Environmental data, zooplankton abundance, and community
structure
With the aim of evaluating the correlations between environmental
parameters and the structure of the zooplankton community, revealing the
spatial structure within each cruise, and revealing seasonal and
inter-annual environmental changes, we analyzed the relationships
between the 8 environmental predictors and the zooplankton distribution.
Those predictors included the average (0-200 m) environmental conditions
(i.e., temperature, oxygen, salinity, fluorescence, and density) as well
as geographic and bathymetric predictors (e.g., latitude, longitude, and
depth). Since the cruises reflect late spring (XIXIMI-05) and summer
(XIXIMI-04 and XIXIMI-06) conditions, season was used as a constraining
factor in subsequent analyses.
The effect of environmental parameters was tested using the proxy of
abundance for each taxa at the family level. A distance-based linear
modeling (DistLM) analysis was applied with a multivariate multiple
regression analysis using Primer 6+P
(K. R. Clarke &
Warwick, 2001). We conducted these analysis to estimate the independent
ordination of all predictors (Marginal test), which determines the
proportion of zooplankton variance explained by each environmental
variable independently, and to obtain an optimal ordination model
(Sequential Test), in which the model partitions the variation in the
data based on a multiple regression model selected by the user (e.g.,
forward, stepwise, or best fit;
K. R. Clarke &
Warwick, 2001). The latter outcome may be considered to be the best
statistical combination of all abiotic predictors. In this study, we
implemented the Akaike Information Corrected Criterion (AICc) and
‘stepwise’ options for model selection. We selected this approach
because stepwise multiple regression adds or subtracts predictor
variables from a model until most of the variation is explained;
variables are excluded if they behave like random variables in terms of
the additional variation explained. A significance evaluation of the
multidimensional model was conducted with a permutation method with 9999
permutations
(Usov, Khaitov,
Smirnov, & Sukhotin, 2019) implemented in Primer 6+P
(K. R. Clarke &
Warwick, 2001).
A distance-based redundancy analysis (dbRDA) was performed using Primer
6+P (K. R. Clarke
& Warwick, 2001) to visualize the relative importance of all predictor
variables
(McArdle &
Anderson, 2001). The potential relationships were evaluated using
normalized environmental data and the fourth root transformed read
abundance of zooplankton at the family level. The fourth root
transformation was deemed to be the most appropriate transformation
since it reduces the weight of highly abundant taxa and facilitates
comparisons among different datasets
(Howson,
Buchanan, & Nickels, 2017; Vause et al., 2019). The resultant data were
converted to a resemblance matrix using the Bray-Curtis similarity
index.
According to the multivariate multiple regression results (reported
below), stations were categorized into discrete categories according to
their environmental profiles. These categories were: low/high oxygen
concentration, warm/cold temperatures, and east/west longitude. With
regard to the latter, east stations were located at longitudes
< 86.30 °W, while the remaining stations (> 86.30
°W) were attributed to the west group. Likewise, stations with
lower/higher values than the average mean values for oxygen and
temperature (specific average threshold values of oxygen and temperature
were calculated according to the stations in each analysis) were grouped
as “Low/High_O2” and “Cold/Warm”, respectively.
These oceanographic variables (averaged for the first 200 m of the water
column) were visualized in contour plots generated with Ocean Data View
software (Schlitzer, 2018). This approach was used to compare spatial
(within each cruise) and temporal patterns (among spring and summer
cruises) and to test for similarities/differences in zooplankton
community structure. In addition, all data for the three cruises were
also included in a comprehensive analysis.
Potential spatial and seasonal segregation of the environmental
parameters after normalizing the data was tested using a Cluster/SIMPROF
analysis in Primer 6+P software. Clustering was carried out with a
Euclidean distance matrix using the group average method. Statistical
differences among groups of stations were evaluated by a PERMANOVA with
4999 permutations using Primer 6+P software, while comparisons of the
average abiotic parameters were conducted with ANOVAs in Statistica
software.