Shifting research paradigms in landscape ecology: insights from bibliometric analysis
Bibliographic record
Abstract
Abstract Context With 2022 marking the 40th anniversary of International Association for Landscape Ecology (IALE), landscape ecology has undergone substantial theoretical and methodological advances. A comprehensive quantitative bibliometric analysis can help better understand how the field has evolved during the past four decades. Objectives The main objectives of this review were: (1) to examine the 40-year developmental trajectory and key research topics in landscape ecology, and (2) to synthesize the shifts in research emphasis or paradigm shifts. Methods We conducted a quantitative analysis of publications in landscape ecology from 1981 to 2024, focusing on their trends, contributors, and hot topics based on the Web of Science core collection database. Results We found that: (1) Annual publications on landscape ecology in the Web of Science Core Collection have significantly increased in the last four decades; (2) The United States leads in publication quantity, citation frequency, and research collaboration, closely working with countries like China and Canada; (3) Key journals include Landscape Ecology, Landscape and Urban Planning, and Ecology, with Landscape Ecology being the most influential; (4) There have been significant shifts in research emphasis over time, with early studies concentrating on landscape structure, pattern and scale, while more recent trends focusing increasingly on ecosystem services and sustainability. Conclusions There has been a paradigm shift from “patch–corridor–matrix” to “pattern–process–scale”, and then to “pattern–process–service–sustainability” in landscape ecology research. To advance landscape ecology toward sustainability, future research needs to focus on developing conceptual frameworks, methodologies, and case studies of the “pattern–process–service–sustainability” paradigm.
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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 | 0.031 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.092 | 0.157 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".