A general framework for quantifying the effects of land-use history on ecosystem dynamics

Land-use legacies are important for explaining present-day ecological patterns and processes. However, an overarching approach to quantify land-use history effects on ecosystem properties is lacking, mainly due to the scarcity of high-quality, complete and detailed data on past land use. We propose a general framework for quantifying the effects of land-use history on ecosystem properties, which is applicable (i) to different ecological processes in various ecosystem types and across trophic levels; and (ii) when historical data are incomplete or of variable quality. The conceptual foundation of our framework is that past land use affects current (and future) ecosystem properties through altering the past values of resources and conditions that are the driving variables of ecosystem responses. We describe and illustrate how Markov chains can be applied to derive past time series of driving variables, and how these time series can be used to improve our understanding of present-day ecosystem properties. We present our framework in a stepwise manner, elucidating its general nature. We illustrate its application through a case study on the importance of past light levels for the contemporary understorey composition of temperate deciduous forest. We found that the understorey shows legacies of past forest management: high past light availability lead to a low proportion of typical forest species in the understorey. Our framework can be a useful tool for quantifying the effect of past land use on ecological patterns and processes and enhancing our understanding of ecosystem dynamics by including legacy effects which have often been ignored

Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N2O emissions from arable soils at regional or continental scales

Evaluation of Markov chains for projecting diameter distributions

Fifteen years of the University of Tennessee\u27s Continuous Forest Inventory data were used to determine if Markov chains could be used to make accurate predictions of future diameter distributions for pulpwood size trees in natural hardwood stands. Distributions projected five and ten years were compared to actual data by means of the Kolmogorov - Smirnoff Test for Goodness of Fit. Projections were made for all hardwood species on each of three forest tracts and for several individual species that were abundant. Markov chain theory is based on the assumption that transition probabilities remain constant through time. To determine if this assumption was met when projecting forest stands, two initial probability matrices were constructed from data at five year intervals. If transition probabilities had not changed, differences between the two matrices would have been minimal. The two matrices were constructed and compared for all hardwoods on each tract and for the individual species. Markov chains and matrix algebra were employed to predict the average number of periods expected to pass before trees in each diameter class reach sawtimber size. Also, the number of periods spent in each diameter class before reaching sawtimber size was predicted. The results of the analysis of projected and actual diameter distributions indicated that Markov chains could be used as a reliable technique to determine future stand conditions. The only significant differences found between actual and projected data were because of the underestimation of the number of trees in upper diameter classes or, more often, the underestimation of mortality. In each of these cases actual mortality was unusually high as a result of an initial high proportion of intolerant trees in the stand which possibly succombed to suppression or competition; unprojected harvest or timber stand improvement; or catastrophic mortality caused by fire, insects or disease. Comparison of the probability matrices constructed at five year intervals indicated that, for each of the forest tracts studied, overall growth rates were not steady. Differences were not large, though, showing that short term projections were reliable. If longer time periods were projected, it would be expected that the differences between actual and projected data would increase. The other Markov chain calculations, average time to reach sawtimber size and average time spent in each diameter class, provided useful information about the growth and productivity of each tract. These predictions may be used in planning and timing various management activities

On the use of the bayesian approach for the calibration, evaluation and comparison of process-based forest models

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaForest ecosystems have been experiencing fast and abrupt changes in the environmental conditions, that can increase their vulnerability to extreme events such as drought, heat waves, storms, fire. Process-based models can draw inferences about future environmental dynamics, but the reliability and robustness of vegetation models are conditional on their structure and their parametrisation. The main objective of the PhD was to implement and apply modern computational techniques, mainly based on Bayesian statistics, in the context of forest modelling. A variety of case studies was presented, spanning from growth predictions models to soil respiration models and process-based models. The great potential of the Bayesian method for reducing uncertainty in parameters and outputs and model evaluation was shown. Furthermore, a new methodology based on a combination of a Bayesian framework and a global sensitivity analysis was developed, with the aim of identifying strengths and weaknesses of process-based models and to test modifications in model structure. Finally, part of the PhD research focused on reducing the computational load to take full advantage of Bayesian statistics. It was shown how parameter screening impacts model performances and a new methodology for parameter screening, based on canonical correlation analysis, was presente

Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid

STUDY ON MONGOLIAN FOREST STAND DYNAMICS USING MATHEMATICAL MODELING

A Global warming, climate change and negative human activities are expected to directly and negatively influence Mongolia’s forest resource area and quality [21]. In 2015, Mongolian forested area was 12,188.2 thousand hectare and in compared with the forested area in 2010, it has decreased by 864.5 thousand hectare. The important causes of deforestation and forest degradation are fire, improper commercial logging, illegal collection of wood for construction and fuel wood, overgrazing, mining activity, and damage by pests and diseases. Mongolian forest stand is not only decreasing in quantity, but also forest age class has been changing into maturity forest classification in recent years. The maturity forest resource has counted for 74 percent of total forest resource is in National Forest Inventory, 2016. There are few study for Mongolian forest stand dynamics and this work is first research that used stochastic process to predict forest stand dynamics in Mongolian case. This paper considered the main factors such as Climate factors and Socio-Economic factors in predicting forest stand dynamics. The factors are chosen based on real situation of forest resource’s changes in Mongolia. The study has estimated coefficients of relationship between forest resource and main factors, as well as main factors and their explanatory variables, using suitable regression model for all estimation. Moreover, Markov chain process has been used to extracted future dynamic of forest stand by age class structure based on imbalanced age structure of total forest resource today. The result of this paper shows that the most important factors that influenced the future forest stand changes are forest fire, commercial logging and afforestation. The estimated model results shows the forest fire will be decreased (9%), commercial logging will be increased (25%) and reforestation will be increased (30%) by 2030. Specially, this paper presented that forest resource will be decreased by 13 percent in future 15 years. Additionally, this decrease is consist of forest age structure changes which is young aged forest would be increased by 27%, middle aged forest would be decreased by 15%, maturing forest would be decreased by 39% and maturity forest would be decreased by 16% in 2030

MAppleT: simulation of apple tree development using mixed stochastic and biomechanical models

International audienceConstruction of architectural databases over years is time consuming and cannot easily capture the event dynamics, especially when both tr ee topology and geometry are considered. The present project aimed to bring together models of topology and geometry in a single simulation such that the architecture of an apple t ree may emerge from process interactions. This integration was performed using L-systems. A m ixed approach was developed based on stochastic models to simulate plant topology and me chanistic model for the geometry. The succession of growth units (GUs) along axes and the ir branching structure were jointly modeled by a hierarchical hidden Markov model. A bi omechanical model, derived from previous studies, was used to calculate stem form a t the metamer scale, taking into account the intra-year dynamics of primary, secondary and f ruit growth. Outputs consist of 3D mock- ups geometric models representing the progression o f tree form over time. To asses these models, a sensitivity analysis was performed and de scriptors were compared between simulated and digitized trees, including the total number of GUs in the entire tree, descriptors of shoot geometry (basal diameter, length), and des criptors of axis geometry (inclination, curvature). In conclusion, in spite of some limitat ions MAppleT constitutes a useful tool for simulating development of apple trees in interactio n with gravity