Data analyses
To study which factors affected the overall invasion extent of exotic
aquatic plants in the sites, we used regression analysis to examine the
relationships between the site-level mean biomass of exotic aquatic
plants (MBSE ) and the site-level richness of
native plants (Rnative_site ), site-level
performance of native plants [Pnative_site , a
metric integrating MBSN andMCSN by principal component analysis (PCA), data
values were scores of PC1 (proportion variances = 0.86)], T ,Nwater , Hsite ,Lsite-town , and Ahabitat .
For each regression analysis, we trialed linear regression and nonlinear
regression (quadratic and cubic) models and selected the best-fitting
models with the highest fitting coefficient
(R2 ) and a significant P-value
(P<0.05).
To study the differences in the effects of the same factors on different
life-form exotic plants, we selected three exotic plants belonging to
different life-forms with the highest frequency in our field surveys:
emergent plant Alternanthera philoxeroides (in 483 quadrats
and145 sites), free-floating plant Eichhornia crassipes (in 515
quadrats and 87 sites), submerged plant Cabomba caroliniana (in
170 quadrats and 25 sites). We used linear regression analysis to
examine the relationships between the quadrat-level population biomass
of the three exotic plants and the quadrat-level richness of native
plants (Rnative_quadrat ), quadrat-level
performance of native plants (Pnative_quadrat , a
metric integrating TBQN andTCQ N by PCA, data values were scores of PC1 and
proportion variances ranged from 0.74~0.87),
quadrat-level performance of co-occurring exotic plants
(Pexotic_quadrat , a metric integratingTBQE and TCQ E of all
exotic plants except the target exotic plant by PCA, data values were
scores of PC1 and proportion variances ranged from
0.82~0.98), T , Nwater ,Hquadrat , and Dleaf .
To investigate how native plants, water nutrient status, climate,
habitat features, herbivory level, and anthropogenic disturbance
influence the overall invasion extent of exotic aquatic plants and the
invasion extent of different life-form plants, we employed structural
equation modeling (SEM) to explore the relationships between the factors
and MBSE and quadrat-level population biomass ofA. philoxeroides , E. crassipes , and C. carolinianaindividually. For MBSE , we constructed a
priori models and hypothesized that (1) all abiotic factors (T ,Nwater , Hsite ,Lsite-town , Ahabitat )
would influence the MBSE ,Rnative_site , andPnative_site , (2)Rnative_site andPnative_site would further influence theMBSE , and Rnative_sitewould predict Pnative_site , (3)Nwater would be predicted byAhabitat , Hsite , andLsite-town (Appendix S1: Fig. S2A). For biomass
of A. philoxeroides , E. crassipes , and C.
caroliniana , we constructed a priori models and hypothesized
that (1) T , Nwater ,Hquadrat ,Rnative_quadrat ,Pnative_quadrat , andPexotic_quadrat would influence biomass of the
three exotics, (2) T , Nwater ,Hquadrat ,Rnative_quadrat , andPnative_quadrat also would influencePexotic_quadrat , (3) bothRnative_quadrat , andPnative_quadrat would be predicted by T ,Nwater , and Hquadrat , andRnative_quadrat would predict thePnative_ quadrat(Appendix S1: Fig. S2B). TheDleafwas not included in the models because it neither reduced the biomass of
exotic plants nor affected other factors.
The SEM models were fitted by the PiecewiseSEM R-package (Lefcheck
2016). Before modeling of the data, we inspected the data for outliers
and multi-collinearity. To facilitate interpretation and ensure model
convergence, we standardized all variables (except the richness of
native plants) to have a mean of zero and a standard deviation. In the
sub-models of Pnative_site andMBSE , we included the random effect of the
sampling area to control the effect of the sampling area on the biomass
of exotic and native plants. In all sub-models of quadrat-level response
variables, we included random effects of the site to account for the
non-independence of quadrats within the same site. Both site-level and
quadrat-level richness of native plants were modeled with generalized
linear models (GLMs) or generalized mixed-effects models (GLMMs) using a
Poisson distribution and log-link function. The other response variables
were modeled using linear models (LMs) or linear mixed-effects models
(LMEs). All LMs, LMEs, GLMs, and GLMMs were performed by the “lme4”
package (Bates et al. 2014). To obtain the best model, we removed the
nonsignificant paths with the highest p-value in a stepwise manner. We
added any link that was initially ignored when the D-separation test
revealed significant missing relationships between variables, and
including the significant missing path would improve the model fit. We
assessed the goodness-of-fit of the model (Fisher’s C statistic) using
Shipley’s test of directional separation. Models with an adequate fit (p
> 0.05) were considered as candidate models, and their AICs
were computed and compared. The model with the lowest AIC value was
considered as the best-fit model. Standardized coefficients for the GLM
and GLMM components of the piecewise SEM (not provided by PiecewiseSEM)
were calculated by the latent theoretic approach (Lefcheck 2019).
To determine the relative importance of various factors that influence
the overall invasion extent of exotic aquatic plants and the invasion
extent of different life-form plants, we employed hierarchical
partitioning (hier.part R package, Walsh and MacNally 2015) to analyze
the independent explanatory power (I) ofRnative_site ,Pnative_site , T ,Nwater , Hsite ,Lsite-town , and Ahabitatfor the MBSE , and the independent explanatory
power (I) of T , Nwater ,Hquadrat ,Rnative_quadrat ,Pnative_quadrat , andPexotic_quadrat for the biomass ofA. philoxeroides , E.
crassipes , and C. caroliniana . The statistical significance of
each I was determined by randomizing the data matrix 200 times to
produce the distribution of I values, with observed I value that
exceeded the 95th percentile considered significant. The results of the
significance tests were expressed as Z-scores. All analyses were
conducted by R v4.2.0 (R Development Team 2022). The biomass of A.
philoxeroides , E. crassipes , and C. caroliniana was
transformed using Ln (x) function.