Socioecological drivers of mutualistic and antagonistic plant-insect interactions and interaction outcomes in suburban landscapes
Bibliographic record
Abstract
Cities are complex socioecological systems, yet most urban ecology research does not include the influence of social processes on ecological outcomes. Much of the research that does address social processes focuses primarily on their effects on biotic community composition, with less attention paid to how social processes affect species interactions. Linking social processes to ecological outcomes is complicated by high spatial heterogeneity in cities and the potential for scale mismatch between social and ecological processes, and the indicators used to assess those processes. Here, we assessed how social and ecological processes jointly influence the frequency and outcomes of species interactions among the native perennial vine Gelsemium sempervirens and its insect pollinators, nectar robbers, and florivores across 28 residential subdivisions in the Research Triangle region, NC, USA. We integrated data on socioeconomic attributes (mean property value, mean property size, subdivision age), vegetation attributes (forest cover and richness and density of managed and unmanaged floral resources), species interactions (conspecific and heterospecific pollen deposition, nectar robbing, florivory), and Gelsemium reproduction (fruit set, seeds per fruit) using structural equation modeling to understand the causal links between socioeconomic attributes, vegetation attributes, and interaction frequency and outcome. Among socioeconomic attributes, property value was the strongest predictor of interaction frequency, having both direct and vegetation-mediated indirect effects on pollination and florivory. However, the effect of socioeconomic attributes on plant reproduction was small. Overall, we were able to explain only a small amount of the variation in any species interaction or reproduction measure. This may be due to the functional similarity of subdivisions, despite large variation in both socioeconomic and vegetation attributes, or may reflect scale mismatch between the ecological and socioeconomic variables. Our findings highlight the need to develop scale-appropriate indicators to improve our understanding of the links between social and ecological processes in urban 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".