University of Amsterdam
Faculty of Science
Wageningen University and Research Centre
Alterra Research Institute
Climate Change & Adaptive Land & Water
Management
Taking the bite out of ‘de Peel’;
Determination of the environmental conditions
that sustain high numbers of Aedes cinereus larvae
in the peat bog areas surrounding Griendtsveen.
Bachelor Thesis
Brian Wals
Supervisor:
Dhr. prof. dr. ir. Piet Verdonschot
Abstract
The town of Griendtsveen (NL) has been exposed to a mosquito nuisance for the past
years and Aedes cinereus has found to be the most abundant mosquito species within
this region. Though research has been done locally on numbers of mature mosquitos,
there is little known about the mosquito larvae and their preferred habitat. This
research aimed to find out what environmental conditions sustain high numbers of
Aedes cinereus larvae. This was done by acquiring data via fieldwork over a period of
6 weeks in the peat bog area surrounding Griendtsveen and subsequently conducting a
statistical analysis of models based on this data. Results showed with a reliability of
39.52% that with regard to substrate content: leaves, mosses, wood, algae and grass
correlated with the number of Aedes cinereus larvae found; as did trees, high grasses
and low grasses with regard to vegetation type. Furthermore, with regard to water
cover, mosses showed a correlation and the variables: DO content, water temperature
and water depth did as well. These new insights can be used for mitigation of the
nuisance or form the basis for further research on this species.
Contents
1. Introduction ...1
1.1. Research question ...2
1.1.1. Research sub-questions ...2
1.2. Aim of the research ...2
1.3. Hypothesis ...2
2. Material & Methods ...3
2.1. Peatland surrounding Griendtsveen ...3
2.2. Data ...4
2.2.1. Mosquito larvae numbers ...4
2.2.2. pH, conductivity, dissolved oxygen concentration and water temperature ...5
2.2.3. Water depth ...5
2.2.4.
Substrate content and properties, vegetation cover and type, water cover and
weather.
...6
2.2.5. Water colour ...6
2.3. List of materials ...7
3. Results ...7
3.1. Environmental conditions ...7
3.2. Aedes cinereus catches ...7
3.3. Correlation between number of Aedes cinereus larvae found and environmental conditions ...8
4. Discussion ...11
5. Conclusion ...11
6. Acknowledgements ...12
References ...12
Appendix
A. Template for digital data copy ...13
B. Newspaper article ‘de Limburger’ (May 28th, 2016) ...14
C. R-code for statistical analysis ...15
D. Master Dataset ...17
1. Introduction
The town of Griendtsveen, located on the
border of the provinces Noord-Brabant and
Limburg, has in recent years experienced
nuisance by mosquitos. The mosquito
population is for the greatest part made up
by marsh mosquitos (82%) and particularly
by the species Aedes cinereus (72%)
(Verdonschot et al., 2015). The most likely
reason for the increase in mosquito
numbers is the hydrological measures
taken to improve the nature quality for peat
formation and the accompanying formation
of temporal pools (Knotters et al., 2008).
This development mainly consists of water
level rise by filling up ditches and trenches
and closing canals to recreate peatland.
This peatland now functions as breeding
places for marsh mosquitos. Breeding
locations for marsh mosquitos are typically
characterised by shallow, isolated, seasonal
waters containing a high amount of organic
material, low oxygen levels and thus few
predators.
This research aimed to find out under what
conditions most of the Aedes cinereus
mosquitos originate within the peatland.
This was done by checking if certain
environmental conditions could be linked
to
mosquito
larvae
numbers.
Environmental conditions that were taken
into account in this research are: dissolved
oxygen, pH, conductivity, colour, substrate
type, and water temperature and depth. For
this research data was ascertained by
conducting field work. This field work was
done during the period of April 19
thmeasuring a multitude of environmental
parameters.
Very little previous research has been done
on Aedes cinereus mosquitos. Previous
research that has been done in this area
was conducted by Alterra Research
Institute and investigated the mosquito
nuisance as a whole. This included the
monitoring of the nuisance, an indication
of to what extent the nuisance was present
and advice with regard to the effects of
measures that were taken. The research
confirmed what species of mosquitos were
present in the area and to what extent each
species was present. It was also concluded
that the range of the mosquitos was 1.5 –
2km (Verdonschot et al., 2015).
Furthermore Verdonschot et al. (2015)
stated that the Aedes cinereus sucks blood
from mammals and birds. Also they note
that eggs are deposited in the period of
June-September in low lying regions that
flood in periods of rain and that, on
average, 60 eggs are laid at a time. The
eggs hatch in April, regardless of whether
they are submerged in water earlier on or
not.
This research provides new insights by
being able to predict breeding locations
within a peatland area. Also it can form the
basis for further research or for the
mitigation of nuisances formed by Aedes
1.1
Research question
What environmental conditions determine
Aedes cinereus larvae number in the peat
bog area surrounding Griendtsveen?
1.1.1 Research sub-questions
1. Which environmental conditions
are present in the peat bog area
surrounding
Griendtsveen?
.
2. Where are most Aedes cinereus
found in the peat bog area
surrounding
Griendtsveen?
.
3. Is there a correlation between the
number of Aedes cinereus larvae
found at a location and the
environmental conditions that are
present?
1.2 Aim of the research
The main objective of this research was to
gain insight in the relation between number
of Aedes cinereus larvae present within a
location and the environmental conditions
that are present within that same location.
This research can be used in order to
predict the number of Aedes cinereus
larvae within an area based on present
environmental conditions thus making it
easier to assess population size. Insights
can be used for further research regarding
the Aedes cinereus or for the mitigation of
nuisances formed by Aedes cinereus.
1.3 Hypothesis
that high numbers of Aedes cinereus larvae
will be present in locations that contain
shallow water and have low concentrations
of dissolved oxygen (Verdonschot et al.,
2015). Shallow water is expected to
correlate with higher water temperatures
which are favourable for the development
of the larvae as they are
(meso-)thermophilic (Verdonschot et al., 2015).
Low concentrations of dissolved oxygen
may cause fewer predators to be present
and are expected to be the result of large
amounts of organic material on the bottom
of the water (Verdonschot et al., 2015).
Because
Aedes
cinereus
is
slightly
acidophilic it is expected that they are
more common in water with pH levels < 7.
Due to the fact that there is very little
scientific knowledge available about the
Aedes
cinereus
it
is
unknown
if
conductivity will have any influence on
larvae number. It is however expected that
fewer Aedes cinereus larvae will be present
in water that has high amounts of moss or
algae cover.
2. Material &
Methods
2.1. Peatland surrounding Griendtsveen
‘De Peel’ is a region in the Southeast of the
Netherlands located on the border of the
provinces of North-Brabant and Limburg.
The region is best known for peat
extraction, a process that had been going
on since the Middle Ages but is currently
no longer done. The region is characterized
by many canals, which were dug for the
purpose of peat extraction. The exact
locations of the peatbog area that were
researched
are:
Deurnsche
Peel,
Kanaalbos,
Grauwveen
and
the
compartments Driehonderd
Bunders and Horster Driehoek within the
Mariapeel. All these locations are within a
2
km
radius
from
the
town
of
Griendtsveen, which has experienced a
considerable mosquito nuisance over the
past years.
The region has undergone some changes as
measures were taken that coincide with the
LIFE+ project. The LIFE+ project is the
European Union’s nature conservation
financial instrument which is used for the
conservation of Natura 2000 areas.
Conservation efforts are commissioned by
Staatsbosbeheer and carried out with
financial support from LIFE+ and the
province Limburg. Measures that have or
will be taken consist of creating shallower
water bodies, the installation of quays,
deforestation of circa 8 ha of woods, an
increased overall water level, further
partitioning of the area and the installation
of a dry compartment.
Current environmental conditions in the
region vary greatly dependent on the
amount of water that is present. Large
bodies of water alternate with forests that
vary in denseness. Vegetation in the area
consists mostly out of purple moor-grass
(Molinia caerule), the grass-like plant
Juncaceae Juncus effucus, commonly
known as pitrus, and woods consisting
mostly out of birch (Betulaceae Betula)
trees. In addition: the dominant moss
species in the area consists of the genus
Sphagnum, commonly known as peat
moss. Heather is also present in certain
areas, as is colonial bent (Agrostis
capillaris
).
2.2. Data
The data used for this research were
acquired between the 19
thof April and the
27
thof June by fieldwork. Fieldwork was
carried out by Keizer (2016), De Meyer
(2016) and Wals (2016) as their bachelor
theses required the same type of data.
Accommodation was provided by Alterra
in the town of Griendtsveen in order to
maximize efficiency during the timeframe
of the research. During the research period
59 locations were sampled either once or
twice. For the sake of convenience a
cinereus was determined for each location
as were the environmental conditions:
dissolved oxygen (DO), pH, conductivity,
colour, substrate content, vegetation cover
and water depth. Different methods were
used for every parameter. A digital copy of
the acquired data was made according to
the spreadsheet found in appendix A
Parameters and accessory measurement
methods that are relevant for this research
will now be addressed and a list of
materials that were used can be found in
subsection
2.3.
2.2.1. Mosquito larvae numbers
For the larval survey a white plastic dipper
equipped with a long solid aluminium
handle, also known as a Clarke dipper, was
used. While operating the Clarke dipper
there are several dipping techniques that
can be appropriate. The dipping methods
that were used during this research were
classified by O’Malley (1995) as ‘partial
submersion’ and ‘flow-in’. Which method
was used per location depended on the
water depth that was present. According to
O’Malley (1995) during partial submersion
“the dipper is submerged at approximately
45° along the emergent vegetation. Water
flows rapidly into the dipper. The dipper is
not moved horizontally. The dipper can be
moved vertically to scrape along the edge
of emergent vegetation.” Flow-in on the
other hand “is used in shallow water that
has a depth < the height of the ladle on the
dipper. The bottom of the dipper is pushed
During sampling an effort was made to
approach the site while facing the sun,
moving vegetation only when necessary
and using quit slow steps. This was done in
order to minimize disturbance of the water
surface as disturbance will stimulate
immature mosquitos to dive from the water
surface (Workman and Walton, 2003)
After a sample had been taken the water
was undone from larger organic material
and subsequently was poured through a
screen. This left the larvae exposed thus
making it easier to place them into the
container that was designated to the
location. This placement was done making
use of tweezers. The container was a
plastic bottle was partially filled with
alcohol in order to simplify species
determination. Species determination was
carried out by Alterra Research Institute at
their lab in Wageningen.
2.2.2. pH, conductivity, dissolved oxygen
concentration and water temperature
Dissolved oxygen concentration and water
temperature were measured in the field
with a Hach Sension 156 Portable
multiparameter meter. This was done by
taking a water sample with the Clarke
dipper and placing the DO probe and
temperature probe in it.
The pH and conductivity were also
measured with a Hach Sension 156
Portable multiparameter meter. However,
they were not measured in the field
because it was a very time consuming
process. Therefore water samples were
completed. All values given by the meters
were rounded up or down to 2 decimal
places for practical reasons.
2.2.3. Water depth
Water depth was measured by a ruler.
During the measurements the ruler was
lowered into the water until the bottom was
reached. No additional pressure was
exercised upon the stick so that the first
bottom was the one measured. Additional
water underneath a potential false bottom
was not taken into account. Measurements
were rounded up to a multitude of 5 cm to
prevent
unjustified
accuracy
from
occurring.
Image 2.2: A Hach Sension 156 Portable
multiparameter meter with DO probe
attached to it. (
www.coleparmer.com
)
2.2.4. Substrate content and properties,
vegetation cover and type, water cover
and weather.
When assessing substrate content and
properties a scale of 0-5 was used to
quantify the data. The values 0-5 can be
interpreted as followed: 0 = 0-5%, 1 =
5-20%, 2 = 20-40%, 3 = 40-60%, 4 =
60-80%, 5 = 80-100%. The assessment of the
substrate was done by ‘digging’ into the
soil with the Clarke dipper and exposing
the content on the surface besides the
water. Subsequently, the substrate was
valued through estimation by sight for the
following
vegetation
related
characteristics: leaves, wood, water plants,
mosses, algae and grass. After the content
had been determined the substrates
properties with regard to the extent of its
decay were assessed. This was done by
valuing the percentage of fine particulate
organic matter (FPOM) and coarse
particulate matter (CPOM) that the
substrate consisted of. This was also done
by means of estimation by visual
observation.
The same method was used to determine
the vegetation cover. Vegetation cover was
estimated for: trees, bushes, heath, high
grasses, low grasses and mosses. Water
cover was subsequently determined by
estimating the percentage of mosses and
algae present on the water surface and
placing them in the scale of 0-5.
Weather was monitored in order to account
wind was present and, if present, sunshine
or
rain.
2.2.5. Water colour
In order to determine the colour of the
water a sample was taken at every location
in a clear plastic container. When all
locations had been sampled the containers
were aligned and categorized on a scale of
1-3. This was done under even lighting
conditions to avoid. Examples of colours
that the samples could have can be found
in image 2.3 where: 1 = very light, 2 =
light, 3 = dark, 4 is very dark. The
categories should be interpreted as
followed: category 1 = the colour of the
locations water sample lies between
example 1 and 2, category 2 = the colour
of the locations water sample lies between
example 2 and 3, category 3 = the colour
of the locations water sample lies between
example 3 and 4.
Image 2.3. The 4 examples of water
samples used to determine category of
water colour with: 1 = very light, 2 =
light, 3 = dark, 4 is very dark.
2.3 List of materials
The table below lists all the instruments
used during the research. Instruments such
as the Clarke dipper, tweezers, sieve,
waders, measuring stick, GPS-meter, Hach
pH/DO were used frequently in the field.
Mobile phones equipped with a camera
and the Collector application for Android,
which were used as well and bicycles were
used as mean of transportation. Plastic
containers were either used to take a water
sample of a location or they were filled
with 75% ethanol, in which case they were
used to contain larvae. Plastic containers
could be washed in order to make them
available for further use again.
Item
Amount
Clarke Dipper (350 mL)
2
Tweezers
2
Sieve (Diameter!!!!)
2
Plastic cups (50 mL)
150
Waders
3
Flora book
1
Measuring stick
2
Jar with 75% ethanol
1
GPS-meter
1
Bike
3
Camera/mobile phone
3
Hach pH
1
Hach DO
1
Collector – Android mobile application
3
3. Results
3.1. Environmental conditions
Environmental
conditions
that
were
measured in this research consisted out of:
dissolved
oxygen,
pH,
conductivity,
colour,
substrate
type,
and
water
temperature and depth. In order to answer
the
first
sub-question:
‘Which
environmental conditions are present in the
peat bog area surrounding Griendtsveen?’
this research’ findings with regard to these
conditions will now be discussed. The
Dissolved oxygen content was found to be
5.42mg/L on average with a minimum
close to anoxic conditions at 0.80mg/L at
location 47 and a maximum of 13.00mg/L
at location 36.
The pH fluctuated between very acidic
(3.30) at location 39 and slightly alkaline
(7.20) at location 56. On average the pH of
the sample sites was 4.47.
After having excluded the outliers,
-1.4µS/cm at location 56 and 511µS/cm at
location 52, the mean conductivity was
and the highest measurement was found at
location 39 with 224µS/cm.
The colour of the water varied between
very light and very dark (image 2.3). Out
of the 77 water samples that were taken it
occurred 23 times that water colour was
classified to be somewhere between very
light and light. In 35 instances colour was
classified to be between light and dark in
and in the remaining 19 cases the water
colour was found to be between dark and
very dark.
Water temperature had a mean of 15.34
ͦC.
Measurements
fluctuated between a
minimum of 6.10 ͦC on April 26
that
location 11 and 23.10
ͦC on May 11
that
location 43.
Average depth of the dipping locations
ranged between 7.86cm at location 38, a
depth lesser than the height of the ladle
thus making it necessary to apply the flow-
in method, and 45.00cm at location 33.
The mean depth of all dips was 16.15cm.
3.2. Aedes cinereus catches
During this research a total of 2199
mosquito larvae were caught. As shown in
chart 3.1 our species accounted for 99% of
the total catch: Ochlerotatus sp. (6%),
Ochlerotatus cantans/annulipes (16%),
Ochlerotatus puntor (27%) and Aedus
cinereus (50%).
Because this study focussed solely on
Aedes cinereus were caught. Amongst the
50 assessments where larvae were caught
22 assessments had >10 catches. The
abundance of larvae, as well as the exact
locations of these specific assessments and
accessory information about them can be
found in chart 3.2, image 3.1 and table 3.1.
3.3. Correlation between number of
Aedes
cinereus
larvae
found
and
environmental conditions
In order to determine if a correlation was
present between environmental conditions
and number of Aedes cinereus larvae found
statistical tests were done in R-Studio. A
Shapiro-Wilk test showed that only the
data for water temperature was likely to be
distributed normally. The data for the
following variables was not distributed
normally as p <= 0.05: larvae catch
(p=2.2e-16), oxygen (p=0.00236), average
water depth (p=1.08e-09) and conductivity
(6.001e-09). Because transformations in
the form of log10, square root or arcsine
did not result in normally distributed data
and catch data was found to be Poisson
distributed a Poisson general linear model
(GLM) was used to test a correlation.
These models were made in compliance
with De Meyer as his research required a
similar model.
GLM’s containing different combinations
of variables, including variables that are
not elaborated upon in this research, were
tested for reliability based on their AIC
within R-Studio, can be found in appendix
C. Outcomes, based on the Δ AICc,
showed that 5 (0.46) and 6 (0.00)
suggested substantial evidence for the
model as they were < 2 (Burnham and
Anderson, 2002). Analysis of these models
indicated that model 5 had a probability of
44% to be the best model among the
candidate models where model 6 had a
probability of 55%. The results of model 5
and 6 will now be presented.
Model 6, which most evidence was
suggested for and which had the highest
probability of being the best model among
the candidate models, contained the
following variables: substrate: all types,
vegetation cover: trees, high grasses and
low
grasses,
water
cover:
mosses,
measured: oxygen, water temperature and
water depth. The AIC value of this model
was 1606.8 and the reliability was found to
be 39.52% (1 – (null deviance/residual
deviance)). All variables, besides substrate
leaves (p=0.005201), vegetation trees
(p=0.001578) and vegetation high grasses
(p=0.002852)
contributed
very
Model 5 contained the same variables as
model 6 but additionally contained ph. For
this model there was slightly less evidence
suggested than for model 6 and it also had
a slightly lesser probability of being the
best model among the candidate models.
Model 5 had an AIC value of 1606.2. The
contribution of every variable, besides
substrate leaves (p=
0.003143), vegetation
high grasses (p=
0.003193) and ph (p=
0.101243), to this AIC was very significant
(p<=0.001).
127 347
600 1105
Mosquito catches per species (#)
other Ochlerotatus sp. Ochlerotatus cantans/annulipes Ochlerotatus puntor Aedus cinereusChart 3.1.
Overview of mosquito species
that were caught and the abundance that
they were caught in.
Table 3.2: Summary output of model 6.
Chart 3.2. A column chart
containing every sample location
with an Aedes cinereus catch >1 0.
Image 3.1. Visualization of the
locations where >10 Aedes
cinereus larvae were caught.
Table 3.1. Details about the
locations that had an Aedes
cinereus catch >10.
131
105
98
82
54 54
48 46
40 39
34 33 33
26 26 23 23
18 16
11 11 11 10
0
20
40
60
80
100
120
140
27
.0
4.0
43
.0
7.0
4.1
32
.0
26
.0
38
.0
31
.0
11
.1
8.0
39
.0
42
.0
41
.0
51
.0
44
.0
50
.0
16
.0
41
.1
15
.0
27
.1
58
.0
45
.0
lar
vae
c
atc
h
Location number
Larvae catch per location (n>10)
location # Larvae catch (#) RD Coordinates Time
Date
27,0 131 191318, 383540 14:00 10-05-16 4,0 105 192933, 383342 15:05 20-04-16 43,0 98 190512, 383504 14:30 11-05-16 7,0 82 192100, 381656 11:01 21-04-16 4,1 54 192907, 383349 11:00 12-05-16 32,0 54 191745, 383266 11:00 10-05-16 26,0 48 191271, 383589 13:00 09-05-16 38,0 46 190288, 382165 16:20 10-05-16 31,0 40 191270, 383322 17:00 10-05-16 11,1 39 188610, 383500 10:40 26-05-16 8,0 34 191937, 381890 13:12 21-04-16 39,0 33 190460, 383379 11:00 11-05-16 42,0 33 190557, 383540 13:30 11-05-16 41,0 26 190623, 383492 12:15 11-05-16 51,0 26 191644, 382597 15:40 12-05-16 44,0 23 190003, 383091 16:00 11-05-16 50,0 23 191965, 382983 14:15 12-05-16 16,0 18 190273, 382904 15:45 02-05-16
4. Discussion
While conducting the research it wasn’t
always possible to ascertain data for all
parameters. Especially early on in the
research it took some time to become
acquainted with the measuring methods
and build-up. This resulted in the absence
of conductivity data until location 22. Due
to the fact that conductivity data was
unlikely to significantly fluctuate within a
relatively short time span (Verdonschot,
personal communication, May 2016) every
location that missed data was assessed a
second time and accessory data was used
for the locations. Besides conductivity,
during this research the DO probe was at
times
irresponsive
thus
making
it
impossible to test the water sample for DO
and water temperature at those times.
Furthermore it should be noted that there
were instances where a location could not
be assessed a second time due to the fact
that the site had fallen dry. If data was
unavailable for a first assessment this
resulted in incomplete data for these
locations.
In the case of missing data with regard to
substrate content, vegetation cover and
type, and water cover, estimations were
made during a second assessment or based
on the photograph taken during the first
assessment. These estimations are not as
accurate as on site estimations.
Subsequently, the dipping technique and
site conditions have played a very large
role during this research. Mosquito larvae
are very easily startled and therefor the
When working in march-like conditions it
can be difficult to leave the water
undisturbed. Also weather conditions such
as precipitation or temperature can result in
unrepresentative catches at locations. It is
also possible that mosquito larvae have a
preferred micro-habitat within an assessed
location. Though dips were taken at
random within a location the exact amount
of times that certain micro-habitats were
dipped in could have influenced the catch
as well.
The statistical analyses that were carried
out showed a reliability of respectively
39.52%. This is a very limited percentage
and thus conclusions drawn based on these
results should be made with caution. Also
noteworthy is that the statistical analysis
was only carried out for locations with
complete data for all variables used:
resulting in a total of 75 locations. It is
very well possible that other statistical
tests, unknown to me or my supervisors, fit
the acquired data in a more suitable
fashion.
5. Conclusion
According to the statistical analyses of
models 5 and 6 it can be concluded with a
reliability of 39.52% that correlations are
present between Aedes cinereus catches
and
the
following
environmental
conditions: substrate: leaves, mosses, wood
algae and grass, vegetation: trees, high
grasses and low grasses, water cover:
mosses, and the variables: DO content,
water temperature and water depth. Due to
the fact that the environmental variables:
concluded that they did not contribute to an
increased and more reliable correlation
model.
It is of great importance for further
research that data is acquired on a greater
scale and over a longer period of time in
order to adequately portray the exact
correlations existent between mosquito
larvae
number
and
environmental
conditions present within the sample site.
6. Acknowledgements
I am highly thankful to Alterra Research
Institute, Wageningen University and
Research and the University of Amsterdam
for providing the possibility and resources
to conduct this research. I would like to
thank Piet Verdonschot for familiarizing
me with the subject and his feedback
throughout the project. Also I would like to
thank Dorine Dekkers for her constant
guidance throughout the field work process
and her extensive work on species
determination in the lab. With regard to the
statistical analysis I would like to thank
Peter Roessingh and Emiel van Loon for
their consultancy. Furthermore I would
like to thank Roos Keizer and Alex de
Meyer for their full-time assistance in
acquiring data and Mario van Ooij & Els
van Geffen for hosting us during the period
of
fieldwork.
References
Anderson, D. R., & Burnham, K. P.
(2002).
Avoiding
pitfalls
when
using
information-theoretic methods. The Journal of
Wildlife Management, 912-918.Crans, W. J.
(2004). A classification system for mosquito
life cycles: life cycle types for mosquitoes of
the northeastern United States. Journal of
Vector Ecology, 29, 1-10.
Knotters, M., van Delft, S. P. J.,
Keizer-Vlek, H. E., Jansen, P. C., von Asmuth,
J. R., Sival, F. P., & van‘t Klooster, C. E.
(2008). Evaluatie monitoring Deurnese Peel en
Mariapeel. Kwantificering van effecten van
maatregelen
en
advies
over
het
monitoringsplan.(Evaluation monitoring of
Deurnese Peel and Mariapeel. Quantifying the
effects of the measures and advice about the
monitoring plan) Alterra Wageningen,
Alterra-rapport, 1717.
O’Malley, Claudia. "Seven ways to a
successful dipping career." Wing Beats 6.4
(1995): 23-24.
Verdonschot, Piet F.M., Dorine D.
Dekkers, Anna A. Besse-Lototskaya, 2015.
Stekende insecten Griendtsveen; Situatie 2015.
Wageningen,
Alterra
Wageningen
UR
(University & Research centre),
Alterra-rapport 2680. 60 blz.; 19 fig.; 9 tab.; 11 ref.
Workman, P. D. and W. E. Walton.
2003. Larval behavior of four Culex (Diptera:
Culicidae)
from
treatment
wetlands
in
southwestern United States. Journal of Vector
Appendix A: Template for digital data copy
Location: Date:
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm) 1 2 3 4 5 6 7 8 9 10 average: GPS: Time: CPOM FPOM leaves wood waterplants mosses algae grass trees bush heath high grasses low grasses mosses mosses algae sunshine partial overcast overcast no wind little wind wind rain width (m) length (m) avarege depth (cm) water cover (%)
estimated total volume (m3) Colour (scale 1-4): Turbidity (mm)
Location image:
Vegetation (scale 1-5): Substrate (scale 1-5):
Water cover (scale 1-5):
Weather (0=no, 1=yes):
Dimensions:
Appendix C: R-code for statistical analysis
install.packages("AICcmodavg")
library(AICcmodavg)
model_list <- list()
For fill of model list see next page: List of General Linear Models (GLM) and
accessory variables.
Modnames = c("Nullmodel", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12",
"13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
"28", "29", "30")
aictab(model_list,modnames = Modnames)
K
AICc
Δ AICc
AICcWt
Cum.Wt LL
6 13
1613.08 0.00
0.55
0.55
-790.40
5 14
1613.54 0.46
0.44
0.99
-789.09
4 17
1621.11 8.03
0.01
1.00
-787.89
26 14
1624.07 10.99
0.00
1.00
-794.35
23 15
1640.81 27.73
0.00
1.00
-801.12
21 11
1647.27 34.19
0.00
1.00
-810.44
7 10
1647.80 34.72
0.00
1.00
-812.09
14 11
1654.87 41.79
0.00
1.00
-814.23
27 14
1657.55 44.47
0.00
1.00
-811.09
24 14
1665.10 52.02
0.00
1.00
-814.87
16 11
1678.20 65.12
0.00
1.00
-825.90
25 15
1678.69 65.61
0.00
1.00
-820.06
8 14
1696.10 83.02
0.00
1.00
-830.36
20 9 1
698.50
85.42
0.00
1.00
-838.80
28 13
1700.36 87.28
0.00
1.00
-834.04
15 11
1700.58 87.50
0.00
1.00
-837.09
30 13
1723.32 110.24
0.00
1.00
-845.52
13 11
1724.05 110.97
0.00
1.00
-848.83
17 11
1758.98 145.90
0.00
1.00
-866.29
19 10
1768.82 155.74
0.00
1.00
-872.61
10 12
1779.23 166.15
0.00
1.00
-874.97
12 11
1784.47 171.38
0.00
1.00
-879.03
11 11
1796.12 183.04
0.00
1.00
-884.86
29 13
1815.38 202.30
0.00
1.00
-891.55
22 11
1831.99 218.91
0.00
1.00
-902.79
9 5
1850.49 237.41
0.00
1.00
-919.79
18 10
2104.87 491.79
0.00
1.00
-1040.63
3 3
2405.26 792.18
0.00
1.00
-1199.45
2 2
2434.45 821.37
0.00
1.00
-1215.14
List of General Linear Models (GLM) and accessory variables:
model_list[[1]] = glm(larvaenormal ~ 1, data=dataallesclean, family = 'poisson' ) #null model with just a constant model_list[[2]] = glm(larvaenormal ~ SUBleaves, data=dataallesclean, family = 'poisson')
model_list[[3]] = glm(larvaenormal ~ SUBleaves + VEGIhigh.grasses, data=dataallesclean, family = 'poisson') model_list[[4]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + VEGImosses + WCmosses + WCalgae + ph + oxy + temp + depth + con, data=dataallesclean, family = 'poisson')
model_list[[5]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson') model_list[[6]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + oxy + temp + depth, data=dataallesclean, family = 'poisson')
model_list[[7]] = glm(larvaenormal ~ SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGIlow.grasses + WCmosses + oxy + temp + depth, data=dataallesclean, family = 'poisson')
model_list[[8]] = glm(larvaenormal ~ SUBleaves + SUBwood + SUBgrass + VEGItrees + VEGIhigh.grasses +
VEGIlow.grasses + VEGImosses + WCmosses + WCalgae + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson') model_list[[9]] = glm(larvaenormal ~ ph + oxy + temp + depth, data=dataallesclean, family = 'poisson')
model_list[[10]] = glm(larvaenormal ~ SUBleaves + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGImosses + WCmosses + WCalgae + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson')
model_list[[11]] = glm(larvaenormal ~ SUBleaves + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGImosses + WCmosses + WCalgae + ph + oxy + temp, data=dataallesclean, family = 'poisson')
model_list[[12]] = glm(larvaenormal ~ SUBleaves + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGImosses + WCalgae + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson')
model_list[[13]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + WCmosses + oxy + temp + depth, data=dataallesclean, family = 'poisson') #SUBwood & Vegilowgrass weg
model_list[[14]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + oxy + temp, data=dataallesclean, family = 'poisson') #depth & SUBwood
model_list[[15]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + oxy + temp + depth, data=dataallesclean, family = 'poisson') #VEGilow & WCmoss
model_list[[16]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + WCmosses + oxy + temp + depth, data=dataallesclean, family = 'poisson') # VEgIhigh &vegilow
model_list[[17]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + temp + depth, data=dataallesclean, family = 'poisson') # oxy & SUbwood
model_list[[18]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBgrass + VEGItrees + VEGIhigh.grasses + WCmosses + oxy + depth, data=dataallesclean, family = 'poisson') #temp & Subalge & Vegilow
model_list[[19]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + VEGItrees + WCmosses + oxy + temp + depth, data=dataallesclean, family = 'poisson') #Subgrass & vegilow & vegihigh
model_list[[20]] = glm(larvaenormal ~ SUBmosses + SUBalgae + SUBgrass + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + oxy + temp, data=dataallesclean, family = 'poisson') #vegitrees & subleaves & subwood & depth
model_list[[21]] = glm(larvaenormal ~ SUBleaves + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + oxy + temp + depth, data=dataallesclean, family = 'poisson') #wcmoss, submoss
model_list[[22]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + WCmosses + temp + depth, data=dataallesclean, family = 'poisson') #oxy &Vegilow
model_list[[23]] = glm(larvaenormal ~ SUBleaves + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + WCmosses + WCalgae + ph + oxy + temp + depth + con, data=dataallesclean, family = 'poisson') # Submoss Vegimoss
model_list[[24]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + VEGImosses + WCmosses + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson') #con SUBalg & wcalg
model_list[[25]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGImosses + WCmosses + WCalgae + ph + oxy + temp + depth + con, data=dataallesclean, family = 'poisson') #Vegilow vegihigh subgrass
model_list[[26]] = glm(larvaenormal ~ SUBmosses + SUBwood + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + VEGImosses + WCmosses + WCalgae + oxy + temp + depth, data=dataallesclean, family = 'poisson') # SUbleaves ph con
model_list[[27]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBalgae + SUBgrass + VEGItrees + VEGIhigh.grasses + VEGIlow.grasses + VEGImosses + WCalgae + ph + oxy + temp + con, data=dataallesclean, family = 'poisson') #depth, wcmos, subwood
model_list[[28]] = glm(larvaenormal ~ SUBleaves + SUBmosses + SUBalgae + SUBgrass + VEGItrees + VEGImosses + WCmosses + WCalgae + ph + oxy + temp + depth, data=dataallesclean, family = 'poisson') # subwood, con, vegigraslow, vegihigh
Appendix D: Master dataset
rv ae c atc h ( #) Ac id ity ( p H) O₂ -c o n ten t (mg /L) W ater temp ( ͦC ) Dep th ( cm) C o n ( u S/ cm) RD C o o rd in ates Ti me W ater p ro p er ti es CP OM FPO M leav es wo o d wat erpl an ts m o sses alg ae grass m o sses alg ae Co lo u r 1 4 .88 3 .55 1 0 .48 3 2 .50 1 0 9 .00 1 9 1 0 5 3 , 3 8 3 6 5 8 10:00 3 3 3 2 0 0 0 0 1 3 1 1 5 .30 2 .60 1 3 .60 2 5 .45 1 0 9 .00 1 9 1 0 5 3 , 3 8 3 6 5 8 14:00 3 3 3 2 0 0 0 0 1 0 1 105 5 .00 1 1 .13 1 4 .20 1 2 .67 1 1 4 .00 1 9 2 9 3 3 , 3 8 3 3 4 2 15:05 4 1 0 0 0 0 0 5 0 2 2 54 4 .50 6 .90 1 8 .30 1 1 .15 1 1 4 .00 1 9 2 9 0 7 , 3 8 3 3 4 9 11:00 4 1 1 0 0 2 0 2 2 1 2 0 6 .05 2 .40 1 5 .05 2 2 .08 1 0 8 .00 1 9 2 6 1 2 , 3 8 2 7 0 4 16:25 4 1 5 2 0 1 0 0 0 2 1 0 5 .30 2 .40 1 3 .40 1 6 .50 1 0 8 .00 1 9 2 6 1 2 , 3 8 2 7 0 4 13:00 4 1 5 2 0 1 0 0 0 2 1 7 5 .30 1 5 .20 1 1 .00 1 9 2 3 0 7 , 3 8 2 7 0 3 16:45 4 1 0 0 0 0 0 5 0 2 1 9 2 3 0 7 , 3 8 2 7 0 3 13:30 4 1 0 0 0 0 0 5 82 4 .53 5 .67 1 2 .07 9 .47 1 8 5 .00 1 9 2 1 0 0 , 3 8 1 6 5 6 11:01 4 1 3 1 0 0 0 3 1 2 2 6 3 .90 3 .60 1 3 .70 8 .00 1 8 5 .00 1 9 2 1 0 0 , 3 8 1 6 5 6 17:10 4 1 3 1 0 0 0 3 1 2 2 34 4 .40 1 0 .40 1 1 .80 2 2 .20 1 8 7 .00 1 9 1 9 3 7 , 3 8 1 8 9 0 13:12 1 4 2 1 0 0 0 1 5 3 2 0 3 .90 1 0 .40 1 4 .10 1 6 .50 1 8 7 .00 1 9 1 9 3 7 , 3 8 1 8 9 0 16:40 1 4 2 1 0 0 0 1 5 3 2 6 5 .10 3 .60 1 6 .10 1 1 .20 1 8 5 .00 1 9 2 1 9 7 , 3 8 1 9 9 8 13:50 4 1 4 1 0 0 0 1 3 1 3 4 3 .90 3 .60 1 3 .00 9 .00 1 8 5 .00 1 9 2 1 9 7 , 3 8 1 9 9 8 16:00 4 1 4 1 0 0 0 1 3 1 3 3 6 .10 6 .60 3 0 .00 1 8 8 6 3 7 , 3 8 3 4 6 9 9:52 3 3 4 1 0 1 0 0 1 1 1 4 .40 6 .20 6 .10 1 7 .55 1 8 8 6 1 0 , 3 8 3 5 0 0 10:46 5 0 1 0 0 0 0 5 1 5 39 4 .40 6 .20 1 4 .40 9 .50 1 8 8 6 1 0 , 3 8 3 5 0 0 10:40 5 0 1 0 0 0 0 5 1 1 6 3 .90 7 .80 2 1 .60 1 8 8 8 9 0 , 3 8 3 6 4 3 11:25 3 3 3 1 0 0 0 3 2 1 0 1 8 8 8 9 0 , 3 8 3 6 4 3 11:30 3 3 3 1 0 0 0 3 2 1 0 4 .80 1 7 .00 3 3 .50 1 8 8 9 0 4 , 3 8 3 5 4 9 14:00 4 1 0 0 0 2 0 4 5 0 0 4 .80 1 7 .00 8 .50 1 8 8 9 0 4 , 3 8 3 5 4 9 11:50 4 1 0 0 0 2 0 4 4 0 0 4 .06 7 .00 1 3 .80 9 .55 1 9 4 .00 1 9 0 7 7 9 , 3 8 2 5 0 8 13:58 4 1 0 0 0 0 1 5 2 1 1 2 3 .80 9 .90 1 5 .80 9 .00 1 9 4 .00 1 9 0 7 7 9 , 3 8 2 5 0 8 16:30 4 1 0 0 0 0 0 5 2 1 1 11 4 .35 8 .80 1 7 .05 2 6 .11 1 0 9 .00 1 9 1 0 2 0 , 3 8 2 6 6 1 14:35 1 4 4 2 0 0 0 0 0 0 2 0 5 .30 3 .10 1 3 .60 1 2 .50 1 0 9 .00 1 9 1 0 2 0 , 3 8 2 6 6 1 13:45 1 4 4 2 0 0 0 0 0 0 2 18 4 .00 5 .70 1 4 .90 1 3 .64 1 7 0 .00 1 9 0 2 7 3 , 3 8 2 9 0 4 15:45 3 3 4 1 0 2 0 0 5 1 1 0 4 .20 3 .50 1 3 .00 1 9 .00 1 7 0 .00 1 9 0 2 7 3 , 3 8 2 9 0 4 14:15 3 3 4 1 0 2 0 0 5 1 1 6 4 .10 9 .70 1 6 .00 1 5 .63 1 9 0 7 6 5 , 3 8 3 1 7 5 16:48 3 3 2 1 0 0 0 3 3 0 2 1 9 0 7 6 5 , 3 8 3 1 7 5 14:30 3 0 2 0 4 .00 3 .20 1 0 .30 1 4 .50 1 9 0 5 7 5 , 3 8 2 2 5 8 11:00 4 1 4 1 0 0 0 2 1 1 1 9 0 5 7 5 , 3 8 2 2 5 8 13:45 0 0 0 3 .70 7 .30 1 0 .30 1 0 .00 1 9 0 5 1 4 , 3 8 2 1 8 0 11:30 4 1 0 0 0 0 1 5 0 1 1 9 0 5 1 4 , 3 8 2 1 8 0 15:15 0 4 .40 4 .70 1 4 .60 1 0 .45 1 9 0 4 0 5 , 3 8 2 1 7 4 12:37 4 2 5 1 0 0 0 0 0 0 1 9 0 4 0 5 , 3 8 2 1 7 4 13:30 6 1 0 .00 1 9 0 2 4 9 , 3 8 2 0 6 5 13:35 4 2 4 1 0 0 0 3 0 0 1 9 0 2 4 9 , 3 8 2 0 6 5 13:35 8 3 .90 1 1 .60 1 4 .60 1 5 .91 1 8 7 .00 1 9 0 4 3 4 , 3 8 1 9 1 6 16:46 4 1 1 1 0 1 0 0 2 1 3 0 3 .90 1 0 .00 1 2 .20 8 .75 1 8 7 .00 1 9 0 4 3 4 , 3 8 1 9 1 6 13:15 3 3 3 1 0 0 0 0 2 2 3 0 5 .90 1 .40 1 3 .40 2 8 .00 7 5 .00 1 8 9 8 2 6 , 3 8 2 8 8 8 11:00 3 3 4 1 0 0 0 1 1 1 3 0 6 .40 4 2 .22 1 9 2 5 9 7 , 3 8 3 6 4 7 13:00 4 1 4 1 0 0 0 0 0 1 0 6 .40 4 2 .22 1 9 2 5 9 7 , 3 8 3 6 4 7 14:30 4 1 4 1 0 0 0 0 0 1 0 6 .40 3 6 .11 1 9 0 8 9 1 , 3 8 3 8 2 6 13:50 4 1 4 1 0 0 0 0 0 1 0 6 .40 3 6 .11 1 9 0 8 9 1 , 3 8 3 8 2 6 14:45 4 1 4 1 0 0 0 0 0 1 48 3 .90 4 .60 2 1 .00 2 3 .85 1 8 7 .00 1 9 1 2 7 1 , 3 8 3 5 8 9 13:00 4 1 1 0 0 0 1 5 1 1 1 3 3 .90 4 .60 1 4 .00 1 4 .50 1 8 7 .00 1 9 1 2 7 1 , 3 8 3 5 8 9 15:00 4 1 1 0 0 0 1 5 1 1 1 131 4 .30 6 .50 2 0 .60 1 1 .50 1 2 5 .50 1 9 1 3 1 8 , 3 8 3 5 4 0 14:00 3 2 4 1 0 0 1 1 2 2 1 11 4 .00 6 .50 1 3 .70 1 0 .50 1 8 2 .00 1 9 1 3 1 8 , 3 8 3 5 4 0 15:30 3 2 4 1 0 0 1 1 2 2 1 0 5 .40 4 .90 2 0 .30 2 1 .50 1 0 4 .60 1 9 1 2 7 9 , 3 8 3 5 1 2 15:00 3 3 4 1 0 0 0 1 0 0 3 0 5 .20 4 .90 1 3 .40 2 1 .50 1 1 5 .00 1 9 1 2 7 9 , 3 8 3 5 1 2 15:45 3 3 4 1 0 0 0 1 0 0 3 0 5 .50 1 9 .40 1 5 .91 1 5 7 .00 1 9 1 2 2 1 , 3 8 3 5 2 8 15:30 4 1 5 2 0 0 0 0 0 1 1 9 1 2 2 1 , 3 8 3 5 2 8 15:30 0 0 1 4 .10 2 .90 2 2 .00 1 3 .50 9 2 .00 1 9 1 2 6 6 , 3 8 3 4 3 0 16:00 4 1 0 0 0 0 0 5 0 0 2 8 3 .80 2 .90 1 2 .40 1 1 .50 1 9 2 .00 1 9 1 2 6 6 , 3 8 3 4 3 0 16:00 4 1 0 0 0 0 0 5 0 0 2 40 3 .60 1 8 .90 1 5 .00 5 4 .00 1 9 1 2 7 0 , 3 8 3 3 2 2 17:00 4 1 3 1 0 0 2 3 2 3 2 0 3 .90 2 .70 1 2 .70 1 2 .86 1 8 8 .00 1 9 1 2 7 0 , 3 8 3 3 2 2 16:45 4 1 3 1 0 0 2 3 2 3 2 54 4 .10 4 .00 1 7 .40 1 1 .00 1 7 2 .00 1 9 1 7 4 5 , 3 8 3 2 6 6 11:00 3 2 1 0 0 0 0 3 0 4 1 0 4 .30 4 .50 1 1 .00 1 0 .50 1 6 5 .00 1 9 1 7 4 5 , 3 8 3 2 6 6 17:45 3 2 3 1 0 0 0 3 0 4 1 1 4 .00 5 .70 1 7 .40 4 5 .00 1 8 6 .00 1 9 1 7 0 6 , 3 8 3 2 6 8 12:00 4 1 2 0 0 0 0 2 0 5 2 6 4 .30 5 .70 1 2 .40 3 8 .00 1 6 5 .00 1 9 1 6 5 6 , 3 8 3 2 7 8 17:00 4 1 2 0 0 0 0 2 1 5 1 5 4 .40 6 .70 1 6 .90 1 6 .50 1 6 4 .00 1 9 1 7 4 0 , 3 8 3 3 2 6 12:15 4 1 0 0 0 0 0 4 0 4 1 0 4 .00 3 .20 1 3 .10 1 3 .50 1 8 1 .00 1 9 1 7 4 0 , 3 8 3 3 2 6 17:15 4 1 0 0 0 0 0 4 1 4 1 6 4 .50 9 .80 1 8 .60 1 6 .36 1 5 3 .00 1 9 1 8 8 4 , 3 8 3 1 7 1 13:30 4 1 0 0 0 0 0 5 0 4 1 0 4 .30 1 3 .00 1 9 .20 8 .00 1 2 6 .00 1 9 1 3 3 2 , 3 8 3 0 3 7 14:30 4 1 5 0 0 0 0 0 0 4 2 0 4 .40 3 .10 1 2 .40 9 .00 1 6 1 .00 1 9 1 3 3 2 , 3 8 3 0 3 7 18:30 4 1 5 2 0 0 0 0 1 0 2 9 4 .30 9 .38 1 6 4 .00 1 9 1 3 6 4 , 3 8 3 0 0 9 14:50 3 2 2 0 0 0 0 2 0 4 3 1 9 1 3 6 4 , 3 8 3 0 0 9 14:50 46 3 .60 7 .50 1 9 .10 7 .86 2 0 4 .00 1 9 0 2 8 8 , 3 8 2 1 6 5 16:20 3 2 2 0 0 1 2 2 0 4 3 1 9 0 2 8 8 , 3 8 2 1 6 5 13:00 0 4 33 3 .30 3 .00 1 7 .40 8 .33 2 2 4 .00 1 9 0 4 6 0 , 3 8 3 3 7 9 11:00 5 0 4 0 0 3 0 2 0 3 3 1 9 0 4 6 0 , 3 8 3 3 7 9 11:00 0 3 .60 2 .00 1 7 .90 1 5 .63 2 1 1 .00 1 9 0 6 2 7 , 3 8 3 4 9 1 11:55 4 1 1 0 0 4 0 0 0 1 2 1 9 0 6 2 7 , 3 8 3 4 9 1 11:30 26 3 .50 8 .80 1 8 .00 1 5 .50 2 0 6 .00 1 9 0 6 2 3 , 3 8 3 4 9 2 12:15 4 1 0 0 0 0 0 5 0 2 1 16 3 .60 2 .00 1 7 .90 1 0 .00 2 1 1 .00 1 9 0 6 2 3 , 3 8 3 4 9 2 11:45 3 3 1 0 0 0 0 5 4 1 1 33 3 .70 6 .80 2 1 .20 1 2 .73 2 0 2 .00 1 9 0 5 5 7 , 3 8 3 5 4 0 13:30 5 0 4 0 0 0 0 1 0 3 3 2 3 .70 4 .40 1 5 .60 9 .50 2 0 2 .00 1 9 0 5 5 7 , 3 8 3 5 4 0 12:30 5 0 4 0 0 0 0 1 5 1 3 98 4 .00 7 .10 2 3 .10 1 9 .50 1 8 0 .00 1 9 0 5 1 2 , 3 8 3 5 0 4 14:30 5 0 1 0 0 0 0 4 0 1 2 23 3 .80 1 0 .50 1 8 .30 1 2 .50 1 9 7 .00 1 9 0 0 0 3 , 3 8 3 0 9 1 16:00 1 4 4 0 0 0 0 1 0 5 2 0 3 .80 3 .00 1 5 .60 1 1 .50 1 9 7 .00 1 9 0 0 0 3 , 3 8 3 0 9 1 14:00 1 4 4 0 0 0 0 1 0 2 2 10 4 .00 3 .20 1 7 .80 1 0 .00 1 8 7 .00 1900145, 383074 16:30 4 1 5 0 0 0 0 0 0 0 2 1900145, 383074 13:30 4 1 5 0 0 0 0 0 2 0 4 .40 0 .80 1 7 .40 1 9 .17 1 4 5 .00 1 9 2 2 7 8 , 3 8 2 9 6 0 12:00 3 2 5 0 0 0 0 0 0 5 2 0 4 .40 1 .60 1 3 .60 1 2 .50 1 4 5 .00 1 9 2 2 7 8 , 3 8 2 9 6 0 13:20 3 2 5 1 0 0 0 0 1 2 3 2 3 .60 8 .20 1 7 .00 1 3 .89 9 9 .50 1 9 2 2 9 1 , 3 8 2 9 7 1 12:30 5 0 0 0 0 0 0 5 0 3 2 8 5 .90 8 .20 1 7 .00 8 .00 7 1 .00 1 9 2 2 9 1 , 3 8 2 9 7 1 13:40 5 0 0 0 0 0 0 5 1 4 2 4 3 .50 2 .00 1 6 .50 2 1 .00 1 0 7 .00 1 9 2 1 7 3 , 3 8 2 7 3 4 12:50 3 2 5 0 0 0 0 0 0 1 3 0 3 .80 1 .00 1 2 .20 1 5 .00 1 9 5 .00 1 9 2 1 7 3 , 3 8 2 7 3 4 14:15 3 2 5 0 0 0 0 0 0 1 2 23 3 .90 1 0 .30 1 8 .70 1 1 .67 1 0 4 .00 1 9 1 9 6 5 , 3 8 2 9 8 3 14:15 4 1 1 0 0 0 0 4 0 3 2 0 3 .60 5 .70 1 4 .20 1 0 .00 2 0 6 .00 1 9 1 9 6 5 , 3 8 2 9 8 3 14:40 5 1 3 1 0 0 0 3 1 4 2 26 4 .00 1 .80 1 8 .60 9 .00 7 8 .00 1 9 1 6 4 4 , 3 8 2 5 9 7 15:40 4 1 5 0 0 0 0 1 0 3 2 1 4 .00 4 .90 1 2 .80 1 3 .00 7 8 .00 1 9 1 6 4 4 , 3 8 2 5 9 7 18:00 4 1 5 0 0 0 0 1 4 1 3 0 6 .60 1 .60 1 6 .70 1 2 .73 1 8 9 6 8 1 , 3 8 3 1 4 5 10:45 2 3 5 0 0 0 0 0 0 0 1 5 3 .78 3 .60 1 9 .40 1 3 .33 1 3 5 .00 1 8 9 7 9 9 , 3 8 2 3 5 5 11:20 2 3 3 0 0 2 0 2 0 2 2 0 4 .63 2 .50 1 9 .00 2 1 .67 1 7 7 .00 1 8 9 9 3 7 , 3 8 2 3 3 9 12:30 5 0 0 0 0 0 0 5 0 3 3 0 3 .90 1 0 .00 1 2 .20 2 2 .00 1 8 7 .00 1 8 9 9 1 9 , 3 8 2 3 3 5 2371903 0:00:00 4 1 0 0 0 0 1 4 1 5 3 0 7 .20 7 .10 1 6 .80 1 7 .50 1 8 9 9 8 8 , 3 8 2 1 1 9 11:30 1 4 1 0 0 0 1 1 0 3 1 0 4 .50 2 .70 1 3 .70 1 8 .00 1 5 0 .00 1 9 0 9 3 4 , 3 8 2 8 3 3 12:30 2 3 4 0 0 1 0 1 2 3 3 11 5 .80 7 .90 1 2 .30 1 1 .00 7 6 .00 1 9 0 6 2 7 , 3 8 2 9 1 8 13:00 1 4 1 0 0 0 0 4 1 5 2 0 5 .20 4 .10 1 4 .70 1 3 .64 1 1 1 .00 1 9 0 7 5 0 , 3 8 2 9 6 6 14:00 4 1 3 0 0 0 0 3 1 5 2 0 6 .00 4 .70 1 5 .40 8 .89 7 1 .00 1 8 9 8 5 2 , 3 8 2 7 9 1 15:00 3 3 0 0 0 0 0 5 1 0 1 8 3 .90 6 .40 1 3 .50 1 1 .50 1 8 7 .00 1 9 2 0 8 2 , 3 8 2 5 7 9 15:15 4 1 3 2 0 0 1 3 1 2 3 4 4 .20 3 .30 1 3 .10 1 5 .50 1 6 8 .00 1 9 0 9 4 2 , 3 8 2 8 7 2 15:10 5 1 5 1 0 0 0 0 0 0 2 1 5 .40 3 .80 1 5 .50 1 3 .00 1 0 6 .00 1 9 1 0 0 6 , 3 8 2 7 1 6 15:30 1 4 0 0 0 1 0 5 1 1 2 Su b str ate: ( sc al e 1 -5) W ater co ve r: ( sc al e 1 -5)Ac id ity ( p H) O ₂-co n ten t (mg /L ) W ater temp ( ͦC ) Dep th ( cm) C o n ( u S/ cm) RD C o o rd in ates Ti me W ater p ro p er ti es CP OM FPO M leav es wo o d wat erpl an ts m o sses alg ae grass m o sses alg ae Co lo u r 0 4 .30 1 3 .00 1 9 .20 8 .00 1 2 6 .00 1 9 1 3 3 2 , 3 8 3 0 3 7 14:30 4 1 5 0 0 0 0 0 0 4 2 0 4 .40 3 .10 1 2 .40 9 .00 1 6 1 .00 1 9 1 3 3 2 , 3 8 3 0 3 7 18:30 4 1 5 2 0 0 0 0 1 0 2 9 4 .30 9 .38 1 6 4 .00 1 9 1 3 6 4 , 3 8 3 0 0 9 14:50 3 2 2 0 0 0 0 2 0 4 3 1 9 1 3 6 4 , 3 8 3 0 0 9 14:50 46 3 .60 7 .50 1 9 .10 7 .86 2 0 4 .00 1 9 0 2 8 8 , 3 8 2 1 6 5 16:20 3 2 2 0 0 1 2 2 0 4 3 1 9 0 2 8 8 , 3 8 2 1 6 5 13:00 0 4 33 3 .30 3 .00 1 7 .40 8 .33 2 2 4 .00 1 9 0 4 6 0 , 3 8 3 3 7 9 11:00 5 0 4 0 0 3 0 2 0 3 3 1 9 0 4 6 0 , 3 8 3 3 7 9 11:00 0 3 .60 2 .00 1 7 .90 1 5 .63 2 1 1 .00 1 9 0 6 2 7 , 3 8 3 4 9 1 11:55 4 1 1 0 0 4 0 0 0 1 2 1 9 0 6 2 7 , 3 8 3 4 9 1 11:30 26 3 .50 8 .80 1 8 .00 1 5 .50 2 0 6 .00 1 9 0 6 2 3 , 3 8 3 4 9 2 12:15 4 1 0 0 0 0 0 5 0 2 1 16 3 .60 2 .00 1 7 .90 1 0 .00 2 1 1 .00 1 9 0 6 2 3 , 3 8 3 4 9 2 11:45 3 3 1 0 0 0 0 5 4 1 1 33 3 .70 6 .80 2 1 .20 1 2 .73 2 0 2 .00 1 9 0 5 5 7 , 3 8 3 5 4 0 13:30 5 0 4 0 0 0 0 1 0 3 3 2 3 .70 4 .40 1 5 .60 9 .50 2 0 2 .00 1 9 0 5 5 7 , 3 8 3 5 4 0 12:30 5 0 4 0 0 0 0 1 5 1 3 98 4 .00 7 .10 2 3 .10 1 9 .50 1 8 0 .00 1 9 0 5 1 2 , 3 8 3 5 0 4 14:30 5 0 1 0 0 0 0 4 0 1 2 23 3 .80 1 0 .50 1 8 .30 1 2 .50 1 9 7 .00 1 9 0 0 0 3 , 3 8 3 0 9 1 16:00 1 4 4 0 0 0 0 1 0 5 2 0 3 .80 3 .00 1 5 .60 1 1 .50 1 9 7 .00 1 9 0 0 0 3 , 3 8 3 0 9 1 14:00 1 4 4 0 0 0 0 1 0 2 2 10 4 .00 3 .20 1 7 .80 1 0 .00 1 8 7 .00 1900145, 383074 16:30 4 1 5 0 0 0 0 0 0 0 2 1900145, 383074 13:30 4 1 5 0 0 0 0 0 2 0 4 .40 0 .80 1 7 .40 1 9 .17 1 4 5 .00 1 9 2 2 7 8 , 3 8 2 9 6 0 12:00 3 2 5 0 0 0 0 0 0 5 2 0 4 .40 1 .60 1 3 .60 1 2 .50 1 4 5 .00 1 9 2 2 7 8 , 3 8 2 9 6 0 13:20 3 2 5 1 0 0 0 0 1 2 3 2 3 .60 8 .20 1 7 .00 1 3 .89 9 9 .50 1 9 2 2 9 1 , 3 8 2 9 7 1 12:30 5 0 0 0 0 0 0 5 0 3 2 8 5 .90 8 .20 1 7 .00 8 .00 7 1 .00 1 9 2 2 9 1 , 3 8 2 9 7 1 13:40 5 0 0 0 0 0 0 5 1 4 2 4 3 .50 2 .00 1 6 .50 2 1 .00 1 0 7 .00 1 9 2 1 7 3 , 3 8 2 7 3 4 12:50 3 2 5 0 0 0 0 0 0 1 3 0 3 .80 1 .00 1 2 .20 1 5 .00 1 9 5 .00 1 9 2 1 7 3 , 3 8 2 7 3 4 14:15 3 2 5 0 0 0 0 0 0 1 2 23 3 .90 1 0 .30 1 8 .70 1 1 .67 1 0 4 .00 1 9 1 9 6 5 , 3 8 2 9 8 3 14:15 4 1 1 0 0 0 0 4 0 3 2 0 3 .60 5 .70 1 4 .20 1 0 .00 2 0 6 .00 1 9 1 9 6 5 , 3 8 2 9 8 3 14:40 5 1 3 1 0 0 0 3 1 4 2 26 4 .00 1 .80 1 8 .60 9 .00 7 8 .00 1 9 1 6 4 4 , 3 8 2 5 9 7 15:40 4 1 5 0 0 0 0 1 0 3 2 1 4 .00 4 .90 1 2 .80 1 3 .00 7 8 .00 1 9 1 6 4 4 , 3 8 2 5 9 7 18:00 4 1 5 0 0 0 0 1 4 1 3 0 6 .60 1 .60 1 6 .70 1 2 .73 1 8 9 6 8 1 , 3 8 3 1 4 5 10:45 2 3 5 0 0 0 0 0 0 0 1 5 3 .78 3 .60 1 9 .40 1 3 .33 1 3 5 .00 1 8 9 7 9 9 , 3 8 2 3 5 5 11:20 2 3 3 0 0 2 0 2 0 2 2 0 4 .63 2 .50 1 9 .00 2 1 .67 1 7 7 .00 1 8 9 9 3 7 , 3 8 2 3 3 9 12:30 5 0 0 0 0 0 0 5 0 3 3 0 3 .90 1 0 .00 1 2 .20 2 2 .00 1 8 7 .00 1 8 9 9 1 9 , 3 8 2 3 3 5 2371903 0:00:00 4 1 0 0 0 0 1 4 1 5 3 0 7 .20 7 .10 1 6 .80 1 7 .50 1 8 9 9 8 8 , 3 8 2 1 1 9 11:30 1 4 1 0 0 0 1 1 0 3 1 0 4 .50 2 .70 1 3 .70 1 8 .00 1 5 0 .00 1 9 0 9 3 4 , 3 8 2 8 3 3 12:30 2 3 4 0 0 1 0 1 2 3 3 11 5 .80 7 .90 1 2 .30 1 1 .00 7 6 .00 1 9 0 6 2 7 , 3 8 2 9 1 8 13:00 1 4 1 0 0 0 0 4 1 5 2 0 5 .20 4 .10 1 4 .70 1 3 .64 1 1 1 .00 1 9 0 7 5 0 , 3 8 2 9 6 6 14:00 4 1 3 0 0 0 0 3 1 5 2 0 6 .00 4 .70 1 5 .40 8 .89 7 1 .00 1 8 9 8 5 2 , 3 8 2 7 9 1 15:00 3 3 0 0 0 0 0 5 1 0 1 8 3 .90 6 .40 1 3 .50 1 1 .50 1 8 7 .00 1 9 2 0 8 2 , 3 8 2 5 7 9 15:15 4 1 3 2 0 0 1 3 1 2 3 4 4 .20 3 .30 1 3 .10 1 5 .50 1 6 8 .00 1 9 0 9 4 2 , 3 8 2 8 7 2 15:10 5 1 5 1 0 0 0 0 0 0 2 1 5 .40 3 .80 1 5 .50 1 3 .00 1 0 6 .00 1 9 1 0 0 6 , 3 8 2 7 1 6 15:30 1 4 0 0 0 1 0 5 1 1 2 Su b str ate: ( sc al e 1 -5) W ater co ve r: ( sc al e 1 -5)
Appendix E: Detailed raw data per sample location
Location: 1 Datum: 20-4-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
1.0.01 4 4.9 2.8 10.2 30 109 1.0.02 6 40 1.0.03 2 20 1.0.04 0 30 1.0.05 0 35 1.0.06 1 4.9 3.7 9.7 35 1.0.07 1 30 1.0.08 0 45 1.0.09 0 40 1.0.10 1 40 1.0.11 0 60 1.0.12 1 4.4 4.5 10.2 40 1.0.13 0 20 1.0.14 1 5 1.0.15 0 30 1.0.16 0 15 1.0.17 0 15 1.0.18 0 25 1.0.19 2 45 1.0.20 1 5.3 3.2 11.8 50 average 1 4.875 3.55 10.475 32.5 GPS: 191053, 383658 tot 191268, 383638 Time: 10:00 Substrate: leaves (%) 3 wood (%) 2 CPOM (%) 3 FPOM (%) 3 waterplants (%) 0 mosses (%) 0 algae (%) 0 grass (%) 0 Vegetation: trees (%) 4 trees (height m) 0 bush (%) 0 bush (height m) 0 heath (%) 0 heather (height cm) 0 high grasses (%) 1
high grasses (height cm) 0
low grasses (%) 3
low grasses (height cm) 0
mosses (%) 0 Water cover: mosses (%) 1 algae (%) 3 Weather: sunshine 1 partial overcast 1 overcast 0 no wind 0 little wind 1 wind 0 rain 0 Dimensions: width (m) 2 length (m) 225 avarege depth (cm) 32.5 water cover (%) 100
estimated total volume (m3) 146.25
Location: 1-1 Datum: 23-5-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
1.1.01 1 5.3 2.6 13.6 20 109 1.1.02 0 20 1.1.03 0 35 1.1.04 0 30 1.1.05 0 15 1.1.06 0 25 1.1.07 1 25 1.1.08 0 15 1.1.09 0 35 1.1.10 0 20 1.1.11 0 40 average 0.18 25.45 GPS: 191053, 383658 Time: 14:00 Substrate: leaves (%) 3 wood (%) 2 CPOM (%) 3 FPOM (%) 3 waterplants (%) 0 mosses (%) 0 algae (%) 0 grass (%) 0 Vegetation: trees (%) 5 trees (height m) 0 bush (%) 0 bush (height m) 0 heath (%) 0 heather (height cm) 0 high grasses (%) 2
high grasses (height cm) 0
low grasses (%) 3
low grasses (height cm) 0
mosses (%) 0 Water cover: mosses (%) 1 algae (%) 0 Weather: sunshine 0 partial overcast 0 overcast 1 no wind 0 little wind 1 wind 0 rain 1 Dimensions: width (m) 2 length (m) 225 avarege depth (cm) 25.45 water cover (%) 90
estimated total volume (m3) 103.07
Water Properties:
Colour 1
Location: 4 Datum: 20-4-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
4.0.01 0 5 11.4 13.9 10 114 4.0.02 1 10 4.0.03 4 10 4.0.04 4 15 4.0.05 3 15 4.0.06 1 15 4.0.07 6 10 4.0.08 9 10 4.0.09 11 20 4.0.10 >20 4.4 14 14.7 15 4.0.11 7 15 4.0.12 9 10 4.0.13 16 15 4.0.14 9 5 4.0.15 5 5.6 8 14 15 average 6.071428571 12.67 GPS: 192933, 383342 Time: 15:05 Substrate: leaves (%) 0 wood (%) 0 CPOM (%) 4 FPOM (%) 1 waterplants (%) 0 mosses (%) 0 algae (%) 0 grass (%) 5 Vegetation: trees (%) 1 trees (height m) bush (%) 0 bush (height m) heath (%) 0 heather (height cm) high grasses (%) 4
high grasses (height cm)
low grasses (%) 0
low grasses (height cm)
mosses (%) 1 Water cover: mosses (%) 0 algae (%) 2 Weather: sunshine 1 partial overcast 0 overcast 0 no wind 1 little wind 0 wind 0 rain 0 Dimensions: width (m) 50 length (m) 50 avarege depth (cm) 12.7 water cover (%) 30
estimated total volume (m3) 95.03
Water Properties:
Colour 2
Location: 4-1 Datum: 12-5-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
4.1.01 0 4.5 6.9 18.3 15 114 4.1.02 0 15 4.1.03 0 15 4.1.04 5 10 4.1.05 2 10 4.1.06 1 15 4.1.07 12 5 4.1.08 2 10 4.1.09 12 10 4.1.10 1 10 4.1.11 7 5 4.1.12 9 10 4.1.13 9 15 average 4.62 11.15 GPS: 192907, 383349 Time: 11:00 Substrate: leaves (%) 1 wood (%) 0 CPOM (%) 4 FPOM (%) 1 waterplants (%) 0 mosses (%) 2 algae (%) 0 grass (%) 2 Vegetation: trees (%) 2 berken trees (height m) bush (%) 0 bush (height m) 0 heath (%) 0 heather (height cm)
high grasses (%) 4 pijpenstrootje
high grasses (height cm)
low grasses (%) 0
low grasses (height cm)
mosses (%) 2 veenmos Water cover: mosses (%) 2 algae (%) 1 Weather: sunshine 1 partial overcast 0 overcast 0 no wind 1 little wind 0 wind 0 rain 0 Dimensions: width (m) 50 length (m) 50 avarege depth (cm) 11.15 water cover (%) 20
Location: 5 Datum: 20-4-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
L 5.0.01 0 6.6 13.8 10 108 R 5.0.02 0 5.5 16.3 15 R 5.0.03 0 30 L 5.0.04 0 25 R 5.0.05 0 25 R 5.0.06 0 25 L 5.0.07 0 30 L 5.0.08 0 40 R 5.0.09 0 40 L 5.0.10 1 10 R 5.0.11 0 10 L 5.0.12 2 5 average 0.25 6.05 15.05 22.08 GPS: 192612, 382704 Time: 16:25 Substrate: leaves (%) 5 wood (%) 2 CPOM (%) 4
FPOM (%) 1 NO IMAGE AVAILABLE
waterplants (%) 0 mosses (%) 1 algae (%) 0 grass (%) 0 Vegetation: trees (%) 1 trees (height m) bush (%) 5 bush (height m) heath (%) 0 heather (height cm) high grasses (%) 1
high grasses (height cm)
low grasses (%) 1
low grasses (height cm)
mosses (%) 1 Water cover: mosses (%) 0 algae (%) 2 Weather: sunshine 1 partial overcast 0 overcast 0 no wind 0 little wind 1 wind 0 rain 0 Dimensions: width 600 length 100 avarege depth 22.08 water cover (%) 100
estimated total volume (m3) 13250.00 Water Properties:
Colour 1
Location: 5-1 Datum: 24-5-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
5.1.01 0 5.3 2.4 13.4 15 108 5.1.02 0 5 5.1.03 0 15 5.1.04 0 20 5.1.05 0 10 5.1.06 0 15 5.1.07 0 5 5.1.08 0 30 5.1.09 0 25 5.1.10 0 25 average 0 16.5 GPS: 192612, 382704 Time: 13:00 Substrate: leaves (%) 5 wood (%) 2 CPOM (%) 4 FPOM (%) 1 waterplants (%) 0 mosses (%) 1 algae (%) 0 grass (%) 0 Vegetation: trees (%) 1 trees (height m) bush (%) 5 bush (height m) heath (%) 0 heather (height cm) high grasses (%) 1
high grasses (height cm)
low grasses (%) 1
low grasses (height cm)
mosses (%) 1 Water cover: mosses (%) 0 algae (%) 2 Weather: sunshine 0 partial overcast 0 overcast 1 no wind 0 little wind 0 wind 1 rain 0 Dimensions: width (m) 600 length (m) 100 avarege depth (cm) 16.5 water cover (%) 100
estimated total volume (m3) 9900 Water Properties:
Colour 1
Location: 6 Datum: 20-4-2016
# Larvae catch (#) Acidity (pH) O₂-content (mg/L) Water temp ( ͦC) Depth (cm) Con (uS/cm)
6.0.01 2 5.3 15.2 7 6.0.02 1 10 6.0.03 2 10 6.0.04 3 15 6.0.05 6 15 6.0.06 8 10 6.0.07 8 10 average 4.29 11 GPS: 192307, 382703 Time: 16:45 Substrate: leaves (%) 0 wood (%) 0 CPOM (%) 4 FPOM (%) 1 waterplants (%) 0 mosses (%) 0 algae (%) 0 grass (%) 5 Vegetation: trees (%) 2 trees (height m) bush (%) 0 bush (height m) heath (%) 0 heather (height cm) high grasses (%) 5
high grasses (height cm)
low grasses (%) 0
low grasses (height cm)
mosses (%) 1 Water cover: mosses (%) 0 algae (%) 2 Weather: sunshine 1 partial overcast 0 overcast 0 no wind 1 little wind 0 wind 0 rain 0 Dimensions: width (m) 60 length (m) 100 avarege depth (cm) 11.0 water cover (%) 5
estimated total volume (m3) 33.00 Water Properties:
Colour Turbidity (mm)
