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Record W2312765105 · doi:10.1242/jeb.069575

Biophysics, bioenergetics and mechanistic approaches to ecology

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

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typeeditorial
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsReductionismEcologyUtopiaPopulationField (mathematics)BiologyCognitive scienceData scienceComputer scienceEpistemologySociologyPsychologyPolitical sciencePhilosophyLaw

Abstract

fetched live from 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.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.019
Scholarly communication0.0040.011
Open science0.0020.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0040.002

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 head0.059
GPT teacher head0.281
Teacher spread0.221 · 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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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