Introduction to the Symposium—Chemicals that Organize Ecology: Towards a Greater Integration of Chemoreception, Neuroscience, Organismal Biology, and Chemical Ecology
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».