Supporting information:
Simulated climate change increases environmental harshness and alters connectivity in a
temporary pool metacommunity
Appendix S1: Guideline for the use of the base code of the hydrological model
The R scripts necessary to run the simulations can be found in the electronic supplementary
material. The example script is written for a fictive pool cluster as an example and can be
adapted to pools with other morphometries and to larger sets of pools. The fictive pool
cluster consists of eight pools and contains two symmetric and two asymmetric connections.
To obtain the necessary pool specific parameters (evaporation factors and parameters for
conductivity), two other scripts should be run beforehand.
Three scripts are available:
1. Script to obtain pool specific evaporation factor C.txt
2. Script to obtain parameters necessary for simulation of conductivity.txt
3. Script for general simulation.txt
The first script ("Script to obtain pool specific evaporation factor C") should be used to
calculate the optimal pool specific evaporation factors.
The necessary input files are:
1. Precipitation (50 days).txt: Input file with Date and Precipitation (50 days)
2. Monthly evaporation data.txt: Input file to calculate daily evaporation based on
monthly evaporation (Number.Month, Name.Month and Monthly.Evaporation)
3. Pool specific data (without C values).txt: Input file poolspecific terms (Pool.Number,
upp, R, opp)
4. Observed water levels (50 days).txt: Input file with observed water levels (50 days)
The second script is used to obtain the parameters necessary for simulation of conductivity.
Necessary input files for this are:
1. Conductivity.txt: Input file with observed conductivity values (26 days)
2. Observed water levels (26 days).txt: Input file with observed water levels (26 days)
The third script is the script to simulate water levels and conductivity and to obtain
hydroregime and connectivity variables. The fictive pool cluster has 2 asymmetric and 2
symmetric connections. The asymmetric connections are between pool 1 and 3 and pool 2
and 4, the asymmetric connections are between pool 5 and 7 and pool 6 and 8. It is good to
split up the script and rank the pools according to the characteristics of their connections
(first the ones without the pools without connections, then the pools with connections,
starting with those that overflow into other pools and ending with those that may receive
overflowing water from donor pools). In the example it is recommended to put all the pools
with an outgoing connection (1 and 2) in a loop, followed by the group that receives input
from these pools (3 and 4) in the same order as the pools they are connected with. For pools
that receive overflow input from several pools, it is necessary to calculate all data first for
the source pools, and place this receiving pool later in the list so data necessary as input data
can be selected from the previously created data.
The necessary input files are:
1. Precipitation.txt: Input file with Date and Precipitation (6 years)
2. Monthly evaporation data.txt: Input file to calculate the daily evaporation
(Number.Month, Name.Month and Monthly.Evaporation)
3. Pool specific data.txt: Input file poolspecific terms (Pool.Number, upp, R, opp, C)
This file should be created using the first script: "Script to obtain pool specific
evaporation"
Appendix S2: Cluster analysis
For illustrative purposes, pool-basins were subdivided into groups of habitats with similar
hydrologies using cluster analysis (Fig. S.1). Hydrological variables included in the analyses
are standardized values of mean hydroperiod, mean inundation frequency and their
corresponding standard deviations. The cluster analysis (hclust function, stats package) was
performed using unweighted pair-group average as linkage rule on a similarity matrix of
Euclidean distances (dist function, stats package).
In general, three main groups of hydroregimes can be defined supplemented with one
grouping of the two most ephemeral basins (E) in a separate clade (basin depth: 5-27 cm)
and one grouping of the two deepest and largest basins (XD, basin depth: 32-38 cm). These
groupings largely corresponded to differences in basin morphometry (Fig. S.2). A first clade
(S) groups shallow pools (4.5-28 cm) with short hydroperiods and high inundation frequency.
A second clade (I) groups pools with an intermediate basin depth (16-37 cm) with longer
hydroperiods, higher SD hydroperiod and a lower inundation frequency. Finally, the last
clade (D) contains the pools with the deepest basins (23-34 cm) which on average
experience the longest hydroperiods, the highest variation in hydroperiod and the lowest
number of inundations per year.
Fig. S.1 Cluster dendogram based on hydrological variables (standardized values of mean
hydroperiod and mean inundation frequency en both their corresponding standard deviations ) using
unweighted pair-group average dividing the pool basins in 5 groups (E, S, I, D and XD).
One-way ANOVAs showed significant differences in basin depth and basin surface area
between pool types defined using cluster analysis (Fig. S.2). Tukey post-hoc tests revealed a
significant difference between the S-clade and both I-and D-clades.
Fig. S.2 Mean (± SE) (a) basin depth and (b) logarithmic transformed basin surface area for the three
major pool types and corresponding F and P-value from an one-way ANOVA. Tukey post-hoc
significances are given by a and b. The two pools of the two additional groups (E and XD) are plotted
as supplementary values (triangles).
Table S1: Parameters estimates (coefficient a and b and corresponding P-values) for the relation between
water level and observed conductivity using a non-linear least square estimation (condt = a x e(-b x dt)) with
corresponding r² and residual sum of squares for 25 consecutive days in 2005 (testing dataset). P-values:
*<0,1;**<0.05; ***<0.01; ****<0.001; *****<0.0001; n.s. non significant.
Pool
1
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
31
32
33
34
35
36
38
39
40
41
42
43
44
Avg
SD
coef
a 74.73
173.05
116.46
64.98
144.99
104.25
585.83
639.43
162.13
319.17
304.00
431.07
354.24
686.60
222.17
825.97
430.59
350.52
254.96
208.86
74.58
1152.57
555.98
360.19
408.10
955.09
1171.70
1819.65
261.88
155.41
413.94
331.54
369.96
217.42
112.53
88.06
134.36
173.71
673.15
88.54
149.84
450.44
229.16
depth versus conductivity
p
coef p
r²
*value 0.01
*****
b
value 0.83
*a
0.04 **
0.76
b
**
0.02 ***
0.78
**
0.00 ***
0.97
**** 0.01 ***
0.99
***** 0.01 ***** 0.87
**** 0.02 **** 0.90
***
0.02 ***
0.80
***
0.01 ***
0.81
***** 0.02 ***** 0.96
***** 0.02 ***** 0.98
***** 0.02 ***** 0.89
***** 0.05 ***** 0.98
***** 0.02 **** 0.95
***** 0.02 ***** 0.99
***** 0.02 ***** 0.99
***
0.02 **** 0.77
***
0.02 ***
0.89
***
0.06 ***
0.92
**
0.02 **** 0.91
**
0.01 ***
1.00
***** 0.02 ***** 0.93
**
0.03 **
0.76
***** 0.02 ***** 0.99
**
0.02 **** 0.69
**
0.02 **** 0.79
***** 0.02 ***** 0.99
***** 0.01 ***** 0.99
**** 0.02 ***** 0.84
**
0.01 ***
0.83
***** 0.01 ***** 0.92
***
0.04 **** 0.77
***
0.02 **** 0.81
***** 0.02 ***** 0.95
**
0.01 ***
0.92
n.s.
0.01 n.s.
0.45
n.s.
0.02 ***
0.74
**
0.03 ***
0.55
**
0.05 **
0.78
n.s.
0.01 *
0.20
n.s.
0.03 n.s.
0.52
**
0.05 **
0.77
**
0.02 ***
0.60
0.83
0.17
Residual.
183.24
sum.of.
1626.70
squares
843.87
12.20
15.87
489.36
5983.36
21707.80
1803.73
534.57
183.20
6481.65
393.68
836.99
74.83
251.09
30860.95
2173.99
849.60
1162.82
0.67
17508.14
17968.61
237.93
31948.12
89673.40
2870.25
12280.41
4904.85
1070.93
4733.33
12177.39
5790.73
299.66
54.54
1217.93
1553.54
5187.88
15968.89
2882.08
1471.43
5693.75
6173.31
Appendix S3: Detailed discussion of model validation
In this study, we used a model based on a simple water balance equation to simulate water
levels in a cluster of temporary rock pools. The strong fit and significant correlation
coefficients between simulated and observed water levels support that predictions are
reliable. Validation was based on a training dataset which was not used for calibration and
parameterization indicating that the model can not only be used to reconstruct, but also to
predict water levels based on historical rainfall and evaporation data. The fit for the 36
deeper pools also considered in a previous study (Vanschoenwinkel et al., 2009) was higher
than the fit for the 8 ephemeral pools. Considering only these 36 pools resulted in a higher
average r² of 96 ± 4.7%.
The fit between observed values of conductivity and model simulated values was very good
for the 36 larger pools. For the 8 ephemeral pools, results were less satisfactory. These pool
basins are often very shallow with water levels < 1 cm, which makes conductivity
measurements at these moment less reliable because the probe cannot be fully submerged
and conductivity has to be measured in a beaker. In addition, these shallow pools show less
variation in conductivity during their inundations. The shorter hydroperiod of these pools
also imply less measuring points compared to the long lived pools in deeper basins. The
combination of these elements can explain the poorer fit for the 8 shallow ephemeral pools.
Fig S3: Model simulated responses (mean ± SE) of hydroperiod (HP), inundation frequency (IF) and
minimum conductivity under different climate change scenarios with potential changes in intraannual distribution of rainfall. Responses are plotted separately for shallow (left panels) and deep
pools (right panels) as well as for pools with an intermediate basin depth (middle panels). E0 = no
change in evaporation, E10 = +10% evaporation, E20 = +20% evaporation. Scenario 0 = present day
situation, scenario 1-4 = +0% total yearly precipitation (scenario 1 = DJF (December, January and
February) rainfall +0%, JJA (June, July and August) rainfall -5%, scenario 2 = DJF rainfall +0%, JJA
rainfall -10%, scenario 3 = DJF rainfall +0%, JJA rainfall -5%, scenario 4 = DJF rainfall +0%, JJA rainfall 10%), scenario 5-8 = -5% total yearly precipitation (scenario 5 = DJF rainfall +0%, JJA rainfall -5%,
scenario 6 = DJF rainfall +0%, JJA rainfall -10%, scenario 7 = DJF rainfall +0%, JJA rainfall -5%, scenario
8 = DJF rainfall +0%, JJA rainfall -10%). Size classes (mean depth ± SE): Shallow: 121.6 ± 19.7;
Intermediate: 240.2 ± 14.9; Deep: 271.4 ± 18.4).
Fig S4: Model simulated responses (mean ± SE) of number of connections per channel per year, total
number of connections per year, the volume of water running through the channels and mean length
of established symmetric connections under different climate change scenarios with potential
changes in intra-annual distribution of rainfall. Responses are plotted separately for symmetric (right
panels) and asymmetric connections, which are plotted separately for pools with a shallow (left
panels) or deeper pool basins (middle panels). The deeper source pools are divided in deep (D) and
intermediate (I) pools. E0 = no change in evaporation, E10 = +10% evaporation, E20 = +20%
evaporation. Scenario 0 = present day situation, scenario 1-4 = +0% total yearly precipitation
(scenario 1 = DJF rainfall +0%, JJA rainfall -5%, scenario 2 = DJF rainfall +0%, JJA rainfall -10%, scenario
3 = DJF rainfall +0%, JJA rainfall -5%, scenario 4 = DJF rainfall +0%, JJA rainfall -10%), scenario 5-8 = 5% total yearly precipitation (scenario 5 = DJF rainfall +0%, JJA rainfall -5%, scenario 6 = DJF rainfall
+0%, JJA rainfall -10%, scenario 7 = DJF rainfall +0%, JJA rainfall -5%, scenario 8 = DJF rainfall +0%, JJA
rainfall -10%). Size classes (mean depth ± SE): Shallow: 121.6 ± 19.7; Intermediate: 240.2 ± 14.9;
Deep: 271.4 ± 18.4).
Fig S5: : Model simulated responses of percentage of suitable inundations (mean ± SE) for the rock
pool fairy shrimp B. wolfi considering only inundations longer than 6 days, inundations for which the
minimum conductivity is lower than 125 µS/cm and inundations for which both conditions are
fulfilled under different climate change scenarios with potential changes in intra-annual distribution
of rainfall. Responses are plotted separately for shallow (left panels) and deep pools (right panels) as
well as for pools with an intermediate basin depth (middle panels). E0 = no change in evaporation,
E10 = +10% evaporation, E20 = +20% evaporation. Scenario 0 = present day situation, scenario 1-4 =
+0% total yearly precipitation (scenario 1 = DJF rainfall +0%, JJA rainfall -5%, scenario 2 = DJF rainfall
+0%, JJA rainfall -10%, scenario 3 = DJF rainfall +0%, JJA rainfall -5%, scenario 4 = DJF rainfall +0%, JJA
rainfall -10%), scenario 5-8 = -5% total yearly precipitation (scenario 5 = DJF rainfall +0%, JJA rainfall 5%, scenario 6 = DJF rainfall +0%, JJA rainfall -10%, scenario 7 = DJF rainfall +0%, JJA rainfall -5%,
scenario 8 = DJF rainfall +0%, JJA rainfall -10%). Size classes (mean depth ± SE): Shallow: 121.6 ± 19.7;
Intermediate: 240.2 ± 14.9; Deep: 271.4 ± 18.4).