Ecology

The following museum acronyms are used (mostly following55):
BMNH, NHM, BM: Natural History Museum, London, formerly British Museum of Natural History (UK);
CAS: California Academy of Sciences, San Francisco (USA);
MHNC: Musée d’histoire naturelle, La Chaux-de-Fonds (CH);
MHNG: Muséum d’Histoire Naturelle, Genève (CH);
MNHN: Muséum national d’Histoire naturelle, Paris (F);
SMNS: Staatliches Museum für Naturkunde, Stuttgart (D);
UWBM The Washington State Museum of Natural History and Culture/Burke Museum, University of Washington (USA);
ZMB: Museum für Naturkunde Berlin, formerly Zoologisches Museum Berlin (D).
Study organism and taxonomic background
Acanthodactylus was first described as a subgenus of Lacerta Cuvier (sic)56, the type species is A. boskianus (Daudin, 1802). The few meristic characters described to be diagnostic for Acanthodactylus56 include: collar connected in the center but free on the sides, tempora squamosal, i.e., temporal region covered by small scales rather than larger shields, ventral scales rectangular and arranged in longitudinal rows, digits acutely fimbriate-denticulate forming “toe fringes”. Fringed toes have evolved in various shapes multiple times in lizards and in Lacertidae, and when triangular, projectional or conical as in Acanthodactylus they are commonly seen as adaptation to windblown sand substrate57.
The enigmatic Acanthodactylus guineensis is among the lesser-known species of the Lacertidae. Only limited information is available regarding the species’ morphology, habitat and distribution (see Meinig & Böhme44 for a review and references therein; and 34, 38, 45, 58). Based on one young specimen, A. guineensis was described as member of the genus Eremias Fitzinger, 1834 by Boulenger40. Boulenger does not mention the slightly projecting third row of scales around the toes and fingers (“fringed toes”) of the type specimen in his quite comprehensive description40, probably because he did not notice it in such a small (SVL 24 mm) specimen and because the projection is indeed rather minor compared to other members of the genus. This is probably also the reason why he did not place guineensis in the genus Acanthodactylus Wiegmann, 1834 but in Eremias Fitzinger, 1834 (in56).
About 30 years later, Boulenger revised the genus Eremias and divided it into five sections he assumed to be natural associations59: (1) Taenieremias Boulenger, 1918—monotypic, type species Eremias guineensis Boulenger, 1887 (currently Acanthodactylus guineensis); (2) Lampreremias Boulenger, 1918—type species Eremias nitida Günther, 1872 (currently Heliobolus nitidus); (3) Pseuderemias Boettger, 1883—type species Eremias mucronata (Blandford, 1870) (currently Pseuderemias mucronata); (4) Mesalina Gray, 1838—type species Eremias rubropunctata (Lichtenstein, 1823) (currently Mesalina rubropunctata); (5) Eremias s. str. Fitzinger, 1834—type Eremias velox (Pallas, 1771) (currently Eremias velox).
Monard47 described Eremias (Taenieremias) benuensis from Cameroon based on a few minor morphological differences compared to E. guineensis, but in 1969 E. benuensis was synonymized with E. guineensis60.
Salvador17 and one year later also Arnold18 in their respective major revisions of the genus Acanthodactylus both found that Eremias (Taenieremias) guineensis agrees with all the characteristic morphological features of Acanthodactylus with the exception of the arrangement of scales around the nostril. However, it was suggested that the E. guineensis condition with an extra suture across the area occupied by the first upper labial scale to produce a smaller first upper labial is easily derived from that found in Acanthodactylus, evidenced by BMNH 1966.430, a juvenile A. erythrurus lineomaculatus (sic) with a similar scale arrangement18. Within Acanthodactylus, both authors placed guineensis in the Western clade and in the Acanthodactylus erythrurus group which was assumed to consist of A. erythrurus, A. savignyi, A. boueti, A. guineensis, and A. blanci which was considered a subspecies of A. savignyi at the time14, 20.
Molecular data and phylogenetic analyses
We aimed at reconstructing a well-supported phylogeny of the genus Acanthodactylus using whole mitochondrial DNA sequences, assembled by means of a shotgun next generation sequencing strategy. Analyses of whole mitogenomes have been shown to resolve many nodes of the lacertid tree with high statistical support (e.g.31). We retrieved muscle tissue from a museum voucher of A. guineensis (ZFMK 59511 from Daroha, near Bobo Dioulasso, Burkina Faso43) that likely was never in contact with formalin for preservation and therefore offered good chances to obtain sufficient amounts of DNA of decent quality for sequencing. We extracted genomic DNA using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the protocol provided by the manufacturer. We additionally sequenced a tissue sample of a freshly caught individual of Acanthodactylus schmidti (SB 642) from Abu Dhabi (UAE) to increase sampling of Acanthodactylus spp. in the mitogenomic tree. The lizard was euthanized by injection of an aqueous solution of benzocaine (20%) into the body cavity. Subsequently, a sample of muscle tissue was taken from the thigh, preserved in 98% Ethanol and stored in − 80 °C. Handling, euthanizing and collection of tissue samples of A. schmidti individuals was approved by the NYUAD Institutional Animal Care and Use Committee (IACUC 19-0002) and UAE No Objection Certificate (NOC 8416), and all applied methods were performed in accordance with the relevant guidelines and regulations.
Genomic DNA of SB 642 was extracted from ethanol-preserved muscle tissue using the Qiagen MagAttract HMW DNA Kit (Qiagen, Hilden, Germany) for high molecular weight DNA. We determined DNA yields on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit. Totals of 52 ng and 80 ng of DNA (diluted in 26 µl 10 mM Tris·Cl, 0.5 mM EDTA, pH 9.0 (AE buffer)) from A. guineensis and A. schmidti, respectively, were used for library preparation.
ZFMK 59511 libraries were prepared with NEB Ultra II FS DNA (New England Biolabs, Ipswich, MA, USA) kit as per protocol instructions with input below 100 ng. For sample SB642, linked reads were generated on a 10X Genomics Chromium following Genome reagent kits v2 instructions. Resulting libraries’ concentration, size distribution and quality were assessed on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit and on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) using a High Sensitivity DNA kit. Subsequently, libraries were normalized and pooled, and pools quantified with a KAPA Library quantification kit for Illumina platforms (Roche Sequencing, Pleasanton, CA, USA) on a ABI StepOnePlus qPCR machine (Thermo Fisher Scientific Inc., Waltham, MA, USA), then loaded on a SP flowcell and paired-end sequenced (2 × 150 bp) on an Illumina NovaSeq 6000 next generation sequencer (Illumina, San Diego, CA, USA), and a S2 flowcell for linked read library. Raw reads were deposited in the Sequence Read Archive (SRA) under BioProject ID PRJNA700414 . All mitogenome assemblies and original alignments were deposited in Figshare under  https://doi.org/10.6084/m9.figshare.13754083.v1 .
Raw FASTQ sequenced reads were first assessed for quality using FastQC v0.11.561. The reads where then passed through Trimmomatic v0.3662 (parameters ILLUMINACLIP: trimmomatic_adapter.fa:2:30:10 TRAILING:3 LEADING:3 SLIDINGWINDOW:4:15 MINLEN:36) for quality trimming and adapter sequence removal. Following the quality trimming, the reads were assessed again using FastQC. The executed workflows were performed using BioSAILs63.
For SB 642 (A. schmidti), we received 436,370,000 single-end reads (average read length 139.5 bases (b)) with a raw coverage of 29.29X and a scaffold N50 of 21.12 kilobases (kb). For the MITObim analysis, read number for SB 642 was randomly reduced to 1,000,000 reads (360,463,035 b) using the awk command. For ZFMK 59511 (A. guineensis), we received 57,936,345 paired-end reads (average read length 151 b, raw coverage ~ 10x). Subsequently, the two sets of reads were converted to interleaved format. We used the quality filtered reads to assemble the mitogenomes of A. guineensis and A. schmidti using an iterative mapping strategy in MITObim v. 1.9.164. We used the Acanthodactylus aureus mitogenome (GB accession number xxxxx; assembly method is provided in the next paragraph) as seed for both samples; this rendered an initial mapping of a conserved region from a more distantly related individual unnecessary64. We therefore applied the –quick option in MITObim and iterations were run until no additional reads could be incorporated into the assembly (14 in A. guineensis, eight in A. schmidti).
We also assembled complete or nearly complete mitogenomes for additional twelve species of Gallotiinae and Eremiadini using anchored hybrid enrichment sequence data from Garcia-Porta et al.6 (see Supplementary methods in6 for extraction protocol and sequencing methods and Supplementary Table S1 online in6) with the Podarcis muralis complete mitogenome (GB accession number NC_011607) as reference sequence. The raw data of each individual was quality filtered using Trimmomatic v0.3662 (parameter MINLEN:45) and assembled using MITObim v. 1.9.1. All multiple alignment files generated in the final MITObim iteration were imported to Geneious R11 (https://www.geneious.com) to check for assembly quality and coverage.
The resulting assemblies were annotated with MITOS65 using defaults settings with the vertebrata database as a reference. The annotated assemblies were imported into PhyloSuite66 together with existing mitogenomic sequences of Lacertidae, and Blanus cinereus (Amphisbaenia; outgroup) available in GenBank (as of July 2020). Details of all mitogenomic sequences included in this study and the corresponding GenBank accession numbers are provided in Supplementary Table S1 online. Using PhyloSuite, we exported all protein coding sequences plus the two rRNAs. The resulting files were aligned with MAFFT67 using “auto” settings. Alignments of coding sequences were refined with MACSE68 to account for open reading frame structure during the alignments. The protein coding and rRNAs alignments were finally concatenated into a single alignment delimiting partitions by marker and, for the protein-coding genes, by codons within markers. Mitogenome phylogenies were inferred using a maximum likelihood approach with IQ-TREE v. 1.5.4 software69 and Bayesian inference (BI) analysis with MrBayes 3.270.
For the maximum likelihood approach, best-fitting partitioning schemes and substitution models were selected based on the Akaike Information Criterion (AIC) using the heuristic algorithms implemented in the MFP + MERGE option (Supplementary Table S3 online). After ML inference, branch support was assessed with 1000 ultrafast bootstrap replicates. As the alignments used for phylogenetic inference contained more than one sequence for some species, these terminals in the resulting tree were collapsed a-posteriori for aesthetic reasons using FigTree v1.4 (as depicted in Fig. 1). An uncollapsed version of this tree is presented in Supplementary Fig. S1 online. For the BI analysis, the best-fitting partition/substitution model scheme, as selected by the ModelFinder algorithm in iQTree (Supplementary Table S4 online), was implemented with MrBayes 3.270. Results of two independent runs of 10 million generations, each comprising four Markov Chains (three heated and one cold), were sampled every 1000 generation. Chain mixing and stationery was assessed by examining the standard deviation of split frequencies and by plotting the –lnL per generation using Tracer 1.5 software71. Samples corresponding to the initial phase of the Markov chain (25%) were discarded as burn-in and the remaining results were combined to obtain a majority rule consensus tree and the respective posterior probabilities of nodes.
An additional, more comprehensive, alignment was produced containing all currently known Acanthodactylus species to obtain a higher resolution perspective on the phylogenetic placement of the target species. Since no nuclear data was available for A. guineensis, we extracted from the newly obtained sequences the four best represented mitochondrial gene fragments across Lacertidae, as compiled by Garcia-Porta et al.6, despite the known shortcomings of partial mitochondrial datasets to resolve lacertid relationships9, 72, 73. The corresponding sequences of the ribosomal RNAs (12S, 16S), COB, and NADH-dehydrogenase subunit (ND4) were later added to the alignment of6 using –add and –keeplength options in MAFFT (Supplementary Table S5 online).
Species distribution modeling
As basis for characterizing the species’ bioclimatic envelopes, we compiled an updated distribution map for A. guineensis and A. boueti including new discoveries in museum collections from the current study (see Supplementary Material online), all known records from the literature16, 34, 37, 38, 40, 42, 44, 45, 47, 58, 60, 74,75,76,77 and from the Global Biodiversity Information Facility (GBIF78). We provide all coordinates as latitude (decimal degrees), longitude (decimal degrees). Due to the general scarcity of records for A. guineensis and A. boueti, we added two localities for A. guineensis (12.5°, − 2.5°; 7.5°, 13.5°) and one locality for A. boueti (− 2.5°, 7.5°) published in Trape et al.45 that were not found on GBIF or in other publications, by extracting the center coordinates of the respective 1 × 1 degree grid. These additional localities have coordinates with very low accuracy and the corresponding environmental variables extracted from a 1 × 1 km resolution grid (30 arc-seconds; see below) therefore have high degrees of uncertainty. When examined, the majority of values fell within the total range for the respective environmental variable (the two exceptions are mentioned in the Results section), we consequently decided to keep them for our analysis. We reduced the final locality dataset to one record per km2 (the resolution of the environmental input data, see below) to avoid pseudoreplication using the R package spThin79. We then tested whether the remaining presence points are randomly dispersed or clustered using the Nearest Neighbor Index NNI in the R packages sp80 and spatialEco81. NNI was 1.4 for A. guineensis and 1.2 for A. boueti, indicating that the filtered point dataset consists of randomly distributed points which are not spatially autocorrelated.
As environmental parameters we downloaded spatial layers of the Terrestrial Ecoregions of the World (TEOW)82, global bioclimate and elevation layers39, data on potential evapotranspiration83 and global aridity index84 with a spatial resolution of 30 arc-seconds (~ 1 km2 near the equator). Although the climate datasets are interpolated from data from weather stations much farther apart from one another and are consequently not measurements of actual environmental conditions, they were rigorously cross-validated with observed data (including satellite data) during the development of the dataset and generally showed high correspondence with observations (especially temperature variables)39. Since for many applications such as our species distribution models data at high spatial resolution (i.e., 1 × 1 km) are preferable over lower resolution to capture variation for example across steep climate gradients in mountains39 we chose the 30 arc-seconds dataset over the 5 arc-minutes dataset (~ 9 × 9 km near the equator) for the ecoregions and climate datasets. In addition, we downloaded spatial layers with forest and grassland/scrub/woodland cover from the harmonized soil database (only available in 5 arc-minutes spatial resolution85). We downscaled the forest and grassland/scrub/woodland dataset to 30 arc-seconds resolution despite the resulting slight inaccuracy, which we kept in mind during interpretation but did not consider relevant for the vegetation datasets.
In order to compare the prevailing climate within the distribution range of A. guineensis and A. boueti to all other species of the genus Acanthodactylus we downloaded all available records of the other currently known Acanthodactylus species from GBIF. We cleaned the downloaded datasheet by deleting all entries with missing data for either latitude, longitude, species epithet, or all of those, and removed duplicates or spelling mistakes. We further removed all GBIF entries with imprecise coordinates, i.e. records with coordinates that when plotted landed just outside of coastal areas in the ocean instead of on land (usually coordinates with only two decimals). In addition, we added several curated locality data from Garcia-Porta et al.6 as available from Figshare (https://doi.org/10.6084/m9.figshare.8866271.v1/). Following Tamar et al.14 we treated A. lineomaculatus as a junior synonym of A. erythrurus and changed the respective records in our database accordingly. For species that did not have records published on GBIF we added further locality data from the literature (Supplementary Table S6 online). The final database contained 4286 records from all 44 currently recognized species of Acanthodactylus. We acknowledge that our database is by no means complete, however, we wanted to follow the most conservative approach and use only records that are supported by a voucher specimen and can be traced back using published databases. We trust that our record list covers a nearly complete representation of the ecological conditions inhabited by all currently accepted Acanthodactylus species.
Using the bioclim dataset39 we plotted annual mean temperature (bio1) and annual precipitation (bio12) for all locality records in our Acanthodactylus species database and compared the respective data for A. guineensis and A. boueti with its congeners. We developed species distribution models using Maxent 3.4.146 for A. guineensis and A. boueti. Maxent applies the maximum entropy principle86 for model fitting under the basic premise that the estimated species distribution deviates from a uniform distribution as minimally as required to explain the observations46. We used the cleaned datasets of A. guineensis and A. boueti with only one record per km2 as input presence locality data. We extracted environmental data for all presence sites from the 19 bioclim variables (bio1-19), elevation (alt), aridity index (AI), percentage cover of forest (forest) and grassland, scrub and woodland (grass) per grid cell, as well as four parameters comprising potential evapotranspiration (PET): PET of the wettest, driest, warmest and coldest quarter of the year (PETwet, PETdry, PETwarm, PETcold). To avoid issues resulting from correlated parameters we reduced the number of environmental predictors by performing multicollinearity analyses using the Variance Inflation Factor (VIF) with a threshold  More

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Physicochemical properties of water
Physicochemical properties of water and associated environmental factors according to the flow directions of surface flow wetlands are shown in Table 1. Analysis of variance for indexes with p values of less than 0.05 showed that all indexes exhibited large variations during different months.
Table 1 Physicochemical characteristics of the surface-flow constructed wetlands in each unit.
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Some indexes exhibited large variations during different months according to the flow directions of surface flow wetlands. For example, DO was first reduced and then increased in May, but increased in August and showed differences compared with that in May and October. The salinity was first reduced and then increased in May but then remained stable from August to October. Some indexes showed similar changes according to the flow directions of surface flow wetlands. For example, ORP showed an initial decrease followed by an increase. At the same time, pH, SpCond, TDSs, and TN showed reduced variability over time. The changes in temperature were minimal, although the temperature was higher in the summer and autumn than in the spring.
Surface flow wetlands are in direct contact with the environment and are greatly influenced by outside environmental factors17. Thus, most physicochemical factors of the water samples showed large variability.
Denitrifying bacteria diversity and abundance
For 10 samples from different seasons showing 97% similarity in clustering analysis, the numbers of OTUs differed in May, August, and October (575, 869, and 741, respectively), and the Fig. 2 showed that the denitrifying bacterial abundance indexes (ACEs) were 686.8, 996.2, and 887.3 in May, August, and October, respectively. Additionally, the Shannon-Weiner indexes (H′) were 3.718, 4.303, and 4.432, respectively, indicating that the abundance tended to increase initially, followed by a decrease, and diversity tended to increase. The different seasons affected both the denitrifying bacteria abundance and diversity. Abundance was the largest in August, but its diversity was lower than that in October. These data suggested that the main species became dominant during August, affecting the structure of the denitrifying bacteria.
Figure 2

Biodiversity and abundance of the surface-flow constructed wetland in each unit.

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For different processing units, the abundance and diversity of denitrifying bacteria varied slightly; both the ACE and H′ index showed low variability. The units F, H, and J showed greater declines than the initial values. In May, the ACE index peaked, with a value of 841 at location I. In August, the ACE index peaked at location E (1251), and that in October peaked at location G (1042). In different months, denitrifying bacteria abundances showed similar changes. Because bacterial diversity in the flowing water and static water were affected by different factors, the surface flow wetlands will be susceptible to various factors, and the bacterial community interactions with internal and external environmental factors will be important for bacterial survival18. Additionally, the number of constructed wetland bacteria decreases as the depth and distance increases19,20, suggesting that denitrifying bacteria may be affected by physical and chemical indicators of changes in water.
Community structure of denitrifying bacteria
Similar OTUs (97% similarity) were identified by sequencing. Database analysis of sequence alignment results revealed that there were many bacteria in the environmental samples that could not be cultivated but that showed high similarity; thus, the denitrifying bacteria were mostly present in the surface flow wetlands and were not cultured. Figure 3 shows statistical analysis of the denitrifying bacterial categories in a histogram format. During the different months, OTUs mainly belonged to seven genera: unclassified bacteria (37.12%), unclassified Proteobacteria (18.16%), Dechloromonas (16.21%), unranked environmental samples (12.51%), unclassified Betaproteobacteria (9.73%), unclassified Rhodocyclaceae (2.14%), Rhodanobacter (1.51%), and other genera (2.62%, representing less than 1% each). Several genera have also been found in surface flow wetlands21 and other types of constructed wetlands22,23,24, albeit with different proportions.
Figure 3

Community structure of the surface-flow constructed wetland in each unit.

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The same processing units showed different denitrifying bacterial community structures during different seasons and were always changing. Unclassified bacteria showed a greater weight during May for the A processing unit, although its weight was lower than that of Dechloromonas in August. In October, unclassified bacteria had become the most dominant group, and the proportion of Dechloromonas was extremely low. For the B processing unit, from May to October, the proportion of Dechloromonas was decreased, and the proportion of unclassified Proteobacteria was increased, overtaking Dechloromonas. For the C and D processing units, unclassified Proteobacteria were dominant in May, and unclassified bacteria were dominant in August and October. For the E, G, H, I, and J processing units, unclassified bacteria were dominant at all time points. For the F processing unit, the bacterial groups were similar to those of the B processing unit, with proportion of Dechloromonas decreasing and the proportion of unclassified bacteria increasing in October.
Figure 4 shows the means and variances of denitrifying bacteria genus proportions among different processing units and seasons. The means and variances of the dominant genus were large at the same time during different seasons. Thus, the dominant genus often determined the changes in denitrifying bacteria community structures during different seasons in the same unit. However, the greatest variance was observed in the genus Dechloromonas, which was the second most dominant genus in May. This suggested that this genus showed greater changes in different processing units than others. In August, the largest variances were observed in the genus Dechloromonas and in unclassified Proteobacteria, which had lower means than unclassified bacteria. Similar results were observed in October. The largest variance was observed in unclassified Proteobacteria, indicating that the denitrifying bacterial community structures were affected by the second dominant genus over time in the different processing units.
Figure 4

Mean and variance of different denitrifying bacterial taxa in the surface-flow constructed wetland (May, August, and October).

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For the denitrifying bacteria community structures in different months, we used nonmetric multidimensional scaling to determine the similarities between different processing units during the same months. As shown in Fig. 5, in May, A, B, C, and E showed high similarity, whereas other samples were more dispersed. The distances between D and H and between G and I were shorter than the other distances. F and J were alone in a group. Sample distributions were concentrated in August; C, D, E, F, G, H, and I were relatively similar, and D and E showed maximum similarity. A and B showed some similarity. In contrast, J was distinct. In October, distributions were more dispersed, and the distances between two points were not relatively similar, whereas the differences between the various processing units were higher.
Figure 5

Nonmetric multidimensional scaling map (May, August, and October).

Full size image

Relationships between denitrifying bacteria and environmental factors
Next, we carried out PCA analysis to determine the main factors affecting denitrifying bacteria. After maximum variance orthogonal rotating (p = 0.05), there were two principal component eigenvalues that were greater than the average. The two top principal components contributed to 53.9% and 28.7% of the variance. The first principal component mainly reflected SpCond, TDSs, ORP, and salinity (factor loading was 0.409, 0.403, 0.398, and 0.403, respectively), and the second principal component reflected DO, TN, pH, and temperature (factor loading was 0.449, 0.449, 0.465, and 0.446, respectively). The load distribution characteristics of different environmental factors showed that the surface flow wetlands were affected by the main environmental factors, including temperature, SpCond, DO, pH, ORP, and TN (Fig. 6).
Figure 6

PCA of various environmental factors in the surface-flow constructed wetlands.

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Table 2 shows bacterial abundance indexes and H′ index for different external environmental factors, as analyzed by Pearson correlation analysis. The results showed that the bacterial abundance was strongly correlated with temperature, DO, and pH, and H′ was strongly correlated with all parameters except TN.
Table 2 Relationships between biodiversity and environment factors.
Full size table

RDA was performed (Fig. 7) for analysis of community distributions and the relationships among environmental factors. For screening of the physicochemical factors of water and the proportions of denitrifying bacterial genera, standardization to center (Monte Carlo permutation) tests were used, and refinement of the information extracted from the first and second axes showed that the total explained variance rate was 80.94%. The results showed that all denitrifying bacterial genera were greatly affected by environmental factors, including temperature and pH, and that the effects of SpCond and ORP were similar. The predominance of unclassified bacteria and unclassified Proteobacter could be explained by positive correlations with temperature, pH, ORP, and SpCond and negative correlations with TN and DO. Dechloromonas showed the opposite trends. In contrast, unranked environmental samples were similar to unclassified Betaproteobacteria, with positive correlations for temperature and pH but negative correlations for TN and DO.
Figure 7

Relationships between denitrifying bacterial community structures and environment factors.

Full size image

Denitrifying bacterial diversity is affected by water nutrient elements and other environmental factors. Most denitrifying bacteria were heterotrophic bacteria. In this study, the autotrophic denitrifying bacteria Dechloromonas accounted for a large proportion in each processing unit25,26,these bacteria can accumulate phosphate and exhibit denitrification activity, partly explaining the lack of TOC removal in association with the observed TN removal. The SpCond of the water reflected its salinity and could be explained by positive correlations with a high proportion of unclassified Proteobacteria. However, SpCond was not generally correlated with denitrifying bacterial abundance. Our results showed that the water SpCond in surface-flow constructed wetlands affected salinity-related denitrifying bacteria but did not affect other denitrifying bacteria. The ORP was positively correlated with denitrifying bacterial genera that were suitable for survival in a strong oxidizing environment, such as unclassified Proteobacteria.
Different physical and chemical properties can influence the structure of the bacterial community owing to the influence of different species on the living environment27,28. In this study, we assessed environmental factors that differed according to season and showed that denitrifying bacteria varied according to some environmental parameters. A comprehensive analysis of the trend of physical and chemical properties of water showed that all parameters except DO and salinity were not highly affected by season and that the trend of the abundances of denitrifying bacteria communities did not change with season along the flow direction of different processing units. However, environmental indicators have a more significant impact on different denitrifying bacteria, which also changes the diversity of denitrifying bacteria community. Accordingly, these results, combined with prediction models of the effects of environmental factors on nitrogen and phosphorus29,30,31, could be used to predict changes in the denitrifying bacterial community structure. More

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