Quesnel Lake Data Visualization

Part I: Physical Environment & Drivers

1. Map Attempt

The spatial visualization of the Quesnel Lake sampling stations was generated in R using a combination of provincial geographic datasets and local project metadata. The two-dimensional shoreline boundary of Quesnel Lake comes from the British Columbia Freshwater Atlas using the bcdata R package, where it was isolated from the provincial dataset, transformed to the standard WGS84 coordinate reference system (EPSG: 4326), and stored locally as a GeoPackage. The physical coordinates for the CTD casts were extracted from the 2023 CTD metadata file. Sites were filtered down to the specific target locations (ST10, ST4, ST8Zoo, ST5, Sill East, and Sill West). The final visualization was constructed using the ggplot2 and sf spatial packages.

2. Continuous Air & Water Temperature Timeline (2023 - 2025)

This block isolates the weather data to create a massive widescreen timeline spanning multiple years. It plots continuous temperature readings from both stations, introduces a 4°C reference line (the temperature of maximum density for fresh water), and highlights historical upwelling.

3. Light Profiles (2024 - 2025)

4. 2023 CTD Profiles during Upwelling Events

This code isolates CTD data (Temperature and Depth) that was captured only during recorded upwelling periods. It then creates individual temperature-by-depth profile graphs for each upwelling day and arrays them into a large grid.

5. 2024 CTD Temperature Profiles Over Time (Top 100m)

This code loops through a directory containing raw 2024 CTD data files, standardizes their naming conventions, and maps them to field metadata. It generates a 6-facet wrapping plot, restricting depth to the top 100m, and uniquely colors the lines using a high-contrast time gradient (viridis turbo) mapped to the date of the cast.

##6. This will be my Schmitt Stability code

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Part II: Multi-Year Chemical & Biological Trends

7. Average Yearly Chlorophyll Concentration (Bar Chart)

This is a bar chart of chlorophyll concentrations averaged each year and shown with error bars. This will highlight how 2023 was higher than other years.

8. Average Yearly Total Phosphorus (TP & TDP)

This is a bar chart of the average TP and TDP concentration across the study period. Each year has its standard deviation shown.

9. Multi-Year Chlorophyll Concentration Timelines

This section iterates over three separate years. It calculates the daily averages for chlorophyll across active sites, underlays upwelling events, and generates an individual graph for each year directly in the HTML output.

10. Multi-Year Total Phosphorus (TP) and TDP Timelines

11. Chlorophyll & Gap-Filled 15-Minute Temperature Timeline

This script aligns low-frequency data (daily chlorophyll averages) with high-frequency data (continuous weather station temperatures). It merges weather datasets from two locations (Plato and West Basin) to fill temporal gaps and plots a dual-axis graph for each year.

12. Stacked Weather and Chlorophyll with Upwelling

This expands on the previous gap-filled timeline by drawing semi-transparent geom_rect polygons over the dual-axis plots to represent when upwelling events were actively occurring.

13. 2024 & 2025 Stacked Phosphorus and Chlorophyll

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Part III: 2023 High-Resolution Deep Dive

14. 2023 Chlorophyll Scatter Plot

15. 2023 Total Phosphorus (TP) Scatter Plot

16. 2023 Total Dissolved Phosphorus (TDP) Scatter Plot

17. Stacked Plot: Chlorophyll & TDP with Upwelling

18. Stacked Plot: Chlorophyll, TP, and TDP with Upwelling

19. 2023 Stacked TP/TDP Overlay and Chlorophyll

20. 2023 Complete Data Stack (TP, TDP, Chla, Weather, Upwelling)

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Part IV: 2025 High-Resolution Deep Dive

21. 2025 Phosphorus data TDP and TP on the same graph

22. 2025 Phosphorus concentration in separate graphs

23. 2025 Phosphorus averaged with error bars

24. 2025 Combined Phosphorus and Chlorophyll Visualization

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Part V: Upwelling Impact & Statistical Analysis

25. Upwelling Point-Biserial Correlation (7-Day Lag)

## [1] "--- Point-Biserial Correlation Results (All Years Combined) ---"

## # A tibble: 2 × 3
##   SiteGroup Point_Biserial_R P_Value
##   <chr>                <dbl>   <dbl>
## 1 Far field           0.0511   0.412
## 2 Sill               -0.0306   0.645

26. Upwelling Point-Biserial Correlation (Fixed 14-Day Lag)

27. Upwelling to Chlorophyll Lag Sweep (2025 Only)

28. Bi-Weekly P-to-Chla Lag Sweep & Optimized Correlation

## [1] "--- Data-Driven Optimal Lag Times (TDP to Chl) ---"

## # A tibble: 3 × 4
## # Groups:   Year [3]
##    Year Best_Lag   Max_R  P_val
##   <dbl>    <dbl>   <dbl>  <dbl>
## 1  2023       56 -0.0501 0.716 
## 2  2024        0  0.366  0.0170
## 3  2025       56  0.0669 0.579

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Part VI: Zooplankton Ecology (2025)

29. Zooplankton Length and Species Diversity

30. Zooplankton Shannon Diversity Index by Region

31. Adult Zooplankton Relative Abundance (Excluding Juveniles)

This section visualizes the relative abundance of the zooplankton community in 2025, specifically filtering out juvenile life stages (nauplii, copepodids, and juvenile cladocera). Because juvenile forms can often dominate raw counts and obscure the community structure of fully developed populations, removing them provides a clearer picture of the established adult taxa diversity across the different sites.

32. Zooplankton Volumetric Density (per Cubic Meter)

This section calculates the estimated total zooplankton abundance standardized per cubic meter (\(m^3\)) of water filtered during the net tow. This is done by estimating the total zooplankton caught in the 30m tow (extrapolating from the subsampled split fractions), calculating the volume of the cylindrical water column pulled through the 20cm diameter net, and dividing the total count by that volume.

33. Total Zooplankton Volumetric Density Over Time (2025)

This graph displays the total volumetric density (zooplankton per cubic meter) over time at each site specifically for the year 2025. This data is aggregated across all species classifications to show the total zooplankton biomass at a given time. The colors distinguish the spatially distinct regions of the lake: Sill sites are represented in shades of blue, while far-field stations (ST) are represented in shades of red.

## 34. Regional Average Zooplankton Density Over Time (2025) This graph groups the individual sampling stations into two broader regions: Sill sites and Far-field (ST) sites. The data calculates the total volumetric density for each site on a given date, and then averages those densities within their respective regions to show broader spatial trends across the lake for 2025. Additionally, known upwelling events are overlaid to visualize potential relationships between physical mixing events and zooplankton biomass.

Part VIII: Exploring Historical Data

35. Historical Nitrogen and Silica Trends

This plot visualizes historical concentrations of Nitrate (NO3), Ammonia (NH3), and Soluble Reactive Silicon (SRS). The dataset is strictly filtered to five key stations, which have been relabeled to match current site naming conventions. Because Nitrogen is measured in µg/L and Silicon in mg/L, the parameters are faceted into separate horizontal panels.

36. Historical Phosphorus Trends (SRP & TP)

This graph displays both Soluble Reactive Phosphorus (SRP) and Total Phosphorus (TP) on the same axes. Overlaying them directly highlights what fraction of the total phosphorus pool was immediately biologically available (SRP) at any given station over time.

37. Historical Zooplankton Abundance and Biomass

This section compares the total raw count of zooplankton against their total estimated biomass. By splitting these metrics into separate panels, we can identify unique ecological events: for example, a spike in abundance without a corresponding spike in biomass indicates an explosion of very small juvenile zooplankton, while high biomass with low counts indicates a population dominated by large, mature individuals.

38. Yearly Average Nitrogen and Silicon Trends

This plot visualizes the macro-level yearly averages of Nitrate (NO3), Ammonia (NH3), and Soluble Reactive Silicon (SRS) across the five primary stations. By aggregating the data annually, short-term seasonal noise is smoothed out, allowing us to identify long-term shifts in the lake’s baseline nutrient loading.

39. Yearly Average Phosphorus Trends (SRP & TP)

This graph displays both the yearly average Soluble Reactive Phosphorus (SRP) and Total Phosphorus (TP) on the same axes. Looking at this on an annual scale highlights years of overall elevated phosphorus loading and whether those increases were driven by immediately bio-available phosphorus (SRP) or bound particulate matter.

40. Yearly Average Zooplankton Abundance and Biomass

This section compares the yearly average raw count of zooplankton against their yearly average estimated biomass. Decoupling these metrics on an annual basis helps identify long-term shifts in the zooplankton community structure—such as a multi-year trend toward higher abundance but lower total biomass, which would indicate a systemic shift toward smaller taxa.