WenJun Zhang School of Life Sciences, Sun Yat-sen University, Guangzhou, China Proceedings of the International Academy of Ecology and Environmental Sciences ISSN 2220-8860 http://www.iaees.org/publications/journals/piaees/online-version.asp 2027 17 1 1 18 International Academy of Ecology and Environmental Sciences Hong Kong 6 August 2026 28 August 2026 1 March 2027 environmental filtering functional traits biodiversity change predictive model hierarchical theory eco-evolutionary feedback trait filtering operator multi-scale integration Current predictions of biodiversity change often rely on statistical correlations between species occurrences and environmental variables, or on single-scale species distribution models. Such approaches do not explicitly represent the causal chain that links environmental change, trait-based performance differences among organisms, and the resulting shifts in taxonomic, functional, and phylogenetic diversity. This study proposes the theory that environmental filtering of species traits is a central mechanistic link between environmental change and biodiversity dynamics, and that this link can be formalized through a trait filtering operator. The study proposes an original framework termed the Trait Filtering-Diversity Dynamics Hierarchy, abbreviated TF-DDH. The framework connects four organizational levels: individual fitness surfaces, population growth rates, community assembly, and regional species pools. At the core of the framework is a measurable mapping from environmental states and trait combinations to persistence probabilities. From this mapping, one can derive a filtering spectrum, trait sensitivity, and diversity change spectra. The study presents a set of quantitative methods to estimate the filtering operator, including hierarchical Bayesian models, structural causal models, and interpretable machine learning. It also describes simulation and empirical designs for testing the framework and illustrates how the framework can be applied to global change scenarios. The scientific significance of the approach is that it moves environmental filtering from a heuristic metaphor toward a formal, testable, and transferable predictive structure. The applied value is that it can improve early warning of biodiversity reorganization and guide trait-based conservation and restoration. http://www.iaees.org/publications/journals/piaees/articles/2027-17(1)/biodiversity-change-driven-by-environmental.pdf