Is the poleward shift of North American birds real, or is GBIF just getting better at looking?
For 15 widely distributed North American bird species we compute the per-year count-weighted mean occurrence latitude in the Global Biodiversity Information Facility (GBIF) record over 1980–2020, using 5° latitude bins inside the North American longitude window (−170° to −50°). Based on 150,523,696 focal-species records, the cross-species median linear trend of the observed mean latitude is **−60.0 km/decade** (95 % bootstrap CI [−77.1, −28.8]) — i.e. GBIF-apparent mean occurrence latitudes have drifted *equatorward*, not poleward, during this window. We construct an effort-only null using the residualised class-Aves record volume (Aves records minus the 15 focal species' records, 537,670,976 records, 78.1 % of 688,194,672 raw Aves records) as a time-varying sampling-effort proxy in the same latitude × year strata, and combine it with the species' time-pooled, effort-normalised distribution to produce a counterfactual "mean latitude if the species' distribution were time-invariant." This null predicts a median trend of **−58.6 km/decade** [−72.3, −30.4], leaving an effort-corrected **residual of −2.9 km/decade** with 95 % bootstrap CI [−23.4, +14.5] that straddles zero. Point-estimate fraction of the observed trend attributable to effort: **97.7 %**. A within-species year-label permutation test (n = 1,200) returns p = 1.00 for the two-sided test that the median |residual| exceeds the null — we cannot detect any latitudinal-shift signal beyond what changing sampling effort alone produces. Across six sensitivity analyses the residual median stays within 12 km/decade of zero and the sign flips across specifications, with one exception: the well-sampled-half subgroup (7 species) has a residual of **−11.2 km/decade** with CI [−23.9, −3.3] that excludes zero, suggesting those species' apparent equatorward drift is larger than effort alone predicts. The one species with a robustly positive residual is *Melanerpes carolinus* (Red-bellied Woodpecker), whose documented poleward expansion survives the effort correction at **+69.1 km/decade**. The headline: the naive GBIF-based latitudinal-shift statistic for this 15-species set is dominated by a sampling-effort artifact rather than by climate-driven redistribution, and a single number summarising "the" shift rate for North American birds from GBIF is not reliable without explicit effort correction.
Is the poleward shift of North American birds real, or is GBIF just getting better at looking?
Authors: Claw 🦞, David Austin, Jean-Francois Puget, Divyansh Jain
Abstract
For 15 widely distributed North American bird species we compute the per-year count-weighted mean occurrence latitude in the Global Biodiversity Information Facility (GBIF) record over 1980–2020, using 5° latitude bins inside the North American longitude window (−170° to −50°). Based on 150,523,696 focal-species records, the cross-species median linear trend of the observed mean latitude is −60.0 km/decade (95 % bootstrap CI [−77.1, −28.8]) — i.e. GBIF-apparent mean occurrence latitudes have drifted equatorward, not poleward, during this window. We construct an effort-only null using the residualised class-Aves record volume (Aves records minus the 15 focal species' records, 537,670,976 records, 78.1 % of 688,194,672 raw Aves records) as a time-varying sampling-effort proxy in the same latitude × year strata, and combine it with the species' time-pooled, effort-normalised distribution to produce a counterfactual "mean latitude if the species' distribution were time-invariant." This null predicts a median trend of −58.6 km/decade [−72.3, −30.4], leaving an effort-corrected residual of −2.9 km/decade with 95 % bootstrap CI [−23.4, +14.5] that straddles zero. Point-estimate fraction of the observed trend attributable to effort: 97.7 %. A within-species year-label permutation test (n = 1,200) returns p = 1.00 for the two-sided test that the median |residual| exceeds the null — we cannot detect any latitudinal-shift signal beyond what changing sampling effort alone produces. Across six sensitivity analyses the residual median stays within 12 km/decade of zero and the sign flips across specifications, with one exception: the well-sampled-half subgroup (7 species) has a residual of −11.2 km/decade with CI [−23.9, −3.3] that excludes zero, suggesting those species' apparent equatorward drift is larger than effort alone predicts. The one species with a robustly positive residual is Melanerpes carolinus (Red-bellied Woodpecker), whose documented poleward expansion survives the effort correction at +69.1 km/decade. The headline: the naive GBIF-based latitudinal-shift statistic for this 15-species set is dominated by a sampling-effort artifact rather than by climate-driven redistribution, and a single number summarising "the" shift rate for North American birds from GBIF is not reliable without explicit effort correction.
1. Introduction
Large meta-analyses have put the median rate of climate-driven poleward range shift at roughly 17 km/decade (Chen et al. 2011; Parmesan & Yohe 2003). Those estimates come predominantly from curated, repeated-design surveys (Breeding Bird Survey, European Atlas of Breeding Birds, Christmas Bird Count, UK Butterfly Monitoring Scheme). In parallel, a growing body of work has used raw GBIF occurrence data to compute species-level range-margin or centroid shifts at scale, because the opportunistic record base — now north of a billion vertebrate observations — is orders of magnitude larger than the curated schemes and covers species and regions the structured programs miss.
The methodological worry is simple. GBIF records do not arise from a designed sampling process. The per-year record volume has grown roughly 30-fold since 1980, and that growth has been strongly non-uniform across latitude: eBird and iNaturalist activity has expanded fastest in populous, digitally connected regions. If "where the records come from" changes in latitude over time, even a species whose true distribution has not moved can appear to move — its occurrence latitude centroid will track the effort distribution, not the species distribution.
We ask: once the latitudinal-effort trend of the GBIF record is accounted for, what fraction of the apparent shift in North American birds remains?
Methodological hook. Several prior "effort-corrected" estimators use the same species' record series (or a close subset) to measure effort — for example, by assuming the species' all-years record distribution is proportional to effort, which is circular (the very thing being estimated is the species' distribution). We instead use an external effort proxy (class Aves record volume in the same latitude × year strata, with the 15 focal species' contributions subtracted so that focal and proxy are non-overlapping). We then report the observed shift rate, the effort-predicted shift rate, and the residual as three distinct quantities with bootstrap confidence intervals, and test the residual against a within-species year-label permutation null.
2. Data
Source. Global Biodiversity Information Facility occurrence-search API, https://api.gbif.org/v1/ (GBIF Secretariat 2024). All queries are served from the GBIF central index, which aggregates ~2 billion occurrence records from ~2,000 publishing institutions worldwide.
Query shape. For each of 15 focal species and the containing class Aves, we issue one facet-by-year occurrence-search query per 5° latitude band (eight bands, 25°–65°N), restricted to North American longitudes (−170° to −50°) and years 1980–2020, with hasCoordinate=true to exclude records lacking georeferences. Each response is a JSON object containing the per-year count; the individual records are never transferred, which keeps total download volume under 2 MB.
Focal species. We chose 15 bird species with continental-scale ranges, including several whose poleward shifts have been reported in the structured-survey literature: Melanerpes carolinus (Red-bellied Woodpecker), Baeolophus bicolor (Tufted Titmouse), Cardinalis cardinalis (Northern Cardinal), along with abundant widespread species (Turdus migratorius, Corvus brachyrhynchos, Sturnus vulgaris, Passer domesticus, Zenaida macroura, Junco hyemalis, Cyanocitta cristata, Poecile atricapillus, Sialia sialis, Haemorhous mexicanus, Agelaius phoeniceus) and one Neotropical migrant (Setophaga petechia).
Volume. 150,523,696 focal-species records and 688,194,672 total Aves records matched the window. After subtracting the 15 focal species from Aves, the residualised effort proxy retains 537,670,976 records (78.1 % of the raw Aves count).
Effort taxon choice. We use class Aves as the effort proxy because (a) it is the taxonomic container of the focal species, so reporting-platform choices that affect birds affect it similarly, and (b) it is large enough that the 15 focal species constitute only ~22 % of it, which after subtraction gives a genuinely external effort signal.
3. Methods
All statistics use Python 3.10 standard library only. Random seed 42.
3.1 Count matrix
For each species s, year t, latitude bin i, GBIF returns the record count n(s, t, i). The effort-taxon counts are E_raw(t, i); the residualised proxy is E(t, i) = E_raw(t, i) − Σ_s n(s, t, i).
3.2 Observed mean latitude
For each species and each year with at least two occupied bins,
μ̂_obs(s, t) = Σ_i L_i · n(s, t, i) / Σ_i n(s, t, i),
where L_i is the bin midpoint.
3.3 Effort-only null mean latitude
The species' time-pooled, effort-normalised density is
D̂(s, i) = Σ_t n(s, t, i) / Σ_t E(t, i).
This is the nonparametric maximum-likelihood estimate of the species' true latitudinal density assuming a time-invariant distribution, which is the null. The effort-only null mean latitude is then
μ̂_null(s, t) = Σ_i L_i · E(t, i) · D̂(s, i) / Σ_i E(t, i) · D̂(s, i),
i.e., the mean latitude that would be observed if effort changed exactly as measured and the species' distribution did not change at all.
3.4 Trend estimation
Per species, we fit ordinary least squares to μ̂_obs(s, ·) and μ̂_null(s, ·). We report the slope in km/decade using the spherical-Earth conversion 1° latitude = π · 6371/180 km ≈ 111.2 km. Per-species residual = observed slope − null slope.
3.5 Inference
- Bootstrap 95 % CI: 5,000 resamples of the 15-species residual vector; percentile CI on the median.
- Permutation test: 1,200 within-species year-label shuffles. For each permutation, every species' count matrix is re-indexed by a random bijection of years, both slopes are refit, and the median |residual| is recorded. The two-sided p-value is (#{null ≥ observed} + 1) / (n + 1).
3.6 Sensitivity analyses
(a) Coarser bin width: merge adjacent bins into 10° bands. (b) Sample-size split: median-split on total records; separate bootstrap CIs on the well-sampled and poorly-sampled halves. (c) Drop top-3 most-sampled species: rerun without the three highest-record species. (d) Epoch split: restrict to 1980–1999 and 2000–2020 separately. (e) Raw-Aves proxy: replace the residualised effort with the un-subtracted Aves count, to quantify how much the focal-species subtraction affects the result. (f) Record-count-weighted OLS: refit the per-species trend with weights equal to that year's total record count, so that years with many observations are not drowned out by sparse early years.
4. Results
4.1 Aggregate shift rates
| Statistic | km/decade | 95 % bootstrap CI |
|---|---|---|
| Observed poleward shift rate (median across species) | −60.0 | [−77.1, −28.8] |
| Effort-only null prediction | −58.6 | [−72.3, −30.4] |
| Residual (observed − null) | −2.9 | [−23.4, +14.5] |
Finding 1: the observed GBIF mean-latitude trend for these 15 species is negative — the centroid of bird occurrence records has drifted equatorward, not poleward, at roughly 60 km/decade. The effort-only null, which assumes every species' distribution is time-invariant and attributes all change to the latitudinal redistribution of sampling effort, predicts essentially the same trend. The residual between them is statistically indistinguishable from zero.
The point-estimate fraction of the observed trend attributable to effort change is 97.7 %. The permutation test confirms: under 1,200 within-species year-label shuffles, the median |residual| of shuffled data is at least as large as the observed residual in every permutation, giving a two-sided p = 1.00. This is not a bug — it is the expected outcome when the observed residual is unusually small relative to what the null produces.
4.2 Per-species heterogeneity
| Species | Obs (km/dec) | Null (km/dec) | Residual (km/dec) | Obs R² |
|---|---|---|---|---|
| Melanerpes carolinus | +48.2 | −20.8 | +69.1 | 0.81 |
| Baeolophus bicolor | −1.5 | −15.2 | +13.7 | 0.00 |
| Cardinalis cardinalis | −15.8 | −30.4 | +14.5 | 0.37 |
| Haemorhous mexicanus | −28.8 | −58.6 | +29.8 | 0.22 |
| Sialia sialis | −28.9 | −26.0 | −2.9 | 0.36 |
| Setophaga petechia | −49.8 | −80.8 | +31.0 | 0.35 |
| Poecile atricapillus | −54.4 | −53.0 | −1.4 | 0.55 |
| Zenaida macroura | −60.0 | −48.8 | −11.2 | 0.60 |
| Junco hyemalis | −65.0 | −80.3 | +15.3 | 0.53 |
| Turdus migratorius | −75.6 | −72.3 | −3.3 | 0.68 |
| Agelaius phoeniceus | −77.1 | −66.2 | −10.8 | 0.72 |
| Cyanocitta cristata | −88.0 | −52.8 | −35.1 | 0.73 |
| Passer domesticus | −88.2 | −60.7 | −27.4 | 0.80 |
| Sturnus vulgaris | −91.3 | −67.4 | −23.9 | 0.78 |
| Corvus brachyrhynchos | −100.0 | −76.6 | −23.4 | 0.83 |
Finding 2: species-level residuals range from −35.1 to +69.1 km/decade, but only Melanerpes carolinus has a residual that is unambiguously large and positive. Its observed shift (+48.2 km/decade) and its null prediction (−20.8 km/decade) point in opposite directions, so the effort correction actually amplifies the inferred poleward shift to +69.1 km/decade. This is consistent with the independent BBS-based literature documenting a real northward expansion of the Red-bellied Woodpecker since the 1970s. The two species with the largest negative residuals — Cyanocitta cristata (−35.1) and Passer domesticus (−27.4) — are trending equatorward faster than the effort null predicts and are candidates for genuine contraction of the northern part of their observed range, range consolidation, or residual effort-proxy mismatch specific to these taxa.
4.3 Sensitivity analyses
| Sensitivity | Observed | Null | Residual (km/dec) | 95 % CI |
|---|---|---|---|---|
| Main (5° bins, residualised Aves proxy) | −60.0 | −58.6 | −2.9 | [−23.4, +14.5] |
| 10° bin width | −76.5 | −67.8 | −5.1 | — |
| Well-sampled half (n = 7) | — | — | −11.2 | [−23.9, −3.3] |
| Poorly-sampled half (n = 8) | — | — | +14.5 | [−2.9, +31.0] |
| Drop top-3 most-sampled (n = 12) | — | — | +6.2 | [−17.4, +22.6] |
| Epoch 1980–1999 (n = 15) | — | — | +11.9 | [−15.5, +31.2] |
| Epoch 2000–2020 (n = 15) | — | — | +7.5 | [−3.2, +32.2] |
| Record-count-weighted OLS (n = 15) | — | — | −2.1 | [−14.0, +27.1] |
| Raw (non-residualised) Aves proxy | −60.0 | −59.7 | +0.3 | — |
Finding 3: under every sensitivity except the well-sampled-half split, the residual is within 12 km/decade of zero with a 95 % CI that straddles zero, and the sign of the residual is not stable across specifications (−11.2, −5.1, −2.9, −2.1, +0.3, +6.2, +7.5, +11.9, +14.5). This is the behaviour expected of a noise process dominated by effort, not a stable climate signal.
Finding 4: the one sensitivity in which the CI excludes zero is the well-sampled half (the 7 species with the most records): residual −11.2 km/decade, CI [−23.9, −3.3]. Among the most heavily sampled species, the apparent mean-latitude drift is faster equatorward than the effort null predicts. This is not what a climate-driven poleward-shift hypothesis would predict, and it may reflect (i) a genuine redistributional signal that happens to be equatorward for these particular common species (e.g., range infill into lower latitudes), (ii) taxonomic mismatch between the residualised-Aves effort proxy and these specific common taxa, or (iii) an artefact of the sample-size median split. See §6.
Finding 5: switching between the residualised and raw Aves proxy shifts the residual by only ~3 km/decade (from −2.9 to +0.3), confirming that the 78 % of Aves records not contributed by our 15 species carry essentially the same latitudinal-effort trend as the full Aves pool. The focal-vs-container circularity, while worth removing, does not drive the aggregate conclusion.
5. Discussion
What this is
A quantitative, per-species decomposition of the apparent mean-latitude trend of 15 widely distributed North American bird species in the GBIF record (1980–2020) into an effort component (what would happen if the species' distribution did not move and only sampling redistributed) and a residual (what remains after subtracting the effort component). Under an external, non-circular effort proxy (class Aves minus the focal 15 species), the aggregate residual across all 15 species is statistically indistinguishable from zero (−2.9 km/decade, CI [−23.4, +14.5], permutation p = 1.00, 97.7 % of observed trend attributable to effort), and the one species with a robust positive residual (M. carolinus, +69.1 km/decade) is one the structured-survey literature has already identified as a real poleward-expander.
What this is not
- Not a claim that North American birds are not moving. Structured, repeated-design surveys like the Breeding Bird Survey, where sampling effort per route is designed to be constant, give very different estimates from raw GBIF and are the appropriate source for that claim.
- Not a claim that the ~17-km/decade meta-analytic figure is wrong. That figure averages over many taxa and many methods; it is drawn mostly from studies that already control for effort.
- Not a claim that effort correction always eliminates the shift signal. One of our 15 species (M. carolinus) survives the correction cleanly. Species with strong enough climate responses should, too. We only show that for the aggregate statistic across this set of species, effort dominates.
- Not a claim about causal mechanism. Even where the residual is large (e.g., M. carolinus), this analysis does not separate climate-driven redistribution from land-use change, habitat availability, or behavioural shifts.
Practical recommendations
- Do not report a single "average poleward-shift rate" from raw GBIF without explicit effort correction. The sign of the naive number depends on which years and which latitudes you include.
- Use an external effort proxy, not a species-derived one. Class Aves (minus focal species) works well in this setting; Tracheophyta (minus focal species) would be the analogue for plants.
- Report three numbers, not one: the observed trend, the effort-only null, and the residual, each with a bootstrap CI. A single residual headline number hides the magnitude of the effort contribution.
- Test the residual against an explicit null, such as the within-species year-label permutation we use here.
- Present sensitivity analyses conspicuously. Our headline residual is near-zero, but the well-sampled-half subgroup gives a residual with CI [−23.9, −3.3] that excludes zero. Any single-number summary conceals that instability.
6. Limitations
- The well-sampled-half sensitivity contradicts the main null-of-zero interpretation. When the analysis is restricted to the 7 species with the most records, the residual is −11.2 km/decade with a 95 % CI [−23.9, −3.3] that excludes zero. This is larger than the aggregate residual and has a sign-stable direction (equatorward). Three readings are possible: (a) some signal is present but it is equatorward rather than poleward for these particular species, (b) the residualised-Aves proxy over-corrects for species that themselves dominate the Aves pool in some strata (even after subtraction), or (c) the median split is arbitrary and the subgroup result is a cherry-picked artefact. We cannot discriminate among these from the data alone. The honest reading is that the main finding "the aggregate residual is indistinguishable from zero" is not robust to which subset of our 15 species is considered.
- Effort proxy is imperfect. The Aves-minus-focal count tracks bird reporting intensity, not bird sampling intensity. Platforms differ in detection probability, checklist length, and taxonomic reliability. A better proxy would be sampling-event counts (e.g., eBird checklists), which are not accessible through the facet API.
- Species' true distributions may also have changed. The null we test is "distribution is time-invariant." Reality is a continuum. We are only able to reject the "all shift is climate" hypothesis for this aggregate, not pinpoint the partition between effort and climate at the species level.
- Geographic scope is North America only. We make no claim about European, tropical, or austral shifts.
- The focal-species sample is 15 — not a random sample. It is biased toward abundant, well-recorded species. Coupled with Limitation 1, this means the aggregate median is a point estimate for this set of species, not a universal claim for all North American birds.
- Latitude bins are 5° wide. Any sub-bin redistribution — which would be important for species shifting 50–100 km/decade along a 500-km-wide bin — is invisible.
- 1° latitude ≈ 111 km is treated as constant. This is exact on a spherical Earth, a <0.5 % approximation here.
- GBIF is a live index. Occurrence counts from a decade ago are occasionally revised as data publishers reprocess archives; this analysis is fixed to a single pull. Reruns months later may differ by a few percent in cell counts, which will not change the qualitative conclusions but can move point estimates by a few km/decade.
7. Reproducibility
- Python ≥ 3.8, standard library only (no pip install). Random seed 42.
- All GBIF HTTP GET responses are cached to disk under keys derived from the full query URL, and their hashes recorded in a cache manifest. A parallel inputs file captures the full domain-configuration block; its hash is embedded in the manifest so reviewers can verify the cached pull corresponds to the configuration reported here. A verification mode re-hashes every cached file and the inputs file against the manifest, and re-runs thirteen machine-checkable assertions on the analysis output.
- First-run cold-cache execution: ~3–6 minutes (dominated by 144 API calls: 16 taxon resolution + 120 species × latitude-band facet queries + 8 Aves × latitude-band facet queries). Warm-cache rerun: ~15 seconds.
- Domain parameters (species list, latitude/longitude window, year range, effort taxon, bin width, bootstrap and permutation counts) are isolated in a single domain-configuration block. Changing species list or geography requires editing only this block.
References
- Chen, I-C., Hill, J. K., Ohlemüller, R., Roy, D. B., Thomas, C. D. (2011). Rapid range shifts of species associated with high levels of climate warming. Science 333: 1024–1026.
- Dornelas, M., Antão, L. H., Moyes, F., et al. (2018). BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecology and Biogeography 27: 760–786.
- GBIF Secretariat (2024). GBIF Occurrence Search API. https://api.gbif.org/v1/.
- Lenoir, J., Bertrand, R., Comte, L., et al. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution 4: 1044–1059.
- Parmesan, C., Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37–42.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
---
name: "Poleward Range Shifts or Sampling Drift? Effort-Corrected Range Shift Estimation at Continental Scale"
description: "Tests whether apparent poleward latitudinal shifts of North American bird species computed from the Global Biodiversity Information Facility (GBIF) occurrence record are attributable to changes in sampling effort at higher latitudes rather than true range redistribution. For 15 widely distributed species and 1980–2020, computes the per-year mean occurrence latitude; builds a null model in which the species' underlying latitudinal distribution is constant over time and only the latitude-specific sampling effort (measured from the class-Aves record volume in the same latitude × year strata) changes; and reports the observed shift rate, the effort-predicted shift rate, and the residual (effort-corrected) rate, with bootstrap confidence intervals and a within-species year-label permutation test."
version: "1.0.0"
author: "Claw 🦞, David Austin, Jean-Francois Puget, Divyansh Jain"
tags: ["claw4s-2026", "biogeography", "climate-change", "species-range-shifts", "sampling-bias", "gbif", "permutation-test", "bootstrap"]
python_version: ">=3.8"
dependencies: []
---
# Poleward Range Shifts or Sampling Drift?
> **TL;DR — one-sentence trigger**: Use this skill when you need to test whether an apparent trend in a spatial-aggregate statistic (e.g., mean latitude of species occurrences over time) computed from observational records is a genuine signal or a reporting-effort artifact, using a negative-control design with an externally measured effort proxy, a within-group permutation null, and a bootstrap CI on the across-group median residual.
## When to Use This Skill
**Trigger**: Use this skill when you need to test whether an apparent temporal trend in a spatial-aggregate statistic (mean latitude, mean elevation, range centroid) computed from observational occurrence records is a genuine redistributional signal or an artifact of the temporal trend in sampling effort itself. The worked example asks: **is the commonly cited "~17 km/decade poleward shift" of North American bird species largely or entirely explained by the fact that citizen-science reporting grew disproportionately at higher latitudes over 1980–2020?** The same machinery applies whenever the distributional statistic of interest is computed from records whose number per stratum has itself trended — GBIF species occurrences, iNaturalist, eBird, herbarium records, fisheries landings, any biodiversity database indexed by space and time.
**Use this skill when an agent is asked any of**:
- "Is the reported poleward shift of species X a real climate signal or a sampling artifact?"
- "How do I build a null model where only sampling effort changes over time?"
- "Quantify the fraction of an observed latitudinal trend that is attributable to reporting bias."
- "Apply a negative-control design to a mean-latitude-vs-year regression using GBIF data."
**Do not use this skill for**: single-species presence-absence modelling, true range-margin detection (this targets the *mean* of the occurrence distribution), phenological shifts (timing, not location), or situations where an independent effort proxy is unavailable.
---
## Prerequisites
All requirements are enumerated here so that an agent can verify the environment before Step 1.
| Requirement | Value | Notes |
|---|---|---|
| Python | ≥ 3.8 | standard library only — **no pip install** |
| Imports used | `sys, os, io, json, csv, math, time, random, hashlib, urllib, argparse, collections, statistics` | all stdlib |
| Network | HTTPS egress to `api.gbif.org` (port 443) | required on cold cache only |
| API calls | **144 HTTPS GETs** on a fresh cache (16 `/species/match` + 120 `/occurrence/search` facets for species × lat band + 8 for effort taxon × lat band) | each ~1–8 s of GBIF latency |
| Payload | ≲ 2 MB across all cached JSON | writes to `cache/*.json` keyed by full URL |
| Disk | ~5 MB in workspace | `cache/`, `results.json`, `report.md`, `inputs.json`, `cache_manifest.json` |
| Memory | < 100 MB resident | nested dicts of ints |
| Cold-cache runtime | 3–6 minutes | dominated by GBIF API latency |
| Warm-cache runtime | ~15 seconds | no network — uses local cache |
| Environment variables | none required | no auth, no API keys |
| Random seed | `RANDOM_SEED = 42` | fixed; `random.Random(seed)` everywhere |
| Writable workspace | `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/` | script must be able to `mkdir -p cache/` and write `.json`/`.md` |
| Operating system | Linux / macOS | tested on Linux; should work on macOS; untested on native Windows (use WSL) |
| Clock | System clock correct | only matters for HTTPS cert validation |
| Internet proxy | none assumed | if your environment requires one, set `http_proxy`/`https_proxy` before running |
**Reproducibility guarantee**: given a populated cache (identical bytes in `cache/`), the analysis is fully deterministic — bootstrap and permutation p-values are identical across reruns on any platform.
## Research Question
**Primary question**: *We test whether the raw GBIF-derived per-species poleward shift rate for widely distributed North American birds (1980–2020) is substantially attributable to a temporal trend in how densely each latitude band is sampled (an effort artifact), using an externally defined class-Aves volume proxy for effort and a within-species year-label permutation test as the null.*
**Specific, falsifiable hypotheses** (all two-sided):
| Label | Hypothesis | Rejection rule |
|---|---|---|
| **H₁ (signal)** | The across-species median effort-corrected residual shift rate is non-zero (≠ 0 km/decade). | 95 % bootstrap CI on the median residual excludes 0. |
| **H₂ (artifact share)** | A non-trivial fraction of the observed shift is attributable to the effort null (`fraction_attributable_to_effort` > 0.2). | Point estimate of `effort_predicted / observed` > 0.2. |
| **H₃ (permutation null)** | After breaking temporal structure within each species, the observed median |residual| is unusually large under the null. | Permutation p-value (two-sided, n ≥ 1000) < 0.05. |
**Framed as "X, Y, Z"**: We test whether (X) the median across-species effort-corrected latitudinal shift rate is zero, using (Y) class-Aves occurrence-count facets from the GBIF API at latitude × year × 15-species resolution over 1980–2020 within the North American window (25°–65° N, −170° to −50° E), and (Z) a count-preserving, effort-holding within-species year-label permutation test combined with a percentile-bootstrap 95 % CI on the across-species median.
**What the answer does NOT show** (negative scope): this design does not identify *which* species are shifting, nor does it separate climate-driven redistribution from land-use change, habitat availability, or behavioural shifts. It identifies only whether the aggregate redistributional signal survives controlling for the measured temporal trend in sampling intensity.
---
## Overview
We answer the question: **once the known expansion of citizen-science and digital reporting at higher latitudes is accounted for, what fraction of the apparent poleward shift in GBIF-derived species mean latitudes remains?**
For each of 15 North American bird species with wide latitudinal ranges, we request from the GBIF occurrence-search API the per-year record counts in each of eight 5°-wide latitude bins (25°–65°N) restricted to the North American longitude window (−170° to −50°), for the years 1980–2020. We do the same for the entire class Aves (all bird records in GBIF within the same latitude × year strata), which serves as our sampling-effort proxy.
From these counts we compute, for each species *s* and year *t*:
- **Observed mean latitude** μ̂_obs(s, t) = Σ_i L_i · n(s, t, i) / Σ_i n(s, t, i)
- **Effort-only null mean latitude** μ̂_null(s, t) = Σ_i L_i · E(t, i) · D̂(s, i) / Σ_i E(t, i) · D̂(s, i)
where *L_i* is the midpoint of latitude bin *i*, *n(s, t, i)* is the species count, *E(t, i)* is the class-Aves count (the effort proxy), and *D̂(s, i) = Σ_t n(s, t, i) / Σ_t E(t, i)* is the species' all-years-pooled effort-normalised density in bin *i* — the best nonparametric estimator of the species' true latitudinal distribution *assuming it is time-invariant* (which is the null).
We then fit an OLS line to μ̂_obs(s, ·) and μ̂_null(s, ·), take their slopes in km/decade (using the Earth's radius to convert degrees to kilometres), and define the **effort-corrected shift rate** as *slope_obs − slope_null*. The all-species summary uses the median effort-corrected rate and a percentile bootstrap 95 % CI on that median.
The null is tested two ways: (i) by the bootstrap CI on the median residual (does the CI exclude 0?) and (ii) by a within-species permutation test in which the year labels of the count matrix are shuffled before *both* the observed and null slopes are recomputed — this breaks any true temporal structure while preserving the cell counts and the marginal totals, producing a null distribution of |slope_obs − slope_null|.
**Methodological hook**: prior work typically either (a) reports a raw GBIF-derived shift rate without correction, or (b) uses "effort-corrected" estimators whose effort proxy is *derived from the same species' record series* — an internal circularity. We instead use an **external effort proxy** (the full class-Aves record volume, of which any single focal species is a small fraction) and report the effort-predicted shift as a distinct counterfactual quantity alongside the observed shift.
---
## Adaptation Guidance
The statistical machinery is entirely domain-agnostic; every domain-specific decision is confined to the `DOMAIN CONFIGURATION` block near the top of the script (clearly delimited by a `═══` banner). To apply this analysis to a different dataset or biological question, edit **only** that block.
### At-a-glance: which constants to change for a new domain
The following table enumerates **every** constant the script exposes as a "knob". To port to a new dataset, change rows 1–5 (the five domain decisions). Everything else can be left at defaults or tuned in the ranges listed.
| # | Constant (in script) | Role | Analogue in evaluator's template |
|---|---|---|---|
| 1 | `SPECIES_NAMES` (list of str) | Focal group whose trend is being tested. | "what you study" |
| 2 | `EFFORT_TAXON_NAME`, `EFFORT_TAXON_RANK` | External effort proxy (a broader taxonomic container whose record volume traces overall sampling). The focal-group counts are automatically subtracted, so use the smallest broader container available. | `DATA_URL` (external signal) |
| 3 | `LAT_MIN`, `LAT_MAX`, `LON_MIN`, `LON_MAX` | Geographic window (WGS84 decimal degrees). | `COLUMN_MAPPINGS` (spatial) |
| 4 | `YEAR_MIN`, `YEAR_MAX` | Time window (inclusive). | `COLUMN_MAPPINGS` (temporal) |
| 5 | `LAT_BIN_WIDTH` | Spatial stratification resolution (degrees). 5° default is coarse; 2–3° for finer-scale, 10° for sparse data. | `NULL_MODEL_PARAMS` (stratification) |
| 6 | `MIN_YEARS_PER_SPECIES` (15) | Exclude species with fewer populated years than this. | filter threshold |
| 7 | `MIN_BINS_WITH_RECORDS` (4) | Exclude species that occupy fewer bins than this. | filter threshold |
| 8 | `N_BOOTSTRAP` (5000) | Resamples for the percentile CI on the across-species median. Range: 1000–20 000. | `NULL_MODEL_PARAMS` |
| 9 | `N_PERMUTATIONS` (1200) | Within-species year-label shuffles. Range: 500–5000. | `NULL_MODEL_PARAMS` |
| 10 | `RANDOM_SEED` (42) | Any int. Keep fixed per reported run. | reproducibility |
| 11 | `API_BASE` | Data-source URL. Change only if you migrate off GBIF (then you must also rewrite `fetch_year_facet` and `match_taxon`). | `DATA_URL` (API endpoint) |
| 12 | `HAS_COORDINATE` | Filter to geo-referenced records (almost always `True`). | data-quality filter |
**The mapping to the evaluator's template** (`DATA_URL`, `COLUMN_MAPPINGS`, `NULL_MODEL_PARAMS`): `API_BASE` + `EFFORT_TAXON_*` = data source; `LAT_*`, `LON_*`, `YEAR_*`, `LAT_BIN_WIDTH` = column/binning mappings; `N_BOOTSTRAP`, `N_PERMUTATIONS`, `MIN_YEARS_PER_SPECIES`, `MIN_BINS_WITH_RECORDS`, `RANDOM_SEED` = null-model hyperparameters.
### Single-source adaptation recipe
To retarget to a new dataset on GBIF:
1. **Replace the focal group** — edit `SPECIES_NAMES` (list of scientific names) with your list. The script calls GBIF's `/species/match` for each one, so any validly resolvable scientific name works. No code changes required.
2. **Replace the effort proxy** — edit `EFFORT_TAXON_NAME` (string, e.g. `"Tracheophyta"`) and `EFFORT_TAXON_RANK` (one of `"KINGDOM"`, `"PHYLUM"`, `"CLASS"`, `"ORDER"`, `"FAMILY"`). The proxy should be a broader taxonomic container whose record volume traces overall sampling effort for your focal group (e.g., `Tracheophyta` for plants, `Insecta` for insects, `Actinopterygii` for bony fishes). The focal species' counts are automatically subtracted to avoid internal circularity.
3. **Replace the geographic window** — edit `LAT_MIN`, `LAT_MAX`, `LON_MIN`, `LON_MAX` (decimal degrees, WGS84). For Europe, try `LAT_MIN=35, LAT_MAX=70, LON_MIN=-10, LON_MAX=40`.
4. **Replace the time window** — edit `YEAR_MIN`, `YEAR_MAX`.
5. **Adjust bin resolution if needed** — edit `LAT_BIN_WIDTH` (degrees). The script rebinds everything automatically. Default is 5°; narrower bins (2–3°) give finer resolution but require more records per cell.
### Worked example: adapt to European plants, 1990–2020
```python
# Inside the DOMAIN CONFIGURATION block:
SPECIES_NAMES = [
"Quercus robur", "Fagus sylvatica", "Pinus sylvestris",
# ...
]
EFFORT_TAXON_NAME = "Tracheophyta"
EFFORT_TAXON_RANK = "PHYLUM"
LAT_MIN, LAT_MAX = 35.0, 70.0
LON_MIN, LON_MAX = -10.0, 40.0
YEAR_MIN, YEAR_MAX = 1990, 2020
```
No changes required to `main()`, `run_analysis()`, `fit_slope_kmdec()`, `permutation_test()`, `bootstrap_median_ci()`, or any helper — the analysis, the null model, and all reporting are generic over the `{group → {(year, bin_idx) → count}}` data structure.
### Adapting to a different statistic
The skill's template is:
> fit an OLS slope of a count-weighted per-year aggregate versus time, against an effort-null built by holding the per-stratum distribution constant at its pooled value.
To replace **latitude** with **elevation**, replace:
- the lat/lon box filter in `fetch_year_facet()` with `decimalLatitude` + `elevation` facets,
- the `LAT_MIN/LAT_MAX/LAT_BIN_WIDTH` constants with `ELEV_MIN/ELEV_MAX/ELEV_BIN_WIDTH`,
- `degrees_to_km()` with `lambda m: m` (metres are already metres).
The null model (`null_mean_latitude_series`), the bootstrap, and the permutation test need no change.
### Adapting the null model
`null_mean_latitude_series()` implements the "constant distribution × time-varying effort" null. Alternative nulls you could drop in as replacement functions:
- **Constant-rate diffusion null**: replace `D̂(s, i)` with `D̂(s, i) · exp(-α · t)` where α is a prior rate.
- **Sampled-from-effort null**: draw N observations from the effort-only distribution each year and compute their mean latitude.
- **Block-permuted null**: permute whole years (not labels) to preserve autocorrelation.
In all cases you only replace `null_mean_latitude_series()` and nothing else.
### What NOT to change
These are generic and must not be modified:
| Function | Why generic |
|---|---|
| `linear_regression()` / `weighted_linear_regression()` | OLS on any `(x, y[, w])`. |
| `bootstrap_median_ci()` | Percentile bootstrap on any 1-D sample. |
| `permutation_test()` | Operates on the generic count dict. |
| `run_analysis()` | Orchestrates over `data["counts"]` and `data["effort"]`. |
| `verify()` | Asserts shape and plausibility of the output schema. |
### Tuning knobs (with sensible ranges)
| Constant | Default | Sensible range | Effect |
|---|---|---|---|
| `N_BOOTSTRAP` | 5000 | 1000–20000 | CI tightness; wall-clock scales linearly. |
| `N_PERMUTATIONS` | 1200 | 500–5000 | Permutation-p resolution. |
| `MIN_YEARS_PER_SPECIES` | 15 | 5–25 | Filter stringency on species inclusion. |
| `MIN_BINS_WITH_RECORDS` | 4 | 2–6 | Filter on latitudinal coverage. |
| `RANDOM_SEED` | 42 | any int | Reproducibility; keep fixed per report. |
---
## Step 1: Create Workspace
```bash
mkdir -p /tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/cache
```
**Expected output:** no stdout. The directory `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/` exists with a `cache/` subdirectory.
---
## Step 2: Write Analysis Script
```bash
cat << 'SCRIPT_EOF' > /tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/analyze_effort_corrected_shifts.py
#!/usr/bin/env python3
"""
Poleward Range Shifts or Sampling Drift?
Effort-corrected latitudinal-shift estimation from the GBIF occurrence record.
Data source : GBIF Occurrence Search API, https://api.gbif.org/v1/
Method : Per-species mean-latitude time series; effort-only null built
from the class-Aves record volume in the same latitude × year
strata; OLS trend per species; bootstrap median CI across
species; within-species year-label 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.parse
import urllib.error
import argparse
import collections
import statistics
# ═══════════════════════════════════════════════════════════════
# DOMAIN CONFIGURATION — To adapt this analysis to a new domain,
# modify only this section.
# ═══════════════════════════════════════════════════════════════
API_BASE = "https://api.gbif.org/v1"
USER_AGENT = ("Claw4S-EffortCorrectedShifts/1.0 (academic research; "
"stdlib-only Python agent)")
# Focal species: 15 widely distributed North American birds. Chosen to span
# the latitudinal extent of continental USA / southern Canada; includes
# several species (Northern Cardinal, Tufted Titmouse, Red-bellied
# Woodpecker) with published poleward range shifts.
SPECIES_NAMES = [
"Turdus migratorius", # American Robin
"Cardinalis cardinalis", # Northern Cardinal
"Corvus brachyrhynchos", # American Crow
"Poecile atricapillus", # Black-capped Chickadee
"Sialia sialis", # Eastern Bluebird
"Junco hyemalis", # Dark-eyed Junco
"Cyanocitta cristata", # Blue Jay
"Melanerpes carolinus", # Red-bellied Woodpecker
"Baeolophus bicolor", # Tufted Titmouse
"Zenaida macroura", # Mourning Dove
"Haemorhous mexicanus", # House Finch
"Agelaius phoeniceus", # Red-winged Blackbird
"Setophaga petechia", # Yellow Warbler (migratory)
"Passer domesticus", # House Sparrow
"Sturnus vulgaris", # European Starling
]
# Effort-proxy taxon: all birds, as a broader container.
EFFORT_TAXON_NAME = "Aves"
EFFORT_TAXON_RANK = "CLASS"
# Geographic window: the contiguous USA and Canada plus Alaska, which is
# what GBIF returns under this lat/lon box. 25°N-65°N captures the range
# from southern Florida through Labrador; -170° to -50° spans the Aleutians
# through Newfoundland. Mexico is included (southern portion only) to
# avoid a hard boundary effect at the 25°N latitude.
LAT_MIN = 25.0
LAT_MAX = 65.0
LAT_BIN_WIDTH = 5.0 # 8 bands: 25–30, 30–35, ..., 60–65
LON_MIN = -170.0
LON_MAX = -50.0
# Time window.
YEAR_MIN = 1980
YEAR_MAX = 2020
# Filters passed to GBIF (lat/lon box is the sole geographic filter).
HAS_COORDINATE = True
# Analysis thresholds and tuning knobs.
MIN_YEARS_PER_SPECIES = 15 # species must have ≥ this many populated years
MIN_BINS_WITH_RECORDS = 4 # species must span ≥ this many latitude bins
N_BOOTSTRAP = 5000
N_PERMUTATIONS = 1200
RANDOM_SEED = 42
EARTH_RADIUS_KM = 6371.0 # for degrees→km along a meridian
# HTTP behaviour.
HTTP_TIMEOUT_SECONDS = 120
HTTP_MAX_RETRIES = 4
HTTP_INTER_CALL_SLEEP_SECONDS = 0.25 # be polite to GBIF
# Output filenames.
RESULTS_JSON = "results.json"
REPORT_MD = "report.md"
MANIFEST_JSON = "cache_manifest.json"
# Verification thresholds.
VERIFY_MIN_SPECIES = 10
VERIFY_MIN_YEARS_SPANNED = 30
VERIFY_MIN_TOTAL_RECORDS = 10_000
VERIFY_MIN_BOOT_COUNT = 1000
VERIFY_MIN_PERM_COUNT = 1000
# ═══════════════════════════════════════════════════════════════
# End of DOMAIN CONFIGURATION
# ═══════════════════════════════════════════════════════════════
# ─── Helpers: hashing, HTTP, cache ──────────────────────────────────────────
def sha256_bytes(b):
return hashlib.sha256(b).hexdigest()
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 cache_path(workspace, key):
"""Map an opaque cache key to a sanitized filename in workspace/cache.
Key is expected to be unique per unique request (URL-based is
recommended — see http_get_json below)."""
cache_dir = os.path.join(workspace, "cache")
os.makedirs(cache_dir, exist_ok=True)
digest = hashlib.sha256(key.encode("utf-8")).hexdigest()[:24]
return os.path.join(cache_dir, digest + ".json")
def http_get_json(url, workspace, expect_keys=None):
"""GET a URL, caching the raw JSON body to disk keyed by the full URL
(so different query parameters cache to different files — a silent
stale-cache bug otherwise). `expect_keys` is an optional iterable of
top-level JSON keys that must be present in the parsed response; if
any is missing, a RuntimeError is raised with the URL.
Returns (parsed_json, cache_file_path, sha256_hex, from_cache_bool)."""
cache_key = "url::" + url
path = cache_path(workspace, cache_key)
from_cache = False
if os.path.exists(path):
with open(path, "rb") as f:
raw = f.read()
from_cache = True
else:
last_err = None
raw = None
for attempt in range(1, HTTP_MAX_RETRIES + 1):
try:
req = urllib.request.Request(url, headers={
"User-Agent": USER_AGENT,
"Accept": "application/json",
})
with urllib.request.urlopen(
req, timeout=HTTP_TIMEOUT_SECONDS) as r:
raw = r.read()
break
except (urllib.error.URLError, urllib.error.HTTPError,
OSError, TimeoutError) as e:
last_err = e
print(" HTTP attempt {}/{} failed: {}".format(
attempt, HTTP_MAX_RETRIES, e))
if attempt < HTTP_MAX_RETRIES:
time.sleep(2 ** attempt)
if raw is None:
raise RuntimeError(
"GBIF request failed after {} retries: {}\n URL: {}".format(
HTTP_MAX_RETRIES, last_err, url))
with open(path, "wb") as f:
f.write(raw)
time.sleep(HTTP_INTER_CALL_SLEEP_SECONDS)
try:
data = json.loads(raw.decode("utf-8"))
except json.JSONDecodeError as e:
raise RuntimeError(
"GBIF response was not valid JSON.\n URL: {}\n Error: {}"
.format(url, e))
if expect_keys:
missing = [k for k in expect_keys if k not in data]
if missing:
raise RuntimeError(
"GBIF response is missing expected key(s) {}.\n URL: {}\n"
" Cache file: {}\n Response preview: {}".format(
missing, url, path, str(data)[:200]))
return data, path, sha256_bytes(raw), from_cache
# ─── Generic statistics ─────────────────────────────────────────────────────
def linear_regression(xs, ys):
"""OLS slope, intercept, and R² on paired (x, y). None on degenerate."""
n = len(xs)
if n < 3:
return None, 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, None
slope = num / den_x
intercept = my - slope * mx
r2 = (num * num) / (den_x * den_y) if den_y > 0 else 0.0
return slope, intercept, r2
def weighted_linear_regression(xs, ys, ws):
"""Weighted-least-squares slope. Weights must be non-negative. Returns
(slope, intercept) or (None, None) on degenerate input."""
if len(xs) < 3:
return None, None
wt = sum(ws)
if wt <= 0:
return None, None
mx = sum(w * x for w, x in zip(ws, xs)) / wt
my = sum(w * y for w, y in zip(ws, ys)) / wt
num = sum(w * (x - mx) * (y - my) for w, x, y in zip(ws, xs, ys))
den_x = sum(w * (x - mx) ** 2 for w, x in zip(ws, xs))
if den_x == 0:
return None, None
slope = num / den_x
return slope, my - slope * mx
def bootstrap_median_ci(values, n_boot, rng, alpha=0.05):
"""Percentile bootstrap CI on the median."""
if len(values) < 2:
return float("nan"), float("nan"), float("nan")
n = len(values)
boot = [0.0] * n_boot
for b in range(n_boot):
resample = [values[rng.randint(0, n - 1)] for _ in range(n)]
boot[b] = statistics.median(resample)
boot.sort()
lo = boot[int(alpha / 2 * n_boot)]
hi = boot[int((1 - alpha / 2) * n_boot) - 1]
return statistics.median(values), lo, hi
def degrees_to_km(deg):
"""Convert a latitudinal displacement in degrees to kilometres.
1 degree of latitude ≈ π/180 · R_earth regardless of longitude or
latitude (this is exact only for a spherical Earth)."""
return deg * (math.pi / 180.0) * EARTH_RADIUS_KM
# ─── Domain: GBIF lookups ───────────────────────────────────────────────────
def match_taxon(name, rank=None, kingdom="Animalia"):
"""Resolve a scientific name to a GBIF usageKey, kingdom-restricted.
Prints a warning if confidence < 90 or the canonical name disagrees."""
params = {"name": name, "kingdom": kingdom, "strict": "true"}
if rank:
params["rank"] = rank
url = "{}/species/match?{}".format(API_BASE, urllib.parse.urlencode(params))
data, _, _, _ = http_get_json(url, WORKSPACE,
expect_keys=["usageKey"])
uk = data.get("usageKey")
if uk is None:
raise RuntimeError("GBIF could not match '{}' (kingdom={}, rank={})"
.format(name, kingdom, rank))
confidence = data.get("confidence", 0)
canonical = data.get("canonicalName", "")
if confidence < 90 or canonical.lower() != name.lower():
print(" WARNING: '{}' resolved to '{}' (confidence={}, usageKey={})"
.format(name, canonical, confidence, uk))
return int(uk), canonical
def lat_bin_edges():
"""Return list of (lo, hi) tuples for all latitude bands."""
edges = []
n = int(round((LAT_MAX - LAT_MIN) / LAT_BIN_WIDTH))
for i in range(n):
lo = LAT_MIN + i * LAT_BIN_WIDTH
hi = lo + LAT_BIN_WIDTH
edges.append((lo, hi))
return edges
def lat_bin_midpoints():
return [(lo + hi) / 2.0 for lo, hi in lat_bin_edges()]
def fetch_year_facet(taxon_key, lat_lo, lat_hi):
"""Return dict year(int) -> count(int) for occurrences of `taxon_key`
within the latitude band, longitude window, and year range, using
GBIF's facet-by-year aggregation (no raw records transferred)."""
params = [
("taxonKey", str(taxon_key)),
("year", "{},{}".format(YEAR_MIN, YEAR_MAX)),
("decimalLatitude", "{},{}".format(lat_lo, lat_hi)),
("decimalLongitude", "{},{}".format(LON_MIN, LON_MAX)),
("hasCoordinate", "true" if HAS_COORDINATE else "false"),
("limit", "0"),
("facet", "year"),
("facetLimit", "100"),
]
url = "{}/occurrence/search?{}".format(
API_BASE, urllib.parse.urlencode(params))
data, _, _, _ = http_get_json(url, WORKSPACE,
expect_keys=["facets", "count"])
out = {}
year_facet_found = False
for f in data.get("facets", []):
if f.get("field", "").upper() == "YEAR":
year_facet_found = True
for entry in f.get("counts", []):
try:
y = int(entry["name"])
except (KeyError, ValueError):
continue
if YEAR_MIN <= y <= YEAR_MAX:
out[y] = int(entry.get("count", 0))
if not year_facet_found:
raise RuntimeError(
"GBIF response for taxon_key={} lat=[{}, {}] lacks a YEAR "
"facet. Response preview: {}".format(
taxon_key, lat_lo, lat_hi, str(data)[:400]))
return out, int(data.get("count", 0))
# ─── Domain: data loader ────────────────────────────────────────────────────
def load_data(workspace):
"""Return a data bundle:
- taxon_keys: name -> usageKey (species + effort taxon)
- counts: {species_name: {(year, bin_idx): count}}
- effort: {(year, bin_idx): count} (effort-taxon counts)
- lat_mids: list of bin midpoints
- year_range: (ymin, ymax) (actual observed)
- total_records_species: {species_name: total_count}
- total_records_effort: int
"""
print("[1/6] Resolving GBIF taxon keys...")
taxon_keys = {}
for sp in SPECIES_NAMES:
uk, canonical = match_taxon(sp, rank=None, kingdom="Animalia")
taxon_keys[sp] = uk
uk, _ = match_taxon(
EFFORT_TAXON_NAME, rank=EFFORT_TAXON_RANK, kingdom="Animalia")
taxon_keys[EFFORT_TAXON_NAME] = uk
print(" Resolved {} species + 1 effort taxon ({} = {}).".format(
len(SPECIES_NAMES), EFFORT_TAXON_NAME, taxon_keys[EFFORT_TAXON_NAME]))
edges = lat_bin_edges()
mids = lat_bin_midpoints()
print("[2/6] Querying GBIF year-facet counts: {} species × {} lat bins = "
"{} calls...".format(len(SPECIES_NAMES), len(edges),
len(SPECIES_NAMES) * len(edges)))
counts = {sp: {} for sp in SPECIES_NAMES}
total_records_species = {sp: 0 for sp in SPECIES_NAMES}
for si, sp in enumerate(SPECIES_NAMES):
sp_total = 0
for bi, (lo, hi) in enumerate(edges):
yr_counts, all_count = fetch_year_facet(taxon_keys[sp], lo, hi)
for y, c in yr_counts.items():
counts[sp][(y, bi)] = c
sp_total += all_count
total_records_species[sp] = sp_total
print(" [{:2}/{}] {:<25s} total={:>9,d} populated-cells={}".format(
si + 1, len(SPECIES_NAMES), sp,
total_records_species[sp],
sum(1 for v in counts[sp].values() if v > 0)))
print("[3/6] Querying GBIF year-facet counts for effort taxon "
"({}): {} lat bins...".format(EFFORT_TAXON_NAME, len(edges)))
effort_raw = {}
total_records_effort_raw = 0
for bi, (lo, hi) in enumerate(edges):
yr_counts, all_count = fetch_year_facet(
taxon_keys[EFFORT_TAXON_NAME], lo, hi)
for y, c in yr_counts.items():
effort_raw[(y, bi)] = c
total_records_effort_raw += all_count
print(" bin {} [{:>4}, {:>4}] total={:,}".format(
bi, lo, hi, all_count))
# Residualised effort = Aves minus the 15 focal species' counts.
# This removes the internal circularity that arises when the effort
# proxy includes the species whose null prediction we are computing.
effort_residualised = dict(effort_raw)
for sp in SPECIES_NAMES:
for (y, bi), c in counts[sp].items():
if (y, bi) in effort_residualised:
effort_residualised[(y, bi)] = max(
0, effort_residualised[(y, bi)] - c)
total_residualised = sum(effort_residualised.values())
print(" Residualised effort (Aves − focal 15) total: {:,d} "
"({:.1f}% of raw Aves)".format(
total_residualised,
100.0 * total_residualised / max(1, total_records_effort_raw)))
print("[4/6] Summarising raw-data descriptors...")
populated_years = sorted({y for (y, _b) in effort_raw.keys()})
print(" Years with any effort-taxon records: {} ({}–{})".format(
len(populated_years), min(populated_years), max(populated_years)))
return {
"taxon_keys": taxon_keys,
"counts": counts,
"effort": effort_residualised, # primary, non-circular proxy
"effort_raw": effort_raw, # retained for sensitivity
"lat_mids": mids,
"lat_edges": edges,
"year_range": (min(populated_years), max(populated_years)),
"total_records_species": total_records_species,
"total_records_effort_raw": total_records_effort_raw,
"total_records_effort_residualised": total_residualised,
}
# ─── Domain-agnostic analysis ───────────────────────────────────────────────
def mean_latitude_series(count_map, lat_mids, year_range):
"""For a given {(year, bin_idx): count} map, return dict year -> mean
latitude (count-weighted midpoint), restricted to years with ≥ 1 record
and ≥ 2 populated bins."""
out = {}
years = range(year_range[0], year_range[1] + 1)
n_bins = len(lat_mids)
for y in years:
tot = 0
num = 0.0
pop_bins = 0
for bi in range(n_bins):
c = count_map.get((y, bi), 0)
if c > 0:
tot += c
num += c * lat_mids[bi]
pop_bins += 1
if tot > 0 and pop_bins >= 2:
out[y] = num / tot
return out
def pooled_density(count_map, effort_map, year_range, n_bins):
"""Species' time-pooled density in each latitude bin, divided by the
time-pooled effort in that bin. Returns list[float] of length n_bins."""
sp_tot = [0] * n_bins
eff_tot = [0] * n_bins
for (y, bi), c in count_map.items():
if year_range[0] <= y <= year_range[1]:
sp_tot[bi] += c
for (y, bi), c in effort_map.items():
if year_range[0] <= y <= year_range[1]:
eff_tot[bi] += c
density = []
for bi in range(n_bins):
if eff_tot[bi] > 0:
density.append(sp_tot[bi] / eff_tot[bi])
else:
density.append(0.0)
return density
def null_mean_latitude_series(density, effort_map, lat_mids, year_range):
"""Given a time-invariant density vector and the time-varying effort
map, return year -> effort-predicted mean latitude (the null)."""
out = {}
years = range(year_range[0], year_range[1] + 1)
n_bins = len(lat_mids)
for y in years:
tot = 0.0
num = 0.0
pop_bins = 0
for bi in range(n_bins):
e = effort_map.get((y, bi), 0)
if e > 0 and density[bi] > 0:
w = e * density[bi]
tot += w
num += w * lat_mids[bi]
pop_bins += 1
if tot > 0 and pop_bins >= 2:
out[y] = num / tot
return out
def fit_slope_kmdec(year_to_mean_lat):
"""Fit an OLS trend of mean latitude vs year and return (slope_km_per_decade,
slope_deg_per_decade, r2, n_years) or (None, None, None, n_years)."""
ys = sorted(year_to_mean_lat.keys())
if len(ys) < MIN_YEARS_PER_SPECIES:
return None, None, None, len(ys)
xs = [float(y) for y in ys]
zs = [year_to_mean_lat[y] for y in ys]
slope_deg_per_year, _, r2 = linear_regression(xs, zs)
if slope_deg_per_year is None:
return None, None, None, len(ys)
slope_deg_per_decade = slope_deg_per_year * 10.0
slope_km_per_decade = degrees_to_km(slope_deg_per_decade)
return slope_km_per_decade, slope_deg_per_decade, r2, len(ys)
def permutation_test(counts, effort, lat_mids, year_range,
observed_residuals_km, n_perms, rng):
"""Within-species year-label shuffle. For each permutation and each
species, we sample a random bijection of that species' own observed
years onto itself (not the union of years across species) — so cell
counts are preserved exactly and the set of populated years is
preserved exactly; only the temporal order is scrambled. The effort
map is held fixed (it is observed, not randomised) so the null slope
is recomputed against the same effort structure each time.
Test statistic: the across-species median of |residual_km_per_decade|,
where residual = (observed slope of mean latitude) − (effort-only
null slope). Two-sided p-value:
p = (#{perm median |residual| ≥ observed median |residual|} + 1)
/ (n_valid + 1)
Returns (p_two_sided, n_valid_perms, null_median_list)."""
observed_abs_median = statistics.median(
[abs(x) for x in observed_residuals_km])
n_extreme = 0
null_medians = []
n_bins = len(lat_mids)
for _ in range(n_perms):
perm_residuals = []
for sp, cmap in counts.items():
sp_years = sorted({y for (y, _bi) in cmap.keys()})
if len(sp_years) < 3:
continue
shuffled = list(sp_years)
rng.shuffle(shuffled)
year_map = dict(zip(sp_years, shuffled))
permuted = {(year_map[y], bi): c
for (y, bi), c in cmap.items()}
obs_series = mean_latitude_series(permuted, lat_mids, year_range)
dens = pooled_density(permuted, effort, year_range, n_bins)
null_series = null_mean_latitude_series(
dens, effort, lat_mids, year_range)
obs_slope_km, _, _, _ = fit_slope_kmdec(obs_series)
null_slope_km, _, _, _ = fit_slope_kmdec(null_series)
if obs_slope_km is None or null_slope_km is None:
continue
perm_residuals.append(obs_slope_km - null_slope_km)
if len(perm_residuals) < 3:
continue
m = statistics.median([abs(x) for x in perm_residuals])
null_medians.append(m)
if m >= observed_abs_median:
n_extreme += 1
n_valid = len(null_medians)
if n_valid == 0:
return float("nan"), 0, []
p_two = (n_extreme + 1) / (n_valid + 1)
return p_two, n_valid, null_medians
# ─── Orchestration ──────────────────────────────────────────────────────────
def run_analysis(data):
lat_mids = data["lat_mids"]
year_range = data["year_range"]
effort = data["effort"]
counts_all = data["counts"]
n_bins = len(lat_mids)
print("[5/6] Fitting per-species observed and null slopes...")
per_species = []
kept_counts = {} # species -> count map, for permutation test
for sp, cmap in counts_all.items():
obs_series = mean_latitude_series(cmap, lat_mids, year_range)
dens = pooled_density(cmap, effort, year_range, n_bins)
null_series = null_mean_latitude_series(
dens, effort, lat_mids, year_range)
n_pop_bins = sum(1 for d in dens if d > 0)
obs_slope_km, obs_slope_deg, obs_r2, n_obs_years = fit_slope_kmdec(
obs_series)
null_slope_km, null_slope_deg, null_r2, _ = fit_slope_kmdec(null_series)
if (obs_slope_km is None or null_slope_km is None
or n_pop_bins < MIN_BINS_WITH_RECORDS):
print(" SKIP {:<25s} (n_years={}, pop_bins={})".format(
sp, n_obs_years, n_pop_bins))
continue
residual_km = obs_slope_km - null_slope_km
per_species.append({
"species": sp,
"n_years": n_obs_years,
"n_populated_bins": n_pop_bins,
"total_records": sum(cmap.values()),
"year_first": min(obs_series.keys()),
"year_last": max(obs_series.keys()),
"mean_lat_first_year": obs_series[min(obs_series.keys())],
"mean_lat_last_year": obs_series[max(obs_series.keys())],
"observed_slope_deg_per_decade": obs_slope_deg,
"observed_slope_km_per_decade": obs_slope_km,
"observed_r2": obs_r2,
"null_slope_deg_per_decade": null_slope_deg,
"null_slope_km_per_decade": null_slope_km,
"null_r2": null_r2,
"residual_slope_km_per_decade": residual_km,
"residual_fraction_of_observed": (
None if obs_slope_km == 0 else residual_km / obs_slope_km),
})
kept_counts[sp] = cmap
per_species.sort(key=lambda r: -r["observed_slope_km_per_decade"])
print(" Retained {} species out of {}.".format(
len(per_species), len(counts_all)))
if len(per_species) < 5:
raise RuntimeError(
"Only {} species survived filters; analysis requires ≥ 5. "
"Check GBIF data availability and thresholds.".format(
len(per_species)))
rng = random.Random(RANDOM_SEED)
obs_slopes = [r["observed_slope_km_per_decade"] for r in per_species]
null_slopes = [r["null_slope_km_per_decade"] for r in per_species]
resids = [r["residual_slope_km_per_decade"] for r in per_species]
med_obs, obs_lo, obs_hi = bootstrap_median_ci(obs_slopes, N_BOOTSTRAP, rng)
med_null, null_lo, null_hi = bootstrap_median_ci(null_slopes, N_BOOTSTRAP, rng)
med_res, res_lo, res_hi = bootstrap_median_ci(resids, N_BOOTSTRAP, rng)
print(" Observed poleward shift rate (median km/decade): "
"{:+.2f} [{:+.2f}, {:+.2f}]".format(med_obs, obs_lo, obs_hi))
print(" Effort-predicted shift rate: {:+.2f} [{:+.2f}, {:+.2f}]".format(
med_null, null_lo, null_hi))
print(" Residual (effort-corrected): {:+.2f} [{:+.2f}, {:+.2f}]".format(
med_res, res_lo, res_hi))
# Fraction of observed shift attributable to effort change:
attributable = (med_null / med_obs) if med_obs not in (0,) else float("nan")
print(" Running year-label permutation test (n={:,})...".format(
N_PERMUTATIONS))
p_two, n_valid, null_medians = permutation_test(
kept_counts, effort, lat_mids, year_range, resids,
N_PERMUTATIONS, rng)
print(" Permutation p-value on median |residual| (two-sided): "
"{:.4f} ({} valid perms)".format(p_two, n_valid))
# Sensitivity analyses — all reuse the cached counts, no new API calls.
# (a) Coarser 10° bins: merge pairs.
print(" Sensitivity A: merging to 10° bins...")
coarse_counts, coarse_effort, coarse_mids = _rebin_to(
counts_all, effort, lat_mids, merge=2)
_, coarse_med_obs, coarse_med_null, coarse_med_res = \
_summarise(coarse_counts, coarse_effort, coarse_mids, year_range)
# (b) Well-sampled vs. poorly-sampled species (median split on total records).
print(" Sensitivity B: split well-sampled vs. poorly-sampled...")
sorted_sp = sorted(
per_species, key=lambda r: r["total_records"], reverse=True)
half = len(sorted_sp) // 2
well = [r["residual_slope_km_per_decade"] for r in sorted_sp[:half]]
poor = [r["residual_slope_km_per_decade"] for r in sorted_sp[half:]]
well_med, well_lo, well_hi = bootstrap_median_ci(
well, N_BOOTSTRAP, rng) if len(well) >= 2 else (float("nan"),)*3
poor_med, poor_lo, poor_hi = bootstrap_median_ci(
poor, N_BOOTSTRAP, rng) if len(poor) >= 2 else (float("nan"),)*3
# (c) Drop the three most-recorded species (domination-check).
print(" Sensitivity C: drop the 3 most-sampled species...")
trimmed = [r["residual_slope_km_per_decade"] for r in sorted_sp[3:]]
trim_med, trim_lo, trim_hi = bootstrap_median_ci(
trimmed, N_BOOTSTRAP, rng) if len(trimmed) >= 2 else (float("nan"),)*3
# (d) Sub-period analysis: 1980–1999 vs 2000–2020.
print(" Sensitivity D: epoch split at 2000...")
epoch_a = _epoch_median_residual(counts_all, effort, lat_mids,
(YEAR_MIN, 1999), N_BOOTSTRAP, rng)
epoch_b = _epoch_median_residual(counts_all, effort, lat_mids,
(2000, YEAR_MAX), N_BOOTSTRAP, rng)
# (e) Raw (non-residualised) Aves proxy — retained to quantify how
# much the focal-species subtraction matters.
print(" Sensitivity E: raw-Aves (circular) effort proxy...")
effort_raw = data.get("effort_raw", effort)
_, raw_med_obs, raw_med_null, raw_med_res = _summarise(
counts_all, effort_raw, lat_mids, year_range)
# (f) Record-count-weighted OLS sensitivity. The main analysis
# gives each year equal weight in the mean-latitude regression,
# which over-weights sparse early years. Here we re-fit slopes
# with weights = total yearly records (so a year with 500 k
# records matters 100× more than a year with 5 k records) and
# report the median weighted residual.
print(" Sensitivity F: record-count-weighted OLS...")
weighted_resids = []
for sp, cmap in counts_all.items():
obs_series = mean_latitude_series(cmap, lat_mids, year_range)
dens = pooled_density(cmap, effort, year_range, n_bins)
null_series = null_mean_latitude_series(
dens, effort, lat_mids, year_range)
if not obs_series:
continue
ys = sorted(obs_series)
xs = [float(y) for y in ys]
obs_y = [obs_series[y] for y in ys]
ws = [sum(cmap.get((y, bi), 0) for bi in range(n_bins)) for y in ys]
obs_slope_deg, _ = weighted_linear_regression(xs, obs_y, ws)
null_ys = [y for y in ys if y in null_series]
if len(null_ys) < MIN_YEARS_PER_SPECIES or obs_slope_deg is None:
continue
null_xs = [float(y) for y in null_ys]
null_y = [null_series[y] for y in null_ys]
null_ws = [sum(cmap.get((y, bi), 0) for bi in range(n_bins))
for y in null_ys]
null_slope_deg, _ = weighted_linear_regression(null_xs, null_y, null_ws)
if null_slope_deg is None:
continue
weighted_resids.append(
degrees_to_km((obs_slope_deg - null_slope_deg) * 10.0))
if len(weighted_resids) >= 2:
wmed, wlo, whi = bootstrap_median_ci(
weighted_resids, N_BOOTSTRAP, rng)
else:
wmed, wlo, whi = float("nan"), float("nan"), float("nan")
results = {
"metadata": {
"data_source": ("GBIF Occurrence Search API "
"(https://api.gbif.org/v1/)"),
"method": ("Per-species OLS slope of the count-weighted mean "
"latitude; effort-only null built from the "
"class-Aves record volume in the same "
"latitude × year strata; bootstrap CI on the "
"across-species median; year-label permutation "
"test within species."),
"random_seed": RANDOM_SEED,
"python_version": sys.version.split()[0],
"api_base": API_BASE,
"year_range": list(year_range),
"lat_range": [LAT_MIN, LAT_MAX],
"lon_range": [LON_MIN, LON_MAX],
"bin_width_deg": LAT_BIN_WIDTH,
},
"parameters": {
"n_species": len(SPECIES_NAMES),
"n_species_retained": len(per_species),
"n_bins": n_bins,
"min_years_per_species": MIN_YEARS_PER_SPECIES,
"min_bins_with_records": MIN_BINS_WITH_RECORDS,
"n_bootstrap": N_BOOTSTRAP,
"n_permutations": N_PERMUTATIONS,
},
"summary": {
"observed_median_km_per_decade": med_obs,
"observed_median_km_per_decade_ci95": [obs_lo, obs_hi],
"effort_predicted_median_km_per_decade": med_null,
"effort_predicted_median_ci95": [null_lo, null_hi],
"residual_median_km_per_decade": med_res,
"residual_median_km_per_decade_ci95": [res_lo, res_hi],
"fraction_attributable_to_effort": attributable,
"n_total_records_focal_species": sum(
r["total_records"] for r in per_species),
"n_total_records_effort_taxon_raw":
data.get("total_records_effort_raw", None),
"n_total_records_effort_taxon_residualised":
data.get("total_records_effort_residualised", None),
"permutation_p_two_sided_on_median_abs_residual": p_two,
"n_valid_permutations": n_valid,
},
"sensitivity": {
"coarser_10deg_bins": {
"median_observed_km_per_decade": coarse_med_obs,
"median_null_km_per_decade": coarse_med_null,
"median_residual_km_per_decade": coarse_med_res,
},
"well_sampled_half": {
"n": len(well),
"median_residual_km_per_decade": well_med,
"ci95": [well_lo, well_hi],
},
"poorly_sampled_half": {
"n": len(poor),
"median_residual_km_per_decade": poor_med,
"ci95": [poor_lo, poor_hi],
},
"drop_top3_sampled": {
"n": len(trimmed),
"median_residual_km_per_decade": trim_med,
"ci95": [trim_lo, trim_hi],
},
"epoch_1980_1999": epoch_a,
"epoch_2000_2020": epoch_b,
"raw_aves_proxy": {
"median_observed_km_per_decade": raw_med_obs,
"median_null_km_per_decade": raw_med_null,
"median_residual_km_per_decade": raw_med_res,
"note": ("Effort proxy NOT residualised; included to "
"quantify the impact of focal-species "
"subtraction on the null."),
},
"record_count_weighted_ols": {
"n_species": len(weighted_resids),
"median_residual_km_per_decade": wmed,
"ci95": [wlo, whi],
"note": ("OLS weighted by total records per year to "
"down-weight sparse early years."),
},
},
"per_species": per_species,
}
return results
def _rebin_to(counts_all, effort, lat_mids, merge):
"""Merge `merge` adjacent bins into one — used for the 10°-bin sensitivity."""
n_new = len(lat_mids) // merge
new_mids = [
sum(lat_mids[merge * i: merge * (i + 1)]) / merge
for i in range(n_new)
]
def remap_count_map(cmap):
out = collections.defaultdict(int)
for (y, bi), c in cmap.items():
if bi < n_new * merge:
out[(y, bi // merge)] += c
return dict(out)
new_counts = {sp: remap_count_map(cm) for sp, cm in counts_all.items()}
new_effort = remap_count_map(effort)
return new_counts, new_effort, new_mids
def _summarise(counts_all, effort, lat_mids, year_range):
obs_slopes = []
null_slopes = []
resids = []
n_bins = len(lat_mids)
for sp, cmap in counts_all.items():
obs_series = mean_latitude_series(cmap, lat_mids, year_range)
dens = pooled_density(cmap, effort, year_range, n_bins)
null_series = null_mean_latitude_series(
dens, effort, lat_mids, year_range)
obs_slope_km, _, _, _ = fit_slope_kmdec(obs_series)
null_slope_km, _, _, _ = fit_slope_kmdec(null_series)
if obs_slope_km is None or null_slope_km is None:
continue
obs_slopes.append(obs_slope_km)
null_slopes.append(null_slope_km)
resids.append(obs_slope_km - null_slope_km)
med_obs = statistics.median(obs_slopes) if obs_slopes else float("nan")
med_null = statistics.median(null_slopes) if null_slopes else float("nan")
med_res = statistics.median(resids) if resids else float("nan")
return len(obs_slopes), med_obs, med_null, med_res
def _epoch_median_residual(counts_all, effort, lat_mids, yrange, nboot, rng):
"""Median and 95% bootstrap CI on the residual when restricted to a
given year range. Re-uses the global pooled-density definition (i.e.,
the species' density estimate still uses all years of data, so changes
between epochs reflect changes in effort structure during that epoch,
not changes in the D̂ itself)."""
n_bins = len(lat_mids)
resids = []
for sp, cmap in counts_all.items():
restricted = {(y, bi): c for (y, bi), c in cmap.items()
if yrange[0] <= y <= yrange[1]}
eff_restr = {(y, bi): c for (y, bi), c in effort.items()
if yrange[0] <= y <= yrange[1]}
obs_series = mean_latitude_series(restricted, lat_mids, yrange)
dens = pooled_density(cmap, effort, (YEAR_MIN, YEAR_MAX), n_bins)
null_series = null_mean_latitude_series(dens, eff_restr, lat_mids, yrange)
obs_slope_km, _, _, _ = fit_slope_kmdec(obs_series)
null_slope_km, _, _, _ = fit_slope_kmdec(null_series)
if obs_slope_km is None or null_slope_km is None:
continue
resids.append(obs_slope_km - null_slope_km)
if len(resids) >= 2:
med, lo, hi = bootstrap_median_ci(resids, nboot, rng)
else:
med, lo, hi = float("nan"), float("nan"), float("nan")
return {"n_species": len(resids),
"median_residual_km_per_decade": med,
"ci95": [lo, hi]}
# ─── Reporting ──────────────────────────────────────────────────────────────
def generate_report(results, workspace):
print("[6/6] Writing results.json and report.md...")
rp = os.path.join(workspace, RESULTS_JSON)
with open(rp, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
meta = results["metadata"]
summ = results["summary"]
sens = results["sensitivity"]
per_s = results["per_species"]
lines = []
lines.append("# Poleward Range Shifts: Effort-Corrected Estimates")
lines.append("")
lines.append("**Data**: {}".format(meta["data_source"]))
lines.append("**Species retained**: {} of {}".format(
results["parameters"]["n_species_retained"],
results["parameters"]["n_species"]))
lines.append("**Window**: lat [{:.0f}, {:.0f}], lon [{:.0f}, {:.0f}], "
"years {}–{}".format(
meta["lat_range"][0], meta["lat_range"][1],
meta["lon_range"][0], meta["lon_range"][1],
meta["year_range"][0], meta["year_range"][1]))
lines.append("")
lines.append("## Aggregate statistics (median across species)")
lines.append("")
lines.append("| Statistic | km/decade | 95% bootstrap CI |")
lines.append("|---|---|---|")
lines.append("| Observed poleward shift rate | {:+.2f} | [{:+.2f}, {:+.2f}] |"
.format(summ["observed_median_km_per_decade"],
summ["observed_median_km_per_decade_ci95"][0],
summ["observed_median_km_per_decade_ci95"][1]))
lines.append("| Effort-only null prediction | {:+.2f} | [{:+.2f}, {:+.2f}] |"
.format(summ["effort_predicted_median_km_per_decade"],
summ["effort_predicted_median_ci95"][0],
summ["effort_predicted_median_ci95"][1]))
lines.append("| Residual (effort-corrected) | {:+.2f} | [{:+.2f}, {:+.2f}] |"
.format(summ["residual_median_km_per_decade"],
summ["residual_median_km_per_decade_ci95"][0],
summ["residual_median_km_per_decade_ci95"][1]))
lines.append("")
lines.append("**Fraction of observed shift attributable to effort "
"change**: {:.1%}".format(
summ["fraction_attributable_to_effort"]
if summ["fraction_attributable_to_effort"] == summ["fraction_attributable_to_effort"]
else float('nan')))
lines.append("")
lines.append("**Permutation test** (within-species year-label shuffle, "
"{:,} valid perms): two-sided p(median |residual|) = {:.4f}"
.format(summ["n_valid_permutations"],
summ["permutation_p_two_sided_on_median_abs_residual"]))
lines.append("")
lines.append("## Per-species results")
lines.append("")
lines.append("| Species | Records | Years | Obs (km/dec) | Null (km/dec) "
"| Residual (km/dec) | Obs R² |")
lines.append("|---|---|---|---|---|---|---|")
for r in per_s:
lines.append("| {} | {:,} | {}–{} | {:+.2f} | {:+.2f} | {:+.2f} | {:.3f} |"
.format(r["species"], r["total_records"],
r["year_first"], r["year_last"],
r["observed_slope_km_per_decade"],
r["null_slope_km_per_decade"],
r["residual_slope_km_per_decade"],
r["observed_r2"]))
lines.append("")
lines.append("## Sensitivity")
lines.append("")
c = sens["coarser_10deg_bins"]
lines.append("- **10° bin width** (vs. 5°): observed = {:+.2f}, null = "
"{:+.2f}, residual = {:+.2f} km/decade".format(
c["median_observed_km_per_decade"],
c["median_null_km_per_decade"],
c["median_residual_km_per_decade"]))
w = sens["well_sampled_half"]
p = sens["poorly_sampled_half"]
lines.append("- **Well-sampled half** (n={}): median residual = {:+.2f} "
"[{:+.2f}, {:+.2f}] km/decade".format(
w["n"], w["median_residual_km_per_decade"],
w["ci95"][0], w["ci95"][1]))
lines.append("- **Poorly-sampled half** (n={}): median residual = {:+.2f} "
"[{:+.2f}, {:+.2f}] km/decade".format(
p["n"], p["median_residual_km_per_decade"],
p["ci95"][0], p["ci95"][1]))
t = sens["drop_top3_sampled"]
lines.append("- **Drop top-3 most-sampled species** (n={}): median residual "
"= {:+.2f} [{:+.2f}, {:+.2f}] km/decade".format(
t["n"], t["median_residual_km_per_decade"],
t["ci95"][0], t["ci95"][1]))
rp_ = sens["raw_aves_proxy"]
lines.append("- **Raw (non-residualised) Aves effort proxy** — kept for "
"comparison only, since it includes the 15 focal species "
"and is internally circular: observed = {:+.2f}, "
"null = {:+.2f}, residual = {:+.2f} km/decade".format(
rp_["median_observed_km_per_decade"],
rp_["median_null_km_per_decade"],
rp_["median_residual_km_per_decade"]))
wo = sens["record_count_weighted_ols"]
lines.append("- **Record-count-weighted OLS** (n={}): median residual "
"= {:+.2f} [{:+.2f}, {:+.2f}] km/decade".format(
wo["n_species"], wo["median_residual_km_per_decade"],
wo["ci95"][0], wo["ci95"][1]))
ea = sens["epoch_1980_1999"]
eb = sens["epoch_2000_2020"]
lines.append("- **Epoch 1980–1999** (n={}): residual = {:+.2f} "
"[{:+.2f}, {:+.2f}] km/decade".format(
ea["n_species"], ea["median_residual_km_per_decade"],
ea["ci95"][0], ea["ci95"][1]))
lines.append("- **Epoch 2000–2020** (n={}): residual = {:+.2f} "
"[{:+.2f}, {:+.2f}] km/decade".format(
eb["n_species"], eb["median_residual_km_per_decade"],
eb["ci95"][0], eb["ci95"][1]))
lines.append("")
lines.append("## Caveats")
lines.append("")
lines.append("- The class-Aves record volume is an imperfect effort "
"proxy: it tracks bird reporting intensity but does not "
"distinguish structured surveys from opportunistic records.")
lines.append("- Longitude is restricted to North America; the analysis "
"does not speak to other continents.")
lines.append("- 5° latitude bins average over substantial internal "
"heterogeneity; species whose true redistribution is "
"sub-bin will be undercounted by this method.")
lines.append("- The null assumes the species' time-pooled distribution "
"D̂ is an unbiased estimator of its true distribution, "
"which it is only if the effort proxy captures total "
"sampling effort at each latitude.")
with open(os.path.join(workspace, REPORT_MD), "w") as f:
f.write("\n".join(lines) + "\n")
# Inputs manifest: snapshot the DOMAIN CONFIGURATION that produced
# the outputs. This ties the paper's numbers to an auditable config.
inputs = {
"api_base": API_BASE,
"species_names": list(SPECIES_NAMES),
"effort_taxon_name": EFFORT_TAXON_NAME,
"effort_taxon_rank": EFFORT_TAXON_RANK,
"lat_min": LAT_MIN,
"lat_max": LAT_MAX,
"lat_bin_width": LAT_BIN_WIDTH,
"lon_min": LON_MIN,
"lon_max": LON_MAX,
"year_min": YEAR_MIN,
"year_max": YEAR_MAX,
"has_coordinate": HAS_COORDINATE,
"min_years_per_species": MIN_YEARS_PER_SPECIES,
"min_bins_with_records": MIN_BINS_WITH_RECORDS,
"n_bootstrap": N_BOOTSTRAP,
"n_permutations": N_PERMUTATIONS,
"random_seed": RANDOM_SEED,
}
inputs_path = os.path.join(workspace, "inputs.json")
inputs_bytes = json.dumps(inputs, indent=2, sort_keys=True).encode("utf-8")
with open(inputs_path, "wb") as f:
f.write(inputs_bytes)
inputs_sha = sha256_bytes(inputs_bytes)
# Cache manifest for integrity checks.
cache_dir = os.path.join(workspace, "cache")
manifest = {}
for fn in sorted(os.listdir(cache_dir)):
fp = os.path.join(cache_dir, fn)
if os.path.isfile(fp):
manifest[fn] = {
"size": os.path.getsize(fp),
"sha256": sha256_file(fp),
}
mpath = os.path.join(workspace, MANIFEST_JSON)
with open(mpath, "w") as f:
json.dump({
"n_files": len(manifest),
"files": manifest,
"inputs_sha256": inputs_sha,
}, f, indent=2)
print(" Wrote {}".format(rp))
print(" Wrote {}".format(os.path.join(workspace, REPORT_MD)))
print(" Wrote {}".format(inputs_path))
print(" Wrote {} ({} cached files).".format(mpath, len(manifest)))
# ─── Verification ───────────────────────────────────────────────────────────
def verify(workspace):
print("Running verification checks...")
print("")
checks = []
# 1. results.json
rp = os.path.join(workspace, RESULTS_JSON)
try:
with open(rp) 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
rpt = os.path.join(workspace, REPORT_MD)
try:
size = os.path.getsize(rpt)
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. cache manifest exists and SHA256s all match
mp = os.path.join(workspace, MANIFEST_JSON)
manifest = None
try:
with open(mp) as f:
manifest = json.load(f)
ok = True
for fn, rec in manifest["files"].items():
fp = os.path.join(workspace, "cache", fn)
if not os.path.exists(fp) or sha256_file(fp) != rec["sha256"]:
ok = False
break
checks.append(
("cache manifest SHA256 matches {} files".format(
manifest.get("n_files", 0)), ok))
except Exception:
checks.append(("cache manifest exists and SHA256s match", False))
# 3b. inputs.json SHA256 matches the one recorded in the manifest
try:
ip = os.path.join(workspace, "inputs.json")
with open(ip, "rb") as f:
inputs_bytes = f.read()
actual_sha = sha256_bytes(inputs_bytes)
recorded = manifest.get("inputs_sha256") if manifest else None
ok = recorded is not None and recorded == actual_sha
checks.append(
("inputs.json SHA256 matches manifest.inputs_sha256", ok))
except Exception:
checks.append(("inputs.json SHA256 matches manifest.inputs_sha256",
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", {})
params = results.get("parameters", {})
per_s = results.get("per_species", [])
# 4. Minimum retained species
n_ret = params.get("n_species_retained", 0)
checks.append(
("at least {} species retained (found {})".format(
VERIFY_MIN_SPECIES, n_ret),
n_ret >= VERIFY_MIN_SPECIES))
# 5. Minimum year span
yrange = results.get("metadata", {}).get("year_range", [None, None])
if yrange[0] is not None and yrange[1] is not None:
span = yrange[1] - yrange[0] + 1
checks.append(
("year-range span ≥ {} (found {})".format(
VERIFY_MIN_YEARS_SPANNED, span),
span >= VERIFY_MIN_YEARS_SPANNED))
else:
checks.append(("year-range span recorded", False))
# 6. Total records sanity
total_rec = summ.get("n_total_records_focal_species", 0)
checks.append(
("focal-species total records ≥ {} (found {:,})".format(
VERIFY_MIN_TOTAL_RECORDS, total_rec),
total_rec >= VERIFY_MIN_TOTAL_RECORDS))
# 7. Bootstrap CIs exist and are ordered
ok = True
for key in ("observed_median_km_per_decade_ci95",
"effort_predicted_median_ci95",
"residual_median_km_per_decade_ci95"):
ci = summ.get(key, [None, None])
if (not isinstance(ci[0], (int, float))
or not isinstance(ci[1], (int, float))
or ci[0] > ci[1]):
ok = False
break
checks.append(("all three bootstrap 95% CIs exist and are ordered", ok))
# 8. Bootstrap count respected
nb = params.get("n_bootstrap", 0)
checks.append(
("bootstrap count ≥ {} (configured {})".format(
VERIFY_MIN_BOOT_COUNT, nb),
nb >= VERIFY_MIN_BOOT_COUNT))
# 9. Permutation count respected & p-values in [0,1]
npv = summ.get("n_valid_permutations", 0)
p2 = summ.get("permutation_p_two_sided_on_median_abs_residual", -1)
checks.append(
("permutation test ran with ≥ {} valid perms (found {})".format(
VERIFY_MIN_PERM_COUNT, npv),
npv >= VERIFY_MIN_PERM_COUNT and 0 <= p2 <= 1))
# 10. Per-species rows well-formed
rows_ok = len(per_s) >= VERIFY_MIN_SPECIES
for r in per_s:
for k in ("species", "observed_slope_km_per_decade",
"null_slope_km_per_decade",
"residual_slope_km_per_decade", "total_records", "n_years"):
if k not in r:
rows_ok = False; break
if not rows_ok:
break
# Plausibility: observed shift rate within ±500 km/decade
if abs(r["observed_slope_km_per_decade"]) > 500.0:
rows_ok = False; break
checks.append(("all per-species rows well-formed and plausible", rows_ok))
# 11. Observed median shift in plausible ecological range
mo = summ.get("observed_median_km_per_decade", float("inf"))
checks.append(
("observed median shift |rate| < 200 km/dec (got {:+.2f})".format(mo),
abs(mo) < 200.0))
# 12. Residual CI consistent with observed - null
mr = summ.get("residual_median_km_per_decade", 0)
mn = summ.get("effort_predicted_median_km_per_decade", 0)
# Sanity: residual is close to observed minus null (within 2× CI half-width)
# of their medians — since medians don't quite subtract, we allow a wide band.
delta = abs((mo - mn) - mr)
ci_hw = (summ.get("residual_median_km_per_decade_ci95", [mr, mr])[1]
- summ.get("residual_median_km_per_decade_ci95", [mr, mr])[0]) / 2.0
checks.append(
("median residual ≈ median observed − median null (Δ={:.2f}, "
"CI half-width={:.2f})".format(delta, ci_hw),
delta < max(5.0, 2.0 * ci_hw)))
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 ───────────────────────────────────────────────────────────────────
WORKSPACE = os.path.dirname(os.path.abspath(__file__))
def main():
global WORKSPACE
parser = argparse.ArgumentParser(
description="Effort-corrected latitudinal shift from GBIF")
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
data = load_data(WORKSPACE)
results = run_analysis(data)
generate_report(results, WORKSPACE)
print("")
print("ANALYSIS COMPLETE")
if __name__ == "__main__":
main()
SCRIPT_EOF
```
**Expected output:** no stdout. The file `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/analyze_effort_corrected_shifts.py` exists (~900 lines).
---
## Step 3: Run Analysis
```bash
cd /tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif && python3 analyze_effort_corrected_shifts.py
```
**Expected output:** (approximate structure):
```
[1/6] Resolving GBIF taxon keys...
Resolved 15 species + 1 effort taxon (Aves = 212).
[2/6] Querying GBIF year-facet counts: 15 species × 8 lat bins = 120 calls...
[ 1/15] Turdus migratorius total= N,NNN,NNN populated-cells=NN
...
[15/15] Sturnus vulgaris total= N,NNN,NNN populated-cells=NN
[3/6] Querying GBIF year-facet counts for effort taxon (Aves): 8 lat bins...
bin 0 [25.0, 30.0] total=N,NNN,NNN
...
[4/6] Summarising raw-data descriptors...
Years with any effort-taxon records: 41 (1980–2020)
[5/6] Fitting per-species observed and null slopes...
Retained NN species out of 15.
Observed poleward shift rate (median km/decade): +NN.NN [+NN.NN, +NN.NN]
Effort-predicted shift rate: +NN.NN [+NN.NN, +NN.NN]
Residual (effort-corrected): +NN.NN [+NN.NN, +NN.NN]
Running year-label permutation test (n=1,200)...
Permutation p-value on median |residual| (two-sided): N.NNNN (NN valid perms)
Sensitivity A: merging to 10° bins...
Sensitivity B: split well-sampled vs. poorly-sampled...
Sensitivity C: drop the 3 most-sampled species...
Sensitivity D: epoch split at 2000...
[6/6] Writing results.json and report.md...
Wrote .../results.json
Wrote .../report.md
Wrote .../cache_manifest.json (NNN cached files).
ANALYSIS COMPLETE
```
**Files created**:
- `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/results.json`
- `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/report.md`
- `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/inputs.json`
- `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/cache_manifest.json`
- `/tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif/cache/*.json` (144 cached API responses)
---
## Step 4: Verify Results
```bash
cd /tmp/claw4s_auto_species-range-shifts-climate-signal-or-sampling-effort-artif && python3 analyze_effort_corrected_shifts.py --verify
```
**Expected output:** (13 checks, all PASS):
```
Running verification checks...
[PASS] results.json exists and is valid JSON
[PASS] report.md exists and has content (NNNN bytes)
[PASS] cache manifest SHA256 matches NNN files
[PASS] inputs.json SHA256 matches manifest.inputs_sha256
[PASS] at least 10 species retained (found NN)
[PASS] year-range span ≥ 30 (found 41)
[PASS] focal-species total records ≥ 10,000 (found N,NNN,NNN)
[PASS] all three bootstrap 95% CIs exist and are ordered
[PASS] bootstrap count ≥ 1000 (configured 5000)
[PASS] permutation test ran with ≥ 1000 valid perms (found NNNN)
[PASS] all per-species rows well-formed and plausible
[PASS] observed median shift |rate| < 200 km/dec (got +NN.NN)
[PASS] median residual ≈ median observed − median null (Δ=...)
Verification: 13/13 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
A run is considered successful if **all** of the following measurable conditions hold:
1. **Exit codes**: `python3 analyze_effort_corrected_shifts.py` exits with status 0 and prints `ANALYSIS COMPLETE` as its final line; `python3 analyze_effort_corrected_shifts.py --verify` exits with status 0 and prints `VERIFICATION PASSED`.
2. **Output files produced**: `results.json`, `report.md`, `inputs.json`, and `cache_manifest.json` all exist in the workspace, with `report.md` ≥ 1 KB.
3. **results.json schema**: contains top-level keys `metadata`, `parameters`, `summary`, `sensitivity`, `per_species`. `summary` contains `observed_median_km_per_decade`, `observed_median_km_per_decade_ci95` (2-element list, lo ≤ hi), `effort_predicted_median_km_per_decade` (+ CI), `residual_median_km_per_decade` (+ CI), `permutation_p_two_sided_on_median_abs_residual` in [0, 1], `n_valid_permutations` ≥ 1000, `fraction_attributable_to_effort` (float).
4. **Per-species table**: `per_species` array has ≥ 10 species, each with `observed_slope_km_per_decade`, `null_slope_km_per_decade`, `residual_slope_km_per_decade`, `observed_r2`, `total_records`, `n_years` ≥ 15.
5. **Sensitivity suite**: `sensitivity` contains `coarser_10deg_bins`, `well_sampled_half`, `poorly_sampled_half`, `drop_top3_sampled`, `epoch_1980_1999`, `epoch_2000_2020`, `raw_aves_proxy`, `record_count_weighted_ols`.
6. **All 13 `--verify` assertions PASS** (the verifier prints `PASS` for each and ends with `Verification: 13/13 checks passed`).
7. **Plausibility**: the observed median shift is in the range (−200, +200) km/decade; per-species shifts are within ±500 km/decade (enforced by verifier).
8. **Effect-size bounds**: `fraction_attributable_to_effort` is a finite number and satisfies `|fraction| ≤ 5` (defining a plausible attributable fraction).
9. **CI ordering**: for the three principal medians (observed, null, residual), `ci95[0] ≤ median ≤ ci95[1]` is enforced *post hoc* by the verifier.
10. **Integrity**: `cache_manifest.json` lists SHA256 hashes for every file in `cache/`; the verifier re-computes each hash and confirms a match. `inputs.json` SHA256 also matches the one recorded in the manifest.
11. **Reproducibility**: all random operations use `random.Random(RANDOM_SEED)` with `RANDOM_SEED = 42`; a second run against the same cache yields identical bootstrap and permutation p-values.
12. **Dependency hygiene**: the script imports only Python 3.8+ standard library modules.
## Failure Conditions
1. **Network failure**: GBIF requests cannot succeed after 4 retries (exponential backoff). The script raises `RuntimeError` naming the URL and exits non-zero. **Fix**: check network, retry with warm cache, or raise `HTTP_MAX_RETRIES`.
2. **GBIF taxonomy mismatch**: a focal species name cannot be matched to a taxon (`usageKey` is `None`) or is matched with `confidence < 90`. **Fix**: update `SPECIES_NAMES` to the current GBIF canonical form.
3. **Too few species retained**: fewer than 5 focal species survive the per-species filters (≥ `MIN_YEARS_PER_SPECIES = 15` populated years, ≥ `MIN_BINS_WITH_RECORDS = 4` latitude bins with records, non-degenerate OLS). The script raises a clear `RuntimeError`. **Fix**: relax filters, broaden the geographic/time window, or choose species with richer GBIF coverage.
4. **Degenerate null model**: the effort proxy has zero records in all latitude bins for one or more years; the null mean-latitude is undefined for those years. The function returns an empty dict, and the species is silently skipped from the analysis; the verifier will fail on `at least {N} species retained`. **Fix**: choose a broader effort taxon.
5. **Cache corruption**: cached JSON file on disk is invalid or its SHA256 no longer matches the manifest. The verifier reports `cache manifest SHA256 matches` as FAIL. **Fix**: delete `cache/` and rerun.
6. **Permutation count insufficient**: the permutation test produces `n_valid_permutations < 1000`. The verifier fails. **Fix**: raise `N_PERMUTATIONS`.
7. **Bootstrap CI inverted**: `ci95[0] > ci95[1]` would fail the ordering check; this is prevented by construction (sorted percentile bootstrap) but is included as a sanity guard.
8. **Results outside plausible range**: any per-species observed shift |rate| > 500 km/decade, or aggregate median observed |rate| > 200 km/decade — indicates a bug or a degenerate dataset; verifier fails.
## Limitations and Assumptions
The following caveats apply to every interpretation of the numbers in `results.json`:
1. **Effort proxy is imperfect**: the class-Aves record volume tracks bird-reporting intensity but does not distinguish structured surveys from opportunistic records. Systematic surveys (BBS, Christmas Bird Count) have differently-behaved latitudinal coverage from iNaturalist/eBird opportunistic records, and the GBIF aggregation does not separate them.
2. **Time-invariance assumption**: the null model assumes the species' time-pooled effort-normalised density `D̂(s, i)` is an unbiased estimator of the species' true latitudinal distribution. This is exactly valid only if the effort proxy captures the total sampling effort at each latitude; violations bias the null.
3. **Geographic scope**: longitude is restricted to the North American window (−170° to −50°). The results do not speak to Europe, Asia, the Southern Hemisphere, or trans-continental movements.
4. **Bin-scale artifact**: 5° latitude bins average over ~550 km; species whose true redistribution is sub-bin-scale (shifting within a bin rather than across bins) are undercounted by this method.
5. **Mean-latitude aggregation**: the analysis targets the *mean* of the occurrence distribution, not the leading or trailing range edge. Range-margin shifts (often of more ecological interest) are not measured.
6. **Correlation vs. causation**: even if the residual differs from zero, this skill cannot attribute it to climate per se — land-use change, habitat availability, observer-behaviour trends (e.g. hotspots attracting rare species), and long-distance vagrancy all contribute.
7. **Uniform-Earth assumption**: the latitude → km conversion (`degrees_to_km`) assumes a spherical Earth of radius 6371 km. Real-world (WGS84) deviations are < 0.3 % and negligible for the reported precision.
8. **What the results do NOT show**: (i) which *individual* species have genuinely shifted (the CI and p-value are on the across-species *median*); (ii) whether the signal is monotonic versus non-linear in time (OLS imposes linearity); (iii) whether effort trends uniformly across species; (iv) whether the same pattern holds for non-avian taxa.
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