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

Abstract:

Background: Since the 1990’s, afforestation programs in the páramo have been implemented to offset carbon emissions through carbon sequestration, mainly using pine plantations. However, several studies have indicated that after the establishment of pine plantations in grasslands, there is an alteration of carbon pools including a decrease of the soil organic carbon (SOC) pool. The aim of this study is to investigate the impact of the establishment of pine plantations on the carbon stocks in different altitudes of the páramo ecosystem of South Ecuador. Results: At seven locations within an elevational gradient from 2780 to 3760 m a.s.l., we measured and compared carbon stocks of three types of land use: natural grassland, grazed páramo, and Pinus patula Schlltdl. & Cham. plantation sites. For a more accurate estimation of pine tree carbon, we developed our own allometric equations. There were significant (p < 0.05) differences between the amounts of carbon stored in the carbon pools aboveground and belowground for the three types of land use. In most of the locations, pine plantations revealed the highest amounts of aboveground and belowground carbon (55.4 and 6.9 tC/ha) followed by natural grassland (23.1 and 2.7 tC/ha) and grazed páramo sites (9.1 and 1.5 tC/ha). Concerning the SOC pools, most of the locations revealed significant lower values of plantations’ SOC in comparison to natural grassland and grazed páramo sites. Higher elevation was associated with lower amounts of pines’ biomass. Conclusions: Even though plantations store high amounts of carbon, natural páramo grassland can also store substantial amounts above and belowground, without negatively affecting the soils and putting other páramo ecosystem services at risk. Consequently, plans for afforestation in the páramo should be assessed case by case, considering not only the limiting factor of elevation, but also the site quality especially affected by the type of previous land use.

Abstract:

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of Tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours Forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Abstract:

Quantification of nutrient stocks and their temporal changes are considered of prime importance in farm-ing systems of the humid tropics to answer the question of sustainable management. The research areain the Southern Ecuadorian Andes included forest, annual (0–5 years old) and different aged perennial(0–5, 6–10, 11–20, 21–30) and pasture (0–5, 6–10, 11–20, 21–30, >30) sites. Soil organic carbon (SOC),total and plant available soil nutrient stocks and nutrient balances were investigated to assess temporalsoil nutrient dynamics in relation to management activities. Forest conversion by slash-and-burn caused a decrease in SOC stocks in all three land-uses amounting between 14% and 19%. This was mainly due tothe absence of an organic layer and losses in the upper five cm of the mineral soil. Stocks of exchangeablebases and pH values increased in annuals which however, had the most negative nutrient balance of allland-uses amounting to ?128, ?25 and ?226 kg ha?1 a?1 for N, P and K, respectively. The abandonmentof annual sites after five years was linked to a shortage of available N and P due to low-external-input management which caused SOC stock decreases. Major soil nutrient changes in perennials and pasturesdid not occur directly but 6–20 years after forest conversion with increases in stocks for total N, P, S and exchangeable bases above forest level. SOC stocks of medium aged perennials and pastures increased above forest level in the mineral soil. Easily available inorganic N and P stocks remained low throughout the chronosequence in perennials and pastures, indicating a quick uptake by plant roots and microor-ganisms. Compared to medium aged sites, oldest perennials and pastures showed a strong decrease in SOC (?28% and ?16%) and soil nutrient stocks. Stocks for total N, P, S and exchangeable bases were up to50% below those of medium aged sites in oldest perennials and pastures.