Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Resumen:

Soil-landscapes are diverse and complex due to the interaction of pedogenetic, geomorphological and hydrological processes. The resulting soil profile reflects the balance of these processes in its properties. Early conceptual models have by now resulted into quantitative soil-landscape models including soil variation and its unpredictability as a key soil attribute. Soils in the Andean mountain rainforest area of southern Ecuador are influenced by hillslope processes and landslides in particular. The lack of knowledge on the distribution of soils and especially physical soil properties to understand slope failure, resulted in the study of this particular soil-landscape by means of statistical models relating soil to terrain attributes, i.e. predictive soil mapping. A 24 terrain classes comprising sampling design for soil investigation in mountainous areas was developed to obtain a representative dataset for statistical modelling. The soils were investigated by 56 profiles and 315 auger points. The Reference Soil Groups (RSGs) Histosol, Stagnosol, Umbrisol, Cambisol, Leptosol and Regosol were identified according to the World Reference Base for Soil Resources (WRB). Classification tree models and a probability scheme based on WRB hierarchy were applied to include RSG prediction uncertainty in a digital soil map. Histosol probability depended on hydrological parameters; highest Stagnosol probability was found on slopes < 40° and above 2146 m a.s.l. Poor model performance, probably due to the prediction of complex categories (RSGs) and WRB inconsequence (absolute and relative value criteria), led to the proposal of ?incomplete soil classification? by relating the thickness of the WRB?s diagnostic horizons as percentage to the upper 100 soil centimetres, including the organic layer. Typical diagnostic horizons histic, humic, umbric, stagnic and cambic were regionalised in their thickness and occurrence probability by classification and regression trees (CART). Prediction uncertainty was addressed with hundredfold model runs based on different random Jackknife partitions of the dataset. Whether the first mineral soil horizon displays stagnic properties or not, likely depends on physical soil properties in addition to terrain parameters. Incomplete soil classification resulted in histic and stagnic soil parts dominating the first 100 cm of the soil volume for most of the research area. While soil profiles and auger points were described in their horizon composition, thickness, Munsell colour and soil texture by finger method (FAO, 2006), soil cohesion, bulk density and texture by pipette and laser were analysed in soil profiles only. Texture results by pipette compared to laser method, showed the expected shift to higher silt and lower clay contents. Linear regression equations were adapted. Pedotransfer functions to predict physical soil properties from the bigger auger dataset analysed by field texture method only, could not be developed, because field texture analysis did not provide satisfying results. It was therefore not possible to correct its results with the more precise laboratory data. Comparing CART and Random Forest (RF) in their model performance to predict topsoil texture and bulk density as well as mineral soil thickness by hundredfold model runs with random Jackknife partitions, RF predictions resulted more powerful. Altitude a.s.l. was the most important predictor for all three soil parameters. Increasing sand/ clay ratios with increasing altitude, on steep slopes and with overland flow distance to the channel network are caused by shallow subsurface flow removing clay particles downslope. Deeper soil layers are not influenced by the same process and therefore showed different texture properties. Terrain parameters could only explain the spatial distribution of topsoil properties to a limited extent, subsoil properties could not be predicted at all. Other parameters that likely influence soil properties within the investigation area are parent material and landslides. Strong evidence was found that topsoil horizons did not form from the bedrock underlying the soil profile. Parent material changes within short distance and often within one soil profile. Landslides have a strong influence on soil-landscape formation in shifting soil and rock material. Soil mechanical and hydrological properties in addition to terrain steepness were hypothesized to be the major factors in causing soil slides. Thus, the factor of safety (FS) was calculated as the soil shear ratio that is necessary to maintain the critical state equilibrium on a potential sliding surface. The depth of the failure plane was assumed at the lower boundary of the stagnic soil layer or complete soil depth, depending on soils being stagnic or non-stagnic. The FS was determined in dependence of soil wetness referring to 0.001, 0.01, 0.1 and 3 mm/h net rainfall rate. Sites with a FS &#8805; 1 at 3 mm/h (complete saturation) were classified as unconditionally stable, sites with a FS < 1 at 0.001 mm/h as unconditionally unstable. The latter coincided quite well with landslide scars from a recent aerial photograph.

Resumen:

Soil landscape modelling is based on understanding the spatial distribution patterns of soil characteristics. A model relating the soil?s properties to its position within the landscape is used to predict soil properties in other similar landscape positions. To develop soil landscape models, the interaction of geographic information technology, advanced statistics and soil science is needed. The focus of this work is to predict the distribution of the different soil types in a tropical mountain forest area in southern Ecuador from relief and hydrological parameters using a classification tree model (CART) for soil regionalisation. Soils were sampled along transects from ridges towards side valley creeks using a sampling design with 24 relief units. Major soil types of the research area are Histosols associated with Stagnosols, Cambisols and Regosols. Umbrisols and Leptosols are present to a lesser degree. Stagnosols gain importance with increasing altitude and with decreasing slope angle. Umbrisols are to be found only on slopes <30°. Cambisols occurrence might be related to landslides.The CART model was established by a data set of 315 auger sampling points. Bedrock and relief curvature had no influence on model development. Applying the CART model to the research area Histosols and Stagnosols were identified as dominant soil types. Model prediction left out Cambisols and overestimated Umbrisols, but showed a realistic prediction for Histosols, Stagnosols and Leptosols.