Wildlife Population Analysis with GIS: Conservation Management of Royal Albatross

Bruce R. McLennan1, Martin K. Purvis1 and Christopher J.R. Robertson2
1Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand.
2Conservation Sciences Centre, Department of Conservation, Wellington, New Zealand.

Abstract
This paper examines the use of geographic information systems (GIS) for wildlife management. A prototype GIS that embodies principles of landscape ecology successfully provided useful information that assists with the management of a breeding colony of Royal Albatross in New Zealand. The prototype also provides information that aids in the exploration of the distribution of nest sites within the colony.

There are numerous examples in the literature of the use of GIS for the assessment of habitat requirements and the prediction of suitable areas of habitat for wildlife species. In most cases the use of GIS in this manner have relied on the spatial data operators that are characteristic of GIS for these analyses and predictions. They are typically based on surrogate variables (eg. landcover) that act as predictors of species presence/absence and normally consider a single temporal snapshot.

The adoption of GIS for ecological research and applications has been somewhat slower than that for habitat analyses. Ecological studies focus on organisms and the emphasis has traditionally been on interactions that affect the number of organisms and the changes in those numbers over time. Spatial interactions between organisms, and between organisms and their environment, were either simplified or ignored altogether. A more recent focus on the principles of landscape ecology which considers the spatial and temporal interaction between landscape characteristics and the distribution of organisms would seem to provide a considerable argument for the adoption of GIS. The facilities GIS offer for the storage, management and retrieval of the large quantities of spatially referenced data required for landscape studies make them particularly useful. But there are some obstacles to be overcome. These include:

This paper describes a prototype spatial information system (SIS) that has been developed by the Department of Information Science at the University of Otago, in collaboration with the Department of Conservation for the collection, maintenance, analysis and reporting of data concerning a breeding colony of Royal Albatross at Taiaroa Head, on the Otago Peninsula. This colony is somewhat unique since it has been the subject of a continuous scientific monitoring and protection effort since the late 1930s. As a result, approximately 60 years of spatial and aspatial breeding data have been collected. The prototype system integrates these data with physical topographic, cultural and natural feature data obtained from aerial and GPS surveys in a manner that allows systematic spatial analyses to be conducted that were not previously feasible.

Three example analyses are discussed that demonstrate the systemís capabilities for the exploration of relationships. This leads to a better understanding of the spatial and temporal relationships between breeding birds and their physical environment and that assist in the management of the colony itself.

The prototype system is significant in that the successful management of the colony is information critical. The albatross population at Taiaroa Head is growing steadily but has yet to reach a level at which it could be considered naturally self-sustaining. The success of the colony therefore depends on the quality of the decisions made regarding its management. The colony has considerable conservation value as it provides a unique opportunity to closely study a species of seabird that is otherwise inaccessible. Itís presence also generates a substantial amount of tourism revenue. The prototype also represents an ecological application of GIS technology that is data rich. Detailed data is maintained for all individuals in the population over a continuous time period at a high spatial scale.