Bibliographic record
Abstract
Invasive species are one of the foremost damaging environmental problems for biodiversity and conservation, and can affect human health and man-made structures. They pose a great challenge for pest management, with little known about their control and few available success stories. Many crustacean species are invasive and can affect both biodiversity and aquaculture. Controlling invasive Crustacea is a complex and arduous process, but success could lead to increased environmental protection and conservation. Invasive Crustacea also comprise a significant pathway for the introduction of invasive pathogens. If these invaders carry pathogens, parasites or commensals to a new site they may threaten native species. Alternatively, pathogens can control their invasive host and could be utilised in a targeted biological control effort as a biocontrol agent. \nLooking specifically at one species of invasive brachyuran crab (Carcinus maenas) collected from the UK, Faroes Islands and Atlantic Canada, and several species of invasive amphipod from the UK and Poland, I explore which groups of microorganisms are carried alongside invasions, and if any could be used as biocontrol agents or whether they pose a threat to native wildlife. \nThis thesis involves wide-scale screening of Carcinus maenas and several amphipod species, identifying a range of metazoans, fungi, protozoa, bacteria and viruses; many new to science. Taxonomic descriptions are provided for previously unknown taxa: Parahepatospora carcini; Cucumispora ornata; Cucumispora roeselii; and Aquarickettsiella crustaci. The application of metagenomics to pathogen invasion ecology is also explored, determining that it can be used as an early screening system to detect rare and/or asymptomatic microbial associations. Finally, I used experimental systems to assess the impact of pathogens carried by Dikerogammarus haemobaphes upon both itself and alternate host species (Dikerogammarus villosus and Gammarus pulex), identifying that C. ornata can infect native species and decrease their chance of survival. \nOverall this thesis describes a research process following through three main steps: i) invasive pathogen detection, ii) taxonomic identification, and iii) host range and pathological risk assessment and impact. Screening invasive and non-native hosts for pathogens is recommended for invasive species entering the UK, to provide a fast and informed risk assessment process for hazardous hitchhiking microbes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".