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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.


Correct assignment versus trait correlation. Correct assignment of individuals to species as a function of the average absolute pairwise correlation between traits in a trait combination, for floral and vegetative trait datasets. Points are sized by the number of traits in a combination (larger points are combinations with more traits). Correct assignment decreased as traits within a combination became more highly correlated, using floral (y = −0.56x + 0.84) and vegetative (y = −0.16x + 0.75) trait datasets
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ece32780-fig-0006: Correct assignment versus trait correlation. Correct assignment of individuals to species as a function of the average absolute pairwise correlation between traits in a trait combination, for floral and vegetative trait datasets. Points are sized by the number of traits in a combination (larger points are combinations with more traits). Correct assignment decreased as traits within a combination became more highly correlated, using floral (y = −0.56x + 0.84) and vegetative (y = −0.16x + 0.75) trait datasets

Mentions: Nonetheless, combinations of less correlated traits were more informative. A decrease in the average absolute pairwise correlation within trait combinations from 0.5 to 0.2 improved the odds of correct assignment 1.3‐ to 2.1‐fold using vegetative or floral traits, respectively (Figure 6). Although these trait datasets appear to show different trends and point spreads (Figure 6), we attribute this primarily to the larger number of vegetative combinations, spanning a wider range of numbers of traits included and assignment success.


Trait dimensionality and population choice alter estimates of phenotypic dissimilarity
Correct assignment versus trait correlation. Correct assignment of individuals to species as a function of the average absolute pairwise correlation between traits in a trait combination, for floral and vegetative trait datasets. Points are sized by the number of traits in a combination (larger points are combinations with more traits). Correct assignment decreased as traits within a combination became more highly correlated, using floral (y = −0.56x + 0.84) and vegetative (y = −0.16x + 0.75) trait datasets
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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

ece32780-fig-0006: Correct assignment versus trait correlation. Correct assignment of individuals to species as a function of the average absolute pairwise correlation between traits in a trait combination, for floral and vegetative trait datasets. Points are sized by the number of traits in a combination (larger points are combinations with more traits). Correct assignment decreased as traits within a combination became more highly correlated, using floral (y = −0.56x + 0.84) and vegetative (y = −0.16x + 0.75) trait datasets
Mentions: Nonetheless, combinations of less correlated traits were more informative. A decrease in the average absolute pairwise correlation within trait combinations from 0.5 to 0.2 improved the odds of correct assignment 1.3‐ to 2.1‐fold using vegetative or floral traits, respectively (Figure 6). Although these trait datasets appear to show different trends and point spreads (Figure 6), we attribute this primarily to the larger number of vegetative combinations, spanning a wider range of numbers of traits included and assignment success.

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.