Limitations and trade‐offs in the use of species distribution maps for protected area planning
Bibliographic record
Abstract
Summary Range maps represent the geographic distribution of species, and they are commonly used to determine species coverage within protected areas and to find additional places needing protection. However, range maps are characterized by commission errors, where species are thought to be present in locations where they are not. When available, habitat suitability models can reduce commission errors in range maps, but these models are not always available. Adopting a coarse spatial resolution is often seen as an alternative approach for reducing the effect of commission errors, but this comes with poorly explored conservation trade‐offs. Here, we characterize these trade‐offs by identifying scenarios of protected area expansion for the world's threatened terrestrial mammals under different resolutions (10–200 km) and distribution data deriving from range maps and habitat suitability models. We found that planning new protected areas using range maps results in an overestimation of the species protection level when compared with habitat suitability models (which are more closely related to species presence). This overestimation increases when more area is selected for protection and is higher when higher spatial resolutions are employed. Adopting coarse resolutions reduced the overestimation of species protection and also halved the spatial incongruence between protected areas prioritized from range maps or habitat suitability models. However, this came at a very high cost, with an area of up to four times greater (12 M km 2 vs. 3 M km 2 ) needed to adequately protect all species. Synthesis and applications . Our findings demonstrate that adopting coarse resolutions in protected area planning results in unsustainable increases in costs, with limited benefits in terms of reducing the effect of commission errors in species range maps. We recommend that, if some level of uncertainty is acceptable to practitioners, using range maps at resolutions of 20–30 km is the best compromise for reducing the effect of commission errors while maintaining cost‐efficiency in conservation analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".