How Developmental and Behavioural Plasticity in the Field Cricket is Influenced by the Acoustic Social Environment and Anthropogenic Noise
Bibliographic record
Abstract
The social environment is an important driver of developmental plasticity in juveniles and behavioural plasticity in adults.An individual's ability to accurately assess cues during development and predict its future social dynamics plays an important role in ensuring that its phenotype at adulthood will match their anticipated environment.However, because the overall plasticity of an individual is the result of a concomitant interaction between development, environment, and behaviour over the entire lifetime, constraints arising from development may hinder adult behaviour.My dissertation examines the influence of the developmental social environment on life-history traits and fitness-conferring adult behaviours to enhance our understanding of how these two life phases interact in the fall field cricket Gryllus pennsylvanicus.Through play-back experiments that altered the density of adult male acoustic signals, my findings reveal that male and female development time decreases and female residual mass at adulthood increases in higher perceived population densities.Contrary to my expectations, neither adult male aggressive nor mate attraction signalling were significantly influenced by developmental social environments.Adult female mate preference behaviour was influenced by developmental social environment, as females raised in social isolation were more responsive than females raised exposed to signals.Furthermore, the acoustic social environment experienced during development had significant indirect effects on all adult behaviours through constraints imposed on adult body size.Crickets reared in the high density environments developed faster, and a faster development time resulted in larger body sizes.Because body size significantly influenced male aggression and signalling behaviour, and female mate preference behaviour, the ability to express I am immensely grateful to my supervisor Dr. Susan Bertram, for her encouragement, support, guidance, and massive patience for my shenanigans over the past seven years.She has been an amazing mentor in academia and life.I thank her for the enormous contribution to my research, the completion of this thesis, her encouragement and support of my teaching, and her beer.Her enthusiasm, reassurance, as well as her friendship kept me going when things seemed impossible.I am extremely
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".