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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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Predicting and Prioritising Community Assembly: Learning Outcomes via Experiments
ABSTRACT Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion, restoration and synthetic ecology. However, predicting and prioritising assembly outcomes remains difficult. We address this challenge via a mechanism‐free approach useful when little data or knowledge exist (LOVE; Learning Outcomes Via Experiments). We carry out assembly experiments (‘actions’, here, random combinations of species additions) potentially in multiple environments, wait, and measure abundance outcomes. We then train a model to predict outcomes of novel actions or prioritise actions that would yield the most desirable outcomes. Across 10 single‐ and multi‐environment datasets, when trained on 89 randomly selected actions,LOVEpredicts outcomes with 0.5%–3.4% mean error, and prioritises actions for maximising richness, maximising abundance, or removing unwanted species, with 94%–99% mean true positive rate and 10%–84% mean true negative rate across tasks.LOVEcomplements existing mechanism‐first approaches for community ecology and may help address numerous applied challenges.
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- Award ID(s):
- 1831944
- PAR ID:
- 10548495
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 27
- Issue:
- 10
- ISSN:
- 1461-023X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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