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Abstract Phylogenetic and species‐based taxonomic descriptions of community structure may provide complementary information about the mechanisms driving community assembly across different environments. Environmental filtering may have similar effects on taxonomic and phylogenetic diversity under the assumption of niche conservatism, whereas competitive exclusion could produce contrasting patterns in these diversity metrics. In grassland restorations, these diversity patterns might then reveal potential assembly mechanisms underlying the impacts of restoration and management conditions on community structure.We compared plant community structure (alpha diversity, composition, and within‐site beta diversity) from both phylogenetic and taxonomic perspectives. Using surveys from 120 tallgrass prairie restorations in four regions of the Midwestern United States, we examined the effects of four potential drivers or environmental gradients: precipitation in the first year of restoration, seed mix richness, time since last prescribed fire, and restoration age, and included soil conditions as a covariate.First‐year precipitation influenced taxonomic community structure, but had weak effects on phylogenetic diversity and composition. Similarly, greater seed mix richness increased taxonomic diversity but did not influence phylogenetic diversity. Taxonomic, but not phylogenetic, diversity generally was lower in older restorations and those with a longer time since the last prescribed fire. These drivers consistently explained more variation in taxonomic than phylogenetic diversity and composition, perhaps in part because species turnover was largely among related species, producing weak impacts on phylogenetic community measures.An impact of precipitation on taxonomic but not phylogenetic diversity suggests that there may not be large differences in drought tolerance among clades that would cause phylogenetic patterns to arise from this environmental filter. Declining taxonomic diversity but not phylogenetic diversity is consistent with competitive exclusion as an assembly mechanism when competition is strongest between related species.Synthesis. This research shows how studying taxonomic and phylogenetic diversity of ecosystem restorations can inform plant community ecology and help natural resource managers better predict the outcomes of restoration actions and management.
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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:
- 10456219
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 227
- Issue:
- 5
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1544-1556
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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