Factors Affect Colonisation Of New Habitats Biology Essay

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Freshwater habitats like ponds are under decline and face threats including alteration of habitats, pollutants, agricultural intensification and urbanisation. Even though recently pond numbers have been increasing many are degraded and of poor quality.

Ponds often contain a considerable amount of diversity and are an important refugee for many threatened species.

To better understand pond communities and to be able to preserve them questions such as what factors affect colonisation of ponds need to be answered.

16 man-made mesocosms installed in 2011 were studied under a 12 week-period using a Latin Square design to see if shade and sediment affected what kind of abundance and diversity was found in the mesocosms.

Only one species (Diaptomus) colonized the ponds whereas 13 species overall were found during the 12-week period.

No difference between treatments was found, suggesting that shade and sediment do not affect which macroinvertebrates and zooplankton colonize the ponds. Vegetation, connectivity and existing communities may play more important roles in structuring pond communities.

CONTENTS

INTRODUCTION 3

MATERIAL & METHODS 8

RESULTS 10

DISCUSSION 13

REFERENCES 20

INTRODUCTION

Understanding the processes behind colonisation and factors affecting aquatic communities is crucial for the ability to preserve freshwater habitats. The biological diversity found in these kinds of habitats is under rapid decline and exceeds that found in many terrestrial ecosystems. Threats include alterations and destructions of habitats, pollutants and the pressures exerted by the growth of the human population (Dudgeon et al. 2006).

Urbanisation, the movement of people into cities from rural areas, is a continuing phenomenon with over half (3.3 billion) of the worlds' population living in urban areas in year 2008 (UNFPA 2007). An urban area is defined as an area with high human density and/or linked to commercial or industrial activities (Loram et al. 2007). 4.9 billion people are expected to be living in urban areas in 2033 and though most growth will occur in developing countries, urban areas in the developed world are still expected to expand 2,5 times by 2030 (UNFPA 2007). The urban area in the UK makes up around 6.8 % of the total area but contains around 80 % of the population (UK National Ecosystem Assessment 2011). Urban areas affects more than just the total area they cover since the activities in cities affect landscapes nearby and they contribute to pollution (Goddard et al. 2010). The population is predicted to grow from the 62 million today to 77 million in 2033 and by 2021 there will be a need for 3.8 million extra households (UK National Ecosystem Assessment 2011). This need does not come only from population growth but also from social changes since more people are living alone and for longer which in turn decreases the average size of households (Gaston et al. 2005b). This process of increased urbanisation has big effects on biodiversity and more often than not the effects are negative. Urbanization has been found to change the land cover, alter and fragment habitats, increase the pollution and ambient temperature to name but a few, negatively affecting the species richness and the complexity of ecosystems (Tratalos et al. 2007; Goddard et al. 2010).

Gardens in urban areas are often considered to positively impact wildlife, especially since they in many cases cover a large amount of the total urban area. In the UK, numbers range between 22-27 % of the total area depending on the city (Goddard et al. 2010). Their impact on biodiversity is attributed by their function as a habitat and their function in connecting habitats to each other (Davies et al. 2009). Gardens, especially in suburban areas, can be very important to wildlife and some species have been found to have more stable populations there than in rural areas due to the heterogeneity present in these kind of areas (Goddard et al. 2010). Gardens could in all probability be used more in conserving biodiversity but there are some issues. Gardens are privately owned so they fall outside the direct control of government or local authorities and the information on how to increase the biodiversity in gardens is limited. However, the research done on biodiversity in gardens in urban areas is increasing, with the Biodiversity in Urban Gardens in Sheffield Project (BUGS) having carried out extensive research on both the flora and fauna and factors affecting their diversity in urban gardens.

Ponds have been pointed out to be a way of enhancing the biodiversity in gardens as well as being an important part of freshwater habitats. Ponds can be described in many ways depending on the focus (e.g. depth, size, type of water supply) and no unanimous definition exists although one common definition is one stated by the Pond Conservation Organisation in the UK: "a body of water which can vary in size between 1 m2 and 2 hectares and which holds water for four months of the year or more" (Oertli et al. 2005; Pond Conservation 2013). Still, because there is no official definition, there is no single value of how many ponds there are in the UK. The Countryside Survey Report in 2007 estimated the number of ponds to be around 478 000 whereas estimates of 3.5 million ponds found only in gardens have also been made (Goddard et al. 2010). These huge differences in numbers is due to the fact that these estimates have used different definitions, with a minimum of 25 m2 in size needed in the first estimate for a water body to constitute as a pond whereas the mean size of a pond in the second estimate was only 1 m2. Ponds can and have been created by natural processes like glaciation and more simple processes like the falling of a tree but in modern time anthropogenic processes have an increasing control over the creation and destruction of ponds (Wood, Greenwood & Agnew 2003; Pond Conservation 2013).

As mentioned before, freshwater habitats are under increasing threat and ponds are especially vulnerable since they in many cases are not recognized as being an important habitat. Ponds are being lost due to agricultural intensification, urbanisation as well as industry and transport developments and this loss has been especially great during the last two decades (Boothby 1995; Wood, Greenwood & Agnew 2003). New ponds are also being created so the turn-over is high, with an estimated 18 000 ponds lost and 70 600 created between 1998 and 2007. Many of the new ponds are of better quality compared to some older ones although mostly it is not know what the quality of the ponds lost were so it is difficult to say whether the gain of new ponds compensates for the loss of the old ones (Countryside Survey 2007).

Many new ponds are created in gardens but these are not usually interchangeable to the ponds lost in the wider landscape due to them often being small, intensely managed and containing ornamental fish species (Loram et al. 2007). There is also evidence that the quality of ponds has declined since 1996 and now around 80 % of ponds are of poor or very poor quality (Countryside Survey 2007). This is especially worrying since ponds have been found to contain much biodiversity, supporting the most species and highest number of rare species thus contributing the most to regional biodiversity in aquatic systems (Biggs et al. 2005). Many species are known to inhabit ponds and over 2/3 of UK freshwater animals and plants are found in ponds, also including many threatened species (Countryside Survey 2007). Despite this, ponds have often been neglected in studies to the favour of other water bodies like rivers and lakes. Long-term datasets and a larger understanding is available for these but ponds are less understood in the terms of how they function and how they should be managed (Wood et al. 2003; Biggs et al. 2005). Without the information on how they function and what factors affect organisms in ponds proper action to conserve them cannot be taken. Just assuming what is best won't work, considering the fact that until recently ponds were not even considered important and past mistakes have been made, for example the action to dredge ephemeral ponds and making them permanent or removing vegetation from ponds and in turn losing some of the fauna that prefer ephemeral or densely vegetated ponds (Biggs et al. 2001). Knowledge of pond ecology and function is becoming increasingly important, since the pressure of urbanisation as well as future climate change are putting ponds under even more pressure (Brönmark & Hansson 2002; Wood, Greenwood & Agnew 2003). To be able to provide habitats for species in the form of ponds, knowledge about possible factors that may affect their colonisation and ability to persist in an aquatic community is important.

Some recent work has been conducted on ponds in both experimental settings and in gardens (Jenkins 1998; Brady et al. 2002; Caeceres & Soluk 2002; Gaston et al. 2005; Frisch & Green 2007) but most of the previous studies regarding freshwater has lasted under a year (Stendera et al. 2012). Many of the studies have also not sunk their ponds into the ground due to different restrictions (Caeceres & Soluk 2002; Gaston et al. 2005). The pond project going on at the University of Reading, which this experiment is a part of, is also providing important information about ponds. These mesocosms are sunk into the ground, providing a more natural lay-out. Hopefully this project will provide information about the changes in aquatic communities and physiochemical data over time, providing a long-term data set.

The main questions this experiment is asking is which physical variables affect the colonisation of newly formed aquatic habitats. Two variables are being tested in a Latin Square Design: light exposure and sediment base layers. Additional questions are whether there is any relationship between the physiochemical results as well as the biological data and the physiochemical results. The biological data is looked at both from an abundance and an diversity viewpoint. The answer to these questions can then possibly be applied to real life situations, for example when installing a pond in a garden to promote biodiversity. Since ponds are so poorly studied and understood these kinds of results will bring vital information to this area of research.

METHODS AND MATERIALS

16 man-made mesocosms equally spaced apart were used in this experiment (Table 1). The experiment location was on campus at the University of Reading. A Latin Square design with 1.5 m x 1.5 m spacing in each Latin Square was applied for this experiment. The ponds were dug out in 2011 and consist of containers sunk into the ground that were filled with tap water and then left to stand to be naturally colonized. The volume of the ponds are between 35 and 40 liters (depending on rainfall) with the approximate measures of 41 cm in diameter and a height of 29 cm. Half of the ponds are shaded, which was done by a construction of wire and nylon sheeting (windbreaker fabric) in a half circle format shading the ponds a 100 % at mid day. The area of a shade is 0,25 m2 with approximate measures of a length of 80 cm and a height of 52 cm. Half of the ponds are seeded, meaning they had received 1.5 kg of soil that was sifted in a 4 mm sieve during the construction of the mesocosms. The soil originated from the grid area so that no new chemicals were introduced to the ponds. Treatment A consisted of seeded and un-shaded ponds, treatment B of seeded and shaded ponds, treatment C of un-seeded and un-shaded ponds and treatment D of un-seeded and shaded ponds and each treatment had 4 replicates. Plant species growing very close or into the ponds were removed.

Physiochemical and biological sampling started on 2.10.2012 and ended on 18.12.2012. The samples were taken of the ponds weekly at around 10 am although some measurements had to be taken later in the day due to personal restraints. The physiochemical measurements taken were ph, temperature, conductivity, dissolved oxygen and water transparency. The ph, temperature, conductivity and dissolved oxygen was measured using electronic meters from Hanna Instruments with the model HI9024 used for ph and temperature, HI9142 for dissolved oxygen and HI8733 for conductivity. The ph probe had problems during week 9 of sampling, showing the same number for all ponds and broke in week 10 of sampling so no results were obtained from week 9 to week 12 and these weeks were ignored in the results. The water transparency was measured using a Secchi disk. Biological samples were always taken after the physiochemical samples had been taken. A small hand net consisting of aquatic fish net (length 9.5 cm and width 7.5 cm) was used to collect specimens by rotating it in a figure eight motion three times at three different levels (close to surface, middle of pond and close to bottom).

After collection specimens were put in tubes (one tube for each pond) that were filled with tap water. When taken to the lab, the tubes were put in a fridge. One tube at a time was then poured into a tray and specimens were identified using keys (Macan 1962; Scourfield & Harding 1966; Disney 1975; Croft 1986; Cranston et al. 1987; Elliot, Humpesch & Macan 1988; Smith 1989; Henderson 1990; Greenhalgh & Ovenden 2007; Alberti et al. 2013) and the total number of each species was counted. Due to the large numbers of waterfleas present an approximate count of these were taken where one eight of the tray was counted and that number was multiplied by eight. The aim was to identify every specimen down to species level but due the presence of specimens notoriously difficult to identify without specialist knowledge some were only identified down to family or genus level. After identification and counting, the contents of the tray were emptied back into the tube which was put back into the fridge. When the contents of all tubes had been looked at, they were transported back to the ponds so as to not deplete the resources of the ponds.

The Kruskal-Wallis one way analysis of variance was used to detect any differences between treatments of abundance and diversity. The Mann-Whitney U-test was used to detect any specific effect of shade or sediment on abundance and diversity. Spearman's rank correlation coefficient was used to detect any relationship between the physiochemical variables as well as any relation between the physiochemical variables and the abundance or diversity.

A1

B1

D1

C1

B2

C2

A2

D2

C3

D3

B3

A3

D4

A4

C4

B4

Table 1. Latin Square Design of the ponds.

RESULTS

13 species were found during the experiment with apparently varying ability to colonize the ponds (Table 2). 5 species colonized all of the ponds: the larvae of common house mosquitoes (Culex pipiens), the larvae of biting midges (Ceratopogonidae), lesser water-boatmen (Sigara nigrolineata) and two species of water fleas (Daphnia pulex and Scapholeberis mucronata). Other species were only found in one or very few ponds: Ghost midge larvae (Chaoborus americanus) in one pond, seed shrimps (Cypridoidea) in two ponds and copepods (Diaptomus) in 5 ponds. Individual ponds contained a maximum value of 7 to 11 taxa.

Common Name

Taxonomy

1-10

11-30

30-100

101-1000

>1000

Number of ponds

Water flea

Daphnia pulex (Daphnidae; Cladocera)

x

16

Water flea

Scapholeberis mucronata (Daphnidae; Cladocera)

x

16

Common House Mosquito

Culex pipiens (Culicidae; Diptera)

x

16

Biting Midge

(Ceratopogonidae; Diptera)

x

16

Lesser waterboatmen

Sigara nigrolineata (Corixidae; Hemiptera)

x

16

Mayfly

Cloeon dipterum (Baetidae; Ephemeroptera)

x

14

Non-biting midge

(Chironomidae; Diptera)

x

11

Mosquito

Anopheles claviger (Culicidae; Diptera)

x

10

Meniscus midge

Dixella aestivalis (Dixidae; Diptera)

x

8

Copepod

Diaptomus (Diaphomidae; Diaptomus)

x

5

Predaceous diving beetles

(Dytiscidae; Coleoptera)

x

4

Seed shrimp

(Cypridoidea;Podocopida)

x

2

Ghost midge

Chaoborus americanus (Chaoboridae; Diptera)

x

1

Table 2. Species inhabiting the ponds. The different numbers represent the maximum number of the same species found during a sampling of one pond and the number of ponds refers to how many ponds it was found in during the experiment. Taxonomic names represent species or genus name and the family and order names are in brackets.

The physiochemical values varied with the weeks (Appendix 1). The ph had a range of 7.76 to 12.47 with an average of 9.86 (± 0.02 SE), the amount of dissolved oxygen had a wide range of 0.2 to 16.1 mg/Lwith an average of 4.2 mg/L (± 0.4 SE) and the temperature ranged from 13.8 degrees to 1.0 degree Celsius with an average of 8.5 degrees (± 0.02 SE) (Figure 1). The conductivity ranged from 126 µS/cm to 320 µS/cm with an average of 190 (± 22 SE) µS/cm (Figure 2). The Secchi disk measurements had a range of 6 cm to 20 cm and the disk was only used in one pond (A3) since the rest of them stayed clear trough-out the experiment (Appendix 2).

Figure 1. The ranges of the physiochemical variables (pH, dissolved oxygen and temperature) among the different treatments. The outliers of dissolved oxygen in treatment A are values of 14,9 and 16,1, in treatment B 14,2 and 15,2, in treatment C 14,6 and in treatment 15,8. These values were all recorded in week 12.

Figure 2. The ranges of conductivity among the different treatments. The outliers consist of values of 320 (Treatment B) and 307 (Treatment C). These were recorded in week 1.

Instead of grouping all the results together, week 1, 6 and 12 was included in the statistical testing. The Kruskal-Wallis one way analysis was performed to check for an effect of the treatment on both abundance and diversity so all in all 6 tests were performed. There was no significant effect of treatment on diversity in week 1 (H(3) = 5,84, p = 0,120), in week 6 (H(3) = 1,61, p = 0,658) or in week 12 (H(3) = 0,87, p= 0,833). There was no significant effect of treatment on abundance in week 1 (H(3) = 1,51, p = 0,680) in week 6 (H(3) = 2,38, p= 0,497) or in week 12 (H(3) = 2,23, p = 0,526). The Mann-Whitney U test was performed to check of any particular influence of sediment or shade separately on the diversity or abundance in week 1, 6 and 12 as well. There was no significant effect of shade on diversity in week 1 (W= 63,5, p= 0,674), in week 6 (W= 73,0, p= 0,618) or in week 12 (W= 67,5, p= 1,00) and the same was true for abundance in week 1 (W= 65,5, p= 0,833), in week 6 (W= 66,0, p= 0,875) or in week 12 (W= 71,0, p= 0,793). There was no significant effect of sediment on diversity in week 1 (W= 81,0, p= 0,131), in week 6 (W= 59,0, p= 0,347) or in week 12 (W= 59,5, p= 0,385) and the same was also true for abundance in week 1 (W= 58,0, p= 0,318), in week 6 (W= 54,0, p= 0,156) or in week 12 (W= 61,0, p= 0,495).

Spearman's rank correlation coefficient was used to detect if there were any significant relationship between the physiochemical variables and the physiochemical variables and abundance or diversity. There were five statistically significant relationships between the physiochemical variables (Figure 3, 4, 5, 6 and 7). These were oxygen and pH (Rs(14)= 0,640, p= <0,01), abundance and pH (Rs(14)= -0,583, p= 0,01), temperature and conductivity (Rs(14)= 0,748, p= <0,005), pH and conductivity (Rs(14)= -0,709, p= <0,005) and oxygen and conductivity (Rs(14)= -0,694, p= <0,01).

Figure 3. The positive correlation between oxygen and pH.

Figure 4. The positive correlation between temperature and conductivity.

Figure 5. The negative correlation between abundance and pH.

Figure 6. The negative correlation between pH and conductivity.

Figure 7. The negative correlation between oxygen and conductivity.

Spearman's rank correlation coeffcicient was also used to see if there was any relationship between mosquitoes (Culex pipiens and Anopheles claviger) and Daphnia pulex.

DISCUSSION

Treatment was not found to significantly affect abundance or diversity and shade or sediment did not individually affect abundance or diversity either. In fact, abundance and diversity seemed to stay rather constant trough out the experiment (Figure 8 and 9 ) even though there were noticeable weekly fluctuations in abundance which either relates to real fluctuations in the abundance or an effect of sampling and estimation of waterflea numbers (Figure 10).

Figure 8. Weekly cumulative total abundance of the different treatments.

Figure 9. Weekly cumulative average diversity of the different treatments.

Figure 10. Fluctuations of weekly total abundance between the treatments.

The physiochemical variables were quite similar in each treatment as can be seen from Figure 1 and 2 and it is likely that these differences were not big enough to affect the species living in the ponds and thus no difference showed in the treatments when comparing abundance and diversity. The conductivity values are quite average for a freshwater environment. For ponds conductivity has been found to range anywhere between 13 and 2560 µS/cm and since our range was relatively small (126 - 320 µS/cm) it is unlikely to have any big impact on the species in the ponds, especially since the mean value was relatively low (Countryside Survey 2007). The mean oxygen value is tolerated by a variety of invertebrates especially since they as a group have a wide range of tolerance, however the lower ranges of oxygen (under 2 mg/L) might have affected the organisms living in the ponds (Canadian Council of the Ministers of the Environment 1999). It is also possible that the equipment was not functioning ideally, especially since it measured consistently low numbers (under 2 and down to 0.2 mg/L) for a few weeks and then the levels suddenly jumped up to as high as 16.1 mg/L in week 12. The low ranges may also have resulted from coverage of ice preventing re-aeration since a week after the ice first appeared the oxygen dropped down low. It is puzzling though that some species seemed to increase in numbers even when the oxygen levels measured dropped down to as low as 0.2 mg/L. One example is the mayfly nymphs, which were found most abundant in week 11 after 4 weeks of low oxygen values even though mayflies have been found to need high oxygen levels in waters. The species present in our ponds, Cloeon dipterum, is one of the more tolerant species and is found in a wide range of oxygen levels (Lock & Goethals 2011), however some weeks the oxygen values dropped to the very end of its tolerance so it is still surprising the species did so well. The pH value is higher than the average values found for ponds in England with the average of 9.86 in our ponds compared to the regional mean of 7.2, although mean values up to 10.1 were also found regionally (Countryside Survey 2007). pH was possibly a limiting factor for some species as for example, pH values of 10.5 to 11 have been found lethal to some species of stoneflies and dragonflies and the highest species richness in invertebrates have been found to be in the ranges of 4.09 to 8.56 and a pH over 9 decreased richness (National Research Council (U.S.); Committee on Water Quality Criteria, 1972; Berezina 2001). This might also explain why pH and abundance was found to have a negative relationship since a drop in pH would have meant that the value was better tolerated by the species. The relationships between the physiochemical variables found significant were positive relationships between oxygen and pH and between conductivity and temperature and negative relationships between conductivity and pH and between conductivity and oxygen. A positive relationship between oxygen and pH has also been found in other studies (Araoye, P.A. 2009) and a positive relationship between conductivity and temperature is also expected since conductivity measures the capability of a solution to pass an electrical current which is higher when there are more ions in the water and as the temperature gets higher the ions become more mobile in the solution which leads to an increase in conductivity (Jenway 2010). The relationship between pH and conductivity is probably due to the fact that both of them can be affected by the same factor, in this case ions in the water, rather than the two factors directly affecting each other. The same might be true for the relationship found for oxygen and conductivity. The amount of oxygen the water can hold decreases with a higher salinity and a higher salinity would lead to a higher value of conductivity as well (Schwartz 2006).

Successful colonization includes two stages: to arrive at a new habitat and to persist in the habitat. Processes involved in structuring communities can be divided into two broad categories: local processes which involve abiotic and biotic factors that affect the persistence of species and regional processes which involve dispersal among sites and the extinction of local populations (Cottenie et al. 2003). If the rate of dispersal is much bigger than extinction rate, habitats will contain the species whereas if dispersal is rare, species will often be absent in habitats. Diverse communities can often make it difficult for new species to persist and so these may have high extinction rates. This is thought to be because of more species use up more of the resources and many of the niches may already be taken (Shurin 2000). Shurin (2000) found that over 91 % of the species he introduced to existing ponds became extinct and that the more species a pond contained, the harder it was for the introduced species to invade. Zooplankton has been found to be able to disperse over large distances and often quickly invade new areas but community structure factors may prevent their establishment in the population (Shurin 2000). Dispersal also apparently limits some zooplankton species and they can vary in their ability to colonize a new habitat (Jenkins 1995; Jenkins & Buikema 1998) Only two species of zooplankton were found in our ponds initially and three briefly managed to co-exist which suggest that not only dispersal limited these animals (Figure 11). Another factor is that the ponds were not new when we started to study them. It could be that environmental factors like shade and sediment and the slight differences in physiochemical variables no longer affect the species distribution but that the species composition in the ponds may effectively stop some species to establish themselves. It is also possible that since the ponds were so close to each other, species were continuously dispersed between ponds and that balanced out any major differences between treatments. Species that share the same trophic level often affect each other and this is more important in longer-lasting habitats than short ephemeral habitats where the density of competitors are more unlikely to be high and where resources might not be depleted as quickly. One of the competitor interactions suggested taking place is that between mosquito-larvae and cladoceran species because both of these may filter feed on phytoplankton. This competition may be especially important in ponds where the hydroperiod is longer, where there are limited resources or predator densities are not that high (Blaustein & Chase 2007). The hydroperiod length in our ponds is very long compared to more ephemeral ponds found in the wider landscape as well as the predator densities being low, with Dytiscidae found in low densities in only 4 ponds and Chaoborus americanus found in low desnities in only one pond. One of the possible species pair competing with each other in the ponds were Culex pipiens larvae and Anopheles claviger larvae competing with Daphnia pulex which all feed on phytoplankton. No significant correlation between Daphnia pulex and the mosquito species were found (Spearmans Rank Correlation coefficient for Daphnia pulex and Culex pipiens: Rs(14)= -0,259, p= >0.1), Spearmans rank correlation coefficient for Daphnia pulex and Anopheles claviger: Rs(14)= 0,380, p= 0.1) and Daphnia pulex was considerably more abundant in our ponds which is not an uncommon pattern (Blaustein & Chase 2007). A study by Stav, Blaustein & Margalita (2005) however found that the effect of Daphnia manga on Culex pipiens were that the larvae developed more slowly and pupated at a smaller size which then affected the fitness of the mosquitoes when adults. This could possibly have been happening at our ponds as well but since we did not take this into account when sampling it cannot be said for certain.

Figure 11. The three species of zooplankton found in our ponds.

The only species that colonized the ponds during the experiment were copepods (Diaptomus), with a first appearance in week 10 of the experiment and at week 12 it was found in the same four ponds as in week 10. This appearance seemed to coincide with a lowering of Scapholeberis mucronata numbers as only a few were found in the samples in week 10 and none in week 12. It is possible that competition from Scapholeberis mucronata added with high concentrations of Daphnia pulex had excluded copepods from ponds in previous weeks. Zooplankton communities in freshwater are often dominated by either large or small species. Small species usually dominate in ponds with fish since the fish tend to take larger zooplankton whereas fishless water is usually dominated by large herbivores and invertebrate predators usually take smaller zooplankton (Brooks & Dodson 1965; Dodson 1974). Not many predators were found in or mesocosms, for example one often mentioned effective zooplankton predator, Chaoborus, was only present in one pond in low numbers.

The dominant zooplankton in our ponds were Daphnia pulex and Scapholeberis mucronata numbers were always lower, possible because of pressure from the competition from Daphnia pulex. Daphnia pulex has been found to suppress smaller species in numbers by the reduction of food and it is possible it is why smaller zooplankton may have had difficulty to establish themselves in our ponds (Vanni 1986). Diaptomus species have been suggested to be constricted by competition for food, water chemistry and predation and low food concentrations have been shown to exclude Diaptoms from some lakes (Elore 1983). The competition from both Daphnia pulex and Scapholeberis mucronata could have been too much for Diaptoms and only when Scapholeberis mucronata reduced in numbers was colonization possible.

Dispersal can happen in three different ways: trough ovipositioning of eggs, trough immigration and by passive dispersal (Shurin 2000). It is likely that the animals found in larval stages in the ponds have got there trough ovipositioning and the adults are aerial so the range of dispersal is likely to be large. The adult forms found in the ponds were very few and even in the case of water boatmen nymphs were found so immigration is most likely less important than ovipositioning regarding these ponds. Passive dispersal plays a big role in the occurrence of microcrustaceans and since these were very abundant, the passive dispersal probably outweighed the immigration as well. Passive dispersal is not so well understood but wind and rain is probably important factors as well as occasionally hitchhiking on other animals (Shurin 2000; Caeceres & Soluk 2002). Colonization of the ponds varied among the different species with some managing to colonize all and some only a few. Jenkins (1995) found that species colonizing multiple ponds did so fast (within a month) and were found in higher densities and for longer whereas species only found in a few ponds were not as densely populated and existed for shorter times which is a pattern similar to our mesocosms. Species with low vagility (capacity to disperse) were also found to have low viability (capacity to maintain in a population) and the same was true in the opposite direction. Jenkins and Buikema (1998) found that when constructing 12 man-made ponds richness increase for the first 6 to 7 months and that there was no difference in taxa or entire communities among ponds and that in similarity to our ponds, some species were found in all ponds and some only managed to colonize a few. Since these ponds already were colonized we don't know if species like mosquitoes and water boatmen colonized them rapidly although it is possible that they did. The species that were not present in all ponds at the start did not colonize more ponds during the experiment, suggesting that there was something inhibiting them from doing so, whether it was difficulty to disperse or inability to persist because of the community already established in the pond.

What other factors could have played in to that there was no difference between treatments? Many other factors have been found to affect species richness and perhaps shade and sediment are not as important as some other variables in ponds. Indeed, both historical, local and regional factors play a role in what communities are found in a pond so abiotic measurements only contribute to a certain degree (Stendera et al. 2012) Size has not been found to affect all taxonomic groups, meaning that a pond does not have to be large to support many species. Oertli et al. (2002) found that the only species groups significantly affected by size were Odonata and Gastropoda, which preferred large ponds. This may be due to an indirect effect since an increased area usually contributes to more habitat diversity and more diverse flora (Oertli et al. 2002; Stendera et al. 2012) The ponds in the experiment were fairly small and did not succeed in attracting any species of the group Odonata and because no sediment sampling was done, Gastropoda was not collected even though they were sometimes visible from the surface. Oertli (2002) also noticed a relationship between shade and Odonata richness so had the ponds managed to attract Odonata there might have been a difference in which ponds they would have chosen, whereas there was no relationship between shade and the other groups examined (Gastropoda and Coleoptera). Other authors have however found a relationship between pond area and Coleopteran species richness (Rundle et al. 2002). Since all of our ponds were the same size they might have attracted some species groups more than others, making the composition more uniform. Pond density has been found to be one of the biggest predictors for species richness: the more ponds in a landscape the greater the richness of invertebrates and plants (Gledhill, James & Davies 2008). Since all of our ponds were situated in close proximity to each other this again would have cancelled out any major difference in species richness. One of the biggest predictors of species richness is the plant species richness found in the pond. Plant species richness have been found to positively affect invertebrate species richness by the effect it has in providing microhabitats, increasing food availability, providing refuge from predators as well as substrate to build retreats (Szalay & Resh 2000; Bazzanti, Della Bella & Seminara 2003; Gledhill, James & Davies 2008). Ponds with thick emergent vegetation have been found to have more diverse communities and especially Coleopteran species richness has been found to be influenced by the amount of submerged vegetation found in a pond (Oertli et al. 2002). Brady et al. (2002) compared mesocosms that had soil and filtered water with mesocosms that had vegetation cores planted in them and found that a wider range of invertebrates inhabited those with vegetation cores. The controls had mostly aerial taxa and had much higher density of mosquitoes, biting midges and true bugs which was also found in our mesocosms. Higher density of vegetation has also been found to lead to higher oxygen production in ponds (Gledhill, James & Davies 2008) Pond vegetation being such an important factor could mean that the same group of insects were attracted to all ponds and that many groups were inhibited to colonize them because of the lack of vegetation in the ponds.

What does these results imply looking at them for a wider angle? Even small ponds without vegetation will attract wildlife so installing ponds in gardens can indeed contribute to the biodiversity of an area. Shade did not seem to affect the colonization of organisms and neither did sediment layers so in all likelihood these will not be the main factors to consider if you want to construct a pond that promotes biodiversity. The Countryside Survey 2007 however found that there was a relationship between increasing tree shade and decreasing pond condition. This survey only considered plants though and it is possible that if we had plants in the ponds these would have been affected by the shade and in turn affected the species so more research with more variables would be beneficial to increase the knowledge in this area. Past results have shown vegetation to be important and proving to attract invertebrates so installing plants in a pond will likely benefit biodiversity. Connectivity of ponds in the wider landscape is also an important factor, since the connectivity is vital for biodiversity and community structure (Stendera et al. 2012), so it is important to get as many people as possible educated on urbanisation and biodiversity because isolation negatively affects biodiversity. Hopefully as more and more people realize how important ponds can be, more research and general knowledge will accumulate and some of this knowledge can be used to off-set the impacts of urbanisation and climate change in the future.

5063

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APPENDIX 1

Physiochemical sampling data

Week 1

pH

Temperature

Dissolved O2

Conductivity

A1

9,22

12,6

5,1

248

B1

8,66

12,6

4,1

236

C1

9,04

13,4

4,5

257

D1

8,75

12,6

4,6

197

A2

8,38

13,0

3,6

267

B2

8,66

12,9

4,4

235

C2

8,87

13,2

2,6

275

D2

8,43

12,7

3,1

253

A3

9,66

13,8

6,9

264

B3

8,58

12,9

3,8

237

C3

8,71

13,2

6,8

233

D3

8,92

12,7

5,5

201

A4

8,55

13,7

4

252

B4

8,55

12,8

2,6

320

C4

8,47

13,4

4

307

D4

8,54

12,7

4

230

Week 2

pH

Temperature

Dissolved O2

Conductivity

A1

9,2

11,6

5,6

210

B1

8,6

11,6

3,5

162

C1

8,6

12

4,2

212

D1

7,95

11,7

5

192

A2

7,97

12,1

4,2

237

B2

8,9

11,8

4,6

204

C2

7,91

12,2

4

233

D2

8,7

11,8

2,1

231

A3

8,7

12,4

4,9

235

B3

8,5

11,9

4

208

C3

8,4

12,1

5,5

188

D3

8,85

11,9

4,8

176

A4

8,4

11,9

4,7

247

B4

8,8

12,4

2,8

272

C4

8,3

12,3

3,3

272

D4

8,34

12,1

4,8

209

Week 3

pH

Temperature

Dissolved O2

Conductivity

A1

9,63

10,3

6,3

194

B1

8,93

9,8

4,9

205

C1

9,28

10,2

5,6

214

D1

8,97

9,8

5,7

189

A2

8,72

10,5

4,6

227

B2

8,98

9,7

6,2

199

C2

7,95

10,4

4,5

222

D2

8,73

10

3,4

228

A3

8,22

11,4

6,1

226

B3

7,76

9,8

5,1

198

C3

8,34

10,7

6,3

179

D3

9,01

9,7

6,7

169

A4

8,31

11

4,7

234

B4

8,56

9,9

4

260

C4

8,09

11,1

4,5

257

D4

8,4

10,1

5,8

198

Week 4

pH

Temperature

Dissolved O2

Conductivity

A1

10,24

13,4

6

178

B1

9,24

13,4

3,9

196

C1

9,95

13,5

5,1

193

D1

9,59

13,4

4,5

126

A2

8,94

13,5

2

219

B2

10,34

13,5

5,2

175

C2

9,41

13,5

2

212

D2

9,37

13,5

1,1

208

A3

9,73

13,7

4,1

208

B3

9,6

13,6

2,4

191

C3

9,32

13,7

3,9

171

D3

9,93

13,6

5,4

161

A4

9,32

13,7

1,6

225

B4

9,31

13,6

2,1

246

C4

9,41

13,6

1,5

257

D4

9,28

13,6

2

193

Week 5

pH

Temperature

Dissolved O2

Conductivity

A1

11,83

8,3

6,5

186

B1

11,13

8

5,2

205

C1

11,89

8,9

6

199

D1

11,33

8,2

5,7

184

A2

11,55

9,2

6,7

221

B2

11,88

8,1

5,9

190

C2

11,42

9,1

5,4

222

D2

11,59

8,6

4,7

222

A3

11,44

10,4

7,4

215

B3

11,51

8,2

5,4

193

C3

11,64

8,6

6,7

170

D3

11,68

8

6,7

165

A4

11,68

9,1

4,5

235

B4

11,45

8,2

4,2

251

C4

11,54

9,4

5,4

254

D4

11,42

8,2

5,4

197

Week 6

pH

Temperature

Dissolved O2

Conductivity

A1

11,31

4,3

9,3

152

B1

10,71

2,4

6,8

170

C1

12,17

4

6,5

173

D1

11,76

2,2

8,2

152

A2

10,06

4,9

4,7

192

B2

12,47

3,5

7,1

153

C2

11,13

4,6

6,6

163

D2

11,31

3,9

5,2

190

A3

11,5

5,3

7

188

B3

12,07

4,2

6,2

164

C3

12,05

4,7

7,7

141

D3

12,26

4

7,4

142

A4

12,07

5,6

6,6

197

B4

10,99

4,6

6,3

218

C4

11,96

5,8

5,8

218

D4

11,93

4,6

6,6

167

Week 7

pH

Temperature

Dissolved O2

Conductivity

A1

10,56

8,6

3

146

B1

9,84

8,8

3,2

181

C1

10,50

8,9

2,6

178

D1

10,67

8,7

2,9

161

A2

10,17

8,9

2,6

197

B2

10,98

9

2

171

C2

9,93

8,8

2,7

198

D2

10,01

8,9

3,1

199

A3

10,70

9,9

1,7

149

B3

10,35

8,9

1,8

174

C3

10,28

9

1,9

148

D3

11,05

9,7

2,2

148

A4

10,18

9

1

209

B4

10,18

9,4

1,4

243

C4

10,21

8,7

1,5

229

D4

10,30

9,6

1,3

174

Week 8

pH

Temperature

Dissolved O2

Conductivity

A1

11,1

9,9

1,3

170

B1

10,32

9,6

1,3

183

C1

10,25

10,5

1,1

175

D1

10,35

10,3

1

163

A2

9,87

10,2

0,9

195

B2

10,08

10,4

0,8

173

C2

9,62

9,7

0,7

202

D2

9,34

9,8

0,9

202

A3

9,63

9,6

0,7

206

B3

9,83

9,9

0,9

175

C3

9,34

9,7

0,6

152

D3

10,22

10,3

0,9

156

A4

9,21

10

0,4

210

B4

9,42

10,3

1,4

233

C4

9,24

10,2

0,9

230

D4

9,39

10,5

0,4

179

Week 9

pH

Temperature

Dissolved O2

Conductivity

A1

10,22

7,7

0,6

160

B1

10,22

8,1

0,4

175

C1

10,02

8,2

0,4

174

D1

10,38

8,1

0,5

159

A2

10,04

8,2

0,5

186

B2

10,26

8,2

0,6

165

C2

9,16

8,2

0,6

187

D2

10,27

8,2

0,4

180

A3

9,95

8,6

2,1

180

B3

9,43

8,4

2,0

162

C3

10,11

8,2

2,0

148

D3

9,89

8,2

2,0

147

A4

9,96

8,5

1,7

196

B4

9,78

12,1

4,4

212

C4

9,73

8,6

3,3

214

D4

9,44

8,6

1,8

167

Week 10

pH

Temperature

Dissolved O2

Conductivity

A1

*

3,1

0,2

153

B1

*

3,1

0

133

C1

*

3,4

0,7

152

D1

*

2,4

0,3

141

A2

*

5,1

1,1

173

B2

*

3,3

1,2

158

C2

*

3,9

1,2

184

D2

*

5,6

1,1

142

A3

*

3,7

0,9

172

B3

*

4,1

0,9

156

C3

*

3,9

0,9

136

D3

*

2,8

1

142

A4

*

3,3

0,8

153

B4

*

3,3

1

213

C4

*

4,1

0,9

186

D4

*

4,1

0,7

164

Week 11

pH

Temperature

Dissolved O2

Conductivity

A1

 *

1

2,1

159

B1

 *

3

2,1

161

C1

 *

6,1

2,3

162

D1

 *

2,4

2,3

136

A2

 *

2,1

2

191

B2

 *

2,5

2,1

169

C2

 *

2,9

2,2

199

D2

 *

3,1

2,2

174

A3

 *

3,2

1,7

206

B3

 *

2,2

1,8

119

C3

 *

2,9

2

145

D3

 *

2

1,8

141

A4

 *

1,9

1,1

140

B4

 *

2,8

1,3

224

C4

 *

2,1

1,1

162

D4

 *

2,8

1

125

Week 12

pH

Temperature

Dissolved O2

Conductivity

A1

*

5,5

8,1

156

B1

*

5,8

10,8

162

C1

*

5,4

14,6

171

D1

*

5,8

11,6

149

A2

*

5,6

16,1

137

B2

*

5,1

15,2

127

C2

*

5,8

13,8

200

D2

*

5,3

15,8

131

A3

*

6,1

12,1

144

B3

*

5,2

15,1

145

C3

*

5,8

13,7

178

D3

*

6,2

15,5

161

A4

*

6,8

14,9

162

B4

*

5,5

14,2

135

C4

*

5,9

14,8

172

D4

*

6

15,9

168

APPENDIX 2

Turbidity values for A3

Week

1

2

3

4

5

6

7

8

9

10

11

12

Turbidity (cm)

0

7,5

10

6

10,5

10,5

13

18

10

8

20

15

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