Quesnel Lake Data Visualization
Dan
2026-09-14
Sample Timeline Check
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. CTD Depth and Temperature Profiles
6. This will be my Schmitt Stability code
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
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
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
19. 2023 Stacked TP/TDP Overlay and Chlorophyll
20. 2023 Complete Data Stack (TP, TDP, Chla, Weather, Upwelling) (don’t like, will change style)
Part IV: 2025 High-Resolution Deep Dive
21. 2025 Phosphorus data TDP and TP on the same graph
When TDP is higher than TP at a given site, the date is highlighted by a red bar. The darker the bar, the more sites on a given day have the TDP vs TP issue. Of the 30 samples days 19 have the TDP issue.
22. 2025 Phosphorus concentration in separate graphs
23. 2025 Phosphorus averaged with error bars
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
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 Silica (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 Silica 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.
41. Historical Chlorophyll Concentration Trends
42. Temporal Trend Analysis of Quesnel Lake Limnological Parameters
This section evaluates long-term monotonic trends in historical daily environmental data, including nutrient concentrations, total chlorophyll, and zooplankton biomass. The non-parametric Mann-Kendall trend test is applied to explicitly handle gaps in collection years and large variations in sampling frequencies. This analysis identifies statistically significant upward or downward shifts in water quality and ecological parameters over the monitoring period.
There is a highly significant downward trend in both total chlorophyll and ammonia.
Nitrate concentrations show a highly significant upward trend.
Total phosphorus, total dissolved phosphorus, and total zooplankton biomass exhibit no statistically significant trends on a daily scale, remaining relatively stable over time.
The daily data indicates a shifting nitrogen cycle where nitrate is accumulating while ammonia depletes. Despite this available nitrate, chl is decreasing. Because zooplankton biomass is stable, this daily decline in chlorophyll is likely not driven by an overall increase in grazing pressure within Quesnel Lake.
##
## --- Total Chlorophyll (TOTCHL) Trend ---## Score = -4680 , Var(Score) = 5118710
## denominator = 63702.18
## tau = -0.0735, 2-sided pvalue =0.03863
## NULL##
## --- Total Phosphorus (TP) Trend ---## Score = 18878 , Var(Score) = 5375950
## denominator = 65406.71
## tau = 0.289, 2-sided pvalue =< 2.22e-16
## NULL##
## --- Nitrate (NO3) Trend ---## Score = 1031 , Var(Score) = 5034070
## denominator = 63158.49
## tau = 0.0163, 2-sided pvalue =0.64618
## NULL##
## --- Ammonia (NH3) Trend ---## Score = -25193 , Var(Score) = 4047042
## denominator = 54448.75
## tau = -0.463, 2-sided pvalue =< 2.22e-16
## NULL##
## --- Total Dissolved Phosphorus (TDP) Trend ---## Score = 3200 , Var(Score) = 1036202
## denominator = 21905.46
## tau = 0.146, 2-sided pvalue =0.0016744
## NULL##
## --- Total Zooplankton Biomass (TOTBIOM) Trend ---## Score = -10353 , Var(Score) = 16776884
## denominator = 141245.5
## tau = -0.0733, 2-sided pvalue =0.011492
## NULL43. Non-Linear Modeling of Daily Chlorophyll Drivers
The Akaike Information Criterion (AIC) analysis reveals that Quesnel Lake primary production is driven by a strict co-limitation of Phosphorus and Silica, rather than classical single-nutrient bottlenecks or comprehensive N+P+Si dependence.
While the “Total Co-limitation” model (N+P+Si) yielded the lowest raw
AIC score, the “Phosphorus + Silica” model (mod_psi)
produced a Delta AIC of only 0.78. In information theory, models with a
Delta AIC of less than 2 are considered statistically indistinguishable
in their predictive power. Applying the Principle of
Parsimony—which dictates that the simplest valid model is
ecologically superior—we can confidently select the Phosphorus + Silica
model because it achieves the same predictive accuracy while requiring
significantly less mathematical complexity (df = 12.1 vs. 16.7).
Conversely, dropping Phosphorus from the equation (the N+Si model) resulted in a Delta AIC of 7.44, indicating a substantial loss of predictive power.
## [1] "--- Expanded AIC Model Ranking (N vs P vs Si Dynamics) ---"## df AIC Delta_AIC
## mod_npsi 16.722320 39.36444 0.0000000
## mod_psi 12.106836 40.15059 0.7861447
## mod_nsi 12.034818 46.80584 7.4413956
## mod_si 7.568916 50.23938 10.874932844. GAM Magnitude and Direction of P-Si Relationship
45.Yearly Temporal Trend Analysis of Quesnel Lake Limnological Parameters
This section evaluates long-term monotonic trends across aggregated yearly historical data. By analyzing annual averages, the Mann-Kendall trend test mitigates seasonal noise and daily fluctuations to reveal broader, macro-level ecological shifts in water quality, nutrient availability, and zooplankton biomass over the monitoring period.
On a yearly scale, both TP and NO3 show highly significant upward trends. The lake is accumulating primary nutrients year over year.
Both total chlorophyll and total zooplankton biomass show significant downward trends.
Ammonia continues to show a highly significant downward trend, mirroring the daily data.
Despite a long-term accumulation of essential nutrients (phosphorus and nitrate), overall biological productivity is shrinking. Both the primary producers (phytoplankton) and primary consumers (zooplankton) are declining year over year, suggesting that a secondary, unmodeled physical or environmental factor is suppressing the lake’s biological carrying capacity.
Yearly Total Chlorophyll (TOTCHL) Trend
Score = -18 , Var(Score) = 212.6667 denominator = 66.00001 tau = -0.273, 2-sided pvalue =0.24372 NULL
Yearly Total Phosphorus (TP) Trend
Score = 32 , Var(Score) = 212.6667 denominator = 66.00001 tau = 0.485, 2-sided pvalue =0.033524 NULL
Yearly Nitrate (NO3) Trend
Score = 8 , Var(Score) = 212.6667 denominator = 66.00001 tau = 0.121, 2-sided pvalue =0.63122 NULL
Yearly Ammonia (NH3) Trend
Score = -41 , Var(Score) = 165 denominator = 55 tau = -0.745, 2-sided pvalue =0.0018457 NULL
Yearly Total Dissolved Phosphorus (TDP) Trend
Score = 10 , Var(Score) = 65.33334 denominator = 28 tau = 0.357, 2-sided pvalue =0.26551 NULL
Yearly Total Zooplankton Biomass (TOTBIOM) Trend
Score = -34 , Var(Score) = 589.3333 denominator = 136 tau = -0.25, 2-sided pvalue =0.17403 NULL