Indirect evolutionary interactions in a multitrophic system
Bibliographic record
Abstract
Indirect evolutionary effects Species interact both directly and indirectly through interactions mediated by a third species. Recently, it has been established that these indirect interactions have important effects on the structure of ecological communities and that they can produce more complex interaction webs than the food webs of direct trophic interactions (Ohgushi 2005, 2007). In this chapter we address the question: can indirectly interacting species separated in space or time influence each other’s evolution? These indirect interactions can evolve if there is a chain reaction where one species exerts selection via a second species on a heritable trait of a third species (Fig. 13.1). If indirect interactions can evolve they may play an important role in community evolution. Measuring indirect interactions is more difficult than measuring direct evolutionary interactions. We will suggest strategies for measuring indirect evolutionary interactions using examples from the community centred on the gall-inducing fly Eurosta solidaginis , its host plants and natural enemies. Goldenrod–herbivore–natural enemy interaction Eurosta has formed three partially reproductively isolated populations on Solidago altissima altissima , S. a. gilvocanescens and S. gigantea which we refer to as the forest altissima , prairie altissima and forest gigantea host races, respectively. The forest altissima and forest gigantea host races occur sympatrically in the forest biome of North America (Waring et al . 1990; Craig et al . 1993, 1997, 2001, 2007a, b; Brown et al . 1996; Itami et al . 1998; Stireman et al . 2005; Horner et al . 2008) and the host races on S. altissima gilvocanescens and S. gigantea occur sympatrically in the prairie (Craig et al . 2007a; Craig and Itami 2011). Along the forest–prairie biome border there is geographic mosaic in the distribution of S. a. altissima and S. a. gilvocanescens growing in close proximity to each other.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".