Editorial: To plant, rewild, or ignore? Linking forest restoration methods to long-term ecological trajectories and ecosystem services
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".