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Enregistrement W6911309288 · doi:10.5281/zenodo.10933648

The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs

2024· dataset· en· W6911309288 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésForest managementTable (database)Forest coverTropicsLoggingTaigaDemocracy

Résumé

récupéré en direct d'OpenAlex

Introduction This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024). Data collection We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs. Overlap analysis All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R. Abstract of the related article Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,165
Score d'incertitude au seuil0,328

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,011
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0110,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,263
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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