Explaining Interspecific Variation in Susceptibility and Resistance to Parasitism in Damselflies
Bibliographic record
Abstract
Parasites are often overlooked in evolutionary and community ecology studies.The interactions between hosts and their parasites can have important implications on community structure.Past research has focused on species-specific characteristics of parasites to explain why different, but phylogenetically closely related host species, are under different selection regimes imposed by parasites.However, the evolutionary ecology of hosts is also expected to have important influence in their associations with parasite species.This thesis explores host factors principally that are expected to influence host susceptibility and resistance, in damselflies parasitized internally by gregarines and externally by water mites.There was often considerable interspecific variation in parasitism.When comparing species grouped into sibling species pairs, gregarine parasitism was explained in part by geographic range size of host species, but in most of the cases where there was difference in parasitism between the two closely related host species, it was the host species with the smaller range that had higher levels of parasitism.A similar pattern was observed in Arrenurus water mites parasitizing the same host species, grouped into species pairs.Additionally, Arrenurus species richness was more similar within species pairs than across species pairs meaning that more closely related hosts share similar Arrenurus fauna.At a higher taxonomic level where the host species were not grouped into species pairs, but where host phylogeny was controlled for through comparative methods, host phenology and geographic range size were better predictors of parasite host interactions than were other host characteristics such as host local abundance and host body size.The best predictor models demonstrated that host species most active in Preface Co-authorship Statement My contributions to the research described in this thesis were: (1) I proposed and developed the research questions in partnership with Dr. M. R. Forbes, and was primarily responsible for the design of the projects used to address these questions; (2) I was primarily responsible for carrying out all the lab work (e.g., dissections, phenoloxidase assays, mounting of larval Arrenurus) from the field work in 2010 at the Queen's University Biological Station (QUBS).I informally supervised a summer lab assistant (A.Morrill) who helped to process damselflies collected from QUBS in 2010.The molecular work, CO1 DNA barcoding of Arrenurus were carried out by Wayne Knee, Ag.Can (Chapters 3, 4), and at the Barcode Institute University of Guelph (Chapter 6); (3) I analysed all of the data; and (4) I wrote all first drafts of the chapters/manuscripts.I used the integrated thesis format and therefore each data chapter was formatted as an independent research article that has either been published in or was submitted to a peer-reviewed journal when this thesis was completed.There is some repetition in introductions and discussions; however I have cross-referenced between chapters to reduce repetition in the methods sections.As indicated above, I always played a major role in the design of the research and in the preparation and writing of each chapter/manuscript.However, I must acknowledge the constructive guidance and advice from my co-authors.My supervisor, Dr. M. R. Forbes contributed his theoretical, statistical and grammatical knowledge to each of my six chapter/manuscript drafts listed below.Dr. C. Hassall was a great asset in helping with the statistical analyses and v contributed grammatical expertise to the drafts of chapters/manuscripts one.Dr. W.Knee, as mentioned before, conducted the molecular barcoding and provided feedback to the drafts of chapters/manuscripts three, four and six.Dr. A. Iserbyt was a provided guidance on the phenoloxidase assay analysis and statistical and grammatical expertise on the drafts for chapter/manuscript five.Lastly, Dr. L.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".