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SuppMat 2: Effects of Network Reconstruction on Food-Web Structure
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SuppMat 2: Effects of Network Reconstruction on Food-Web Structure

Additional Results and Analyses

Effects of Network Reconstruction on Food-Web Structure

Table S1. Descriptive statistics (mean ± standard deviation) of network metrics by model

In [1]:
library(knitr)
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
readr::read_csv("tables/Table_S1_descriptive_stats.csv") %>%
kable()
Rows: 13 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (7): statistic, ADBM, ATN, Body-size ratio, Niche, PFIM, Random

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
statistic ADBM ATN Body-size ratio Niche PFIM Random
Complexity 0.779 ±1 0.794 ±1 0.865 ±1 0.895 ±1 0.871 ±1 0.744 ±1
Connectance 0.298 ±0 0.225 ±0 0.172 ±0 0.118 ±0 0.119 ±0 0.221 ±0
Diameter 1 ±1 1.38 ±1 4.37 ±4.4 3.8 ±3.8 3.3 ±3.3 2.66 ±2.7
Distance 2 ±2 2.35 ±2 2.81 ±2.81 2.19 ±2.2 2.5 ±2.5 1.02 ±1
Generality 0.902 ±1 1.03 ±1 0.637 ±1 1.12 ±1 1.72 ±2 0.326 ±0
Max trophic level 3.58 ±4 3.2 ±3 6.25 ±6.2 4.03 ±4 2.48 ±2 5.89 ±5.89
No. of apparent competition motifs 1.39 ±1 1.17 ±1 0.501 ±1 0.323 ±0 0.523 ±1 0.0297 ±0
No. of direct competition motifs 1.45 ±1.5 1.34 ±1.3 0.529 ±1 0.143 ±0 0.0644 ±0 1.62 ±1.6
No. of linear chains 0 ±0 0.0291 ±0 0.749 ±1 0.192 ±0 0.104 ±0 0.0108 ±0
No. of omnivory motifs 1.19 ±1 0.456 ±0 0.141 ±0 0.109 ±0 0.139 ±0 0.181 ±0
Redundancy 9.44 ±9.438 6.86 ±6.857 5.07 ±5.07 3.02 ±3.02 2.92 ±2.9 6.77 ±6.765
Richness 34.8 ±34.75 34.8 ±34.75 34.8 ±34.75 34 ±33.99 34.8 ±34.75 34.7 ±34.71
Vulnerability 0.889 ±1 1.07 ±1 0.69 ±1 0.648 ±1 0.633 ±1 1.7 ±2

Table S2. Canonical discriminant analysis

In [2]:
readr::read_csv("tables/canonical_loadings.csv") %>%
kable()
Rows: 8 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Metric
dbl (3): Can1, Can2, Can3

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Metric Can1 Can2 Can3
connectance -0.75 0.36 -0.45
trophic_level -0.31 -0.75 -0.25
generality 0.73 0.58 0.32
vulnerability -0.86 -0.20 0.38
S1 0.38 -0.60 -0.48
S2 -0.35 0.62 -0.53
S4 -0.81 0.14 -0.11
S5 -0.12 0.74 -0.52

Figure S1. Canonical Loadings

Canonical loadings for the first two canonical variates (CV1, CV2) from the canonical discriminant analysis of network metrics. Arrows indicate the contribution of each metric to the multivariate separation among reconstruction models. Colours denote the scale of each metric: Macro (light brown), Meso (brown), Micro (sienna). Metric labels are shown for the most influential variables.

PERMANOVA Variance Partitioning

To quantify the relative contributions of reconstruction framework and temporal turnover to variation in inferred network structure, we conducted permutational multivariate analysis of variance (PERMANOVA). Euclidean distance matrices were calculated from standardised (z-transformed) network metrics. Reconstruction framework (‘model’) and extinction phase (‘time’) were analysed separately to estimate their total contributions to variance, and in combination to assess interaction effects. Significance was assessed using 999 permutations.

Robustness of model effects after temporal centering

To determine whether the dominance of reconstruction framework reflected absolute structural shifts among extinction phases, we repeated the analysis after centering network metrics within each time bin. This procedure removes mean temporal differences while preserving within-phase structural variation. Even after temporal centering, reconstruction framework explained 84.8% of multivariate variance (R² = 0.848, p < 0.001), exceeding the variance explained in the uncentered analysis. Thus, the strong influence of model identity is not attributable to temporal mean differences, but reflects intrinsic structural divergence among reconstruction frameworks.

Statistical Drivers of Network Variation

Statistical Robustness and Assumptions

Factorial ANOVA assumptions were validated via residual analysis. Despite significant heteroscedasticity (Levene’s test, p<0.001), the perfectly balanced design (n = 100 per cell) and large sample size (N = 2400) ensure the robustness of the F-test. Visual inspection of Q-Q plots and Residuals-vs-Fitted plots confirmed that the distributions were sufficiently symmetric for parametric analysis.

Figure S2. Temporal Trajectories of Network Structure by Model

Detailed shifts in network properties across the four extinction phases, categorised by organisational scale (Macro, Meso, Micro). Each line represents the mean value for a specific reconstruction framework, with error bars denoting standard error. This figure illustrates the “baseline” differences between models—such as the Niche model’s tendency to over-estimate motif counts—and their divergent responses to species loss.

Table S3. Variance Partitioning of Framework, Time, and Interaction Effects.

Summary of the two-way factorial ANOVA results for all eight metrics. Values represent partial eta-squared (\(\eta^{2}_{p}\)), which quantifies the proportion of variance explained by each factor. The dominance of the ‘Model’ term across all scales confirms that framework choice is the primary determinant of network topology.

In [3]:
readr::read_csv("../tables/ANOVA_Results.csv") %>%
kable()
Rows: 8 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Metric
dbl (3): Model, Time, Interaction

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Metric Model Time Interaction
Connectance 0.871 0.029 0.171
Max trophic level 0.821 0.322 0.324
Generality 0.959 0.230 0.405
Vulnerability 0.900 0.010 0.168
No. of linear chains 0.959 0.514 0.783
No. of omnivory motifs 0.955 0.673 0.782
No. of direct competition motifs 0.973 0.915 0.863
No. of apparent competition motifs 0.959 0.771 0.605

Figure S3. Model Disagreement (CV%) Across Extinction Phases

Trends in inter-model disagreement, quantified as the Coefficient of Variation (CV%) between framework means. The Y-axis is standardised across panels to facilitate comparison between organisational scales. A characteristic “dip” at the ‘during’ phase in several meso-scale metrics illustrates the structural canalisation effect, where severe species loss forces a temporary convergence in model predictions.

Table S4. Percentage Disagreement Between Frameworks Across Extinction Phases.

Calculated inter-model CV% for each metric at each time step. These data points underpin the bubble sizes in Figure 2 and the trajectories in Figure S3. Note the reduction in CV% for linear chains and omnivory motifs during the peak extinction phase (During).

In [4]:
readr::read_csv("../tables/Model_Agreement_CV.csv") %>%
kable()
Rows: 8 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): statistic, level
dbl (4): Pre-extinction, During extinction, Early extinction, Late extinction

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
statistic level Pre-extinction During extinction Early extinction Late extinction
Connectance Macro 39.68855 32.15691 34.79191 40.54695
Generality Micro 58.51606 41.01746 48.53126 51.75989
Max trophic level Macro 41.65860 31.46020 32.30812 39.32523
No. of apparent competition motifs Meso 74.26024 83.35463 84.28280 79.62819
No. of direct competition motifs Meso 80.49528 81.63039 79.94947 82.73417
No. of linear chains Meso 173.66964 131.61905 156.24100 160.33886
No. of omnivory motifs Meso 119.22142 102.05423 101.35425 123.04302
Vulnerability Micro 47.92763 41.32709 39.99020 47.67690

Figure S4. Pairwise Framework Comparisons (Tukey HSD)

Heatmap showing significant differences between specific pairs of reconstruction frameworks across each extinction phase. Colours represent the magnitude and direction of the difference (estimate); asterisks (\(*\)) indicate statistical significance (p<0.05). This identifies which specific models drive the high CV values seen in Figure 2.

Reconstruction approach choice influences inferred extinction dynamics

Table S5. Kendall’s Tau Correlation Coefficients

Kendalls Tau (\(\tau\)) values quantifying the amount of agreement between reconstruction approach as to the main driver of extinctions. This is determined based on the mean absolute difference rankings. Significant correlations (p<0.05) are denoted with an asterisk (*). The variability in \(\tau\) values across models for the same metric highlights how reconstruction choice can lead to divergent interpretations of extinction drivers in network structure.

In [5]:
readr::read_csv("tables/kendall_tau_coefficients.csv") %>%
kable()
Rows: 360 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): Model1, Model2, metric
dbl (1): tau

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Model1 Model2 tau metric
ADBM ADBM 1.0000000 S1
ATN ADBM 0.8129201 S1
Body-size ratio ADBM -0.1935524 S1
Niche ADBM 0.1161314 S1
PFIM ADBM 0.2709734 S1
Random ADBM 0.0387105 S1
ADBM ATN 0.8129201 S1
ATN ATN 1.0000000 S1
Body-size ratio ATN -0.1794872 S1
Niche ATN 0.0769231 S1
PFIM ATN 0.2820513 S1
Random ATN 0.0512821 S1
ADBM Body-size ratio -0.1935524 S1
ATN Body-size ratio -0.1794872 S1
Body-size ratio Body-size ratio 1.0000000 S1
Niche Body-size ratio 0.4358974 S1
PFIM Body-size ratio 0.1794872 S1
Random Body-size ratio -0.0512821 S1
ADBM Niche 0.1161314 S1
ATN Niche 0.0769231 S1
Body-size ratio Niche 0.4358974 S1
Niche Niche 1.0000000 S1
PFIM Niche -0.1794872 S1
Random Niche -0.1025641 S1
ADBM PFIM 0.2709734 S1
ATN PFIM 0.2820513 S1
Body-size ratio PFIM 0.1794872 S1
Niche PFIM -0.1794872 S1
PFIM PFIM 1.0000000 S1
Random PFIM 0.3076923 S1
ADBM Random 0.0387105 S1
ATN Random 0.0512821 S1
Body-size ratio Random -0.0512821 S1
Niche Random -0.1025641 S1
PFIM Random 0.3076923 S1
Random Random 1.0000000 S1
ADBM ADBM 1.0000000 S2
ATN ADBM 0.5641026 S2
Body-size ratio ADBM 0.4615385 S2
Niche ADBM 0.4871795 S2
PFIM ADBM -0.0769231 S2
Random ADBM 0.2051282 S2
ADBM ATN 0.5641026 S2
ATN ATN 1.0000000 S2
Body-size ratio ATN 0.4871795 S2
Niche ATN 0.3076923 S2
PFIM ATN 0.2564103 S2
Random ATN 0.1282051 S2
ADBM Body-size ratio 0.4615385 S2
ATN Body-size ratio 0.4871795 S2
Body-size ratio Body-size ratio 1.0000000 S2
Niche Body-size ratio 0.3076923 S2
PFIM Body-size ratio -0.1538462 S2
Random Body-size ratio 0.0769231 S2
ADBM Niche 0.4871795 S2
ATN Niche 0.3076923 S2
Body-size ratio Niche 0.3076923 S2
Niche Niche 1.0000000 S2
PFIM Niche -0.3333333 S2
Random Niche 0.0000000 S2
ADBM PFIM -0.0769231 S2
ATN PFIM 0.2564103 S2
Body-size ratio PFIM -0.1538462 S2
Niche PFIM -0.3333333 S2
PFIM PFIM 1.0000000 S2
Random PFIM 0.3076923 S2
ADBM Random 0.2051282 S2
ATN Random 0.1282051 S2
Body-size ratio Random 0.0769231 S2
Niche Random 0.0000000 S2
PFIM Random 0.3076923 S2
Random Random 1.0000000 S2
ADBM ADBM 1.0000000 S4
ATN ADBM 0.6153846 S4
Body-size ratio ADBM 0.3589744 S4
Niche ADBM -0.2051282 S4
PFIM ADBM 0.0256410 S4
Random ADBM 0.1282051 S4
ADBM ATN 0.6153846 S4
ATN ATN 1.0000000 S4
Body-size ratio ATN 0.3846154 S4
Niche ATN -0.3846154 S4
PFIM ATN -0.0512821 S4
Random ATN 0.3076923 S4
ADBM Body-size ratio 0.3589744 S4
ATN Body-size ratio 0.3846154 S4
Body-size ratio Body-size ratio 1.0000000 S4
Niche Body-size ratio 0.0769231 S4
PFIM Body-size ratio -0.1025641 S4
Random Body-size ratio 0.3076923 S4
ADBM Niche -0.2051282 S4
ATN Niche -0.3846154 S4
Body-size ratio Niche 0.0769231 S4
Niche Niche 1.0000000 S4
PFIM Niche 0.2051282 S4
Random Niche -0.3076923 S4
ADBM PFIM 0.0256410 S4
ATN PFIM -0.0512821 S4
Body-size ratio PFIM -0.1025641 S4
Niche PFIM 0.2051282 S4
PFIM PFIM 1.0000000 S4
Random PFIM -0.6410256 S4
ADBM Random 0.1282051 S4
ATN Random 0.3076923 S4
Body-size ratio Random 0.3076923 S4
Niche Random -0.3076923 S4
PFIM Random -0.6410256 S4
Random Random 1.0000000 S4
ADBM ADBM 1.0000000 S5
ATN ADBM 0.2564103 S5
Body-size ratio ADBM 0.0512821 S5
Niche ADBM 0.0000000 S5
PFIM ADBM 0.0512821 S5
Random ADBM -0.0256410 S5
ADBM ATN 0.2564103 S5
ATN ATN 1.0000000 S5
Body-size ratio ATN 0.3846154 S5
Niche ATN 0.0256410 S5
PFIM ATN 0.2820513 S5
Random ATN 0.4615385 S5
ADBM Body-size ratio 0.0512821 S5
ATN Body-size ratio 0.3846154 S5
Body-size ratio Body-size ratio 1.0000000 S5
Niche Body-size ratio -0.2307692 S5
PFIM Body-size ratio -0.0769231 S5
Random Body-size ratio -0.0512821 S5
ADBM Niche 0.0000000 S5
ATN Niche 0.0256410 S5
Body-size ratio Niche -0.2307692 S5
Niche Niche 1.0000000 S5
PFIM Niche -0.0769231 S5
Random Niche 0.0000000 S5
ADBM PFIM 0.0512821 S5
ATN PFIM 0.2820513 S5
Body-size ratio PFIM -0.0769231 S5
Niche PFIM -0.0769231 S5
PFIM PFIM 1.0000000 S5
Random PFIM 0.1025641 S5
ADBM Random -0.0256410 S5
ATN Random 0.4615385 S5
Body-size ratio Random -0.0512821 S5
Niche Random 0.0000000 S5
PFIM Random 0.1025641 S5
Random Random 1.0000000 S5
ADBM ADBM 1.0000000 connectance
ATN ADBM 0.8205128 connectance
Body-size ratio ADBM 0.3076923 connectance
Niche ADBM 0.4615385 connectance
PFIM ADBM 0.1794872 connectance
Random ADBM 0.0256410 connectance
ADBM ATN 0.8205128 connectance
ATN ATN 1.0000000 connectance
Body-size ratio ATN 0.3846154 connectance
Niche ATN 0.3846154 connectance
PFIM ATN 0.0512821 connectance
Random ATN 0.1025641 connectance
ADBM Body-size ratio 0.3076923 connectance
ATN Body-size ratio 0.3846154 connectance
Body-size ratio Body-size ratio 1.0000000 connectance
Niche Body-size ratio 0.2820513 connectance
PFIM Body-size ratio 0.0000000 connectance
Random Body-size ratio 0.5128205 connectance
ADBM Niche 0.4615385 connectance
ATN Niche 0.3846154 connectance
Body-size ratio Niche 0.2820513 connectance
Niche Niche 1.0000000 connectance
PFIM Niche 0.0512821 connectance
Random Niche 0.3589744 connectance
ADBM PFIM 0.1794872 connectance
ATN PFIM 0.0512821 connectance
Body-size ratio PFIM 0.0000000 connectance
Niche PFIM 0.0512821 connectance
PFIM PFIM 1.0000000 connectance
Random PFIM -0.1282051 connectance
ADBM Random 0.0256410 connectance
ATN Random 0.1025641 connectance
Body-size ratio Random 0.5128205 connectance
Niche Random 0.3589744 connectance
PFIM Random -0.1282051 connectance
Random Random 1.0000000 connectance
ADBM ADBM 1.0000000 generality
ATN ADBM 0.7692308 generality
Body-size ratio ADBM -0.2564103 generality
Niche ADBM -0.1538462 generality
PFIM ADBM 0.0512821 generality
Random ADBM -0.0512821 generality
ADBM ATN 0.7692308 generality
ATN ATN 1.0000000 generality
Body-size ratio ATN -0.1282051 generality
Niche ATN 0.0256410 generality
PFIM ATN 0.2820513 generality
Random ATN 0.1794872 generality
ADBM Body-size ratio -0.2564103 generality
ATN Body-size ratio -0.1282051 generality
Body-size ratio Body-size ratio 1.0000000 generality
Niche Body-size ratio -0.1282051 generality
PFIM Body-size ratio 0.0256410 generality
Random Body-size ratio 0.4358974 generality
ADBM Niche -0.1538462 generality
ATN Niche 0.0256410 generality
Body-size ratio Niche -0.1282051 generality
Niche Niche 1.0000000 generality
PFIM Niche 0.4871795 generality
Random Niche 0.2820513 generality
ADBM PFIM 0.0512821 generality
ATN PFIM 0.2820513 generality
Body-size ratio PFIM 0.0256410 generality
Niche PFIM 0.4871795 generality
PFIM PFIM 1.0000000 generality
Random PFIM 0.4871795 generality
ADBM Random -0.0512821 generality
ATN Random 0.1794872 generality
Body-size ratio Random 0.4358974 generality
Niche Random 0.2820513 generality
PFIM Random 0.4871795 generality
Random Random 1.0000000 generality
ADBM ADBM 1.0000000 trophic_level
ATN ADBM 0.5641026 trophic_level
Body-size ratio ADBM -0.2564103 trophic_level
Niche ADBM 0.3076923 trophic_level
PFIM ADBM 0.2564103 trophic_level
Random ADBM -0.3589744 trophic_level
ADBM ATN 0.5641026 trophic_level
ATN ATN 1.0000000 trophic_level
Body-size ratio ATN -0.1282051 trophic_level
Niche ATN -0.0256410 trophic_level
PFIM ATN 0.3333333 trophic_level
Random ATN -0.1282051 trophic_level
ADBM Body-size ratio -0.2564103 trophic_level
ATN Body-size ratio -0.1282051 trophic_level
Body-size ratio Body-size ratio 1.0000000 trophic_level
Niche Body-size ratio -0.0769231 trophic_level
PFIM Body-size ratio 0.4871795 trophic_level
Random Body-size ratio -0.1282051 trophic_level
ADBM Niche 0.3076923 trophic_level
ATN Niche -0.0256410 trophic_level
Body-size ratio Niche -0.0769231 trophic_level
Niche Niche 1.0000000 trophic_level
PFIM Niche 0.0769231 trophic_level
Random Niche -0.1794872 trophic_level
ADBM PFIM 0.2564103 trophic_level
ATN PFIM 0.3333333 trophic_level
Body-size ratio PFIM 0.4871795 trophic_level
Niche PFIM 0.0769231 trophic_level
PFIM PFIM 1.0000000 trophic_level
Random PFIM -0.3846154 trophic_level
ADBM Random -0.3589744 trophic_level
ATN Random -0.1282051 trophic_level
Body-size ratio Random -0.1282051 trophic_level
Niche Random -0.1794872 trophic_level
PFIM Random -0.3846154 trophic_level
Random Random 1.0000000 trophic_level
ADBM ADBM 1.0000000 vulnerability
ATN ADBM 0.9230769 vulnerability
Body-size ratio ADBM 0.0000000 vulnerability
Niche ADBM 0.0000000 vulnerability
PFIM ADBM 0.0000000 vulnerability
Random ADBM 0.1025641 vulnerability
ADBM ATN 0.9230769 vulnerability
ATN ATN 1.0000000 vulnerability
Body-size ratio ATN -0.0256410 vulnerability
Niche ATN -0.0769231 vulnerability
PFIM ATN -0.0256410 vulnerability
Random ATN 0.0256410 vulnerability
ADBM Body-size ratio 0.0000000 vulnerability
ATN Body-size ratio -0.0256410 vulnerability
Body-size ratio Body-size ratio 1.0000000 vulnerability
Niche Body-size ratio 0.1282051 vulnerability
PFIM Body-size ratio 0.3333333 vulnerability
Random Body-size ratio 0.1794872 vulnerability
ADBM Niche 0.0000000 vulnerability
ATN Niche -0.0769231 vulnerability
Body-size ratio Niche 0.1282051 vulnerability
Niche Niche 1.0000000 vulnerability
PFIM Niche 0.1794872 vulnerability
Random Niche 0.1794872 vulnerability
ADBM PFIM 0.0000000 vulnerability
ATN PFIM -0.0256410 vulnerability
Body-size ratio PFIM 0.3333333 vulnerability
Niche PFIM 0.1794872 vulnerability
PFIM PFIM 1.0000000 vulnerability
Random PFIM 0.2820513 vulnerability
ADBM Random 0.1025641 vulnerability
ATN Random 0.0256410 vulnerability
Body-size ratio Random 0.1794872 vulnerability
Niche Random 0.1794872 vulnerability
PFIM Random 0.2820513 vulnerability
Random Random 1.0000000 vulnerability
ADBM ADBM 1.0000000 Link
Body-size ratio ADBM 0.2307692 Link
ATN ADBM 0.8205128 Link
Niche ADBM -0.3846154 Link
PFIM ADBM 0.3076923 Link
Random ADBM 0.1282051 Link
ADBM Body-size ratio 0.2307692 Link
Body-size ratio Body-size ratio 1.0000000 Link
ATN Body-size ratio 0.3076923 Link
Niche Body-size ratio -0.4871795 Link
PFIM Body-size ratio 0.1538462 Link
Random Body-size ratio 0.4871795 Link
ADBM ATN 0.8205128 Link
Body-size ratio ATN 0.3076923 Link
ATN ATN 1.0000000 Link
Niche ATN -0.3076923 Link
PFIM ATN 0.1794872 Link
Random ATN 0.2051282 Link
ADBM Niche -0.3846154 Link
Body-size ratio Niche -0.4871795 Link
ATN Niche -0.3076923 Link
Niche Niche 1.0000000 Link
PFIM Niche -0.2564103 Link
Random Niche -0.3846154 Link
ADBM PFIM 0.3076923 Link
Body-size ratio PFIM 0.1538462 Link
ATN PFIM 0.1794872 Link
Niche PFIM -0.2564103 Link
PFIM PFIM 1.0000000 Link
Random PFIM 0.2564103 Link
ADBM Random 0.1282051 Link
Body-size ratio Random 0.4871795 Link
ATN Random 0.2051282 Link
Niche Random -0.3846154 Link
PFIM Random 0.2564103 Link
Random Random 1.0000000 Link
ADBM ADBM 1.0000000 Node
Body-size ratio ADBM 0.2564103 Node
ATN ADBM 0.8974359 Node
Niche ADBM 0.5128205 Node
PFIM ADBM 0.5897436 Node
Random ADBM 0.6666667 Node
ADBM Body-size ratio 0.2564103 Node
Body-size ratio Body-size ratio 1.0000000 Node
ATN Body-size ratio 0.2564103 Node
Niche Body-size ratio 0.6410256 Node
PFIM Body-size ratio 0.3076923 Node
Random Body-size ratio 0.3846154 Node
ADBM ATN 0.8974359 Node
Body-size ratio ATN 0.2564103 Node
ATN ATN 1.0000000 Node
Niche ATN 0.5641026 Node
PFIM ATN 0.5384615 Node
Random ATN 0.7692308 Node
ADBM Niche 0.5128205 Node
Body-size ratio Niche 0.6410256 Node
ATN Niche 0.5641026 Node
Niche Niche 1.0000000 Node
PFIM Niche 0.3589744 Node
Random Niche 0.5384615 Node
ADBM PFIM 0.5897436 Node
Body-size ratio PFIM 0.3076923 Node
ATN PFIM 0.5384615 Node
Niche PFIM 0.3589744 Node
PFIM PFIM 1.0000000 Node
Random PFIM 0.7692308 Node
ADBM Random 0.6666667 Node
Body-size ratio Random 0.3846154 Node
ATN Random 0.7692308 Node
Niche Random 0.5384615 Node
PFIM Random 0.7692308 Node
Random Random 1.0000000 Node