Distinguishing geographical range shifts from artefacts of detectability and sampling effort
Bibliographic record
Abstract
Abstract Aim The redistribution of species with climate change is well documented. Even so, the relative contribution of species detectability to the variation in measured range shift rates among species is poorly understood. How can true range shifts be discerned from sampling artefacts? Location Australia. Methods We simulate range shifts for species which differ in their abundance for comparison to patterns derived from empirical range shift data from two regional‐scale (100s km) empirical studies. We demonstrate the use of spatial occupancy data in a distance‐to‐edge ( DTE ) model to assess changes in geographical range edges of fish species within a temperate reef fish community. Results Simulations identified how sampling design can produce relatively larger error in range shift estimates in less abundant species, patterns that correspond with those observed in real data. Application of the DTE model allowed us to estimate the location of the true range edge with high accuracy in common species. In addition, upper confidence bounds for range edge estimates identified species with range edges that have likely shifted in location. Conclusions Simulation and modelling approaches used to quantify the level of confidence that can be placed in observed range shifts are particularly valuable for studies of marine species, where observations are typically few and patchy. Given the observed variability in range shift estimates, the inclusion of confidence bounds on estimates of geographical range edges will advance our capacity to disentangle true distributional change from artefacts of sampling design.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".