2.3 Data analysis
We used multivariate techniques to examine the influence of connectivity
and seasons on amphibian, fish, benthos, and plankton assemblages,
respectively, and also all biota together. Density data of all faunal
groups were standardized using the decostand(XX, method=”max”)
function in the vegan package (Oksanen et al., 2020) in R prior to
analysis so that the density data of each taxon range between 0 and 1.
We then analysed the assemblage data at 25 sites in three seasons (as 78
data) by nonmetric multidimensional scaling (NMDS) using Bray-Curtis
dissimilarity index as pairwise β-diversity values. We applied two-way
PERMANOVA procedures to the β‐diversity values to test for statistical
differences in the biota by the connectivity gradient and by seasons
using the adonis function in the vegan package (Oksanen et al., 2020).
When there were significant interactions of connectivity and seasons, we
applied PERMANOVA for the 25 data of each season to examine the effect
of connectivity on the assemblages in each season.
We then used generalized dissimilarity modelling (GDM) to analyse the
relative contribution of connectivity and local environment on the
β-diversity in each season. GDM is a nonlinear extension of matrix
regression for analysing and predicting patterns of assemblage
dissimilarity in relation to environmental gradients. Dissimilarities
between all possible pairs of assemblage were calculated and were
modelled as a function of environmental dissimilarities assuming their
nonlinear relationships (Ferrier et al., 2002, 2007). We applied GDM to
25 assemblage data for each season. Using the gdm package (Manion et
al., 2018), we first converted each dissimilarity matrix and the
environmental data to site-pair format, applied GDM with function gdm,
and estimated importance of environmental data using function
gdm.varImp. As connectivity variable, we used the four connectivity
categories “No flow”, “Early”, “Late”, and “Flowing” based on
the presence of inflow from the main stream over time as described
above. As environmental variables, we included area and depth of each
pond, average flow velocity, downstream connectivity of each pond to the
river, grain size, water temperature, and dissolved oxygen. pH and
conductivity were not included for the analysis because of their high
association with DO (positive) and temperature (negative).
To partition variation in biotic assemblages, each group of explanatory
variables was first screened with forward selection with a Monte-Carlo
randomisation test and only variables significantly related to
assemblage structure in forward selection were retained in the final
models. We then carried out three model fittings for each assemblage
data set along with a different set of environmental data matrix.
Matrix-(I) was constrained by both connectivity and local environments
(a +b +c ; where a describes pure effects of
environmental variables, b shared environmental and connectivity
effects, and c pure effects of connectivity); matrix-(II) was
constrained by environmental variables only (a +b ); and
matrix-(III) was constrained by connectivity only (b +c ).
Variation in assemblage structure was subsequently partitioned into
shared environment and connectivity [b =(a +b )
+(b +c ) −(a +b +c )], pure local
environment [a =(a +b ) −(b )], pure
connectivity [c =(b +c ) −(b )], and
unexplained fractions [d =1 −(a +b +c )]
(Borcard et al. 1992; Legendre and Legendre 1998). In addition, in order
to illustrate autumn benthic macroinvertebrate and amphibian assemblage
patterns in relation to local environments (Table S1) a canonical
correspondence analysis was conducted.
Finally, to identify key taxa driving the spatial and temporal
variations in aquatic assemblages, we applied a similarity percentage
analysis (SIMPER) to examine the contribution of each taxon to the
differences in the whole aquatic assemblage of each season. We also
carried out SIMPER to examine the seasonal change in aquatic assemblages
and tested the significance in the seasonal decline or increase of each
taxon from spring to autumn.