Disentangling direct and indirect effects of landscape structure on urban bird richness and functional diversity
Bibliographic record
Abstract
As fragmented landscapes become increasingly common around the world, managing the spatial arrangement of landscape elements (i.e., landscape configuration) may help to promote the conservation of biodiversity. However, the relative effects of landscape configuration on different dimensions of biodiversity across species assemblages are largely unknown. Thus, a key challenge consists in understanding when it is necessary to focus on landscape configuration, in addition to landscape composition, to achieve multifunctional landscapes. We tested the effects of landscape composition (the percentage of tree cover and built infrastructure) and landscape configuration (degree of fragmentation) on landscape-level species richness and different metrics of functional diversity of urban birds. We collected data on different bird guilds (nectarivores/frugivores, insectivores) from Brisbane, Australia. Using structural equation models, we found that landscape structure (landscape composition and configuration) affected functional diversity via two main pathways: (1) through effects of landscape composition, mediated by landscape configuration (indirect effects), and (2) through direct ("independent") effects of landscape composition and configuration, filtering species with extreme trait values. Our results show that landscape-level species richness declined with the extent of built infrastructure, but patterns of trait diversity did not necessarily correlate with this variable. Landscape configuration had a stronger mediating effect on some metrics of the functional diversity of insectivores than on the functional diversity of frugivores/nectarivores. In addition, fragmentation increased the effects of built infrastructure for some traits (body size and dispersal capacity), but not for others (habitat plasticity and foraging behavior). These results suggest that differential approaches to managing landscape structure are needed depending on whether the focus is on protecting functional diversity or species richness and what the target guild is. Managing landscape fragmentation in areas with high levels of built infrastructure is important if the objective is to protect insectivore species with uncommon traits, even if it is not possible to preserve high levels of species richness. However, if the target is to enhance both functional diversity and species richness of multiple guilds, the focus should be on improving composition through the reduction of negative effects of built infrastructure, rather than promoting specific landscape configurations in growing cities.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".