Diversity and Trait-Based Approaches

🧑🏻‍💻 Masatoshi Katabuchi @ XTBG, CAS

  • mattocci27@gmail.com
  • @mattocci
  • github.com/mattocci27/phy-fun-div
  • https://mattocci27.github.io



November 11, 2024 XTBG AFEC

Objective

We Learn:

  • Why we use functional and phylogenetic diversity

  • How to calculate functional and phylogenetic diversity

  • Trait-based approaches

Outline

  • Community Ecology

  • Simple diversity indices

  • Phylogenetic diversity

  • Functional traits and diversity

  • R examples

Community Assembly and Species Coexistence

For over a century, field ecologist have been characterizing patterns in ecological communities and trying to draw theoretical inferences form the resulting data.

Central Questions:

  • Why do species occurs in specific locations?

  • Why do some species coexist while others do not?

  • Environmental filtering:
    • Ecologically similar species should coexist in ecologically similar environments.
  • Limiting similarity:
    • Ecologically dissimilar species should coexist because too similar species competing for the same resources cannot stably coexist.
  • Neutral theory:
    • Dispersal and stochastic demographic processes explain species coexistence and species differences are not important.

How can we quantify ecological similarity of coexisting species?

How to quantify ecological communities 🍁

  • Species

  • Species + Site information (1950s ~)

  • Species + Site information + Species information (2000s ~)

1a) First-order properties of single communities

  • A vector of species abundance

  • Species composition

Site1
Sp. 1 4
Sp. 2 300
Sp. 3 56
Sp. 4 23

  • Species richness = 4
  • Simpson’s evenness = 1/ Σfreqi2 = (4/383)2 + (300/383)2 + (56/383)2 + (23/383)2

1a) First-order properties of single communities

Which community is more diverse?

  • Species richness = 2

  • What is the chance to get the same species?

  • A: \(\frac{9}{10} \times \frac{8}{9} + \frac{1}{10} \times \frac{0}{9} = 0.8\)

  • B: \(\frac{5}{10} \times \frac{4}{9} + \frac{5}{10} \times \frac{4}{9} \simeq 0.44\)

1a) First-order properties of single communities

Which community is more diverse?

  • A: \(\frac{9}{10} \times \frac{8}{9} + \frac{1}{10} \times \frac{0}{9} = 0.8\)

  • B: \(\frac{5}{10} \times \frac{4}{9} + \frac{5}{10} \times \frac{4}{9} \simeq 0.44\)

  • We prefer that large values indicate more diverse communities.

  • Diversity of A: 1 - 0.8 = 0.2

  • Diversity of B: 1 - 0.44 = 0.56

  • Simpson’s Index of Diversity: \(D = 1 - \Sigma\frac{n_i(n_i - 1)}{N_i(N_i - 1)}\)

  • Simpson’s Index of Diversity (ver. 2): \(D = 1 - \Sigma p_i^2\)

1a) First-order properties of single communities

Another simple way to describe diversity?

  • A: \(p_1\) = 0.9, \(p_2\) = 0.1

  • B: \(p_1\) = 0.5, \(p_2\) = 0.5

  • Diversity of A: 0.9 \(\times\) 0.1 = 0.09?

  • Diversity of B: 0.5 \(\times\) 0.5 = 0.25?

  • Diversity \(\times\) Diversity? What is the unit?

  • \(\mathrm{log}(x \times y) = \mathrm{log}(x) + \mathrm{log}(y)\)
  • Expectations:
    • A: \(0.9 \times \mathrm{log}(0.9) + 0.1 \times \mathrm{log}(0.1) \simeq -0.32\)
    • B: \(0.5 \times \mathrm{log}(0.5) + 0.5 \times \mathrm{log}(0.5) \simeq -0.69\)
  • We prefer that large values indicate more diverse communities.
    • A: \(-1 \times (-0.32) = 0.32\)
    • B: \(-1 \times (-0.69) = 0.69\)
  • Shannon Diversity Index: \(H' = -\Sigma p_i\mathrm{log}p_i\)

1b) First-order properties of multiple communities (Beta diversity)

  • Species \(\times\) site matrix
  • Metacommunity
Site 1 Site 2 Site 3 Site 4
Sp. 1 4 0 315 23
Sp. 2 300 250 0 18
Sp. 3 56 120 74 0
Sp. 4 23 18 101 0
  • Dissimilarity matrix (site \(\times\) site)
Site 1 Site 2 Site 3 Site 4
Site 1 0.00 0.16 0.81 0.90
Site 2 0.16 0.00 0.79 0.92
Site 3 0.81 0.79 0.00 0.91
Site 4 0.90 0.92 0.91 0.00

e.g., Bray–Curtis dissimilarity

\(BC_{ij}=1-2\frac{\sum min\left(S_{A,i}\mbox{, } S_{B,i}\right)}{\sum S_{A,i}+\sum S_{B,i}}\)

Site 1 vs Site 2: 1 - (2 * (0 + 250 + 56 + 18) / (4 +300 + 56 + 23 + 0 + 250 + 120 + 18)) = 0.16

2a) Second-Order properties with site characteristics (1950s ~)

Site 1 Site 2 Site 3 Site 4
Abundance
Sp. 1 4 0 315 23
Sp. 2 300 250 0 18
Sp. 3 56 120 74 0
Sp. 4 23 18 101 0
Env
Env. 1 780 2500 480 1200
Env. 2 21 11 24 19
Env. 3 1500 1900 700 4500
  • “Species \(\times\) site” and “site \(\times\) environment”

2a) Second-Order properties with site characteristics (1950s ~)

Site 1 Site 2 Site 3 Site 4
Abundance
Sp. 1 4 0 315 23
Sp. 2 300 250 0 18
Sp. 3 56 120 74 0
Sp. 4 23 18 101 0
Env
Elevation (m) 780 2500 480 1200
MAT (℃) 21 11 24 19
MAP (mm) 1500 1900 700 4500
  • “Species \(\times\) site” and “site \(\times\) environment”
  • Diversity-environment relationships
  • Composition-environment relationships
    • Multivariate ordination: placing the survey plots “in order” based on their multivariate species composition.

2b) Second-Order properties with species characteristics (2000s ~)

Site 1 Site 2 Site 3 Site 4
Abundance
Sp. 1 4 0 315 23
Sp. 2 300 250 0 18
Sp. 3 56 120 74 0
Sp. 4 23 18 101 0
Env
Elevation (m) 780 2500 480 1200
MAT (℃) 21 11 24 19
MAP (mm) 1500 1900 700 4500
Trait 1 Trait 2 Trait 3 Trait 4
Sp 1 1.3 4.8 1.8 30.0
Sp 2 1.7 12.5 2.1 22.4
Sp 3 7.0 5.9 5.7 11.5
Sp 4 2.1 2.1 3.4 119.9
  • “Trait \(\times\) species”, “species \(\times\) site”, “site \(\times\) environment”

2b) Second-Order properties with species characteristics (2000s ~)

Site 1 Site 2 Site 3 Site 4
Abundance
Sp. 1 4 0 315 23
Sp. 2 300 250 0 18
Sp. 3 56 120 74 0
Sp. 4 23 18 101 0
Env
Elevation (m) 780 2500 480 1200
MAT (℃) 21 11 24 19
MAP (mm) 1500 1900 700 4500
Leaf N Amax Rdark LL
Sp 1 1.3 4.8 1.8 30.0
Sp 2 1.7 12.5 2.1 22.4
Sp 3 7.0 5.9 5.7 11.5
Sp 4 2.1 2.1 3.4 119.9
  • “Trait \(\times\) species”, “species \(\times\) site”, “site \(\times\) environment”
  • Trait diversity and its role in species composition

  • Trait composition-environment relationships

How to measure species characteristics?

Photosynthetic rates

Genus:species ratio

  • The genus:species ratio type of study in plant community ecology started ~1910 and was popular until 1990’s
  • A large criticism of genus:species ratio analyses is that they do not take account for the different ages of genera and species
    • Two species in a relatively young genus may be expected to be more similar than two species in a relatively old genus.

Solution for the genus:species ratio problem = Use phylogenetic trees

Phylodiversity

  • In the 1990’s conservation biologists recognized the biodiversity is not only species diversity

    • Biodiversity has several axes or dimensions including genetic, taxonomic, phylogenetic and functional diversity

Phylodiversity

  • Phylogenetic diversity was first formalized by Dan Faith in 1992
  • He proposed a metric called PD that is also commonly referred to as Faith’s Index
  • Many additional metrics have now been generated but this metric is still widely used, especially in the context of conservation Index

Faith’s Index (PD)

  • Total branch length = 18
  • PD is the sum of the lengths of all those branches that are members of the corresponding minimum spanning path
  • PD is the phylogenetic analogue of taxon richness and is expressed as the number of tree units which are found in a sample
  • PD will correlate with species richness

Faith’s Index (PD)

  • Total branch length = 9

Faith’s Index (PD)

  • Total branch length = 14

Pethcey’s functional diversity (FD)

  • FD is proposed by Owen Petchey in 2002
  • FD is the total branch length of the functional dendrogram.
  • Analogous to PD

Beyond Faith’s Index (PD)

  • Solution for genus:species = Use phylogenetic trees to estimate the relatedness of coexisting species

    • This solution was first proposed by Cam Webb in 2000

Distance matrix

    A B C D E
  B 1        
  C 2 2      
  D 4 4 3    
  E 5 5 4 2  
  F 5 5 4 2 1

Mean Nearest Nodal Distance (MNTD) and Nearest Taxa Index (NTI)

Greatest possible nearest nodal distance for a community of 4 taxa = 2 (A, C, D, F; A to C = 2, D to F = 2)

    A B C D E
  B 1        
  C 2 2      
  D 4 4 3    
  E 5 5 4 2  
  F 5 5 4 2 1

Mean Nearest Nodal Distance (MNTD) and Nearest Taxa Index (NTI)

Community 1; A, B, C, D

  • A -> B
  • B -> A
  • C -> (A, B)
  • D -> C
    A B C
  B 1    
  C 2 2  
  D 4 4 3

MNTD = (1 + 1 + 2 + 3) / 4 = 1.75

NTI = 1 - (1.75 / 2.0) = 0.125

Community 2; A, B, E, F

  • A -> B
  • B -> A
  • E -> F
  • F -> E
    A B E
  B 1    
  E 5 5  
  F 5 5 1

MNTD = (1 + 1 + 1 + 1) / 4 = 1

NTI = 1 - (1 / 2.0) = 0.5

Community 2 is more phylogenetically similar (in tips).

Sparks community phylogeny

Functional dendrogram vs. phylogeny (Anole example)

  • A: Functional dendrogram based on ecomorph

  • B: Phylogeny indicates frequent evolution of traits

  • They do not match at all (!!)

  • Phylogenetically similar = Functional (ecologically) similar??

Putting traits on the tips of phylogeny: phylogenetic signal

  • What is Phylogenetic Signal (K)?
    • Phylogenetic signal measures the degree to which related species share similar traits, quantifying the inheritance of traits from either recent or more ancient common ancestors.
  • Interpretation of K Values:
    • Large K (phylogenetic conservatism): Trait similarity is high among closely related species, suggesting that traits are conserved across lineages.
    • K = 1: Traits are evolving under Brownian motion.
    • Small K (phylogenetic divergence): Trait similarity is low among closely related species, indicating that traits have diverged.
  • Calculating K:
    • K is the ratio of the Mean Squared Error (MSE) of observed trait values to the MSE expected under Brownian motion (random evolutionary change).

Phylogenetic conservatism matters

Phylogenetic conservatism matters

The phylogenetic middleman problem

  • Phylogeny as a proxy for the functional or ecological similarity of species.

  • Measuring trait data and arraying it on the phylogenetic tree to demonstrate phylogenetic signal in function so that their phylogenetically-based inferences could be supported.

  • Compared to simply measuring the trait dispersion, this approach is very indirect.

  • This approach should be avoided! (phylogeny and traits are useful to make meaningful evolutionary inferences)

Plant functional traits

Measurable properties of plants that are indicative of ecological strategies

“Hard” traits: e.g., Photosynthetic rates

“Soft” traits: e.g., LMA (leaf mass per area)

Leaf Economic Spectrum (LES)

  • LES describes pairwise correlations among a bunch of leaf traits from the global leaf database called GLOPNET
  • Global leaf function constrained to a single axis (75 % of the variation in the 6 traits)
  • Multidimensional (leaf) functional diversity can be mapped into a one-dimensional index
  • Controversial (!!)

Rebuilding community ecology from functional traits

  • Non-trait based statement

    • Campanula aparinoides is found only in infertile habitats.
  • Trait-based statement

    • Compact plants with canopy area < 30 cm 2 and small or absent leaves are restricted to marshes with < 18 \(\mu\) g g -1 soil P.

Rebuilding community ecology from functional traits

  • Go beyond ‘How many species and why?’ to ask ‘How much variation in traits and why?
  • Go beyond ‘In what environments does a species occur?’ to ask ‘What traits and environmental variables are most important in determining fundamental niche?
  • Go beyond ‘What are the most important niche dimensions?’ to ask ‘What traits are most decisive in translating from fundamental niche to realized niche?
  • Go beyond ‘How does population dynamics determine abundance?’ to ask ‘How does the performance of species in the interaction milieu determine their ranking of abundance or biomass?
  • Go beyond ‘How does space affect population dynamics?’ to ask ‘How do environmental gradients affect community structuring?

Convex hull volume (functional richness)

  • California woody-plant communities (43 plots, 54 species, 3 traits)

  • Is the trait volume of California woody-plant communities significantly less than expected by chance?

    • Environmental filtering

Convex hull volume (functional richness)

  • Species in 40 out of 43 plots occupied less trait space than would be expected by chance

  • Consistent with environmental filtering

Community assembly and trait distribution

Environmental filtering and limiting similarity can occur at the same time

  • Yasuni tropical tree communities, 25ha, 625 20m x 20m quadrats, 1089 species!

  • Consistent with environmental filtering

  • A: Ridgetops have lower than expected SLA and valleys have higher

    • Traits match with environmental conditions
  • B: Seed mass shows broader distribution than expected - Limiting similarity

  • C: Range of SLA is smaller than expected - Environmental filtering

Environmental filtering can occur within dipterocarp trees

How ecological processes might influence community assembly

Process / Facotr Pattern
Biotic factor
Environmental filtering Clustring
Abiotic factor (competition)
Limiting similarity / Compitative exclusion Overdispersion
Competitive hierarchy / directional compation Clustring
Herbivors / Prasites / Pathogens Overdispersion
Pollinator-mediated competition Overdispersion
Abiotic factor (facilitation)
Nurse plants Overdispersion
Pollinator facilitation Clustering
Stochastic process
Neutral theory Random

Competitive hierarchy

Limiting similarity

Competitive interaction strengths between species will increase with decreasing niche distance, measured as their absolute traits distance \(|t_A - t_B|\)

Competitive hierarchy

Competitive effects of species A on species B will increase with increasing \(t_A - t_B\).

Plant–herbivore interactions

  • 6000+ secondary metabolites from nearly 100 species in a diverse Neotropical plant clade across the whole Amazonia

  • More differences in their defensive chemistry than expected by chance

  • Plant–herbivore interactions promote species diversity

Facilitation

  • Alpine plants in the Andes

  • Functional dispersion in harsh environments (higher potential solar radiation)

  • Facilitation tends to dominate interactions when environmental harshness increases

Trait-based ecology: where are we now?

Predicting future biodiversity may require community, species, and indidivual-level analyses

  • 50ha Forest Dynamics Plot on Barro Colorado Island, Panama

  • The changes in community-weighted mean (CWM) of wood density over time seem to suggest a community-level response in climate change (i.e, drier conditions).

  • No clear species-level pattern: just two of 300 species account for 60% of the temporal shifts in CWM, likely due to species-specific pathogens rather than climate change response.

Using harder-to-measure physiological traits may be not enough

We may need metrics somewhere between functional traits and invasion growth rate (model parameter) to predict coexistence.

Summary 🍺

  • Why do we use trait and phylogenetic diversity?

    • We want to quantify ecological similarities and biodiversity dimensions.

    • We need to be very careful when we use or develop ways to map multiple dimensions of biodiversity to a few dimensions of diversity.

  • Trait-based approaches

    • Moving ecology towards more quantitative and predictive methods.

    • We only focused on a “snapshot” of biodiversity (predicting future biodiversity is another story)

    • Developing more effective metrics may be necessary, beyond simply measuring traits.

  • How to calculate trait and phylogenetic diversity anyway?

    • We have some R practice.

About the slides