Dynamic macroecology on ecological time‐scales
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".