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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.

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Correct assignment versus number of traits, by species and populations. The effect of the number of traits used on correct assignment of individuals to species, for all trait datasets (panels). For most species (a) and M. primuloides populations (b), species appear more distinct when more traits are considered. Variation among species (a) and among populations (b) in measurable species differences are of similar magnitude. Note that both (a) and (b) display how readily species were distinguished from other Mimulus species; only one M. primuloides was sampled in a given run, as part of a multispecies comparison. SE bars are shown. 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
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ece32780-fig-0007: Correct assignment versus number of traits, by species and populations. The effect of the number of traits used on correct assignment of individuals to species, for all trait datasets (panels). For most species (a) and M. primuloides populations (b), species appear more distinct when more traits are considered. Variation among species (a) and among populations (b) in measurable species differences are of similar magnitude. Note that both (a) and (b) display how readily species were distinguished from other Mimulus species; only one M. primuloides was sampled in a given run, as part of a multispecies comparison. SE bars are shown. 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

Mentions: Both the average assignment success and its relationship with the number of traits included varied among species. For example, using four vegetative traits, the average correct assignment was 80.9% for M. lewisii but only 46.2% for M. mephiticus (Figure 7a). Correct assignment also varied with trait dataset (panels in Figure 7a); as a case in point, M. lewisii was much better distinguished using vegetative rather than floral traits (80.9% vs. 53.8% success, respectively). For most species, correct assignment showed either a slight and plateauing or strong positive relationship with the number of traits, using either combined trait dataset (Figure 7a). However, using more traits did not improve correct assignment for two of seven species when a subset of traits was used (vegetative or floral), and the identity of these species differed depending on the subset used (Figure 7a). These dissimilar patterns in LDA assignment, among species and among trait datasets, are understood by examining phenotypic overlap among species in multivariate trait space. Species overlapping heavily in either vegetative (Figure 1) or floral (Figure 2) trait space were poorly discriminated using that trait dataset, even when numerous traits were considered (Figure 7a). Therefore, in speciose assemblages, multiple suites of traits would best capture species' phenotypic differences.


Trait dimensionality and population choice alter estimates of phenotypic dissimilarity
Correct assignment versus number of traits, by species and populations. The effect of the number of traits used on correct assignment of individuals to species, for all trait datasets (panels). For most species (a) and M. primuloides populations (b), species appear more distinct when more traits are considered. Variation among species (a) and among populations (b) in measurable species differences are of similar magnitude. Note that both (a) and (b) display how readily species were distinguished from other Mimulus species; only one M. primuloides was sampled in a given run, as part of a multispecies comparison. SE bars are shown. 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
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5383497&req=5

ece32780-fig-0007: Correct assignment versus number of traits, by species and populations. The effect of the number of traits used on correct assignment of individuals to species, for all trait datasets (panels). For most species (a) and M. primuloides populations (b), species appear more distinct when more traits are considered. Variation among species (a) and among populations (b) in measurable species differences are of similar magnitude. Note that both (a) and (b) display how readily species were distinguished from other Mimulus species; only one M. primuloides was sampled in a given run, as part of a multispecies comparison. SE bars are shown. 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
Mentions: Both the average assignment success and its relationship with the number of traits included varied among species. For example, using four vegetative traits, the average correct assignment was 80.9% for M. lewisii but only 46.2% for M. mephiticus (Figure 7a). Correct assignment also varied with trait dataset (panels in Figure 7a); as a case in point, M. lewisii was much better distinguished using vegetative rather than floral traits (80.9% vs. 53.8% success, respectively). For most species, correct assignment showed either a slight and plateauing or strong positive relationship with the number of traits, using either combined trait dataset (Figure 7a). However, using more traits did not improve correct assignment for two of seven species when a subset of traits was used (vegetative or floral), and the identity of these species differed depending on the subset used (Figure 7a). These dissimilar patterns in LDA assignment, among species and among trait datasets, are understood by examining phenotypic overlap among species in multivariate trait space. Species overlapping heavily in either vegetative (Figure 1) or floral (Figure 2) trait space were poorly discriminated using that trait dataset, even when numerous traits were considered (Figure 7a). Therefore, in speciose assemblages, multiple suites of traits would best capture species' phenotypic differences.

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