Methods (original) (raw)
Related papers
Spatio‐temporal development of forests–current trends in field methods and models
2004
Studies of forest dynamics, ie of the changes of forest composition and structure over time, have received much scientific attention since the early concepts of forest succession by Cowles and Clements (Cowles 1899, Clements 1916 cited in GlennÁ/Lewin and van der Maarel 1992). The spatio-temporal development of forests may be described as changes of tree populations due to birth and colonization, growth, and death of trees.
Spatial modelling of succession-disturbance dynamics in forest ecosystems: Concepts and examples
Perspectives in Plant Ecology, Evolution and …, 2008
Over the last few decades it has become increasingly obvious that disturbance, whether natural or anthropogenic in origin, is ubiquitous in ecosystems. Disturbance-related processes are now considered to be important determinants of the composition, structure and function of ecological systems. However, because disturbance and succession processes occur across a wide range of spatio-temporal scales their empirical investigation is difficult. To counter these difficulties much use has been made of spatial modelling to explore the response of ecological systems to disturbance(s) occurring at spatial scales from the individual to the landscape and above, and temporal scales from minutes to centuries. Here we consider such models by contrasting two alternative motivations for their development and use: prediction and exploration, with a focus on forested ecosystems. We consider the two approaches to be complementary rather than competing. Predictive modelling aims to combine knowledge (understanding and data) with the goal of predicting system dynamics; conversely, exploratory models focus on developing understanding in systems where uncertainty is high. Examples of exploratory modelling include model-based explorations of generic issues of criticality in ecological systems, whereas predictive models tend to be more heavily data-driven (e.g. species distribution models). By considering predictive and exploratory modelling alongside each other, we aim to illustrate the range of methods used to model succession and disturbance dynamics and the challenges involved in the model-building and evaluation processes in this arena.
Analytical Models of Forest Dynamics
The hierarchical system of forest ecosystem models based on the theory of individual-based (structured) models of populations and communities is briefly described. New self-thinning models are integrated with tree stand models within a hierarchical system of models, aimed at assessing (quasi-) stable states of a forest ecosystem under given environment and management. Within the structural model of succession, the "ergodic theorems in biology" (announced earlier) are proved. They assert that the areas of biocenoses in a climax state of association should be proportional to the "proper times" of their development in a succession line; the other characteristics of biocenoses should be also proportional to their own "proper regeneration times" in a climax state. A simple theoretical approach to estimating the deviation of the current state of forest ecosystem from its steady state is derived and applied to data from the Prioksko-Terrasny Reserve. The results obtained can serve as a theoretical basis in numerical processing of data on ecological monitoring of undisturbed forest lands.
Models of forest dynamics based on roles of tree species
Ecological Modelling, 1996
A linkage between the two major modeling approaches to forest dynamics, transition Markovian models and JABOWA-FORET type simulators, is generated by developing a compact model of forest dynamics. This patch transition model utilizes functional roles instead of species. The roles or types are based on the regeneration and mortality characteristics of tree species; specifically, the requirements of canopy gaps for regeneration and the capacity to create canopy gaps upon death. A gap-size plot can be assigned to each of a set of states defined according to dominance of one of the roles. Transition probabilities among these states and mean holding times in each transition lead to semi-Markovian analytical calculations of the stationary state probabilities. Forest dynamics, as the proportions of total canopy space occupied by each role in a collection of gap-size plots, can be analyzed and simulated using a chain of first-order differential equations to emulate the distributed time-delays. Additional fixed time-delays in the transition of every pair of states is also included to account for long latencies. In addition to simplifying the simulations, the resulting model can also utilize available results of the theory of semi-Markov processes; and therefore, can provide analytical guidance to the simulations, the feasibility of direct exploration of hypothesis and the possibility of fast computation from closed-form solutions and formulae. These advantages can especially be useful in the simulation of landscape dynamics and species-rich tropical forests.
Annals of Forest Science, 2012
& Context Forest resource projections are required as part of an appropriate framework for sustainable forest management. Suitable large-scale projection models are usually based on national forest inventory (NFI) data. However, sound projections are difficult to make for heterogeneous resources as they vary greatly with respect to the factors that are assumed to drive forest dynamics on a large spatial scale, e.g. geographically varying growth conditions (here represented by NFI regions), tree species composition (here broadleafdominated, conifer-dominated and broadleaf-conifer mixed stands) and stand structure (here high forest, coppice forest and high-coppice forest mixture). Handling Editor: Matthias Dobbertin Contribution of the co-authors H.W. coordinated the presented work on the model, performed the model sensitivity analysis, and wrote the manuscript; A.C. and H.C. calibrated the model and ran the simulations, provided expertise on the model, and contributed to manuscript writing; J.-D.B. contributed to the set-up and performance of the scientific work on the model (scope and positioning, conceptual design), brought up structuring ideas in view of further model development, and contributed to manuscript writing; G.P. provided expertise on the model, wrote an NFI intern technical model description including approaches to further model development; S.C. brought up a thematic context that lead to the initiation of the work on the model; J.-M.L. contributed to the scientific positioning of the work on the model; J.-C. H. contributed to orientation and structure of work on the model, and provided expertise on the model; M.F. initiated the work on the model and contributed to its scientific positioning, and promoted the research project including funding.
An individual-based process model to simulate landscape-scale forest ecosystem dynamics
Ecological Modelling, 2012
Ecological field theory Hierarchical multi-scale modeling Forest structure and functioning a b s t r a c t Forest ecosystem dynamics emerges from nonlinear interactions between adaptive biotic agents (i.e., individual trees) and their relationship with a spatially and temporally heterogeneous abiotic environment. Understanding and predicting the dynamics resulting from these complex interactions is crucial for the sustainable stewardship of ecosystems, particularly in the context of rapidly changing environmental conditions. Here we present iLand (the individual-based forest landscape and disturbance model), a novel approach to simulating forest dynamics as an emergent property of environmental drivers, ecosystem processes and dynamic interactions across scales. Our specific objectives were (i) to describe the model, in particular its novel approach to simulate spatially explicit individual-tree competition for resources over large scales within a process-based framework of physiological resource use, and (ii) to present a suite of evaluation experiments assessing iLands ability to simulate tree growth and mortality for a wide range of forest ecosystems. Adopting an approach rooted in ecological field theory, iLand calculates a continuous field of light availability over the landscape, with every tree represented by a mechanistically derived, size-and species-dependent pattern of light interference. Within a hierarchical multi-scale framework productivity is derived at stand-level by means of a light-use efficiency approach, and downscaled to individuals via local light availability. Allocation (based on allometric ratios) and mortality (resulting from carbon starvation) are modeled at the individual-tree level, accounting for adaptive behavior of trees in response to their environment. To evaluate the model we conducted simulations over the extended environmental gradient of a longitudinal transect in Oregon, USA, and successfully compared results against independently observed productivity estimates (63.4% of variation explained) and mortality patterns in even-aged stands. This transect experiment was furthermore replicated for a different set of species and ecosystems in the Austrian Alps, documenting the robustness and generality of our approach. Model performance was also successfully evaluated for structurally and compositionally complex old-growth forests in the western Cascades of Oregon. Finally, the ability of our approach to address forest ecosystem dynamics at landscape scales was demonstrated by a computational scaling experiment. In simulating the emergence of ecosystem patterns and dynamics as a result of complex process interactions across scales our approach has the potential to contribute crucial capacities to understanding and fostering forest ecosystem resilience under changing climatic conditions.
Analysis Of Neighborhood Dynamics Of Forest Ecosystems Using Likelihood Methods And Modeling
Ecological Applications, 2006
Advances in computing power in the past 20 years have led to a proliferation of spatially explicit, individual-based models of population and ecosystem dynamics. In forest ecosystems, the individual-based models encapsulate an emerging theory of ''neighborhood'' dynamics, in which fine-scale spatial interactions regulate the demography of component tree species. The spatial distribution of component species, in turn, regulates spatial variation in a whole host of community and ecosystem properties, with subsequent feedbacks on component species. The development of these models has been facilitated by development of new methods of analysis of field data, in which critical demographic rates and ecosystem processes are analyzed in terms of the spatial distributions of neighboring trees and physical environmental factors. The analyses are based on likelihood methods and information theory, and they allow a tight linkage between the models and explicit parameterization of the models from field data. Maximum likelihood methods have a long history of use for point and interval estimation in statistics. In contrast, likelihood principles have only more gradually emerged in ecology as the foundation for an alternative to traditional hypothesis testing. The alternative framework stresses the process of identifying and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. There are four general steps involved in a likelihood analysis: (1) model specification, (2) parameter estimation using maximum likelihood methods, (3) model comparison, and (4) model evaluation. Our goal in this paper is to review recent developments in the use of likelihood methods and modeling for the analysis of neighborhood processes in forest ecosystems. We will focus on a single class of processes, seed dispersal and seedling dispersion, because recent papers provide compelling evidence of the potential power of the approach, and illustrate some of the statistical challenges in applying the methods.
Tackling unresolved questions in forest ecology: The past and future role of simulation models
Ecology and Evolution, 2021
1. Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations. 2. We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.
Forest Ecology and Management, 2008
Forest landscape models, a tool for understanding the effect of the large-scale and long-term landscape processes Forest landscape models have become important tools for understanding large-scale and long-term landscape (spatial) processes such as climate change, fire, windthrow, seed dispersal, insect outbreak, disease propagation, forest harvest, and fuel treatment, because controlled field experiments designed to study the effects of these processes are often not possible (Shifley et al., 2006). In the past decade and a half, significant advances in theory and technology have been incorporated into the development of forest landscape models (Mladenoff and Baker, 1999; Gardner and Urban, 2003; Keane et al., 2004; Brown et al., 2006). From a theoretical perspective, forest landscape models continue to build upon the rich ecological theories of disturbance, succession, and equilibrium and non-equilibrium dynamics of ecosystems processes (Mladenoff, 2004). It is now widely acknowledged that the future status of forest ecosystems is constrained by both localscale (ecosystem) and large-scale (landscape) processes (Turner et al., 1993; Wu et al., 2006). From a technological perspective, forest landscape models have benefited greatly from the rapid development of computing capacity, GIS, and software engineering. Determining which ecological processes to incorporate into a forest landscape model, how to represent those processes, and how to simulate the interactions among such processes can be facilitated by improved software products with features such as fully modularized model design and interchangeable module components (Fall and Fall, 2001; He et al., 2002). To facilitate the exchange of the progress made in theory and application of forest landscape models, the China Natural Science Foundation and the International Association of Landscape Ecology sponsored an international workshop of forest landscape modeling in June 2006. The workshop was organized by the Institute of Applied Ecology, Chinese Academy of Sciences. The purpose of the workshop was to study and discuss the strengths and weaknesses as well as opportunities and limitations of the modeling approach and applications embodied in forest landscape modeling. Over 50 papers were presented at the workshop, of which 12 were selected in this special issue, and one paper (Zollner et al., 2008) was invited. We have organized the papers into three sections that describe current activities in forest landscape modeling: (1) effects of climate change on forest vegetation, (2) forest landscape model applications, and (3) model research and development.