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Record 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 on OpenAlexaff
James A. Murray, Russell C. Wyeth

Bibliographic record

VenueIntegrative and Comparative Biology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsSt. Francis Xavier University
FundersCompany of BiologistsSociety for Integrative and Comparative Biology (SICB)
KeywordsEcologyChemical ecologyBiologyChemoreceptor

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0020.004
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0600.025

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.

Opus teacher head0.041
GPT teacher head0.319
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations2
Published2015
Admission routes1
Has abstractyes

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