Machine Learning Identification of Ecological Corridors for Medium-Sized Carnivores

Abstract

Introduction: Habitat fragmentation is among the most pressing issues in the contemporary loss of global biodiversity, calling for the development of ecological corridors to ensure connectivity of the species' populations. Medium-sized carnivores play an important role as indicators of ecological conditions and dispersers of seeds, however, are increasingly threatened by man-made matrix barriers and degraded landscapes. Conventional corridor identification methodologies, such as least-cost path and circuit theory approaches, commonly involve static parameters and heuristically defined variables that cannot effectively account for non-linear processes occurring in ecosystems. Materials & Methods: In this theoretical study, a multi-scale landscape connectivity modeling framework is devised, incorporating the latest developments in machine learning algorithms including Random Forests, MaxEnt, and Gradient Boosting Machines with a computational network analysis approach. A theoretical matrix was developed by employing artificially constructed environmental friction gradients and topography layers, as well as dispersal kernel parameters corresponding to the biological characteristics of medium-sized carnivores. The novel theoretical model introduces dynamic resistance surfaces in which permeability values are optimized with the help of machine learning classifiers based on movement trajectories and multi-variable habitat suitability indices, thus eliminating shortcomings of the classical heuristics-based approach. Results: The theory-based study suggests that machine learning-based methods significantly improve corridor prediction efficiency because they allow mapping of non-linear spatial relationships and identifying latent habitat interactions which are overlooked by conventional models. The computational outputs obtained by synthesis prove that the algorithm optimization leads to reduction of spatial omission errors and high-resolution recognition of movement bottlenecks, ecological stepping stones, and structural pinch points at various spatial scales. Also, sensitivity analysis within the simulated framework shows that machine learning models exhibit high stability during prediction regardless of spatial resolution and environmental resistance parameter variation. Conclusion: Machine learning application in the theoretical framework of landscape ecology represents an adaptive and scalable approach for identification of wildlife corridors. This paradigm change will help to optimize spatial prioritization in conservation planning and thus protect regional metapopulations from extinction and avoid negative ecological impacts of habitat fragmentation for medium-sized carnivores during global environmental changes.