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Abstract AimAmazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. LocationAmazonia. TaxonAngiosperms (Magnoliids; Monocots; Eudicots). MethodsData for the abundance of 5082 tree species in 1989 plots were combined with a mega‐phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. ResultsIn the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white‐sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main ConclusionNumerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long‐standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions.
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Sensitivity of Codispersion to Noise and Error in Ecological and Environmental Data
Understanding relationships among tree species, or between tree diversity, distribution, and underlying environmental gradients, is a central concern for forest ecologists, managers, and management agencies. The spatial processes underlying observed spatial patterns of trees or edaphic variables often are complex and violate two fundamental assumptions—isotropy and stationarity—of spatial statistics. Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to data from large forest plots has provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying edaphic and topographic gradients. However, these data are not collected without error, and the performance of the codispersion coefficient when there is noise or measurement error (“contamination”) in the data heretofore has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many forest datasets. Three of these involved comparing codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codispersion between tree species and soil variables, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination was relatively low (<15%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice.
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- Award ID(s):
- 1832210
- PAR ID:
- 10154591
- Date Published:
- Journal Name:
- Forests
- Volume:
- 9
- Issue:
- 11
- ISSN:
- 1999-4907
- Page Range / eLocation ID:
- 679
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/f9110679
