Ecological Niche Modeling: Predicting Species Distribution and Abundance"

Abstract

Ecological niche modeling (ENM) has emerged as a central analytical framework for predicting species distribution and abundance across heterogeneous landscapes and changing environmental conditions. This study critically evaluates the effectiveness of ENM in forecasting both the geographic occurrence and population density of species, with a focus on integrating occurrence‑based and abundance‑based modeling approaches. Using a combination of presence‑only and presence‑abundance datasets, alongside high‑resolution environmental predictors (climate, topography, and land cover), the analysis applies multiple algorithms generalized linear models (GLM), generalized additive models (GAM), boosted regression trees (BRT), and maximum entropy (MaxEnt) to generate spatially explicit suitability and abundance surfaces. Model performance is assessed through cross‑validation, ensemble modeling, and sensitivity analyses, highlighting the importance of variable selection, pseudo‑absence strategy, and spatial resolution in shaping prediction accuracy and uncertainty. Results show that ENMs can reliably identify core habitat zones, potential refugia, and areas of high conservation priority, while also revealing substantial uncertainty at range margins and in data‑limited regions. The integration of abundance‑based SDMs further refines predictions by scaling local population density relative to habitat suitability, thereby improving the relevance of ENMs for conservation planning, climate‑change adaptation, and ecosystem management. The study concludes that ENM, when grounded in robust data and transparent validation, offers a powerful and flexible tool for understanding species environment relationships and guiding policy decisions in biodiversity‑rich and climate‑sensitive regions such as Pakistan.