Genetic Monitoring of Insect Populations Over the Long Term in a Changing World
Bibliographic record
Abstract
Biodiversity loss is a defining challenge of the Anthropocene, with species extinctions and population declines threatening ecosystem function and stability worldwide. Yet, beneath these visible impacts lies the silent erosion of genetic diversity, an often overlooked phenomenon. As the foundation of evolutionary potential, genetic variation is critical for population resilience, adaptation, and long-term survival. Despite its importance, genetic diversity is neglected in most conservation assessments and is rarely monitored over time. This thesis addresses both gaps by applying state-of-the-art genomic approaches to investigate the dynamics of genetic diversity in insect populations, a group with key roles in ecosystems that remains underrepresented in monitoring programs and policy frameworks. In Chapter 1, the development of a new genomic monitoring framework based on ultra- conserved elements (UCEs) and universal single-copy orthologs (USCOs) is presented. This molecular tool was applied to Swiss Orthoptera during the latest Red List update. Drawing upon a sampling of 645 samples including all Orthoptera species in Switzerland, the study provided an updated phylogeny of the group, explored species structuring, and generated within-species genetic diversity estimates. Notably, we demonstrate that genetic diversity levels are not correlated with IUCN threat status, advocating for the integration of genetic indicators into risk assessments and providing a practical tool for future national biodiversity strategies. In Chapter 2, we investigate the population dynamics through time and space in seven insect species considered as Least concern by the IUCN, all widely distributed and associated with agricultural landscapes, by combining historical DNA from museum specimens with contemporary genomic data. Using hybridization-based hyRAD sequencing on nearly 1,500 museum specimens and ddRAD sequencing on approximately 800 modern individuals, the study identifies signatures of genetic erosion. These include species-specific declines in genetic diversity, with tentative indications of recent recovery in some cases, and a trend toward both genetic homogenization and fragmentation over time. This work demonstrates that genetic erosion is not confined to rare or endangered species but also affects widespread taxa that appear demographically stable. It further shows how natural history collections can be leveraged for long-term genomic monitoring and calls for their broader inclusion in conservation frameworks. Together, these findings highlight the urgent need to integrate genomic indicators into conservation policy, as outlined by the Kunming–Montreal Global Biodiversity Framework, which now explicitly includes the goal of maintaining and restoring genetic diversity as a core target of global conservation efforts. Genetic erosion reduces the capacity of populations to adapt to environmental change, including the many stressors driven by human activity. Though invisible to traditional monitoring, its loss is irreversible on evolutionary timescales. Detecting and preventing genetic erosion must therefore become a central pillar of conservation in the decades ahead.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".