November is peak tourist season, not peak biology. 4 of 6 groups change their apparent peak month after effort correction.
November has 10.7× more cross-taxon observations than May — not because November is richer in wildlife, but because it has more eyes. Corrected index: monthly count ÷ total observations that month across all taxa. Produces encounters per unit of observer effort, allowing fair month-to-month comparison. Applied across 6 taxa, 11,180 observations, 2000–2025.
Total cross-taxon observations per month (all 6 taxa combined, 2000–2025, n=11,180). This is the effort proxy used in all normalizations below.
November (2,390 obs) has 10.7× more observations than May (224 obs) — the largest month-to-month ratio in the dataset. January (1,539) is elevated by humpback whale records that are themselves biologically real. The effective gap between Nov and the low-season months (Mar–Jun) ranges from 4× to 10×.
corrected index = observations of species in month ÷ total observations across all groups in that month
Not a population density — a relative signal for fair cross-month comparison. Species with <10 total observations excluded.
| Taxon | Raw peak | Effort-normalized peak | Species analyzed | Species with shifted peak |
|---|---|---|---|---|
|
Insects
Lepidoptera, Odonata + others
|
November | July ↑ flipped | 18 | 13 of 18 |
|
Lagoon wildlife
Crocodile, iguana, black iguana
|
November | May ↑ flipped | 3 | 2 of 3 |
|
Plants
Vascular + marine algae (taxon-level only)
|
November | April ↑ flipped | — | — |
|
Whales & dolphins
Humpback, spotted dolphin, bottlenose
|
January | February ↑ adjusted | 4 | 2 of 4 |
|
Mushrooms
Fungi, bracket fungi, agarics
|
September | September ✓ confirmed | 16 | 4 of 16 |
|
Sea turtles
Olive Ridley, Green, Leatherback
|
April | April ✓ confirmed | 3 | 0 of 3 |
4 of 6 groups changed their apparent peak month after the correction. The whale January→February adjustment is a fine-grained shift within the confirmed winter migration window, not a major bias correction. Plants: individual species' monthly counts are not available in the source data, so only group-level correction was possible.
Each strip shows relative intensity per month. Top row = raw observation counts. Bottom row = corrected index (observations per unit of observer effort). Bar height is scaled to that row's maximum. November bars shrink dramatically after correction for groups heavily influenced by tourism patterns.
Species whose November ranking changed most dramatically. Rank 1 = highest month. A species dropping from rank #1 to rank #8 after normalization was strongly misread by raw counts.
In raw counts, November is the green iguana's top month by a wide margin (33 observations), appearing to make November the peak activity period. After effort normalization, November falls to rank #8. The iguana's true behavioral year is more evenly distributed: warm-season months (May–August) score highest per unit effort, when iguanas are active for breeding and thermoregulation but fewer tourists are observing. The November spike is almost entirely an artifact of elevated human presence at the lagoon edge.
22 of this butterfly's 46 total observations fall in November in the raw record, placing it as a strong November species. After normalization, the November dominance dissolves: it had essentially zero wet-season coverage (0 observations in Jul–Aug) but this reflects that photographers weren't there to observe, not that the butterfly was absent. The normalized window shifts to the Jul–Oct period consistent with known Nymphalidae tropical activity patterns.
The highest observer-effort amplification factor in the insect dataset. In raw counts this is a November butterfly. After normalization by monthly effort, May becomes its peak — a striking result, because May has very few total observations (224 cross-taxon), so even a small count in May signals genuine presence at a time when almost no one is recording. The wet-season and early dry-season window (May–Jun) emerges as the true activity period.
These peaks hold after effort-correction and are cross-checked against published literature. They are real biological signals, not observer artifacts.
The Jan–Feb humpback peak survives normalization and matches the documented winter breeding migration of the Central American–Mexican Pacific population in peer-reviewed literature. The fine shift (raw peak Jan → normalized peak Feb) reflects that January has more observers across all taxa, but both months remain firmly peak. No bias correction needed here.
The September nesting peak for Lepidochelys olivacea is confirmed by literature for the Guerrero coast (Jul–Dec season, peak Sep–Oct) and survives normalization intact. The November secondary spike in raw data is partially effort-driven; September's dominance is real. This validates the source artifact's nesting_season annotation.
The September fruiting peak for coastal Guerrero fungi is confirmed by normalization and is consistent with the wet-season fruiting pattern in tropical dry-forest ecosystems (peak precipitation July–September). After normalization, September becomes even more dominant relative to October–November, confirming the rainy-season origin of the signal.
The American crocodile's January raw peak (25 observations) survives normalization as the highest normalized month. January is also dry-season onset when basking behavior increases — consistent with literature on tropical crocodilian thermoregulation. The Nov spike in raw croc data (14 obs) drops substantially after normalization, confirming it is largely effort-driven.
The most scientifically valuable observations you can contribute are from months that are currently under-sampled. Even a small number of uploads from low-effort months has outsized value for normalizing the record.
The most under-sampled window: combined cross-taxon effort is 4–10× lower than November. If you visit in these months, photograph everything. Insects, fungi, and lagoon animals observed in March–May are worth disproportionately more to the normalized record than the same species in November.
The wet-season mushroom and insect peak. Relatively few visitors are present, but biological activity is high. Fungal fruiting bodies are particularly rich in August–September and almost exclusively recorded by local guides and hardy off-season travelers. Upload with as much habitat context as possible.
Post-dry-season transition: sea surface temperatures are rising, butterfly activity is beginning, and the crocodile is actively basking. Currently nearly invisible in the dataset. Night-active species (bats, moths, nightjars) are essentially unrecorded for this window.
For sea turtles, record nesting activity and track strandings. For plants, photograph in-flower specimens with location data — the algae-heavy plant record needs more terrestrial coverage outside November–January. Consistent documentation at the lagoon edge, even in peak season, helps build the baseline that makes normalization more reliable.
4 of 6 groups shift peak month. The biological year peaks in the wet-season transition (Jul–Oct) and dry-season onset (Jan–Feb for marine species) — not November tourist season.
Insects: raw November peak (1,439 obs) inverts to July–August after correction. "November butterflies" — *Anartia fatima*, ruddy daggerwing — are wet-season species obscured by tourist patterns. Green iguana: raw rank #1 in November drops to #8 after correction (7-rank drop, largest reversal in dataset). Crocodile January peak survives — consistent with dry-season basking. Mushrooms and sea turtles are self-validating: same peak before and after. Humpbacks: January–February peak is real and literature-confirmed; correction shifts the fine peak Jan→Feb, reinforcing February as the more concentrated whale month per unit effort.
iNaturalist + GBIF, 2000–2025, 25–50 km radius. Effort proxy = total monthly observations across all 6 groups. Plants: group-level only (no per-species monthly counts in source data). March–May: low effort AND low biodiversity — correction can over-interpret low-count months. Caution warranted. Script: /scripts/analyze_observer_effort.py → /functions/api/_findings_observer_effort.js.