Editorial: Long-term monitoring in ecology and evolution: establishing a sound baseline to help inform our future
Bibliographic record
Abstract
Long-term monitoring in ecology and evolution: establishing a sound baseline to help inform our future Since the earliest days of research in ecology and evolution, organisms have been observed and tracked in their natural environments.Observations are a necessary first step in understanding biological processes, serving as an initial glimpse into natural phenomena such as species occupancy and abundance, associations between individuals and their environments, system responses to natural or anthropogenic disturbance, and processes underlying life history variation (Sagarin and Pauchard, 2010;Clutton-Brock and Sheldon, 2010).When extended through time, observational data form the basis of longterm monitoring, documenting changes in populations, species or systems over prolonged periods ranging from years to decades (Lindenmayer and Likens, 2018).Long-term monitoring has the potential to detect and explain complex temporal variation in ecological or evolutionary processes, and thus may be well-positioned to act as a cornerstone for tracking some of the big challenges currently facing environments and society.However, long-term monitoring is criticized for being too descriptive and not relying sufficiently on hypotheses and testable predictions, meaning that research results can be inconclusive or even spurious (Lovett et al., 2007).Long-term monitoring often lacks focus because relevant species or interactions are not tracked with the rigor or temporal variability needed for strong inference.It follows that much long-term monitoring may not be sufficiently nimble to address emerging or priority research topics, and targeted approaches like field experiments or before-after-control-impact studies can yield stronger findings in a more time-and resource-efficient manner.Thus, long-term monitoring may be less attractive to researchers seeking to maximize productivity or to funding agencies expecting conclusive findings through a typical 3-5 year grant cycle.Collectively, these concerns may cast doubt on the current and potential future importance of long-term monitoring in ecology.While it is indisputable that long-term monitoring faces challenges of legitimacy and perceived relevance in today's research landscape, we feel that this investigative approach remains crucial for addressing a range of questions where patterns are only revealed
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".