Data and Rscripts from: An integrated experimental and mathematical approach to inferring the role of food exploitation and interference interactions in shaping life history
Bibliographic record
Abstract
Intraspecific interactions can occur through many ways but the mechanisms can be broadly categorized as food exploitation and interference interactions. Identifying how intraspecific interactions impact life history is crucial to accurately predict how population density and structure influence dynamics. However, disentangling the effects of interference interactions from exploitation using experiments, is challenging for most biological systems. Here we propose an approach that combines experiments with modeling to infer the pathways of intraspecific interactions in a system. First, a consumer-resource model is built without intraspecific interactions. Then, the model is parameterized by fitting it to life-history data from a first experiment in which food abundance was varied. Next, hypothesized scenarios of intraspecific interactions are incorporated into the model which is then used to predict life histories with increasing competitor density. Lastly, model predictions are compared against data from a second experiment which raised groups of competitors of different densities. This comparison allows us to infer the role of interference and exploitation in shaping life history. We demonstrated the approach using the smaller tea tortrix Adoxophyes honmai across a range of temperature. We investigated five scenarios of interactions that included exploitation and three pathways for interference through some effects either on energetics to represent changes in ingestion or activity, or on mortality to model deadly interactions, or on mortality and ingestion to model cannibalism. Overall, intraspecific interactions in tea tortrix are best explained by a high level of deadly interactions along with some level of interference that acts on energy such as escaping and blocking access to food. Deadly interactions increase with temperature while interference that acts on energy is strongest close to the optimal temperature for reproduction. Interestingly, exploitation is more important than interference at low competitor density. The combination of mathematical modeling and experimentation allowed us to mechanistically characterize the intraspecific interactions in tea tortrix in a way that is readily incorporated into population-level mathematical models. The primary value of this approach, however, is that it can be applied to a much wider range of taxa than is possible with pure experimental approaches.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".