Bombing for Biodiversity in the United States: Response to Zentelis & Lindenmayer 2015
Notice bibliographique
Résumé
Zentelis & Lindenmayer (2015) contend military training areas (MTAs) “have the potential to make a significant formal contribution to biodiversity conservation,” yet their conservation value has not been rigorously assessed. We believe their paper is an important step in raising awareness of the potential conservation value of MTAs to policy makers, scientists, and conservation professionals. Here, we offer an empirical evaluation of their statements regarding size, distribution, and representation of ecological systems (i.e., vegetation communities) within MTAs for the contiguous United States (CONUS) by comparing MTAs with lands managed by other U.S. federal agencies. We used lands managed by Department of Defense (DoD) as a proxy for MTAs. By combining the Protected Areas Database of the U.S. (PAD-US; USGS-GAP 2012) and the National GAP Land Cover (USGS-GAP 2011), we determined total number of ecological systems across all units of DoD and other agencies; and proportion of each ecological system that each agency represents across all lands. DoD lands occur in every state (Figure S1) and represent 467 of 565 total ecological systems within CONUS on 8.1 million hectares (Table S1). This ecological diversity is exceeded only by the National Park Service (NPS), which represents 479 ecological systems across 10.2 million hectares. In contrast, U.S. Forest Service (USFS) and Bureau of Land Management (BLM) lands, despite being 8.5 and 8.6 times larger than DoD lands, represent only 458 and 293 ecosystems, respectively. Therefore, even though DoD lands comprise only 5% of the total area of federal lands, they represent 82.6% of the diversity of ecological systems, whereas USFS and BLM comprise 42% and 43% of the total federal land area, but neither represents as much diversity as DoD lands. Similarly, Stein et al. (2008) found DoD lands disproportionately represented more imperiled species (e.g., vascular plants) per unit area than other federal lands. DoD lands also contribute to total representation of ecological systems on federal lands, as three ecological systems occur on DoD lands only. These ecological systems are relatively rare (i.e., occur on <10,000 hectares throughout CONUS) and have >50–100% of their area on federal lands within DoD. Similar to other federal agencies, the majority of ecological systems have <10% of their entire or federal distribution within DoD lands (Figure S2, USFS is an exception). As part of the entire collective of federal lands, DoD lands increase federal representation of 50 ecological systems by >5% (Figure S3). Our results are likely a consequence of a mandate that DoD lands be intentionally distributed across the U.S. to train the military under a variety of geographic conditions. They contrast with lands managed by BLM, NPS, USFS, and U.S. Fish and Wildlife Service (FWS), which have been obtained through various opportunities and agency-specific conservation priorities (Aycrigg et al. 2013) and not specifically established to maximize biological diversity (Scott et al. 2001). Our empirical analysis of DoD lands within CONUS support the contentions of Zentelis & Lindenmayer (2015) that DoD lands (i.e., MTAs) contribute to biodiversity conservation and should be considered a conservation asset. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0,000 |
| 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 ».