Spatial bias in landscape ecological simulations a case study of accounting for spatial dependence in a raster-based stochastic model using a region approach.

Cover of: Spatial bias in landscape ecological simulations |

Published by National Library of Canada in Ottawa .

Written in English

Read online

Edition Notes

Thesis (M.Sc.) -- University of Toronto, 2002.

Book details

SeriesCanadian theses = -- Th`eses canadiennes
The Physical Object
Pagination2 microfiches : negative.
ID Numbers
Open LibraryOL19193766M
ISBN 100612687414

Download Spatial bias in landscape ecological simulations

This book should be a required reading for anyone interested either casually or deeply in the design and implementation of spatial simulation models ." (Shivanand Balram, Suzana Dragicevic, Ecological Economics, Vol. 52 (1), )Brand: Springer-Verlag New York.

This book should be a required reading for anyone interested either casually or deeply in the design and implementation of spatial simulation models ." (Shivanand Balram, Suzana Dragicevic, Ecological Economics, Vol.

52 (1), ). Paula García-Llamas, Leonor Calvo, Marcelino De la Cruz, Susana Suárez-Seoane, Landscape heterogeneity as a surrogate of biodiversity in mountain systems: What is the most appropriate spatial analytical unit?, Ecological Indicators, /d, 85, (), ().Cited by: About Landscape Ecology: Determine persistence in a spatially heterogeneous landscape Virtual Lab Simulation Not all areas within a landscape are created equal.

When studying ecological processes in an environment, it is important to remember that landscapes may be spatially heterogeneous. Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines.

Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation. Landscape planning can be defined as the practice of planning for the sustainable use of physical, biological, and cultural resources.

It seeks the protection of unique, scarce, and rare resources. Abstract. This chapter seeks to evaluate the degree to which issues of spatial scale and scaling problems have been incorporated into different research problems in landscape ecology and to identify research methods applicable to problems of spatial scale.

This paper examines the role of spatial effects in ecological inference. Both formally and through simulation experiments, we consider the problems associated with ecological inference and cross-level inference methods in the presence of increasing degrees of spatial autocorrelation.

1. Introduction. One of the most difficult challenges in predicting large-scale ecological change is the inclusion of non-equilibrium dynamics, disturbance regimes, extreme events, and spatial relationships into ecological simulation models (Solomon,Dale and Rauscher,Gardner et al.,Fosberg et al., ).Theoretical community models and patch-scale vegetation models.

Realistic simulation of Spatial bias in landscape ecological simulations book effects of abundance distribution and spatial heterogeneity on non-parametric estimators of species richness. Écoscience: Vol. 9, No. 2, pp. Remotely sensed data for Southeastern United States (Standard Federal Region 4) are used to examine the scale problems involved in reporting landscape pattern for a large, heterogeneous region.

Frequency distributions of landscape indices illustrate problems associated with the grain or resolution of the data. Grain should Spatial bias in landscape ecological simulations book 2 to 5 times smaller than the spatial features of interest. Landscape: a complex fragmented spatial ecological system Studies, databases and conservation plans usually concern local or global scales.

May () showed that a great deal of ecological research focuses on single species or interactions between two species, usually on spatial scales that are often smaller than the characteristic distance. Nearly all landscape models are spatially explicit, since landscapes are fundamentally spatial entities.

However, not all landscape-relevant questions require a spatial model to address. For example, many (earlier) metapopulation viability models are spatially implicit, not explicit. That is, the spatial subdivision and separation of habitat.

The LANDIS-II forest landscape model simulates future forests (both trees and shrubs) at decadal to multi-century time scales and spatial scales spanning hundreds to millions of hectares. The model simulates change as a function of growth and succession and, optionally, as they are influenced by range of disturbances (e.g., fire, wind, insects.

Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology is an excellent reference for professionals such as modeling and simulation experts, GIS experts, spatial analysts, mathematicians, statisticians, epidemiologists, health policy makers, as well as researchers and.

A critical part of ecological studies is to quantify how landscape spatial heterogeneity affects species’ distributions. With advancements in remote sensing technology and GIS, we now live in a data-rich era allowing us to investigate species–environment relationships in heterogeneous landscapes at multiple spatial.

Analyzing multi-scale changes in landscape connectivity is an important way to study landscape ecological processes and also an important method to maintain regional biodiversity. In this study, graph-based connectivity was used to analyze the dynamics of the connectivity of natural habitats in the Long Yangxia basin of upper Yellow River valley from to This study aims at quantifying the landscape patterns and ecological processes or clearly linking pattern to process to identify green space changes and their driving forces, based on gradient.

Scheller’s research examines past and future landscape change due to climate change and human activities, management, and values.

He has published more than manuscripts and book chapters, and his first book, “Managing Landscapes for Change” (Springer) is due to be published in Dr. important issue for ecological change in both the western and eastern United States. Advances occurring in the past ten years regarding spatial data availability, micro-econometric modeling, spatially-explicit landscape simulation approaches, and policy applications are highlighted.

The. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation ( m resolution) with field measurements and.

Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters.

Information on the relationship between encounter probabilities, sources of. In this study, we explored how spatial structure can be a source of bias, and present an approach that allows uncertain landscape data to be incorporated into model output with minimal bias.

There are many different landscape models in the literature (see for a recent and comprehensive list), all of which allow a process (population) model to. Site‐selection bias also depended on the spatial distribution of individuals and was stronger when individuals were aggregated in the landscape: For a true richness change of zero, the bias decreased the observed richness decline to − (relative to − in the standard model, [Supporting information]).

In addition, the authors discuss how to most effectively integrate spatial ABMs with a GIS. The book concludes with a combination of knowledge from entomological, epidemiological, simulation-based, and geo-spatial domains in order to identify and analyze relationships between various transmission variables of.

Spatial extrapolation in ecology tends to follow a general framework in which (a) the objectives are defined and a conceptual model is derived; (b) a statistical or simulation model is developed to generate predictions, possibly entailing scaling functions when extrapolating to broad scales; and (c) the results are evaluated against new data.

Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines. Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial : $ Biodiversity in Drylands, the first internationally based synthesis volume in the Long-Term Ecological Research (LTER) Network Series, unifies the concepts of species and landscape diversity with respect to deserts.

Within this framework, the book treats several emerging themes, among them: how animal biodiversity can be supported in deserts diversity's relation to habitat structure. Introduction. Dramatic changes in landscape composition and increasing concerns about global biodiversity losses through the 20th century motivated the emergence of the field of landscape ecology, which addresses ecological dynamics in relation to the spatial pattern of landscapes.

As an example, in landscape ecology, MAUP effects on scale, that is aggregating to larger spatial units, and rezoning, or moving the boundaries of an area measured, both have significant results in result evaluation.

Areas under ecological stress, for instance, could be less severe or underestimate in their severity. He, H. A simulation study of landscape scale forest succession in northeastern China.

15 th International Association of International Association of Landscape Ecology. Lincoln, NE USA. He, H. Spatial simulation of forest landscape response to different harvest scenarios under global climate warming. Geoinformatics. Generating reliable ENMs for such species is a challenge, however, owing to issues that arise from spatial sampling bias, such as model inaccuracy and overfitting.

Here, using virtual scenarios, we assess the utility of integrating occurrence data for closely related species with varying degrees of niche overlap into ENMs of focal species. To acquire a more thorough understanding of the complexity of natural systems, researchers have sought the assistance of advanced computer-based technologies in the development of integrated modeling and simulation systems.

Computer simulations have been utilized in a variety of natural resource management applications from modeling animal populations, to forest fires, to hydrologic systems.

Tools to explicitly report the spatial distribution of the bias and lack of sampling effort across a study region include maps of ignorance that provide information on sampling coverage and reliability (Rocchini et al.Ruete ), maps of collecting effort (Schulman et al.

) or spatial modeling of the distribution of effort based on. These GCMs lack orographic detail, having a coarse spatial resolution with a grid-cell size on the order of ° × ° (approximately × km 2), which is far too coarse for landscape or basin-scale models that investigate hydrologic or ecological implications of climate change.

The meso-scale (1 to km) climate surfaces provided. Natural variability is defined as spatial and temporal variation in the ecological conditions that are relatively unaffected by people, within a period of time and geographical area appropriate to an expressed goal (Landres et al.

Improved understanding is needed of how natural disturbance-generated landscape mosaics differ from human. Single-Village Simulations Spatial Simulations: Garki District Madagascar Discussion Appendix A Enzyme Kinetics Model for Vector Growth and Development A.1 Overview A.2 Stochastic Thermodynamic Models A.3 Poikilothermic Development Models A.4 The Sharpe and DeMichele Model Appendix S3, Table 2) reported greater bias from a misspecified null model with α 2 = 1 (–188% to –195%) than my average (–166%).

Their simulations included telemetry data for 2–16 animals and appear to have been based on a single realization of the spatial covariate (Royle et al. Recent work has shown that death assemblages can offer high-quality spatial data and can be used to reconstruct season-specific landscape use, birthing grounds, hunting grounds, and even ecological gradients (Tomašových and Kidwell b; Terry a; Miller ; Miller et al.

It is not surprising that bone surveys, which record bones. Field plot-based P:H ratio (i.e., forest canopy architecture, see Methods) estimates showed an average bias of 15% in the lowland landscapes but extreme biases averaging 98% were found in montane landscapes ().All density distributions of landscape P:H ratio were skewed with the exception of the two lowland erosional terra firme (ETF) landscapes (Fig.

2B). These include selection effects, denominator bias, exposure inaccuracy bias and the errors-in-variables problem, spatial dependency, significance tests, and ecological bias.

The chapter also discusses socio-economic confounding as this is a major potential source of bias in spatial epidemiology. Introduction. A succession of spatially explicit ecological models in the early s indicated that large-scale regular spatial patterns could arise within homogeneous landscapes from local biotic interactions alone –, with potentially profound implications for the maintenance of biodiversity and ecological stability.At first, large-scale ordered patterns were harder to find in natural.In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in, and RSD simulated in over Zhengzhou city, Henan province, China.

Three major findings can be .

33017 views Saturday, November 14, 2020