Impacts of urban land-cover on plant community structure and biodiversity in a multi-use landscape
Bibliographic record
Abstract
While research and policy alike have recognized the importance of conserving biodiversity, the rapid and continued expansion of urban areas hinders many conservation efforts, particularly as many high-value conservation areas are found in landscapes already modified by human use. Research into the impact of landscape mosaics—their composition and configuration in particular—is important to understanding the impact that human induced land-use change may have on biodiversity, biotic communities, and thus the ecological processes within these areas. The objectives of this research paper are to determine the impacts of the landscape composition surrounding conservation forests has on the plant communities of the forest understory communities. We also seek to outline the possible mechanisms by which the landscape can indirectly impact plant communities, and in so doing, provide a deeper understanding of how natural areas within mosaic landscapes may sustain biodiversity. Using plant community measures from the Credit Valley Conservation Authority in Ontario, Canada, and open-sourced spatial data on Canada’s landcover, we calculated the land cover composition of urban and natural lands surrounding each forest site, and the biodiversity of the understory community in each forest. We used both individual species richness and abundance (NMDS, TITAN), as well as aggregate biodiversity measures (linear regression) to test for significant relationships between the plant community metrics and the composition of the surrounding landscape. Natural land cover, urban land cover, and continuous forest size were all significantly associated with species changes in the NMDS at all scales, and the direction of the urban cover vector was nearly opposite of the natural cover vector in the NMDS space. The output of the TITAN analysis identified both positive and negative responses of individual species to land cover composition at the three scales considered, indicating that indicator species had strong responses to changes in the land cover, with different species being associated with urban vs. natural land cover. The TITAN and NMDS both showed that many more species were positively associated with natural land cover. Only a few species responded positively to high urban cover, and those forests had much lower populations. A series of linear regressions revealed a negative relationship between urban land cover and plant diversity metrics, and positive relationships between natural land cover and plant biodiversity at all scales. Both species richness and species abundance changed significantly with the surrounding land cover composition, but species richness (that is the total number of species present in a community) had the most consistent and statistically significant response—indicating that an areas ability to sustain a certain number of species is affected by the surrounding landscape. The significant findings of both species-level and community level changes associated with land cover confirm our expectations that land cover in mosaic landscapes does indeed have significant impact on plant communities, and can impact forest’s potential to support biodiversity, even when the changes are indirect changes. Forest understory vegetation shows a significant relationship to surrounding land cover composition, with changes associated with urban and natural land cover being consistently significant at 1 km, 2 km, and 5 km scales. This indicates that the forest understory communities of the CVC are not random assemblages, but communities found in predictable patterns that are associated with the composition of the landscape around each site.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".