Fieldworks Freshwater module Help:

Freshwater species and their adaptations: Fauna (Animals) or Flora (Plants)

General characteristics of rivers and lakesGeneral characteristics of plants living in freshwaterGeneral characteristics of animals living in freshwaterBackground Information:1. Abiotic factors affecting distribution

1.1 Velocity1.2.Oxygen 1.3 Carbon dioxide

1.4 pH1.5 Temperature1.6 Light1.7 Suspended solids1.8 Sediment size

2. Biotic factors affecting distribution2.1 Competition2.2 Predation2.3 Dispersal2.4. Community organisation: competition, predation and

disturbance3.Productivity4.Succession5.Oligotrophic and Eutrophic lakes

6.Monitoring water quality6.1. Chemical and Physical tests6.2. Biological tests :

Trent Biotic Index, Chandler Score Biological Monitoring Working Party -BMWPGammarus:Asellus ratioRIVPACS

6.3. Biochemical Oxygen Demand (BOD)6.4. Levels of substances in potable water6.5 Water Treatment and Domestic water consumption

7. Sampling methods7.1 Biological7.2 Physical

8. Pollution 8.1 Organic pollutionEffects of organic pollution on riversEutrophication

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Nitrates and the environmentPhosphates and the Environment

8.2 Inorganic chemicals8.3 Toxic chemicals

Effect on otters8.4 Thermal pollution

8.5 Radioactive substanceseffect of the Chernobyl accident on aquatic wildlife

8.6 Oestrogenic compounds 8.7 Acid rain8.8 Pollution references

9. Management of rivers10. Project suggestions11.Freshwater references12.Risk Assessment for Field Work13.Equipment Suppliers14.Program support15.Minimum system requirements

16.About Fieldwork17.Licence and Warranty

Sampling methods - Biological

Sampling invertebrates in rivers and streams

1. Effort based - 'Kick' samplingPlace a net on the stream bed downstream of the area to be sampled. Remove any large stones making sure any organisms on the stones get washed into the net. Dislodge organsims on the stream bed by disturbing the substrate using the foot. remove the organisms from the net into a sample tray containing water. Sort out the invertebrates and record the number of different species found. The same amount of effort should be made for each sample taken. The correct time of kicking or number of kicks can be found by sampling for an increasing time or kicks and constructing the following graph:

The correct sampling time is where the graph starts to level off (ie 120 seconds): less time would mean that not all the species had been sampled, more time would mean effort was wasted.

2. Area based

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Use a 'surber sampler' (Surber, 1970) which is basically a quadrat with a net behind it. This means assessment can be made of a known area ie how many species per unit area, or how many of each species per unit area.

3. Drift nets

A surber sampler can be used or a net devised specifically for this purpose. Make sure it is securely fastened to the base of the stream

Sampling invertebrates in ponds and lakes1. Effort based - sweep netsSweep a net through the pond for a certain number of times, this can be repeated and a graph constructed (as above) to work out the optimum number of sweeps to get the majority of species. The same amount of effort should be made for each sample taken.

2. Plankton sampling - using a plankton netThis can be used by dragging it through the pond or lake, it is usually most effective to do it from a boat. If there is too much vegetation it will be impossible to do.

Vegetation sampling - TransectThe vegetation can be sampled from the bank into the water and a kite diagram (or similar) constructed (see FieldWorks species distribution module)

For other methods see Doberski and Brodie (1991)

Sampling methods - Physical

1. Flow rate or velocity

a. Using a flow meter:For a flow meter, the propeller must be facing up-stream and should be positioned approximately 1/3rd of the way up the water column from the bottom.

Sources of flow metersIt is possible to obtain a range of flow meters, from complex electronic to more simple designs.All are provided with calibration curves or equations.eg.MJP

Tel :01837 810195Digital flow meterCalibration : Velocity (m/s)= 0.000854xC + 0.05 where C is the number of propellor revolutions in one minuteOwens and Boyd Field Study Products

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Tel :01656 660311Owens and Boyd produce the Hydro-Prop flow meter which is an 'intermediate technology' design using no electronics, though it does require a stop watch. Calibration : Velocity (m/s) = 0.0277 + (3.2805 / propeller travel time in secs)

b. Using the 'orange' or 'dog biscuit' method

A floating object such as an orange, dog biscuit, weighted cork, is timed over a certain distance. All floats must be unaffected by any wind and different floats may float at different speeds eg. dog biscuits float on aaverage 15% slower than oranges (Frew, 1993)Use the equation

Velocity (m/sec) = Distance (m) / Time(sec)

Calculate average time taken for c. 3 floats to move a set distance.

With floats, only the surface speed has been measured. This needs to be corrected to give mean water velocity by multiplying by 0.85. (Lenon and Cleves,1994)

2. Oxygen

a. Using an oxygen meter

These are available from most biological suppliers. They are expensive and fragile

b. Using the Winkler Method

Austin (1983) gives a simple version of the classic Winkler method for measuring dissolved oxygen

c. Using Oxygen kits

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These are relatively inexpensive and the results are as, or often more, reliable than meters. They involve a mini-titration and can be performed in the field. Some of the chemical used are potentially hazardous so rubber gloves should be used.

They are available from Biological suppliers eg Philip Harris

3. Light

This is usually measured using a Secchi disc. This is a metal disc which is divided into four and painted alternately black and white. It can be bought or made using a weighted plastic disc. The disc is lowered using a graduated line into the lake or pond and the depth recorded when it disappears from view. The disc is lowered still further and then raised up and the depth recorded when it comes into view again. The mean of the depths gives a measure of light penetration. However it is known that plants can photosynthesise 2.5 times the depth recorded with the disc.

4. pH

A variety of pH tests kits are available: Philip Harris supply Hanna and Palintest kits. Or they can be obtained direstly from the manufacturer: Palintest, Kingsway, Team Valley, Gateshead, Tyne & Wear, NE11 0NS

5. Temperature

A range of thermometers are available and can be used for surface temperatures. However, if more detailed analysis at depth is required, temperature probes can be used, for example to look at change in temperature through the thermocline in a lake.

6. Nitrate, phosphate, ammonia, chlorine

A variety of tests kits are available: Philip Harris supply Hanna and Palintest kits. Or they can be obtained direstly from the manufacturer: Palintest, Kingsway, Team Valley, Gateshead, Tyne & Wear, NE11 0NS

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7. B.O.D.

The test involves collecting two water samples, the oxygen content of one is measured immediately, the second is incubated for 5 days at 20°C in the dark (to prevent any photosynthesis of diatoms etc.) after which the oxygen concentration is measured. The BOD is the difference in the oxygen concentration between the two samples.

A test kit is available from Palintest, Kingsway, Team Valley, Gateshead, Tyne & Wear, NE11 0NS. This will give an estimate of B.O.D. in half an hour

Chemical and Physical TestsFor example temperature, pH, dissolved oxygen, ammonia, suspended solids, nitrate, chloride,detergents,organic matter, Biochemical Oxygen Demand (BOD)

Advantages of chemical and physical tests1. Very low levels of substances can be detected.

Disadvantages of chemical and physical tests1. Measurements can vary depending when and where they are taken2. Intermittent pollution events will not be detected (this may be overcome by continuous monitoring systems)

3. Chemical sampling will only detect those chemicals for which analyses are carried out4. Expensive to operate

Sampling methods - Physical

Biological Tests

It is important to choose the right organisms for study that will relate to water quality not an ecological factor. The method of assessment must be quantifiable so that comparisons can be made with other sites and should reflect the conditions at the point of sampling only. Generally it is unwise to choose a single species as the indicator: the species may vary both spatially and over time due to other biotic factors rather than due to pollution. Species such as bacteria, algae and macrophytes have all been used (see Mason, 1996), but more widely used are the macroinvertebrates, the presence of certain species are thought to indicate clean water, others to indicate polluted water, these are called indicator species, however certain species which may indicate clean water may not be present for other reasons than pollution, for example stoneflies are not common in lowland streams. A biotic index (eg Trent Biotic Index, Chandler Score, Biological Monitoring Working Party -BMWP, Gammarus:Asellus ratio will give more meaningful results. Diversity indicies (eg Shannon-

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Weiner, Simpson) can be useful but variations occur with the season, and seasonal variations at one site have been found to be greater than differences between different sites (Murphy, 1978).

The most recent and sophisticated system involves using environmental data to predict what organisms will be found. A software package, River Invertebrate Prediction and Classification System (RIVPACS), (Wright et al 1993) is used.

Simple Indicators of water quality:

1. Very clean waterMayfly and Stonefly nymphs, some Caddis can only occur in clean water, though others can survive in water which is quite polluted, Caddis at order level are therefore not such a good indicator species. Trout and salmon occur.

2. Clean waterAs above but coarse fish (roach, perch etc.) replacing trout and salmon

3. Fairly cleanFew fish. Common invertebrates are Ascellus (Freshwater hog louse) and Gammarus (Freshwater shrimp)

4. DoubtfulAs above but with no fish

5. BadOnly Chironomids and Tubifex worms will survive

Advantages of biological tests1. Cheap to operate2. Does not require expensive equipment and can be done on the spot.

3. Effect of a pollution incident may be reflected for a long time in the biota therefore pollution can be picked up sometimes days after it has occurred

Disadvantages of biological tests1. Does not specify the cause of pollution2. Sampling must allow for natural variations in species: indicator species may disappear for other reasons than pollution.3. Species which are sensitive to one pollutant may be tolerant of another eg stonefly nymphs cannot tolerate organic pollution but can tolerate high concentrations of heavy metals.

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4. Macro-invertebrates show a patchy distribution in a stream so one sample may catch only a few whereas another may catch a lot purely by chance not because they are not present.5. Skill in identifying species

Sampling methods- Biological

Chandler Score

This is additiveand relies on species abundance as well as presence or absence. It assumes a 3 minute kick sample.The results are dependent on the sampling effort and sampling effectiveness in covering all habitats. It requires a higher level of identification skill than the Trent Biotic Index, but is more sensitive.

Number in kick sample Present Few Common Abundant Very Abundant

Groups present in sample 1-2 3-10 11-50 51-100 100+

each species of Stonefly 90 94 98 99 100

each species of mayfly 79 84 90 94 97

each species of casedcaddis and alderfly 75 80 86 91 94

each species of freshwater limpet 70 75 82 87 91genera of midge (fly) larvae 60 65 72 78 84genus Similium (black fly larvae) 56 61 67 73 75genera of beetles 51 55 61 66 72genera of Baetis (mayfly) 44 46 48 50 52genera of Gammarus (shrimp) 40 40 40 40 40each species of uncased caddis 38 36 35 33 31each species of flatworm 35 33 31 29 25genera of water mites 32 30 28 25 21each species of Mollusc 30 28 25 22 18each species of Chironomid 28 25 21 18 15

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each species of freshwater hog louse 25 22 18 14 0each species of leech 24 20 16 12 8each species of Tubifex 22 18 13 12 9each species of air breathers 19 15 9 5 1

ReferencesChandler, J.R. (1970) A biological approach to water quality management. Water Pollution Control 1970, 415-421

BMWP (Biological Monitoring Working Party)

This has superseded the Trent Biotic Index and the Chandler Score. The method is a 3 minute sample in all the habitat types. NB. the results are dependent on the sampling effort and sampling effectiveness in covering all habitats.Identification is to family level and no account is taken of abundance. Species which are least tolerant of pollution are given the highest score. The total score is then added up. To standardise the technique the final score is divided by the numbers of taxa to give the Average Score Per Taxon (ASPT). This is thought to be more accurate as a larger sample is likely to have more families. Another criticism of these indicies is that they vary seasonally, however Armitage et al (1983) found no such effect though Zamora-Munoz et al (1995) working in Spain found temperature did affect the results of ASPT, but not BMWP.

A Simplified BMWP (Biological Monitoring Working Party) Score System

Phylum Family ScoreStonefly (Plecoptera) All except:Nemouridae 107Mayfly (Ephemeroptera) Leptophlebiidae 10

Ephemerellidae 10 Ephemeridae 10 Ecdyonuridae 10

Caddis flies (Trichoptera)Cased All except:Limnephilidae 107

Crustacea Astacidae (freshwater crayfish) 8

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Dragonflies (Odonata) All river families 8

Caddis flies (Trichoptera)Caseless Families range: 5-8

Psychomyiidae, Philopotamidae 8 Rhyacophilidae, Polycentropidae 7 Hydroptilidae 6 Hydropsychidae 5

Mayfly (Ephemeroptera) Caenidae 7

Molluscs nerite snail (Neritidae) 6 Banded snail (Viviperidae) 6 Freshwater limpet (Ancylidae) 6 Freshwater mussel (Unionidae) 6

Crustacea Freshwater shrimp (Gammaridae) 6

Damselflies (Odonata) Most river families 6Beetles and Water Bugs (Coloeptera and Hemiptera) Most river families 5Fly larvae (Diptera) All families except:Chironomidae 52Flatworms (Tricladida) All families 5Mayfly (Ephemeroptera) Baetidae 4Alderfly (Neuroptera) Sialidae 4Leeches (Hirudinea) All families except 3

Piscicolidae 4Molluscs Valve snail (Valvatidae) 3

Spire snail (Hydrobiidae) 3 Bladder snail (Physidae) 3 Ramshorn snail (Planorbidae) 3 Pea mussel (Sphaeriidae) 3

Crustacea Water hog louse (Asellidae) 3Oligochaeta True worms 1

ReferencesChesters, R.K. (1980) Biological Monitoring Working Party. The 1978 National Testing Exercise. Department of the Environment. Water Data Technology Memo, 19, 1-37

Biochemical Oxygen Demand (BOD)

Biochemical Oxygen Demand (BOD) is often used as an indicator of the pollution potential of organic wastes. BOD is a measure of the amount of oxygen required by micro-organisms to break down the biodegradable material present. It is an index of water quality: the lower the BOD the less organic pollution there is. The test involves collecting two water samples, the oxygen content of one is measured immediately, the second is

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incubated for 5 days at 20°C in the dark (to prevent any photosynthesis of diatoms etc.) after which the oxygen concentration is measured. The BOD is the difference in the oxygen concentration between the two samples.

Examples of typical BOD levels (mg/litre)

BOD(mg/litre)treated domestic sewage 20-60raw domestic sewage 300-400cattle slurry 10,000 - 20,000milk 140,000silage 50,000

Estimating the Biochemical Oxygen Demand using Methylene Blue (Byrne, 1993)1. Use a 250ml sample bottle with a ground glass stopper. Fill this completely with river water so no air gets in.

2. Add 1 ml of 0.1% methylene blue below the surface of the water and replace the stopper.

3. Gently shake the bottle to mix and incubate at 20°C in the dark.

4. Check the bottle at regular interval and note the time when the colour disappears.

Project suggestions

Projects involving river and pond work need to be carefully thought out as it is difficult to separate out any one environmental factor, they are all interrelated. The following gives some examples and where appropriate, the types of statistical analysis that can be applied to the data.

Rivers and streams1. Comparison of velocity with distribution2. Comparison of sediment size with number of different species or with number of a particular species

3. Colonisation of sediment4. Invertebrate drift over time.5. Size of Gammarus (freshwater shrimp) in relation to substrate size6. Distribution of species in one stretch of river7. Seasonal occurrence of mayfly species8. Distribution of species from an upstream to estuary site9. Influence of pollution10 Estimation of the Biochemical Oxygen Demand

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11 The Gammarus:Asellus ratio

12. Oxygen consumption of invertebrates13. The effect of plant detritus on organisms found on the stream bed.

Ponds1. Distribution of plants from the bankside into water2. Predation experiments3. Photosynthetic rate of microscopic algae using light and dark bottle method.4. Compare ponds from two different areas5. Oxygen concentration from the surface of the pond to the mud over 24 hours

6. Choice of macrophyte species by gastropod molluscs

Other1. Effect of oestrogenic substances on freshwater organisms:2. Feeding rate of blackfly larvae

PROJECTS1. Comparison of velocity with distribution:

Many studies have shown that certain species are found in areas of high velocity and others found in slower flowing areas (Brooker and Morris, 1980, Edington, 1968, Scullion et al, 1982) although it is suggested that velocity is of secondary importance in the distribution of species when compared with substrate and food availablility (Ulfstrand 1967 , Cummins and Lauff 1969 ). This work suggested that the microdistribution of invertebrates was influenced by velocity, physical and chemical factors in the water, food and substrate particle size but that the latter two factors were the most important.

The velocity recorded using a flow meter will be considerably greater than the velocity experienced by the organisms on the stream bed, most will avoid the current by using the boundary layer on the surface of the substrate, where the flow is greatly reduced.

Method: Measure the velocity using a flow meter or by timing a floating object over a metre distance. Sample the organisms at that point, using kick sampling or a surber sampler. Repeat at different flow rates. Record the number of a particular species, for example Ecdyonurus sp. (mayfly) or a net-spinning caddis, Hydropsyche sp (Edington, 1968)., or a blackfly, Similium spp (Hart, 1986, Armitage et al, 1974). See if there is any correlation between flow and a particular species. A Spearman's Rank Correlation coefficient can be calculated on the results if there are more than 7 sets of data.

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This graph would suggest a positive correlation between the flow rate and numbers of a particular species.

High riffle densities may be associated with requirements connected with respiration, or the lack of silt on the stone surface which may affect some species or prevent them attaching (eg Simulium spp.), or, in the case of predators, the occurrence of their prey.

Distribution of species from an upstream to estuary site

Changes in species from the headwaters to the estuary can be recorded, especially if it possible to find a stream where the distance between the headwaters and the estuary is not great. Try to standardise other abiotic factors (velocity, substrate, depth etc). Certain species are tolerant of salinity changes (eg. Gammarus spp) whereas others are intolerant. Other species which are usually found in saline water migrate up rivers (eg. the flounder, Platichthys flesus).

Draw bar charts or similar to compare the regions studied. Account for any patterns observed especially in relation to salinity. Similarity indicies and diversity indices can be calculated.

Influence of pollutionFind a site where there is an input of a pollutant eg from farming, from forestry, from a sewage works. Look at the effect of the pollutant on species diversity (eg Shannon Weiner and Simpson) and calculate biotic indicies (see Trent Biotic Index, Chandler Score and BMWP). The stream should be sampled upstream of the point of discharge, and immediately below the effluent plus further downstream until, if possible, the stream has recovered. Also measure abiotic factors which are appropriate (eg pH, nitrate levels). Remember the pollutant may not be continually being discharged and therefore chemicals may not be detected (see . Chemical and Physical tests for further details).

IMPORTANT: When handling potentially polluted water take precautions! see Risk Assessment for Field Work

Effects of organic pollution on rivers

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ReferencesAston, R.J. and A.G.P. Miller (1980) A comparison of populations of the isopod Asellus aquaticus above and below power stations in organically polluted reaches of the River Trent. Freshwater Biology, 10, 1-14Byrne, K (1993) Biology of Freshwater Pollution. Ecology and Environmental Science Factsheet. Number 5. The Environment Press.

Chandler, J.R. (1970) A biological approach to water quality management. Water Pollution Control 1970, 415-421Davies, L.J. and Hawkes, H.A. (1981) Some effects of organic pollution on the distribution and seasonal incidence of Chironomidae in riffles in the River Cole. Freshwater Biology, 11, 549-559Hynes, H.B.N. (1960) The Biology of Polluted Waters. Liverpool University PressLearner, M.A., Densem, J.W. and T.C. Iles (1983) A comparison of some classification methods used to determine benthic macro-invertebrate species associations in river survey work based on data obtained from the River Ely, South Wales. Freshwater Biology, 13, 13-26

Mason, C.F. (1996) The Biology of Freshwater Pollution. Longman. 3rd Edition.Scullion, J., and R.W. Edwards (1980) The effects of coal industry pollutants on the macro-invertebrate fauna of a small river in the South Wales coalfield. Freshwater Biology, 10, 141-162Watton, A.J. and Hawkes, H.A. (1984) Studies on the effects of sewage effluent on gastropod populations in experimental streams. Water Research, 18, 1235-1247Whitehurst, I.T. and B.I. Lindsey (1990) The impact of organic enrichment on the benthic macroinvertebrate communities of a lowland river. Water Research, 24, 625-630

Woodiwiss, F.S. (1964) The biological system of stream classification used by the Trent River Board. Chemistry and Industry, March 14, 1964, pp443-447Wright, J.F., Moss, D, Armitage, P.D. and Furse, M.T. (1984) A preliminary classification of running-water sites in Great Birtain based on macro-invertebrate species and the prediction of community type using environmental data. Freshwater Biology, 14, 221-256

FIELDWORKS SPECIES DISTRIBUTION MODULE HELP:

ContentsHow to..... information

Worked examples ( eg.species distributions)

Background information on a range of habitats

Sand dunes, Salt marshes, Rocky shores,, Moorland

Information on species adaptations and distribution patterns

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Sand dune species, Rocky shore species, Salt marsh species Moorland species

Help system contents

Tutorial

Sample data files

Setting preferences

Program support

Minimum system requirements

About FieldWorks

About Hallsannery Field Centre

Licence and Warranty

Edition

1. SAMPLING METHODS

It is impossible to look at everything in the area to be examined unless it is small, therefore a sample should be examined. It can also be very destructive if the whole area is trampled over.The sampling method used depends on the habitat to be studied and the aims of the investigation. Sampling must be random or systematic to eliminate bias, throwing a quadrat is haphazard, not random..

1.1.Where there is an environmental gradient

A transect (ie. sample line)is generally used to investigate changes in flora and fauna (ie zonation) which may be related to a change in a habitat factor causing an environmental gradient to occur. For example a change in the salinity of soil water across a salt marsh can affect the distribution of salt marsh vegetation.The line of the transect should be placed at right angles to the zones and can be either systematic or random

1.1.1.Systematic

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LINE TRANSECTData is collected along a single line and records are made of every plant that touches

the line. This is unlikely to give a comprehensive picture of the site.CONTINUOUS BELT TRANSECTA belt usually 0.5 or 1m in width is laid out and everything recorded, usually using

sequential quadratsINTERRUPTED BELT TRANSECTEverything in a belt of chosen width is recorded at fixed intervals or interrupts (the

size of the interrupt, or interval, is variable, depending on the habitat, the rate of change of vegetation types or environmental variables, time, etc.) along the transect line

1.1.2 .Random - STRATIFIED RANDOM SAMPLINGThe areas (or strata) to be sampled along the transect are chosen. These could be,

for example at 10m intervals. At each strata quadrat(s) are placed along a line at right angles to the

transect line, using random numbers from a random number table . A coin can be used to decide whether to go

left or right of the line.

1.2Where no environmental gradient occurs

1.2.1.Systematic

The use of random sampling can result in some important areas of ground not being sampled. Systematic sampling can overcome this. An example of systematic quadrat sampling is to record at regular intervals across a grid

1.2.2.Random

Random numbers should be used

a. Grid method

Lay out a grid of eg 10m x 10m. Always using the same axis as x and y, choose 2 random numbers as the x and y coordinates, this is the position of the quadrat. Repeat for subsequent quadrats

b. Random walking

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Use pairs of random numbers for the number of paces in one direction and then the number at right angles(either left or right depending on the spin of a coin).Subsequent quadrats are located in the same way, preferably starting from the same place each time. Walking over the sample grid in this way causes high levels of trampling, which may not be acceptable.

2. What do we use to take our samples?

A quadrat is used. These can be either frame or point quadrats. Size of frame quadrats vary and the correct size to use should be worked out for each particular habitat to be studied. A point quadrat consists of a single bar with 10 holes, usually spaced every 5 cm, through which pins can slide freely and vertically.

FRAME QUADRAT

a. Size of quadrat

The size, morphology and distribution pattern of the plants being sampled influence the choice of quadrat size. As a general rule a small quadrat tends to be used if the species are small and numerous and a large quadrat is chosen for species which are large and/or thinly scattered.

To find the optimum size of quadrat to use :

1. When sampling an area use the smallest frame first and count the number of species in this first quadrat

2. Double the size of the first quadrat. Record the number of new species found.

3. Continue to double the quadrat size and count the new species present until there is no significant increase in the number of new species.

4. Plot a cumulative frequency curve. The optimum size of quadrat which is suitable to use for the habitat is the point at which the curve levels out on the graph. From this point an increase in the size of the quadrat is unlikely to record new species unless another habitat is entered. This method saves wasted effort when the quadrat is too large and avoids inaccurate records caused when the quadrat is too small.

b.Number of quadrats (or samples)

If only one size of quadrat is available then the correct number of quadrats of that size needs to be worked out for each area sampled in the same way as for correct quadrat size.

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Put out a series of quadrats and count the number of species in the first. Then for each successive quadrat count the number of new species. A graph of number of quadrats (x axis) versus cumulative number of species (y axis) can be constructed.

c. Divided quadrats

Quadrats are often subdivided, for example a 50cm x 50cm quadrat can be subdivided into 100 5 x 5cm units. These are most useful when recording frequency and cover (see later). They are not suitable for use when the vegetation is high as it is then flattened or when animal species need to be removed from the quadrat to be recorded (eg on the seashore).

POINT QUADRATS

When using a frame quadrat for assessment of plant cover the result tends to be subjective. A more objective approach can be achieved by reducing the area of the quadrat to a very small area ie a point. This is useful when plants are small but not suitable for vegetation that is taller than the bar of the frame. Care should also be taken to avoid trampling the area to be sampled. One, few or all pin positions can be used at each sample site.

Point quadrats can be used for determining abundance of vegetation in a number of ways:

1. Total cover

All ‘hits’ of the pin are recorded as it is lowered to the ground. This gives a measure of the bulk of plant material and can be related to yield and biomass. This method is detailed and lengthy.

2. Percentage cover

The first ‘hit’ on each new species is recorded. The records for each species are then represented as a percentage of the total number of point quadrats recorded (ie hits and misses)eg

The sum of the figures for percentage cover for all the species is likely to be greater than 100% because of overlap between aerial parts of adjacent plants.

3. Top cover

(Canopy shading)Only the first hit of each pin is recorded. For each species the percentage cover can be calculated

4. Frequency

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For each point record whether a species is present or absent ie whether or not the species is ‘hit’ as the pin is lowered to the ground. This is then expressed as a percentage of the total number of hits and misses (ie total number of point quadrats)

Disadvantages:

1. Should not be used when the vegetation is higher than the bar.

2. Plants with different shaped leaves may be over- or under-estimated

3. The vegetation must not be trampled.

FRAME AND POINT QUADRATS can be used together in the following ways:

1. Use a divided quadrat and the points are the intersections of the lines. This can be used to assesss top cover (percentage cover) within the area of the frame quadrat. This is less subjective than a frame estimate of cover

2. Use a pin frame systematically across the quadrat area.

3. How should the data be recorded ?

3.1. Qualitative or subjective measurements

3.1.1. The simplest method would be to list the species present

3.1.2. The relative abundance of each species can be used eg ACFOR (A=abundant, C= common,

F= frequent, O= occasional, R=rare) The terms are defined and applied anew at each site with considerable personal bias. The ACFOR scale has been quantified for seashore organisms and in this case can be used for some statistical tests.

Disadvantages

As this is used on subjective estimates it cannot be used where abundance data is processed mathematically.

Some organisms tend to be over-estimated eg. plants with large leaves or those in flower

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3.2.Quantitative

These are essential if tests of significance are to be applied and if mean scores are calculated.

3.2.1. DENSITY The number of plants, clumps, flowering shoots per unit area.

The number of individuals of the species can be counted in randomly placed quadrats.The data can be used in the following ways

a. The average number of individuals per quadrat is determined and density is expressed as numbers per unit area (eg per m2, hectare etc) which allows for comparison between sites.

b. Density is expressed as a total number for the whole area.Higher numbers of quadrats give increased accuracy.

A population estimate can be made for an individual species :

Estimated population (N)= area of plot x total no. of plants counted /area of quadrat x no. of quadrats

Disadvantages:

1. Can only be used for plants which can be easily identified as separate, individual plants , not plants with rhizomes, tillers, stolons etc. Also difficult to use in dense clumps of vegetation.

2. Measuring density is very time consuming.

3.2.2. PERCENTAGE COVER

Proportion of the ground covered by the aerial parts of a plant species. It is usually expressed as a percentage of the total quadrat area and a mean value for each plant species at a given site can be calculated.Three ways of estimating:

a. Using scales - these are semi-quantitative.

Cover-abundance scales such as Domin and Braun-Blanquet have a subjective element.

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b. Direct estimate: disadvantage of personal bias and is unlikely to produce accurate results though can be used when the divided quadrat cannot.

c. Divided quadratThe frame quadrat is divided into sub-quadrats. All sub quadrats in which the species occupies more than 50% of the area is recorded.

Disadvantages:

1. Difficult where vegetation is tall or of different heights.

2. Smaller species are often ignored

3. Large leafy plants are often overestimated

4. Time consuming unless only a small number of species are being sampled.

3.2.3.FREQUENCY

This is the number of percentage of sampling units (eg quadrats) in which the species occurs. Therefore if a species has a frequency of 20% it should occur once in every 5 quadrats, providing the sample is large enough.It can be measured using a divided quadrat in which case it is known as local frequency.Frequency should also be standardised into root or shoot frequency. Root frequency records a plant as present if it is rooted in the quadrat (which relates to density) and shoot frequency records a plant as present if part of its leaves overlap into the quadrat (which is related to cover)Its advantage is that it is quick and reduces the problem of under estimating small plants.

Disadvantages:

a. The size of quadrat used can influence the results.

b. Size of the individuals of different species ie larger species with the same density as a smaller species will often have a higher frequency.

c. Spatial distribution of the individuals, eg if plants are clumped their frequency will be reduced.

References

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Chalmers and Parker (1986)The OU Project Guide. Field Studies Occasional Publication N0.9. Field Studies Council.

Sampling - belt transectA belt usually 0.5 or 1m in width is laid out and species recorded along the belt using consecutive quadrats

transect line

ACFOR scale

AbundantCommonFrequentOccasionalRare

or sometimes used as DAFORDominantAbundantFrequentOccassionalRare

These abundance scales are the most subjective and descriptive methods of describing vegetation abundance, unless the categories are well defined.

A rocky shore version, with precise definitions,using different quadrat sizes for different groups of species and adding two extra categories Extremely abundant and Super abundant, is given in Chalmers and Parker,(1986)

Categorical measurements

Categorical measurements are where you can say that an object belongs only to a category.eg.

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if investigating sea anemone distribution, possible categories might be 'in a rock pool' or 'out of a rock pool'

Statistical references Interval measurementsOrdinal measurements

Statistics references

Chalmers, N. and Parker, P. (1986). The OU Project Guide. Fieldwork and Statistics for Ecological Projects. Field Studies Council Occasional Publication No.9.Field Studies Council. 108pp. Ebdon, D.(1988). Statistics in Geography. Blackwell, Oxford.232pp.Hampton, R.E. (1994). Introductory Biological Statistics. W.C.Brown. Oxford,. 233pp.Hutcheson, K. (1970). A test for comparing diversities based on the Shannon formula. Journal of Theoretical Biology 29, 151-4

Kent, M and Coker,P.(1992). Vegetation Description and Analysis : A Practical Approach. Belhaven Press, London.363pp.Lenon, B.J. and Cleves, P.G.(1983). Techniques and Fieldwork in Geography. Bell and Hyman, London.124ppMcIntosh, R.P. (1967). An index of diversity and the relation of certain concepts of diversity. Ecology 48, 392-404.Magurran, A. (1988). Ecological Diversity and its Measurement. Croom Helm. London.Slingsby, D and Cook, C.(1986). Practical Ecology.Macmillan Education, Basingstoke. 213pp

Williams, B.W. (1993). Biostatistics : Concepts and Applications for Biologists. Chapman and Hall, London. 201pp.

Interval level measurements

Interval level measurements are variables such as length, height, width, weight, etc. Further reading is recommended in Statistical references

Ordinal level measurements

Ordinal level measurements are measurements where there is an element of subjectivity eg, the Powers scale of Roundness where it is possible to say that any sediment has a particular Powers score and to say that one piece of sediment has a higher score than another and therefore the values can be ranked, or ordered .It is not however possible to say that a Powers score of 4 represents a roundness twice that of a score of 2 . Other examples would include the Domin, Braun-Blanquet and ACFOR scales of vegetation abundance.

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Categorical measurements

Categorical measurements are where you can say that an object belongs only to a category.eg. if investigating retail distribution, possible categories might be shop types or function.

Profile data recording

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Ranging poles

1. The simplest method is to use two ranging poles, preferably marked in centimetres so that readings can be taken directly, and a piece of string held taught and level to determine height differences.

The disadvantage of this method is that it is difficult to keep the string taught over distances greater than about 15m. Levels of accuracy are reasonable, but not as good as those obtained using expensive professional levelling equipment. Hanging spirit levels are readily available from builders merchants and DIY stores. Similar results can be obtained using a water level, instead of the string and hanging spirit level.

Sample recording sheet for profiling:

Station nº D1 D2 H1 H2 H1-H2 Total height

RankingPole

H 2

Hanging spirit level

string

D 2

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RankingPole

H 1

D 1

TRANSECT DATA - TYPICAL METHODS OF DATA PRESENTATION

Transect data is traditionally represented as either kite diagrams, bar charts or centralised bar charts.

The choice of presentation depends on the type of data being plotted, eg.whether it is :

- vegetation data recorded as %cover, % frequency, shoot number, etc - vegetation data recorded as Domin class, Braun-Blanquet class, ACFOR class, - numbers of animals

- the distance between sampling stations - personal preferences and perceived ease of interpretation

Good summaries of the options and the advantages and disadvantages of each method can be found in the references given below.Plotting graphs

References Crothers, J.H. (1981). On the graphical presentation of quantitative data. Field Studies,5, 487-511Slingsby,D and Cook, C.(1986) Practical Ecology.Macmillan Education, Basingstoke. 213pp

Kite diagrams

Kite diagrams are used to present transect data as they are visually effective

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and are relatively easy to interpret eg.some simple seashore data.

The data value for each station is divided in half, and half plotted above the centre line and half below as points; the points are then joined and the resulting kites shaded.There are however some problems with their use which are discussed in Crothers (1981).The problems include the assumption that by drawing a line between stations that there is a relationship between the stations and that sampling at any point in between would give a value approximating to that indicated by the line. This may not be a problem over short distances, or where species distribution changes gradually, but over long distances and where vegetation changes abruptly eg. between dunes and slacks, kite diagrams may not be appropriate.

- if using ordinal data ie. a scale such as Domin, Braun-Blanquet or ACFOR, then mathematically meaningful lines cannot be drawn between different sampling stations - this is because, for example, using the Domin scale a 10 would indicate 91-100% and a 5 would indicate 11-25%, and though 5 is half of 10, 11-25% is not half of 91-100%. Similar problems apply to all scale data and in addition it is not possible to take a mean of this type of data directly.

There is a transformation available for the Domin scale to allow mathematical operations to be performed and the above limitations do not apply if using the transformed Domin .

Plotting graphsReferences

Bar charts

A typical example is given from a rocky shore. No assumptions are made about species abundance between sample stations.

Plotting graphs

Domin scaleVegetation abundance is often measured using subjective scales such as the Domin.There are several published variations of the Domin scale eg. Kent and Coker(1992) and Slingsby and Cook(1986) which differ slightly at the lower end of the cover scale. The table below is a composite.The Domin scale translates estimates of %cover value for a given species into a score and is often used to allow different recorders to produce similar, reproducible results. It is a non-linear score and therefore theoretically should not be used in any mathematical or statistical calculations unless it is transformed

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% cover Domin Score

91- 100 1076-90 951-75 834-50 726-33 611-25 55-10 4<5% many individuals 3< 5% several individuals 2< 5% few individuals 1

The scale as originally published includes a lowest value of + , and this is not acceptable to any computer package for data analysis or presentation; it is therefore suggested that if required , any vegetation present at a cover value of less than 5%, with only 1 or 2 individuals should be recorded as 0.5, or alternatively simply recorded as a 1 in the Domin scale.

Domin references

Sand dunes : typical transect data

This data was collected using stratified random sampling, with dunes and slacks as the main strata.A 10m x 10m grid was placed in each strata and quadrat positions determined using random numbers as x y co-ordinates. Quadrat size was 50c x 50cm and vegetation was recorded using the Domin scale.Presentation uses a bar chart as distance between sample stations was >100m - using a kite diagram would make unreasonable assumptions about species distribution between sample sites.

The Shannon-Weiner Index () of species diversity

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NB.If species abundance data is recorded using an abundance scale, such as the non-linear Domin scale, the data should not theoretically be used in any mathematical or statistical calculations unless it is transformed . This applies to any ordinal data.

The index is calculated using the formula :

H = - sum of (Ni / N x ln (Ni / N)

In the species distribution module, the diversity of each sample station or site can be calculated

(see how to calculate species diversity..) This is particularly useful for investigating changes in species diversity with succession, or with changes in environmental factors in general. It is useful to compare the results obtained with the Simpson Index , Margalefs Index. and the McIntosh Index

It can be simply calculated by hand if the data is arranged in a similar format to the table below :

Worked example for a single site where data on abundance of several species has been collected:

Species Number of individuals Proportion ln Proportion Proportion x ln ProportionNi Ni/N ln(Ni/N) Ni/N x ln(Ni/N)

---------------------------------------------------------------------------------------------

a 24 0.216 -1.53 -0.33

b 12 0.108 -2.22 -0.24

c 29 0.261 -1.34 -0.35

d 2 0.018 -4.02 -0.07

e 7 0.063 -2.76 -0.17

f 37 0.333 -1.10 -0.37

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--------------------------------------------------------------------------------------------- N= 111 = 1.53

This is probably the most widely used non-parametric diversity index. Reliability increases as sample size increases, with greatest reliability if the sample size is above 200.

depends on both the evenness of distribution and abundance of species and the range is from 0 to a maximum value of about 5 (using natural logarithms).

The evenness of distribution (E) is calculated by :

E = / lnN

It is possible to use a t-test to test for significant differences between species diversities in two samples.

It is possible to use a t-test to test for significant differences between species diversities in two samples.

The Simpson Index of Species Diversity

NB.If species abundance data is recorded using an abundance scale, such as the non-linear Domin scale, the data should not theoretically be used in any mathematical or statistical calculations unless it is transformed . This applies to any ordinal data.

This index is based on the probability that two individuals picked at random will belong to the same species .For any sample :

C = å Ni (Ni-1) / N (N-1)

In the species distribution module, the diversity of each sample station or site can be calculated (how to calculate species diversity). This is particularly useful for investigating changes in species diversity with succession, or with changes in environmental factors in

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general. It is useful to compare the results obtained with Margalef’s Index ,the Shannon-Weiner Index. and the McIntosh Index

Species Number of individuals

Ni Ni(Ni-1)

--------------------------------------------------------------------------------------

a 24 24(23)

b 12 12(11)

c 29 29(28)

d 2 2(1)

e 7 7(6)

f 37 37(36)

-------------------------------------------------------------------------------------- N=111 åNi(Ni-1)=11787 C = 11787 / 111(110)

C = 0.49

NB. Often the reciprocal is used so that higher values represent greater species diversity D= 1/C = 2.03

The Sorenson Qualitative Index of Similarity (Cn)

This is generally used for a simple comparison of species composition of two sites. It uses only presence or absence of a species and takes no account of species abundance.

Cn = 2j / (a+b)

where j = the number of species common to both sites; a= the number of species at site a;

b= the number of species at site b

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It can easily be calculated in the Species Distribution module (see how to...)

The following data set represents the numbers of individuals of each species found at two separate sites:

sp1 sp2 sp3 sp4 sp5

-----------------------------------------------------------------------------------------

Site a 23 0 3 8 15

Site b 0 12 0 18 1

-----------------------------------------------------------------------------------------

j = 2, a = 4, b = 3

Cn = 2 (2) / 4 + 3

Cn = 0.57

The index will have a minimum value of 0 and a maximum value of 1, where the same species occur at each site.

For more detailed comparisons, use Sorensons Quantitative Index.

The Sorenson Quantitative Index of Similarity (CN)

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This is different from the Sorensons Qualitative Index in that it uses the abundance of species, rather than simply their presence or absence.

CN = 2 x sum of jN / ( sum of aN + sum of bN)

where

sum of jN = the sum of the lower of the two abundances for species occuring at both sites

sum of aN = the sum of the abundances of all species at site a

sum of bN= the sum of the abundances of all species at site b

It can easily be calculated in the Species Distribution module (see how to...)

The following data set represents the numbers of individuals of each species found at two separate sites:

eg. the following data set represents the numbers of individuals of each species found at two separate sites:

sp1 sp2 sp3 sp4 sp5

-----------------------------------------------------------------------------------------

Site a 23 0 3 8 15

Site b 0 12 0 18 1

-----------------------------------------------------------------------------------------

sum of jN = 8 + 1 = 9

sum of aN = 23 + 3 + 8 + 15 = 49

sum of bN = 31

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CN = 2 (9) / 49 + 31 = 0.225

The index will have a minimum value of 0 and a maximum value of 1, where the same species occur at each site with the same abundance values.

FIELDWORKS : HYDROLOGY MODULE HELP

ContentsAs well as the usual how to.. information , the Help system contains detailed background information on a range of river features and a case study of the Lyn drainage basin

How to....use Fieldworks hydrology moduleIndex of how to...

Planning an investigationField methods

Channel cross section measurementVelocity measurementBedload measurementField recording sheetsSafety

Stream characteristics and featurespoolsrifflesmeandersbankfull level

overland flowflooding

Case studiesLyn drainage basin

GlossaryReferencesEdition

Pools/Riffles

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Riffles normally occur at spacing of between 5 to 6 times the channel width (Waugh, 1990)

Pool/riffle sequence on R.Yeo, North Devon. River width is approximately 5m and riffle spacing is 20-30m

MeandersA typical meander (Jan.1998)on an upland stream on Exmoor, with deposition on the inside of the bend and erosion on the outside

Bankfull levelWhen a river is flowing at bankfull level,this means that the river channel is full and it is close to spilling over on to the flood plain. it represents the most effective discharge for sediment transport and maintenance of channel width(Callow and Petts, 1994) and the highest energy state.

Bankfull discharge generally has a return period between 1 and 3 years, but may be longer - up to 32 years on some rivers (Callow and Petts, 1994)

Tributary of R.Torridge, North Devon, at bankfull (December 1997)

Overland flow occurring after several weeks of heavy rain, December 1997 atHallsannery, Bideford, North Devon

DESCRIPTIVE STATISTICS (STATISTICS MODULE)

Mean

The mean is an 'average' of a group of values and is obtained by adding the values together and dividing the total by the number of individuals

Interpretive Solutions Ltd.1999, 2000

Sample standard deviation

The sample standard deviation measures the spread of the measurements around the mean

If a set of data has a near normal distribution then approximately :68% of the values will fall within one standard deviation of the mean (+ and -) 95% of the values will fall within 2 standard deviations (+ and -)100% will fall within 3 standard deviations of the mean (+ and -)

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A small standard deviation indicates that there is little variation from the mean and that the population is fairly homogenous.

The variance of the sample, , can then be calculated if required.

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