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Use of Macroinvertebrates to Identify Cultivated
Wetlands in the Prairie Pothole Region

Methods


Using National Wetlands Inventory (NWI) (Wilen and Bates 1995) wetland designations, we sampled 32 temporary and 32 seasonal wetlands in 1992. These wetlands were randomly selected from agricultural fields within 10.4-km² plots monitored by the U.S. Fish and Wildlife Service to estimate waterfowl recruitment using the mallard model (Cowardin et al. 1988). However, we dropped 2 temporary wetlands from our sample because we determined that our field crew had not collected samples from the correct location. We focused on the glaciated drift prairie in North Dakota, South Dakota, and Minnesota because it has been extensively developed for agricultural production and contains many seasonal and temporary wetlands. Only wetlands that were dry and intensively farmed when visited were included in this study. If on our initial site visit, a wetland was found to have been artificially drained, we excluded that wetland from our sample and selected an alternate wetland from the sample universe described above. Our goal was to evaluate the technique on the shortest hydroperiod wetlands in the PPR: those that were highly modified by agriculture and were difficult or impossible to identify using conventional techniques during dry periods. In August and September, we collected 12 soil samples at each site: six from the deepest portion of the wetland basin and six from the adjacent upland. We used a laser plane surveying instrument to locate the deepest portion of each wetland. We also used the laser plane to determine the elevation at which each wetland would overflow during flooding. We then defined 3 transects that radiated from the deepest portion of each wetland to the adjacent upland along random compass bearings. Four soil samples were collected from each transect; duplicate 500-cm³ wetland samples were collected 1 m from the deepest portion of each wetland, and duplicate 500-cm³ upland samples were collected at an elevation of 15 cm above the overflow elevation of each wetland. We designed this sampling scheme to ensure that samples from only wetland and upland habitats were collected. Soil samples were collected by coring 10 cm deep with a 8-cm-diameter coring device. Samples were frozen until processed in our laboratory.

We examined the soil samples in two ways. First, we visually examined one duplicate of the three wetland and the three upland soil samples from each wetland for recalcitrant remains of invertebrates under a low-magnification dissecting scope after concentrating remains by sieving through a 0.5-mm-mesh screen. Second, we incubated the remaining soil samples in 37.9-L aquaria under standardized light (12-hour day length), specific conductance (700 ÁS cm-1), and temperature regimes (four weeks at 8°C followed by four weeks at 22°C). In order to promote the emergence of the maximum number of taxa, we used the above dual temperature regime meant to simulate temperatures of wetlands following early spring snowmelt and after major summer precipitation events. At the end of the first 4-week incubation, we siphoned the contents of each aquarium through a 0.5-mm-mesh screen and returned the sieved water to its original aquarium for a second incubation at the alternate temperature. Invertebrates retained by the 0.5-mm-mesh screen during each 4-week incubation were combined and processed as a single sample. Invertebrates were sorted into taxonomic groupings according to Pennak (1989) and enumerated from both the visually examined field samples and our incubated aquarium samples, hereafter termed field and incubated samples, respectively.


Statistical Methods

We calculated taxon richness and counts of individuals by taxon, and taxon richness and counts of individuals weighted by the affinity of each taxon for wetlands. We determined the wetland association categories for each invertebrate taxon based on autecological relationships in published sources (Barnes 1968, Borror et al. 1981, Clarke 1981). The categories and weights we used were as follows: wetland obligate (taxon occurs only in wetlands), 1.0; facultative wetland (taxon occurs usually in wetlands), 0.75; facultative (taxon occurs regularly in both wetlands and uplands), 0.5; facultative upland (taxon occurs usually in uplands), 0.25; and upland obligate (taxon occurs only in uplands), 0.0. Because of the patchy distribution of aquatic invertebrates (Elliott 1977), we took a logarithmic transformation of the counts and used the variable log(count + 1), hereafter termed LogCount.

To facilitate use by those not very familiar with invertebrate identification, we also did an analysis with a simplified taxonomy. For this purpose, we combined into single groupings all planorbid snail shells, lymnaid snail shells, physid snail shells, cladoceran resting eggs (ephippia), ostracod shells, and trichopteran cases for the visually examined field samples, as well as all Cladocera, Copepoda, Ostracoda, Anostraca, and Conchostraca individuals for the incubated samples.

Data from the three transects were summed to provide a single value for each wetland and another for the adjacent upland. We computed means and standard errors of number of taxa and LogCount, unweighted and weighted by wetland-obligate status, for field and incubated samples, within location (upland, wetland), and by wetland class (seasonal, temporary). We tested for differences between upland and wetland sites and between wetland classes with a randomized-incomplete block analysis of variance using PROC MIXED (SAS Institute 1997), where habitat type (upland, temporary wetland, seasonal wetland) was the explanatory variable and sites were blocks. The response variables were number of taxa and LogCount; the explanatory variable was habitat type (upland, temporary wetland, seasonal wetland). We also computed least-squares means for each response variable by habitat type (SAS Institute 1997); separation among habitat types was performed using Fisher's protected LSD procedure following significant F-tests in ANOVAs (Milliken and Johnson 1984). This procedure was performed for both field and incubated samples and for complete and simplified taxonomies.

To classify wetland sites, we found a straight line in the Taxon Richness—LogCount plane (i.e., a two dimensional, flat surface) that best distinguished upland from wetland sites on the basis of those two variables (Figure 1). This was done through an iterative trial-and-error procedure by successively calculating lines and then determining the number of misclassified sites: the number of wetland sites below that line plus the number of upland sites above the line. A line that produced the minimum number of misclassifications was selected. This was done separately for the field and incubated samples. We chose this method because of its simplicity and the fact that the data did not meet assumptions of other straightforward classification methods, such as linear discriminant function analysis.


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