Using a novel model approach to assess the distribution and conservation status of the endangered Baird's tapir
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Bibliographic record
Abstract
Abstract Aim We test a new species distribution modelling ( SDM ) framework, while comparing results to more common distribution modelling techniques. This framework allows for the combination of presence‐only ( PO ) and presence‐absence ( PA ) data and accounts for imperfect detection and spatial bias in presence data. The new framework tested here is based on a Poisson point process model, which allows for predictions of population size. We compared these estimates to those provided by experts on the species. Species and Location Presence data on Baird's tapir ( Tapirus bairdii ) throughout its range from southern México to northern Colombia were used in this research, primarily from the years 2000 to 2016. Methods Four SDM frameworks are compared as follows: (1) Maxent, (2) a presence‐only ( PO ) SDM based on a Poisson point process model ( PPM ), (3) a presence‐absence ( PA ) SDM also based on a PPM and (4) an Integrated framework which combines the previous two models. Model averaging was used to produce a single set of coefficient estimates and predictive maps for each model framework. A hotspot analysis (Gi*) was used to identify habitat cores from the predicted intensity of the Integrated model framework. Results Important variables to model the distribution of Baird's tapir included land cover, human pressure and topography. Accounting for spatial bias in the presence data affected which variables were important in the model. Maxent and the Integrated model produced predictive maps with similar patterns and were considered to be more in agreement with expert knowledge compared to the PO and PA models. Main conclusions Total abundance as predicted by the model was higher than expert opinion on the species, but local density estimates from our model were similar to available independent assessments. We suggest that these results warrant further validation and testing through collection of independent test data, development of more precise predictor layers and improvements to the model framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it