Quantifying Local and Landscape Indicators of Urban Pollination Service with High-Resolution Imagery and Field Sampling
Bibliographic record
Abstract
Urbanization is a driver of global change that can result in the loss, fragmentation, and alteration of habitat, all of which are threats to biodiversity and ecosystem functioning. The ecology of pollinating insects, especially bees, is relatively well described in urban environments. Urban green spaces can support pollinator diversity, including designed spaces, such as community gardens, as well as remnant or restored semi-natural habitats. Insect pollinators in turn provide cultural and regulatory pollination services to UGSs. The impacts of urbanization on biodiversity and ecosystem functioning are regularly described from the landscape perspective, where urbanization is described as the compositional replacement of land cover with impervious surfaces. Intensely urbanized landscapes are negatively correlated with pollinating insect diversity. But less is known about the effects of landscape configurational heterogeneity on the maintenance of pollinator diversity, and especially pollination services. While convenient to measure, fractional urban cover oversimplifies urbanization and thus may limit ecological inferences on patterns operating at the landscape or local scale. For instance, the presence of urban elements, such as buildings, may reduce the permeability of the urban matrix. On the local scale, the abundance and diversity of flowers is related to habitat quality. But invasive plant species pose a significant pressure to the biodiversity and ecosystem functioning of these spaces. Although this is the case, the impacts of plant invasion on pollinator diversity and pollination service are relatively underexplored. In this thesis, a variety of open-source and low-cost remote sensing data products were used to characterize urbanization and to describe patterns in urban pollinator diversity and pollination service delivery. From patches to landscapes, remote sensing data provide information on the ecological conditions altered by urbanization and the pressures imposed on biodiversity and ecosystem functioning. Applied to pollinators, the patterns detected using the approaches applied in this thesis provide ecological inferences into the processes that underly both pollinator diversity and service delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".