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Enregistrement W4403700096 · doi:10.3389/ffgc.2024.1491218

Editorial: To plant, rewild, or ignore? Linking forest restoration methods to long-term ecological trajectories and ecosystem services

2024· editorial· en· W4403700096 sur OpenAlexaff
Sarah Jane Wilson, Bonnie G. Waring, Matthew E. Fagan, Meredith Root‐Bernstein, Leland K. Werden

Notice bibliographique

RevueFrontiers in Forests and Global Change · 2024
Typeeditorial
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueForest Ecology and Biodiversity Studies
Établissements canadiensUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésRestoration ecologyForest restorationEnvironmental resource managementContext (archaeology)Ecosystem servicesRegeneration (biology)EcosystemEcologyForest ecologyEnvironmental scienceEnvironmental planningGeographyBiology

Résumé

récupéré en direct d'OpenAlex

forest recovery. Forest restoration methods span a broad gradient of management intensity: active/assisted recovery (which often involves some form of tree planting or direct seeding); assisted natural regeneration (encompassing techniques such enrichment planting or removal of competing species); and passive/natural regeneration (which involves little to no direct intervention). Additionally, 'rewilding' is an umbrella term for interventions that seek to restore self-regulating ecosystem processes, e.g., by re-introducing keystone species. 5 How might we determine which sites require intensive intervention to enable ecosystem recovery, and which have the capacity to regenerate naturally? What are long-term implications of these choices for forest dynamics? How do social conditions mediate forest conservation (or clearing) as regrowing forests mature?The papers in this special issue explore the linkages between restoration methods and restoration outcomes, embracing a wide array of methodological approaches and inter-disciplinary perspectives. Collectively they address two of the main challenges restoration practitioners face: the technical challenge of how to restore ecosystems to meet specific goals, and the ecological and socioeconomic challenges of ensuring that restored forests persist on the landscape. They demonstrate that choosing the 'right' restoration intervention requires an in-depth understanding of the local site conditions, and the landscape context in which sites are situated. Clear goals for restoration are important for deciding on an acceptable timeframe, degree of predictability, and level of recovery.From selecting tree species or cultivars, to selecting restoration sites, choosing an approach that fits local conditions and responds to specific goals is paramount.Ray and colleagues (2022) discuss how intra-specific genetic diversity affects how forests respond to disturbance and climate change. They conclude that although natural regeneration-based approaches might fit some contexts and goals, tree breeding programmes can be a powerful tool in contexts where goals are more specific, depend on a given timeframe, or where forest recovery requires a high degree of resilience in the face of a changing climate. They can also help to create more predictable forest recovery trajectories in a given context.How quickly and how completely forests recover in a given site will also determine how much intervention is needed, and therefore whether passive restoration is a viable option. Recovery depends on both landscape and site is, but it may have been camelids (currently extirpated) or extinct megafauna. In their analysis of contemporary biotic interactions between the palm and its probable current disperser, the rodent Octodon degus, they show that there is a spatial association between Octodon degus burrows and natural palm regeneration at both large and small scales. The likely mechanism is that stored palm seeds that are not eaten by O. degus later germinate, but the interactions between O. degus and other seed predators (humans and introduced rodents) are unknown. This study emphasizes the importance of basic research to better understand and therefore conserve the dispersal mechanisms and biotic interactions by which rare and understudied plants regenerate.Equally important for persistent forest recovery is the social landscape. Holl et al. ( 2022) unpack the concept of 'abandoned land', i.e., land that is often presumed to be available for restoration based upon remote sensing datasets. But what a satellite 'sees' as abandoned land at a particular snapshot in time could actually be productive land in fallow, for example. Or such land may be unused now, but could be rapidly brought into production again should markets or demographics shift. Understanding the drivers of past forest clearing as well as land governance and access rights is critical to understand how and why forests are converted to agricultural land, and whether regenerating forests will persist in the future. This type of due diligence is also necessary to avoid unintentional 'green grabs,' through which local communities' access to their customary land is usurped by national or international institutions. 8Understanding local site conditions and landscape context is vital to determine which restoration approach is most suitable in a given context to meet specific goals. A blend of scientific study and local knowledge can provide a holistic picture while engaging local stakeholders. Indigenous peoples and local communities often play vital roles in forest protection and regeneration, 9,10 but are too often excluded from dialogues about how best to implement large-scale restoration projects. Although nascent, these partnerships present a promising model through which forest restoration efforts can make use of scientific insights as well as Indigenous and traditional knowledge to collectively set meaningful goals for restoration, locate it on the landscape, and quantify system recovery using project-tailored ecological, social, cultural, and economic indicators.The contribution from Root-Bernstein et al. (2024) also argues for a highly contextual approach to developing more naturalistic forms of tree-based restoration.They argue that there is scope for moving towards a rewilding-inspired approach to restoration with "foundation plants", that is trees, shrubs, palms and cacti, focusing on functionalist rather than compositionalist aims, on natural or naturalistic habitat structures and formations, and on spontaneous successional trajectories led by natural processes. However, designing such approaches should consider social histories of land use and current socio-economic contexts and governance challenges.Focusing on the Sahel and the Southern Cone of South America, they point out how ecological similarities and social differences have shaped both how restoration, conservation and rewilding have taken shape in both regions, and how a rewilding inspired approach could be adapted to each region. Comparisons and knowledge exchange between regions may also catalyze new approaches.The articles in this Special Issue illustrate that the determinants of restoration outcomes span multiple levels of biological organization, from genes to communities to ecosystems; as well as social considerations including governance, land use patterns, and community needs. Too often, projects fail to invest in the research required to plan restoration to meet specific goals. Deep, strategic planning is required to best match local and regional contexts with the array of approaches available. It is imperative that adequate funding and resources are provided to understand the local and landscape context before interventions are undertaken. Co-creating restoration approaches with Indigenous peoples, local communities, and scientists can serve to engage people, help to set locally relevant goals, and provide the types of knowledge that are required to effectively restore complex ecosystems that will persist into the future.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,267
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,026
Tête enseignante GPT0,282
Écart entre enseignants0,257 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

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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