Predicting the distribution of poorly-documented species, Northern black widow (Latrodectus variolus) and Black purse-web spider (Sphodros niger), using museum specimens and citizen science data
Bibliographic record
Abstract
Predicting species distributions requires substantial numbers of georeferenced occurrences and access to remotely sensed climate and land cover data. Reliable estimates of the distribution of most species are unavailable, either because digitized georeferenced distributional data are rare or not digitized. The emergence of online biodiversity information databases and citizen science platforms dramatically improves the amount of information available to establish current and historical distribution of lesser-documented species. We demonstrate how the combination of museum and online citizen science databases can be used to build reliable distribution maps for poorly documented species. To do so, we investigated the distribution and the potential range expansions of two north-eastern North American spider species (Arachnida: Araneae), the Northern black widow (Latrodectus variolus) and the Black purse-web spider (Sphodros niger). Our results provide the first predictions of distribution for these two species. We also found that the Northern black widow has expanded north of its previously known range providing valuable information for public health education. For the Black purse-web spider, we identify potential habitats outside of its currently known range, thus providing a better understanding of the ecology of this poorly-documented species. We demonstrate that increasingly available online biodiversity databases are rapidly expanding biogeography research for conservation, ecology, and in specific cases, epidemiology, of lesser known taxa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".