Insect ecology and conservation in urban areas: An overview of knowledge and needs
Bibliographic record
Abstract
Abstract Urban expansion across the globe profoundly impacts local biodiversity. The growing body of urban ecology research on animals has largely focused on mammals and birds, whereas knowledge of insect ecology and conservation in urban areas remains limited. To anchor this Special Issue (SI), we have taken a broad approach to editorial and conducted a structured literature search to set the scene. We provide here an overview of existing literature reviews on urban insect ecology and conservation, indicate where the articles included in this SI contribute to developing our understanding and point to priority areas for further investigation. Key themes in the growing literature (at individual, species, and/or community level) include the influence of habitat quality, quantity and land use type on insect diversity; the impacts of anthropogenic pollution (for instance, heat, noise, light and chemicals); habitat connectivity and changes in habitat structure and impacts of urban density on genetic diversity. Insect diversity and abundance broadly decline with urban density and loss of habitat. Beyond this, variation in responses of different taxa, or in different regions, and methodological limitations of individual studies make it challenging to identify general patterns. Insect ecology and conservation research in urban environments should focus on applying ecological theory to understand variation in diversity patterns; investigating interactions between climate change and urban contexts; identifying impacts of novel environments on insect biodiversity; addressing methodological limitations and harmonising methodological approaches; and exploring the influence of social and historical factors on urban insect biodiversity. Insect conservation must also consider research into how best to communicate the value of urban insects to urban humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".