Introduction to the Symposium—Chemicals that Organize Ecology: Towards a Greater Integration of Chemoreception, Neuroscience, Organismal Biology, and Chemical Ecology
Bibliographic record
Abstract
Ecology as a process is shaped by biotic and abiotic influences. In some cases, a single type of chemical can have effects on an ecosystem, effects that are disproportionate to its relative mass, such that the ecosystem would organize differently in the absence of this type of chemical. The hallmark examples are those ‘‘molecules of keystone significance’’ (Ferrer and Zimmer 2012), such as the biosequestered alkaloid tetrodotoxin. Keystone molecules are analogous to keystone species in their capacity to mediate large ecological effects disproportionate to their mass. Some predators/grazers may evolve the ability to sense toxins being released from potential prey, making the toxin a semiochemical, molecules that serve as information-bearing cues among organisms (Ferrer and Zimmer 2012). Not all semiochemicals may be of keystone significance but they do have a greater effect on ecology that would be predicted, based on their abundance relative to all biomolecules, and could be said to ‘‘organize ecology.’’ Zimmer and Derby (2011) created a conceptual framework relating chemical defenses and neurobiological function in the SICB 2011 symposium entitled ‘‘Neural Determinants of Ecological Processes from Individuals to Ecosystems.’’ They advocated for an approach to defensive chemicals that integrated the isolation and identification of chemicals with study of behavioral responses, sensory and neuronal mechanisms of action, and ecological effects and adaptation. Their goals included learning how defensive chemicals affect the abundance and distribution of organisms and species. An integrative approach would include not only analytical chemistry, illustrating distributions of chemical within individual organs and across trophic levels, but would also attempt to link the chemicals to behavioral and physiological effects, and, ultimately, to reproductive success so as to define the mechanisms of the selective forces that shape ecology. Defensive chemicals can be released and repel competitors or predators/herbivores (van Alstyne et al. 2015, this issue), and one might predict that these competitors would eventually evolve an insensitivity to such cues unless the released chemicals were toxic. Alternatively, defensive chemicals can be accumulated or sequestered and they can affect trophic interactions upon contact or after partial ingestion. Some of these accumulated chemicals, such as saxitoxin (Ferrer et al. 2015, this issue) can be biosequestered over multiple trophic levels and keystone molecules are prime examples (Ferrer and Zimmer 2012). Also, chemicals with less comprehensive roles can also contribute to organizing ecological interactions. Other effects of defensive chemicals include the evolution of counter-measures such as avoidance of, or resistance to, toxins, and evolution of mechanisms of sensation (Lunceford and Kubanek 2015, this issue). One goal of this symposium was to extend the causes of ecology to the exchange of semiochemicals (pheromones, kairomones, allomones, and synomones) (Sbarbati and Osculati 2006). Thus, Integrative and Comparative Biology
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".