Solving the fourth‐corner problem: forecasting ecosystem primary production from spatial multispecies trait‐based models

Forecasting productivity and stress across an ecosystem is complicated by the multiple interactions between competing species, unknown levels of intra- interspecific trait plasticity, dependencies those traits within individuals. Integrating these features into a trait-based quantitative framework requires conceptual synthesis how species their functional interact respond to changing environments, challenge known in community ecology as “fourth-corner problem.” We propose such novel synthesis, implemented integrated Bayesian hierarchical model. This allows us (1) simultaneously model trait–trait trait–environment relationships explicitly accounting for both variabilities single analysis using all available data types, (2) quantify strength relationships, (3) identify trade-offs (4) faithfully propagate our modeling uncertainties when making species-specific community-wide predictions, reducing false confidence spatial prediction results. apply this approach world’s largest mangrove forest, Sundarbans, sentinel impacted climate change types human exploitation. The Sundarbans presents extensive variability environmental variables, salinity siltation, driven seawater from south freshwater damming north. find that tree growing under have typical response drivers with inter-specific around average, amount further contingent upon nature magnitude drivers. Our captures retreat related resource acquisition plastic enhancement conservation, clear indications stress. predict that, if historical increases siltation are maintained, one-third whole-ecosystem will be lost 2050. bridges through correlations at organismal, community, levels; provides generalizable foundation powerful trait-environment linkages environments consequences on functioning services; readily applicable Earth’s ecosystems.