Rare plant detection methods and their application to the Canadian context
Bibliographic record
Abstract
Rare species can be particularly vulnerable to population decline and extinction due to inbreeding depression, genetic drift, Allee effects, stochastic events, and anthropogenic disturbances. However, information about these species is often limited because they may be difficult to detect in the field. This is especially true for rare plants, whose detection in field surveys is poorly understood. The objective of my thesis was to examine the practical utility of both novel techniques (i.e., community science, remote sensing, environmental DNA) and traditional field survey methods (i.e., transect-based and unstructured search surveys) for detecting rare plants. We found that community science has mainly been used to detect herbs in urban areas, highlighting the conservation value of some urban spaces. Remote sensing has mainly been used to detect trees but also a small number of herbs in forests, suggesting that, given the right survey timing, it may be possible to detect plants via remote sensing in closed environments. Environmental DNA has mainly been used to detect herbs in semi-open habitats, such as shrublands and wetlands, but due to the limited number of eDNA studies, we could not determine the most suitable habitats for this technique. In addition, we empirically demonstrated that drones could detect fading, partially hidden plants, and some plants potentially missed by transect-based surveys. However, our drone survey could not provide exact plant counts and was more expensive and time-consuming. Regarding traditional field survey methods, we found no difference in detectability between transect-based and unstructured search surveys. However, transect-based surveys covered a slightly larger portion of our study area, while unstructured surveys detected slightly more plants per time unit. This suggests that when time is critical, unstructured surveys may be more efficient but when the habitat preferences of the target species are unknown, transect-based surveys may be better to ensure a comprehensive coverage of the study area. By evaluating the efficiency of field survey methods for detecting rare plants, we can develop more effective monitoring strategies and make optimal use of limited time and financial resources. This can better inform conservation efforts to protect rare plants from extinction.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".