What evidence exists for the use of urban forest management in nature-based carbon solutions and bird conservation. A systematic map protocol
Bibliographic record
Abstract
BACKGROUND: There is global interest in finding innovative solutions that address current climate and societal challenges in an urban context. Cities are often on the front lines of environmental change, meaning urban greening strategies have high potential to provide benefits across human communities, while protecting global biodiversity. There is growing consensus that nature-based solutions can provide multiple benefits to people and nature while also mitigating the effects of climate change. Urban forest management is well-suited to a nature-based solutions framework due to the wide variety of services trees provide our communities. Effective approaches to urban forest management also have the potential to promote other forms of urban biodiversity, particularly birds and species at risk. However, studies that integrate strategies for both climate and biodiversity conservation are rare. The goal of this systematic map is to gather and describe information on two desired outcomes of urban forest management: (1) conserving avian diversity and species at risk (2) carbon storage and sequestration (i.e., nature-based climate solutions). METHODS: We will identify relevant articles from two separate searches for inclusion in our systematic map that address (1) urban forestry and avian and species at risk conservation and, (2) urban forestry and carbon storage and sequestration. We will search two bibliographic databases, consult 20 relevant organizational websites, and solicit grey literature through an open call for evidence. Eligibility screening will be conducted at two stages: (1) title and abstract and (2) full text. Relevant information from included papers will be extracted and entered in a searchable, coded database. Synthesis of evidence will describe the key characteristics of each study (e.g., geographic locations, interventions, outcomes, species studied) and identify knowledge gaps and clusters of evidence. Our systematic map will guide further research on opportunities for multiple benefits using nature-based solutions, particularly as they relate to urban forest management. Furthermore, our evidence base will support both management and funding decisions to ensure the effective use of resources for maximum benefits across people and ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.054 | 0.031 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".