Spatio‐temporal development of forests – current trends in field methods and models
Bibliographic record
Abstract
We present a critical review of current trends in research of spatio‐temporal development of forests. The paper addresses (1) field methods for the development of spatially‐explicit models of forest dynamics and their integration in models of forest dynamics, (2) strengths and limitations of traditional patch models versus spatially‐explicit, individual‐based models, and (3) the potential for moment‐based methods in the analysis of forest dynamics. These topics are discussed with reference to their potential for solving open questions in the studies of forest dynamics. The study of spatio‐temporal processes provides a link between pattern and process in plant communities, and plays a crucial role in understanding ecosystem dynamics. In the last decade, the development of spatially‐explicit, individual‐based models shifted the focus of forest dynamics modelling from the dynamics of discrete patches to the interactions among individual organisms, thus encapsulating the theory of “neighbourhood” dynamics. In turn, the stochastic properties and the complexity of spatially‐explicit, individual‐based models gave rise to the development of a new suite of so‐called moment‐based models. These new models describe the dynamics of individuals and of pairs of individuals in terms of their densities, thus directly capturing second‐order information on spatial structure. So far, this approach has not been applied to forests; we indicate extensions needed for such applications. Moment‐based models may be an important complement to spatially explicit individual‐based models in developing a general spatial theory of forest dynamics. However, both kinds of models currently focus on fine scales, whereas a critical issue in forest dynamics is to understand the interaction of fine‐scale processes with coarser‐scale disturbances. To obtain a more complete picture of forest dynamics, the relevant links and interactions between fine‐, intermediate‐, and coarse‐scale processes ought to be identified. Intensive links between modelling work and field studies designed across different scales are a promising means to create a new perspective on forest dynamics.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".