Magpie Goose Survey - A Case Study

Note: although based in fact, the data and scenarios presented in this tutorial have been simplified and adapted to suit the current purpose.

• Background
• The role of spatial technology in survey work
• Collate existing data
• Process data to suit the survey aim
• Sampling strategy
• Fieldwork
• Spatial analysis
• Extrapolate
• Evaluation and outcomes
• Acknowledgments

Background
Magpie Geese (Anseranas semipalmata) are an important natural resource in the Northern Territory as the species is a significant component of the fauna of floodplains, provides food for Aboriginal communities, sport for hunters, and is highly visible making it a key tourist attraction. Distribution patterns of the Magpie Goose in Australia are known to have diminished since the arrival of Europeans and conservation of the species is an issue of concern to wildlife managers in northern Australia. The NT Parks and Wildlife Commission have conducted surveys of Magpie Geese and their nests on an annual basis for many years in order to gain a better understanding of the ecology of the species, its distribution and abundance, and habitat relations.

A subset of this data - a single year (1993) in a single catchment (Mary River) - has been selected to illustrate the important role that spatial technology plays in the ongoing collection and analysis of this data. A typical sequence of events relating to the planning and execution of surveys in the field and the subsequent integration and analysis of the data into a geographic information system is portrayed.

Figure 1: The Mary River Floodplain in the Top End
of the Northern Territory.

The role of spatial technology in survey work
Spatial information has a role to play before, during and after surveys.
A typical sequence for Magpie Geese might be as follows.

Before the survey

• collate existing data (and understand the study area)
• process data to suit the survey aim
• determine the most appropriate sampling strategy

During the survey

• field maps and navigation
• GPS location fixes
• record the collected data.

After the survey

• analyse the sampled data
• extend the results to areas not surveyed
• evaluate and verify results
• report outcomes and recommendations to management.

A section for each of the above headings follows. Each section contains a brief overview of the role of spatial information at that point and is accompanied by a more specific description of the Magpie Goose survey. Each of these is accompanied by illustrative maps, tables, and other spatial information from the geographic information system as appropriate.

Collate existing data

A baseline database would normally contain topographic data such as drainage, roads, places and elevations to provide a context for the more specific datasets associated with the study. In this study of Magpie Geese, baseline digital data is AUSLIG TOPO 250K data at 1:250,000 scale (see Figure 1). Datasets specific to this project and available for this area included a vegetation map compiled by D. Lynch of the Department of Lands, Planning and Environment. The vegetation map describes 47 vegetation types within the Mary River floodplain area and is illustrated in Figure 2. It is known from detailed ecological studies that Magpie Geese favour specific habitats for nesting, feeding and roosting. This vegetation map will then be used to provide the underlying framework onto which survey data can be mapped and finally presented.

Data exploration

The vegetation map by Lynch contains information on 47 vegetation communities in the Mary River floodplain. These spatial units are linked in a database table to other attributes. Attributes available for each vegetation type include the area and perimeter measurements for each vegetation community. Data is often collected for specific projects and may need to be processed before it is suitable for use. Typical data exploration would include a description of the area covered by each community and the proportion of area each community comprises of the total area. This information is needed to determine whether the data was suitable for this project and help in determining the suitability of different sampling strategies.

Figure 3: Subset of the Lynch vegetation map with legend to indicate vegetation classes.

Figure 4: GIS functions includes statistical analysis of fields in a table.
In this case 46 vegetation communities covering 1,078 square kilometers.

Process to suit the survey aim

The Lynch vegetation map is based on a combination of vegetation floristics and structure and soil type. This level of detail is more than is necessary for estimating goose numbers and generalisation of the data was achieved by reclassifying the map into 18 classes. These classes were suggested by Peter Whitehead on the basis of his work on the autoecology of the Magpie Goose. These new units were based solely on floristics and structure and not on other variables. The effect of generalisation on the display of the map can be seen in Figures 5 & 6.

Figure 5

Figure 6

Sampling Strategy

Sampling extensive floodplains during the wet season at the resolution required to observe individual Magpie Geese can only be achieved in an effective manner using aerial survey techniques. Initial calculations of flight times and costs can be gained by plotting the transects over the area of interest, calculating their total length, and examining the vegetation communities they cross to ensure than a representative sample is obtained - that is, that your sampling is 'stratified' with respect to vegetation unit. An east-west system of transects was designed at intervals of approximately 5 kms. This provided a total transect distance of 1142 kms and provided a representative sample of vegetation communities proportional to the total area of each community.

Figure 8: Total length of the 21 transects required to survey the study area can be calculated in a GIS. The cost of the survey can be estimated from this information.

Fieldwork

Observers on both sides of the aircraft counted geese and recorded their observations frequently. The field of view for counting was 200 meters on each side of the aircraft which provided a 400 m swathe along the flight path. A GPS was used to register the geographic coordinates of each goose observation and the coordinates and the count entered into a data logger in real time. Maps prepared specifically for the survey were marked with the time and direction of flight for each transect.

Spatial Analysis

Table 2: Density of geese found within each vegetation type can be calculated using a GIS.

The"reclassified" vegetation map provides information on vegetation communities included within the sampled area (Figure 10). The area sampled can be derived from within the GIS by mapping the transects and then creating a buffer around the data to account for the field of view of the flight path flown. Analysis within the GIS can then provide information on the area for each vegetation type within the sampled area and the number of geese observed in each of these. The density of geese for each vegetation community can then be derived. This information provides a quantitative assessment of which community types Magpie Geese favoured at the time of the survey and provides a starting point for understanding the habitat relations.

Extrapolate

By using a GIS to analyse the information gained during the field trip in association with vegetation types, the density of Magpie Geese within each vegetation type could be estimated for the sampled area. This data can be used to estimate the density of Magpie Geese throughout the total floodplain. This is done by 'scaling up' the count data by the ratio of sampled vegetation to total vegetation communities. For example, if 100 geese were observed in 100 km2 of community type A, a density of 1 goose/km2 can be calculated. If the total area of community A is 300 km2 then the total Magpie Geese population can be estimated at 300 for this community. In this way the total population in the area can be estimated in addition to the relative preferences for different community types.

Table 3: Total numbers of geese in the floodplain system can be calculated using field data in a GIS.

Evaluation and outcomes

Results can be evaluated by comparison with results from previous years and catchment areas. Sampling strategies can be reviewed in light of the results obtained. For example, when the transects flown were assessed against the reclassified Lynch vegetation map, it became apparent that not all vegetation types were represented in the sample. The missing vegetation types could not be analysed for the occurrence of magpie geese. Future sampling strategies should include all vegetation types to find occurrence of magpie geese, and additional sampling may be scheduled.

After evaluation of the results and methodology used, techniques of analysis can be refined and applied to datasets. An example of this is the application of the methodology to analyse density of Magpie Goose nests on the Mary River floodplain. The methodology could also be applied to other catchment areas, for other species and at other times.

Figure 12: Illustrates the extent of the 1993 survey of Magpie Geese nests across the Top End of the Northern Territory. Evaluation of results from individual areas can be compared.

Acknowledgments
This case study was prepared by Chris Devonport and Janice Crerar. The following people and organisations are acknowledged for their contributions: Dr. Peter Whitehead, Mathew Fegan and Tom Colley of the Charles Darwin University; D. Lynch of the Department of Lands, Planning & Environment and; Keith Saalfeld of the NT Parks & Wildlife Commission.