Multiscale Information-Shaping of Adaptation (MISA): A Framework for Understanding Rapid Evolutionary Responses to Environmental Constraints

Rapid evolutionary changes — such as antibiotic resistance emerging within days, seasonal adaptation in natural populations, and responses to climate stressors — often occur faster than predicted by traditional gradual models. We introduce the Multiscale Information-Shaping of Adaptation (MISA) framework, which models environmental constraints as binary signals (0 = viability, 1 = threat) that efficiently prune non-viable evolutionary pathways across biological scales. Grounded in Fisher information maximization and non-equilibrium thermodynamics, MISA integrates key Extended Evolutionary Synthesis (EES) concepts (niche construction, evolvability, developmental bias, ecological inheritance) into a quantitative model. Empirical support includes data from long-term evolution experiments (LTEE with E. coli; MuLTEE with snowflake yeast) and natural systems (phytoplankton, Daphnia, coral reefs), showing consistent scale-invariant fractal patterns (α ≈ –1.92) and strong constraint-adaptation correlations (r = 0.78–0.82). We present six falsifiable predictions with explicit thresholds (overall R² ≈ 0.90) and propose three concrete experimental designs for direct testing in ecological contexts: seasonal thermal cycles in Daphnia, drought gradients in plant mesocosms, and urban-rural gradients in house sparrows. MISA offers ecologists a predictive tool for studying rapid evolution, forecasting ecosystem resilience, and addressing challenges such as climate change adaptation and invasive species dynamics. Keywords: rapid evolution, environmental constraints, scale integration, Extended Evolutionary Synthesis, fractal patterns, climate change adaptation, predictive ecology, phenotypic plasticity, adaptive dynamics