Protected area effectiveness: evaluation of biological outcomes in protected areas
Notice bibliographique
Résumé
Protected areas are the primary form of intervention applied to achieve biodiversity conservation in response to anthropogenic threat. More than 14% of terrestrial land surface area is within protected areas worldwide, and the CBD Strategic Plan developed in Nagoya established new targets of 17% global coverage of protected areas. However, biodiversity continues to decline, including within some protected areas. The ability of protected areas to retain their conservation values has been questioned. Understanding of whether protected areas are retaining their biodiversity values, and under what circumstances is limited. In particular, the crucial role of protected area performance in maintaining populations of species remains poorly understood. Long-term systematic population monitoring data are exceptionally rare, but critical for determining species and community level changes in natural values. Given the level of investment in protected areas, especially in terms of opportunity cost, it is critical that we understand the mechanisms underlying protected area effectiveness in retaining biological values. I use a variety of tools to evaluate the trends in fauna within protected areas, and to identify critical correlates of effectiveness in maintaining species populations. I undertook a comprehensive literature analysis to evaluate the impact of protected areas and factors likely to impact their success. I identified a broad suite of potential variables likely to influence outcomes in protected areas. These included design, ecology, management and socio-economic factors. In general protected areas appear effective for protecting habitats, although leakage (the impact still occurring, but in another place) is a problem and enforcement is important. However, a key finding was poor counterfactual monitoring in species population studies, making relative impact difficult to determine. To evaluate the influence of critical correlates I lead a global evaluation to identify key correlates of biological effectiveness of protected areas using trends in terrestrial vertebrates. Using mixed effects analysis several emergent trends were identified. Species body mass and indicators of human wellbeing were strongly positively correlated with population trends of monitored vertebrate fauna (birds and mammals), a finding that is consistent across taxonomic classes and geographic realms. I also explored the influence of factors for which it is difficult to obtain good broadscale data (such as resources) using a case study in Canada. Investment and staff time appear to be the best predictors of species outcomes in Canada, although the model explained little of the overall variance, indicating that there are also likely to be factors that have not been considered at play. In iiaddition to the rarity of population monitoring data, a key limitation of protected areas impact evaluation is the lack of monitoring outside of protected areas. Further, I investigated and developed methods to estimate relative impact of protection using species list data (lists of species found on a single visit in a defined geographic location) collected by volunteers and citizen scientists. In the absence of other available data, or historical data, this approach can give us a first approximation of potential trends for all species in a community. I have applied these methods (List Length Analysis) to a case study in the Australian Wet Tropics (AWT). Using this approach I was able to finding that although absolute trends are variable, most endemics are stable in the AWT bioregion. However there was no difference in endemic avifaunal trends within and outside of protected areas: rather any remaining habitat is equally as good at retaining these species. The outcomes of this work are likely to yield tangible conservation benefits through application to policy and practice in both the short and long term. Better understanding of the impact of protection and the mechanisms that may be underlying protected area effectiveness in retaining biological values will facilitate improved outcomes by informing the management process and policy and investment decision-making. It will therefore be possible to maximise the marginal benefit of existing and new protected areas.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».