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Geographic Information Systems

A GIS Application in Waterfowl Management


Management of waterfowl populations involves changing either the recruitment or survival rates of a population. Recruitment and survival are often functions of the amount and type of habitat; therefore, much of waterfowl management consists of preserving, adding, or modifying habitat. Selecting those habitat manipulations that alone or in combination will have the desired effect on a population is a difficult procedure because of the number of variables involved and the complex biological interactions between species biology and habitat.

The same advances in computer technology that have led to recent developments in the use of GISs have led to increases in the use of simulation models that attempt to portray the population biology of waterfowl species. Population data required by these simulation models usually are difficult and expensive to obtain. For many species, habitat availability and quality and various population parameters are correlated. Fortunately, recent advances in remote sensing have made inexpensive estimates of the availability of habitat over vast areas available to waterfowl managers. A marriage between GIS and simulation modeling is the logical outcome of these technological developments. As an example, we will illustrate the use of such a combined GIS and population simulation model for evaluating management practices for mallards in the prairie pothole region of North Dakota.

For the following example, we selected a 10.4-km2 plot from typical duck habitat in central North Dakota. The area is glaciated and has numerous small wetland basins. The uplands are used almost exclusively for agriculture. Landsat TM data from May and September of 1986 processed by methods developed by Koeln and Wesley (1987) and Koeln et al. (1988) were used. The digital image processing techniques created a GIS data layer for land use and land cover. The retrieval and reporting functions of a GIS were used to report the areas for three wetland and 10 upland classes. This habitat information was altered to represent the habitats available to birds arriving in early May. For example, areas of growing grain were assumed to have been bare soil (recently plowed ground) when birds arrived in late April. Habitat classes conformed with the classes (Cowardin et al. 1988b) required as data input for a mallard productivity model (Johnson et al. 1987). That model allows the user to vary population parameters and habitat availability to predict the resulting production of young. Cowardin et al. (1988b) illustrated the use of the model in combination with other models that predict the number of breeding birds settling in an area. The same models have been linked with habitat data derived from high-altitude photography and aerial video (Cowardin et al. 1988a).

Population parameters used in the example were based on information from Cowardin et al. (1988b) and Klett et al. (1988). The habitat data (Table 3) can be displayed on the monitor of a microcomputer. These data then were transmitted to the mallard productivity model, and the model was executed to obtain estimates of expected breeding population and young produced as well as other important population parameters (Table 4). For our demonstration, the control data represent current conditions.

Table 3. Availability of nesting habitat for a 10.4-km2 area in central North Dakota used to illustrate linkage of a population simulation model to a GIS with habitat data derived from Landsat TM.
Habitat
Percent of area per treatmenta
Control
Impoundment
Conservation reserve
Barrier fence
Autumn-plowed crop
51.86
49.63
28.53
51.86
Grain stubble
1.21
1.02
1.21
1.21
Summer fallow
3.18
3.18
3.18
0.58
No-till winter wheat
11.19
11.19
11.19
11.19
Grassland
13.61
9.72
13.61
13.61
Hayland
2.00
2.00
2.00
2.00
Right-of-way
0.46
0.46
0.46
0.46
Shallow wetlandb
8.88
7.13
8.88
8.88
Deep wetlandb
1.42
3.81
1.42
1.42
Permanent water
0.25
0.25
0.25
0.25
Fenced cover
0.00
0.00
0.00
2.61
Conservation reserve
0.00
0.00
23.33
0.00
Barren
5.94
11.24
5.94
5.94
a The treatments were: (0) control, no management; (1) creation of an impoundment; (2) half of cropland converted to conservation reserve; (3) addition of a 148-ha predator barrier fence.
b Only the portion of the wetland representing nesting cover is shown; the remainder is included in barren.

The problem for the waterfowl manager is to select from an array of potential management alternatives the one most likely to maximize production from an area. The simulation models also can be combined with economic models to produce a planning tool (Nelson and Wishart 1988). The system demonstrated here combines GIS and simulation modeling. It cannot tell the manager what management should be applied, but it is a tool to assist evaluation of the outcome of various alternatives. The software has the advantage of allowing this to be done by modifying the habitat displayed on the computer monitor. For illustration, we used the editing functions of the GIS to simulate three management techniques. These techniques were represented by three different land use and land cover data files. In one simulation, we created a large, semipermanent wetland. In another simulation we converted one-half of the cropland to planted cover under the USDA's CRP. In the third simulation, we constructed a predator barrier fence around 148 ha of tall, dense nesting cover (Figs. 17-19). The predicted results are highly dependent on the assumptions made for many population parameters in the model. As a first approximation we used the same assumptions made by Cowardin et al. (1988b) and unpublished nest survival rates prepared for data in Klett et al. (1988).

Results of the simulations are summarized in Table 4. All treatments show some increase in the number of recruits produced. Creation of the large impoundment was the only treatment that increased the amount of wetland used by breeding pairs. The model predicted a corresponding increase in breeding population and recruits produced, but the increase in nest success was negligible. Conservation reserve was modeled under two different nest survival rates for that cover because there were no published data. We therefore used nest survival of 13.2% (unpubl. data, Northern Prairie Wildl. Res. Cent., Jamestown, N.D.) for planted cover in simulation (a) because that cover is similar to what is expected under conservation reserve. Some might argue that nest success in conservation reserve should be higher than planted cover because the large amount of conservation reserve may dilute the effect of predation. In conservation reserve (b), without real data, we chose a nest success rate of 20% which is relatively high but reasonable. The treatment then produced as many recruits as the impoundment from the same number of pairs used in the control. Addition of the predator barrier fence produced the most recruits of all treatments.

Table 4. Estimates of mallard population parameters for a 10.4-ha area produced by using habitat availability data deriverd from Landsat TM, a GIS, and a population simulation mmodel applied to a control and three habitat enhancement practices.
Treatmenta
Breeding population (pairs)
Nest success (%)
Hen success (%)
Recruits produced
Control
28
13.4
25.0
26.4
Impoundment
36
14.3
26.3
35.3
Conservation reserve (a)
28
14.9
27.4
28.5
Conservation reserve (b)
28
19.7
33.9
35.6
Barrier fence
28
32.2
50.1
53.0
a The treatments were: (0) control, no management; (1) creation of an impoundment; (2) half of cropland converted to conservation reserve, for (a) nest success in conservation reserve = 13.3%, for (b) nest success in conservation reserve = 20.0%; (3) addition of a 148-ha predator barrier fence.

Results obtained from the model are extremely sensitive to the input data (Johnson et al. 1987, Cowardin et al. 1988b). It was designed as a tool to assist in comparing the potential outcome to be obtained from treatments. In the example, we used a range of reasonable input data, but there was an absence of data for conservation reserve. Both the input data and the assumptions upon which the model was based must be considered carefully when model results are interpreted.

The model is also sensitive to the amounts of habitat present in the area to be evaluated. Fortunately, these areas can be measured more accurately than some of the mallard population parameters by use of remote sensing techniques. In previous applications of the model, construction and modification of data sets that simulated various treatments were time- and labor-intensive. The combination of GIS technology with the model overcame this problem. Furthermore, the display of a real landscape and the rapid modification of that landscape by means of GIS editing functions are easily understandable by a land manager. Through the efforts of the North American Waterfowl Management Plan (NAWMP), many waterfowl habitat-enhancement techniques are being applied to the prairie pothole region. By comparing land cover and land use data derived from the 1986 satellite data with data derived from more recent satellite data, we will have an excellent record of the success of the NAWMP in changing the landscape for improving waterfowl habitat. The effects of these actual changes will be evaluated with the GIS and mallard model approach described above.


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