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Title: Ecological niche models (ENMs) analysis for Carpornis melanocephala (Passeriformes: Cotingidae) in Rio de Janeiro State,Brazil.

Author : Tainá Rocha

Here we use ENMs to map climatic suitability for Black-headed berryeater (C.melanocephala) in Rio de Janeiro state. Our goal it's find new populations in future fieldwork.

Status: Finished

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Scripts Folder structure:

• [x] Exploratory data analysis : to study the georeferenced data in all climatic variables which we use in this study. We use climatic data from Worldclim database (http://www.worldclim.com/version2 in 30 seconds resolution (~1 km²)).

• [x] Pearson_Correlation: to verify the pairwise variables correlation and after choose variables less autocorrelated.

• [x] Data cleaning: filters to applie on records dataset in order to reduce sample bias (reduce spatial correlation). Here, we use setupsdma_data funtion of modleR package (https://model-r.github.io/modleR/)

• [x] Crop_M_BAM_Area: select and crop movement (M) area (see Barve et al 2011, https://www.researchgate.net/publication/230691635_The_crucial_role_of_the_accessible_area_in_ecological_niche_modeling_and_species_distribution_modeling )

• [x] Bioclim_for_PA: We run a BIOCLIM algorithm to select pseudoabsence area i.e, low suitability area to generate pseudoabsen records.

• [x] ENMs: workflow based on modleR package to performing ENMs. We have two subfolder:

  • 4_ENMs_Variables: for models based on 4 climatic vriables
  • 6_ENMs_Variables: for models based on 6 climatic variables.

• [x] BoxPlots:

  • Boxplots of variables in record database
  • Boxplot of models evaluation metrics

• [x] Models post-processing: Select only suitable climate where are forests (according to MapBiomas collection 4)