TY - JOUR
T1 - A scalable model of vegetation transitions using deep neural networks
AU - Rammer, Werner
AU - Seidl, Rupert
N1 - Publisher Copyright:
© 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
PY - 2019/6
Y1 - 2019/6
N2 - In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent. We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (103–106) of potential states based on its structure, composition and functioning. Transition probabilities between states are estimated via a deep neural network (DNN) trained on observed or simulated vegetation transitions in combination with environmental variables. The impact of vegetation transitions on important ecological indicators is quantified by probabilistically linking attributes such as carbon storage and biodiversity to vegetation states. Here, we describe the SVD approach and present results of applying the framework in a meta-modelling context. We trained a DNN using simulations of a process-based forest landscape model for a complex mountain forest landscape under different climate scenarios. Subsequently, we evaluated the ability of SVD to project long-term vegetation dynamics and the resulting changes in forest carbon storage and biodiversity. SVD captured spatial (e.g. elevational gradients) and temporal (e.g. species succession) patterns of vegetation dynamics well, and responded realistically to changing environmental conditions. In addition, we tested the computational efficiency of the approach, highlighting the utility of SVD for country- to continental scale applications. SVD is the—to our knowledge—first vegetation model harnessing deep neural networks. The approach has high predictive accuracy and is able to generalize well beyond training data. SVD was designed to run on widely available input data (e.g. vegetation states defined from remote sensing, gridded global climate datasets) and exceeds the computational performance of currently available highly optimized landscape models by three to four orders of magnitude. We conclude that SVD is a promising approach for combining detailed process knowledge on fine-grained ecosystem processes with the increasingly available big ecological datasets for improved large-scale projections of vegetation dynamics.
AB - In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent. We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (103–106) of potential states based on its structure, composition and functioning. Transition probabilities between states are estimated via a deep neural network (DNN) trained on observed or simulated vegetation transitions in combination with environmental variables. The impact of vegetation transitions on important ecological indicators is quantified by probabilistically linking attributes such as carbon storage and biodiversity to vegetation states. Here, we describe the SVD approach and present results of applying the framework in a meta-modelling context. We trained a DNN using simulations of a process-based forest landscape model for a complex mountain forest landscape under different climate scenarios. Subsequently, we evaluated the ability of SVD to project long-term vegetation dynamics and the resulting changes in forest carbon storage and biodiversity. SVD captured spatial (e.g. elevational gradients) and temporal (e.g. species succession) patterns of vegetation dynamics well, and responded realistically to changing environmental conditions. In addition, we tested the computational efficiency of the approach, highlighting the utility of SVD for country- to continental scale applications. SVD is the—to our knowledge—first vegetation model harnessing deep neural networks. The approach has high predictive accuracy and is able to generalize well beyond training data. SVD was designed to run on widely available input data (e.g. vegetation states defined from remote sensing, gridded global climate datasets) and exceeds the computational performance of currently available highly optimized landscape models by three to four orders of magnitude. We conclude that SVD is a promising approach for combining detailed process knowledge on fine-grained ecosystem processes with the increasingly available big ecological datasets for improved large-scale projections of vegetation dynamics.
KW - deep neural networks
KW - ecological forecasting
KW - simulation modelling
KW - state and transition modelling
KW - upscaling
KW - vegetation dynamics
KW - vegetation transitions
UR - http://www.scopus.com/inward/record.url?scp=85063132305&partnerID=8YFLogxK
U2 - 10.1111/2041-210X.13171
DO - 10.1111/2041-210X.13171
M3 - Article
AN - SCOPUS:85063132305
SN - 2041-210X
VL - 10
SP - 879
EP - 890
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
IS - 6
ER -