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Trait dimensionality and population choice alter estimates of phenotypic dissimilarity

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ABSTRACT

The ecological niche is a multi‐dimensional concept including aspects of resource use, environmental tolerance, and interspecific interactions, and the degree to which niches overlap is central to many ecological questions. Plant phenotypic traits are increasingly used as surrogates of species niches, but we lack an understanding of how key sampling decisions affect our ability to capture phenotypic differences among species. Using trait data of ecologically distinct monkeyflower (Mimulus) congeners, we employed linear discriminant analysis to determine how (1) dimensionality (the number and type of traits) and (2) variation within species influence how well measured traits reflect phenotypic differences among species. We conducted analyses using vegetative and floral traits in different combinations of up to 13 traits and compared the performance of commonly used functional traits such as specific leaf area against other morphological traits. We tested the importance of intraspecific variation by assessing how population choice changed our ability to discriminate species. Neither using key functional traits nor sampling across plant functions and organs maximized species discrimination. When using few traits, vegetative traits performed better than combinations of vegetative and floral traits or floral traits alone. Overall, including more traits increased our ability to detect phenotypic differences among species. Population choice and the number of traits used had comparable impacts on discriminating species. We addressed methodological challenges that have undermined cross‐study comparability of trait‐based approaches. Our results emphasize the importance of sampling among‐population trait variation and suggest that a high‐dimensional approach may best capture phenotypic variation among species with distinct niches.

No MeSH data available.


Phenotypic overlap of species in multivariate vegetative trait space. The principal coordinates analysis (PCoA) used Gower's distance on the full dataset (all populations and individuals) and all standardized vegetative traits
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ece32780-fig-0001: Phenotypic overlap of species in multivariate vegetative trait space. The principal coordinates analysis (PCoA) used Gower's distance on the full dataset (all populations and individuals) and all standardized vegetative traits

Mentions: In a common solution to sample‐size‐based mathematical constraints of LDA (few individuals, many traits), we first used principal coordinates analysis (PCoA) on the Gower's distance matrix constructed from each selected combination of traits as a preprocessing step (Baker & Logue, 2003; Fukunaga, 1990; Sharma & Paliwal, 2015) and passed the first two major axes as input “traits” to the LDA. These first two axes should capture the vast majority of phenotypic variation: in ordinations of the full dataset, the first two PCoA axes in combination explained 86.8% of variation using vegetative traits (Figure 1), and 84.3% using floral traits (Figure 2). To further ensure that only using the first two PCoA axes from this preprocessing step did not drive the results, we conducted smaller (3 species with 12+ individuals each) parallel analyses using two and eight PCoA axes in the LDA (details in SI). With the exception of the floral‐only dataset for M. lewisii and M. primuloides population 7 (which showed greater assignment success with eight PCoA axes), the results were qualitatively very similar using two and eight PCoA axes (Figs. S2 and S3). Therefore, we report results from the larger dataset, using two PCoA axes. In previous work, “dimensionality” refers to the number of composite orthogonal phenotypic axes used (Maire et al., 2015; Villéger et al., 2011); thus, dimensionality encapsulates both the number and type of traits used to estimate phenotypic space. Our definition of dimensionality follows this concept but differs operationally. We vary the number and type of input traits, but as outlined above, our reported results are all generated using the same number of composite orthogonal trait axes (two).


Trait dimensionality and population choice alter estimates of phenotypic dissimilarity
Phenotypic overlap of species in multivariate vegetative trait space. The principal coordinates analysis (PCoA) used Gower's distance on the full dataset (all populations and individuals) and all standardized vegetative traits
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC5383497&req=5

ece32780-fig-0001: Phenotypic overlap of species in multivariate vegetative trait space. The principal coordinates analysis (PCoA) used Gower's distance on the full dataset (all populations and individuals) and all standardized vegetative traits
Mentions: In a common solution to sample‐size‐based mathematical constraints of LDA (few individuals, many traits), we first used principal coordinates analysis (PCoA) on the Gower's distance matrix constructed from each selected combination of traits as a preprocessing step (Baker & Logue, 2003; Fukunaga, 1990; Sharma & Paliwal, 2015) and passed the first two major axes as input “traits” to the LDA. These first two axes should capture the vast majority of phenotypic variation: in ordinations of the full dataset, the first two PCoA axes in combination explained 86.8% of variation using vegetative traits (Figure 1), and 84.3% using floral traits (Figure 2). To further ensure that only using the first two PCoA axes from this preprocessing step did not drive the results, we conducted smaller (3 species with 12+ individuals each) parallel analyses using two and eight PCoA axes in the LDA (details in SI). With the exception of the floral‐only dataset for M. lewisii and M. primuloides population 7 (which showed greater assignment success with eight PCoA axes), the results were qualitatively very similar using two and eight PCoA axes (Figs. S2 and S3). Therefore, we report results from the larger dataset, using two PCoA axes. In previous work, “dimensionality” refers to the number of composite orthogonal phenotypic axes used (Maire et al., 2015; Villéger et al., 2011); thus, dimensionality encapsulates both the number and type of traits used to estimate phenotypic space. Our definition of dimensionality follows this concept but differs operationally. We vary the number and type of input traits, but as outlined above, our reported results are all generated using the same number of composite orthogonal trait axes (two).

View Article: PubMed Central - PubMed

ABSTRACT

The ecological niche is a multi‐dimensional concept including aspects of resource use, environmental tolerance, and interspecific interactions, and the degree to which niches overlap is central to many ecological questions. Plant phenotypic traits are increasingly used as surrogates of species niches, but we lack an understanding of how key sampling decisions affect our ability to capture phenotypic differences among species. Using trait data of ecologically distinct monkeyflower (Mimulus) congeners, we employed linear discriminant analysis to determine how (1) dimensionality (the number and type of traits) and (2) variation within species influence how well measured traits reflect phenotypic differences among species. We conducted analyses using vegetative and floral traits in different combinations of up to 13 traits and compared the performance of commonly used functional traits such as specific leaf area against other morphological traits. We tested the importance of intraspecific variation by assessing how population choice changed our ability to discriminate species. Neither using key functional traits nor sampling across plant functions and organs maximized species discrimination. When using few traits, vegetative traits performed better than combinations of vegetative and floral traits or floral traits alone. Overall, including more traits increased our ability to detect phenotypic differences among species. Population choice and the number of traits used had comparable impacts on discriminating species. We addressed methodological challenges that have undermined cross‐study comparability of trait‐based approaches. Our results emphasize the importance of sampling among‐population trait variation and suggest that a high‐dimensional approach may best capture phenotypic variation among species with distinct niches.

No MeSH data available.