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Enregistrement W2312765105 · doi:10.1242/jeb.069575

Biophysics, bioenergetics and mechanistic approaches to ecology

2012· editorial· en· W2312765105 sur OpenAlexaboutno aff
Mark W. Denny

Notice bibliographique

RevueJournal of Experimental Biology · 2012
Typeeditorial
Langueen
DomaineEnvironmental Science
ThématiqueSpecies Distribution and Climate Change
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReductionismEcologyUtopiaPopulationField (mathematics)BiologyCognitive scienceData scienceComputer scienceEpistemologySociologyPsychologyPolitical sciencePhilosophyLaw

Résumé

récupéré en direct d'OpenAlex

In 1986, Tom Schoener evaluated the current state of community ecology, came to the conclusion that a reductionist approach to the subject was feasible (at least in theory) and proposed what he called ‘a mechanistic ecologist’s utopia’ in which the dynamics of populations and communities could be predicted from information about the structure, physiology and behavior of individual organisms (Schoener, 1986). In the quarter of a century since Schoener’s ‘call to arms’, his utopia has not been realized. The prevailing sentiment among ecologists has been that the mechanistic approach’s immense informational requirements – and the complications inherent in synthesizing that information – put it beyond practical reach. Indeed, the extreme complexity of population and community dynamics has led some ecologists to question whether a mechanistic, predictive understanding of community ecology – elucidation of general laws – can ever be achieved (e.g. Lawton, 1999; Simberloff, 2004). This is a worrisome thought. In this time of rapid climate change (Intergovernmental Panel on Climate Change, 2007), it is discouraging to suppose that the complex nature of interactions among organisms – and among organisms and their environment – might preclude science from providing reliable guidance as to what the future has in store and how humankind should cope.Recently, ecologists have begun to reconsider Schoener’s proposition. Advances in technology have sparked optimism that the detailed information required for the mechanistic ecologist’s utopia can actually be obtained. Using the wizardry of solid-state electronics, field ecologists can make measurements of physiology, behavior and the environment with an ease that could scarcely be imagined 25 years ago. Equally extraordinary advances in molecular biology allow physiologists, evolutionary and population biologists, and ecologists to explore the genetic underpinnings of their science in unprecedented detail. Similarly detailed environmental information is now readily available via remote sensing, and computing power has increased exponentially, making it practical for theoretical ecologists to manipulate models incorporating increased detail, even to the level of individual organisms. Based in part on this ‘optimism of information’, calls have gone out for new efforts to incorporate mechanistic understanding of individual organisms into the study of ecology (McGill et al., 2006; Kearney et al., 2008; Denny and Helmuth, 2009; McGill and Nekola, 2010; Monaco and Helmuth, 2011).It is here that The Journal of Experimental Biology (JEB) enters the picture. Since its inception in the 1920s, JEB has championed development of mechanistic approaches to the study of physiology and biomechanics, research perspectives that use the tools of physics, chemistry and engineering to explain how individual plants and animals function. The time seems ripe to couple these mechanistic approaches (and the information already available at the individual level) to the efforts of population and community ecologists, forming a grand ‘constructionist’ perspective extending from genetics to ecosystems. Where might this marriage of mechanistic approaches be advantageous? Where is it even feasible? What areas of research need priority attention? These questions formed the impetus for a symposium on Biophysics, Bioenergetics and Mechanistic Approaches to Ecology held in Cambridge, UK in March 2011. The results are offered here.No single volume could do justice to the multitudinous details of mechanistic approaches in ecology. Instead, what you will find in this compendium is a selection of topics that span the breadth of the subject. From the mechanics of individual molecules to the long-term viability of entire coral reefs. From ocean waves to waves of grain. From the genetic capacity for adaptation to the mechanics of reproduction and dispersal, to theories predicting evolved responses to rising temperature. Bacteria, phytoplankton and seaweeds; salmon and jellyfish; vultures, dragonflies, mussels and lizards; we have them all. The hope is that these articles will raise the awareness of JEB’s traditional audience regarding the application of their interest and expertise to issues in ecology and that the topics addressed will raise the awareness of ecologists to the vast potential of mechanistic approaches.A note about terminology. The combination of individual-level and ecological-level mechanistic approaches needs a name, and none of the traditional ones will do. ‘Biophysics’, ‘bioenergetics’ and ‘biomechanics’, while certainly part of the program, don’t acknowledge ecology. ‘Physiological ecology’ and ‘ecological physiology’ traditionally do not emphasize the depth of physical and genetic detail espoused here. Instead, the term ‘ecomechanics’ [short for ‘ecological mechanics’ (Wainwright et al., 1976)] will be employed. As explained previously (Denny and Gaylord, 2010), the intent is not to exclude any field from a mechanistic approach to ecology but rather to provide a convenient shorthand for the whole broad perspective.I thank the symposium participants and the editorial and production staff at JEB for their steadfast efforts, interest and good humor in bringing this special issue to fruition.

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)0,004
score de la tête « metaresearch » (Gemma)0,005
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: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0020,019
Communication savante0,0040,011
Science ouverte0,0020,003
Intégrité de la recherche0,0050,010
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

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 Opus0,059
Tête enseignante GPT0,281
Écart entre enseignants0,221 · 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

Citations7
Publié2012
Routes d'admission1
Résumé présentoui

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