MétaCan
Menu
Back to cohort

Dynamic macroecology on ecological time‐scales

2009· article· en· W1530501861 on OpenAlexafffund
Jonathan A. D. Fisher, Kenneth T. Frank, William C. Leggett

Bibliographic record

VenueGlobal Ecology and Biogeography · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsBedford Institute of OceanographyQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMacroecologyEcologyTemporal scalesEcosystemEnvironmental changeRange (aeronautics)Climate changeBiodiversityBiology

Abstract

fetched live from OpenAlex

ABSTRACT Aim The discipline of macroecology is increasingly being regarded as an effective vehicle for the evaluation of recent population‐ to ecosystem‐level responses to widespread human and environmental influences. However, due to the prevalent use of time‐averaged and cumulative data in macroecological analyses, the majority of the patterns that emerge from research in this field can be regarded as static. Here we review the application of dynamic macroecological analyses to changes in relationships between macroecological variables on seasonal to decadal scales. We illustrate the strength of this perspective for documenting changing patterns and testing hypotheses related to these dynamics on ecological time‐scales. Location Studies were compiled and reviewed from terrestrial and aquatic ecosystems. Methods We review examples of temporal changes in macroecological patterns driven by recent anthropogenic influences and environmental change. Results The dynamic nature of macroecological patterns on ecological time‐scales has been revealed in recent years across a wide range of ecosystems, largely through the development, maintenance and analysis of biotic and environmental monitoring time series. The resultant analyses complement examinations of dynamics over evolutionary time and have similarly revealed that static portrayals can conceal important temporal dynamics that underlie the patterns of interest. As a consequence, static depictions, resting as they do on comparative analyses in which the validity of space‐for‐time substitutions is assumed, may be of limited use for testing hypotheses related to the mechanisms underlying the patterns revealed and, by extension, the development of reliable predictions of future states. Main conclusions Recent dynamic macroecological analyses have demonstrated the utility of combined spatial and temporal replication, and have contributed to hypothesis testing related to the mechanistic processes underlying changes in macroecological patterns on ecological time‐scales. We suggest four specific avenues of future research to further the development and application of temporal approaches on similar time‐scales within the field of macroecology.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.002
GPT teacher head0.212
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations80
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueGlobal Ecology and BiogeographySame topicIsotope Analysis in EcologyFrench-language works237,207