Members:

• Evaldinolia
• Maristela
• Pedro
• Sérgio

Abstract: The loss of animal habitat and the greater use of forests by humans in various parts of the world have increased the conflict between humans and wildlife, especially for those animals with geographically large home ranges. As a result, much effort has gone into preserving a network of reserves of the last remaining prime habitat. Biologists, however, have realized that these areas alone are not sufficient to sustain populations and that the multiple-use regions that surround these core areas are essential for species' survival. While the concept of multiple use is attractive, finding the right balance between competing uses is difficult or impossible without a computational framework in which to analyze these competing uses. The advent of object-based geographic data structures have given scientists such a framework and has spawned a numner of dynamic individual-based models for analyzing the interaction of animals with their environment. One of the most difficult challenges associated with these models is the simulation of movement and the relationship between movement and behaviour. Various analytic models have been proposed, and implementation of state-based movement has been designed using knowledge from domain experts. /with the recent use of GPS collars for tracking animals, scientists now have quantitative information on the nature of animal movement. This quantitative data offers an unprecedented opportunity to better understand the relationship between an animal's state and its behaviour at different spatial and temporal scales. The findings from these analyses can be used to calibrate and test the individual-based object models in an effort to understand the short- and long-term consequences of geographically specific management proposals and practices. This chapter explores these issues in the context of the tiger as a prototype for modeling the interaction between humans and animals in the wild.

Abstract: The loss of animal habitat and the greater use of forests by humans in various parts of the world have increased the conflict between humans and wildlife, especially for those animals with geographically large home ranges. As a result, much effort has gone into preserving a network of reserves of the last remaining prime habitat. Biologists, however, have realized that these areas alone are not sufficient to sustain populations and that the multiple-use regions that surround these core areas are essential for species' survival. While the concept of multiple use is attractive, finding the right balance between competing uses is difficult or impossible without a computational framework in which to analyze these competing uses. The advent of object-based geographic data structures have given scientists such a framework and has spawned a numner of dynamic individual-based models for analyzing the interaction of animals with their environment. One of the most difficult challenges associated with these models is the simulation of movement and the relationship between movement and behaviour. Various analytic models have been proposed, and implementation of state-based movement has been designed using knowledge from domain experts. /with the recent use of GPS collars for tracking animals, scientists now have quantitative information on the nature of animal movement. This quantitative data offers an unprecedented opportunity to better understand the relationship between an animal's state and its behaviour at different spatial and temporal scales. The findings from these analyses can be used to calibrate and test the individual-based object models in an effort to understand the short- and long-term consequences of geographically specific management proposals and practices. This chapter explores these issues in the context of the tiger as a prototype for modeling the interaction between humans and animals in the wild.

Abstract: A common ingredient to most predictive GIS-base models of land cover change is the empirical modeling of transition potentials-the likelihood that land would change from one cover type to another. However, there are many different approaches currently in use for the empirical modeling for transition potentials.This paper are , they undertook a comparative evaluation of twelve empirical procedures using case data over three time periods from Bolivia.

Abstract: Issues related to the scale of ecological phenomena are of fundamental importance to their study. The causes and consequences of environmental change can, of course, be measured at different levels and along multiple scales. While the natural sciences have long understood the importance of scale, research regarding scale in the social sciences has been less explicit, less precise, and more variable. The growing need for interdisciplinary work across the natural:social science divide, however, demands that each achieve some common understandings about scaling issues. This survey seeks to facilitate the dialogue between natural and social scientists by reviewing some of the more important aspects of the concept of scale employed in the social sciences, especially as they relate to the human dimensions of global environmental change. The survey presents the fundamentals of scale, examines four general scaling issues typical of social science, and explores how different social science disciplines have used scale in their research.

Abstract: We used a configurational comparative approach, the Qualitative Comparative Analysis (QCA), to study multiple causal interactions characterizing deforestation in the Brazilian Amazon. Our data set is based on seven local case studies at three time periods. Results reveal a limited number of pathways describing the articulation of causes of deforestation in the Brazilian Amazon under different contexts. Roads are often combined with biophysical conditions and the occurrence of extractive activities in the explanation of deforestation. This study reached conclusions via a reproducible and formal procedure that was applied at a regional scale while accounting for the geographic diversity of land-use trajectories.

Abstract: The use of agent-based models (ABMs) for investigating land-use science ques- tions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning— Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.