Examining the Sustainability of Tropical Island Forests: Advances and Challenges in Measurement, Monitoring, and Reporting in the U.S. Caribbean and Pacific
Notice bibliographique
Résumé
Forests across the U.S. and U.S. affiliated islands of the Caribbean and Pacific constitute rich and dynamic social-ecological systems that, while heterogeneous in many ways, share certain characteristics and trends that underscore the utility of sustainability assessments that go beyond single jurisdictional efforts. This paper summarizes a recent effort to assess the sustainability of tropical island forests of and politically affiliated with the U.S. using the Montréal Process criteria and indicator framework (MP C&I), which address ecological, social, economic, and institutional dimensions of forests. Forests cover 45 percent of the total area and more than 50 percent of each island jurisdiction, except Hawaii (36 percent). Forest cover is generally stable over much of the area in terms of recent reference conditions. The history of human occupation and land alteration is a prominent determinant of current conditions throughout the islands, which exhibit relatively high rates of threatened species in comparison to mainland counterparts and particularly where endemism is high. The islands also harbor significant areas of new or novel assemblages of native and non-native forest species, predominately on abandoned agricultural lands cleared of native forests long ago, which have been shown to contribute to the restoration of these degraded lands and provide many other ecosystem services at levels as high as and in some cases higher than native forests. Although industrial-level commercial timber harvest is small to nonexistent on most islands, socioeconomic and cultural linkages to forests are extensive but difficult to quantify. Amassing a foundation of data sufficient to address the MP C&I was challenging, in part due to the heterogeneity of the islands, island geography, and limited reporting capacities. We document significant improvements in the availability of data important for sustainability assessments in the last decade or so, especially with the extension of the U.S. Department of Agriculture Forest Service Forest Inventory and Analysis program to the islands. Likewise, we find the MP C&I to be a useful tool for organizing and presenting information important for assessing forest sustainability. Nevertheless, considerable data gaps remain in the areas of biodiversity, forest functions and processes, and socioeconomic conditions of forests, which are critical elements to track across the islands, particularly in the context of climate change and ongoing anthropogenic pressures.
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Comment cette classification a été obtenuedéplier
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 ».