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Enregistrement W2163821433 · doi:10.1093/icb/icv089

Introduction to the Symposium—Chemicals that Organize Ecology: Towards a Greater Integration of Chemoreception, Neuroscience, Organismal Biology, and Chemical Ecology

2015· article· en· W2163821433 sur OpenAlexaff
James A. Murray, Russell C. Wyeth

Notice bibliographique

RevueIntegrative and Comparative Biology · 2015
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNicotinic Acetylcholine Receptors Study
Établissements canadiensSt. Francis Xavier University
Organismes subventionnairesCompany of BiologistsSociety for Integrative and Comparative Biology (SICB)
Mots-clésEcologyChemical ecologyBiologyChemoreceptor

Résumé

récupéré en direct d'OpenAlex

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), 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) 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). 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, one of the main ideas explored in this symposium concerns how natural selection influences the release-of and the responses-to semiochemicals, in addition to the ecological effects of toxins on potential trophic interactions. In other words, how do chemicals (be they informative cues or defensive toxins) influence movement, locations, and distribution of organisms? Furthermore, chemical cues can synergistically reinforce and/or replace redundant informative cues from other sensory modalities (Goyret and Michael 2015), thus expanding the ecological niches of the organisms that employ such cues. Although pheromones and synomones relay signals that are mutually beneficial to both signaler and receiver—and are thus clearly adaptive—some chemical cues may result in maladaptive behavior on the part of the receiver (allomones; “lures”). Conversely, other chemical cues (kairomones; e.g., odors of predator or prey) are released despite selective forces against their release (Vasey et al. 2015). There is strong selective pressure on the predators/grazers to increase their sensitivity to the inadvertent release of kairomone cues on the part of prey, and vice versa for the prey to increase sensitivity to kairomones released by a predator/grazer, so as to induce defensive structures and trigger defensive behaviors. Chemical cues may be attractive, including odors of food, mates, and ideal habitat. Gardiner et al. (2015) describe how sharks use chemical cues in natal homing. Other chemical cues are repulsive, including conspecific alarm signals, odors of predators, or anti-settlement cues. Some chemicals may have toxic effects as well as provide allomone cues that reduce the fitness of competitors and/or herbivores, as described by Van Alstyne et al. (2015). Many studies of chemoreception are limited to a focus on the receptor/ligand interaction or on the molecular basis of the transduction of semiochemical binding into intracellular changes. We hope that this symposium contributes to extending chemoreceptive studies to their ecological effects via comparative evolution of receptor-protein sequences, neural processing of cues, and behavioral outputs of evolved neuronal circuits (Lunceford and Kubanek 2015). Neuroethology can serve as a bridge between neuronal circuits and adaptive behavior, but rarely is this approach extended to higher levels of organization at which variations in behavior are mapped to evolving populations in response to ecological pressures. Chemical ecology has been successful in relating single chemicals, or suites of chemicals, to ecological effects, but is sometimes limited by a lack of mechanistic explanation of the physiological mechanisms within the organism. The physiological mechanisms could include digestion and sequestration in the case of defensive chemicals, or sensory transduction and neural integration in the case of semiochemicals. Greater integration of these levels of analysis will coincide with better communication between laboratories that focus on their respective levels, as well as with cross-disciplinary training of students in various fields. The ultimate goal is to build ecological models with a clear causal chain based on chemistry, but to reach this goal we will need to build computational tools (Vasey et al. 2015) that integrate lower levels of information into community-level models, including selective forces that can successfully predict organismal distribution and diversity based, in part, on chemical interactions between these organisms. Edison et al. (2015) describe how the traditional natural products chemistry approach to identify active biocompounds via bioassay activity-guided fractionation and analysis can be reinforced and informed by a metabolomics approach that seeks to identify unanticipated chemical differences statistically between complex mixtures of biochemicals taken from two or more groups of organisms. In the future, we hope these papers will help researchers decide when an integrated approach is particularly beneficial for understanding the role of chemicals in organizing ecology. We consider this symposium another step toward facilitating the collaboration of ecologists, physiologists, molecular biologists, and chemists. Together, we will need to develop new molecular, physiological, and computational tools to define analytically the connections among, and to integrate, the various levels of analysis. An integral part of chemoecology includes how chemicals are transferred between organisms, and, since many semiochemicals are fluid-borne, it is imperative to understand how diffusion, bulk flow, and turbulence affect sensory reception and, thus, select for behavioral strategies that maximize chances of finding (or avoiding) chemical sources (Vasey et al. 2015; Bau and Cardé 2015). Jeff Riffell (University of Washington) will organize another symposium on neuroecology at SICB in 2016, and we hope the reader can join us again in Portland, Oregon. The participants in this symposium gratefully acknowledge the support of SICB and its Divisions of Neurobiology, Invertebrate Zoology, and of Comparative Physiology and Biochemistry. We also acknowledge the generous support of the Company of Biologists, and of A-M Systems.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,060
Score d'incertitude au seuil0,200

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0050,004
Science ouverte0,0020,004
Intégrité de la recherche0,0060,011
Charge utile insuffisante (le modèle a refusé de juger)0,0600,025

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,319
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2015
Routes d'admission1
Résumé présentoui

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