Effectiveness of GIS in assesinggrizzly

grizzly GRIZZLY BEAR HABITAT

In the Central Coast of Brittish Columbia.

Conceptual Idea
This research project explores the ways in which institutional objectives that are reflected in GIS data models
influence the resultant data and maps used to advise the decision making process.



bear with salmon    When multiple agencies or organizations create similar GIS analyses around a controversial resource management issue, are institutional mandates and objectives of the respective agencies responsible for variations in the maps and data produced?  The technology of GIS has greatly enhanced the ability of natural resource data to be visualized and this data is playing an increasing role in representations of wildlife habitat (IBM Report).  Two ministries within the British Columbia government and numerous ENGO's are using GIS to analyze spatial data in order to produce grizzly bear (Ursos arctos) capability and suitability maps.   The descriptive potential of the maps produced from such analyses serve as powerful tools for shaping public perception and the decision making process (McKendry, 2000).  The Ministry of Water, Land and Air Protection (MoWLA) is working with the Grizzly Bear Scientific Panel and the Wildlife Branch to develop sustainable harvesting levels of grizzly bears in BC.  The Ministry of Sustainable Development is using gizzly bear habitat maps as one of its base spatial information data sets to advise the Central Coast Land and Resources Management Plan (LRMP).  In both of these cases, GIS is employed for analysis by the BC government early on in the planning process and the decisions are subject to other types of information gathering and methods of validation.     The Round River Conservation Institute completed a regional Conservation Area Design (Jeo, 1998) and are in the process of creating another analysis of grizzly bear habitat in British Columbia with the Craighead Institute.  Assuming that agendas are indeed encoded into models of reality (Schurman, 2002) then the resultant data of a GIS analysis will reveal a view of reality held by the creating agency.  This is not to suggest that these agencies and organizations are not aware of the inadequacies of any spatial modeling strategy.  In fact, increasingly agencies are trying to incorporte local knowledge, field methodology and other forms of verification into the modeling proccess in order to better simulate reality.  Nonetheless, the data resulting from the GIS analysis is very influential in decisions made and strategies adopted by these agencies.
    There is a great deal of uncertainty around how to effectively model grizzly bear habitat and estimate population densities.  Model building in GIS has been recognized as a key point where social influences enter and thus influence the predictive value of GIS (Schuurman, 2002).   Shurman further describes models as ways of simplifying reality in order to better understand environments and that the problem arising is that they are often confused with reality when they are but representations of reality that reveal institutional objectives.  Mills et al, (1996) studied how the use of different population viability computer modeling programs run on the same data set produced different results due to slight differences in the ways in which the data was inputted into the models.  This resea4rch is interesting because it suggest that slight modifications in the way that data itself is entered into the analysis could have profound influence upon the results.  

forest river        Many issues of habitat assessment are wrought with problems of data quality, data reliability and data interpretation. The development of the individual layers in the data model can represent sources of inaccuracy and misrepresentation of the landscape.  For example, the definition of old forest by the BTM data (trees older than 140yrs) verses a more complex definition of old growth structure used by the Sierra Club (multi-structured in terms of species  and structural diversity & trees older than 250 years) will produce very different data sets.  Likewise, will the determination of which land classifications in the landscape represent prime habitat.  This project seeks to study how the final evaluation of criteria in the model building process can influence the overall outcome.  It asks what would happen if habitat structures were emphasized in the decision making process as opposed to emphasizing the influence that human activity might have?  Recognizing the influence that social objectives have in influencing the development of a GIS analysis are important because once a model is encoded in GIS, in manufactures entities that gain a materiality as they enter into the parlance of science and society (Schuurman, 2002).  They may initially be treated as 'hypothetical', as in the early stages of  the LRMP, but over time they become 'normalized and institutionalized' (ibid).  Nonetheless, the predictive abilities of GIS to help scientists and policy makers better understand the world coupled with a society that embraces the creation of scientific knowledge ensure the continued use of GIS derived data in the decision making process.  

     This analysis begins with the creation of a simple habitat model for grizzly bear in eight watersheds in the Central Coast.  Seven factors linked to grizzly bear habitat were analyzed and represented spatially using fuzzy logic.  In an attempt to analyze the influence that institutional perspective might have in the final steps of the spatial analysis, these 7 factors were then analyzed using three different Multi-Criteria Evaluations (MCE).  The first MCE weighted heavily habitat factors, the second weighted heavily human influence, and the third adopted a more neutral position.  Comparing the resultant data from these three MCE can offer insight as to how institutional bias might influence the derivation of data used to advise the decision making process. It also reveals the ways in which GIS can be used to produce multiple final analyses that can be then selected from in order to meet objective or mandates, particularly when there are multiple stakeholders and interests.



Conceptual idea   Introduction   Data Collection      Project Design    Spatial Analysis    Conclusions   Problems

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