A comparison of approaches for including connectivity in systematic conservation planning
Bibliographic record
Abstract
Abstract Plans for expanding protected area systems (prioritizations) often aim to facilitate connectivity. To achieve this, many approaches—based on different assumptions and datasets—have been developed. However, little is known about how such approaches influence prioritizations. We examine eight approaches that aim to promote connectivity in prioritizations. Using Washington State (USA) and its avifauna as a case study, we generated prioritizations that aimed to meet species' representation targets and promote connectivity by (a) maximizing total area; (b) further maximizing species representation; (c) minimizing boundary length; and connecting areas based on (d) minimizing human pressure, (e) minimizing naturalness‐based landscape resistance, (f) minimizing focal species landscape resistance, (g) minimizing habitat heterogeneity and (h) maximizing environmental similarity. We controlled for total expenditure, species' representation, and existing land use policies to enable comparisons among prioritizations. We then used a hierarchical cluster analysis to compare prioritizations, based on which areas they selected. We also evaluated how well each approach facilitated connectivity as measured by the other approaches. We found that different approaches for promoting connectivity can lead to very different or very similar prioritizations, depending on their underlying assumptions. In particular, the boundary length approach—which is widely used in systematic conservation planning—resulted in a prioritization that was highly dissimilar to all other prioritizations. Surprisingly, approaches based on very different underlying assumptions produced similar prioritizations, such as maximizing total area and minimizing focal species landscape resistance approaches. Moreover, when comparing the prioritizations based on the level of connectivity they could facilitate, we found that none of the prioritizations facilitated a high level of connectivity for all eight approaches. Synthesis and applications. We recommend carefully considering the assumptions and limitations that underpin approaches for promoting connectivity. Our findings demonstrate that different connectivity approaches can produce marked differences in priorities and, in turn, produce trade‐offs between different approaches. Indeed, despite the ubiquity of the boundary length approach, practitioners might find that other approaches can better achieve conservation objectives. Practitioners can use our methodology for comparing different connectivity approaches to help to navigate trade‐offs among them.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".