Modeling Species Distribution And Habitat Suitability With Machine Learning Approaches

Abstract

Models Habitat suitability analysis and species distribution modeling (SDM) is an important ecological and conservation biology instrument, extremum ecological management instrument. Trite statistical techniques are often ineffective at summarising the complex, non-linear, interactions between the environmental factors and the species occurrence. Machine learning (ML) algorithms, particularly the-not-so-old computing techniques include the randomly foresting, support machine learning and deep neural networks have also proven to be quite a ground-shattering challenge to the factual and robustness of estimating the species location. The paper discusses the application of ML models in SDM with specific attention paid to the idea of how these models can process large quantities of data, consider a host of environmental predictors, and extrapolate across spatial scales. Efficiency of models are available, relevance of features discussed, and predictive uncertainty analysed by use of representative case studies. Results indicate that the ML-based solution is better than the traditional one in the capacity to reach ecologic complexity and strengthen the predictory powers. Nevertheless, there are still practically-supported constraints, such as overfitting, interpretability, computational requirement, and dependence on high quality presence-absence or presence-only data. The next step in the research is to combine explainable AI methods, hybrid models, and the use of climate change forecasts to improve the process of making decisions to preserve biodiversity and manage habitats.