A Predictability Framework for Conservation Strategy: Empirical Evidence from Estuarine Biodiversity Forecasting

Conservation biology increasingly relies on ecological forecasting, yet the biodiversity components most urgently targeted by conservation, such as rare species, local assemblages, and hotspot-defined communities, are often those whose dynamics are least predictable. Understanding how predictability varies across biodiversity is therefore essential for aligning management tools with their targets. This study tests whether predictability varies along three axes, how diversity is measured, the spatial scale of observation, and the temporal forecast horizon (which together govern the effective signal-to-noise ratio of ecological dynamics), and uses these patterns to inform conservation strategies. Using long-term monitoring data from seven estuaries along the Texas Gulf Coast, forecasting performance was evaluated for Hill diversity (q = 0, 1, 2) and population-level abundance of eight dominant taxa at local (bay) and regional (coastwide) scales across near-term (1-month) and long-term (12-month) horizons. Multiple time-series model classes were assessed within a rolling-origin cross-validation framework, with performance measured as improvement in root mean square error over a seasonal naive baseline. Forecasting performance increased consistently with Hill number order, reflecting reduced stochastic variation as dominant species are emphasized. The effects of spatial aggregation differed between systems. Aggregation generally improved performance for littoral assemblages but provided limited or no benefit for demersal assemblages, consistent with differences in how predictive signals are distributed across space. Forecast skill declined from 1-to 12-month horizons, with slower decay for dominance-weighted diversity and demersal assemblages than for rare-species-weighted richness and littoral assemblages. Environmental covariates provided limited near-term gains but became an increasingly important source of predictive information at longer horizons for a subset of demersal and crustacean targets. These results define a predictability landscape structured by diversity measurement, spatial scale, and forecast horizon. Three conservation domains, stochastic, transitional, and structured, emerge from this framework, each associated with distinct predictability regimes and management strategies. Aligning conservation approaches with the predictability properties of their targets provides a principled basis for determining when forecast-based management is informative and when precautionary approaches are more appropriate.