Marine microbial communities perform critical functions in biogeochemical transformations such as carbon fixation, respiration and remineralization of organic dissolved material. More specifically in the Western English Channel (WEC) these processes help facilitate the carbon movement from surface waters to the pelagic (Azam and Malfatti, 2007), In a mechanism known as the microbial carbon pump. Costal ecosystems are however increasingly subjected to climate change driven by for example anthropogenic pressures. Rising sea levels, ocean acidification, altered nutrient compositions and stratification can reshuffle nutrients and therefore microbial communities, having consequences on ecosystem assemblage.
In this study we analyse microbial community dynamics in the WEC using long term time series data spanning multiple years. Including amplicon sequence variant (ASV) counts with their corresponding environmental parameters and taxonomic annotations. This integrative approach allows us to identify potential environmental drivers of microbial alpha diversity and to evaluate seasonal patterns in specific microbial groups. Understanding the sensitivity of microbial community structure and function to environmental variability is especially urgent in coastal regions where biogeochemical fluxes are tightly coupled to microbial diversity. Recent studies highlight that shifts in microbial taxonomic composition, particularly at the family or genus level, can reflect changes in functional traits relevant to carbon processing, including metabolic flexibility, particle association, and nutrient utilization (Louca et al., 2016). We focus on Magnetospiraceae a family within the Alphaproteobacteria that includes magnetotactic bacteria known for their ability to orient along geomagnetic fields due to the presence of intracellular magnetosomes. These bacteria are often found at oxic-anoxic interfaces and contribute to the cycling of iron, sulfur, and carbon compounds (Lin et al., 2014). While Magnetospiraceae are traditionally studied in sediments, emerging evidence suggests their presence and potential activity in coastal water columns, particularly under stratified or hypoxic conditions. Their metabolic versatility and microaerophilic lifestyles position them as key responders to environmental gradients in oxygen, redox potential, and nutrient availability By exploring the environmental correlates of Magnetospiraceae dynamics alongside broader microbial community trends, this study enhances our understanding of how coastal microbial assemblages adapt to changing conditions and what that means for the future of carbon cycling in dynamic marine environments.
Methods
Three datasets were used (1) Amplicon sequence variant ASV count data across a temporal timeseries (WEC_ASV_data.csv), (2) environmental metadata associated with each sample (WEC_environmental_data.csv), and (3) taxonomic assignments for each ASV (WEC_taxonomic_data.csv). ASV count data were transposed, converted to relative abundance. Used to calculate Shannon diversity (alpha diversity). Environmental data were cleaned and joined by sample ID. Data were processed in R (v4.3.1) To assess which environmental variable best explained variation in alpha diversity over time, univariate linear regressions were run for each numeric environmental parameter. Each model used Shannon diversity as the response variable. The R-squared and p-value were extracted to rank predictors. A stepwise multiple linear regression model was then constructed, beginning with all numeric environmental variables as predictors. This allowed the selection of an optimised model explaining the variance in alpha diversity. To explore seasonal variation, a linear model including interaction between season and nitrate concentration was constructed. Shannon diversity was plotted against nitrate nitrite across seasons, and individual regression lines were overlaid. ASVs belonging to the family Magnetospiraceae were extracted using taxonomy data. Alpha diversity and total relative abundance were calculated per sample for this family. These metrics were plotted over time. Fourier transform (FFT) was applied to the relative abundance time series (after interpolation to weekly intervals) to identify dominant periodic signals in community dynamics.
Results
firstly using the Using the whole community data, a single environmental parameter that best explains variance in alpha diversity over time was found to be Nitrate_Nitrate
Warning: package 'tidyverse' was built under R version 4.2.3
Warning: package 'ggplot2' was built under R version 4.2.3
Warning: package 'tibble' was built under R version 4.2.3
Warning: package 'tidyr' was built under R version 4.2.3
Warning: package 'readr' was built under R version 4.2.3
Warning: package 'purrr' was built under R version 4.2.3
Warning: package 'dplyr' was built under R version 4.2.3
Warning: package 'stringr' was built under R version 4.2.3
Warning: package 'forcats' was built under R version 4.2.3
Warning: package 'lubridate' was built under R version 4.2.3
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Loading required package: permute
Warning: package 'permute' was built under R version 4.2.3
Loading required package: lattice
This is vegan 2.6-8
Warning: package 'zoo' was built under R version 4.2.3
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
asv <-read.csv("WEC_ASV_data.csv", row.names =1)env <-read.csv("WEC_environmental_data.csv")tax <-read.csv("WEC_taxonomic_data.csv")# change wec to columns asvv <-t(asv)# convert to Relative Abundanceasv_rel <- asvv /rowSums(asvv)# calculate Shannon Diversityalpha_div <-diversity(asv_rel, index ="shannon")# add shannon to the env table env$shannon <- alpha_div[match(env$Sample, rownames(asvv))]# date to characterenv$Date <-as.character(env$Date)env$Date <-as.Date(env$Date)env$Year <-format(env$Date, "%Y")# seeing whioch varuiable is bestenv_numeric <- env %>%select(where(is.numeric)) %>%select(-shannon) # Remove numeric variables with more than 25% missing valuesenv_numeric <- env_numeric %>%select(where(~mean(!is.na(.)) >0.75))model_results <-map_dfr(names(env_numeric), function(var) { x <- env_numeric[[var]]# Skip variables with all NA or zero varianceif (all(is.na(x)) ||var(x, na.rm =TRUE) ==0) return(NULL) model <-lm(env$shannon ~ x) model_summary <-summary(model)tibble(variable = var,r_squared = model_summary$r.squared,p_value =coef(model_summary)[2, 4] )})# View top predictors NITRATE_nitrate model_results %>%arrange(desc(r_squared)) %>%head(20)
To identify the environmental variable that best explains variation in alpha diversity across the time series, individual linear models were fitted for each environmental predictor. The results revealed that NITRATE_NITRITE was the strongest single predictor, explaining approximately 39.4% of the variance in alpha diversity (R² = 0.394, p < 2.2e-16). Other top contributors included PHOSPHATE (R² = 0.362), SILICATE (R² = 0.346), and maximum weekly temperature (R² = 0.335), all of which were highly statistically significant. This finding is consistent with previous research by Fuhrman et al. (2006), who demonstrated a strong positive correlation between nitrate concentrations and bacterial diversity in coastal waters.Nitrate acts as a fundamental nutrient that fuels primary production by phytoplankton, which in turn supplies organic matter to heterotrophic microbial communities. Seasonal mixing and upwelling events that inject nitrate into surface waters may drive periods of increased microbial diversity.
Next is a optimized multiple parameter regression that explains variance in alpha diversity over time.
# Use multiple parameter regression to create an optimised model that explains variance in alpha diversity over time model_full <-lm(shannon ~ PHOSPHATE * season + NITRATE_NITRITE + temp_C + salinity_PSU, data = env)summary(model_full)
Call:
lm(formula = shannon ~ PHOSPHATE * season + NITRATE_NITRITE +
temp_C + salinity_PSU, data = env)
Residuals:
Min 1Q Median 3Q Max
-1.07458 -0.16190 0.01951 0.21766 0.57568
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.416329 8.798962 0.502 0.616
PHOSPHATE 0.113172 0.662849 0.171 0.865
seasonspring -0.435993 0.209894 -2.077 0.039 *
seasonsummer -0.242125 0.209464 -1.156 0.249
seasonwinter 0.272782 0.395345 0.690 0.491
NITRATE_NITRITE 0.044213 0.032975 1.341 0.181
temp_C -0.007486 0.018794 -0.398 0.691
salinity_PSU 0.001631 0.247976 0.007 0.995
PHOSPHATE:seasonspring 0.426070 0.561581 0.759 0.449
PHOSPHATE:seasonsummer 0.343239 0.815900 0.421 0.674
PHOSPHATE:seasonwinter -0.274581 0.846884 -0.324 0.746
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3095 on 206 degrees of freedom
(47 observations deleted due to missingness)
Multiple R-squared: 0.4855, Adjusted R-squared: 0.4605
F-statistic: 19.44 on 10 and 206 DF, p-value: < 2.2e-16
# Define colors for each seasonseason_colors <-c("Spring"="#66C2A5","Summer"="#FC9272","Autumn"="#FDAE6B","Winter"="#6BAED6")# Capitalize the first letter of each seasonenv$season <- stringr::str_to_title(env$season)# Then rerun the plotggplot(env, aes(x = NITRATE_NITRITE, y = shannon, color = season)) +geom_point(alpha =0.6) +geom_smooth(method ="lm", se =FALSE, size =1.2) +facet_wrap(~season) +scale_color_manual(values = season_colors) +labs(title ="Seasonal Relationship Between Nitrate Concentration and Shannon Diversity",x ="Nitrate + Nitrite Concentration (µM)",y ="Shannon Diversity Index",color ="Season" ) +theme_minimal(base_size =14) +theme(axis.line =element_line(color ="black"),panel.border =element_rect(color ="black", fill =NA, linewidth =0.5),strip.text =element_text(face ="bold", size =12),legend.position ="none" )
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 14 rows containing missing values or values outside the scale range
(`geom_point()`).
# Get R² for each seasonr2_by_season <- env %>%group_by(season) %>%summarise(r_squared =summary(lm(shannon ~ NITRATE_NITRITE, data =cur_data()))$r.squared )
Warning: There was 1 warning in `summarise()`.
ℹ In argument: `r_squared = summary(lm(shannon ~ NITRATE_NITRITE, data =
cur_data()))$r.squared`.
ℹ In group 1: `season = "Autumn"`.
Caused by warning:
! `cur_data()` was deprecated in dplyr 1.1.0.
ℹ Please use `pick()` instead.
# View the resultsprint(r2_by_season)
# A tibble: 4 × 2
season r_squared
<chr> <dbl>
1 Autumn 0.119
2 Spring 0.275
3 Summer 0.0398
4 Winter 0.0350
Figure 1. Seasonal Relationship Between Nitrate and Shannon Diversity. Scatter plots with regression lines for each season.
To explore whether the relationship between nitrate concentrations and alpha diversity varied seasonally, the data were visualized using a faceted regression plot and analyzed with separate linear models for each season. The resulting figure shows clear differences in the strength of association across seasons. In spring, nitrate concentrations explained approximately 27.5% of the variance in Shannon diversity (R² = 0.275), indicating a moderate positive relationship. In autumn, the relationship was weaker (R² = 0.119), while in summer and winter, nitrate had minimal explanatory power (R² = 0.040 and R² = 0.035, respectively). These findings suggest that the influence of nitrate on microbial alpha diversity is strongest in spring,These findings suggest that the influence of nitrate on microbial alpha diversity is strongest in spring, likely due to seasonal stratification and nutrient replenishment during winter mixing, which fuel spring phytoplankton blooms and stimulate microbial community diversification (Gilbert et al., 2009). The faceted plot visually supports this conclusion, with the regression line in spring showing a noticeably stronger slope compared to the flatter trends in other panels.
Next a taxanomic plot of Magnetospiraceae and their alpha diversity and relative abundance over time. Using Fourier analysis to determine if there is a seasonal pattern in relative abundance Magnetospiraceae.
# Subset taxonomy to just Magnetospiraceaemagneto_only <-subset(tax, Family =="Magnetospiraceae")# Extract list of ASV IDs for this familymagneto_asvs <- magneto_only %>%pull(ASV) # Replace 'ASV' with the correct column name for ASV IDs if different# Subset ASV table to only those ASVsasv_magneto <- asv_rel[, colnames(asv_rel) %in% magneto_asvs]# Calculate Relative Abundance & Diversity# Total relative abundance of Magnetospiraceae per sampleenv$magneto_abund <-rowSums(asv_magneto, na.rm =TRUE)# Shannon diversity (within-Magnetospiraceae)env$magneto_alpha <- vegan::diversity(asv_magneto, index ="shannon")# Plotting Time Series# Ensure Date is correctly formattedenv$Date <-as.Date(env$Date)# Set darker custom season colorsenv$season_color <-case_when( env$season =="Winter"~"#6BAED6", # blue env$season =="Spring"~"#66C2A5", # green env$season =="Summer"~"#FC9272", # red env$season =="Autumn"~"#FDAE6B"# orange)# Create shading blocks based on seasonseason_blocks <- env %>%select(Date, season_color) %>%distinct() %>%arrange(Date) %>%mutate(Date_end =lead(Date, default =last(Date)))# Plot 1: Relative Abundancep1 <-ggplot() +geom_rect(data = season_blocks, aes(xmin = Date, xmax = Date_end,ymin =-Inf, ymax =Inf, fill = season_color), alpha =0.25) +geom_line(data = env, aes(x = Date, y = magneto_abund), color ="black", size =0.8) +geom_smooth(data = env, aes(x = Date, y = magneto_abund), method ="loess", se =FALSE,color ="red", linetype ="dashed") +scale_fill_identity(guide ="legend", name ="Season",labels =c("Winter", "Spring", "Summer", "Autumn"),breaks =c("#6BAED6", "#66C2A5", "#FC9272", "#FDAE6B") ) +theme_minimal() +theme(axis.line =element_line(color ="black", size =1),legend.position ="top",plot.title =element_text(face ="bold") ) +labs(title ="Magnetospiraceae Relative Abundance",x =NULL, y ="Relative Abundance" )
Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
figure 2: Temporal patterns in Magnetospiraceae relative abundance and alpha diversity in the Western English Channel. displays seasonal dynamics in both the relative abundance (top panel) and Shannon diversity (bottom panel) of ASVs classified under the family Magnetospiraceae. Each point represents a weekly sample, and shaded backgrounds indicate seasons: Winter (blue), Spring (green), Summer (red), and Autumn (orange). Dashed red lines show LOESS-smoothed trends to highlight overall temporal patterns.
Peaks in relative abundance appear to occur regularly in late spring and summer, suggesting that environmental conditions during these months may favor the growth of Magnetospiraceae. These peaks coincide with increases in alpha diversity, indicating that not only are these microbes more abundant in these periods, but there may also be a greater variety of ASVs within the family. Conversely, diversity and abundance tend to drop during winter, implying potential environmental constraints such as temperature or light limitation.
Lastly Fourier Analysis of Magnetospiraceae Abundance
# Ensure Date is Date typeenv$Date <-as.Date(env$Date)# Filter and prepare Magnetospiraceae abundancemagneto_df <- env %>%select(Date, magneto_abund) %>%filter(!is.na(Date) &!is.na(magneto_abund))# Create complete weekly sequenceweekly_seq <-seq(min(magneto_df$Date), max(magneto_df$Date), by ="week")# Merge and interpolatez <-zoo(magneto_df$magneto_abund, order.by = magneto_df$Date)z_full <-merge(z, zoo(, weekly_seq), all =TRUE)z_interp <-na.approx(z_full)# FFTmagneto_ts <-as.numeric(z_interp)fft_result <-abs(fft(magneto_ts))# Prepare for ggplotfft_df <-data.frame(Frequency =seq_along(fft_result) /length(fft_result),Amplitude = fft_result)# Plot using ggplot2ggplot(fft_df[1:30, ], aes(x = Frequency, y = Amplitude)) +geom_col(fill ="steelblue") +labs(title ="Fourier Transform: Magnetospiraceae Relative Abundance",x ="Frequency (cycles/sample)",y ="Amplitude" ) +theme_minimal(base_size =14) +theme(axis.line.x =element_line(size =1.2, color ="black"),axis.line.y =element_line(size =1.2, color ="black"),panel.grid =element_blank() )
Figure 3. Fourier Analysis of Magnetospiraceae Abundance. Amplitude spectrum from FFT of interpolated weekly relative abundance. A strong peak at ~0.019 cycles/sample suggests annual periodicity.
Fourier analysis of the interpolated weekly relative abundance of Magnetospiraceae revealed a distinct periodic signal in the microbial community. The dominant frequency occurred at approximately 0.019 cycles/sample, corresponding to an annual cycle (∼52 weeks), suggesting a strong seasonal pattern in the abundance of this family. Several smaller peaks at higher frequencies were also observed, indicating the presence of potential sub-seasonal fluctuations. These findings support the presence of regular temporal dynamics, likely driven by seasonal environmental changes in the Western English Channel. This seasonal pattern could be due to cyclical changes in temperature, nutrient availability, and water column stratification, all of which strongly influence microbial community composition throughout the year (Fuhrman, Cram and Needham, 2015).
Limitations include occasional missing data points, irregular sampling frequency, and limited taxonomic resolution for some ASVs. Interpolation and relative abundance calculations mitigate these issues but introduce assumptions such as only using variables with less than 25% na values. Additionally, the analysis focuses on one microbial family; broader patterns may differ across taxa. Despite these caveats, the findings highlight clear and interpretable seasonal signals in community diversity and taxon-specific dynamics.
Generative AI statement
STUDENT DECLARATION OF GEN AI IN THIS ASSESSMENT i acknowledge the following uses of GenAI tools in this assessment: i have used ChatGPT for the following tasks: - to help generate code to answer questions - to find errors in code i declare that i have referenced use of GenAI outputs within my assessment in line with the University referencing guidelines and saved a record of all GenAI outputs, that i can make available on request.
REFERNCES
Azam, F. and Malfatti, F. (2007) ‘Microbial structuring of marine ecosystems’, Nature Reviews Microbiology, 5(10), pp. 782–791. Available at: https://doi.org/10.1038/nrmicro1747. Falkowski, P.G., Fenchel, T. and Delong, E.F. (2008) ‘The Microbial Engines That Drive Earth’s Biogeochemical Cycles’, Science, 320(5879), pp. 1034–1039. Available at: https://doi.org/10.1126/science.1153213. Fuhrman, J.A. et al. (2006) ‘Annually reoccurring bacterial communities are predictable from ocean conditions’, Proceedings of the National Academy of Sciences, 103(35), pp. 13104–13109. Available at: https://doi.org/10.1073/pnas.0602399103. Fuhrman, J.A., Cram, J.A. and Needham, D.M. (2015) ‘Marine microbial community dynamics and their ecological interpretation’, Nature Reviews. Microbiology, 13(3), pp. 133–146. Available at: https://doi.org/10.1038/nrmicro3417. Gilbert, J.A. et al. (2009) ‘The seasonal structure of microbial communities in the Western English Channel’, Environmental Microbiology, 11(12), pp. 3132–3139. Available at: https://doi.org/10.1111/j.1462-2920.2009.02017.x. Lin, W. et al. (2018) ‘Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution’, The ISME journal, 12(6), pp. 1508–1519. Available at: https://doi.org/10.1038/s41396-018-0098-9. Louca, S. et al. (2016) ‘High taxonomic variability despite stable functional structure across microbial communities’, Nature Ecology & Evolution, 1(1), pp. 1–12. Available at: https://doi.org/10.1038/s41559-016-0015.
