A comparison of approaches for including connectivity in systematic conservation planning
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».