Ecological Solutions and Evidence (Jul 2024)

NIMO: A graphical user interface‐based R package for species distribution modelling

  • Stanislas Mahussi Gandaho,
  • Etotépé A. Sogbohossou,
  • Lindy Jane Thompson

DOI
https://doi.org/10.1002/2688-8319.12385
Journal volume & issue
Vol. 5, no. 3
pp. n/a – n/a

Abstract

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Abstract Species distribution modelling (SDM) is essential for understanding and predicting biodiversity patterns globally. However, the complexities of data preparation, algorithm selection and model evaluation can present significant challenges. This paper introduces NIMO, an R package with a user‐friendly graphical interface aimed at streamlining the entire SDM workflow. In SDM, data preparation is a crucial and time‐consuming process, which requires the acquisition of spatiotemporal occurrence records. The Global Biodiversity Information Facility (GBIF) database plays a key role in addressing data scarcity by providing extensive community science‐based repositories. However, compiling, aligning and verifying spatial references can be laborious. NIMO addresses these challenges by offering a flexible workflow that allows users to more easily query biodiversity data. NIMO organizes its features into three main steps: pre‐modelling, modelling and post‐modelling. It empowers users to optimize data for modelling with a variety of algorithms. NIMO allows for default hyperparameter values or extensive tuning, providing users with flexibility in selecting models. Practical implication: To demonstrate the effectiveness of NIMO, we presented a case study focusing on the critically endangered white‐backed vulture (Gyps africanus) in Kruger National Park, South Africa. We used NIMO to collect GBIF occurrence data and integrated environmental predictors to map suitable habitat (probability of occurrence). The distribution modelling workflow included area calibration, collinearity check, generation of training and testing data sets, model fitting and model combination. The results showcase the discriminative ability of fitted models, especially the generalized linear model, support vector machine and random forest, that constituted the ensemble model. Spatial prediction using the ensemble model revealed that the species has a higher occurrence probability close to roads, indicating a possible increased risk of mortality for the vultures in Kruger National Park from motor vehicle collisions.

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