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Record W1496143141 · doi:10.1111/conl.12044

A More Realistic Portrayal of Tropical Forestry: Response to Kormos and Zimmerman

2013· article· en· W1496143141 on OpenAlexaff
Francis E. Putz, Pieter A. Zuidema, T.J. Synnott, Marielos Peña‐Claros, Michelle A. Pinard, Douglas Sheil, Jerome K. Vanclay, Plínio Sist, Sylvie Gourlet‐Fleury, John Palmer, Roderick Zagt, Bronson W. Griscom

Bibliographic record

VenueConservation Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLoggingStewardship (theology)Government (linguistics)Illegal loggingForest managementSustainable forest managementTropicsBiodiversityBusinessAgroforestrySubsidyTropical forestTropical and subtropical moist broadleaf forestsCommunity forestryDeforestation (computer science)GeographyForestryEcologyPolitical sciencePoliticsEnvironmental science

Abstract

fetched live from OpenAlex

In their response to our recent article (Putz et al. 2012), Kormos and Zimmerman (K&Z) do not take issue with the result of our meta-analysis of more than 100 published studies that biodiversity and carbon stocks are mostly retained in selectively logged tropical forests. Instead, they object to what they misconstrue as our advocacy of subsidies for logging operations. To be clear, what we advocate is support for efforts to improve tropical forest management and the safety of forest workers. For example, we endorse efforts to restrict logging in riparian buffer zones and on steep slopes, to promote careful planning of harvesting operations, and to provide worker training and safety gear. K&Z disregard the contributions of groups like the Forest Stewardship Council (FSC), the Tropical Forest Foundation, the Borneo Initiative, and the various environmental and social welfare groups that are helping to develop ways to compensate companies and communities for the costs of retaining more carbon in living trees through REDD+ and other mechanisms. K&Z's portrayal of all tropical forests as lawless frontiers is not accurate. Tropical forest logging is admittedly a messy business and apparently the areas in Brazil where K&Z's work are particularly problematic, but control over production forests is often substantial. Evidence for this claim is accumulating from government-issued forest concessions in Indonesia (Gaveau et al. 2012) to community-managed forests in Mexico (Duran-Medina et al. 2005) and elsewhere in the tropics (Porter-Bolland et al. 2012). Furthermore, while governance failures still occur far too frequently, steady increase in the area of natural tropical forest certified as responsibly managed by the FSC (now >13 million hectares) provides evidence that forest owners are increasingly able to protect and manage their resources. We applaud K&Z's advocacy of community-based forest management but question their assumptions about the fates of forests under community control. Although in many places in the tropics, rural livelihoods can only be maintained by forest clearing for agriculture, under some conditions communities try to retain their forest. To derive financial benefits from these forests and to mobilize logging capacity, communities increasingly employ industrial forestry models, often by partnering with industrial forestry firms, which means that the focus on good management practices should remain a priority. What should be avoided are community–company contracts that are unsatisfactory on either environmental or social grounds (e.g., Pokorny et al. 2010). Fortunately, there are already good examples of communities working effectively with industrial forestry firms under clear and well-defined contracts (Benneker 2008). Given the unlikelihood of huge expansions of strictly protected areas in the tropics, it seems logical to focus conservation efforts on forests from which timber will be harvested. Substantial improvements in management practices are possible, but their implementation will require the concerted efforts of the full range of environmental advocates (Sabogal & Casaza 2010). Disregard of these opportunities benefits no one (Sheil & Meijaard 2010). We agree with K&Z that some forests should be spared from logging, but where the likely and lucrative alternative to forest management for timber involves conversion, efforts should be made to increase the financial value of standing forests for all of their benefits, including their wood resources. Finally, given that wood is one of the lowest carbon-footprint structural materials (Perez-Garcia et al. 2005), banning industrial logging would have some perverse environmental outcomes. Although forest management has been widely demonized, and often for good reason, we should be prepared to look beyond weak generalizations and examine the evidence. Providing evidence for the conservation values of selectively logged tropical forests was exactly the aim of the meta-analysis in our 2012 article (Putz et al. 2012). Now the challenge is to discover the best ways to improve management practices so that even more of these values are maintained.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.200
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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