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B I O P O R E DFG-Project 2007-2011
Boris Schröder Loes van Schaik Juliane Palm Institute of Earth & Environmental Sciences University of Potsdam
Erwin Zehe Julian Klaus
KIT Karlsruhe
Linking spatial patterns of anecic earthworm populations, preferential flow pathways and
agrochemical transport in rural catchments: an ecohydrological model approach
© w
ww
.osf
.uk.
com
Biopore
Ecology Hydrology
Ecosystem function
Species distribution
Population dynamics Dynamics
Retention
Risk assessment
Management
Transport
B I O
P O
R E
Aims B
I O P
O R
E
Integrated ecohydrological model linking Spatiotemporal distribution patterns and population dynamics of anecic earthworms Spatiotemporal patterns of connective preferential flowpaths (i.e. earthworm burrows) Spatiotemporal patterns of transport and degradation of agrochemicals considering feedbacks between abiotic and biotic processes
Preferential transport in agroecosystems
Weiherbach catchment: tile-drained arable land (1000 m2) Isoproturon application in three phases, tracer pulse after 10 min
Fast breakthrough in 1.2 m depth Tracer: 20 min, IPU peak: 50 min
0 50 100 150 200 250 300
0.0
0.5
1.0
1.5
2.0
2.5
CBr
end of tracer input
CB
r [m
g/l]
time [min]
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35 CIPU
CIP
U [
mg
/l]
0 50 100 150 200 250 300
0.3
0.4
0.5
0.6
0.7
time [min]
q [
l/s]
q
qbas
Zehe E, Flühler H, 2001. Preferential transport of isoproturon at a plot scale and a field scale tile-drained site. J Hydrol 247: 100-115.
M O
T I
V A
T I O
N
Preferential transport
Tracer experiments Earthworm burrows as transport pathways Fast transport: up to 340 mg/kg into 1 m depth within 2 h
Zehe E, Flühler H, 2001. Slope scale distribution of flow patterns in soil profiles. J Hydrol 247: 116-132.
M O
T I
V A
T I O
N
© J Klaus
additional experiments in 2008/09
Spatial patterns on hillslope scale
Habitat preferences and erosion catena spatial organisation of transport patterns patterns of biogenic structures control transport essential for mobility of pesticides
Catena
Pararendzina
Colluvium
without earthworms
with earthworms
Zehe E, Flühler H, 2001. Slope scale distribution of flow patterns in soil profiles. J Hydrol 247: 116-132.
M O
T I
V A
T I O
N
Transport and Environmental fate of pesticides
Experiments to investigate microbial IPU-degradation Top soil : 8-30 d Deeper soil layers (0,80 – 1m) : depending on location Earthworm burrows : 15 d (first order decay, co-metabolic) Soil matrix : > 150 d
Spritzschlauch Makropore
(Regenwurmröhre)
Bowdenzug
mit Schutzkappe
Austritts-
öffnung
Stechzylinder
a.) Verm essung und b.) Beim pfung der c.) konzentrische Beprobung der
Markierung der Endröhre Endröhre und angrenzender
Makropore Matrix
Nr.,Tiefe
Bolduan R, Zehe E, 2006. Mikrobieller Abbau des Heribizids Isoproturon in Bioporen & der Bodenmatrix …. J Plant Nutr Soil Sci 169: 87-94.
M O
T I
V A
T I O
N
First synthetic modelling approach
Zehe E, Blöschl G, 2004. Predictability of hydrologic response at the plot and catchment scales …. Water Resour Res 40: W10202.
F I R
S T
M O
D E
L S
1) Generation of realistic heterogeneous media Soil matrix: turning bands (mean ks= 10-6 m/s, Variance log(ks)=1, range 3m/50 cm, Zehe et al., HESS 2006) Macropores Density : Poisson-distributed (data: Zehe & Blöschl 2004) Length : Gaussian Burrowing activity : random walk Infiltration capacity : measurements
2) Simulation with CATFLOW
Erwin Zehe
identische Matrixeigenschaften log10(ks/ms-1)
First synthetic modelling approach
Zehe E, Blöschl G, 2004. Predictability of hydrologic response at the plot and catchment scales …. Water Resour Res 40: W10202.
F I R
S T
M O
D E
L S
1) Generation of realistic heterogeneous media Matrix properties are identical on both cases
Erwin Zehe
C [g/kg]
25 mm/5h
F I R
S T
M O
D E
L S
First synthetic modelling approach
2) Simulation with CATFLOW
Erwin Zehe Niklas Hartmann
Hypothesis Feedback between earthworms and transport characteristics determines agroecosystem functioning with respect to the environmental fate of pesticides
Microbial degradation upper soil layer : 15 d lower soil layer : > 150 d
Two study sites Weiherbach Hassberge
B I O
P O
R E
Loess soils with high erodibility and intensive agriculture.
Pelosol soils with high clay content and extensive agriculture, partly nature reserve
Potsdam
Protagonists E
C O
H Y
D R
O L
O G
Y
E C
O S
Y S
T E
M E
N G
I N
E E
R S
Jones CG, Lawton JH & Shachak M 1994. Organisms as ecosystem engineers. - Oikos 69: 373-386. Robinson, C. T., Tockner, K. and Ward, J. V. 2002. The fauna of dynamic riverine landscapes. - Freshwater Biology 47: 661-678.
Crooks JA 2002. Characterizing ecosystem-level consequences of biological invasions: the role of ecosystem engineers. - Oikos 97: 153-166.
Ecosystem engineers / ecosystem engineering Abiotic and biotic effects of ecosystem engineers
E C
O S
Y S
T E
M E
N G
I N
E E
R S
Ecosystem engineers are organisms that directly or indirectly modulate the availability of resources to other organisms by causing physical state changes in biotic or abiotic materials.
www.nrri.umn.edu/worms/identification/ecology_groups.html
Life forms (Bouché 1975)
Bouché MB, 1975. Action de la faune sur les états de la matière organique dans les écosystèmes. In: Kilbertius G, O. R, A. M, Cancela da Fonseca JA (eds.), Humification et biodégradation. Pierron, pp. 157-168.
E A
R T
H W
O R
M S
Earthworms
pH
till vs no-till
Compaction
Texture
Moisture
Organic layer
Intensity
Soil properties
Climate
Land use
Nutrients, C/N
Competition
Habitat factors
Biotic interactions
Predation
Temperature Precipitation
Vegetation
E C
O L
O G
Y
Food
Org. matter
Vegetation / litter
controlling earthworm distributions
A) Hierarchical, multi-scale earthworm distribution model Understanding and prediction of distribution patterns depending on soil, terrain and land use parameters … … and observational data
Modules 1/3 B
I O P
O R
E
Schröder B, 2008. Challenges of species distribution modelling belowground. J Plant Nutr Soil Sci 171: 325-337.
Earthworm extraction with mustard solution on a 50 50 cm2 plots (stratified random sampling)
Counting macropores in different soil depths
Unbalanced nested sampling design to analyse spatial heterogeneity
Multi-scale spatial distribution of soil organisms
Ettema CH, Wardle DA, 2002. Spatial soil ecology. Trends Ecol Evol 17: 177-183.
S P
E C
I E S
D I
S T
R I B
U T
I O
N
Presence/absence data Abundance data
&
Pred
icti
on
P > 0.8
P < 0.2 0.2 < P < 0.5 0.5 < P < 0.8
Spatial extrapolation
Statistics
0.0
0.2
0.4
0.6
0.8
1.0
Prob
abili
ty o
f oc
curr
ence
Habitat factor
Disturbance
Ressources data [0|1]
Expl
anat
ion
Habitat factors soil attributes disturbance frequency patch isolation land use Re
leva
nce
model
data [ ]
Limit. factors
S P
E C
I E S
D I
S T
R I B
U T
I O
N
Species distribution models / realised niche
Validation
Independent data
&
Schröder B, 2008. Species in dynamic landscapes. Habil. Thesis, Potsdam University
M E
T H
O D
S Species distribution modelling – statistical methods
Standard /"Simple" methods Generalised linear models GLM Generalised additive models GAM Classification and regression trees CART Ensemble forecasting methods (machine learning) Random Forest RF Boosted Regression Trees BRT Multivariate adaptive regression splines MARS
Several methods for variable selection, regularisation, multi-model inference Internal validation via bootstrapping or crossvalidation or external Check/controlling for residual spatial autocorrelation
1-specificity (false positives)
Sens
itivi
ty (t
rue
posi
tives
)
with ploughing without ploughing
Occ
urre
nce
prob
abili
ty
SDMs – first results (simple logistic regressions) F
I R S
T R
E S
U L
T S
Performance: ROC-plot AUC = 0.86, R²N=0.43
Strong effect of land use practice (no-till > tillage) Low elevation = higher groundwater level Low penetration depth = soil compaction
Juliane Palm
R E
S U
L T
S
Further predictors (contribution) Heat load (9.2%) pH (6.5%) Compaction (6.0%)
Wetness index 35.3% Ploughing 23.0% Soil org. matter 10.3 %
yes no
Juliane Palm
Species distribution models – Lumbricus terrestris
Higher occurrence probability in areas with low wetness index, no ploughing and higher soil organic matter content
Occ
urre
nce
prob
abili
ty
logi
t sc
ale
Good model performance after cross- validation
AUC = 0.5 : null model 0.8 AUC 0.9 : excellent AUC = 1 : perfect classification
Predicted 1-Specificity (false positives)
Sens
itivi
ty (t
rue
posi
tives
)
Obs
erve
d
AUC.train=0.90 AUC.cv=0.76 R2
N=0.43
Model performance
Boosted regression trees: Partial dependency plots
[%]
10 30 50
without ploughing
with ploughing
Depth [cm] N
umbe
rs o
f m
acro
pore
s
Macropores F
I R S
T R
E S
U L
T S
Juliane Palm
Stage-structured population dynamic model Understanding and prediction of population dynamics of anecic earthworms … … depending on soil properties (temperature, moisture), resource availability and disturbance (land use) … considering active and passive dispersal Matrix population models (Klok et al. 1997; Pelosi et al. 2008), dynamic energy budget model (Jager et al. 2006)
Modules 2/3 B
I O P
O R
E
Pelosi Cet al. 2008. WORMDYN: A model of Lumbricus terrestris population dynamics in agricultural fields. Ecol Model 218: 219-234. Jager T, Reinecke SA, Reinecke AJ, 2006. Using process-based modelling to analyse earthworm life cycles. Soil Biol Biochem 38: 1-6.
Klok C et al. 1997. Assessing the effects of abiotic environmental stress on population growth in Lumbricus rubellus. SBB 29: 287-293.
Grow
th r
ate
Temperature [°C]
O‘Neill functions
Life cycle graph - stage-structured population model A
P P
R O
A C
H
43
32
21
41
Pθ)(T,G00
0Pθ)(T,G0
00Pθ)(T,G
(T)F00P
M
Transition matrix
Implementation: Anett Schibalski
cocoon juvenile subadult adult G2 G1 G3
P1
F4
P2 P3 P4
Life cycle graph
ttt nTMn ,1
Simulation model
Pagel J, Fritzsch K, Biedermann R, Schröder B, 2008. Annual plants under cyclic disturbance regimes …Ecol Appl 18: 2000-2015. Söndgerath D, Schröder B, 2002. Population dynamics and habitat connectivity affecting spatial spread …. Landscape Ecol 17: 57-70.
Modules 3/3 B
I O P
O R
E
C) Stochastic transport model Predicting infiltration, transport and sorption of tracers and pesticides … … depending on spatiotemporal distribution of connective macropores (earthworm burrows). CATFLOW
Maurer T, 1997. Physikalisch begründete, zeitkontinuierliche Modellierung des Wassertransports in kleinen ländlichen Einzugsgebieten. PhD thesis, Universität Karlsruhe
Zehe E, Flühler H, 2001. Slope scale distribution of flow patterns in soil profiles. J Hydrol 247: 116-132.
Loes van Schaik Erwin Zehe Julian Klaus
Summary
Results from previous projects show a) Strong effects of earthworm burrows on transport of pesticides b) Strong need for an integrated ecohydrological modelling approach
Preliminary results show a) Importance of management (no-till) for earthworm distribution b) Strong effect of soil moisture and temperature on earthworm abundance c) High spatial and seasonal variability in earthworm abundances at both sites d) Significant preferential flow at both study sites … still a long way to go …
B I O
P O
R E