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