Spotlight on insects: trends, threats and conservation challenges
Bibliographic record
Abstract
Abstract There is mounting concern over the conservation status and long‐term trends in insect populations. Many insect populations have been reported to be falling and many species are threatened with extinction. While this is true, the evidence does not support unqualified statements of ‘global insect decline’. Global environmental change does not affect all species equally, and there are clear winners as well as losers from anthropogenic impacts. In this special issue of Insect Conservation and Diversity , we draw together articles that (i) identify key challenges in robust inference about insect population trends, (ii) present new empirical evidence for declines (and increases) in insect populations, spanning whole communities down to single species, in both aquatic and terrestrial ecosystems, and (iii) address the interacting drivers of population change, from empirical studies of environmental correlates, to experimental manipulation of driving mechanisms. We argue that the way forward for insect conservation includes more nuanced language and approaches when communicating ecological evidence to peer and public audiences, beyond just a simplistic focus on the insect decline narrative. This will require an expanded portfolio of approaches to promote the value of insects to society, which in turn, should reinforce the social licence to prioritise insect conservation research. This should help us to deliver the rigorous science necessary to document ongoing trends and understand the drivers and mechanisms of population change. Only then will we be able to mitigate or reverse declining populations.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".