Modeling of regional- and local-scale distribution of the genus Montrichardia Crueg. (Araceae)
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Knowledge of the environmental correlates of species’ distributions is essential for understanding population dynamics, responses to environmental changes, biodiversity patterns, and the impacts of conservation plans. Here we examine how environment controls the distribution of the neotropical genus Montrichardia at regional and local spatial scales using species distribution models (SDMs) and logistic regression, respectively. Montrichardia is a genus of aquatic macrophytes with two species, Montrichardia linifera and Montrichardia arborescens, and is often an important component of flooded habitats. We find that for each species, altitude, precipitation and temperature of the driest month figure in the best performing SDMs as the most important factors controlling large-scale distributions, suggesting that the range limits of both species are climatically constrained by plant water-energy balance and cold intolerance. At small spatial scales, logistic regression models indicate the species partition types of aquatic habitat along local gradients of water pH, conductivity, and water transparency. In summary, a hierarchy of factors may control Montrichardia distribution from large to small spatial scales. While at large spatial scales, evolutionarily conserved climatic niches may control the range limits of the genus, at small spatial scales niche differentiation allows individual species to grow in environmentally distinct aquatic habitats.
KeywordsWetlands Aquatic macrophytes Species distribution modeling Maxent Amazon
This work is support by INCT ADAPTA—Brazilian Ministry of Science, Technology and Innovation (Conselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq/Fundação de Amparo à Pesquisa do Estado do Amazonas—FAPEAM), and the Universal CNPq (14/2009; 14/2011), PRONEX “Áreas alagáveis” (CNPq/FAPEAM), PELD MAUA (CNPq/FAPEAM) and FAPEAM EDITAL N. 017/2014—FIXAM/AM Nº Processo: 062.01174/2015 to Dr. Aline Lopes. We thank Marcelo Santos Junior and Marina Anciães for their help with MAXENT Software, and Conceição Lucia Costa, Celso R. Costa, and Valdeney de A. Azevedo for their efforts in collecting field data. Aline Lopes thanks CNPq for her Doctoral Grant and the MAUA Research Group at INPA for logistical and technical support.
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