates nearer to the empirical values than MEM , which in turn strongly underestimated richness. The correlation between S‐ SDM and observed richness suggests that climate is the major (indirect) driver of drosophilid richness in the Brazilian savanna. Richness estimates based on macroecological modelling are, however, almost certainly affected by inventory incompleteness and sampling bias. We emphasise that S‐ SDM can be a valuable approach to explore species richness patterns in poorly sampled regions.