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Record W1567507933 · doi:10.1111/conl.12043

Response to: Putz <i>et al., Sustaining Conservation Values in Selectively Logged Tropical Forests: The Attained and the Attainable</i>

2013· article· en· W1567507933 on OpenAlexaff
Cyril Kormos, Barbara L. Zimmerman

Bibliographic record

VenueConservation Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsWorld Wildlife Fund Canada
Fundersnot available
KeywordsLoggingDeforestation (computer science)AgroforestryIllegal loggingRainforestBusinessBiodiversityAgricultureNatural resource economicsEnvironmental scienceGeographyEcologyForestryEconomicsComputer scienceBiology

Abstract

fetched live from OpenAlex

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).

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)0.002
metaresearch head score (Gemma)0.001
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 score0.154
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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