Advancements in ecological niche models for forest adaptation to climate change: a comprehensive review
Bibliographic record
Abstract
Climate change poses significant challenges to the health and functions of forest ecosystems. Ecological niche models have emerged as crucial tools for understanding the impact of climate change on forests at the population, species, and ecosystem levels. These models also play a pivotal role in developing adaptive forest conservation and management strategies. Recent advancements in niche model development have led to enhanced prediction accuracy and broadened applications of niche models, driven using high-quality climate data, improved model algorithms, and the application of landscape genomic information. In this review, we start by elucidating the concept and rationale behind niche models in the context of forestry adaptation to climate change. We then provide an overview of the advancements in occurrence-based, trait-based, and genomics-based models, contributing to a more comprehensive understanding of species responses to climate change. In addition, we summarize findings from 338 studies to highlight the progress made in niche models for forest tree species, including data sources, model algorithms, future climate scenarios used and diverse applications. To assist researchers and practitioners, we provide an exemplar data set and accompanying source code as a tutorial, demonstrating the integration of population genetics into niche models. This paper aims to provide a concise yet comprehensive overview of the continuous advancements and refinements of niche models, serving as a valuable resource for effectively addressing the challenges posed by a changing climate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".