T H E I N F L U E N C E O F L E S S E R B L AC K
B AC K E D G U L L S A N D H E R R I N G G U L L S
O N T H E S O I L C H E M I S T RY O F T H E
D U N E S I N T E X E L
Bachelor thesis of:
Charlotte Aimee Gevaert - 10540601 Future planet studies - Earth sciences University of Amsterdam
Under supervision of:
Erik Cammeraat & Judy Shamoun-Baranes
University of Amsterdam
IBED
04/07/2016
Student Contact details:
Charlotte Aimee Gevaert - 10540601 +31 6 46428069
Future planet studies - Earth sciences charlotte.gevaert@student.uva.nl University of Amsterdam Supervisors Erik Cammeraat +31 6 50261547 IBED L.H.Cammeraat@uva.nl Judy Shamoun-Baranes +31 6 23884449 IBED J.Z.Shamoun-Baranes@uva.nl Lab supervisor John Vissers +31 20 5256570 IBED J.G.J.Visser@uva.nl Field supervisor Kees Camphuysen +31 2 22369488 NIOZ Kees.Camphuysen@nioz.nl
Involved
List of abbreviations
LBG Lesser black backed gull HEG Herring gull
COP Control point CMB Combined nest EC Electrical conductivity TOC Total organic carbon TIC Total inorganic carbon TC Total carbon
Seabirds breed in large colonies and release excessive amounts of biological litter around their nesting areas. The input the gulls provide consists of guano, the
excrements of the birds, feathers, eggshells, mussel shells and dead chicks. Previous research has shown this input can lead to shifts in vegetation patterns and population dynamics. This research was on the influence of the input of lesser black backed and herring gulls on the soil chemistry of the dunes of the Dutch peninsula Texel, where one of the largest breeding areas of both species is located. Soil samples were collected and tested in the lab where significant results showed a higher nitrogen, phosphorus, carbonate and soluble salts content in the samples taken around the nests in the breeding area. The pH and electrical conductivity were also higher in the soil samples taken from the nests with respect to the samples taken at control points, which lie outside the breeding area of the birds. A distinction between the influence of lesser black backed gulls and herring gulls could be made, which is due to the
difference in diets of the species.
Keywords
Gulls, Larus fuscus, Larus argentatus, soil chemistry, guano, nutrients, soluble salts
A B S T R AC T
1. Introduction 5
2. Research Question 7
2.1 Main research question 7
2.2 Subquestions 8 3. Methods 9 3.1 Fieldwork 9 3.1.1 Research area 3.1.2 Sampling 3.2 Lab work 11 3.2.1 ph and EC 3.2.2 ICP and AA analysis 3.2.3 CNS analysis 3.2.4 TOC 3.2.5 Carbonate content 3.3 Statistical analysis 14 4. Results 15 4.1 Lab work 15 4.1.1 pH and EC 4.1.2 ICP and AA 4.1.3 CNS analysis 4.1.4 Carbonate content 4.2 Statistical analysis 23 5. Discussion 25 5.1 Results 25 5.2 Statistics 26 5.3 Lab work 28 5.4 Recommendations 29 6. Conclusion 30 7. Literature 31 8. Acknowledgements 33 9. Appendices 34 A - Fieldwork Area 34 B - Lab journal 35 C - Matlab script 37
I N D E X
The excrements of birds, which is referred to as guano, has been known for its nutrient richness. It has long been recognized as a fertilizer. At the end of the 19th century guano was mined excessively and 20 million tons were exported overseas (Szpak et al., 2012). Next to its function as a fertilizer, guano also plays an interesting role in ecological processes. When a colony of birds is
considered dominant in a region, the presence of these birds is determinative for the nature of the ecosystem (Hutchinson, 1950).
Considering the diets of gulls, which often consists of marine animals, it is concluded these birds play a role in transporting nutrients across ecosystem boundaries. The gulls feed from animals from marine ecosystems and deposit nutrients in terrestrial
ecosystems. This can have an influence on local population dynamics and resource availability for terrestrial species and in some cases the presence of birds in an area play a role in establishing a productive food web (Ellis et al., 2006; Polis & Hurd, 1996). This forms the basis of the relevance of researching the influence of gulls on their breeding areas, regarding soil chemistry of their ecosystems.
Guano can reach concentrations of 11% P and 9% N, depending on the species of birds. When released on the soils of their breeding areas, guano generally leads to a higher nutrient content (Hutchinson, 1950; Szpak et al., 2012). This affects the vegetation of the breeding areas. Plant growth is often limited by nitrogen and phosphorus content in soils, which suggests increased vegetation growth will take place due to the input of guano deriving from piscivorous (fish-consuming) birds, such as cormorants. However, large amounts of nutrients will limit growth and therefore a shift in species composition is expected (Ellis et al., 2006). The effect of guano on this shift in vegetation is related to the proportion of the change in chemical composition. In some areas the local vegetation can be completely destroyed by the influence of piscivorous birds (Ligeza & Smal, 2003).
What has been concluded in previous research, is the fact the influence of seabirds on soil chemistry is spatially variable. In semi-arid areas guano leads to increasing
production in combination with rainfall, due to leaching of excessive nutrients
(Anderson & Polis, 1999). Likewise, the results of a study of Wait et al. (2005) led to the conclusion that the excessive input of nutrients by guano deposition led to higher
Figure 2. A lesser black backed gull nesting
productivity in a semi-arid area. In Arctic and tundra regions, where scarcity of nutrients can form a problem for vegetation, marine birds tend to complement nutrient
contents, enhancing soil properties and vegetation productivity in coastal areas (Speir & Cowling, 1983; Zwolicki et al., 2016).
This research will focus on the Dutch peninsula Texel. Two different species of gulls will be examined: lesser black backed gulls (Larus fuscus) and herring gulls (Larus
argentatus), see Figure 3 and Figure 4. At the end of 20th and the beginning of the 21st century, a shift in population size of the lesser black backed gulls and herring gulls had been noticed. The population of the herring gulls had declined, while the
population size of the lesser black backed gulls had increased (Camphuysen & Gronert, 2012). In the Southern dunes of Texel, one of the largest breeding areas of both species is located (Camphuysen & Gronert, 2012). More fluctuations in
population dynamics of the gulls could have a significant influence on the soils of their breeding area, which could have an effect on the dunes as a whole ecosystem.
Soil samples were collected in the field and examined in the lab to evaluate the influence of the gulls on various soil properties. Sampling was performed before and during the breeding period to be able to conclude the effect of the presence of the gulls, as the gulls are only present in the research area from March to August before migrating annually (Camphuysen, 2013). Another reason why two sampling dates have been distinguished is because the gulls require a slight change in their diets during the breeding season, due to a higher energy demand for chick care. Therefore, the gulls tend to consume more nutrient-rich fish, leading to the expectation a difference in the input of the gulls can be recognized over the breeding period (Camphuysen, 2013).
It is important to understand the influence of the gulls on nutrient availability. When changes in population size of the gulls take place in the future, there will be more understanding of the consequences for their breeding environments.
2.1 Main research question
What is the impact of the biological input of lesser black backed gulls and herring gulls on the soil chemistry of their breeding places in the dunes of Texel?
The aim of this research was to evaluate the influence of the input of the excrements of gulls on soil chemistry. The birds do not only release guano around their nests in the breeding area, but also egg shells, mussel shells, dead chicks and feathers (Anderson & Polis, 1999). The influence of the gulls was determined by measuring the electrical conductivity, pH, (total/organic) carbon, nitrogen, soluble salts and carbonate content of soil samples taken from their breeding area.
The pH influences numerable biological, chemical and physical processes within a soil and is therefore considered to be one of the master variables of soil properties (Brandy & Weil, 1999). Altering of the pH by the high nutrient input has been found to affect the viability of seeds of some vegetation species, which also influences the way a plant can survive within a contaminated area (Boutin, 2011). Electrical conductivity is a measurement for the cation exchange capacity of a soil, which supports plant
productivity (Grisso et al., 2005). This is in favor of the necessity of examining the pH and electrical conductivity of the samples. Previous research on the input of gulls has shown contradicting results in pH measurements, which resulted to be either higher or lower due to the guano (Wait et al., 2005; Loder et al., 1996). The presence of shells around the nests will most probably lead to a significant carbonate content in the samples taken from the nests, which is expected to lead to a higher pH in this case. High contents of soluble salts and macro-ions are predicted in the samples due to the excessive input of the gulls. Electrical conductivity is linked to the soluble salts content in the soil, which is why it is assumed the electrical conductivity will be higher around the gull nests as well (Grisso et al., 2005). Previous research has shown the influence of guano can lead to over 50 times higher amounts of potassium in samples deriving from areas where piscivorous birds nest (Ligeza & Smal, 2003). As the soil chemistry of the dunes around the research area shows a low calcium, magnesium and iron content, significant results in the nutrient and soluble salt concentrations will stand out and support the hypothesis the gulls influence the soil properties of their breeding areas (Berendsen, 2008).
Next to researching the general input of the gulls, there has also been a focus on to what extent there might be a difference in the input of the different gull species, how this can be linked to their diets and the influence of the other inputs from the gulls, such as shells. Therefore, the following sub questions have been formulated, supporting the main research question.
2.2 Sub questions
1. How significant is the difference between the influence of lesser black backed gulls and herring gulls on the soil chemistry of the dunes in Texel?
2. How can these differences in the influence on soil chemistry of the lesser black backed gulls and herring gulls be linked to the diets of these two species?
Gull diets are examined in several ways, ranging from observations, collecting excrements and food droppings to even killing the birds for examining their gut contents (Barret et al., 2007). Individual birds have been found to prefer certain prey and do not have a large range in dietary preferences. However, a slight distinction can be made in the diets of the two species examined in this research (Camphuysen, 2013). The herring gulls prefer carbonate rich mussels, crabs and cockles, while the lesser black backed gulls tend to consume more fish, high in phosphorus and nitrogen (Noordhuis, 1987; Kim & Monaghan, 2006). Therefore, the composition of the guano and nutrient input of the two species is expected to be substantially different. During the breeding period, the diets of herring gulls shift more towards fish due to a higher nutrient requirement for chick care (Camphuysen, 2013). Therefore, a higher nutrient content is expected in the samples deriving from the second sampling date in the breeding period.
Not only a difference in diets of the two species is expected to influence a possible distinction between the results of the lesser black backed gulls and herring gulls, but also their nesting habits. Herring gulls tend to have a higher nest attendance than the lesser black backed gulls (Calladine, 2007). This leads to the expectation the nests of the herring gulls will be more polluted by the presence of the gulls in comparison to the overall cleaner nests of the lesser black backed gulls (Camphuysen, 2013). By sampling around the nests of both species, it was possible to distinguish a possible difference in the input of nutrients and soluble salts of the gulls. The hypothesis for this subquestion is based on the expectation lesser black backed gulls and herring gulls have a different influence on the soil chemistry of the dunes of Texel. Before the data on the nutrient and soluble salt concentration was obtained in the lab, visible
differences in the nests of the two species were already clear during sampling. Dietary differences are expected to lead to a significant amount of nitrates and phosphorus in the samples of the lesser black backed gulls and a high carbonate content in those of the herring gulls.
3.1 Fieldwork
3.1.1 Research area
The Wadden area is known for its suitability and use as a breeding area for birds (Camphuysen, 2013). The area of research, the Kelderhuispolder, is located in the southern dunes of the Dutch peninsula Texel in the Wadden area. Here, there is a protected breeding area for the gulls. This way the birds suffer minimal amounts of disturbances and can stay there the whole breeding period. The Kelderhuispolder is a valley covered in short vegetation, grass and some bushes. It is located in between the dunes and 8ha large. It is one of the largest breeding places where both the lesser black backed gulls and herring gulls breed (Camphuysen & Gronert, 2012). Over 11,500 pairs of lesser black backed gulls and 5000 pairs of herring gulls nest there during the breeding season (Camphuysen, 2011).
3.1.2 Sampling
In the fieldwork area, three types of samples have been taken the first sampling date. First of all, control points have been set out. These samples were located in areas outside the protected breeding area of the gulls. This way, the soil surrounding the breeding area could be compared with the soil within the area, which may be affected by the gulls. To make a distinction between the different species of gulls, samples have been taken from lesser black backed gull nests and herring gull nests. One sampling location was a combined nest of both gulls.
Sampling was done in triplo, for each type of location three random points within 1 meter around the centre have been set out. From each of these points, three samples were taken according to depth under the soil surface. One from the top 5cm, one from 5-10cm and the last from 10-15cm. Looking at different depths under the soil could give an understanding of possible infiltration of the nutrients.
To be able to evaluate the influence of the sea gulls on the soil chemistry of the breeding areas, there have been multiple sampling dates. The first sampling date was planned on the 18th of March, before the dominant presence of the gulls. The second sampling date was set during the breeding period at the 11th of May, so it was
possible to distinguish a potential increase of nutrients and soluble salts in the samples of the breeding areas. To prevent influences of external factors, it was important the sampling dates were planned on days with the same weather conditions. Since the
fieldwork had to be done as quick as possible to limit the disturbance to the breeding gulls and the nests were located near each other, the sampling took a few hours. To limit disturbance during the breeding season, only two depths per sample were gathered the second sampling date (0-5cm and 5-10cm).
Below in Figure 5, an image of the sampling location with all the points is shown. For an overview of where the field area was located with respect to Texel as a whole, refer to Figure 13 in Appendix A. The coordinates of the sampling locations have been imported into Google Earth to retrieve this overview of the distribution. Two control points, three lesser black backed gull nests, three herring gull nests and one combined nest of the two species have been set out as sampling locations. These are labeled from “Soil 1” to “Soil 9”. As the soils are sampled in triplo, with three samples per point, a total of 81 samples have been taken the first sampling date. In the end, 135 samples have been taken over both sampling dates, due to the fact only two depths were taken during the breeding season.
Figure 5. An overview of all the sampling points in the research area: control points (green), herring gull nests (blue), lesser black backed gull nests (red) and one combined nest (cyan).
3.2 Lab work
The influence of the gulls on the soil chemistry has been evaluated in the lab. Several tests have been carried out to evaluate the difference in pH, electrical conductivity, soluble salts, (organic) carbon, nitrogen and carbonates content. Only the samples deriving from the top layer of the soil (0-5cm) have been examined to evaluate the impact of the shells and diets of the gulls on the soil chemistry of their breeding places, due to lack of time and availability in the lab.
For administration, a lab journal has been kept during all lab work, which can be found in Appendix B. By keeping track of the order of sampling during the tests and noting all exceptional events (e.g. spilling of chemicals), outliers in the results may be explained. Below, a short overview of the outline of how the tests have been performed in the lab is given.
3.2.1 pH and EC
The pH has been tested after drying the samples overnight. This has been done in an oven set at a temperature of 40 degrees Celsius. The samples were sieved with a 2mm sieve after cooling and breaking the soil aggregates. After this, the sieved samples were weighed to 20 grams and placed in a test tube, to which 50mL of demineralised water was added. This was done to obtain a 2:5 soil/water ratio for measurement. After, the bottled samples were placed on an oscillator, set at 130RPM, for two hours. Next, all 54 samples of the top layer from both sampling dates were measured for their pH after calibration of the device, by using solutions with a pH of 4 and 7. The electrical conductivity could be measured straight after, as the preparation of the samples was the same for these tests. The device used for this was calibrated with a solution of 0.01mol/L potassium chloride at a conductivity of 1413microS/cm. The data obtained from the EC measurements was displayed in microS/cm as a unit. The measurements of both tests, pH and EC, were performed in a random order.
3.2.2 ICP and AA analysis
The rest of the water extracts were used to determine the amount of soluble salts. Professional lab analysts of the University of Amsterdam Leen de Lange and Peter Serné performed these analyses with the ICP/AES Optima 3000XL (Elemental Analysis, 2013), Perkin Elmer and the Auto Analyser: Continuous Flow Analyser, SKALAR (GmbH, 2013). The ICP tested the following: Ca, Mg, K, Na, Fe and P. The auto-analyser was used for testing NO3+NO2, Cl2, NH4, DON and total nitrogen. As the analysis on the
P2O5 was not calibrated correctly, due to higher concentrations than expected, this
analysis was performed manually with the use of a spectrophotometer.
The concentrations of the macro-ions retrieved from the ICP and auto-analyser were expressed in mmol/kg or μmol/kg. The obtained data (See Appendix E) was used during the statistical analysis to search for significant differences between the groups.
3.2.3 CNS analysis
After drying, about 20 grams of the sieved soil samples was taken and placed in the milling machine. The milling machine was set at 400RPM and the samples were milled for 7 minutes in batches of four samples per run. After milling, the remnants were placed in labeled tubes. 25-45mg of the milled samples were placed in tin boats and folded for the CNS analysis. This test was performed in duplo for accuracy, therefore two samples per sampling location were tested. In total 108 tin boats containing milled sample were folded. For calibration and testing of reliability, 28 tin boats with 5-12mg of sulphanilic with a known concentration (41.6% C, 8.1% N, 4.1% H and 18.5% S) were made as well. The analysis was performed in two batches of 54 samples and 14 sulphanilic acid samples. Therefore, the first batch was run with samples taken from the first sampling date and the second analysis corresponds with the samples from the second sampling date. Once the samples were placed in the CNS-analyzer, data was obtained on the percentages of carbon, nitrogen, hydrogen and sulphur present in the samples.
3.2.4 TOC
The 2:5 soil/water ratio solutions which were used for the pH and EC analysis, were used again for the TOC, ICP and AA analysis. Before these tests could be performed, the samples were placed on the oscillator for 14 hours. As a preparation for the previously mentioned tests, the samples had to be filtered to make water extracts. The shaken samples were placed in the Herolab centrifuge for 20 minutes after they were shaken for another hour. After this, the samples were ready for filtration, which was done over a <0,2μm membrane filter with the help of vacuum. As the filters used for filtration were very delicate and small, not all liquid of the samples could filter through and be used for the water extract filtrate. Once the water extracts were made, the
Only a few millilitres of the water extracts was needed for the TOC test. However, 10 samples were thinned as there would not be enough left for the ICP/AA analyses. This was corrected in the data. Labeled test tubes were placed in the TOC device and sealed with aluminium foil to prevent contamination of CO2 of the air. The TOC test
gave data on the total organic carbon and total carbon, shown in mg/L. By subtracting the TOC from the TC, the TIC could be calculated.
3.2.5 Carbonate content
To test the inorganic carbon content of the samples, the Wesemaeler test was performed. For this test, the milled samples were used. 2-3 grams were weighed to four decimals and placed in 200mL cut flasks. Also, three references were made by using 150-250mg CaCO3 as a sample. Then, a test tube with HCl (4mol/L) was placed
in the flasks with tweezers and the flasks were closed with a top filled with silica gel. These flasks were weighed again and the weights were noted. Once the final top was placed on the previously placed top of the flask, the HCl was poured out of the test tube within the flask by tilting it carefully. The samples were set on a oscillator for 26 hours, which moved every half hour. The next day the second tops were removed and the weight was noted again. By using the following formula, the carbonate content in percentages could be obtained.
CO2 % = (P * S * 44) / (Q * 100 * R) x 100%
P = Weight loss of sample (g) S = CaCO3 (g)
R = Weight of dry, milled sample used (g) Q = Weight loss CaCO3 (g)
3.3 Statistical analysis
Once the samples were tested in the lab, the influence of the guano on the soils was evaluated by performing statistical analysis. This was done by importing the data obtained from the lab tests into MATLAB. This way it was possible to compare and visualize the characteristics of the soil chemistry of the samples taken and the difference between the samples of the lesser black backed gull and those of the herring gull nests could be distinguished. As the data provided is analyzed in these subgroups, boxplots are a preferred method for visualization. Also, the outliers are easily distinguishable in boxplots.
Lilliefors tests were performed on the data to conclude whether the subgroups were normally distributed, based on a null hypothesis which assumes the data provided is normally distributed. In MATLAB this test is performed with a 5% significance level (Mathworks, 2005). Once it was clear the data often was not normally distributed, the Wilcoxon test was elected for determining whether the subgroups agreed with each other. The corresponding rank sum test in MATLAB compares two groups of data assuming these groups derive from distributions with the same median regardless of the size making the test suitable for smaller data sets (Mathworks, 2005). When many outliers are present in a data set, the mean will be influenced more than the median (Burt et al., 2009). As the subgroups of the obtained data do not have equal lengths and contain many outliers, the Wilcoxon test was chosen for analyzing the obtained data.
The most significant results are described below, the remaining results can be found in Appendix D. The means of the samples taken from the lesser black backed gull nests, herring gull nests and control points are shown in tables. Next to this, the means of the values of the first and second sampling date of the nests of both species are shown and complimented with corresponding boxplots as a visualisation of the different soil properties which have been tested in the lab.
4.1 Lab work
4.1.1 pH and EC
As can be seen in Table 1, the pH values are overall quite neutral. The highest values are found in the samples taken from the herring gull nests and the lowest derive from the control points. There is only a slight difference between the two sampling dates of the nests as can be seen in Figure 6. This difference is more significant in the results of the electrical conductivity of the samples, which can be considered statistically relevant due to the minimal p-values. The electrical conductivity of the second sampling dates of both species are more than twice as high than the first sampling date. A large variation is also visible in the electrical conductivity of the samples when comparing the nests with the control groups. The lesser black backed gull samples are more than twice as high than the control groups, while the herring gull nest samples are more than three times as high as the control points.
The results of the combined nest lie between the lesser black backed and herring gull nests for the pH and electrical conductivity.
Table 1. The means of the pH and EC of the subgroups: lesser black backed gulls (sampling date I and II), herring gulls (sampling date I and II), control points. Also, the p-values of comparing two subgroups by the Wilcoxon tests are shown. *= where the p-values exceed 0,05, corresponding with the level of significance (95%) and can be classified as insignificant.
Table 1. Means of pH and EC
Group pH [-] EC [μS/cm] LBG 6,97 280,4 LBG I 6,95 151,2 LBG II 6,98 409,6 p-value I vs II 0,0859* 0,0040 HEG 7,32 442,5 HEG I 7,38 225,4 HEG II 7,26 659,6 p-value I vs II 0,0378 0,0028 COP 6,75 116,6 p-value COP vs LBG 0,0731* 0,0004 p-value COP vs HEG 0,00002 0,0004
4 - R E S U LT S
4.1.2 ICP and AA analysis
In Table 2 and 3 the results of the ICP and AA analysis on the water extracts of the samples are displayed. As there are many parameters, a boxplot of all substances per test have been made for a clear overview of the differences per soil property.
Considering Table 2 and Figure 7, which present the results of the ICP analysis, again nearly all means of the samples deriving from the gull nests are higher than those of the control groups. There is only one exception, the measured Fe concentration. This was significantly higher in the control groups. Also, the difference between the Fe concentration of the samples from the first and second sampling date of the gull nests did not differ.
Figure 6. Boxplot of the pH and EC results of the different subgroups: control points, herring gull, lesser black backed gull and combined nests per sampling date.
The phosphorus content differed the most in the ICP test when comparing the results of the gull nests with the control points showing significant p-values. When focussing on the herring gull nests, the change between before and during the breeding season was the most significant in the potassium content, of which the corresponding p-values are insignificant. While, for the lesser black backed gull nests the change in the
concentrations of macro-ions between the two sampling dates was the largest in sodium. All p-values of the Wilcoxon test comparing the first and second sampling date of the lesser black backed gull nests proved not to be significant.
Figure 7. Boxplot of the ICP test results of the different subgroups: control points, herring gull nests, lesser black backed gull and combined nests.
The results of the AA analysis, shown in Table 3 and Figure 8 present information on the nitrate, (dissolved organic) nitrogen, sulphate, chlorine and phosphate content of the samples. The p-values of the Wilcoxon tests proved to be randomly insignificant in the tests performed on the different subgroups.
For each substance the samples from the gull nests gave significantly higher values than the samples from the control points. The average values of the total nitrogen (Ntot), dissolved organic nitrogen (DON), nitrogen dioxide (NO2), nitrate (NO3) and
ammonium (NH4) contents of the herring gull nests are more than three times the
corresponding average values of the control points. Considering phosphate (P2O5),
the herring gull nests even showed a four times higher concentration than the control points.
The lesser black backed gull nests had higher values in comparison with the control samples in the ICP results as well. The most significant difference was found in NO2 +
NO3 and Cl. The change in nutrient content between the samples taken during the
breeding season and the ones before the breeding season was the highest in Cl for the lesser black backed gull nests and highest in ammonium for the herring gull nests.
Group P
[μmol/L] Fe [μmol/L] Ca [μmol/L] Na [μmol/L] K [μmol/L] Mg [μmol/L]
LBG 122,12 2,48 1003,0 726,5 177,0 191,0 LBG I 89,7 2,82 835,0 434,0 87,4 161,2 LBG II 154,6 2,15 1170,9 1019,0 266,7 220,9 p-value I vs II 0,9141* 0,5* 0,0503* 0,0770* 0,0939* 0,2244* HEG 244,9 1,76 1443,0 933,0 261,6 290,5 HEG I 147,4 1,66 1258,2 468,5 95,68 215,7 HEG II 342,4 1,85 1627,9 1397,5 427,5 365,3 p-value I vs II 0,0315 0,5* 0,1359* 0,0040 0,0056 0,0503* COP 76,8 4,22 556,8 350,3 123,4 171,1 p-value COP vs LBG 0,0024 0,0162 0,0206 0,0086 0,7360* 0,6694* p-value COP vs HEG 0,0015 0,0032 0,0007 0,0037 0,4315* 0,0366
Table 2. The means of the results of the ICP analysis of the data, divided in subgroups: lesser black backed gulls (sampling date I and II), herring gulls (sampling date I and II), control points. Also, the p-values of comparing two subgroups by the Wilcoxon tests are shown. *= where the p-values exceed 0,05, corresponding with the level of significance (95%) and can be classified as insignificant.
Group NO2+NO3
[μmol/L] NH4 [μmol/L] Ntot [μmolN/L] DON [μmolN/L] Cl [μmol/L] SO4 [μmol/L] P2O5 [μmol/ L] LBG 479,5 400,7 1231,2 351,1 190,8 81,0 20,3 LBG I 408,7 77,4 658,7 172,6 63,0 43,4 12,5 LBG II 550,3 723,9 1803,8 529,6 318,7 118,6 28,0 p-value I vs II 1* 0,0008 0,0625* 0,0188 0,0102 0,3995* 0,0229 HEG 488,8 776,9 1888,4 545,7 337,6 193,9 55,1 HEG I 644,3 207,9 1095,7 223,4 137,6 87,8 36,8 HEG II 313,2 1346,0 2527,1 867,9 515,4 313,3 73,4 p-value I vs II 0,1422* 0,0012 0,0106 0,0012 0,0152 0,2650* 0,1135* COP 143,9 254,5 548,6 150,3 54,4 54,9 13,6 p-value COP vs LBG 0,0024 0,4860* 0,0050 0,6530* 0,0504* 0,7191* 0,0111
Table 3. The means of the results of the AA analysis of the data, divided in subgroups: lesser black backed gulls (sampling date I and II), herring gulls (sampling date I and II), control points. Also, the p-values of comparing two subgroups by the Wilcoxon tests are shown. *= where the p-values exceed 0,05, corresponding with the level of significance (95%) and can be classified as insignificant.
4.1.3 CNS analysis
The CNS analysis of the samples was quite accurate as can be seen in Table 11 of Appendix E. The test was performed in duplo and the two results from the same sample differed minimally, which make the results of the test, visible in Table 4 and Figure 9, quite reliable. The data obtained by the CNS analyzer also contained
information on the hydrogen and sulphur content, however due to relevance, the focus is on the carbon and nitrogen content. Overall, the carbon content was significantly higher than the nitrogen content, which is also visible in Table 11 in Appendix E as the C/N ratio. The values of the gull nests during the breeding season are higher for both carbon and nitrogen content than the corresponding ones taken before the breeding period. Also for this experiment, the results of the gull nests are clearly distinguishable from the control points with significant corresponding p-values.
Figure 9. Boxplot displaying the carbon (CNSC) and nitrogen content (CNSN) derived from CNS analysis results of the different subgroups: control points, herring gull nests, lesser black backed gull and combined nests over the first and the second sampling date.
4.1.4 TOC
The TOC test of the samples gave information on the organic, inorganic and total carbon of the samples. The means per subgroup of the outcomes of the organic, inorganic and total carbon content can be seen in Table 5 in Appendix D. Below in Figure 10, boxplots reveal the samples of the gull nests consisted of a higher organic and inorganic carbon content than the control points and the values of the samples of the second sampling date were higher than those of before the breeding season. Extreme outliers are visible in the organic carbon content of the herring and lesser black backed gull nests.
Figure 10. Boxplot of the TOC analysis results of the different subgroups: control points, herring gull nests, lesser black backed gull and combined nests.
Group
C
[%]
N
[%]
CO
2[%]
LBG 2,281 0,207 0,227 LBG I 1,890 0,182 0,137 LBG II 2,671 0,232 0,313 p-value I vs II 0,4363* 0,3865* 0,0400 HEG 3,203 0,247 2,560 HEG I 3,188 0,250 1,022 HEG II 3,217 0,244 3,585 p-value I vs II 0,3734* 0,4363* 0,4363* COP 1,47 0,119 0,0944 p-value COP vs LBG 0,0061 0,00004 0,0323 p-value COP vs HEG 0,0041 0,0005 0,0000007Table 4. The means of the results of the CNS analysis and inorganic carbonate content of the data, divided in subgroups: lesser black backed gulls (sampling date I and II), herring gulls (sampling date I and II), control points. Also, the p-values of comparing two subgroups by the Wilcoxon tests are shown. *= where the p-values exceed 0,05, corresponding with the level of significance (95%) and can be classified as insignificant.
4.1.4 Carbonate content
The Wesemaeler test was performed to obtain data on the inorganic carbonate content of the samples. The results are presented by CO2 percentages, shown in
the last column of Table 4. The results were very significant for the herring gull nests as can be see in Figure 11 on the right. The carbonate content of the lesser black backed gull nests were also higher than those of the control points. All samples from the second sampling date were distinguishable from the samples taken before the breeding season. The statistical analysis showed promising results from the Wilcoxon test. Only the p-value for the test which compared the first and second sampling date of the herring gull subgroup could not be considered statistically reliable.
4.2 Statistics
As came forward in the lilliefors tests, not all of the obtained data was normally distributed. In Table 6, displayed in Appendix D, the results of the lilliefors and Wilcoxon tests are presented with the corresponding p-values. The tests were performed at a 95% significance level, indicating the p-values above 0.05 are not statistically relevant. The values with an acceptable p-value have been highlighted in the table. By doing this, it becomes clear almost half of the results are not reliable. Therefore, the relative mean and median against the first and second sampling date have been presented in Figure 12. As can be seen, the change in mean is overall higher than the change in median.
Figure 11. Boxplot of the results from the Wesemaeler test on the inorganic carbonate content of the subgroups: control points (COP), herring gull nests (HEG), lesser black backed gull nests (LBG) and combined nest (CMB) over the first and second sampling date.
Figure 12 displays the relative change in median and mean in the measured
substances over the first and second sampling date. When the median of a sample is approaches zero, the relative median is more likely to be influenced, which can be seen in the K and Cl data of the herring gull nests. The x-axis is sorted by the relative change of mean of the herring gull nests, as these projected the largest differences. However, an overall descending trend is visible for the lesser black backed gull nests, control groups and combined nest as well. The largest difference between the samples of the gull nests of the first and second sampling date of the gull nests is seen in NH4.
Appendix C provides more information on how the relative mean and median was calculated and visualized.
Figure 12. Graphs on the relative mean and median on the differences between the results of the first and second sampling dates. The x-axis is sorted by the difference of the herring gull nests.
5.1 Results
The change in pH between the two sampling dates was almost negligible, while the difference in pH between the sampling groups was in favor of the hypothesis
supporting the fact the gulls influenced the pH. Nonetheless, the difference between the subgroups was not very significant. Loder et al. (1996) had found more promising results as the pH of coastal pools in their research area could reach a pH of 10 or even higher due to the guano contamination. The results of the electrical conductivity measurements were more in line with the hypothesis, showing more extreme differences between the gull nests and control groups. The electrical conductivity of the samples was plotted against the sum of cations and presented in Figure 14 of Appendix D. A strong correlation between the electrical conductivity and cations is visible, which is in line with what was found in literature (Grisso et al., 2005).
The input of guano had been expected to lead to higher concentrations of Mg, K, N and P. As base cations and nitrates are more likely to leach in the soil due to rainfall, higher contents of P were expected due to fixation (Allaway & Ashford, 1989; Breuning-Masden et al., 2008). For Mg, K, N and P all results showed higher
concentrations in the gull nests than the control points. The concentrations were also higher in the second sampling date, supporting the hypothesis these high
concentrations can be explained by the presence of the gulls. Unfortunately, a higher P content with respect to K and Mg was not clearly visible in the obtained results. This could have to do with the fact both sampling dates were under dry conditions, limiting the effect of infiltration by rainfall.
The CNS analysis was performed in duplo, of which the results are presented in Table 11, Appendix E. Fortunately, when considering the similarity of the carbon and nitrogen content of the two tests performed per sample, it can be concluded these results are quite reliable. The AA tested the total nitrogen content, as well as the dissolved nitrogen, nitrate, nitrite and ammonium content. Also the CNS analysis provided results on the total nitrogen concentration of the samples. As the nitrogen content of the subgroups show the same proportion in both tests, it is possible to conclude the results of both tests are quite reliable.
The same can be concluded for the carbon content. The TOC test gave information on the inorganic, organic and total carbon content. The CNS analysis provided results on the total carbon content and the Wesemaeler test obtained data on the total inorganic carbon content. All tests showed the highest carbon content in the herring gull nests and the lowest results in the control groups. It is especially relevant to consider the results on the inorganic carbon content, as this can be explained by the input of the gulls. In the results of the Wesemaeler test, the difference between the herring gull nests and the other subgroups was the largest, corresponding with strong outliers visible in the results. It is presumable this corresponds with the large amount of shells which can be found around these nests due to the diets of the herring gulls. Even though the samples were sieved with a 2mm sieve, smaller particles of shells could have infiltrated in the soil samples before milling, causing the extreme concentrations of carbonates in the samples taken before the breeding season.
Other outliers seen in the remaining tests could possibly be explained by local contamination of the soil. When looking in detail at the unprocessed data it becomes clear the outliers in the tests are often found in the same samples, which derive from gull nests (sample 2, 36, 41, 49 and 51, see Table 7 Appendix E). These samples could have been taken where gulls had lost their excrements or food scraps quite recently, for example. As the weather of both sampling dates was quite dry, there was no possibility for fresh biological litter to infiltrate.
5.2 Statistics
When considering Table 6 in Appendix D it is clear most data from the samples of the gull nests is not normally distributed. This is in contrast with the samples taken from the control points, which are overall normally distributed. This could be linked to the higher amount of outliers in the gull nests, which is in favor of the explanation the outliers are caused by local contamination of the gulls. The rejection or acceptance of the null hypothesis is only considered significant if the p-value does not exceed the significance level (<0.05) and these results have been highlighted in Table 6. It turns out, the outcomes of the lilliefors tests are only statistically relevant in rejections of the null hypothesis, which assumes the data is normally distributed. Therefore, the
Table 6 also presents the results of the Wilcoxon test, which were performed on the subgroups of the data. Again, a p-value was displayed when running the tests in MATLAB and considered statistically relevant when not exceeding 0.05. When
comparing the data from the herring gull nests with the control points, it was clear the distributions differed significantly. 16 out of the 21 measured soil characteristics showed a rejection of the null hypothesis with relevant corresponding p-values. This indicates the distributions of the samples from the herring gull nests differed
significantly with respect to the control points, while this difference was less convincing in the results when comparing the samples of the lesser black backed gull nests with the control points.
The samples from the herring gull and lesser black backed gull nests were separated per sampling date and compared to attain information on a possible statistical
distinction in the the distributions of these subgroups. The results of the Wilcoxon tests showed only the data on the rejection of the null hypothesis, which indicates the samples from both sampling dates derived from continuous distributions with equal medians, was relevant. For both species, only about half of the nutrients were said to differ statistically between the sampling dates. The results on the acceptance of the null hypothesis all had extreme p-values, making these values not quite reliable. Therefore, the graph on the relative change in mean and median between the two sampling dates provided more useful information on how the soil properties changed over time.
The phosphorous, phosphate, total nitrogen and electrical conductivity tests provided the most reliable statistical results with respect to the other measured substances as the p-values of the lilliefors and Wilcoxon tests were quite low for these substances. Highlighting the most relevant results with respect to the research, it is interesting to focus on the variation in the distributions of the samples of the gull nests when comparing those with the control points. The substances which showed statistically significant results when running the Wilcoxon test, were the following: NO2+NO3, total
nitrogen, phosphorous, P2O5, Na, Ca and the results of the CNS analysis. All these
substances showed a rejection of the null hypothesis with a reliable p-value, indicating a statistically relevant difference in the distributions of the subgroups. This can be linked back to the diets of the gulls, as fish causes nutrient-rich input, explaining the significant difference in phosphorus and nitrate content.
5.3 Lab work
Although the results were quite in favor with the hypothesis supporting the fact the gulls do indeed influence the soil chemistry, within a research there is always room for improvement. First of all, the lab work was performed by hand. This makes inaccuracy an issue, even though the work was performed precisely. Some problems did in fact arise during the lab period. For example, the test tubes of some samples fractured in the centrifuge, therefore new filtrates had to be prepared, influencing the
heterogeneity of the treatment of the samples. Another error occurred in the the calibration of the AA test on P2O5, which was not adjusted correctly. Therefore, this test
had to be performed manually. This could have influenced the results of the
concentrations of phosphate in the samples. However, the dissimilarity between the gull nests and the control groups was still clearly visible in the obtained results
presented in different units, limiting the expected error. Additionally, the proportion of the change between the samples of the first and second sampling date is correct when comparing it to the other substances measured by the AA.
5.4 Recommendations
Despite the fact almost all tests led to promising results, the influence of gulls on the soil chemistry should be researched more thoroughly. Only the samples deriving from the top layer of the soil were examined in the lab in this research. As the gulls release their excrements and drop food scraps on the top of the soil around their nests, the topsoil is influenced strongly by the biological litter. Therefore, the most interesting results concerning soil chemistry are expected to be found in the top layer of the soil. It would be recommended for further research to examine the underlying layers of the soil as well. This way, the possible leaching of nutrients in the soil could be
determined, which would be expected referring to previous research (Breuning- Masden, 2008).
It would be recommended for further research to gather more samples in the field. Only 54 samples were examined in the lab, which made the data obtained by the tests not large enough to perform proper statistical analysis. Due to the outliers, almost all data was not normally distributed, making the relevance questionable. However, despite the outliers, the influence of the gulls was clearly noticeable.
In addition, a third sampling date after the breeding season could provide more relevant information on the influence of gulls on the soil. Within this research, a clear distinction could be made in the concentration of nutrients before and during the breeding season. By examining the soil after the birds leave for migration, the duration of the influence of the gulls could be determined. Also, it could be concluded whether the input of the gulls is only temporary during their stay in the breeding period.
Another field of research which can be taken into consideration is a biological study on the affects of the gulls on the vegetation in this area. Previous research has found the affected soil chemistry can have a large impact on the species richness of the
vegetation of their breeding areas (Ellis et al., 2006; Ligeza & Smal, 2003; García et al., 2002). It would be interesting to see whether this is also the case in the
Kelderhuispolder, which would support the necessity for further research.
By combining potential results of the influence of the gulls on underlying soil layers with those of research on the soil composition after the breeding period, it can be possible to form an understanding of the longterm effects of gulls on the soil chemistry and ecosystem as a whole of their breeding areas.
Considering the obtained results, the gulls clearly have an influence on the soil chemistry of their breeding area in the Kelderhuispolder in Texel. The hypothesis was based on literature of previous research on the influence of the input piscivorous birds. The effect of the gulls on the soil characteristics was distinguishable despite the higher rainfall in the area of research when comparing this to the known significant influence in semi-arid areas. The biological input of the gulls by releasing excrements and food scraps have led to higher organic matter, soluble salts and nutrient content around the nests. As well as a higher pH and EC.
Referring to the first subquestion, it is possible to conclude there was a distinction in the influence of the two gull species. The herring gulls overall had a higher influence on the soil chemistry than the lesser black backed gulls. This could be linked to their diets and nesting habits. Herring gulls consume more mussels and deposit shells around their nests, explaining the higher inorganic carbon content which arose from the results of the Wesemaeler test. Also, herring gulls have a higher nest attendance with respect to lesser black backed gulls, explaining the overall higher amount of nutrients and soluble salts in their soil samples. Lesser black backed gulls tend to have cleaner nesting habits, which supports the fact the nutrient concentration was lower than those of the herring gulls. As the diets of the lesser black backed gulls contain more fish, which is quite nutrient rich, this agrees with the higher NO2 + NO3 content
in their nests.
Finally, the influence of the presence of the gulls on the soil properties of their breeding areas stood out in the fact the concentrations of the measured substances were overall higher in the samples retrieved from the second sampling date, according to the hypothesis. The change of nutrients during the breeding period was clearly noticeable in the relative mean graph. This can also be explained by the shift to a more nutrient-rich diet of the gulls, which takes place during the breeding season.
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First of all, I would like to dedicate this thesis to my two supervisors Erik Cammeraat and Judy Shamoun-Baranes because of their valuable advice. The different
backgrounds in study areas of the two complemented each other perfectly for my somewhat interdisciplinary research on the gulls and soil chemistry. Kees Camphuysen earns credit for teaching me a lot about the gulls and helping perform the fieldwork in Texel. Also, special thanks to John Visser, who has guided me patiently throughout the whole period in the lab. Next to this, I must mention my sister Anouk Gevaert for her support as well.
Finally, I am thankful for the University of Amsterdam for giving me the opportunity for establishing this research by providing excellent staff, materials and facilities.
A - Fieldwork Area
Figure 13. The fieldwork area with respect to the whole island of Texel.
9 - A P P E N D I C E S
B - Lab journal
Wednesday March 30th- All the samples taken on 18th of March were sorted and placed in the cooling cell
Tuesday May 2nd
- All the samples taken from the first sampling date were sorted and checked for correctness of the labels.
- Drying cartons were folded
- All the soil samples derived from the top layer were placed in the oven, set at 40 degrees Celsius, for drying
Wednesday May 3rd
- The soil samples were taken out of the oven and weighed
Monday May 9
- All 27 samples from the top layer from the first sampling date were sorted and sieved with a 2mm sieve. After storage of the remnant, about 20 grams was extracted for in the milling machine
- +/- 20 grams of the sieved sample was placed in the milling machine. This could be done by four samples at a time.
- The milling machine was set at 400RPM and the duration was set at 7 minutes.
- During the running of the milling machine, the following four samples could be prepared. This was done by weighing the glass jar with sticker and resetting the scale, this way the amount of sample could be weighed to about 20 grams
- The remnant of the milling machine was placed back in the bottles and weighed again, this way the potential loss of sample during the milling phase could be detected.
Tuesday May 10th
- 28 tin boxes were folded in the lab as a preparation for the CNS analysis. 5-12mg of sulphanilic acid were weighed and stored in the boxes. The sulphanilic acid used for this contained of 41.6% C, 8.1% N, 4.1% H and 18.5% S
- As there are 54 samples in total and this analysis will be done in duplo, another 108 boxes should be folded with about 30mg of milled sample
- Next to this, the storage materials for the AA and ICP analysis were prepared by cleaning the test tubes thoroughly. First they were rinsed with acid, after that twice with demi water and finally with ELGA water.
Once cleaned, these bottles could be placed in the oven set at 70 degrees for drying
Wednesday May 11
- The second sampling date, again 27 samples were retrieved from the top layer - Next to this, 27 samples were taken from the underlying layer
Thursday May 12
- The samples from the second sampling date were placed in the oven set at 40 degrees for drying
- First, 2-3 grams of milled material was weighed and placed in a flask, together with a test tube of 4mol/L HCl. Once the DOPPEN were placed on the flasks, the HCl was poured out of the test tube by tilting the flask.
- After this, the flasks were placed on the oscillator for shaking every half hour for 26 hours in total.
Friday May 13
- The samples of the carbonate analysis were taken from the oscillator and weighed again - The flasks and test tubes used for this analysis were cleaned and placed in the oven - Calculations were made on the carbonate content
- The tin boxes for the CNS analysis were folded for the first batch
Tuesday May 17
- The 27 samples of the second sampling date were taken out of the oven and sieved with a 2mm sieve.
- About 20 grams of the sieved samples were milled for 7 minutes at 400RPM.
50mL of demi water was added on a weighing scale to test tube with 20 grams of the sieved material. These samples were shaked for 2 hours 130RPM
- A progress was made on folding the boxes for the second batch for the CNS analysis with the milled samples made
- The first batch was placed in the CNS analyzer
Wednesday May 18
- The rest of the tin boxes for the second batch of CNS analysis were folded with about 30mg of milled sample
- The samples with water were shaken once more for half an hour at 100 RPM.
- Before the pH and EC analysis could be performed, the devices for measurement had to be calibrated. The pH was calibrated with liquids set at a pH of 7 and 4. The EC device was calibrated with a solution of 0.01mol/L Potassium chloride set at a conductivity of 1413 microS/ cm
- After calibration, all 54 solutions were measured for pH and EC and placed on the oscillator for 14 hours at 130 RPM for preparation of filtering the solutions
The second batch was placed in the device for CNS analysis
Thursday May 19
- Preparations were made for the carbonate analysis of the second batch of samples (28-54). This was done in the same way as described at May 12
- All samples were placed in the centrifuge for 20 minutes at a speed of 3000 RPM
- Unfortunately the test tubes of samples 23, 24 and 53 broke during centrifuging. Therefore, 20 grams of samples 23 and 24 were mixed with 20 mL of demi water and placed on the shaker for 14 hours at 130 RPM.
C - Matlab script
%%%%%%%%%%%%%%%%%%%%%%% % Bachelor thesis Charlotte Gevaert % June 2016
clc close all %% Defining data % Folder met de data folder = 'Data/';
% Load all data and get variable names
files = dir([folder 'Single data/*.mat']); % List file names of all datasets
varnames = cellfun(@(x) x(1:end-4), {files.name}, 'UniformOutput', false); % Get names of all variables % Indexes for sorting
dom = [ones(27,1);ones(27,1)*2]; % measurement date site = repmat(1:9,[3 1]);
site = [site(:); site(:)]; % site number species = zeros(size(dom));
species(site==2|site==9) = 1; % control sites species(site==1|site==3|site==8) = 2; % HG species(site==4|site==5|site==7) = 3; % LBBG species(site==6) = 4; % combined % Put all data into one matrix
data = nan(54,numel(varnames)); for i = 1:numel(varnames); % Load nutrient
dat = load(['Single data/' varnames{i} '.mat']); fieldname = fieldnames(dat);
eval(['dat = dat.' fieldname{1} ';'])
% Put data into matrix data(:,i) = dat; clearvars dat fieldname end
% Statistics per group
[medians means meancis] = grpstats(data,[species dom],{'nanmedian','nanmean','meanci'}); % Plot settings
speciesLAB = {'CP','HG','LBBG','BOTH'}; % species group names
speciesLAB2 = {'COP','II','HEG','II','LBG','II','CMB','II'}; % species group names colors = 'kbgc'; % Colors for plot
%% Scatter plot of all values figure(3)
clf
for i = 1:numel(varnames) % For every nutrient subplot(3,7,i)
for j = 1:max(species) % For every species group id = species==j; scatter(dom(id),data(id,i),[],colors(j)) hold on title(varnames{i}) end xlabel('Day')
xlim([0.5 2.5])
lsline % Add line of best fit end legend(speciesLAB) %% Boxplots icp = [3 8 7 9 12 14]; aa = [4 16 15 5 10 11 13]; CNS = [1 2]; TOC = [19 18 17]; pHEC = [21 6]; figure(1) clf
for i = 1:numel(icp) % For every nutrient subplot(2,3,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,icp(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) % if i==5 % ylim([0 2000]) % end title(varnames{icp(i)}) end figure(2) clf
for i = 1:numel(aa) % For every nutrient subplot(2,4,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,aa(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) % if i==1 % ylim([0 1000]) % end % if i== 6 % ylim([0 1500]) % end title(varnames{aa(i)}) end % figure(3) clf
for i = 1:numel(CNS) % For every nutrient subplot(1,2,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,CNS(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) % if i==1 % ylim([0 7]) % end % if i== 2 % ylim([0 0.5]) % end
for i = 1:numel(TOC) % For every nutrient subplot(1,3,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,TOC(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) % for i==1 % ylim([0 120]) % end % if i== 2 % ylim([0 120]) % end title(varnames{TOC(i)}) end figure(5) clf
for i = 1:numel(pHEC) % For every nutrient subplot(1,2,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,pHEC(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) title(varnames{phEC(i)}) end %% WE = [20]; figure(6) clf
for i = 1:numel(WE) % For every nutrient subplot(1,1,i)
% Make boxplot, grouped by species group and date
boxplot(data(:,WE(i)),[species dom],'plotstyle','compact','colors','kkbbggcc','labels',speciesLAB2) title(varnames{WE(i)}) for i = 1 ylim = ([0 20]); end end
%% Relative median and mean
relmedian = reshape(medians,[2,numel(medians)/2]); relmedian = relmedian(2,:)./relmedian(1,:); relmedian = reshape(relmedian,numel(speciesLAB),numel(varnames)); relmean = reshape(means,[2,numel(means)/2]); relmean = relmean(2,:)./relmean(1,:); relmean = reshape(relmean,numel(speciesLAB),numel(varnames)); % Sort data by decreasing HG
srt = relmean(2,:); % Choose a species to base the sorting on (column) % and choose either relmedian or relmean
[~,idx] = sort(srt','descend'); % Apply sorting index to all variables relmean = relmean(:,idx); relmedian = relmedian(:,idx); relvarnames = varnames(idx); % Figure figure(6) clf subplot(2,1,1)
set(gca, 'ColorOrder', [0.5 0.5 0.5; 0 0 1; 0 1 0; 0 1 1; 0 0 0], 'NextPlot', 'replacechildren'); plot((1:numel(varnames))',relmean'); leg = legend(speciesLAB); hold on plot([0 22],[1 1],'k') xlim([0.5 21.5])
ylabel('Relative change of mean [-]') set(gca,'xtick',1:21,'xticklabel',relvarnames) subplot(2,1,2)
set(gca, 'ColorOrder', [0.5 0.5 0.5; 0 0 1; 0 1 0; 0 1 1; 0 0 0], 'NextPlot', 'replacechildren'); plot((1:numel(varnames))',relmedian');
leg = legend(speciesLAB); hold on
plot([0 22],[1 1],'k') xlim([0.5 21.5])
ylabel('Relative change of median [-]') set(gca,'xtick',1:21,'xticklabel',relvarnames) %
% subplot(2,1,2)
% set(gca, 'ColorOrder', [0.5 0.5 0.5; 0 0 1; 0 1 0; 0 1 1; 0 0 0], 'NextPlot', 'replacechildren'); % plot((1:numel(varnames))',relmedian') % hold on % plot([0 22],[1 1],'k') % xlim([0.5 21.5]) % ylim([0 10]) % leg = legend(speciesLAB); % set(leg,'box','off')
% ylabel('Relative change of median [-]') % set(gca,'xtick',1:21,'xticklabel',relvarnames)
D - Remaining results
0 2000 4000 6000 8000 10000 12000 00 200 400 600 800 1,000 1,200 1,400 1,600 Sum c a' ons [µ eq/L] Ec [µS/cm]Sum ca'ons - Ec
Figure 14. The sum of the cations of the ICP analysis against the EC values.
Table 5. The mean values of the TIC and TC of the soil samples of all subgroups: lesser black backed gull (sampling date I and II), herring gulls (sampling date I and II) and the control points.
