Predictive ecological records under fixed constraints and delayed observation

We quantify how much satellite leaf area adds to carbon-flux prediction after recent flux measurements are already available, and how observation timing changes the gain that a fitted reader can achieve. At 42 flux towers, leaf area adds 0.67% [−1.12%, 2.95%] of seasonal-reference prediction error with ERA5 and −0.17% [−1.81%, 1.36%] with tower weather at a 30-day horizon. Observed target-period weather defines this task as a conditional hindcast at monitored sites. A four-model comparison on identical origins estimates that including recent flux reduces the leaf-area increment by 2.02 percentage points [−0.34, 3.88] and 2.31 [0.06, 4.56], respectively. Paired Gaussian networks hold the latent process fixed while changing aggregation, release delay or noise. At a 3% daily linear-reference setting, state-gain detections decrease from 54 of 64 networks with daily observation to 29 with an eight-day composite and eight after further delay. An exactly solvable fixed-constraint model defines the comparison between record-blind and record-reading prediction: configurations with identical graph-divergence charges evolve under one Markov generator, and a noisy observation yields explicit future laws and risks. A support-aware sufficient criterion includes estimation and reader errors. The empirical results quantify the conditional return from an available vegetation record; the finite model explains the state, observation and risk distinctions needed to interpret that return.