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Second-Growth Forests Exhibit Higher Sensitivity to Dry and Wet Years than Long-Existing Ones

ECOSYSTEMS 2025年第1期28卷 1-13页

作者： Balaguer-Romano, Rodrigo De Caceres, Miquel Espelta, Josep Maria CREAF Bellaterra 08193 Catalonia Spain

Second-growth forest establishment over abandoned agricultural lands has been envisaged as a nature-based solution to reduce atmospheric CO2 through carbon sequestration. However, we still have a very limited knowledge about the climate sensitivity of these second-growth forests in comparison to forests with a more continuous land-use history. Here, we compare the climate sensitivity of recently established forests (post-1956) and long-existing forests (pre-1956), by analyzing their responses to climate variability at 456 inventory plots in NE Spain. We analyzed remotely sensed estimates of leaf area index (LAI) and normalized difference vegetation index (NDVI) from 2000 to 2022, relating yearly variation patterns with annual meteorological drought intensity. Then, we used process-based simulations of forest functioning to explore whether different responses of both forest types to climate variability are consistent with differences in root allocation patterns modulated by land-use legacies. Although recent- and long-existing forests had a very similar structure (that is, similar LAI), NDVI variation in recent-established forests was more strongly related to meteorological drought indices, recording steeper NDVI decreases during drier periods, but also higher NDVI increases during rainy years. This pattern is consistent with differences in root allocation between the two forest types, as simulated and remotely sensed forest responses to climate exhibited higher correlation when we inputted recent-established forest with shallower root systems or lower fine root density in comparison with long-existing forests. A higher climate sensitivity of second-growth forests during drier but also wetter years entails the potential for compensating drought impacts, but it also indicates a long-term higher vulnerability to climatic disturbances.

关键词： Forest expansion Land-use legacies Forest vulnerability process-based modeling Forest productivity Fine roots Water-balance simulation Remote sensing

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PDG-Arena: an ecophysiological model for characterizing tree-tree interactions in heterogeneous and mixed stands

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ANNALS OF FOREST SCIENCE 2025年第1期82卷 1-21页

作者： Rouet, Camille Davi, Hendrik Druel, Arsene Fady, Bruno Morin, Xavier INRAE URFM Domaine St Paul Site Agroparc F-84914 Avignon 9 France ADEME 20 Ave Gresille F-49004 Angers 1 France Univ Montpellier EPHE CEFE CNRSIRD 1919 Route Mende F-34293 Montpellier 5 France

Key messagePDG-Arena, a new individual- and process-based ecophysiological model, was developed to simulate the functioning of mixed-species forests. It was evaluated using annual growth data on beech-fir stands of the French *** the context of the ongoing climate and biodiversity crises, mixed forest stands are increasingly considered as a sustainable management alternative to monospecific and even-aged *** developed a new individual- and process-based forest growth model, PDG-Arena, to simulate mixed forest growth and functioning, and test ecophysiological interactions among trees in mixed *** model builds upon the validated ecophysiological stand-scale model CASTANEA and integrates tree competition for light and water. We evaluated the performance of PDG-Arena by comparing the simulated growth with annual radial growth data from 37 common beech and silver fir monospecific and mixed plots in the French ***-Arena performed slightly better than CASTANEA when simulating even-age and monospecific forests (r2 of 32.1 versus 29.5%). When using structure-diverse and species-diverse inventories, PDG-Arena performed better than CASTANEA in pure beech (38.3 versus 22.9%) and mixed stands (40.5 versus 36.3%), but not in pure fir stands (39.8 versus 42.0%). The new model also showed a significant positive effect of species mixing on gross primary production (+ 5.5%), canopy absorbance (+ 11.1%), and transpiration (+ 15.8%) in the tested *** results show that tree-level process-based models such as PDG-Arena, formally simulating interspecific interactions, can serve as a valuable tool to understand and simulate the carbon, radiative, and water dynamics of mixed stands.

关键词： process-based modeling Mixed forest Competition Overyielding Drought French Alps

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Sand Dunes as a Nature-based Solution to Mitigate Salt Intrusion in Stratified Estuaries

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JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 2025年第1期130卷

作者： Geerts, Sem J. van der Sande, Wessel M. Hulscher, Suzanne J. M. H. Geurts, Bernard J. Roos, Pieter C. Univ Twente Fac Engn Technol Water Engn & Management Enschede Netherlands Univ Twente Fac Elect Engn Math & Comp Sci Math Multiscale Modelling & Simulat 3MS Enschede Netherlands

Salt intrusion in estuaries can pose a threat to drinking water extraction, irrigation, industries, and ecology. Key factors influencing the salt intrusion length include freshwater river flushing and vertical salt mixing. This study investigates the potential of estuarine sand dunes, that is, large-scale rhythmic patterns, to serve as a nature-based solution to mitigate salt intrusion. To this end, we adopt a two-dimensional vertical (2DV) modeling approach, which numerically solves the flow and salt transport in an idealized single-channel estuary. By varying the tidal amplitude and river discharge, three types of estuaries are modeled;a salt wedge, a strongly stratified and a strain-induced periodic stratification estuary. Two types of dune configurations are considered: 'depth-neutral' dunes (preserving mean water depth) and 'deepening' dunes (crest level equal to original bed level, rest of dune profile entirely below). Model results demonstrate that the presence of dunes can effectively reduce the salt intrusion length by several kilometers, compared to the scenario without dunes. Systematically varying dune geometry demonstrates that steeper (higher and shorter) dunes yield a stronger reduction in the salt intrusion length. Deepening dunes have the potential to generate sufficient mixing to overcome the adverse effects of channel deepening and, as such, can mitigate salt intrusion while maintaining navigability. As a naturally existing bedform, estuarine sand dunes present a promising nature-based solution against salt intrusion without compromising accessibility to seaports.

关键词： estuarine sand dunes nature-based solution process-based modeling salt intrusion stratified flow mixing

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A process-based MODEL FOR CATTLE MANURE COMPOST WINDROWS: PART 1. MODEL DESCRIPTION

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TRANSACTIONS OF THE ASABE 2017年第3期60卷 877-892页

作者： Bonifacio, H. F. Rotz, C. A. Richard, T. L. Penn Dept Environm Protect Northwest Reg Off Meadville PA USA ARS USDA Pasture Syst & Watershed Management Res Unit University Pk PA USA Penn State Univ Dept Agr & Biol Engn University Pk PA 16802 USA

Composting is an alternative management practice for handling and storing manure in intensive cattle production systems. With composting, cattle manure is converted into a soil amendment with improved nutrient and physical properties and is easier to handle. Despite its benefits, composting can produce large amounts of gaseous carbon (C) and nitrogen (N) emissions that include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ammonia (NH3). A process-based model for cattle manure compost windrows was developed and incorporated into the Integrated Farm System Model (IFSM, v. 4.3), a whole-farm simulation model of crop, dairy, and beef production systems. Designed to simulate the different processes that influence C and N balances in windrows, the compost windrow model predicts changes in C (organic C, microbial C) and N (organic N, microbial N, ammonium (NH4+-N), nitrate (NO3--N)) contents in the windrow;CO2, CH4, N2O, and NH3 emissions throughout composting;and corresponding C and N losses. To increase its accuracy in simulating the different processes occurring during composting, the compost windrow model was also designed to predict environmental conditions within windrows, which include moisture content, temperature, and oxygen availability, and changes in windrow material physical properties, such as bulk and particle densities. modeling routines and relationships of the compost windrow model are described. Evaluation of its performance in predicting windrow environmental conditions, physical and chemical properties, and gaseous emissions is documented in an accompanying article.

关键词： Cattle manure Composting Emissions IFSM process-based modeling

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A process-based MODEL FOR CATTLE MANURE COMPOST WINDROWS: PART 2. MODEL PERFORMANCE AND APPLICATION

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TRANSACTIONS OF THE ASABE 2017年第3期60卷 893-913页

A model was developed and incorporated into the Integrated Farm System Model (IFSM, v.4.3) that simulates important processes occurring during windrow composting of manure. The model, documented in an accompanying article, predicts changes in windrow properties and conditions and the resulting emissions of C and N. Our objective in this article was to evaluate the performance of the compost windrow model. Model predictions were compared to published data from an independent cattle manure composting study that characterized static (no turning) and turned windrows composted over a 188-day period. The model did very well in simulating C and N losses for the turned windrow treatment, with predicted and measured cumulative C losses of 78% and 77%, respectively, and cumulative N losses of 55% and 57%, respectively, by day 99 of composting. By day 188 of composting, predicted cumulative N loss (56%) differed from measured (58%) by 2 percentage points only, whereas predicted cumulative C loss matched the corresponding measured value (80%). For the static windrow treatment, the model also did well in simulating cumulative C loss by day 99, with a difference of 5 percentage points between predicted (50%) and measured (45%) values. On the other hand, there was a difference of 12 percentage points between predicted (31%) and measured (19%) cumulative N losses for the static windrow by day 99. This discrepancy in simulating N loss for the static windrow treatment was not surprising, as the NH3 biofiltration effect of the finished compost cover on the actual static windrow was not modeled. After simulating the turning of the static windrow treatment on day 99, predicted and measured cumulative N losses by day 188 were equal at 44%. With the compost windrow model as a component, IFSM can be used to evaluate gaseous emissions from composted manure as influenced by windrow management practices and environmental conditions, along with other aspects of performance, environmental impac

关键词： Cattle manure Composting Emissions IFSM process-based modeling

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Predicting long-term population dynamics with bagging and boosting of process-based models

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第22期42卷 8484-8496页

作者： Simidjievski, Nikola Todorovski, Ljupco Dzeroski, Saso Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia Univ Ljubljana Fac Adm Ljubljana 1000 Slovenia

process-based modeling is an approach to learning understandable, explanatory models of dynamic systems from domain knowledge and data. Although their utility has been proven on many tasks of modeling dynamic systems in various domains, their ability to accurately predict the future behavior of an observed system is limited. To address this limitation, we propose the use of a standard approach to improving the predictive performance of machine learning methods, i.e., the approach of learning ensemble models. Previous work on ensembles of process-based models has been limited to proof-of-principle experiments with a single ensemble method (bagging) and in the limited perspective of explaining the currently observed system behavior v.s. predicting future system behavior. In this paper, we design a general methodology for adapting ensemble methods to the context of process-based modeling. Using the methodology, we implement the two approaches bagging and boosting of process-based models. We perform an empirical evaluation of the implemented methods on three real-world modeling problems from the domain of population dynamics in aquatic ecosystems. The results of the empirical evaluation show that ensembles of process-based models can lead to long-term predictions of the population dynamics that are more accurate than the ones obtained with a single process-based model. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Ensembles process-based modeling Bagging Boosting Machine learning Predictive modeling Population dynamics

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A process-based model for erosion of Macoma balthica-affected mud beds

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CONTINENTAL SHELF RESEARCH 2011年第6期31卷 527-538页

作者： van Prooijen, Bram C. Montserrat, Francesc Herman, Peter M. J. Delft Univ Technol Fac Civil Engn & Geosci Sect Hydraul Engn NL-2600 GA Delft Netherlands Netherlands Inst Ecol Ctr Estuarine & Marine Ecol NL-4401 NT Yerseke Netherlands

modeling the effect of biota on sediment dynamics is a difficult task. in this paper we re-analyze experimental results of Willows et al. (1998) on the effects of Macoma balthica on sediment erosion. A process-based framework is proposed, fully compatible with a physical description of erosion processes in case of no biogenic influences. The bed is represented by a fluff layer on top of a substrate. A characteristic feature of the framework is that the sediment is represented by a probability density distribution for the critical shear stress, allowing for Type land Type II erosion. M. balthica increases the sediment mass in the fluff layer. This increase is determined by considering the action radius, the overlap of feeding areas, and the feeding rate per animal. The calibrated action radius and feeding rate were in the range as found in the literature. The distribution of sediment over the erodibility classes and the erosion rate parameter are hardly influenced at all. Due to overlapping feeding areas, the effect is non-linear with density of the animals. The model results are in close agreement with the measured results, suggesting that no further formulations of biological effects are needed to simulate the experiments of Willows et al. (1998). In nature, other effects like disrupting the biofilm by grazing can be of importance and should be included in a later stage. This study emphasizes the crucial role of sediment availability and the effect of biota on it. This aspect needs more attention in future experiments. The proposed model turned out to work well for the effects of M. balthica and offers opportunities to include other biogenic effects in a process-based way as well. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Sediment dynamics Macoma balthica Bioturbation process-based modeling Mud beds Erosion

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Learning stochastic process-based models of dynamical systems from knowledge and data

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BMC SYSTEMS BIOLOGY 2016年第1期10卷 1-17页

作者： Tanevski, Jovan Todorovski, Ljupco Dzeroski, Saso Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Jamova Cesta 39 Ljubljana 1000 Slovenia Univ Ljubljana Gosarjeva Ulica 5 Ljubljana 1000 Slovenia

Background: Identifying a proper model structure, using methods that address both structural and parameter uncertainty, is a crucial problem within the systems approach to biology. And yet, it has a marginal presence in the recent literature. While many existing approaches integrate methods for simulation and parameter estimation of a single model to address parameter uncertainty, only few of them address structural uncertainty at the same time. The methods for handling structure uncertainty often oversimplify the problem by allowing the human modeler to explicitly enumerate a relatively small number of alternative model structures. On the other hand, process-based modeling methods provide flexible modular formalisms for specifying large classes of plausible model structures, but their scope is limited to deterministic models. Here, we aim at extending the scope of process-based modeling methods to inductively learn stochastic models from knowledge and data. Results: We combine the flexibility of process-based modeling in terms of addressing structural uncertainty with the benefits of stochastic modeling. The proposed method combines search trough the space of plausible model structures, the parsimony principle and parameter estimation to identify a model with optimal structure and parameters. We illustrate the utility of the proposed method on four stochastic modeling tasks in two domains: gene regulatory networks and epidemiology. Within the first domain, using synthetically generated data, the method successfully recovers the structure and parameters of known regulatory networks from simulations. In the epidemiology domain, the method successfully reconstructs previously established models of epidemic outbreaks from real, sparse and noisy measurement data. Conclusions: The method represents a unified approach to modeling dynamical systems that allows for flexible formalization of the space of candidate model structures, deterministic and stochastic interpretation

关键词： process-based modeling Structural uncertainty Dynamical systems Stochastic models Genetic regulatory networks Compartmental epidemiological models

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Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change

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ECOLOGY 2009年第5期90卷 1301-1313页

作者： Morin, Xavier Thuiller, Wilfried CNRS UMR 5175 Ctr Ecol Fonct & Evolut Equipe BIOFLUX F-34293 Montpellier 5 France McGill Univ Dept Biol Montreal PQ H3A 1B1 Canada Univ Grenoble 1 CNRS UMR 5553 Lab Ecol Alpine F-38041 Grenoble 9 France

Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.

关键词： biodiversity biogeography climate change extinction migration niche-based modeling process-based modeling species distribution

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A process-based model of forest ecosystems driven by meteorology

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ECOLOGICAL MODELLING 2004年第3期179卷 317-348页

作者： Schwalm, CR Ek, AR Univ Minnesota Coll Nat Resources Dept Forest Resources St Paul MN 55108 USA

Although anthropogenic climate change is generally acknowledged as a reality, tree level models that respond to the boundary conditions expected to change as a result of global warming are largely non-existent. Consequently, a process-based model of individual tree growth driven by meteorology was developed. The model, Forest v5.1 predicts the growth of deciduous and coniferous species for the Great Lakes Region of North America. The model uses a daily time step and was written with two overriding design tenets in mind: (i) model drivers must mimic controls on plant growth as they exist in nature and (ii) model initialization must be achievable through the use of typical forest inventory field plot data. Forest v5.1 predicts the carbon, nutrient and water cycle as these influence tree growth and with particular emphasis on light interception and assimilation. Model outputs are both in dimension as well as biomass. Sensitivity analysis shows the importance of parameters that characterize maximum photosynthetic potential and scaling factors. A comparison of observed and predicted growth trajectories for 25 years indicates tree diameter development exhibits useful levels of precision (-0.12 to 0.11 cm year(-1)) relative to an empirical model. The model, using HadCM2-generated weather, projects that water use efficiency will increase as a result of climate change. Net basal area increment increases on average by 0.17 m(2) ha(-1) year(-1) relative to a projection of current climate. (C) 2004 Elsevier B.V. All rights reserved.

关键词： climate change forested ecosystem process-based modeling growth and yield model validation

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