Carbon dynamics and management in Canadian boreal forests : triplex-flux model development, validation, and applications

La forêt boréale, seconde aire biotique terrestre sur Terre, est actuellement considérée comme un réservoir important de carbone pour l'atmosphère. Les modèles basés sur le processus des écosystèmes terrestres jouent un rôle important dans l'écologie terrestre et dans la gestion des ressources naturelles. Cette thèse examine le développement, la validation et l'application aux pratiques de gestion des forêts d'un tel modèle. Tout d'abord, le module récemment développé d'échange du carbone TRIPLEX-Flux (avec des intervalles de temps d'une demi heure) est utilisé pour simuler les échanges de carbone des écosystèmes d'une forêt au peuplement boréal et mixte de 75 ans dans le nord est de l'Ontario, d'une forêt avec un peuplement d'épinette noire de 110 ans localisée dans le sud de Saskatchewan, et d'une forêt avec un peuplement d'épinette noire de 160 ans située au nord du Manitoba au Canada. Les résultats des échanges nets de l'écosystème (ENE) simulés par TRIPLEX-Flux sur l'année 2004 sont comparés à ceux mesurés par les "tours de mesures de covariance des turbulences" et montrent une bonne correspondance générale entre les simulations du modèle et les observations de terrain. Le coefficient de détermination moyen (R2) est approximativement de 0.77 pour le peuplement mixte boréal, et de 0.62 et 0.65 pour les deux forêts d'épinette noire situées au centre du Canada. Le modèle est capable d'intégrer les variations diurnes de l'échange net de l'écosystème (ENE) de la période de pousse (de mai à août) de 2004 sur les trois sites. Le peuplement boréal mixte ainsi que les peuplements d'épinette noire agissaient tous deux comme des réservoirs de carbone pour l'atmosphère durant la période de pousse de 2004. Cependant le peuplement boréal mixte montre une plus grande productivité de l'écosystème, un plus grand piégeage du carbone ainsi qu'un meilleur taux de carbone utilisé comparé aux peuplements d'épinette noire. L'analyse de la sensibilité a mis en évidence une différence de sensibilité entre le matin et le milieu de journée, ainsi qu'entre une concentration habituelle et une concentration doublée de CO2. De plus, la comparaison de différents algorithmes pour calculer la conductance stomatale a montré que la production nette de l'écosystème (PNE) modélisée, utilisant une itération d'algorithme est conforme avec les résultats utilisant des rapports Ci/Ca constants de 0.74 et de 0.81 respectivement pour les concentrations courantes et doublées de CO2. Une variation des paramètres et des données variables de plus ou moins 10% a entrainé, respectivement pour les concentrations courantes et doublées de CO2, une réponse du modèle inférieure ou égale à 27.6% et à 27.4%. La plupart des paramètres sont plus sensibles en milieu de journée que le matin excepté pour ceux en lien avec la température de l'air, ce qui suggère que la température a des effets considérables sur la sensibilité du modèle pour ces paramètres/variables. L'effet de la température de l'air était plus important dans une atmosphère dont la concentration de CO2 était doublée. En revanche, la sensibilité du modèle au CO2 qui diminuait lorsque la concentration de CO2 était doublée. \ud Sachant que, les incertitudes de prédiction des modèles proviennent majoritairement des hétérogénéités spatio-temporelles au cœur des écosystèmes terrestres, à la suite du développement du modèle et de l'analyse de sa sensibilité, sept sites forestiers à tour de mesures de flux (comportant trois forêts à feuilles caduques, trois forêts tempérées à feuillage persistant et une forêt boréale à feuillage persistant) ont été sélectionnés pour faciliter la compréhension des variations mensuelles des paramètres du modèle. La méthode de Monte Carlo par Markov Chain (MCMC) à été appliquée pour estimer les paramètres clefs de la sensibilité dans le modèle basé sur le processus de l'écosystème, TRIPLEX-Flux. Les quatre paramètres clefs sélectionnés comportent: un taux maximum de carboxylation photosynthétique à 25°C (Vmax), un taux du transport d'un électron (Jmax) saturé en lumière lors du cycle photosynthétique de réduction du carbone, un coefficient de conductance stomatale (m), et un taux de référence de respiration à 10°C (R10). Les mesures de covariance des flux turbulents du CO2 échangé ont été assimilées afin d'optimiser les paramètres pour tous les mois de l'année 2006. Après que l'optimisation et l'ajustement des paramètres ait été réalisée, la prédiction de la production nette de l'écosystème s'est améliorée significativement (d'environ 25%) en comparaison avec les mesures de flux de CO2 réalisés sur les sept sites d'écosystèmes forestiers. Les résultats suggèrent, dans le respect des paramètres sélectionnés, qu'une variabilité plus importante se produit dans les forêts à feuilles larges que dans les forêts d'arbres à aiguilles. De plus, les résultats montrent que l'approche par la fusion des données du modèle incorporant la méthode MCMC peut être utilisée pour estimer les paramètres basés sur les mesures de flux, et que des paramètres saisonniers optimisés peuvent considérablement améliorer la précision d'un modèle d'écosystème lors de la simulation de sa productivité nette et cela pour différents écosystèmes forestiers situés à travers l'Amérique du Nord. Finalement, quelques uns de ces paramètres et algorithmes testés ont été utilisés pour mettre à jour l'ancienne version de TRIPLEX comportant des intervalles de temps mensuels. En outre, le volume d'un peuplement et la quantité de carbone de la biomasse au dessus du sol des forêts d'épinette noire au Québec sont simulés en relation avec un peuplement des âges, cela à des fins de gestion forestière. Ce modèle a été validé en utilisant à la fois une tour de mesure de flux et des données d'un inventaire forestier. Les simulations se sont avérées réussies. Les corrélations entre les données observées et les données simulées (R2) étaient de 0.94, 0.93 et 0.71 respectivement pour le diamètre à l.3 m, la moyenne de la hauteur du peuplement et la productivité nette de l'écosystème. En se basant sur les résultats à long terme de la simulation, il est possible de déterminer l'âge de maturité du carbone du peuplement considéré comme prenant place à l'époque où le peuplement de la forêt prélève le maximum de carbone, avant que la récolte finale ne soit réalisée. Après avoir comparé l'âge de maturité du volume des peuplements considérés (d'environ 65 ans) et l'âge de maturité du carbone des peuplements considérés (d'environ 85 ans), les résultats suggèrent que la récolte d'un même peuplement à son âge de maturité de volume est prématuré. Décaler la récolte d'environ vingt ans et permettre au peuplement considéré d'atteindre l'âge auquel sa maturité du carbone prend place, mènera à la formation d'un réservoir potentiellement important de carbone. Aussi, un nouveau diagramme de la gestion de la densité du carbone du peuplement considéré, basé sur les résultats de la simulation, a été développé pour démontrer quantitativement les relations entre les densités de peuplement, le volume de peuplement et la quantité de carbone de la biomasse au dessus du sol à des stades de développement variés, dans le but d'établir des régimes de gestion de la densité optimaux pour le rendement de volume et le stockage du carbone. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : écosystème forestier, flux de CO2, production nette de l'écosystème, eddy covariance, TRIPLEX-Flux module, validation d'un modèle, Markov Chain Monte Carlo, estimation des paramètres, assimilation des données, maturité du carbone, diagramme de gestion de la densité de peuplemen

Silviculture of Mixed-Species and Structurally Complex Boreal Stands

Understanding structurally complex boreal stands is crucial for designing ecosystem management strategies that promote forest resilience under global change. However, current management practices lead to the homogenization and simplification of forest structures in the boreal biome. In this chapter, we illustrate two options for managing productive and resilient forests: (1) the managing of two-aged mixed-species forests; and (2) the managing of multi-aged, structurally complex stands. Results demonstrate that multi-aged and mixed stand management are powerful silvicultural tools to promote the resilience of boreal forests under global change

Assessment of a process-based model to predict the growth and yield of Eucalyptus grandis plantations in South Africa.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2005.It is believed that the process-based model 3-PG (Physiological Principles Predicting Growth; Landsberg and Waring, 1997) can potentially play a useful role within South African forestry, both as an operational and a strategic tool. Strategic applications may include the prediction of potential productivity on a site-by-site basis; broadscale productivity estimates based on remote sensing and the spatial application of 3-PG; identification of production constraints; and estimation of carbon fluxes to help address sustainability issues. Operationally, 3-PG could complement empiricallybased models or be used in conjunction with them as a hybridised product. The challenges of this study were therefore to see whether it is possible to adapt 3-PG to predict the growth and yield of E. grandis under South African conditions, test that model predictions are consistent with observed growth data and are biologically reasonable, and to assess the practicality of using 3-PG as either a strategic or operational tool. The main emphasis of this study was to understand the internal logic of 3-PG and how physiological processes are represented, and to develop methods to objectively parameterise and initialise the model. Thereafter a detailed model validation study was performed, ending off with selected potential applications of 3-PG within the South African context. The sensitivity of predicted stand volume (SV) and leaf area index (LAI) to the values of the species-specific parameters in 3-PG was examined. These analyses enabled the development of three distinct parameter sensitivity classes: insensitive parameters (i.e. those that can be varied widely without affecting the outputs studied), sensitive parameters (i.e. those whose value strongly affects the outputs, and non-linear parameters (i.e. those for which the outputs depend in a non-linear manner on the parameter value). Minimum data requirements for the parameterisation and initialisation of 3-PG are considered in detail. Conventional methods used for the parameterisation of models, specifically 3-PG, are reflected upon. An automated parameter estimation technique was examined and used for the estimation of selected parameters. Species-specific parameters were categorised according to data source estimation and sensitivity classes. Parameters describing allometric and age-dependent relationships were assigned values using observed data from biomass harvests. Critical parameters that could not be directly assigned using observed data were the ratio of foliage to stem allocation (i.e. P2 and p2o), allocation of net primary production (NPP) to roots (TJRX and T]Rn), optimum temperature for growth (7^,) and maximum canopy quantum efficiency (acx)- These were estimated using Parameter ESTimation, by fitting model output to corresponding observed growth data. As well as species-specific parameter values, mandatory inputs required by 3-PG include weather data, site-specific factors such as site fertility (FR) and physical properties of the soils, and stand initialisation data. Objective techniques to determine these site-specific factors and the initial values for the biomass pools were proposed. Most of these data are readily available for sites where experimental trials exist, or where monitoring networks are in place. However, this is the exception rather than the rule, so alternative data and information sources are required. These, together with the need for accurate weather inputs (especially monthly rainfall) and physical properties (especially soil texture, maximum available soil water and FR) of the sites being modelled were explored. 3-PG was validated using four simple tests by comparing predicted versus observed SV. Results showed that 3-PG predictions are relatively consistent with observed stand data. Analyses performed using time-series data showed model predictions accurately tracked observed growth in response to erratic and fluctuating weather conditions. Results from the initial model validation showed production on high and low productivity sites was under- and over-predicted, respectively. Further results presented here show a similar, but less marked trend (i.e. over- and under-predictions are not as extreme), and individual biases are less than those from predictions made using another locally developed parameter set. The application of 3-PG showed that the model is able to make estimates of tree growth that are consistent with those used within the forestry site classification. This showed the considerable potential 3-PG has for strategic planning by the forest industry (i.e. projected wood supplies etc) and in research planning (refining existing site classifications). The model could be useful in predicting growth in various areas where E. grandis is not grown, assisting in future decision making. 3-PG was able to identify growth constraints on a site-by-site basis and distinguish among them, and was able to identify environmental and site limitations to plantation growth, and how they vary in space and time. These results together with predictions of site productivity demonstrate the potential for 3-PG to be used to improve existing forest site classifications. The model comparison study between empirically-based models and 3-PG showed that although the empirical models made accurate predictions of volume under static climatic conditions, under fluctuating weather conditions empirical estimates of volume were less accurate than those made with 3-PG. 3-PG can therefore be used operationally with minimum input data to predict growth using enumeration data. This is useful in saving time and cutting costs. The use of process-based models (PBMs) in general, and 3-PG in particular, needs to be "championed'' to the South African forest industry. This is necessary for two reasons. Firstly, the model and the manner with which it describes physiological processes of growth need to be explained in layman's terms. This will demonstrate how and why a process-based model can work better in a fluctuating environment than empirically based models. Secondly the comparison between 3-PG and the local empirical models needs to be presented as an example of how 3-PG can be applied on an operational basis. It is accepted that much convincing is still required

Computer-based tools for supporting forest management. The experience and the expertise world-wide

Report of Cost Action FP 0804 Forest Management Decision Support Systems (FORSYS)Computer-based tools for supporting forest management. The experience and the expertise world-wide answers a call from both the research and the professional communities for a synthesis of current knowledge about the use of computerized tools in forest management planning. According to the aims of the Forest Management Decision Support Systems (FORSYS) (http://fp0804.emu.ee/) this synthesis is a critical success factor to develop a comprehensive quality reference for forest management decision support systems. The emphasis of the book is on identifying and assessing the support provided by computerized tools to enhance forest management planning in real-world contexts. The book thus identifies the management planning problems that prevail world-wide to discuss the architecture and the components of the tools used to address them. Of importance is the report of architecture approaches, models and methods, knowledge management and participatory planning techniques used to address specific management planning problems. We think that this synthesis may provide effective support to research and outreach activities that focus on the development of forest management decision support systems. It may contribute further to support forest managers when defining the requirements for a tool that best meets their needs. The first chapter of the book provides an introduction to the use of decision support systems in the forest sector and lays out the FORSYS framework for reporting the experience and expertise acquired in each country. Emphasis is on the FORSYS ontology to facilitate the sharing of experiences needed to characterize and evaluate the use of computerized tools when addressing forest management planning problems. The twenty six country reports share a structure designed to underline a problem-centric focus. Specifically, they all start with the identification of the management planning problems that are prevalent in the country and they move on to the characterization and assessment of the computerized tools used to address them. The reports were led by researchers with background and expertise in areas that range from ecological modeling to forest modeling, management planning and information and communication technology development. They benefited from the input provided by forest practitioners and by organizations that are responsible for developing and implementing forest management plans. A conclusions chapter highlights the success of bringing together such a wide range of disciplines and perspectives. This book benefited from voluntary contributions by 94 authors and from the involvement of several forest stakeholders from twenty six countries in Europe, North and South America, Africa and Asia over a three-year period. We, the chair of FORSYS and the editorial committee of the publication, acknowledge and thank for the valuable contributions from all authors, editors, stakeholders and FORSYS actors involved in this project

State of Knowledge of Soil Biodiversity – Status, Challenges and Potentialities, Report 2020

Our well-being and the livelihoods of human societies are highly dependent on biodiversity and the ecosystem services it provides. It is essential that we understand these links and the consequences of biodiversity loss for the various global challenges we currently face, including food insecurity and malnutrition, climate change, poverty and diseases. The Agenda 2030 for Sustainable Development sets out a transformative approach to achieve socio-economic development while conserving the environment. There is increasing attention on the importance of biodiversity for food security and nutrition, especially above-ground biodiversity such as plants and animals. However, less attention is being paid to the biodiversity beneath our feet, soil biodiversity. Yet, the rich diversity of soil organisms drives many processes that produce food, regenerate soil or purify water. In 2002, the Conference of the Parties (COP) to the Convention on Biological Diversity (CBD) decided at its 6th meeting to establish an International Initiative for the Conservation and Sustainable Use of Soil Biodiversity and since then, the Food and Agriculture Organization of the United Nations (FAO) has been facilitating this initiative. In 2012, FAO members established the Global Soil Partnership to promote sustainable soil management and increase attention to this hidden resource. The Status of the World’s Soil Resources (FAO, 2015) concluded that the loss of soil biodiversity is considered one of the main global threats to soils in many regions of the world. The 14th Conference of the Parties invited FAO, in collaboration with other organizations, to consider the preparation of a report on the state of knowledge on soil biodiversity covering its current status, challenges and potentialities. This report is the result of an inclusive process involving 300 scientists from around the world under the auspices of the FAO’s Global Soil Partnership and its Intergovernmental Technical Panel on Soils, the Convention on Biological Diversity, the Global Soil Biodiversity Initiative and the European Commission. The report presents the state of knowledge on soil biodiversity, the threats to it, the solutions that soil biodiversity can provide to problems in different fields, including agriculture, environmental conservation, climate change adaptation and mitigation, nutrition, medicine and pharmaceuticals, remediation of polluted sites, and many others. The report will make a valuable contribution to raising awareness of the importance of soil biodiversity and highlighting its role in finding solutions to today’s global threats; it is a cross-cutting topic at the heart of the alignment of several international policy frameworks, including the Sustainable Development Goals (SDGs) and multilateral environmental agreements. Furthermore, soil biodiversity and the ecosystem services it provides will be critical to the success of the recently declared UN Decade on Ecosystem Restoration (2021-2030) and the upcoming Post- 2020 Global Biodiversity Framework. Soil biodiversity could constitute, if an enabling environment is built, a real nature-based solution to most of the problems humanity is facing today, from the field to the global scale. Therefore efforts to conserve and protect biodiversity should include the vast array of soil organisms that make up more than 25% of the total biodiversity of our planet

State of Knowledge of Soil Biodiversity: Status, Challenges, and Potentialities

This report presents the threats to soil biodiversity and the solutions that soil biodiversity can provide to problems in different fields, including agriculture, environmental conservation, climate change adaptation and mitigation, nutrition, medicine and pharmaceuticals, remediation of polluted sites, and many others. There is increasing attention on the importance of biodiversity for food security and nutrition, especially above-ground biodiversity such as plants and animals. Less attention is being paid to the biodiversity beneath our feet: soil biodiversity. Yet the rich diversity of soil organisms drives many processes that produce food, regenerate soil or purify water. This report is the result of an inclusive process involving more than 300 scientists from around the world under the auspices of FAO's Global Soil Partnership and its Intergovernmental Technical Panel on Soils, the Convention on Biological Diversity, the Global Soil Biodiversity Initiative, and the European Commission