Response to: Putz <i>et al., Sustaining Conservation Values in Selectively Logged Tropical Forests: The Attained and the Attainable</i>
Notice bibliographique
Résumé
Putz et al. (2012) suggest that subsidizing selective logging operations that implement best practices is a “middle way” between conventional logging and strict conservation that can reduce tropical deforestation. Putz et al. do not define “selective logging.” To the extent that selective logging includes industrial logging operations, we believe that this approach will only lead to continued deforestation. Few if any industrial logging operations in primary tropical forests are currently sustainable (Blaser et al. 2011; Shearman et al. 2012; Zimmerman & Kormos 2012) and Putz et al. acknowledge that making industrial logging sustainable would also make it economically unviable. Instead, they recommend abandoning sustained timber yields in favor of a lower standard: maintaining 50% of initial yields in subsequent harvests by logging multiple species. However, this “middle way” cannot solve the problem of tropical deforestation because the strong economic pressure to mine forests of valuable timber and then clear them for agriculture (Asner et al. 2006) will be the same. Nor does this “middle way” explain how forests will be protected before, during or after logging given that governance in most tropical forest sectors remains as weak as the incentive to convert forest land to agriculture remains powerful (Bowles et al. 1998; Asner et al. 2006; ITTO and FAO 2010; Shearman et al. 2012). In any case, given that industrial logging of tropical hardwoods from natural forests is biologically unsustainable under virtually any scenario that approximates financial viability, logging in these forests will end in the not-too-distant future (Shearman et al. 2012; Zimmerman & Kormos 2012). Sooner or later protection will be necessary to prevent conversion: why not use REDD subsidies to keep tropical forests intact, a much easier task when there are few or no roads (Laurance et al. 2009), rather than seeking to protect them after they have been degraded? Clearly, logged forest conserves more biodiversity and carbon than cattle pasture or oil palm plantations (Lindenmayer & Laurance 2012). However, Putz et al. perhaps overstate the benefits. The studies cited measure biodiversity shortly after logging and before a second harvest, when it may not be possible to know the full effects of logging on species’ populations (Bawa & Seidler 1998; Lindenmayer & Laurance 2012). We do not know the degree to which natural communities survive long term in large, logged tracts of forest because those tracts do not themselves survive (Asner et al. 2006; Shearman et al. 2012). Nor do the biodiversity totals cited by the authors specify the degree of fragmentation of forest blocks or their proximity to undisturbed forests that would act as source areas for recolonization (Barlow et al. 2007). Putz et al. also present selective logging with best practices as advantageous from a carbon standpoint, though acknowledging that logging with best practices results in a loss of 24% of a forest's carbon. This loss translates into substantial carbon emissions that will take decades to recapture (Huang & Asner 2010). Subsidizing industrial logging in one of the world's most endangered and sensitive ecosystems in regions of weak governance will add fuel to the fires of deforestation and serve only to benefit logging companies—many of whom will then invest in agriculture (Shearman et al. 2012). REDD+ subsidies should be invested in activities that maximize carbon, biodiversity, and local community benefits by keeping forests intact: community and indigenous management based on known design principles that lead to success, and effective management of new and existing protected areas (Ricketts et al. 2010).
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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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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 ».