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

Abstract:

In undisturbed tropical montane rainforests massive organic layers accommodate the majority of roots and only a small fraction of roots penetrate the mineral soil. We investigated the contribution of vegetation to slope stability in such environments by modifying a standard model for slope stability to include an organic layer with distinct mechanical properties. The importance of individual model parameters was evaluated using detailed measurements of soil and vegetation properties to reproduce the observed depth of 11 shallow landslides in the Andes of southern Ecuador. By distinguishing mineral soil, organic layer and aboveground biomass, it is shown that in this environment vegetation provides a destabilizing effect mainly due to its contribution to the mass of the organic layer (up to 973 t ha
1 under wet conditions). Sensitivity analysis shows that the destabilizing effect of the mass of soil and vegetation can only be effective on slopes steeper than 37.9. This situation applies to 36% of the study area. Thus, on the steep slopes of this megadiverse ecosystem, the mass of the growing forest promotes landsliding, which in turn promotes a new cycle of succession. This feedback mechanism is worth consideration in further investigations of the impact of landslides on plant diversity in similar environments.

Abstract:

Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion. Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the ?black box? of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency. With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes.

Abstract:

Shallow landslides are an important type of natural ecosystem disturbance in tropical montane forests. Due to landslides, vegetation and often also the upper soil layer are removed, and space for primary succession under altered environmental conditions is created. Little is known about how these altered conditions affect important aspects of forest recovery such as the establishment of new tree biomass and species composition. To address these questions we utilize a process-based forest simulation model and develop potential forest regrowth scenarios. We investigate how changes in different trees species characteristics influence forest recovery on landslide sites. The applied regrowth scenarios are: undisturbed regrowth (all tree species characteristics remain like in the undisturbed forest), reduced tree growth (induced by nutrient limitation), reduced tree establishment (due to thicket-forming vegetation and dispersal limitation) and increased tree mortality (due to post-landslide erosion and increased susceptibility). We then apply these scenarios to an evergreen tropical montane forest in southern Ecuador where landslides constitute a major source of natural disturbance. Our most important findings are (a) On the local scale of a single landslide tree biomass recovers within the first 80 years after landslides for most scenarios, but it takes at least 200 years for the post-landslide forest to reach a structure (in terms of stem size distribution) similar to a mature forest. On this scale forest productivity is reduced for most regrowth scenarios. Changes in different tree species characteristics produce distinct spatio-temporal patterns of tree biomass distribution in the first decades of recovery within the landslide disturbed area. These patterns can potentially be used for identifying the dominant processes that drive forest recovery on landslide disturbed sites. (b) On the larger scale of the landscape overall tree biomass is reduced by 9?15% due to landslide disturbances. Overall forest productivity is only slightly reduced (<6%), but landslides increase landscape heterogeneity and produce hotspots of biomass loss and ?blind spots? of forest productivity. Thus landslides have a strong impact on the distribution of biomass in tropical montane forests. This study demonstrates that dynamic forest models are useful tools for complementing field based studies on landslides; they allow for testing alternative hypotheses on different sources of heterogeneity across spatial scales and investigating the influence of landslides on long-term forest dynamics.