Suman Pant, Deepak Srivastava, Vibhor Sharma and Ashutosh Kumar Choudhary
Abstract
Habitat fragmentation, which can also lead to ecosystem degradation and species extinction, is one of the leading drivers of biodiversity loss by fragmenting habitats and changing the functioning of ecosystems. Typical fragmentation indices, like the ones applied in FRAGSTATS, do not always reflect the complexity and the non-linear structure of fragmented landscapes. Meanwhile, fractal geometry offers another way of quantifying the scale dependent landscape structures found in this landscape through self-similarity, and explainable machine learning enhances landscape ecology predictions by providing increased transparency of the models. This paper introduces a conceptual model combining Global Biodiversity Information Facility (GBIF) species occurrence data, fractal landscape analysis, ensemble machine learning, and explainable artificial intelligence (XAI) to assess habitat fragmentation. It outlines the collection of biodiversity data, spatial data preprocessing and integration, calculation of fractal and landscape metrics, fragmentation classification with the help of machine learning, and the interpretation of the results with the aid of SHAP (Shapley Additive exPlanations). It includes mathematical definitions of box-counting fractal dimension, patch density, edge density, and SHAP feature attribution and compares landscape metrics and machine learning indicators. By combining biodiversity informatics, fractal geometry, and interpretable machine learning, the proposed framework provides a transparent, reproducible, and evidence-based approach to support habitat fragmentation assessment and conservation planning.