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Similar weather can accompany different histories

Finding 04 · Matched-pair diagnostics

The mismatches are scientifically useful because they identify cases where the current explanatory layer is insufficient—and where new measurements can be most informative.

Question

When fires are closely matched on weather and declared nuisance variables, do they necessarily share a developmental history? And do similar histories necessarily share weather?

Answer

No. Unique matched pairs include both convergence and divergence. But the observed mismatch fractions are compatible with conditional permutation references, so this analysis does not establish excess mismatch or its ecological prevalence.

Coverage, conditional-null comparisons, and representative morphology- and weather-matched pairs.

What to notice. The weather-matched example reaches the same endpoint through visibly different allocations. The plotted examples are near the population median mismatch, not adversarial extremes. They are two independent pairs—not a two-by-two matching design. Technical caption

How to read this figure
- **Panels:** A reports matching coverage; B places observed mismatch against conditional-permutation references; C and D show two independent example pairs. - **Look here:** In B, the observed dots sit inside the conditional reference distributions. In D, similar measured weather accompanies visibly different histories. - **Comparison:** The examples are representative separate pairs, not a reciprocal two-by-two matching design. - **Supported conclusion:** Matching locates controlled study candidates. It does not establish excess mismatch, prevalence, or a missing mechanism.

Challenge this result. If the examples depended entirely on one caliper or on poor partner coverage, they would be weak study candidates. Sensitivity changes coverage, and the null comparison prevents an excess-mismatch claim. See the matching-caliper test →

Evidence trail

ClaimUseful matched contrasts, without excess mismatch
FigureFigure 5 · 3,710 weather pairs; 3,145 morphology pairs
AnalysisUnique caliper-constrained pairs and conditional permutations
80.5%eligible fires assigned a weather-space partner
68.3%eligible fires assigned a morphology-space partner
49.7%observed mismatch among weather matches
50.4%conditional-permutation mean

What we measured

Matching starts with 9,212 complete primary fires. Candidate partners share region, season, duration, and observation count, differ in area by no more than a factor of two, and fall within a declared standardized-distance caliper. Partners cannot be reused. Mismatch outcomes never influence pair selection.

How well does it hold up?

Coverage changes when the caliper, candidate-neighbor count, or distance metric changes. At the declared design, morphology-matched mismatch is 39.5% compared with a 40.2% conditional-permutation mean. These agreements make the pairs useful as representative study candidates while preventing a claim that observed mismatch is unusually common.

These mismatches tell us what to measure next

  • fuel load and continuity along the active edge
  • terrain and slope aligned to dated progression
  • directional wind and sub-grid weather heterogeneity
  • ignition context and suppression history
  • active-edge geometry and independent operational observations
  • uncertainty in satellite burn date and event reconstruction

These are hypotheses named in the manuscript, not mechanisms established by the current pair analysis. Fire VASE supplies the common response against which those next layers can be tested.

What this does not show

The matching is observational, greedy rather than globally optimal, constrained by candidate search and exact nuisance strata, and sensitive to the declared caliper. A diagnostic threshold of standardized other-space distance greater than one is not a natural class boundary.

Technical details

See matching and reproducibility and the matching outputs.