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Record W2981939107 · doi:10.3390/f10110946

Examining the Sustainability of Tropical Island Forests: Advances and Challenges in Measurement, Monitoring, and Reporting in the U.S. Caribbean and Pacific

2019· article· en· W2981939107 on OpenAlexaboutno aff
Kathleen McGinley, Guy Robertson, Kathleen Friday

Bibliographic record

VenueForests · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersBrandeis University
KeywordsGeographySustainabilityThreatened speciesEcosystem servicesMainlandEndemismEcologyBiodiversitySmall Island Developing StatesAgroforestryEcosystemEnvironmental resource managementClimate changeHabitatEnvironmental science

Abstract

fetched live from OpenAlex

Forests across the U.S. and U.S. affiliated islands of the Caribbean and Pacific constitute rich and dynamic social-ecological systems that, while heterogeneous in many ways, share certain characteristics and trends that underscore the utility of sustainability assessments that go beyond single jurisdictional efforts. This paper summarizes a recent effort to assess the sustainability of tropical island forests of and politically affiliated with the U.S. using the Montréal Process criteria and indicator framework (MP C&I), which address ecological, social, economic, and institutional dimensions of forests. Forests cover 45 percent of the total area and more than 50 percent of each island jurisdiction, except Hawaii (36 percent). Forest cover is generally stable over much of the area in terms of recent reference conditions. The history of human occupation and land alteration is a prominent determinant of current conditions throughout the islands, which exhibit relatively high rates of threatened species in comparison to mainland counterparts and particularly where endemism is high. The islands also harbor significant areas of new or novel assemblages of native and non-native forest species, predominately on abandoned agricultural lands cleared of native forests long ago, which have been shown to contribute to the restoration of these degraded lands and provide many other ecosystem services at levels as high as and in some cases higher than native forests. Although industrial-level commercial timber harvest is small to nonexistent on most islands, socioeconomic and cultural linkages to forests are extensive but difficult to quantify. Amassing a foundation of data sufficient to address the MP C&I was challenging, in part due to the heterogeneity of the islands, island geography, and limited reporting capacities. We document significant improvements in the availability of data important for sustainability assessments in the last decade or so, especially with the extension of the U.S. Department of Agriculture Forest Service Forest Inventory and Analysis program to the islands. Likewise, we find the MP C&I to be a useful tool for organizing and presenting information important for assessing forest sustainability. Nevertheless, considerable data gaps remain in the areas of biodiversity, forest functions and processes, and socioeconomic conditions of forests, which are critical elements to track across the islands, particularly in the context of climate change and ongoing anthropogenic pressures.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.486
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.248
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2019
Admission routes1
Has abstractyes

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