How well do we understand landscape effects on pollinators and pollination services?
Bibliographic record
Abstract
Many studies in the past decade, mostly in temperate countries, have documented the effects of habitat loss and fragmentation on species richness, composition, and abundance and the behaviour of pollinators. Changes in landscape structure are considered to be the primary causes of the limitation of pollination services in agricultural systems. Here, we review evidence of general patterns as well as gaps in knowledge that could be used to support the development of policies for pollinator conservation and the restoration of degraded landscapes. Our results indicate a recent increase in the number of studies on the relationships between pollination processes and landscape patterns, with some key trends already being established. Many authors indicate, for example, that the spatial organization of a landscape has a great influence on the survival and dispersal capacity of many pollinators, as spatial organization affects resource availability and determines the functional connectivity of the landscape. Additionally, the shape, size and spatial arrangement of the patches of each type of natural environment, as well as the occurrence of different types of land use, can create sites with different degrees of connectivity or even barriers to movement between patches, which can deeply modify pollinator flows through the landscape and consequently the success of cross-pollination. However, there are still some gaps, such as in the knowledge of which critical values of habitat loss can lead to drastic increases in pollinator extinction rates, information that is needed to evaluate at what point plant-pollinator interactions may collapse. We also need to concentrate research effort on improving a landscape’s capacity to facilitate pollinator flow (connectivity) between crops and nesting/foraging areas.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".