WenJun Zhang School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China Computational Ecology and Software ISSN 2220-721X http://www.iaees.org/publications/journals/ces/online-version.asp 2027 17 3 124 137 International Academy of Ecology and Environmental Sciences Hong Kong 2 September 2026 26 September 2026 1 September 2027 novel ecosystems constraint-aware prediction predictable domain emergence boundary ecological novelty regime shifts social-ecological systems adaptive governance Bayesian predictive contraction transferability Forecasting methodologies for novel ecosystems share a structural dependence on prior information: historical baselines, structural priors about bifurcation neighborhoods, or assumptions of pattern transferability from training distributions. Ecological novelty does not invalidate these priors uniformly. It erodes them differentially across dynamical features, and existing frameworks lack a systematic language for expressing which features remain constrainable under a given novelty profile and which do not. This paper develops Constraint-Aware Prediction (CAP), a methodological framework that reframes novelty from a property of predicted outcomes into an informational condition of prediction itself. CAP partitions dynamical features into three domains. The constrained domain contains features for which predictive distributions contract relative to priors in a testable manner. The coupled domain contains features governed by cross-scale transmission whose conductance can be estimated but whose parameters are uncertain under novelty. The emergent domain contains features arising from interactions among novel species combinations whose specific configurations cannot be deduced from prior information. For each domain, the framework specifies an operational criterion. Constraint degree is estimated through Bayesian predictive contraction, cross-condition functional stability, and network-topological extrapolability. Social processes are modeled not as external scenario inputs but as structural generators of the constraint set, capable of expanding or compressing the constrained domain through governance responses, institutional lock-in, and knowledge production. CAP does not aim to eliminate uncertainty. It aims to make prediction honestly bounded and therefore usable under conditions without historical precedent. Three testable inferences are derived, and the framework is connected to hierarchical predictive theories of biodiversity change and to the literature on regime shifts, machine learning transferability, and social-ecological feedbacks. http://www.iaees.org/publications/journals/ces/articles/2027-17(3)/constraint-aware-prediction-for-novel-ecosystems.pdf