Analysis of Phosphorus and Chlorophyll Data from Loch Shiel and Loch Garry

Transcription

1 Analysis of Phosphorus and Chlorophyll Data from Loch Shiel and Loch Garry TL Science LTD Unit 3a-b Industrial Estate John O Groats, KW14YR

2 Executive Summary 1. There is no statistical evidence for any overall year-on-year change or trend in Phosphorus or Chlorophyll level in Loch Shiel or Garry according to either Marine Harvest ( undertaken by Stirling University) or SEPA datasets. 2. This also means that there is no evidence of any change in Phosphorus level brought about by the production increase in 2008 in Loch Shiel or at any other time through the period of the data coverage at either loch. 3. Both data sets contain a large amount of variation when looking at data from individual sample locations and this would indicate the importance of using a multisampling point regime in order to capture a representative profile of Loch Phosphorus and Chlorophyll dynamically changing levels. 4. An argument can be made that the Stirling University sampling regime for Marine Harvest is more likely to reflect a robust Mean of the overall Phosphorus levels throughout the lochs. 5. Multiple sampling sites, as used by Stirling, would be better able to capture the special environmental variability and thus provide a more representative indicator of Phosphorus and Chlorophyll levels. The single site sampling method used by SEPA would be less robust in terms of natural environment variability.

3 Methodology Data was provided spanning the period May 2004 to March 2016 for Total Phosphorus and Chlorophyll ( ug/ L), sampled at 12 sites by Marine Harvest ( MH) and 1 site sampled by SEPA at each Loch. MH site sampling occurred mostly every 2-months, while SEPA sampled mostly every month. MH has contracted the University of Stirling to independently monitor Phosphorus levels since Samples were taken twice per year until 2005 at which point the frequency of the regime was increased to bi-monthly to provide 6 sampling episodes per year. From the outset the sampling has been designed to provide an indication of levels throughout the entire loch and therefore samples are taken from a spread of locations as shown below for Loch Shiel and similar pattern for Loch Gary. At each location samples are gathered from 0m depth and 5m depth. No information was provided on the subsequent analytical methodologies used and only the final reported values were provided to TL Science. In order to compare the MH and SEPA data sets they first had to be aligned by month. In order not to lose any data points it was decided to align the MH data to that of the SEPA data. Exact sampling dates for the SEPA data were provided, only the month was provided for the MH data. In a few instances SEPA sample dates near the beginning or the end of the month were transposed to the proceeding/ preceding month in order to align to MH data. Statistical analysis was performed using Univariate and Multivariate tests performed in Unscrambler version (CAMO Software AS. Oslo Science Park, Gaustadalléen Oslo NORWAY).

4 Loch Shiel Visualisation of P data The Means ( Blue bars) and Standard Deviations ( StDev) ( black lines) were calculated by combining the data from all 12 MH sites ( Fig. 1) and for the single SEPA site (Fig. 2) for each sampling time points between A visual inspection showed no obvious overall trend in changing P and levels for the vast majority of time points remain within the Ultra- Oligotrophic/ Oligotrophic set ranges and the SEPA recommended management upper limit of 8ug/ L. In Contrast the SEPA site appears to show somewhat more variation but a visual inspection showed no overall increasing or decreasing trend and rarely exceeds the management upper limit of ~8ug/ L. The SEPA values did appear to have some obviously higher values that the MH measurements. To investigate this further the Mean values of the 12 MH sampling sited was calculated and compared to the SEPA sampling point values. An F test (Fig. 3) showed that the Means of the MH the SEPA data had unequal statistical variance. Thus a student s t test taking account of this unequal variance was performed ( fig. 4). A P value of was returned indicating there is no significant difference in overall Phosphorus levels between the SEPA and MH (Mean) data sets. As another way to view of the data was to calculate the Means and StDev. s for each individual site combined over the time-period, these were then plotted (Fig. 5). This revealed no overall observed difference between their average P levels. Finally, in order to get a broad overview of the any additional structure to the data a Principle Components Analysis was performed thus combining data for site and sampling time (Fig. 6). A few sites/ years fell outside the 95% model limit of Hotellings T2 limit (elliptical) but overall there appeared to be no particular patterns in the data. PC1 and PC2 explained 69% of the variance, further indicting that the MH data showed no trending of P levels on year-by-year basis.

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6 Visualisation of Chlorophyll data The Means (Blue bars) and Standard Deviations ( StDev) ( black lines) for each of the 12 MH sites (Fig. 7) and for the SEPA site ( Fig. 8) were calculated and plotted as a single value for each sampling time points between In the MH data a clear cycling of Chlorophyll levels on an annual basis at each sampling site was observed; such that it dropped below the level of detection in the mid- winter months but there was no obvious year on year trend for the maximum summer levels. Although clearly still cycling annually the SEPA data had a less robust pattern of this and generally appeared to not fall to levels below detection limits. It also appeared to show something of a possible decrease in maximum summer Chlorophyll from 2010 onwards To investigate further the values of the 12 MH sampling sited were combined into Means for the individual time period between ed and compared to the SEPA sampling point values. An F test (Fig. 9) showed that the MH Mean and the SEPA data had unequal variance. Thus a student s t test accounting for this was performed ( Fig. 10). A P value of <001 was returned indicating there was a very significant difference between the SEPA and MH (Mean) data set Means. When the Means and StDev. s for each site over the time- period were also calculated and plotted ( Fig. 11) it was observed that the Mean and StDev for the SEPA data is larger. Finally, in order to get broad overview of the any additional structure to the data a Principle Components Analysis was performed on the data for each site and sampling time point(fig. 12). A few data points ell outside the 95% model limit of Hotellings T2 limit (elliptical) but overall there appears to be no particular patterns in the data. PC1 and PC2 when plotted explaining 69% of the variance, further indicting that the MH data showed no trending of P levels on year by year basis.

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8 Loch Garry Visualisation of P data The Means ( Blue bars) and Standard Deviations ( StDev) ( black lines) for each of the 12 MH sites ( Fig. 13) and for the SEPA site (Fig. 14) were calculated and plotted as a single value for each sampling time points between There was no obvious overall trend in P for the man values, and levels for the vast majority of time remain within the Ultra- Oligotrophic/ Oligotrophic set ranges. There are a time points in the MH and SEPA that did spike above the recommended management upper limit of ~8ug/ L. The SEPA site appeared to show somewhat more variation but this data too showed no overall increasing or decreasing trend and rarely exceeds the management upper limit of 8ug/ L. To investigate further the Mean values of the 12 MH sampling sited was calculated and compared to the SEPA sampling point values. An F test ( Fig. 15) showed that the MH(Mean) and the SEPA data had unequal variance. Thus a student s t test taking account of unequal variance was performed ( Fig. 16). A P value of <001 was returned indicating there was a very significant difference between the SEPA and MH (Mean) data set. When the Means and StDev. s for each site over the timeperiod were also calculated and plotted (Fig. 17) it was observed that the Mean and SDev for the SEPA data was slightly larger. Finally, in order to get broad overview of the any additional structure to the data a Principle Components analysis was performed on the data for each site and sampling time point (Fig. 18). A few data points fall outside the 95% model limit of Hotellings T2 limit elliptical) but overall there appeared to be no particular patterns in the data plot for PC1 and PC2 plotted (explaining 76% of the variance), further indicting that the MH data showed no trending of P levels on year by year basis.

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10 Visualisation of Chlorophyll data The Means (Blue bars) and Standard Deviations ( StDev) ( black lines) for each of the 12 MH sites (Fig. 19) and for the SEPA site (Fig. 20) were calculated and plotted as a single value for each sampling time points between In the MH data a clear annual cycling of Chlorophyll levels on an annual basis at each sampling site; such that falls below the level of detection in the mid- winter months occurred but there was no obvious year on year trend. The SEPA data had a less robust pattern of this and generally appeared to not fall to the levels below detection limits and appeared to show marked decrease from 2010 onwards To investigate further the Mean values of the 12 MH sampling sited were calculated and compared to the SEPA sampling point values. An F test (Fig. 21) showed that the MH Mean and the SEPA data had unequal variance. Thus a student s t test taking account of unequal variance was performed ( Fig. 22). A P value of <001 was returned indicating there was a very significant difference between the SEPA and MH (Mean) data set. When the Means and StDev. s for each site over the timeperiod are also calculated and plotted (Fig. 23) it was observed that the Mean and SDev for the SEPA data was larger. Finally, in order to get a broad overview of the any additional structure a Principle Components analysis was performed combining on the data for each site and sampling time point Fig. 24). A few data points fall outside the 95% model limit of Hotellings T2 limit (elliptical) but overall there appears to be no particular patterns in the data plot for PC1 and PC2 plotted explaining 91% of the variance), further indicting that the MH data showed no trending of P levels on year by year basis which is in somewhat contras to the SEPA data.

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12 Summary and Conclusions The is no statistical evidence that there has been any consistent change in the Oligotrophic state of Loch Shiel or Loch Garry between 2004 to 2016 from the data provided. There was some complication in analysing the MH and SEPA data sets due to their very different structures in that the MH data had far more replicate sampling sites covering the length of the Lochs while SEPA uses a single sampling point station but collect at double the frequency. In general, the MH data had consistently less variation within its overall calculated Mean values for both Phosphorus and Chlorophyll than SEPA and a visual inspection of the plotted data backed this assertion up. Both datasets contained a large amount of variation when looking at data from individual sample locations and this indicated the importance of using a multi-sampling point regime in order to capture a representative profile of Loch Phosphorus and Chlorophyll dynamically changing levels. The MH data seemed to show a general progressive nature along the profile of the sampling sites, as might be expected given their physical distribution. An example of this using a Principle Components Analysis plots ( which captures both the site and time dimensions in one analysis) is shown in Fig. 25. This further backs up the importance of multi-site sampling. Figure 25, Principle Components Analysis of P values for each sampling site at Loch Shiel Overall the MH sampling methodology ( generating 6-times more independent data) appeared to better capture the profile of Phosphorus and Chlorophyll within the two Lochs. It was not clear from the data provided which MH sampling stations were closest to that of SEPA and it would of interest to know this and then examine their profile and individual variability in more detail. Also no information was provided on the analytical laboratory instrumentation methods used by MH thought University of Stirling) or SEPA s so an understanding of any variability contributed by difference between these could not be assessed.