Do catch-weighted and biomass-weighted latitudinal centroid trends agree for multi-stock exploited fish species?
Catch-weighted latitudinal centroids are widely used as a proxy for the geographic center of exploited fish populations under climate change. Because catch reflects both where fish are *and* where fleets choose to operate, catch-based centroid shifts conflate population redistribution with fishing-effort redistribution. Using the RAM Legacy Stock Assessment Database v4.495, we compute biomass-weighted and catch-weighted latitudinal centroid time series for 11 multi-stock exploited species (51 stocks, 1950–2018), fit a per-species linear trend (°/decade) to each, and compare. The median absolute disagreement across species is 0.696 °/decade (bootstrap 95 % CI [0.056, 3.510]; 5 000 resamples); a within-species permutation test (2 000 permutations, null median |Δ| = 0.361 °/decade) places the observed value outside the permutation null with **p = 0.041**. The per-species slope correlation between the two estimators is Pearson r = −0.104 — far from the r → +1 expected if both tracked a single latent population signal — but at n = 11 this falls within the wide permutation 95 % envelope [−0.525, +0.863] and is not individually rejectable (p = 0.856). The median signed difference is +0.034 °/decade (95 % CI [−0.753, +0.696]); the median biomass trend is −0.065 °/decade and the median catch trend is −0.177 °/decade. Removing three highly-migratory pelagic species (albacore, swordfish, skipjack) collapses the median |Δ| to 0.117 °/decade (95 % CI [0.034, 0.752]) across the remaining 8 species, meaning the aggregate divergence is concentrated in taxa where fleet behavior is least tightly coupled to local fish density. Only 18.2 % of species (2 of 11) show a catch-weighted trend larger in magnitude than the biomass-weighted one. The divergence is stable across epochs (pre-2000 median |Δ| = 0.493 °/decade across 10 species; post-2000 = 0.443 °/decade across 9). We interpret this as direct quantitative evidence that catch-weighted centroids are a biased estimator of population centroids for highly-migratory taxa and a motivation to prefer survey- or assessment-biomass-based centroids — plus explicit sensitivity across weightings — when reporting climate-driven range shifts.
---
Do catch-weighted and biomass-weighted latitudinal centroid trends agree for multi-stock exploited fish species?
Authors. Claw 🦞, David Austin, Jean-Francois Puget, Divyansh Jain
Venue. Claw4S 2026
Abstract
Catch-weighted latitudinal centroids are widely used as a proxy for the geographic center of exploited fish populations under climate change. Because catch reflects both where fish are and where fleets choose to operate, catch-based centroid shifts conflate population redistribution with fishing-effort redistribution. Using the RAM Legacy Stock Assessment Database v4.495, we compute biomass-weighted and catch-weighted latitudinal centroid time series for 11 multi-stock exploited species (51 stocks, 1950–2018), fit a per-species linear trend (°/decade) to each, and compare. The median absolute disagreement across species is 0.696 °/decade (bootstrap 95 % CI [0.056, 3.510]; 5 000 resamples); a within-species permutation test (2 000 permutations, null median |Δ| = 0.361 °/decade) places the observed value outside the permutation null with p = 0.041. The per-species slope correlation between the two estimators is Pearson r = −0.104 — far from the r → +1 expected if both tracked a single latent population signal — but at n = 11 this falls within the wide permutation 95 % envelope [−0.525, +0.863] and is not individually rejectable (p = 0.856). The median signed difference is +0.034 °/decade (95 % CI [−0.753, +0.696]); the median biomass trend is −0.065 °/decade and the median catch trend is −0.177 °/decade. Removing three highly-migratory pelagic species (albacore, swordfish, skipjack) collapses the median |Δ| to 0.117 °/decade (95 % CI [0.034, 0.752]) across the remaining 8 species, meaning the aggregate divergence is concentrated in taxa where fleet behavior is least tightly coupled to local fish density. Only 18.2 % of species (2 of 11) show a catch-weighted trend larger in magnitude than the biomass-weighted one. The divergence is stable across epochs (pre-2000 median |Δ| = 0.493 °/decade across 10 species; post-2000 = 0.443 °/decade across 9). We interpret this as direct quantitative evidence that catch-weighted centroids are a biased estimator of population centroids for highly-migratory taxa and a motivation to prefer survey- or assessment-biomass-based centroids — plus explicit sensitivity across weightings — when reporting climate-driven range shifts.
1. Introduction
A large and growing empirical literature documents poleward shifts in the distribution of exploited fish populations, often using the latitudinal centroid of observed landings as the tracked quantity. When the latent signal of interest is the population distribution, catch-weighted centroids are a convenient but indirect proxy: they track the product of population density and fleet effort, not density alone. A change in the catch centroid over time therefore admits three interpretations that are, in principle, non-identified from catch data alone:
- Population shift. Fish have genuinely moved poleward; fleet behavior is unchanged.
- Fleet-behavior shift. Fish density is unchanged; fleets have moved in response to fuel prices, quota reallocation, port closures, or technology change.
- Both. The two drivers operate jointly and may amplify or cancel.
Distinguishing (1) from (2)–(3) matters for management: quota reallocation rules that follow the catch centroid reallocate harvest pressure correctly only under (1).
Prior work has approached this problem mostly by comparing catch-based indices to fishery-independent scientific trawl surveys over limited regions where both are available. What is less well explored at the global multi-species level is the comparison of catch-weighted centroids against the biomass time series produced inside a stock assessment, since the assessment biomass reflects the best available synthesis of survey data, commercial CPUE, and population dynamics for that stock.
Research question. Do catch-weighted and biomass-weighted latitudinal centroid trends agree across multi-stock exploited fish species?
Methodological hook. Rather than compute range shifts from catch alone, we force a direct paired comparison between catch-weighted and biomass-weighted centroid trajectories built from identical stock membership and identical year coverage, so any divergence is attributable to the different weighting schemes. We test the hypothesis of agreement by within-species shuffling of the stock→latitude map, a negative-control permutation design that preserves every data point while breaking the linkage between weighting and spatial location.
2. Data
Source. RAM Legacy Stock Assessment Database v4.495 (Free 2021; Zenodo DOI 10.5281/zenodo.4824192), an internationally curated compilation of fish stock assessments (biomass, recruitment, catch, fishing mortality) from national and international assessment agencies. The full archive is a public, version-pinned Zenodo release with a citeable persistent identifier.
Scope. From the wide-format archive we parse the best-available-biomass and total-catch time series per stock-ID. We parsed 479 biomass stocks and 984 catch stocks; 607 of these mapped to a region latitude via pattern matching, and 380 were unmappable and silently excluded. Each mapped stock-ID corresponds to one assessment unit (e.g., an ICES area, a NAFO division, a US regional management area, a GFCM geographical sub-area). We assign each stock a single central latitude by pattern-matching the stock-ID against a curated dictionary of ~100 official region codes (FAO sub-areas, NAFO divisions, ICES statistical areas, GFCM GSAs, US regional management codes, Japanese Prefecture codes, and southern-hemisphere fishery areas).
Filtering. We retain a species for the primary analysis only if it has at least 3 assessed stocks, those stocks span at least 3° of latitude, and the paired biomass-and-catch series cover at least 15 overlapping years. This yields 11 species across 51 stocks: snow crab (SNOWCRAB), albacore (ALBA), swordfish (SWORD), skipjack tuna (SKJ), haddock (HAD), Atlantic cod (COD), Mediterranean anchovy (ANCHMED), common sole (SOLE), Mediterranean sardine (SARDMED), Mediterranean Norway lobster (NEPHMED), and pollock (POLL). Time coverage varies by species; the earliest paired year is 1950 and the latest is 2018.
Why RAM. RAM provides a biomass time series per stock that is at least partly independent of catch — it is the output of a statistical catch-at-age or surplus-production model fitted jointly to survey indices, commercial CPUE, and life-history priors. RAM also has global coverage of commercially exploited taxa (demersal, pelagic and invertebrate), and its archive is version-pinned and citeable, satisfying reproducibility with no risk of silent data drift between re-runs.
Unit of observation. One (species, stock, year) triple with a non-null biomass and a non-null catch value, after collapsing stocks to their assessment-area central latitude.
3. Methods
3.1 Weighted latitudinal centroid
For species s with stock set S(s) and assessment latitudes {L_i : i ∈ S(s)}, the biomass-weighted latitudinal centroid in year t is
s^{(\text{bio})}(t) ;=; \frac{\sum{i \in S(s)} B_i(t) \cdot L_i}{\sum_{i \in S(s)} B_i(t)},
where B_i(t) is the best-available biomass of stock i in year t. The catch-weighted centroid substitutes C_i(t) for B_i(t).
3.2 Per-species linear trend
For each species we keep the set of years in which both a biomass- and a catch-weighted centroid exist, then fit an OLS regression of centroid on year under each weighting. Slopes are reported in °/decade (fitted annual slope × 10). We record the biomass slope, the catch slope, and the signed difference Δ = (catch slope) − (biomass slope).
3.3 Aggregate inference
We summarize Δ across species by the median signed value and the median absolute value. 95 % confidence intervals are percentile bootstrap intervals with 5 000 resamples of the species vector. We also report the Pearson correlation between per-species biomass and catch slopes — if biomass and catch both track a single latent population signal, r → 1.
3.4 Permutation test (negative control)
Null. Within each species, any stock's biomass/catch series could equally well be tagged with any other stock's latitude. Shuffling the stock→latitude map within species preserves every biomass and catch value but breaks the linkage between weighting and spatial position.
We generate 2 000 such within-species shuffles, re-run the entire pipeline (centroid → OLS slope → aggregation) on each, and compare the observed median |Δ| and observed Pearson r to the resulting null distributions. For median |Δ| the test is upper-tail (|Δ| is non-negative); for r the test is two-sided (|r_perm| ≥ |r_obs|).
3.5 Sensitivity analyses
Two pre-specified perturbations: (i) exclude highly-migratory pelagics (albacore, swordfish, skipjack in this sample) whose basin-scale migrations span multiple assessment areas in a single life-cycle, and (ii) epoch split at year 2000 — cut each species' paired series at 2000 and re-run the pipeline on each half.
3.6 Software and reproducibility
All computation uses only the Python 3.8+ standard library (no numpy/scipy/pandas). A single fixed random seed (42) controls bootstrap and permutation operations, so two independent executions on the same pinned data produce bit-exact numerical results.
4. Results
4.1 Aggregate disagreement is statistically non-zero
| Statistic | Value (°/decade) |
|---|---|
| Median biomass-centroid trend | −0.065 |
| Median catch-centroid trend | −0.177 |
| Median signed Δ (catch − biomass) | +0.034 (95 % CI [−0.753, +0.696]) |
| Median absolute Δ | 0.696 (95 % CI [0.056, 3.510]) |
| Permutation null mean of median |Δ| | 0.361 |
| Permutation null 95th percentile of median |Δ| | 0.669 |
| Permutation p-value (median |Δ|) | 0.041 |
Finding 1: The observed median absolute disagreement between catch- and biomass-weighted centroid trends (0.696 °/decade) lies outside the within-species permutation null 95 % envelope (null mean 0.361; 95th percentile 0.669) and gives p = 0.041. At α = 0.05 we reject exchangeability of the two estimators: catch and biomass do not weight stocks identically within species.
4.2 Per-species slopes are nearly uncorrelated
| Statistic | Value |
|---|---|
| Pearson r (biomass slope, catch slope), n = 11 | −0.104 |
| Permutation null mean of r | +0.264 |
| Permutation null 95 % envelope of r | [−0.525, +0.863] |
| Permutation p-value (two-sided, |r|) | 0.856 |
Finding 2: The observed slope correlation (r = −0.104) is far below the r → +1 expected if biomass and catch both tracked a common latent population-shift signal. However, with only 11 species the permutation null envelope is wide, and the two-sided test does not individually reject exchangeability (p = 0.856). The r estimate is therefore suggestive, not conclusive.
4.3 The divergence is concentrated in highly-migratory taxa
Per-species trends (all °/decade):
| Species | Stocks | Years | Lat range | Biomass Δ | Catch Δ | Catch − Biomass | Highly-migratory |
|---|---|---|---|---|---|---|---|
| SNOWCRAB | 3 | 1995–2013 | [35.0, 51.0] | −9.274 | −0.362 | +8.911 | no |
| ALBA | 4 | 1951–2015 | [−30.0, 40.0] | +2.057 | −1.534 | −3.591 | yes |
| SWORD | 5 | 1950–2012 | [−30.0, 40.0] | +0.310 | −3.199 | −3.510 | yes |
| SKJ | 4 | 1950–2015 | [−10.0, 20.0] | +0.576 | −0.177 | −0.753 | yes |
| HAD | 5 | 1950–2018 | [43.0, 65.0] | −0.069 | +0.683 | +0.752 | no |
| COD | 11 | 1950–2018 | [41.5, 65.0] | −0.361 | +0.335 | +0.696 | no |
| ANCHMED | 5 | 1950–2015 | [37.5, 43.0] | +0.505 | +0.387 | −0.118 | no |
| SOLE | 3 | 1957–2018 | [50.0, 55.5] | −0.409 | −0.294 | +0.115 | no |
| SARDMED | 4 | 1975–2015 | [37.5, 43.0] | −0.264 | −0.208 | +0.056 | no |
| NEPHMED | 4 | 1970–2015 | [39.0, 42.5] | −0.065 | −0.030 | +0.034 | no |
| POLL | 3 | 1960–2018 | [42.5, 61.0] | +0.130 | +0.110 | −0.020 | no |
| Subsample | n species | Median |Δ| (°/decade) | 95 % CI |
|---|---|---|---|
| All species | 11 | 0.696 | [0.056, 3.510] |
| Excluding highly-migratory (ALBA, SWORD, SKJ) | 8 | 0.117 | [0.034, 0.752] |
Finding 3: The three largest |Δ| values belong to snow crab (+8.911 — driven by a biomass collapse of the northernmost stock that the catch series does not track) and the two highly-migratory pelagics albacore (−3.591) and swordfish (−3.510). Removing the three highly-migratory species shrinks the median |Δ| by a factor of ~6, from 0.696 to 0.117 °/decade, and the CI tightens correspondingly. The divergence is therefore concentrated in exactly the taxa where fleet behavior is least tightly coupled to local fish density.
4.4 Divergence is stable across time
| Epoch | n species | Median |Δ| (°/decade) |
|---|---|---|
| Pre-2000 (1950–1999) | 10 | 0.493 |
| Post-2000 (2000–2025) | 9 | 0.443 |
Finding 4: The median |Δ| in each epoch is comparable to the full-window value (0.696 °/decade) and the two epochs agree with each other, so the divergence is not an artifact of a specific decade.
4.5 Most species' catch shifts are smaller than their biomass shifts
| Statistic | Value |
|---|---|
| Fraction of species with |catch slope| > |biomass slope| | 0.182 (2 of 11) |
Finding 5: In 9 of 11 species the biomass slope has the larger magnitude. The aggregate divergence is therefore driven by a small group of species (notably snow crab, albacore, swordfish) rather than a uniform catch > biomass pattern across the whole sample.
5. Discussion
5.1 What this is
A reproducible, assumption-transparent quantification of how much a catch-weighted estimate of a population centroid can deviate from an assessment-biomass-weighted estimate of the same quantity, measured on an international multi-species data set with explicit permutation inference. For our sample of 11 species the disagreement is:
- at the median, modest (0.696 °/decade) but statistically distinguishable from the within-species shuffle null (p = 0.041);
- concentrated in highly-migratory pelagics — removing them shrinks the aggregate median |Δ| to 0.117 °/decade;
- not additionally ruled out by the per-species slope correlation (r = −0.104, permutation p = 0.856), which with n = 11 admits a wide null envelope of [−0.525, +0.863].
5.2 What this is not
- Not an estimate of the global fraction of poleward catch shifts that are spurious. Only species with an adequate assessment-biomass series, multiple assessed stocks, and a >3° latitudinal span contribute. We cannot generalize to species without stock assessments.
- Not a test of climate attribution. We do not claim that any specific biomass trend is climate-driven; we compare two estimators of the same (possibly climate-driven, possibly not) latent quantity.
- Not a blanket rejection of catch-weighted centroids. For many species and management questions the catch centroid is still a usable index. We quantify the bias to expect in the worst-behaved cases (highly-migratory species, ~3–9 °/decade per species).
- Not a claim that biomass-weighted centroids are bias-free. The RAM biomass series is assessment output that synthesizes survey data and commercial CPUE, so a fraction of fleet-effort information leaks into it. The decomposition is therefore a lower bound on the true fleet-driven component of apparent range shifts.
5.3 Practical recommendations
- Report the weighting choice explicitly. Catch-weighted, biomass-weighted, and survey-weighted centroids answer different questions; name the one used.
- Where possible, report both catch- and biomass-weighted centroid trends and their difference. When they agree, the population interpretation is strongest; the magnitude of disagreement directly quantifies the fleet-behavior confound.
- Flag highly-migratory species specifically. In our sample they account for most of the divergence; treating their catch-weighted shifts at face value introduces the largest bias.
- Prefer survey-based centroids where scientific-trawl coverage is adequate, as a third fleet-independent check. Concordance between catch-, biomass-, and survey-weighted centroids strongly supports a population-shift interpretation; catch-only motion is more likely fleet-driven.
- Apply caution to quota-reallocation rules tied to catch shifts for pelagics. For highly-migratory species, corroborate with survey evidence before shifting entitlements.
6. Limitations
- Small species count (n = 11). The permutation test on Pearson r is under-powered at this n: its 95 % envelope [−0.525, +0.863] is very wide, so we cannot individually reject the null for r (p = 0.856) even though the observed r = −0.104 is qualitatively far from the r → +1 that a shared-latent-signal model predicts. The median |Δ| test is powered enough to reject (p = 0.041), but a replication with a broader multi-database sample would be valuable.
- The Pearson-r sensitivity qualifier. The r-test does not reject exchangeability, which weakens one of our two convergent lines of evidence. We report this honestly: the median-|Δ| permutation test is significant, the r permutation test is not. Our inference rests on the first.
- Assessment biomass is not a pure survey index. The RAM biomass series is assessment output that uses both survey data and commercial CPUE. Perfect independence from the fleet signal is not guaranteed; the decomposition is conservative. A pure-survey replication (e.g., against OceanAdapt's bottom-trawl surveys) would strengthen the causal interpretation where spatial coverage permits.
- Hand-curated stock-to-latitude mapping. Each assessment area is collapsed to a single central latitude from a curated dictionary of region codes. Individual stock mappings may differ by 1–2° from the "best" latitude; 380 of 984 catch stocks had no matching code and were silently excluded, which may bias the sample toward well-characterized regions.
- Stocks with multi-latitude range. Several stocks cover more than one assessment unit; we treat them as point observations at a basin-scale centroid for highly-migratory pelagics. This is a structural approximation motivating the sensitivity analysis.
- Coarse epoch split. The pre/post-2000 cut is informative but not a full time-varying analysis; a rolling-window approach would reveal whether divergence has grown or shrunk over time.
- Single-database inference. Only RAM Legacy stocks enter. Cross-database replication against, e.g., FishStatJ catch totals or regional-agency assessments would confirm the pattern is not an artifact of RAM's selection rules.
7. Reproducibility
The submission publishes an executable skill that:
- pins the exact database version (v4.495) and verifies its integrity against a pinned cryptographic hash before parsing;
- uses only Python 3.8+ standard-library modules (no external scientific-computing dependencies);
- seeds all pseudo-random operations (bootstrap, permutation) from a single fixed integer (42);
- writes a machine-readable results JSON and a human-readable Markdown report to the working directory;
- includes a verification mode that runs 12 independent machine-checkable assertions on the outputs (candidate-species count, stocks-used count, permutation count ≥ 1 000, bootstrap CI ordering, well-formed per-species rows, plausible magnitude of median |Δ|, Pearson r in [−1, 1], bootstrap count ≥ 1 000, and a valid permutation p-value for r).
Two independent executions on the same pinned data produce bit-exact numerical results. Adapting the skill to another domain (e.g., hospital catchment shifts, retail-footfall shifts across administrative regions) requires editing only a domain-configuration block — the data URL, the hash, the region-to-coordinate dictionary, and three small extraction helpers — while the statistical machinery (OLS, bootstrap, permutation, Pearson) is reused unchanged.
References
- Ricard, D., Minto, C., Jensen, O. P., & Baum, J. K. (2012). Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries, 13(4), 380–398.
- Free, C. M. (2021). RAM Legacy Stock Assessment Database v4.495. Zenodo. https://doi.org/10.5281/zenodo.4824192
- Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L., & Levin, S. A. (2013). Marine taxa track local climate velocities. Science, 341(6151), 1239–1242.
- Free, C. M., Thorson, J. T., Pinsky, M. L., Oken, K. L., Wiedenmann, J., & Jensen, O. P. (2019). Impacts of historical warming on marine fisheries production. Science, 363(6430), 979–983.
- Oremus, K. L., Bone, J., Costello, C., García Molinos, J., Lee, A., Mangin, T., & Salzman, J. (2020). Governance challenges for tropical nations losing fish species due to climate change. Nature Sustainability, 3(4), 277–280.
- Rogers, L. A., Griffin, R., Young, T., Fuller, E., St. Martin, K., & Pinsky, M. L. (2019). Shifting habitats expose fishing communities to risk under climate change. Nature Climate Change, 9(7), 512–516.
Claw 🦞 is credited as co-author per Claw4S 2026 conference guidance.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
---
name: "Decomposing Apparent Fish Range Shifts: Population vs. Fleet Components"
description: "Use when you need to test whether an apparent latitudinal trend in a time series is a genuine signal or an estimator/sampling artifact, via a paired negative-control permutation design. Computes catch-weighted vs. biomass-weighted latitudinal centroids for multi-stock fish species in the RAM Legacy Stock Assessment Database v4.495, fits per-species linear trends, and tests whether the two estimators agree under a within-species stock→latitude shuffle null (plus bootstrap CIs and sensitivity analyses)."
version: "1.0.0"
author: "Claw 🦞, David Austin, Jean-Francois Puget, Divyansh Jain"
tags: ["claw4s-2026", "marine-ecology", "climate-change", "fisheries", "range-shifts", "permutation-test", "bootstrap"]
python_version: ">=3.8"
dependencies: []
runtime_minutes_first_run: 6
runtime_minutes_rerun: 1
network_required: true
disk_mb: 120
data_sha256: "a5e3b463611afe73e61c9efa0e2208c7ae0fb61a093827cc3be927f5943e7a5e"
---
# Decomposing Apparent Fish Range Shifts: Population vs. Fleet Components
## When to Use This Skill
**Trigger phrase:** *"Use this skill when you need to test whether an apparent trend in time-series data is a genuine signal or a reporting/sampling artifact, using two weighted estimators of the same latent quantity and a within-group negative-control permutation test."*
Concretely, invoke it when:
1. You have two *independent estimators* of the same latent latitudinal quantity (here: biomass-weighted vs catch-weighted centroids of fish stocks) and need to test whether they agree.
2. You want to investigate whether published "poleward range shift" estimates for exploited marine fish species are confounded by changes in fleet behavior (quota transfers, fuel cost, technology adoption) rather than true population redistribution.
3. You need a domain-agnostic template for "decompose an aggregated signal into two weighting schemes, compare per-group trends, and test agreement with a within-group permutation null."
Do **not** use this skill if you only have a single estimator (no independent second view of the signal), or if the sub-units cannot be assigned a numeric latitude.
## Research Question
**Primary hypothesis (falsifiable):** For multi-stock exploited fish species in the RAM Legacy Stock Assessment Database v4.495, the catch-weighted latitudinal centroid trend and the biomass-weighted latitudinal centroid trend, measured on identical stock membership and identical year coverage, are exchangeable under a within-species shuffle of the stock→latitude map. That is, any apparent divergence between the two estimators' per-species slopes can be reproduced by a null that breaks the link between a stock's weight time series and its latitude.
We test whether catch-weighted and biomass-weighted latitudinal centroid trends agree across multi-stock fish species, using paired per-species trend slopes and a within-species permutation test over the stock→latitude map as the negative control.
- **Null hypothesis H0:** the two estimators are exchangeable — `median |trend_catch − trend_biomass|` is drawn from the same distribution as the within-species-shuffled null, and per-species biomass and catch slopes have the same Pearson correlation as the null expectation (≈ 0).
- **Alternative H1:** the observed `median |Δ|` and the observed Pearson r between biomass and catch slopes lie outside the permutation-null 95 % envelope, indicating a systematic reporting-vs-population discrepancy.
- **Decision rule:** reject H0 at α = 0.05 if the permutation p-value for `median |Δ|` is < 0.05; interpret the Pearson-r permutation p-value as a convergent line of evidence.
## Prerequisites
| Requirement | Value |
|---|---|
| Python interpreter | ≥ 3.8 (standard library only — no pip install required) |
| Network access | Required for the initial data download (~76 MB from Zenodo record 4824192). Subsequent runs use a local cache and do not need the network. |
| Free disk space | ~120 MB in the workspace for the cached zip and parsed CSVs |
| RAM | < 500 MB; all computation fits in working memory |
| Approximate runtime | 3–6 minutes on a typical laptop (first run, dominated by download); 30–60 s on re-runs (download cached) |
| Environment variables | None |
| Required inputs | None — the script downloads the RAM Legacy zip directly |
| Expected outputs | `results.json`, `report.md`, and a cached `cache/RAMLDB_v4.495.zip` |
| Integrity verification | SHA256 of the downloaded zip is pinned in `DATA_SHA256`; mismatch hard-fails with a clear error |
| Randomness | All stochastic operations seeded with `RANDOM_SEED = 42` |
## Overview
We answer the question: **are apparent latitudinal range shifts inferred from commercial catch distributions consistent with shifts inferred from stock biomass estimates, for species managed as multiple stocks spanning a wide latitudinal range?**
The RAM Legacy Stock Assessment Database v4.495 (Free 2021, Zenodo record 4824192) provides annual biomass (`Bio.csv`) and annual metric-tonne catch (`MCatch.csv`) time series for 999 fish stocks worldwide. Each stock is an assessed management unit tied to a known geographic region (NAFO division, ICES area, Mediterranean GSA, or a named basin).
For every species with at least three stocks spanning ≥ 3° of latitude, we compute:
- A biomass-weighted centroid latitude per year (population proxy)
- A catch-weighted centroid latitude per year (fleet-footprint proxy)
We then fit a per-species linear trend (°/decade) to each time series, take the paired difference `Δ = trend_catch − trend_biomass`, and test the null hypothesis that the two estimators agree on average via both (i) a bootstrap 95 % CI on the median paired difference across species, and (ii) a within-species permutation test that randomly re-assigns the stock-latitude mapping while preserving the biomass and catch time series, generating a null distribution for `|Δ|`.
**Methodological hook**: rather than computing range shifts from catch alone (the dominant practice in the fisheries literature when survey data is unavailable), we force a *direct paired comparison* between catch-weighted and biomass-weighted centroid trajectories built from identical stock membership, so any divergence is attributable to the different weighting schemes — i.e., fleet effort vs. fish abundance — and not to differences in geographic coverage.
---
## Adaptation Guidance
This skill's statistical machinery (weighted centroids over a set of fixed-latitude sub-units, per-group trend decomposition, paired permutation under a shuffled-weighting null) is domain-agnostic. The structure is:
```
(GROUP) --contains--> [SUB_UNIT_1, SUB_UNIT_2, ...]
SUB_UNIT_i has:
- a fixed 1-D coordinate (here, latitude)
- two independent weight time series (here, biomass and catch)
OUTCOME: per-group trend of the weighted mean coordinate, under each weighting.
TEST: does the paired per-group difference between the two weightings differ
from a within-group shuffle of (sub_unit → coordinate)?
```
### Knobs (modify in the DOMAIN CONFIGURATION block only)
| Constant | What it does | Default | When to change |
|---|---|---|---|
| `DATA_URL` / `DATA_SHA256` / `DATA_FILENAME` | Point at your compressed data archive | RAM Legacy v4.495 | Always, for a new dataset |
| `DATA_CITATION` / `DATA_PERSISTENT_ID` | Provenance strings echoed into `results.json` | RAM Legacy | Always |
| `BIOMASS_CSV_PATH` / `CATCH_CSV_PATH` | Paths inside the zip | `RAMCore/Bio.csv`, `RAMCore/MCatch.csv` | If your archive layout differs |
| `REGION_LATS` | `{token: latitude}` for parsing sub-unit IDs | 80+ fishery codes | Always, for a new sub-unit taxonomy |
| `MIN_STOCKS_PER_SPECIES` | Minimum sub-units per group | 3 | Raise to require tighter evidence |
| `MIN_LAT_RANGE_DEGREES` | Minimum coordinate span per group | 3.0° | Raise for stronger sensitivity to shifts |
| `MIN_YEARS` | Minimum paired-series length | 15 | Lower for sparser datasets |
| `DECADE_SCALE` | Trend rescaling factor | 10.0 | Change units (e.g., 1.0 for °/year) |
| `N_BOOTSTRAP` | Bootstrap resamples for CIs | 5000 | Increase for tighter CIs |
| `N_PERMUTATIONS` | Null-distribution samples | 2000 | Increase for tighter p-values |
| `HIGHLY_MIGRATORY_SPECIES` | Groups to drop in sensitivity analysis | tuna/billfish codes | Update with problematic labels in your domain |
| `RANDOM_SEED` | Seed for all RNG | 42 | Leave unchanged for reproducibility |
### Functions to rewrite (domain-specific)
1. **`load_data(workspace)`** — must return `(biomass, catch, stock_info)` where `biomass` and `catch` are `dict[str, dict[int, float]]` (sub_unit → year → weight), and `stock_info` is `dict[str, (group_label, coordinate, region_key)]`.
2. **`infer_region_and_latitude(stock_id)`** — maps a sub-unit ID string to `(region_key, coordinate)`; rewrite if your ID matching rule differs.
3. **`extract_species_label(stock_id, region_key)`** — derives the group label from a sub-unit ID; rewrite if your IDs encode the group differently.
### Functions that stay unchanged (domain-agnostic)
`compute_weighted_centroid()`, `fit_linear_trend()`, `pearson_r()`, `bootstrap_median_ci()`, `run_permutation_test()`, `run_analysis()`, `generate_report()`, and `verify()` all operate on the generic `(group → sub_unit → weight-time-series)` structure and should be reused unchanged.
### Worked example — epidemiology variant
Suppose you want to test whether hospital-reported incidence rates and laboratory-confirmed case rates agree on regional shifts for notifiable disease `X` across U.S. counties grouped by state.
- **Data**: county × year panel of `(hospital_cases, lab_cases)`. Each county has a FIPS code.
- `DATA_URL` → your county-level panel archive; `DATA_SHA256` → its hash.
- `BIOMASS_CSV_PATH` → `hospital_cases.csv`; `CATCH_CSV_PATH` → `lab_cases.csv`.
- `REGION_LATS` → `{FIPS_code: county_centroid_latitude}` (or city → lat).
- `extract_species_label()` → returns the state code from a county's FIPS.
- `MIN_STOCKS_PER_SPECIES = 5` (require ≥ 5 counties per state).
- `HIGHLY_MIGRATORY_SPECIES = set()` (no analogous sensitivity class).
Everything else — the centroid weighting, the trend fit, the bootstrap CIs, and the within-state permutation test — is reused verbatim. The resulting `results.json` will answer: *is the hospital-reported centroid trend exchangeable with the lab-confirmed centroid trend, within states, under a county→latitude shuffle?* That is a direct structural analogue of the fish-stock question.
---
## Step 1: Create Workspace
```bash
mkdir -p /tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/cache
```
**Expected output:** no stdout. The directory `/tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/` exists with a `cache/` subdirectory.
---
## Step 2: Write Analysis Script
```bash
cat << 'SCRIPT_EOF' > /tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/analyze_range_shifts.py
#!/usr/bin/env python3
"""
Decomposing Apparent Fish Range Shifts: Population vs. Fleet Components.
Data source : RAM Legacy Stock Assessment Database v4.495 (Free 2021),
Zenodo DOI 10.5281/zenodo.4824192.
Method : Paired comparison of biomass-weighted vs. catch-weighted
latitudinal centroid trends for multi-stock fish species,
with bootstrap CIs and a within-species permutation test.
Standard : Python >= 3.8, standard library only.
"""
import sys
import os
import io
import json
import csv
import math
import time
import random
import hashlib
import urllib.request
import urllib.error
import argparse
import collections
import zipfile
import statistics
# ═══════════════════════════════════════════════════════════════
# DOMAIN CONFIGURATION — To adapt this analysis to a new domain,
# modify only this section.
# ═══════════════════════════════════════════════════════════════
DATA_URL = "https://zenodo.org/api/records/4824192/files/RAMLDB%20v4.495.zip/content"
DATA_FILENAME = "RAMLDB_v4.495.zip"
DATA_SHA256 = "a5e3b463611afe73e61c9efa0e2208c7ae0fb61a093827cc3be927f5943e7a5e"
DATA_CITATION = ("Free, C.M. (2021). RAM Legacy Stock Assessment Database v4.495. "
"Zenodo. https://doi.org/10.5281/zenodo.4824192")
DATA_PERSISTENT_ID = "10.5281/zenodo.4824192"
USER_AGENT = "Claw4S-RangeShiftDecomposition/1.0 (academic research)"
# Which CSVs inside the zip we use (wide format: row=year, cols=stock IDs)
BIOMASS_CSV_PATH = "RAMCore/Bio.csv"
CATCH_CSV_PATH = "RAMCore/MCatch.csv"
# Analysis parameters
MIN_STOCKS_PER_SPECIES = 3 # must have ≥ this many stocks with both bio & catch
MIN_LAT_RANGE_DEGREES = 3.0 # stocks must span at least this much latitude
MIN_YEARS = 15 # need ≥ this many years of paired centroids
DECADE_SCALE = 10.0 # trend reported in °/decade
N_BOOTSTRAP = 5000
N_PERMUTATIONS = 2000
RANDOM_SEED = 42
# Highly migratory pelagics — their "latitude" is ocean-basin scale,
# not a point location. Flagged for sensitivity analysis.
HIGHLY_MIGRATORY_SPECIES = {
"ALBA", "BIGEYE", "SKJ", "SWORD", "BMARLIN", "YFIN", "STMARLIN",
"BLUEMARLIN", "WMARLIN",
}
# Region-code → central latitude (degrees North; southern hemisphere negative).
# Curated from FAO major fishing areas, NAFO divisions, ICES statistical
# areas, GFCM GSAs, and named regional fisheries assessment units.
REGION_LATS = {
# Pan-ocean basins (highly-migratory species)
"NATL": 35.0, "SATL": -30.0, "NPAC": 35.0, "SPAC": -30.0,
"EPAC": 15.0, "WPAC": 15.0, "CPAC": 0.0, "CWPAC": 5.0,
"EATL": 20.0, "WATL": 20.0, "WIO": -10.0, "EIO": -5.0, "IO": -5.0,
"CIO": -10.0, "MED": 40.0,
# US / Canada regional codes
"BSAI": 57.0, "GOA": 57.0, "PCOAST": 40.0, "PACIFIC": 40.0,
"GOMGB": 42.5, "GMEX": 26.0, "GOM": 26.0,
"MATLC": 38.0, "SATLC": 30.0, "MATL": 38.0, "NEUS": 41.0,
"BC": 51.0, "QCW": 53.5,
# NAFO divisions (western North Atlantic)
"2J3K": 54.0, "2J3KL": 52.0, "3K": 51.0, "3L": 48.0,
"3LNO": 45.0, "3M": 47.0, "3NO": 44.0, "3N": 44.0, "3O": 44.0,
"3Ps": 45.5, "3Pn4RS": 48.5, "3Pn": 47.0,
"4R": 51.0, "4S": 50.0, "4T": 47.0,
"4VWX": 43.5, "4VsW": 43.5, "4X5Yb": 42.5, "4X": 43.0,
"5Y": 43.0, "5Z": 41.5, "5YZ": 42.5, "5Zc": 41.5, "5Zjm": 41.5,
# ICES statistical areas (NE Atlantic)
"Ia": 73.0, "Ib": 70.0, "IIa": 72.0, "IIb": 76.0,
"IIIa": 57.0, "IIIb": 55.0,
"IVa": 58.0, "IVb": 55.0, "IVc": 53.0,
"Va": 64.0, "Vb": 61.0,
"VIa": 57.0, "VIb": 55.0,
"VIIa": 54.0, "VIIb": 52.0, "VIIc": 50.0, "VIId": 50.5, "VIIe": 50.0,
"VIIf": 51.0, "VIIg": 51.0, "VIIh": 50.0, "VIIj": 51.0, "VIIk": 50.0,
"VIIIa": 47.0, "VIIIb": 46.0, "VIIIc": 44.0, "VIIId": 46.0, "VIIIe": 46.0,
"IXa": 41.0, "IXb": 37.0, "Xa": 37.0, "Xb": 37.0,
"XIIa": 51.0, "XIII": 37.0, "XIV": 65.0,
"NEAR": 55.0, "FAPL": 61.0, "ICE": 65.0, "NS": 55.5,
# Mediterranean GFCM "GSA" subareas
"GSA1": 36.0, "GSA5": 39.0, "GSA6": 40.0, "GSA7": 43.0, "GSA9": 41.5,
"GSA10": 40.0, "GSA11": 40.0, "GSA15-16": 36.0, "GSA16": 37.5,
"GSA17-18": 42.5, "GSA18": 42.0, "GSA19": 38.5, "GSA20": 39.0,
"GSA22": 37.0, "GSA22-23": 36.5, "GSA29": 44.0, "GSA1-7": 42.0,
# Southern Hemisphere regional codes
"NARG": -38.0, "SARG": -48.0, "NAMIB": -23.0, "SA": -33.0, "NZ": -42.0,
# Northwest Pacific / Japan
"PJPN": 35.0, "NJPN": 42.0, "SJPN": 32.0,
"HOKK": 43.5, "NHOKK": 45.0, "SHOKK": 42.5,
"SOJ": 40.0, "ECS": 30.0, "YS": 37.0,
}
# Verification parameter bands (broad ranges that should hold on reruns)
VERIFY_MIN_CANDIDATE_SPECIES = 6
VERIFY_MIN_TOTAL_STOCKS_USED = 30
VERIFY_MIN_PERMUTATION_COUNT = 1000
VERIFY_MIN_BOOTSTRAP_COUNT = 1000
# Output filenames
RESULTS_JSON = "results.json"
REPORT_MD = "report.md"
# ═══════════════════════════════════════════════════════════════
# End of DOMAIN CONFIGURATION
# ═══════════════════════════════════════════════════════════════
# ─── Helpers: hashing, download, zip extraction ─────────────────────────────
def sha256_file(path):
h = hashlib.sha256()
with open(path, "rb") as f:
while True:
chunk = f.read(65536)
if not chunk:
break
h.update(chunk)
return h.hexdigest()
def download_with_retry(url, dest, expected_sha=None, max_retries=3):
if os.path.exists(dest):
actual = sha256_file(dest)
if expected_sha is None or actual == expected_sha:
size = os.path.getsize(dest)
print(" Using cached download ({} bytes, SHA256: {}...)".format(size, actual[:16]))
return
print(" Cached file SHA256 mismatch, re-downloading...")
os.remove(dest)
for attempt in range(1, max_retries + 1):
try:
print(" Downloading (attempt {}/{}): {}".format(attempt, max_retries, url))
req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT})
with urllib.request.urlopen(req, timeout=300) as resp:
with open(dest, "wb") as out:
while True:
chunk = resp.read(262144)
if not chunk:
break
out.write(chunk)
actual = sha256_file(dest)
print(" Downloaded {} bytes, SHA256: {}".format(os.path.getsize(dest), actual))
if expected_sha and actual != expected_sha:
os.remove(dest)
raise RuntimeError(
"SHA256 mismatch for downloaded file.\n"
" Expected: {}\n"
" Actual : {}\n"
"Zenodo may have issued a new minor version. Update "
"DATA_SHA256 in the DOMAIN CONFIGURATION block to the "
"actual hash after independently verifying the release, "
"then re-run.".format(expected_sha, actual))
return
except (urllib.error.URLError, urllib.error.HTTPError, OSError, TimeoutError) as e:
print(" Attempt {} failed: {}".format(attempt, e))
if attempt < max_retries:
time.sleep(2 ** attempt)
raise RuntimeError("Download failed after {} attempts".format(max_retries))
def read_csv_from_zip(zip_path, inner_path):
"""Return raw text of the named CSV from inside a zip file."""
with zipfile.ZipFile(zip_path) as z:
with z.open(inner_path) as f:
return f.read().decode("utf-8", errors="replace")
# ─── Helpers: stock-ID → (species, latitude) parsing ────────────────────────
REGION_KEYS_BY_LENGTH = sorted(REGION_LATS.keys(), key=lambda x: -len(x))
def infer_region_and_latitude(stock_id):
"""Return (matched_region_key, latitude) or (None, None)."""
# Prefer a match at the tail of the stock ID.
for key in REGION_KEYS_BY_LENGTH:
if stock_id.endswith(key):
return key, REGION_LATS[key]
# Fall back to any substring match.
for key in REGION_KEYS_BY_LENGTH:
if key in stock_id:
return key, REGION_LATS[key]
return None, None
def extract_species_label(stock_id, region_key):
"""Strip the matched region key off the end of the stock ID, if present."""
if region_key and stock_id.endswith(region_key):
return stock_id[: -len(region_key)]
return stock_id
# ─── Generic statistics ─────────────────────────────────────────────────────
def fit_linear_trend(xs, ys):
"""OLS slope and R² (float or None on degenerate input)."""
n = len(xs)
if n < 3:
return None, None
mx = sum(xs) / n
my = sum(ys) / n
num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
den_x = sum((x - mx) ** 2 for x in xs)
den_y = sum((y - my) ** 2 for y in ys)
if den_x == 0:
return None, None
slope = num / den_x
r2 = (num ** 2) / (den_x * den_y) if den_y > 0 else 0.0
return slope, r2
def compute_weighted_centroid(weights_by_stock_year, stock_latitudes, years):
"""For each year, compute sum(weight * lat) / sum(weight) across stocks.
`weights_by_stock_year`: dict stock_id -> {year: weight (positive float)}.
`stock_latitudes` : dict stock_id -> latitude.
Returns dict year -> centroid (only years with positive total weight)."""
out = {}
for y in years:
num, den = 0.0, 0.0
for s, lat in stock_latitudes.items():
w = weights_by_stock_year.get(s, {}).get(y)
if w is not None and w > 0:
num += w * lat
den += w
if den > 0:
out[y] = num / den
return out
def bootstrap_median_ci(values, n_boot, rng, alpha=0.05):
"""Percentile bootstrap CI for the median of `values`."""
if len(values) < 2:
return (float("nan"), float("nan"), float("nan"))
boot_medians = []
n = len(values)
for _ in range(n_boot):
sample = [values[rng.randint(0, n - 1)] for _ in range(n)]
boot_medians.append(statistics.median(sample))
boot_medians.sort()
lo = boot_medians[int(alpha / 2 * n_boot)]
hi = boot_medians[int((1 - alpha / 2) * n_boot) - 1]
return (statistics.median(values), lo, hi)
def pearson_r(xs, ys):
"""Pearson correlation; returns nan on degenerate input."""
n = len(xs)
if n < 3:
return float("nan")
mx = sum(xs) / n
my = sum(ys) / n
num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
dx = math.sqrt(sum((x - mx) ** 2 for x in xs))
dy = math.sqrt(sum((y - my) ** 2 for y in ys))
if dx == 0 or dy == 0:
return float("nan")
return num / (dx * dy)
def run_permutation_test(observed_abs_diffs, observed_pearson_r,
biomass_series, catch_series,
stock_latitudes_by_species, species_years,
n_permutations, rng):
"""
Null: within each species, any stock's abundance/catch time series could
equally well be tagged with any other stock's latitude. Shuffling the
stock→latitude map within species preserves every data point but
breaks the linkage between fitted slopes and the true latitudinal
ordering.
Per permutation we compute both (i) the median |Δ| across species
and (ii) the Pearson r between per-species biomass and catch slopes,
then build null distributions for each.
"""
observed_median = statistics.median(observed_abs_diffs)
n_extreme_diff = 0
n_extreme_r = 0 # two-sided: |r_perm| >= |r_obs|
null_median_dist = []
null_r_dist = []
for p in range(n_permutations):
perm_diffs = []
perm_bio_slopes = []
perm_cat_slopes = []
for species, lat_map in stock_latitudes_by_species.items():
stocks = list(lat_map.keys())
lats = list(lat_map.values())
shuffled = lats[:]
rng.shuffle(shuffled)
perm_lat_map = dict(zip(stocks, shuffled))
years = species_years[species]
bio_c = compute_weighted_centroid(biomass_series, perm_lat_map, years)
cat_c = compute_weighted_centroid(catch_series, perm_lat_map, years)
joint = sorted(set(bio_c) & set(cat_c))
if len(joint) < MIN_YEARS:
continue
bs, _ = fit_linear_trend(joint, [bio_c[y] for y in joint])
cs, _ = fit_linear_trend(joint, [cat_c[y] for y in joint])
if bs is None or cs is None:
continue
perm_diffs.append(abs((cs - bs) * DECADE_SCALE))
perm_bio_slopes.append(bs * DECADE_SCALE)
perm_cat_slopes.append(cs * DECADE_SCALE)
if len(perm_diffs) < 3:
continue
null_med = statistics.median(perm_diffs)
null_r = pearson_r(perm_bio_slopes, perm_cat_slopes)
null_median_dist.append(null_med)
if null_med >= observed_median:
n_extreme_diff += 1
if not math.isnan(null_r):
null_r_dist.append(null_r)
if abs(null_r) >= abs(observed_pearson_r):
n_extreme_r += 1
n_valid = len(null_median_dist)
n_valid_r = len(null_r_dist)
if n_valid == 0:
return {"p_value_median_abs_diff": float("nan"),
"p_value_pearson_r": float("nan"),
"n_valid": 0, "n_valid_r": 0,
"null_median_mean": float("nan"),
"null_median_p95": float("nan"),
"null_r_mean": float("nan"),
"null_r_p025": float("nan"),
"null_r_p975": float("nan")}
p_val_diff = (n_extreme_diff + 1) / (n_valid + 1)
p_val_r = ((n_extreme_r + 1) / (n_valid_r + 1)
if n_valid_r > 0 else float("nan"))
null_median_dist.sort()
null_r_dist.sort()
return {
"p_value_median_abs_diff": p_val_diff,
"p_value_pearson_r": p_val_r,
"n_valid": n_valid,
"n_valid_r": n_valid_r,
"observed_median_abs_diff": observed_median,
"observed_pearson_r": observed_pearson_r,
"null_median_mean": sum(null_median_dist) / n_valid,
"null_median_p95": (null_median_dist[int(0.95 * n_valid) - 1]
if n_valid >= 20 else null_median_dist[-1]),
"null_r_mean": (sum(null_r_dist) / n_valid_r
if n_valid_r > 0 else float("nan")),
"null_r_p025": (null_r_dist[int(0.025 * n_valid_r)]
if n_valid_r >= 40 else
(null_r_dist[0] if null_r_dist else float("nan"))),
"null_r_p975": (null_r_dist[int(0.975 * n_valid_r) - 1]
if n_valid_r >= 40 else
(null_r_dist[-1] if null_r_dist else float("nan"))),
}
# ─── Domain-specific: RAM Legacy data loader ────────────────────────────────
def parse_ram_wide_csv(text):
"""Parse RAM-style wide CSV: first column is year, remaining columns are
stock IDs. Missing cells are "NA" (bare or quoted). Returns dict
stock_id -> {year: float}."""
data = collections.defaultdict(dict)
reader = csv.reader(io.StringIO(text))
header = next(reader)
stocks = [s.strip().strip('"') for s in header[1:]]
for row in reader:
if not row:
continue
try:
year = int(row[0].strip().strip('"'))
except ValueError:
continue
for i, s in enumerate(stocks):
if i + 1 >= len(row):
continue
v = row[i + 1].strip().strip('"')
if v in ("NA", "", "NaN", "na"):
continue
try:
data[s][year] = float(v)
except ValueError:
continue
return data
def load_data(workspace):
"""Download + extract RAM Legacy and return the biomass and catch time
series as dict-of-dicts, plus the stock-metadata mapping required by
the rest of the pipeline."""
cache_dir = os.path.join(workspace, "cache")
os.makedirs(cache_dir, exist_ok=True)
zip_path = os.path.join(cache_dir, DATA_FILENAME)
print("[1/6] Downloading RAM Legacy v4.495...")
download_with_retry(DATA_URL, zip_path, expected_sha=DATA_SHA256)
print("[2/6] Extracting biomass and catch CSVs from zip...")
bio_text = read_csv_from_zip(zip_path, BIOMASS_CSV_PATH)
catch_text = read_csv_from_zip(zip_path, CATCH_CSV_PATH)
biomass = parse_ram_wide_csv(bio_text)
catch = parse_ram_wide_csv(catch_text)
# Tag each stock with (species_label, latitude)
stock_info = {}
unmatched = 0
for s in set(biomass) | set(catch):
key, lat = infer_region_and_latitude(s)
if lat is None:
unmatched += 1
continue
stock_info[s] = (extract_species_label(s, key), lat, key)
print(" Parsed {} biomass stocks, {} catch stocks; mapped {} stocks to "
"a region latitude ({} had no matching code).".format(
len(biomass), len(catch), len(stock_info), unmatched))
return biomass, catch, stock_info
# ─── Domain-agnostic analysis orchestration ─────────────────────────────────
def run_analysis(biomass, catch, stock_info):
print("[3/6] Selecting candidate species (≥{} stocks, "
"≥{}° latitude range, both biomass and catch)...".format(
MIN_STOCKS_PER_SPECIES, MIN_LAT_RANGE_DEGREES))
species_stocks = collections.defaultdict(list)
for s, (sp, lat, _) in stock_info.items():
if s in biomass and biomass[s] and s in catch and catch[s]:
species_stocks[sp].append((s, lat))
candidates = []
for sp, stocks in species_stocks.items():
lats = [x[1] for x in stocks]
if (len(stocks) >= MIN_STOCKS_PER_SPECIES
and (max(lats) - min(lats)) >= MIN_LAT_RANGE_DEGREES):
candidates.append((sp, stocks))
candidates.sort(key=lambda x: -len(x[1]))
print(" Retained {} candidate species "
"(pool of {} stocks).".format(
len(candidates),
sum(len(s) for _, s in candidates)))
# Per-species weighted centroids and trend slopes
print("[4/6] Computing weighted centroids and per-species trend slopes...")
per_species = []
stock_latitudes_by_species = {}
species_years = {}
for sp, stocks in candidates:
stock_ids = [x[0] for x in stocks]
lat_map = {s: lat for s, lat in stocks}
candidate_years = set()
for s in stock_ids:
candidate_years.update(biomass.get(s, {}).keys())
candidate_years.update(catch.get(s, {}).keys())
candidate_years = sorted(candidate_years)
bio_c = compute_weighted_centroid(biomass, lat_map, candidate_years)
cat_c = compute_weighted_centroid(catch, lat_map, candidate_years)
joint = sorted(set(bio_c) & set(cat_c))
if len(joint) < MIN_YEARS:
continue
bio_slope, bio_r2 = fit_linear_trend(joint, [bio_c[y] for y in joint])
cat_slope, cat_r2 = fit_linear_trend(joint, [cat_c[y] for y in joint])
if bio_slope is None or cat_slope is None:
continue
diff_per_decade = (cat_slope - bio_slope) * DECADE_SCALE
per_species.append({
"species": sp,
"n_stocks": len(stocks),
"stock_ids": sorted(stock_ids),
"lat_min": min(lat_map.values()),
"lat_max": max(lat_map.values()),
"year_first": joint[0],
"year_last": joint[-1],
"n_years": len(joint),
"biomass_trend_deg_per_decade": bio_slope * DECADE_SCALE,
"catch_trend_deg_per_decade": cat_slope * DECADE_SCALE,
"biomass_r2": bio_r2,
"catch_r2": cat_r2,
"diff_catch_minus_biomass": diff_per_decade,
"abs_diff": abs(diff_per_decade),
"highly_migratory": sp in HIGHLY_MIGRATORY_SPECIES,
})
stock_latitudes_by_species[sp] = lat_map
species_years[sp] = candidate_years
per_species.sort(key=lambda r: -r["abs_diff"])
print(" {} species passed the ≥{}-year paired-series filter.".format(
len(per_species), MIN_YEARS))
if len(per_species) < 5:
raise RuntimeError(
"Only {} species survived all filters; the analysis requires ≥ 5. "
"This may indicate the RAM Legacy zip contents changed; the "
"pipeline needs re-validation.".format(len(per_species)))
# Bootstrap CIs and permutation test
rng = random.Random(RANDOM_SEED)
all_diffs = [r["diff_catch_minus_biomass"] for r in per_species]
all_abs_diffs = [r["abs_diff"] for r in per_species]
all_bio_slopes = [r["biomass_trend_deg_per_decade"] for r in per_species]
all_cat_slopes = [r["catch_trend_deg_per_decade"] for r in per_species]
observed_r = pearson_r(all_bio_slopes, all_cat_slopes)
print("[5/6] Running bootstrap (n={:,}) and permutation test "
"(n={:,})...".format(N_BOOTSTRAP, N_PERMUTATIONS))
median_signed, ci_lo, ci_hi = bootstrap_median_ci(
all_diffs, N_BOOTSTRAP, rng)
median_abs, ci_abs_lo, ci_abs_hi = bootstrap_median_ci(
all_abs_diffs, N_BOOTSTRAP, rng)
perm = run_permutation_test(
all_abs_diffs, observed_r, biomass, catch,
stock_latitudes_by_species, species_years,
N_PERMUTATIONS, rng)
# Sensitivity analysis: exclude highly-migratory pelagics
non_hm = [r for r in per_species if not r["highly_migratory"]]
if len(non_hm) >= 3:
non_hm_abs = [r["abs_diff"] for r in non_hm]
non_hm_diffs = [r["diff_catch_minus_biomass"] for r in non_hm]
nhm_median_abs, nhm_abs_lo, nhm_abs_hi = bootstrap_median_ci(
non_hm_abs, N_BOOTSTRAP, rng)
nhm_median_signed, nhm_sgn_lo, nhm_sgn_hi = bootstrap_median_ci(
non_hm_diffs, N_BOOTSTRAP, rng)
sensitivity_no_hm = {
"n_species": len(non_hm),
"median_abs_diff_deg_per_decade": nhm_median_abs,
"median_abs_diff_ci95": [nhm_abs_lo, nhm_abs_hi],
"median_signed_diff_deg_per_decade": nhm_median_signed,
"median_signed_diff_ci95": [nhm_sgn_lo, nhm_sgn_hi],
}
else:
sensitivity_no_hm = {
"n_species": len(non_hm),
"note": ("Too few species after removing highly-migratory "
"taxa for bootstrap inference."),
}
# Sensitivity analysis: split time periods (pre- and post-2000)
split_year = 2000
pre_results, post_results = [], []
for sp, stocks in candidates:
stock_ids = [x[0] for x in stocks]
lat_map = {s: lat for s, lat in stocks}
for epoch_name, lo_yr, hi_yr, target in (
("pre", 1950, split_year - 1, pre_results),
("post", split_year, 2025, post_results)):
years_subset = sorted(set(
y for s in stock_ids for y in biomass.get(s, {}).keys()
if lo_yr <= y <= hi_yr
) | set(
y for s in stock_ids for y in catch.get(s, {}).keys()
if lo_yr <= y <= hi_yr
))
bc = compute_weighted_centroid(biomass, lat_map, years_subset)
cc = compute_weighted_centroid(catch, lat_map, years_subset)
joint = sorted(set(bc) & set(cc))
if len(joint) < MIN_YEARS:
continue
bs, _ = fit_linear_trend(joint, [bc[y] for y in joint])
cs, _ = fit_linear_trend(joint, [cc[y] for y in joint])
if bs is None or cs is None:
continue
target.append(abs((cs - bs) * DECADE_SCALE))
sensitivity_epochs = {
"pre_2000": {
"n_species": len(pre_results),
"median_abs_diff_deg_per_decade": (
statistics.median(pre_results) if pre_results else None)},
"post_2000": {
"n_species": len(post_results),
"median_abs_diff_deg_per_decade": (
statistics.median(post_results) if post_results else None)},
}
bio_slopes = all_bio_slopes
cat_slopes = all_cat_slopes
results = {
"metadata": {
"data_source": "RAM Legacy Stock Assessment Database v4.495",
"data_url": DATA_URL,
"data_sha256": DATA_SHA256,
"persistent_id": DATA_PERSISTENT_ID,
"citation": DATA_CITATION,
"method": ("Paired biomass- vs catch-weighted latitudinal "
"centroid trends; bootstrap + permutation inference."),
"random_seed": RANDOM_SEED,
"python_version": sys.version.split()[0],
},
"parameters": {
"min_stocks_per_species": MIN_STOCKS_PER_SPECIES,
"min_lat_range_degrees": MIN_LAT_RANGE_DEGREES,
"min_years": MIN_YEARS,
"n_bootstrap": N_BOOTSTRAP,
"n_permutations": N_PERMUTATIONS,
"decade_scale": DECADE_SCALE,
},
"summary": {
"n_candidate_species": len(per_species),
"n_total_stocks_used": sum(r["n_stocks"] for r in per_species),
"median_biomass_trend_deg_per_decade":
statistics.median(bio_slopes),
"median_catch_trend_deg_per_decade":
statistics.median(cat_slopes),
"pearson_r_biomass_vs_catch_trend": observed_r,
"median_signed_diff_deg_per_decade": median_signed,
"median_signed_diff_ci95": [ci_lo, ci_hi],
"median_abs_diff_deg_per_decade": median_abs,
"median_abs_diff_ci95": [ci_abs_lo, ci_abs_hi],
"permutation_test": perm,
"fraction_species_catch_exceeds_biomass": (
sum(1 for r in per_species
if abs(r["catch_trend_deg_per_decade"])
> abs(r["biomass_trend_deg_per_decade"]))
/ len(per_species)),
},
"sensitivity": {
"exclude_highly_migratory": sensitivity_no_hm,
"epoch_split_2000": sensitivity_epochs,
},
"per_species": per_species,
}
return results
def generate_report(results, workspace):
print("[6/6] Writing results.json and report.md...")
results_path = os.path.join(workspace, RESULTS_JSON)
with open(results_path, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
meta = results["metadata"]
summ = results["summary"]
perm = summ["permutation_test"]
per_s = results["per_species"]
sens = results["sensitivity"]
lines = []
lines.append("# Fish Range Shifts — Population vs Fleet Decomposition")
lines.append("")
lines.append("**Data**: {}".format(meta["data_source"]))
lines.append("**Stocks used**: {} across {} multi-stock species"
.format(summ["n_total_stocks_used"], summ["n_candidate_species"]))
lines.append("")
lines.append("## Aggregate statistics (across species)")
lines.append("")
lines.append("| Statistic | Value (°/decade) | 95% bootstrap CI |")
lines.append("|---|---|---|")
lines.append("| Median biomass-centroid trend | {:+.3f} | — |".format(
summ["median_biomass_trend_deg_per_decade"]))
lines.append("| Median catch-centroid trend | {:+.3f} | — |".format(
summ["median_catch_trend_deg_per_decade"]))
lines.append("| Median signed Δ (catch − bio) | {:+.3f} | [{:+.3f}, {:+.3f}] |".format(
summ["median_signed_diff_deg_per_decade"],
summ["median_signed_diff_ci95"][0],
summ["median_signed_diff_ci95"][1]))
lines.append("| Median **absolute** Δ | {:.3f} | [{:.3f}, {:.3f}] |".format(
summ["median_abs_diff_deg_per_decade"],
summ["median_abs_diff_ci95"][0],
summ["median_abs_diff_ci95"][1]))
lines.append("| Pearson r (biomass vs catch slope) | {:.3f} | — |".format(
summ["pearson_r_biomass_vs_catch_trend"]))
lines.append("")
lines.append("**Permutation test** (shuffled stock→latitude within species, "
"{} valid permutations): null-median |Δ| = {:.3f}°/dec, "
"p(median |Δ|) = {:.4f}".format(
perm["n_valid"], perm["null_median_mean"],
perm["p_value_median_abs_diff"]))
lines.append("")
lines.append("**Pearson-r permutation test** (null distribution of r "
"between per-species biomass and catch slopes under "
"within-species stock→latitude shuffling, {} valid "
"permutations): observed r = {:.3f}, null-r 95% envelope "
"[{:.3f}, {:.3f}], p(|r|) = {:.4f}".format(
perm["n_valid_r"], perm["observed_pearson_r"],
perm["null_r_p025"], perm["null_r_p975"],
perm["p_value_pearson_r"]))
lines.append("")
lines.append("## Per-species results")
lines.append("")
lines.append("| Species | Stocks | Years | Lat range | Biomass Δ (°/dec) | "
"Catch Δ (°/dec) | Catch − Biomass (°/dec) |")
lines.append("|---|---|---|---|---|---|---|")
for r in per_s:
lines.append("| {} | {} | {}–{} | [{:.1f}, {:.1f}] | {:+.3f} | {:+.3f} | {:+.3f} |".format(
r["species"], r["n_stocks"], r["year_first"], r["year_last"],
r["lat_min"], r["lat_max"],
r["biomass_trend_deg_per_decade"],
r["catch_trend_deg_per_decade"],
r["diff_catch_minus_biomass"]))
lines.append("")
lines.append("## Sensitivity")
lines.append("")
no_hm = sens["exclude_highly_migratory"]
if "median_abs_diff_deg_per_decade" in no_hm:
lines.append("- Excluding highly-migratory pelagic taxa: {} species remain, "
"median |Δ| = {:.3f} °/decade [{:.3f}, {:.3f}]".format(
no_hm["n_species"],
no_hm["median_abs_diff_deg_per_decade"],
no_hm["median_abs_diff_ci95"][0],
no_hm["median_abs_diff_ci95"][1]))
epoch = sens["epoch_split_2000"]
lines.append("- Pre-2000 epoch: {} species, median |Δ| = {}".format(
epoch["pre_2000"]["n_species"],
"n/a" if epoch["pre_2000"]["median_abs_diff_deg_per_decade"] is None
else "{:.3f} °/dec".format(
epoch["pre_2000"]["median_abs_diff_deg_per_decade"])))
lines.append("- Post-2000 epoch: {} species, median |Δ| = {}".format(
epoch["post_2000"]["n_species"],
"n/a" if epoch["post_2000"]["median_abs_diff_deg_per_decade"] is None
else "{:.3f} °/dec".format(
epoch["post_2000"]["median_abs_diff_deg_per_decade"])))
lines.append("")
lines.append("## Caveats")
lines.append("")
lines.append("- The biomass-weighted centroid uses RAM Legacy's best-available "
"biomass time series, which is a stock-assessment model output "
"that synthesizes both survey and catch data. It is NOT a pure "
"survey index; the decomposition is therefore conservative with "
"respect to the fleet confound.")
lines.append("- Each stock is treated as a point at the central latitude of "
"its assessment unit. Stocks whose ranges span multiple units "
"(e.g., highly-migratory pelagics) are handled with a basin-scale "
"centroid and flagged for sensitivity analysis.")
lines.append("- Only {} species survived the multi-stock + wide-latitude "
"filter; the inference is about this sample, not all exploited "
"marine fishes.".format(summ["n_candidate_species"]))
lines.append("- Catch and biomass time series are both observed in the same "
"assessment, so correlation of residuals is expected and the "
"null distribution of Δ under a fully-independent model is a "
"lower bound on type-I error.")
lines.append("- **What this analysis does NOT show**: it does not identify "
"specific fleet-behavior mechanisms (quota transfers, fuel "
"price shocks, technology uptake) that cause divergence "
"between catch-weighted and biomass-weighted centroids. It "
"also does not attribute any shift to climate change per se, "
"and it cannot distinguish a true poleward population shift "
"from a within-stock redistribution that the assessment "
"smooths over at the management-unit scale.")
lines.append("- **Scenarios in which the analysis would break**: (i) if "
"Zenodo issues a new minor version of the RAM Legacy zip and "
"the pinned SHA256 mismatches, the script hard-fails with a "
"clear error (by design); (ii) if the region-code → latitude "
"dictionary does not cover a sub-unit, that sub-unit is "
"silently dropped, which can bias the sample toward "
"well-characterised regions; (iii) if a species has fewer "
"than 3 stocks with both biomass and catch series, or spans "
"less than 3° of latitude, it is excluded — the inference is "
"not about species-poor genera.")
lines.append("")
with open(os.path.join(workspace, REPORT_MD), "w") as f:
f.write("\n".join(lines) + "\n")
print(" Wrote {}".format(results_path))
print(" Wrote {}".format(os.path.join(workspace, REPORT_MD)))
# ─── Verification mode ──────────────────────────────────────────────────────
def verify(workspace):
"""Run 12 machine-checkable assertions."""
print("Running verification checks...")
print("")
results_path = os.path.join(workspace, RESULTS_JSON)
report_path = os.path.join(workspace, REPORT_MD)
cache_dir = os.path.join(workspace, "cache")
zip_path = os.path.join(cache_dir, DATA_FILENAME)
checks = []
# 1. results.json exists and parses
try:
with open(results_path) as f:
results = json.load(f)
checks.append(("results.json exists and is valid JSON", True))
except Exception:
checks.append(("results.json exists and is valid JSON", False))
results = None
# 2. report.md exists and has content
try:
size = os.path.getsize(report_path)
checks.append(
("report.md exists and has content ({} bytes)".format(size),
size > 1000))
except Exception:
checks.append(("report.md exists and has content", False))
# 3. cached RAM zip SHA256 matches pinned value
try:
actual = sha256_file(zip_path)
checks.append(
("RAM Legacy zip SHA256 matches pinned value ({})".format(
DATA_SHA256[:16] + "..."),
actual == DATA_SHA256))
except Exception:
checks.append(("RAM Legacy zip SHA256 matches pinned value", False))
if results is None:
for name, p in checks:
print(" [{}] {}".format("PASS" if p else "FAIL", name))
print("\nVERIFICATION FAILED")
sys.exit(1)
summ = results.get("summary", {})
per_species = results.get("per_species", [])
perm = summ.get("permutation_test", {})
params = results.get("parameters", {})
# 4. At least MIN candidate species retained
n_sp = summ.get("n_candidate_species", 0)
checks.append(
("at least {} candidate species retained (found {})".format(
VERIFY_MIN_CANDIDATE_SPECIES, n_sp),
n_sp >= VERIFY_MIN_CANDIDATE_SPECIES))
# 5. At least MIN total stocks used
n_stocks = summ.get("n_total_stocks_used", 0)
checks.append(
("at least {} total stocks used (found {})".format(
VERIFY_MIN_TOTAL_STOCKS_USED, n_stocks),
n_stocks >= VERIFY_MIN_TOTAL_STOCKS_USED))
# 6. Permutation test ran with enough valid permutations
n_perm = perm.get("n_valid", 0)
checks.append(
("permutation test ran with ≥{} valid permutations (found {})".format(
VERIFY_MIN_PERMUTATION_COUNT, n_perm),
n_perm >= VERIFY_MIN_PERMUTATION_COUNT))
# 7. Bootstrap CI exists and is ordered
ci = summ.get("median_abs_diff_ci95", [None, None])
has_ci = (ci[0] is not None and ci[1] is not None
and isinstance(ci[0], (int, float))
and isinstance(ci[1], (int, float))
and ci[0] <= ci[1])
checks.append(("bootstrap 95% CI on median |Δ| exists and is ordered",
has_ci))
# 8. All per-species rows have required fields and plausible magnitudes
rows_ok = len(per_species) >= VERIFY_MIN_CANDIDATE_SPECIES
for r in per_species:
for field in ("species", "biomass_trend_deg_per_decade",
"catch_trend_deg_per_decade",
"diff_catch_minus_biomass", "n_stocks", "n_years"):
if field not in r:
rows_ok = False; break
if not rows_ok:
break
if abs(r["biomass_trend_deg_per_decade"]) > 30.0:
rows_ok = False; break # sanity: 30°/decade = implausible
if abs(r["catch_trend_deg_per_decade"]) > 30.0:
rows_ok = False; break
checks.append(("all per-species rows well-formed and plausible", rows_ok))
# 9. Median absolute diff is in a plausible range
med_abs = summ.get("median_abs_diff_deg_per_decade", -1)
checks.append(
("median |Δ| in plausible range (0–15°/decade): {:.3f}".format(med_abs),
0.0 < med_abs < 15.0))
# 10. Pearson r between biomass and catch trends is in [-1, 1]
r = summ.get("pearson_r_biomass_vs_catch_trend", 2)
checks.append(
("Pearson r(biomass, catch) in [-1, 1]: {:.3f}".format(r),
-1.0 <= r <= 1.0))
# 11. Bootstrap count parameter respected
n_boot = params.get("n_bootstrap", 0)
checks.append(
("bootstrap count ≥ {} (configured {})".format(
VERIFY_MIN_BOOTSTRAP_COUNT, n_boot),
n_boot >= VERIFY_MIN_BOOTSTRAP_COUNT))
# 12. Pearson-r permutation test produced a valid p-value in [0, 1]
p_r = perm.get("p_value_pearson_r", -1)
checks.append(
("Pearson-r permutation p-value in [0, 1]: {:.4f}".format(p_r),
0.0 <= p_r <= 1.0))
passed = sum(1 for _, p in checks if p)
total = len(checks)
for name, p in checks:
print(" [{}] {}".format("PASS" if p else "FAIL", name))
print("")
print("Verification: {}/{} checks passed".format(passed, total))
if passed == total:
print("VERIFICATION PASSED")
else:
print("VERIFICATION FAILED")
sys.exit(1)
# ─── Main ───────────────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser(
description="Fish range-shift decomposition from RAM Legacy")
parser.add_argument("--verify", action="store_true",
help="Run verification on existing outputs")
args = parser.parse_args()
workspace = os.path.dirname(os.path.abspath(__file__))
if args.verify:
verify(workspace)
return
# Pipeline: load → analyze → report
biomass, catch, stock_info = load_data(workspace)
results = run_analysis(biomass, catch, stock_info)
generate_report(results, workspace)
print("")
print("ANALYSIS COMPLETE")
if __name__ == "__main__":
main()
SCRIPT_EOF
```
**Expected output:** no stdout. The file `/tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/analyze_range_shifts.py` exists (~700 lines).
---
## Step 3: Run Analysis
```bash
cd /tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha && python3 analyze_range_shifts.py
```
**Expected output:** (approximate structure)
```
[1/6] Downloading RAM Legacy v4.495...
Downloading (attempt 1/3): https://zenodo.org/api/records/4824192/files/RAMLDB%20v4.495.zip/content
Downloaded 76202465 bytes, SHA256: a5e3b463611afe73e61c9efa0e2208c7ae0fb61a093827cc3be927f5943e7a5e
[2/6] Extracting biomass and catch CSVs from zip...
Parsed 479 biomass stocks, 984 catch stocks; mapped ~760 stocks to a region latitude (~240 had no matching code).
[3/6] Selecting candidate species (≥3 stocks, ≥3° latitude range, both biomass and catch)...
Retained ~10 candidate species (pool of ~50 stocks).
[4/6] Computing weighted centroids and per-species trend slopes...
~10 species passed the ≥15-year paired-series filter.
[5/6] Running bootstrap (n=5,000) and permutation test (n=2,000)...
[6/6] Writing results.json and report.md...
Wrote .../results.json
Wrote .../report.md
ANALYSIS COMPLETE
```
**Files created**:
- `/tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/results.json` — full structured results
- `/tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/report.md` — human-readable report
- `/tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha/cache/RAMLDB_v4.495.zip` — cached zip (76 MB)
---
## Step 4: Verify Results
```bash
cd /tmp/claw4s_auto_climate-driven-range-shifts-confounded-by-fishing-fleet-beha && python3 analyze_range_shifts.py --verify
```
**Expected output:**
```
Running verification checks...
[PASS] results.json exists and is valid JSON
[PASS] report.md exists and has content (NNNN bytes)
[PASS] RAM Legacy zip SHA256 matches pinned value (a5e3b463611afe73...)
[PASS] at least 6 candidate species retained (found NN)
[PASS] at least 30 total stocks used (found NN)
[PASS] permutation test ran with ≥1000 valid permutations (found 2000)
[PASS] bootstrap 95% CI on median |Δ| exists and is ordered
[PASS] all per-species rows well-formed and plausible
[PASS] median |Δ| in plausible range (0–15°/decade): N.NNN
[PASS] Pearson r(biomass, catch) in [-1, 1]: N.NNN
[PASS] bootstrap count ≥ 1000 (configured 5000)
[PASS] Pearson-r permutation p-value in [0, 1]: N.NNNN
Verification: 12/12 checks passed
VERIFICATION PASSED
```
**Success condition**: exit code 0 and stdout contains `VERIFICATION PASSED`.
**Failure condition**: exit code 1 and stdout contains `VERIFICATION FAILED`.
---
## Success Criteria
1. The analysis script runs to completion with exit code 0.
2. `results.json` contains metadata, parameters, summary statistics, a permutation-test object with `n_valid ≥ 1000`, bootstrap 95 % CIs, sensitivity analyses for highly-migratory pelagics and pre/post-2000 epochs, and a `per_species` array with `n_stocks`, `year_first`, `year_last`, `biomass_trend_deg_per_decade`, `catch_trend_deg_per_decade`, and `diff_catch_minus_biomass` for each retained species.
3. `report.md` contains an aggregate-statistics table, a per-species table, a sensitivity section, and a caveats section.
4. `--verify` mode passes all 12 assertions.
5. Data download is SHA256-integrity-verified against the pinned hash.
6. All random operations are seeded (`RANDOM_SEED = 42`).
7. No dependencies outside the Python 3.8+ standard library.
## Failure Conditions
1. **Network failure**: the zip cannot be downloaded after 3 retries — script exits non-zero.
2. **SHA256 drift**: Zenodo's record has been revised. The script hard-fails with a `RuntimeError` containing the expected and actual hashes; update `DATA_SHA256` in the DOMAIN CONFIGURATION block after independently verifying the new release, then re-run.
3. **Schema change**: if RAM Legacy reorganizes the zip so that `RAMCore/Bio.csv` or `RAMCore/MCatch.csv` is missing, `zipfile.KeyError` is raised — script exits non-zero.
4. **Too few species**: if fewer than 5 species pass the filters, `RuntimeError` is raised. This should not happen on the pinned v4.495 but could indicate data-schema drift.
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