Bombing for Biodiversity in the United States: Response to Zentelis & Lindenmayer 2015
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".