Estimating the number of undiscovered rare plant occurences in Southern Ontario
Bibliographic record
Abstract
We often do not know the total number of extant populations of species of conservation concern.Species distribution models (SDMs) can be used to predict the probability of species' occurrence.We tested the use of validated SDMs to estimate the number of occurrences of rare plant species across Southern Ontario.We built SDMs for six rare species using known occurrence records and then surveyed 282 new sites and used presence/absence records from these sites to predict probability of occurrence based on the SDM output.We summed these probabilities to estimate the number of occurrences on the landscape.We then used simulation exercises to estimate the likelihood that our sample size was large enough to make a confident estimate.Simulation results showed that the true number of extant occurrences can be overestimated with fewer than 1,000 SDM-directed survey sites.Therefore, our estimates may be overestimates of the true number of extant occurrences, and more surveys will be required to obtain more accurate estimates.This technique for estimating the number of remaining rare species occurrences will inform researchers and managers as they prioritize time and money towards decisions around species recovery and protection, and where to allocate additional survey effort.Without Dr. Shaun Coutts this project would not have been possible.Thank you, Dr.Coutts, for your advanced R coding and modelling skills and your willingness to coach a beginner coder such as myself.Dr. Coutts wrote the R code for the simulation of presences, random and informed sampling and occurrences estimates.Thank you to Ben Hilna for additional R code support and troubleshooting, for his patience and willingness to teach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".