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Coupling spatial variations in earthworm density and soil structure, a modelling approach Sébastien Barot Jean-Pierre Rossi Patrick Lavelle UMR 137 Laboratoire d’Écologie des Sols Tropicaux IRD
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Soil fauna tends to have heterogeneous spatial distributions Earthworms Large patches with higher densities (A) Density of the earthworm Chuniodrilus zielae and (B) Millsonia anomala (juvenile) in the savanna of Lamto (Rossi & Lavelle, 1998) A B
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What are the causes of soil fauna distribution? Preexisting soil heterogeneity? Heterogeneous distribution of plant litter and roots Heterogeneity of soil structure (granulometry, soil aggregate size) Heterogeneity in chemical properties Content in organic matter and mineral nutrients
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But the greatest part of the heterogeneity in soil fauna density is not explained by soil heterogeneity (Decaëns 2001, Whalen 2003) Yet, data analyses show that Soil heterogeneity is correlated with soil fauna distribution Can the own dynamics of soil fauna lead to complex spatial patterns? Mobility? Mortality? Spatially dependent factors of auto-regulations? This hypothesis was tested using a spatially explicit simulation model
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Large aggregates are broken into smaller ones by weathering, roots, and earthworms of the eudrilidea family, which are able to dig into large aggregates, and produce small casts (5 mm>Ø ) Description of the model 1: the biology In the savannas of Lamto (Côte d’Ivoire), the earthworm Millsonia anomala compacts the soil by only ingesting small aggregates and by producing large size casts (Ø> 5 mm ) (Blanchard 1997) Experiments suggest that mortality increases when soil structure becomes too unfavorable: not enough small aggregates Hypothesis of auto-regulation by the availability of small aggregates
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Fecundity ( ), minimum mortality ( min ), sensitivity of mortality to % of thin aggregates (e ) A cellular automaton (50 X 50 cells), each cell (1 m 2 ) defined by M. anomala density (n T ), and the percentage of soil mass in small aggregates (sp 1 ) Dispersal follows a normal law Annual rate of production of coarse aggregates by an earthworm (C), rate of destruction of these aggregates for a mean eudrilidea density (D) Description of the model parameters
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Analysis of the model Comparison with observed patterns Variance and mean of the density Spatial distribution Distance Semivariance All parameters but the mobility and the sensitivity of mortality to soil aggregation can be assessed using field studies Spatial autocorrelation
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First results 1: fecundity = 2, only mortality depends on soil structure, mortality then dispersal
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Distance Semivariance C0C0 C0+CC0+C a First results 2: fecundity = 2, only mortality depends on soil structure, mortality then dispersal Spherical model
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How do we get some spatial structure? Increased fecundity Dispersal before mortality Dependence of mortality and fecundity on soil aggregation is sufficient to get long range spatial structures Dependence of dispersal on soil aggregation is not sufficient Very complex spatial patterns arise for certain combinations of parameters values
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An example: fecundity = 4, only mortality depends on soil structure, dispersal then mortality Semivariance Distance (m) 50 m 30 150 0
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Discussion 1 : interpretation of the results The own dynamics of earthworms can lead to long range spatial structures This arises when sensitivity of fecundity or mortality to soil aggregation is high, and when mobility is very low This suggests that it is really the case In these cases the simulated mean and standard deviations of the density are compatible with values observed in the field
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Discussion 2 : limitations and further analyses No size structure, no temporal variation in parameters although they probably depend on climatic variations The dynamic of decompacting earthworms is not taken into account Soil organic matter is not taken into account Link earthworm demographic parameters to ecosystem properties such as the mineralization rate Experimental work To measure the sensitivity of parameters to soil aggregation To measure mobility
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