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In a previous study18, we used bioinformatics tools to analyze the metagenome and the amplicon 16S sequences to gain an insight into microbial diversity in Moroccan lagoons, namely Marchica and Oualidia. 16S rRNA gene classification revealed a high percentage of bacteria in both lagoons. On average, bacteria accounted for 90% of the total prokaryotes in Marchica and ~ 70% in Oualidia. The five phyla that were the most abundant in both lagoons, Marchica and Oualidia, respectively, were Proteobacteria (53.62%, 29.18%), Bacteroidetes (16.46%, 43.49%), Cyanobacteria (0.53%, 34.35%), Verrucomicrobia (1.75%, 15.82%), and Actinobacteria (7.42%, 13.98%). At the genus level, we found that the highest assigned hits were attributed to Synechococcus, which was highly abundant in Marchica (32%) compared to Oualidia (0.07%) in 2014. This amount dropped to 22% in Marchica and 0.04% in Oualidia in 2015. Hence, in this study we performed the analysis of the Synechococcus genus community using oligotyping to investigate their dynamics and understand their co-occurrence and covariation in space and time within fragile ecosystems such as lagoons.We may divide our results into two emerging Synechococcus communities: one dominated in 2014 and the other was less present in 2015, each composed of different cooccurring Synechococcus oligotypes. The abundant Synechococcus community in Marchica in 2014 consisted of clades I, 5.3, III, IV, and VII. These clades are typically found in either warmer or more oligotrophic environments19,20. This result is in accordance with Marchica’s environmental characteristics; it is an oligotrophic ecosystem with high primary production and warmer water in summer21. The community included clades CB5 and WPC1 in Marchica 2014 and 2015 when the number of Synechococcus reads was lower. Strains belonging to the CB5 clade lack phycourobilin (PUB), contain one motile strain22,23, are present in temperate coastal waters and are prevalent in polar/subpolar waters24,25,26. WPC1 strains are observed in open-ocean and near-shore waters1,24,27. Clades IV and I usually co-occur and are more prevalent in cold coastal waters19,28,29,30. Interestingly, Clade III was prominent in Marchica. This clade is known to be motile and restricted to warm, oligotrophic water19,20,30. Although at a smaller read number, clade III was also observed in Oualidia, where the temperature is cooler compared to Marchica. Furthermore, we found that clade III growth has been shown to be severely affected at low temperatures30. Moreover, representatives of both clades I and IV were present in Oualidia in both the summers of 2014 and 2015. Some Synechococcus strains, which are known to prefer cooler water temperatures and salinities, were in higher relative abundance in the waters of Marchica. This result agrees with a previous study showing that Synechococcus isolates of clades I and IV exhibited temperature preferences31. Their growth rates were marginally lower at low temperatures in strains from clades I and IV, which were dominant in temperate regions.Nitrate levels are typically low or undetectable in these lagoons, which allows the persistence of clades that would not typically thrive in coastal waters at other times of the year. In 2014, the nitrate concentration was higher than the average of 10 mg/l, which could be due to increased agricultural activities and wastewater treatment plant effluent21. The decreasing nitrate concentration in Marchica in 2015 could be explained by the newly installed inlet in 2010, which was designed to improve water exchange with the open sea and reduce the amount of suspended matter21. Temperature and salinity have a large effect on nitrate in marine ecosystems32; the highest nitrate degradation rates were observed at 35 °C and at increasing salinity rates. Therefore, we expected to see correlations between salinity, temperature and nitrate concentrations. Interestingly, clades CB5 in Marchica and IV in Oualidia increased in relative abundance in summer 2015 compared to 2014, when the nitrate concentration decreased. Moreover, the Synechococcus microbial community diversity and density are variables depending on the variations in the physical and chemical parameters. These parameters are strongly influenced by the marine waters passing through the artificial inlets, which have an impact on the internal hydrodynamics of both lagoons and hence the distribution and co-occurrence of Synechococcus strains. In addition, anthropogenic activities also have a great influence on Synechococcales population growth and interactions with their viruses33,34.This study revealed some differences between Marchica and Oualidia in identified Synechococcus clades. The Marchica lagoon showed more heterogeneity (clades I, II, III, IV, VII, VIII, 5.3, WPC1, CB5, and IX) than the Oualidia lagoon, where fewer clades were identified (I, III, IV, and VII). There was a clear variation in the pattern of correlation between oligotypes of the same or different clades for both the 2014 and 2015 samplings. Furthermore, we observed complex patterns of co-occurrence among oligotypes; in 2014 (clades I, III, IV, 5.3, VII), and in 2015, we found clades CB5 and WPC1. In Oualidia, values decreased in comparison to Marchica in both 2014 and 2015 summer samplings, following a pattern of co-occurrence, especially for both clades I and IV in both sampling years. Many studies have shown that the relative proportions of cooccurring Synechococcus populations to each other at the clade and subclade levels vary in space and time based on environmental factors such as seasonal temperature fluctuations, nutrient availability and upwelling, circulation patterns, and abundance of other phytoplankton8.We presume that the greater variability in oligotype co-occurrence behavior observed in Marchica Lagoon, especially in the summer of 2014, could be due to the higher abundance and diversity of Synechococcus oligotypes, physico-chemical parameter fluctuations or rehabilitation of the lagoon.Less abundant oligotypes could also be considered potential bioindicators of Synechococcus genetic diversity. Their seasonal occurrence might contribute to changing ecological and biogeochemical characteristics of the marine environment35. The Synechococcus relative abundance count revealed that the Marchica Synechococcus community included the least abundant oligotypes in 2015. For instance, O7 and O8 were detected in 2014 and were absent in 2015 (Table 1). It is unclear which factors served to constrain the relative abundances of these least present oligotypes, but temperature and salinity could have an impact on their distribution in Marchica (Fig. 4) and the opposite for Oualidia, which are cooler-temperature adapted ones. We noticed that the relative abundance of cooccurring Synechococcus was not constant. For instance, oligotype 4 belonging to Clade IV showed higher values in summer 2014 (974 reads) in Marchica compared to summer 2015 (319 reads), and the opposite was observed in Oualidia, with a lower abundance compared to Marchica. Increased values of cooccurring clade I oligotypes (14, 26, and 6) were detected in the summer of 2014 in both lagoons.Figure 4Principle component analysis of Synechococcus oligotype relative abundance. The plot is generated using the relative abundance of each oligotype, T temperature, S Salinity, and NO3− Nitrate. Each point represents an oligotype. Colors represent the year of sampling; red for 2014 and blue for 2015. The shape of point indicates the sampling site; rounded points refer to Marchica lagoon, and triangles refer to Oualidia. Circles represent the normal distribution of oligotypes; the red circle refers to 2014, and the blue one refers to 2015.Full size imageIn comparing our results with a study from Little Sippewissett Marsh (LSM)8 that used oligotyping to investigate the distribution of the genus Synechococcus in space and time sequencing the V4-V6 hypervariable region of the 16S rRNA gene, we found 31 oligotypes, while they identified 12. In both studies, the proportion of Synechococcus oligotypes increased in summer and in coastal waters compared to estuaries. In addition, Clades I and IV were more abundant in saline conditions, such as Marchica Lagoon. However, these clades were found in greater relative abundances at cold temperatures, in contrast to our study, where they were identified in Marchica’s warm waters. Moreover, clade CB5 tended to be prominent at relatively warm temperatures (17–20 °C)6. In our work, it was not prevalent either in cooler or warmer water. Notably, the relative abundance of rare oligotypes was higher in warm hypersaline estuary waters8,18, while in our case study, they occurred in cooler moderately saline Oualidia waters.The dominance of a certain clade could have many different ecological ramifications, especially as the clades can be incredibly diverse in their growth, loss, nutrient utilization and other attributes. The dominant clade’s growth and loss patterns will set the stage for the population dynamics. For instance, if the dominant clade only blooms in a given environmental factor such as temperature, light, or salinity, it will then affect the timing of blooms, and follow-on the effects of subsequent grazing, lysis or even biogeochemical cycling. Even if the population is diverse, the dynamics as a whole will be a composite response of each individual clade’s ecophysiology, making it important to understand their composition and how it changes over space and time.While the rpoC1 gene is a higher resolution diversity marker36, 16S amplicon data can be used for exploring the entire bacterial assemblage including Synechococcus clade designations via oligotyping35. The latter has a great advantage in answering unexplained diversity contained in taxa using 16S rRNA gene sequences. Nevertheless, it has some limitations, as it acts optimally only when performed on taxa that are closely related. Regarding distantly related taxa, the high number of increased-entropy locations makes the supervision steps difficult. In addition, although oligotyping does not rely on clustering conditions or availability of existing reads within reference databases, it demands preliminary operational taxonomic unit clustering to find closely related species appropriate for the analysis. This method is under continuous improvement to better exploit the information within subtle variations in 16S rRNA gene sequences5.In conclusion, we explored the patterns of Synechococcus diversity in space and time using an oligotyping approach to examine these populations in lagoon waters of Mediterranean Marchica and Atlantic Oualidia, in Morocco. Patterns that have been observed at the clade and subclade levels, such as Synechococcus, relative abundance and the co-occurrence of groups from different clades, were shown to occur among oligotypes. The Marchica Lagoon showed a heterogeneous Synechococcus diversity compared to Oualidia in summer 2014. Thirty-one Synechococcus oligotypes were identified. Two distinct communities emerged in the 2014 and 2015 summer samplings, abundant and rare Synechococcus species, each comprising cooccurring Synechococcus oligotypes from different clades. Network analysis showed that six oligotypes were exclusive to Marchica Lagoon. The identified clades I, III, IV, VII, and 5.3 in Marchica were in accordance with its environmental characteristics. In addition, the relative abundance of some cooccurring Synechococcus strains was not constant over time and space (e.g., clades I and IV). Using gene oligotyping, we illustrated some of the challenges associated with the identification of novel Synechococcus strains or studied their co-occurrence in space and time. Oligotyping has been instrumental in discriminating closely related Synechococcus strains. However, this study leaves open questions about how samples differ by location and whether locations differ from year to year. Do cooccurring oligotypes interact with each other and to what extent do they correlate with physicochemical parameters? What triggers the coexistence of clades I and IV with clade III in warm water or 5.3 with VII, which do not know much about. Finally, how do relative abundances change over seasons. Hence, future work needs to consider additional stations and seasons to provide better statistical support for our findings and to better understand their correlation with physical and chemical environmental parameters. Other factors were not considered in this study, such as nutrient availability, chlorophyll, irradiance, viral lysis, and greater sequencing depth, which could also influence the observed seasonal dynamics. More

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LightGBMBefore explaining LightGBM23, it is necessary to introduce XGBoost24, which is also based on the gradient boosting decision tree (GBDT) algorithm30. XGBoost integrates multiple classification and regression trees (CART) to compensate for the lack of prediction accuracy of a single CART. It is an improved boosting algorithm based on GBDT, which is popular due to its high processing speed, high regression accuracy and ability to process large-scale data31. However, XGBoost uses a presorted algorithm to find data segmentation points, which takes up considerable memory in the calculation and seriously affects cache optimization.LightGBM is improved based on XGBoost. It uses a histogram algorithm to find the best data segmentation point, which occupies less memory and has a lower complexity of data segmentation. The flow of the histogram algorithm to find the optimal segmentation point is shown in Fig. 3.Figure 3Histogram algorithm.Full size imageMoreover, LightGBM abandons the levelwise decision tree growth strategy used by most GBDT tools and uses the leafwise algorithm with depth limitations. This leaf-by-leaf growth strategy can reduce more errors and obtain better accuracy. Decision trees in boosting algorithms may grow too deep while training, leading to model overfitting. Therefore, LightGBM adds a maximum depth limit to the leafwise growth strategy to prevent this from happening and maintains its high computational efficiency. To summarize, LightGBM can be better and faster used in industrial practice and is also very suitable as the base model in our tide level prediction task. The layer-by-layer growth strategy and leaf-by-leaf growth strategy are shown in Fig. 4.Figure 4Two GBDT growth strategies.Full size imageCNN-BiGRUConvolutional neural networkA convolutional neural network (CNN) is a deep feedforward neural network with the characteristics of local connection and weight sharing. It was first used in the field of computer vision and achieved great success32,33. In recent years, CNNs have also been widely used in time series processing. For example, Bai et al.34 proposed a temporal convolutional network (TCN) based on a convolutional neural network and residual connections, which is not worse than recurrent neural networks such as LSTM in some time series analysis tasks. At present, a convolutional neural network is generally composed of convolution layers, pooling layers and a fully connected layer. Its network structure is shown in Fig. 5. The pooling layer is usually added after the convolution layers. The maximum pooling layer can retain the strong features in the data after the convolution operation, eliminate the weak features to reduce the number of parameters in a network and avoid overfitting of the model.Figure 5Schematic diagram of a convolutional neural network.Full size imageBidirectional GRUIn previous attempts at tide level prediction by scholars, bidirectional long short-term memory networks35 have achieved good prediction results. However, in our subsequent experiments, the bidirectional gated recurrent unit achieved higher prediction accuracy than BiLSTM, so we used the BiGRU network for subsequent prediction tasks.The GRU network36 adds a gating mechanism to control information updating in a recurrent neural network. Different from the mechanism in LSTM, GRU consists of only two gates called the update gate ({z}_{t}) and the reset door ({r}_{t}).The recurrent unit structure of the GRU network is shown in Fig. 6.Figure 6Recurrent unit structure of the GRU network.Full size imageEach unit of GRU is calculated as follows:$${z}_{t}= sigma ({W}_{z}{x}_{t}+{U}_{z}{h}_{t-1}+{b}_{z})$$
(7)
$${r}_{t}= sigma ({W}_{r}{x}_{t}+{U}_{r}{h}_{t-1}+{b}_{r})$$
(8)
$${widetilde{h}}_{t}=tanh({W}_{h}{x}_{t}+{U}_{h}left({r}_{t}odot {h}_{t-1}right)+{b}_{h})$$
(9)
$${h}_{t}={z}_{t}odot {h}_{t-1}+left(1-{z}_{t}right)odot {widetilde{h}}_{t}$$
(10)
In the above formula, ({z}_{t}) represents the update gate, which controls how much information is retained from the previous state ({h}_{t-1}) (without nonlinear transformation) when calculating the current state ({h}_{t}). Meanwhile, it also controls how much information will be accepted by ({h}_{t}) from the candidate states ({widetilde{h}}_{t}). ({r}_{t}) represents the reset gate, which is used to ensure whether the calculation of the candidate state ({widetilde{h}}_{t}) depends on the previous state ({h}_{t-1}). (upsigma ) is the standard sigmoid activation function; (tanh(cdot )) is the hyperbolic tangent activation function; and (odot ) indicates the Hadamard product. The weight matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({W}_{z},{W}_{r},{W}_{h}); the coefficient matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({U}_{z},{U}_{r},{U}_{h}); and the offset vectors of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({b}_{z},{b}_{r},{b}_{h}).A bidirectional gated recurrent unit network37 is a combination of two GRUs whose information propagating directions are reversed, and it has independent parameters in each, which makes it able to fit both forward and backward data at first and then join up the results from two directions. BiGRU can capture sequence patterns that may be ignored by unidirectional GRU. The structure of BiGRU is shown in Fig. 7.Figure 7The structure of BiGRU.Full size imageTaking the BiGRU’s forward hidden state vector at time (t) as ({h}_{t}^{(1)}) and taking the BiGRU’s backward hidden state vector at time (t) as ({h}_{t}^{(2)}), (upsigma ) indicates the standard sigmoid activation function, and (oplus ) indicates a vector splicing operation. We can calculate the output ({y}_{t}) of a BiGRU network as follows:$${h}_{t}={h}_{t}^{(1)}oplus {h}_{t}^{(2)}$$
(11)
$${y}_{t}=sigma ({h}_{t} )$$
(12)
CNN-BiGRU prediction modelBecause CNN has significant advantages in extracting useful features from a picture or a sequence and BiGRU is good at processing time series, we combine CNN and BiGRU to build the CNN-BiGRU model. The model can be mainly divided into an input layer, a convolution layer, a BiGRU network layer, a dropout layer, a fully connected layer and an output layer. The CNN layer and BiGRU layer are the core structures of the model. The function of the dropout layer is to avoid model overfitting. The CNN layer consists of two one-dimensional convolution (Conv1D) layers and a one-dimensional maximum pooling (MaxPooling1D) layer. The input of BiGRU is the output sequence of the CNN layer, and the BiGRU network is set as a one-hidden-layer structure. The structure of the CNN-BiGRU combination model is shown in Fig. 8.Figure 8The structure of CNN-BiGRU.Full size imageVariable weight combination modelWhen we analyze and predict relatively stationary tide level time series, LightGBM can perform well. However, due to environmental factors such as air pressure, wind force and terrain in reality, most tide level observation sequences are sometimes not relatively stationary, which requires that our tide level prediction model be reasonably able to “extrapolate” based on the sample observations, that is, be capable of generating values that are not in the sample. LightGBM is a tree-based model, which leads to our prediction results being between the maximum and minimum values of sequences. Therefore, LightGBM will not be able to accurately predict the situation or tidal change trend that did not appear in previous observations. However, the CNN-BiGRU model, which is a kind of neural network, has no such problem in theory and will be able to find the trend information that may be hidden in the tide level series. Therefore, we consider providing an appropriate weight for a single base model to build a combination model to improve the accuracy of the tide level prediction task.Principle of the residual weight combination model and improved variable weight combination modelTo improve the prediction accuracy of the combination model, a simple and effective idea is to determine the base models’ weights in the combination model according to the error between the prediction value and the real value. This method is also called the residual weight method, and its calculation formulas for determining the weights are:$$gleft({x}_{t}right)= sum_{i=1}^{m}{omega }_{i}left(t-1right){f}_{i}({x}_{t})$$
(13)
$${omega }_{i}left(t-1right)=frac{frac{1}{overline{{varphi }_{i}}left(t-1right)}}{sum_{i=1}^{m}frac{1}{overline{{varphi }_{i}}left(t-1right)}}$$
(14)
$$sum_{i=1}^{m}{omega }_{i}left(t-1right)=1,{omega }_{i}left(t-1right)ge 0$$
(15)

where ({omega }_{i}left(t-1right)) denotes the weight of the (i) th model at the moment (t-1), ({f}_{i}left({x}_{t}right)) denotes the prediction value of the (i) th model at the moment (t), (gleft({x}_{t}right)) denotes the prediction value of the combination model at the moment (t), and (overline{{varphi }_{i}}left(t-1right)) is the square sum of the predictive errors of the (i) th model at the moment (t-1).Our LightGBM-CNN-BiGRU (combination model) is based on the improved residual weight method. We call it the variable weight combination model. We use the weights calculated by formula (9) and formula (11) to calculate a series of new weights. The new weights from formula (11) will take the residual weight changes in (d) time steps into consideration by averaging the old weights in (d) time steps to improve the stability of the residual weight method.$${omega }_{j}left(tright)=frac{1}{d}sum_{k=1}^{d}{omega }_{i}left(t-kright)left(d=4right)$$
(16)
After obtaining a series of weights through formula (9) and formula (11), we take the absolute value of the error between the prediction value and the true value of each combination model at the moment of (t) as ({delta }_{i,t}) and ({delta }_{j,t}), respectively:$${delta }_{i,t}=mid sum_{i=1}^{m}{omega }_{i}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
(17)
$${delta }_{j,t}=mid sum_{i=1}^{m}{omega }_{j}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
(18)
Comparing ({delta }_{i,t}) and ({delta }_{j,t}), if ({delta }_{i,t} >{delta }_{j,t}), the combination model uses the new weight ({omega }_{j}left(tright)) in place of the original weight ({omega }_{i}left(tright)). Otherwise, the weight of the combination model remains unchanged.Parameter optimization of the combination modelBecause the LightGBM-CNN-BiGRU (combination model) is a variable weight combination of the prediction results from two base models, the performance of the combination model can be directly improved by separately optimizing the super parameters of the two base models. We mainly use the grid search algorithm and K-fold cross validation method to optimize the parameters. The grid search algorithm is a method to improve the performance of a certain model by iterating over a given set of parameters. With the help of the K-fold cross validation method, we can calculate the performance score of the LightGBM model on the training set and easily optimize its superparameters. The final parameters of the LightGBM model are set to num_leaves = 26, learning_rate = 0.05, and n_estimators = 46.For the CNN-BiGRU network, we mainly improve the prediction accuracy of the model by adjusting the size and number of hidden layers in the BiGRU structure and prevent the model from overfitting by changing the dropout ratio and tracking the validation loss of the network while training.The LightGBM and CNN-BiGRU variable weight combination modelThe workflow of our tide level prediction model is shown in Fig. 9. It mainly includes data preprocessing; training, optimization and prediction of the base models; construction of a variable weight combination prediction model; and evaluation and analysis of the combination model’s performance.

(1)

Data preprocessing: The quality of the data directly determines the upper limit of the prediction and generalization ability of a certain machine learning model. Standard, clean and continuous data are conducive to model training. The data used in this study are from the Irish National Tide Gauge Network, and all of them are subject to quality control. We filled in a small number of missing values and normalized the data to speed up the model training.

(2)

Construction and optimization of base models: We divide the dataset into a training set, a validation set and a test set according to the proportion of 7:1:2 and train the LightGBM model and CNN-BiGRU model with data on the training set. We optimize the parameters and monitor whether the model has been overfitted by tracking the validation loss of the network while training. Finally, we put the data into two base models for training and then obtain the prediction results of a single base model.

(3)

Construction of the variable weight combination model. Based on the prediction results of two single base models obtained in step (2), we calculate the weight of each base model according to the principle of the improved variable weight combination method and then obtain the prediction results of the variable weight combination model.

(4)

Model evaluation and analysis: According to the indexes of the model evaluation, the variable weight combination model is compared with other basic models to analyze its prediction performance after being improved.

Figure 9Prediction flow of the LightGBM-CNN-BiGRU variable weight combination model.Full size image More

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