Modelling spatial dependence in an irregular natural forest

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.

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.

International Journal of Geographical Information Science, 2004

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from relevant protective laws and regulations and therefore free for general use.

Forest Science, 1985

Mathematical methods of assessing the levels of spatial autocorrelation in forest stands were identified. These methods were used to investigate spatial autocorrelation associated with tree characteristics such as product, defect, species class, and basal area. With the exception of the species classification, significant levels of spatial autocorrelation were not associated with any discrete variable. For individual tree basal area, the levels of spatial autocorrelation tended to be positive for low levels of competition, negative at intermediate levels of competition, and positive again at high levels of competition.

Forest Science, 2021

Models at various levels of resolution are commonly used for both forest management and ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between various approaches. Accounting for crown and root morphological plasticity in the limit where equilibrium among neighbors is reached (perfect plasticity) transforms spatial models into nonspatial, distance-independent versions. The links between spatial and nonspatial models obtained through a perfect plasticity assumption are more realistic than ignoring spatial structure by a mean field approximation. This article also reviews the connection between distance-independent models and size distributions and how distributions evolve over time and relate to whole-stand descriptions. In addition, some ways in which stand-level knowledge feeds back into detailed individual-tree formulations are demonstrated. This presentation is intended to be accessible to n...

Fire Ecology, 2000

Characterizing spatial patterns of fire behavior is an important and rarely considered means of understanding patterns of vegetation recovery following a fire event. Using geostatistics, we characterized spatial patterns of pre-burn fuel loads, fire temperature and duration during prescribed burns, and post-burn fuel loads in four longleaf pine stands in the southeastern USA. Fire temperatures exhibited moderate to strong spatial dependence over medium spatial scales. Variograms suggest that 61-99% of sample population variance was spatially dependent at scales of 27-157 m. Patterns of pre-burn fuel loads were only moderately related to patterns of mean fire temperature, confirming that fuel loads alone cannot predict fire patchiness. Other fuel parameters and microscale changes in wind and relative humidity likely influenced patterns of fire intensity as well. Strength and scale of fuel load spatial patterns were altered by fire as indicated by preand post-burn measurements. Spatial analysis provides a useful way to quantify burn patchiness and can help to identify which patch size may be desirable for different management goals. Studies that examine fire effects need to recognize spatial autocorrelation when characterizing fire behavior and account for this variation at appropriate scales.

Tree Physiology, 1986

Geofísica Internacional

Este artículo tiene por objetivos: (i) presentar, (ii) probar y (iii) validar un conjunto de técnicas matemáticas para predecir el número de incendios (No), la superficie incendiada (A), y la superficie promedio incendiada (AM), en bases anuales a escalas regionales. Una fuente de datos extensa proveniente de los bosques de coníferas de Durango se usó para ajustar (1970-2011) y validar (2012-2016) las técnicas de modelaje. La mayoría de los modelos probabilísticos y estocásticos explican menos del 70% de la varianza de los incendios. Sin embargo, el método de teleconecciones que emplea variables a escala oceánica y a escala local del hidroclima aumentó el nivel de precisión hasta cerca del 80% de la varianza total para ambas bases de datos. Los resultados muestran la complejidad de los factores que interactúan, incluyendo los procesos estocásticos y los físicos que hacen que las técnicas de predicción sean modificadas substancialmente. Por estas razones, se propone un modelo más fís...

Forest Science, 2020

Forest tillage experiments regularly use long-term evaluations of large plots creating temporal and/or spatial correlations among observations. Not modeling these correlations could compromise treatment comparisons. The aim of this study was to evaluate the effect of modeling spatio-temporal (ST) variability in forest tillage experiments. We used different strategies that incorporate spatial and/or temporal correlations in the evaluation of tillage intensity effect in initial Eucalyptus growth as well as evaluate the effect of intraplot mortality and competition dynamics. Three tillage intensities in two contrasting soil conditions were compared for tree height and wood volume. Additionally, we compared the use of three individual growth curves for plant height to evaluate the time needed to reach 2 m in height (T2m). We modeled the spatial correlation of T2m using mixed models. In both sites, ST models were superior for plant height and wood volume per hectare, whereas for individu...

Landscape Ecology, 2017

Context Wildfires play a crucial role in maintaining ecological and societal functions of North American boreal forests. Because of their contagious way of spreading, using statistical methods dealing with spatial autocorrelation has become a major challenge in fire studies analyzing how environmental factors affect their spatial variability. Objectives We aimed to demonstrate the performance of a spatially explicit method accounting for spatial autocorrelation in burn rates modelling, and to use this method to determine the relative contribution of climate, physical environment and vegetation to the spatial variability of burn rates between 1972 and 2015. Methods Using a 482,000 km 2 territory located in the coniferous boreal forest of eastern Canada, we built and compared burn rates models with and without accounting for spatial autocorrelation. The relative contribution of climate, physical environment and vegetation to the burn rates variability was identified with variance partitioning. Results Accounting for spatial autocorrelation improved the models' performance by a factor of 1.5. Our method allowed the unadulterated extraction of the contribution of climate, physical environment and vegetation to the spatial variability of burn rates. This contribution was similar for the three groups of factors. The spatial autocorrelation extent was linked to the fire size distribution. Conclusions Accounting for spatial autocorrelation can highly improve models and avoids biased results and misinterpretation. Considering climate, physical environment and vegetation altogether is essential, especially when attempting to predict future area burned. In addition to the direct effect of climate, changes in vegetation could have important impacts on future burn rates.