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The harsh geophysical template characterized by the urban environment combined with people’s choices has led ecologists to invoke environmental filtering as the main ecological phenomena explaining urban biodiversity patterns. Yet, dispersal is often overlooked as a driving factor, especially on expanding vacant land. Does overcoming dispersal limitation by seeding native species in urban environments and increasing the functional or phylogenetic diversity of the seeding pool increase native plant species diversity and abundance in urban vacant land? We took an experimental approach to learn how different dimensions of plant biodiversity within an augmented regional species pool, via seed additions, can explain variation in community structure over a 3-year period. Vacant lots were cleared and manipulated with seeding treatments of high or low phylogenetic and functional diversities from a pool of 28 native species. Establishment success, total native cover and native species richness were followed and compared to cleared, unseeded control lots as well as un-manipulated lots. Seeding increased native plant abundance and richness over uncleared plots, as well as cleared and unseeded control plots. Phylogenetically diverse seed mixtures had greater establishment success than mixtures composed of closely related species. Diversifying seed mixtures increased the likelihood of including species that are better able to establish on vacant land. However, there were no differences in varying levels of either functional or phylogenetic diversity. Augmenting the regional species pool via diverse seed mixtures can enhance native plant cover and richness under the harsh environmental conditions conferred by land abandonment.
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Machine learning predicts large scale declines in native plant phylogenetic diversity
Summary Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon‐specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro‐environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.
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- PAR ID:
- 10164952
- Date Published:
- Journal Name:
- New Phytologist
- ISSN:
- 0028-646X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/nph.16621
