The disentangled bank: How loss of habitat fragments and disassembles ecological networks
Bibliographic record
Abstract
Habitat transformation is one of the leading causes of changes in biodiversity and the breakdown of ecosystem function and services. The impacts of habitat transformation on biodiversity are complex and can be difficult to test and demonstrate. Network approaches to biodiversity science have provided a powerful set of tools and models that are beginning to present new insight into the structural and functional effects of habitat transformation on complex ecological systems. We propose a framework for studying the ways in which habitat loss and fragmentation jointly affect biodiversity by altering both habitat and ecological interaction networks. That is, the explicit study of "networks of networks" is required to understand the impacts of habitat change on biodiversity. We conduct a broad review of network methods and results, with the aim of revealing the common approaches used by landscape ecology and community ecology. We find that while a lot is known about the consequences of habitat transformation for habitat network topology and for the structure and function of simple antagonistic and mutualistic interaction networks, few studies have evaluated the consequences for large interaction networks with complex and spatially explicit architectures. Moreover, almost no studies have been focused on the continuous feedback between the spatial structure and dynamics of the habitat network and the structure and dynamics of the interaction networks inhabiting the habitat network. We conclude that theory and experiments that tackle the ecology of networks of networks are needed to provide a deeper understanding of biodiversity change in fragmented landscapes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".