Defining optimal sampling effort for large-scale monitoring of invasive alien plants: a Bayesian method for estimating abundance and distribution
Bibliographic record
Abstract
1. Monitoring the abundance and spatial structure of invasive alien plant populations is important for designing and measuring the efficacy of long-term management strategies. However, methods for monitoring over large areas with minimum sampling effort, but with sufficient accuracy, are lacking. Although sophisticated sampling techniques are available for increasing sampling efficiency, they are often difficult to implement for large-scale monitoring, thus necessitating a robust yet practical method. 2. We explored this problem over a large area (c.20 000 km2), using ad hoc presence–absence records routinely collected over 4 years in Kruger National Park (KNP), South Africa. Using a Bayesian method designed to solve the pseudo-absence (or false-negative) dilemma, we estimated the abundance and spatial structure of all invasive alien plants in KNP. Five sampling schemes, with different spatially weighted sampling efforts, were assessed and the optimal sampling effort estimated. 3. Although most taxa have very few records (50% of the species have only one record), the more abundant species showed a log-normal species-abundance distribution, with the 29 most abundant taxa being represented by an estimated total of 2·22 million individuals, with most exhibiting positive spatial autocorrelation. 4. Estimations from all sampling schemes approached the real situation with increasing sampling effort. An equal-weighted (uniform) sampling scheme performed best for abundance estimation (optimal efforts of 68 records per km2), but showed no advantage in detecting spatial autocorrelation (247 records per km2 required). With increasing sampling effort, the accuracy of abundance estimation followed an exponential form, whereas the accuracy of distribution estimation showed diverse forms. Overall, a power law relationship between taxon density (as well as the spatial autocorrelation) and the optimal sampling effort was determined. 5. Synthesis and applications. The use of Bayesian methods to estimate optimal sampling effort indicates that for large-scale monitoring, reliable and accurate schemes are feasible. These methods can be used to determine optimal schemes in areas of different sizes and situations. In a large area like KNP, the uniform equal-weighted sampling scheme performs optimally for monitoring abundance and distribution of invasive alien plants, and is recommended as a protocol for large-scale monitoring in other protected areas as well.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".