@misc{BendixAguireBecketal., author = {Bendix, J{\"o}rg and Aguire, Nicolay and Beck, Erwin and Br{\"a}uning, Achim and Brandl, Roland and Breuer, Lutz and B{\"o}hning‑Gaese, Katrin and Paula, Mateus Dantas de and Hickler, Thomas and Homeier, J{\"u}rgen and Inclan, Diego and Leuschner, Christoph and Neuschulz, Eike L. and Schleuning, Matthias and Suarez, Juan P. and Trachte, Katja and Wilcke, Wolfgang and Windhorst, David and Farwig, Nina}, title = {A research framework for projecting ecosystem change in highly diverse tropical mountain ecosystems}, series = {Oecologia}, volume = {195}, journal = {Oecologia}, number = {3}, issn = {0029-8549}, doi = {10.1007/s00442-021-04852-8}, pages = {589 -- 600}, abstract = {Tropical mountain ecosystems are threatened by climate and land-use changes. Their diversity and complexity make projections how they respond to environmental changes challenging. A suitable way are trait-based approaches, by distinguishing between response traits that determine the resistance of species to environmental changes and effect traits that are relevant for species' interactions, biotic processes, and ecosystem functions. The combination of those approaches with land surface models (LSM) linking the functional community composition to ecosystem functions provides new ways to project the response of ecosystems to environmental changes. With the interdisciplinary project RESPECT, we propose a research framework that uses a trait-based response-effect-framework (REF) to quantify relationships between abiotic conditions, the diversity of functional traits in communities, and associated biotic processes, informing a biodiversity-LSM. We apply the framework to a megadiverse tropical mountain forest. We use a plot design along an elevation and a land-use gradient to collect data on abiotic drivers, functional traits, and biotic processes. We integrate these data to build the biodiversity-LSM and illustrate how to test the model. REF results show that aboveground biomass production is not directly related to changing climatic conditions, but indirectly through associated changes in functional traits. Herbivory is directly related to changing abiotic conditions. The biodiversity-LSM informed by local functional trait and soil data improved the simulation of biomass production substantially. We conclude that local data, also derived from previous projects (platform Ecuador), are key elements of the research framework. We specify essential datasets to apply this framework to other mountain ecosystems.}, language = {en} } @misc{GrigusovaLimbergerMurkuteetal., author = {Grigusova, Paulina and Limberger, Oliver and Murkute, Charuta and Pucha-Cofrep, Franz and Gonzalez-Jaramillo, Victor Hugo and Fries, Andreas and Windhorst, David and Breuer, Lutz and Dantas de Paula, Mateus and Hickler, Thomas and Trachte, Katja and Bendix, J{\"o}rg}, title = {Radiation partitioning in a cloud-rich tropical mountain rain forest of the S-Ecuadorian Andes for use in plot-based land surface modelling}, series = {Dynamics of atmospheres and oceans : planetary fluid, climatic and biogeochemical systems}, volume = {110}, journal = {Dynamics of atmospheres and oceans : planetary fluid, climatic and biogeochemical systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0377-0265}, doi = {10.1016/j.dynatmoce.2025.101553}, pages = {1 -- 16}, abstract = {Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (\%Dif; R²=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R²=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R² (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.}, language = {en} } @misc{BendixLimburgerBreueretal., author = {Bendix, J{\"o}rg and Limburger, Oliver and Breuer, Lutz and Dantas de Paula, Mateus and Fries, Andreas and Gonzalez-Jaramillo, Victor Hugo and Grigusova, Paulina and Hickler, Thomas and Murkute, Charuta and Pucha-Cofrep, Franz and Trachte, Katja and Windhorst, David}, title = {Simulation of latent heat flux over a high altitude pasture in the tropical Andes with a coupled land surface framework}, series = {The science of the total environment}, volume = {981}, journal = {The science of the total environment}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-1026}, doi = {10.1016/j.scitotenv.2025.179510}, pages = {1 -- 21}, abstract = {Latent heat flux is a central element of land-atmosphere interactions under climate change. Knowledge is particularly poor in the biodiversity hotspot of the Andes, where heat flux measurements using eddy covariance stations are scarce and land surface models (LSMs) often oversimplify the complexity of the ecosystems. The main objective of this study is to perform latent heat flux simulations for the tropical South Eastern (SE) Ecuadorian Andes using a coupled LSM framework, and to test the performance with heat flux and soil moisture data collected from a tropical high-altitude pasture. Prior to testing, we applied multi-criteria model calibration of sensitive model parameters, focusing on improving simulated soil water conditions and radiation fluxes as a prerequisite for proper heat flux simulations. The most sensitive parameters to improve soil moisture and radiation flux simulations were soil porosity, saturated hydraulic conductivity, leaf area index, soil colour and NIR (Near Infrared) leaf optical properties. The best calibrated model run showed a very good performance for half-hourly latent heat flux simulations with an R² of 0.8 and an RMSE of 34.0 W m⁻², outperforming simulations with uncalibrated and uncoupled LSM simulations in comparable areas. The slight overall overestimation in the simulated latent heat flux can be related to (i) simulation uncertainties in the canopy heat budget, (ii) an imbalance in the observed flux data and (iii) slight overestimations in the simulated soil moisture. Although our study focuses on latent heat fluxes and their relation to simulated radiation fluxes and soil moisture, model outputs of sensible heat fluxes were also discussed. The systematic overestimation of sensible heat flux in the model seems to be mainly a result of overestimated canopy temperatures. The improved simulation for latent heat flux has a high translational potential to support land use strategies in the tropical Andes under climate change.}, language = {en} }