The Delta as Changing Landscapes
Bibliographic record
Abstract
What happens at one place in a landscape influences and is influenced by what happens in other places. Consequently, management and restoration that focus on individual places may fail to recognize and incorporate interactions across entire landscapes. The science of landscape ecology, which emphasizes the interplay of landscape structure, function, and change at multiple scales, offers a perspective that can integrate the spatial relationships of ecological processes and the functional interconnections of land and water in the Delta. Although the Delta is one of the most studied estuaries in the world, applications of landscape science have been limited. We describe why it is important to incorporate landscape science into management and restoration, emphasizing how Delta landscapes have changed over the past centuries. The land–water linkages of the past have been broken, waterways have been over-connected, and hard boundaries have replaced the gradual and dynamic transitions among landscape patches. The contemporary landscape also has new, novel assemblages of species and stressors that were not there in the past. This historical perspective indicates how knowledge of past landscape functions can contribute to the restoration and management of contemporary landscapes. We illustrate these points with case studies of inundation dynamics and riparian woodlands, and use a third example to describe a landscape approach to restoration. We propose that science that encompasses the multiple, interacting components of functional landscapes in the Delta will foster resilient and enduring restoration and management outcomes that benefit both people and wildlife. We suggest several ways of moving landscape science to the forefront of management and restoration in the Delta.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".