Trend in Landscape Ecology Topic Research Based on Web of Science: A Bibliometric Analysis
Bibliographic record
Abstract
Abstract This studies highlighting the state of research and the dominant issues in Landscape Ecology. Based on Web of Science database and using the bibliometric analysis method, the 4,496 papers were analyzed in the field of Landscape Ecology topic research from 1976 to March 15, 2025. Most papers were written in in English (4,408, 98.043 %), were from 130 countries or regions, 3,651 organizations, and published in 824 journals and 12 book series. The top five journals are Landscape Ecology (434, 9.653 %), Landscape and Urban Planning (120, 2.669 %), Ecological Applications (90, 2.002 %), Ecology (86, 1.913 %), Ecological Indicators (84, 1.868 %), each journal published more than 84 papers. Top five countries are USA, Peoples R China, Canada, Australia, England, each published more than 304 papers. The top five organizations are United States Department of Agriculture USDA, United States Forest Service, University of California System, Chinese Academy of Sciences, Centre National De La Recherche Scientifique CNRS, each with more than 138 papers. With co-occurrence network visualization by VOSviewer, all keywords were separated into eight clusters topic research. By the all keywords occurrence during the different period, we can found the earlier and front research keywords and cluster. Based on ESI database, there are twenty-one top papers of all highly cited papers. The most papers are focused on the five Sustainable Development Goals of 15 Life On Land (3,486, 77.536 %), 13 Climate Action (2,897, 64.435 %), 14 Life Below Water (2,739, 60.921 %), 02 Zero Hunger (1,121, 24.933 %), 11 Sustainable Cities and Communities (982, 21.842 %). The results will help researchers clarify the research current situation, but also provide guidance for future research. This work is also useful for student identifying graduate schools and researchers selecting journals for publishing the most papers or top papers.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.142 | 0.170 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".