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

Abstract:

Background: Tropical forests play an important role in the global carbon (C) cycle. However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood. Montane forests are highly endangered due to logging, land-use and climate change. Our objective was to analyse how the carbon balance changes during forest succession. Methods: In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models. We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions (ravines and lower slopes vs upper slopes and ridges). Results: The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange (NEE) of 9.3 Mg C?(ha?yr)?1 during its early successional stage (0–100 years). In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C?(ha?yr) –1. The simulated variability of the NEE was within the range of the field data. We discovered several forest attributes (e.g., basal area or the relative amount of pioneer trees) that can serve as predictors for NEE for young forest stands (0–100 years) but not for those in the late successional stage (500–1,000 years). In case of young forest stands these correlations are high, especially between stand basal area and NEE. Conclusion: In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity. To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests. With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes. These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change.

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:

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individualbased model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Abstract:

While many studies analyse patterns of tropical land management with a backward-oriented approach that utilises data of the past, we propose to consider future-oriented modelling approaches to find sustainable land-use options. This proposal is illustrated with application examples for advanced growth modelling in tropical forests, a short overview on financial performance analyses for tropical land uses, and the introduction of a newmodelling approach. Thismodelling approach sees tropical land management as a financial portfolio of land-use options. Its advantage is the ability to make transparent effects of financial risk reduction that arise from mixing forestry and agriculture-based land-use options. The approach thus does not analyse land uses as stand-alone options, like most other analyses do. The land-use portfolio modelling shows that sustainable land use may also be financially attractive for farmers, if abandoned farm lands are reforested (with a native tree species in our case) and sustainable management in natural forests is carried out. We conclude that the combination of advanced growth with sound financial modelling may lead to improved bioeconomic models. Developed bioeconomic models are necessary to increase the biological realism and acceptability of the results obtained.

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.

Abstract:

Tropical forests are well known for their exceptional species richness ? high diversity of plant species constitute the basis for an equivalently rich fauna. An astonishing variety of plant life strategies has evolved, manifesting itself also in different compositions of life history traits in trees. This thesis investigates the role of tree life history traits (growth, mortality and recruitment) on different processes structuring species-rich forests. Our study system is a montane rainforest located in the Tropical Andes hotspot of biodiversity in southern Ecuador. Here, we find a mosaic of steep ridges and deeply incised valleys, covered with predominantly broadleaf forest. Forest structure and species composition differ considerably depending on altitude and topographic position. The forest cover is frequently interrupted by scars of landslides, which constitute an important type of natural disturbance in this ecosystem. We utilize ecological models as tools to gain deeper insights into key processes driving the maintenance of tree species richness and affecting forest recovery after landslides. The first part of this thesis concerns the question of species coexistence. We develop a theoretical model to analyze how different trade-offs between life history traits (tree growth, seed dispersal, tree mortality) affect tree species coexistence. We find that the considered trade-offs alone are not sufficient to explain long-term species coexistence. Additional ?stabilizing? mechanisms seem to be indispensable to facilitate coexistence in species-rich forests. Such mechanisms could result from biotic interactions, that alter the relation between inter- and intra-specific competition depending on (local) species abundances (e.g. density-dependent mortality). Other possible coexistence mechanisms likely to be relevant to our particular study system are driven by external, abiotic factors like a complex topography resulting in locally differing habitat types (each supporting a different set of species), or the character of a prevailing disturbance regime (e.g. shallow landslides). In the second part of the thesis, we investigate the growth dynamics of the ridge forest in our study system. To this end, we utilize the process-based forest growth model FORMIND. We show that after calibration, the model successfully reproduces forest dynamics on different levels of complexity (e.g. basal area and stem size distribution). We then use this forest model to investigate the influence of landslide disturbances on forest dynamics both on the local scale of a single landslide and on the landscape scale. On landslide sites, changes in environmental conditions might lead to changes in different tree life history traits. We analyze scenarios with changes in different traits (tree recruitment, tree growth, tree mortality) and find that while tree biomass can recover within the first hundred years after a landslide, the time until forest structure and species composition is restored is considerably longer (approximately 200 years). Changes in different traits result in differing spatial distributions of tree biomass: reduced tree growth leads to a more homogeneous distribution of biomass, whereas reduced recruitment and increased mortality yield a more heterogeneous biomass distribution (?patchy? vegetation). On the landscape level, overall forest biomass is substantially reduced by landslides (8 - I 14%), compared to only 2 -3% of the area marked by visible traces of landslides. Thus this particular type of disturbance considerably influences the total forest carbon balance. In a complementary investigation we study abiotic and biotic factors that potentially trigger landslide occurrence in our study system. For this, we develop an extension of a standard physically-based model of slope stability. We find that due to the predominantly shallow tree roots, some of the observed landslides might be triggered by the vegetation itself. This thesis demonstrates that ecological models are useful tools to gain deeper insights into important processes shaping forest communities. They can be applied for theoretical questions such as the question of species coexistence, as well as for more applied, management related questions like predicting forest recovery after disturbances.

Abstract:

The montane forests of Ecuador are part of one of the world?s hotspots of biodiversity and they also suffer the highest deforestation rate amongst South American countries. The processes that drive the dynamics of these highly diverse ecosystems are poorly understood. This is particularly true for transient dynamics, which are crucial for the protection and sustainable management of such forests. Dynamic simulation models can be used to analyse the growth of forests, but so far they have been applied mostly to temperate forests and to some few tropical lowland forests. In this study we investigate whether a process-based, individual-oriented simulation model like FORMIND is capable of reproducing the dynamics of tropical montane forests. For this purpose we develop a parameterisation for the model and validate the model against field observations of different (structural) patterns. We then analyse the predicted succession dynamics. The model is capable of reproducing the structure and dynamics of mature ridge forest on different levels of complexity. The main results indicate that, in terms of relative abundances of different species groups and stem size distribution in the tree community, our model predicts the observed patterns in the field. Additional field studies and model modifications are required to simulate the succession processes that follow different types of disturbances. FORMIND is a promising tool for the extrapolation of local measurements and for simulating the dynamics of tropical montane forests. Parameterisations of the model for further forest types within the research area are intended. The model has a number of potential applications, ranging from investigating the impact of (different) natural disturbances on forest structure and tree species diversity to analysing different potential management strategies.