Searching for non‐indigenous species: rapidly delimiting the invasion boundary
Bibliographic record
Abstract
Abstract Aim At first detection, little information is typically known about an invader’s characteristics, true arrival date or spatial extent. Yet, before management options such as control or eradication can be considered, we need to know where a nuisance species has already spread. This is particularly difficult because of stochastic processes. Here, we develop an approach that requires little a priori information, yet accurately delimits the range of a biological invader. Location We used a simulated landscape, subjected to stochasticity inherent in establishment and spread, to test novel theory for delimiting locally spreading populations. Methods We distinguish three stages to identify the boundary of an invasion, which we term Approach, Decline, Delimit (ADD). Our ADD algorithm uses general characteristics of the invasion pattern, obtained during a search for occupied sites, in combination with sampling and probability theory to delimit the invasion. We compare ADD against four naïve delimitation strategies, for long and normal dispersal kernels. Results Our results illustrate the potential difficulty in delimiting invasions. Naïve strategies, such as stopping when the invader is absent, typically failed to properly delimit the invasion. In contrast, ADD operated relatively efficiently, and was robust to habitat heterogeneity and knowledge of the true epicentre, but was sensitive to the sparseness of the invasion. For long‐distance dispersal kernels, ADD had 80% accurate delimitations when c. 5% or more of the cells were occupied within the invasion boundary; for normal dispersal kernels, ADD had 95% accurate delimitations when c. 2.5% or more of the cells were occupied. Main conclusions There is virtually no existing theory for delimiting invasions. ADD is efficient and accurate, even with unknown time of invasion, unknown dispersal kernels, stochastic establishment dynamics and spatial heterogeneity, except for very low invasion densities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".