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Trait dimensionality and population choice alter estimates of phenotypic dissimilarity

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.


Related in: MedlinePlus

Correct assignment of individuals to species versus number of traits. Correct assignment of individuals to species increased on average as more traits were considered and varied with trait dataset used. Vegetative traits outperformed floral or combined datasets at comparable numbers of traits. The combined constrained trait dataset used separate principal coordinates analyses in linear discriminant analysis (LDA) preprocessing such that one axis subsequently input into the LDA was constrained to be solely vegetative, and the other floral. SE bars are shown
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ece32780-fig-0004: Correct assignment of individuals to species versus number of traits. Correct assignment of individuals to species increased on average as more traits were considered and varied with trait dataset used. Vegetative traits outperformed floral or combined datasets at comparable numbers of traits. The combined constrained trait dataset used separate principal coordinates analyses in linear discriminant analysis (LDA) preprocessing such that one axis subsequently input into the LDA was constrained to be solely vegetative, and the other floral. SE bars are shown

Mentions: All multidimensional approaches outperformed the average single trait (41% correct assignment; Figure 3), and overall, including more traits increased correct assignment. On average, the odds of correct assignment increased about twofold over the range of numbers of traits investigated, as the proportion of correct assignment rose from 0.644 using four traits to 0.788 using 13 (Figure 4). Using few traits, combinations of vegetative traits most easily discriminated species (Figure 4). Within both vegetative and floral trait datasets, traits varied from virtually orthogonal to strongly correlated (absolute values of r .00–.87; Table S1), making it unlikely that differences in trait correlations drove the different performances of multidimensional vegetative and floral trait combinations. Counter to our prediction that drawing across floral and vegetative trait axes would be most informative, when few traits were used, both combined trait datasets performed below even an average of the independent vegetative and floral success (Figure 4). Using six traits, for example, the odds of correct assignment using vegetative traits were 1.4‐fold greater than with the combined constrained dataset. When more traits were used, the combined trait dataset yielded the greatest correct assignment of individuals to species (81.5% at 13 traits). However, the correct assignment rate using eight vegetative traits was similar (77.8%) and performed as well as 10 traits from the combined trait dataset. The odds of correct assignment using eight vegetative traits were 1.1 times better than using even the full combined constrained dataset (13 traits).


Trait dimensionality and population choice alter estimates of phenotypic dissimilarity
Correct assignment of individuals to species versus number of traits. Correct assignment of individuals to species increased on average as more traits were considered and varied with trait dataset used. Vegetative traits outperformed floral or combined datasets at comparable numbers of traits. The combined constrained trait dataset used separate principal coordinates analyses in linear discriminant analysis (LDA) preprocessing such that one axis subsequently input into the LDA was constrained to be solely vegetative, and the other floral. SE bars are shown
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5383497&req=5

ece32780-fig-0004: Correct assignment of individuals to species versus number of traits. Correct assignment of individuals to species increased on average as more traits were considered and varied with trait dataset used. Vegetative traits outperformed floral or combined datasets at comparable numbers of traits. The combined constrained trait dataset used separate principal coordinates analyses in linear discriminant analysis (LDA) preprocessing such that one axis subsequently input into the LDA was constrained to be solely vegetative, and the other floral. SE bars are shown
Mentions: All multidimensional approaches outperformed the average single trait (41% correct assignment; Figure 3), and overall, including more traits increased correct assignment. On average, the odds of correct assignment increased about twofold over the range of numbers of traits investigated, as the proportion of correct assignment rose from 0.644 using four traits to 0.788 using 13 (Figure 4). Using few traits, combinations of vegetative traits most easily discriminated species (Figure 4). Within both vegetative and floral trait datasets, traits varied from virtually orthogonal to strongly correlated (absolute values of r .00–.87; Table S1), making it unlikely that differences in trait correlations drove the different performances of multidimensional vegetative and floral trait combinations. Counter to our prediction that drawing across floral and vegetative trait axes would be most informative, when few traits were used, both combined trait datasets performed below even an average of the independent vegetative and floral success (Figure 4). Using six traits, for example, the odds of correct assignment using vegetative traits were 1.4‐fold greater than with the combined constrained dataset. When more traits were used, the combined trait dataset yielded the greatest correct assignment of individuals to species (81.5% at 13 traits). However, the correct assignment rate using eight vegetative traits was similar (77.8%) and performed as well as 10 traits from the combined trait dataset. The odds of correct assignment using eight vegetative traits were 1.1 times better than using even the full combined constrained dataset (13 traits).

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.


Related in: MedlinePlus