Using R in Landscape Ecology: an Overview

Keywords:
Biodiversity, Mapping, Protected Habitats, Endemic Species, IT Tools

Abstract:
This paper provides an overview of the use of the R programming language and selected specialized packages in a variety of analytical tasks within landscape ecology. It highlights the broad applicability of R across different analytical domains, from numerical data processing and biodiversity analysis to species conservation and spatial visualization. This study integrates quantitative biodiversity assessment with spatial analysis to evaluate plant diversity and conservation patterns. First, the study provides an example of quantitative biodiversity assessment by calculating alpha diversity through species richness, Shannon, Simpson, Berger–Parker, and Fisher’s alpha indices, while beta diversity was evaluated using Jaccard distance and visualized with heatmaps to illustrate compositional differences among sites. Next, the paper presents a mapping of protected areas using the example of Fruška Gora National Park. Finally, an example of species-focused habitat analysis is given through detailed mapping of the endemic species Ramonda serbica and Ramonda nathaliae, including distribution patterns, habitat overlap, and clustering of R. serbica habitats in Serbia. Together, these examples illustrate how R can be effectively applied to a wide range of landscape ecology tasks. By presenting practical case studies across multiple domains, the paper aims to promote the broader adoption of R as a flexible and powerful tool for landscape analysis, research, and conservation planning.

CITATION:

IEEE format

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M. Lakićević, M. Gazdić, “Using R in Landscape Ecology: an Overview,” in Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 201-207. doi:10.15308/Sinteza-2026-201-207

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Lakićević, M., Gazdić, M. (2026). Using R in Landscape Ecology: an Overview. Paper presented at Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-201-207

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