Monitoring forest biodiversity - from the policy level to the management unit
Bibliographic record
Abstract
elements of biodiversity, such as functional con nectivity of different land cover types and ecosystem func tions, are understood by different stakeholders. Monitoring the whole land base Depending on the history of forests and woodland in the actual landscape, the maintenance of forest biodiversity encompasses either of two sets of broad visions. First, poli cies related to the biodiversity of European forests and woodland make explicit reference to the concept of natu ralness (Anon. 1993, 2003b). In spite of the ambiguity of this concept (Balee 1998, Egan and Howell 2001), it is obvious that forest biodiversity indicators should represent elements found in naturally dynamic forests (Peterken 1996). Second, in both Europe (Kirby and Watkins 1998, Agnoletti 2000, Rackham 2003, Angelstam et al. 2003a) and North America (Stevenson and Webb 2004), the maintenance of values found in pre-industrial cultural landscapes is highlighted. The latter, although influenced by human land use, contained structural components such as dead wood, large old trees and old-growth stands that are typically found in naturally dynamic forests. As a con sequence, remnants of the pre-industrial cultural land scape provide a refuge for many species that were adapted to a pristine or near-natural forest environment (Kirby and Watkins 1998). Ideally, the development of forest biodi versity indicators should reflect both of these visions. The natural potential vegetation of Europe's terrestrial ecosystems at mid and northern latitudes is forest (Mayer 1984, Bohn 1994). With continuous change in historical land use including habitat loss, forest restoration and glo bal change, the potential for forest biodiversity can be found in at least four types of landscapes: 1) forest; 2) cul tural woodland; 3) plantations; and finally 4) land subject to future afforestation. In the first three cases there is a re quirement for protection, management and restoration; the fourth case represents opportunities for re-creation of forests. Consequently, we argue that monitoring forest biodiversity should not only deal with what is forest or an cient cultural woodland today, but be applied in geograph ically contiguous units representing actual landscapes. Monitoring should thus be integrated across biotopes in cluding woodland and non-woodland. This would also al leviate the integration of management of woods in pre dominantly agricultural landscapes and of management in forests (Mikusi?ski et al. 2003). There is also a growing insight into the complex interactions between the terrestri al and aquatic systems (Wiens 2002), many of which re quire transdisciplinary landscape approaches (Rabeni and Sowa 2002). In Europe the EC Water Framework Direc tive (Anon. 2000) has recently reinforced a drainage basin perspective on biodiversity that sets the stage and in fact demands integrated monitoring. The capacity to gather information over large areas and perform spatial analysis greatly enhances our ability to study the large-scale pat terns and processes caused by natural and human distur bances, and also to make predictions about the future (Young and Sanchez-Azofeifa 2004). Given the technolo gy now available, the major advances made in ecological research, and the need for integrated management of inte gral landscapes, piecemeal interventions should be re placed by scientifically-based monitoring at multiple scales. Acknowledgements We thank Ulf Grandin, Jonathan Hum phrey and Marc-Andre Villard for valuable comments on the manuscript. J.-M. R. is grateful to the Natural Sciences and Engi neering Research Council of Canada (NSERC) for financial sup port while doing this study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".