Journal of Environmental Management (1984) 19, 239-248
Testing an EcosystemRegionalization
Robert G. Bailey*
WashingtonOffice Land ManagementPlanning Staff, USDA Forest Service,
Fort Collins, Colorado80524, U.S.A.
Received 24 ,April
1984
As a meansof developing reliable estimatesof ecosystemproductivity, landscapes
need to be stratified into homogeneousgeographicregions. Such ecosystemregions
are hypothesizedto be productively different in important ways. One measureof
the difference is hydrologic productivity (i.e. runoff per unit area). To test the
hypothesis that hydrologic productivity is significantly different from region to
region, data from 53 hydrologic bench-markstations within major ecosystem
regions of the conterminous United Stateswere subjectedto discriminant analysis.
The ecosystemregions tested in this study exhibit a high degreeof ability to
circumscribestations with similar hydrologic productivity. Discriminant analysis
seem~to be a particularly appropriate method for testing the validity of land units
at all scalesof interest.
Keywords: ecosystem, region, ecological land classification, landscape ecology,
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1. Introduction
Land managementdeals with productivity systems,that is ecosystems,from which it
attempts to extract efficiently and continuously a product, suchas wood or water. These
products are commonly referred to as renewableresources.
Estimatesof ecosystemproductivity are required for the purposeof assessment
and
managementof theseresources.In order to make suchestimates,relationships between
the needed production information and ecosystemclassesmust be developed.These
relationships are called rules (Davis, 1980). Rules take on many forms, from simple
experience-based judgments to multivariate regression models and complex
mathematicalsimulations. Application of theserules is basedupon conceptsof transfer
by analogy. Thus, the rules necessaryfor estimation of productivity are extrapolated
from experimentalsites or from managementexperienceto analogous areasdefined by
classification.
* Formerly with the Resources Evaluation Techniques Program, Rocky Mountain Forest and Range
Experiment Station, USDA Forest Service,Fort Collins, Colorado, U.S.A.
239
0301-4797/84/070239 + 10 103.00/0
(!;;) 1984 Academic Press Inc. (London) Limited
240
Testing an ecosystemregionalization
Such methods are based on the hypothesis that all replications of a particular
ecosystemclasswill have fairly similar productivity. This hypothesishas beenquestioned
by a number of workers on the grounds that correlations betweenecosystemclassesand
production are generallylow.
One way to establishreliable ecosystem-productionrelationships is to stratify the
landscapeinto "relatively homogeneous"geographicregions where similar ecosystems
have developed on sites having similar properties (Rowe, 1962). For example, similar
sites (i.e. those having the same landform, slope, parent material and drainage
characteristics)may be found in severalclimatic regions. Within a region,thesesiteswill
support the same vegetationcommunities, but in other regions vegetation on the sites
will be different. Thus, beachridges in the tu~dra climatic region support low-growing
shrubs and forbs, whereasbeachesin the subarctic region usually have densegrowth of
black spruce or jack pine. Soils display similar trends, as the kind and developmentof
soil properties vary from region to region on similar sites.
Such ecosystemregions, or ecoregions,have at least two important functions for
management. First, a map of such regions suggestsover what area the productivity
relationshipsderived from experimentsand e:xperience
can be applied without too much
adjustment. Second,they provide the necessarygeographicalstratification for designing
cost-efficientsampling programs to estimate ecosystemproductivity.
The mapping of theseregions has beenworked out in a preliminary way by Bailey
(1976) from conceptsadvancedby Crowley (1967).Subdivisionson the map showunits
that (theoretically) should be relatively homogeneous.This homogeneity has been
partially tested using biophysical parameters (Olson et al., 1982),aquatic ecosystems
(Omernik et al., 1982)and wildlife communities (Inkley and Anderson, 1982).However,
the degree of correlation between these regions and productivity has not been
determined.The objective of this study wasto use actual data to determinethe degreeto
which ecosystemregions representproductivity regions.Effectiveresourcemanagement
basedon ecosystem-productionrelationshipsrequires that productivity be distributed in
a manner similar to the ecosystemregionalization used.
2. Approachto testing
As Rowe and Sheard (1981)point out, mapsare hypothesesto be testedand improved.
The units expressa senseof what is the,orizedto be important in the landscape.Regional
boundaries on Bailey's map are based on climatic and vegetational features (Bailey,
1983).The approachrests upon the hypothesisof a relationship betweenthe featuresof
the environmentusedto delimit the region and the properties of the region. In this case,
regions bounded by changes in macro-features of the climate and vegetation are
hypothesized to be productively different in important ways. If actual data on
productivity are assembledfor the regions,this hypothesiscan be testedstatistically and
the validity of the regional structure (map) can be evaluated objectively. This test is
independentinasmuchas productivity did not enter into the initial regionalization.
The use of statistical tests which analyse or test previously de";eioped regional
structureswas pioneered by Zobler (1957).Thesetestsinvolve the use of a significance
test--:chi square or variance analysis-to ascertainif an associationexists betweenthe
spatial frame, which is the way an areahas beenpar,titioned,and the areal dispersionof
specificproperties of this frame. Recently,the use of multivariate discriminant analysis
to test the validity of land units is attracting attention (Casetti, 1964; Steiner, 1965;
Pavlik and Hole, 1977; ami et aZ.,1979; Rowe and Sheard, 1981).This method of
analysiswas viewed as the statistical method best suited for this studv.
r
R. G. Bailey
241
There are different approachesfor l1Jeasuringecosystel1J
productivity. The question
is then: what l1Jeasureof groductivity is to be used for testing?
The traditional measureof productivity is net primary productivity. It is defined as
the total amount of organic matter produced annually by an ecosystem(Sharpe,1975).
It is an expressionof a combination of vegetationand environmental site characteristics.
Becauseit can be applied to a wide range of ecosystems,at many levels, it is extremely
useful in comparing structure and dynamics of ecosystemswhich differ in floristics and
physiognomy. Analysis of regional structure necessitatesproductivity data that are not
only accurate,but cover large geographicareas.The existenceof such data, however,is
limited. Actual measurementsof primary productivity are difficult to make and are
usually taken from small areas. Different methods of measurement make data
comparison difficult, if not impossible.Thus, there does not exist a comprehensivedata
baseon primary productivity that is suitable for nationwide testing.
Another measure of ecosystemproductivity is the normal runoff-the amount of
water runoff, on average,produced during a period from a unit area of land surface.
Because runoff data are readily available for the entire country, this measure of
productivity was used in this study, and is referred to here as hydrologicproductivity.
3. Data and methods
To make unbiased comparisons of hydrologic productivity, data are needed on the
runoff characteristics of "natural" basins which are unaffected by urbanization, manmade storage,diversion, or ground water pumping. The data usedwere the streamflow
observations collected by the U.S. Geological Survey at 57 hydrologic bench-mark
stations (Cobb and Biesecker,1971).These data for stations from all over the United
States are available in a computerized data library called WATSTORE at the USGS
computer in Reston,Virginia (Hutchison, 1975).Locations of the stations are shown in
Figure 1 and listed in Table 1.
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242
Testing an ecosystemregionalization
TABLE1. Locations of hydrologic bench-mark stations
1.
2.
3.
4.
Blackwater River near Bradley, Alabama
SipseyFork near Grayson, Alabama
Wet Bottom Creek near Childs, Arizona
Cossatot
Arkansas River near Vandervoort'
5. North Sylamore Creek near Fifty Six,
Arkansas
6. Elder Creek near Branscomb,California
7. Merced River near Yosemite,California
8. Wi1drose
Creek near Wi1drose Station'
California
9. Halfmoon Creek near Malta, Colorado
10. Vallecito Creek near Bayfield, Colorado
II. SopchoppyRiver near Sopchoppy,Florida
12. Falling Creek near Juliette, Georgia
13. Tallulah River near Clayton, Georgia
14. Honolii Stream near Papaikou, Hawaii
15. Hayden Creek below North Fork, near
Hayden Lake, Idaho
16. Wickahoney Creek near Bruneau,Idaho
17. South Hogan Creek near Dillsboro,
Indiana
18. Elk Creek near Decatur City,.Iowa
19. Big Creek at Pollock, Louisiana
20. Wild River at Gilead, Maine
21. Washington Creek at Windigo, Isle
Royale, Michigan
22. Kawishiwi River near Ely" Minnesota
23. North Fork Whitewater River near Elba,
Minnesota
24. Cypress Creek near Janice, Mississippi
25. Beauvais Creek near St. Xavier, Montana
26. Swiftcurrent Creek at Many Glacier,
Montana
27. Dismal River near Thedford, Nebraska
28. South Twin River near Round Mountain,
Nevada
29. Steptoe Creek near Ely, Nevada
30. McDonalds Branch in Lebanon State
Forest, New Jersey
31.
32.
33.
34.
Mogollon Creek near Cliff, New Mexico
Rio Mora near Tererro, New Mexico
Esopus Creek at Shandaken,New York
Cataloochee Creek near Cataloochee,
North Carolina
35. Bear Den Creek near Mandaree, North
Dakota
36. Beaver Creek near Finley, North Dakota
37. Upper Twin Creek at McGaw, Ohio
38. Blue Beaver Creek near Cache,Oklahoma
39. Kiamichi River nearBig Cedar,Oklahoma
40. Crater Lake near Crater Lake, Oregon
41. Minam River at Minam, Oregon
42. Young Woman's Creek near Renova,
Pennsylvania
43. ScapeOre Swamp near Bishopville, South
Carolina
44. Upper Three Runs near New Ellenton,
South Carolina
45. Castle Creek near Hill City, South Dakota
46. Little Vermillion River near Salen, South
Dakota
47. Buffalo River near Flat Woods, Tennessee
48. Little River above Townsend,Tennessee
49. Limpia Creek above Fort Davis, Texas
50. South Fork Rocky Creek near Briggs,
Texas
51. Red Butte Creek near Salt Lake City, Utah
52. Holiday Creek near Andersonville,
Virginia
53. Andrews
Creek
near
Mazama,
Washington
54. North Fork Quinault River near Amanda
Park, Washington
55. Popple River near Fence,Wisconsin
56. Cache Creek near Jackson,Wyoming
57. Encampment River near Encampment,
Wyoming
For this study, data from 53 stations were analysed.Station Nos 8, 25, and 40 were
not included in the analysisbecausethe station recordswere incomplete. StationNo. 14
(Hawaii) was not included becauseit is outside the study area. Mean monthly runoff
values, in inchesper squaremile, werecalculated for the period 1971-80for eachstation
(Table 2). Each station was placedinto one of two ecoregiongroups as describedbelow.
The l2 variables (monthly averagesof the runoff) for each station, along with the
ecoregiongroup of the station, constituted the data for analysis.
The ecoregionsystemis organizedinto a four-level hierarchy, eachlevel representing
R. G. Bailey
243
a subdivision of the area encompassedby the preceding hierarchical level. Theselevels,
from upper to lower, are ecodomain,ecodivision,ecoprovinceand ecosection.For the
purposes of this analysis, the ecodomain level, which differentiates regions primarily
accordingto broad climatic similarity, was chosenbecauseof the small samplesize.With
a sample of 53 stations and 12 variables, only two groups could be properly analysed
(Lachenbruch, 1975).Excluding the southern tip of Florida, the conterminous United
States is partitioned into two ecoregions at the ecodomain level-dry and humid
temperate.The two ecodomainsof the conterminousUnited Statesare shown in Figure
1, which also showsthe location of the hydrologic bench-markstations. Fifteen stations
comprised the dry group; 38 stations comprisedthe humId temperategroup.
Discriminant analysis, a technique for analysing a priori grouped data, was used to
test whether the two ecoregionswere different on the basis of hydrologic productiyity.
Discriminating variables (those characteristics on which groups are hypothesized to
differ) are linearly combined and weighted. The analysis tests how successfulthe
discriminating variables are in significantly distinguishing between groups. Once a
significant (P < 0'05) set of discriminating variables has been found, the original set of
cases(stations)is classifiedas to group membership,given those variables and weights.
This classification is done by calculating a discriminant score for each caseand then
comparing it to the mean (average)score for each of the two groups. By comparing
predicted versus actual group membership, one can measure the successof group
discrimination.
The two ecoregionswere analysedvia the computer program BMDP designedfor
multivariate discriminant analysis (Dixon, 1981). All of the discriminating variables
exceptAugust were significant and were used in the analysis.
4. Resultsand discussion
Calculations of the discriminant scoresfor the 15 dry and 38 humid temperatestations
gavevalues ranging from -3.06 to + 2.99{Table 2). In general,the lower values of the
discriminant score represent dry stations, and the higher values represent humid
temperatestations. The frequenciesof stations for various levels of discriminant scores
are shown in Table 3. Although there is overlapping of discriminant scoresfor dry and
humid temperategroups, this table clearly showsthe clustering within the two groups.
The mean value of the discriminant scoresfor the two groups was:
dry
humid temperate
-1.29
midpoint value
-0.39.
0-51
In theory, when the discriminant score is less than the midpoint value -0.39, the
station wi!l be classifiedas dry, and whenthe scoreis greaterthan -0.39 the station will
be classifiedas humid temperate.
The classificationresults are shown in Table 4. The numbers of stations that were
correctly classified are given as diagonal elementsof the matrix and the numbers of
incorrectly classifiedstations appearas off-diagonal elements.Sevenof 53 stations were
misclassified.The misclassifiedstations include 23, 27, 32, 36, 46, 50, and 53 (Figure 2).
All but one (Station No. 32)of theseare in the humid temperateecodomain.The overall
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246
Testing an ecosystemregionalization
TABLE3. Frequenciesof humid temperateand dry stations for various levelsof
discriminant scores
Class limits for
discriminant score
Total
-~
-3.15 to -2.80
-2.79 to -2.10
-2.09to-l.40
-1.39 to -0.70
-0.69 to.O.OO
4
0.01 to 0-70
12
0-71 to 1.40
1.41 to 2-10
2.11 to 2.80
10
4
2.81 to 3-50
Total
2
2
9
1
14
2
5
7
12
10
4
2
38
2
15
53
TABLE4. Classificationresults for ecodomainsbasedon a linear
discriminant function utilizing averagemonthly runoff data
Predicted group membership
Percentageof grouped stations correctly classified: 86.79%
percentageof grouped stations correctly classifiedwas 87%.
The high percentage of correctly classified stations indicates a high degree of
discrimination betweenthe two ecodomains.Thus, the regionalecosystemstestedin this
study exhibit a high degree of ability to circumscribestations with similar hydrologic
productivity. Although classification results can theoretically achievea level of 100%,
inherent natural variation or inadequacyof the variables is such that this level can
probably never be reached. With this in mind, 87% is good discriminating power,
consideringthe smallnessof the sample.Lachenbruchand Mickey (1968)indicated that
using a classification function to classifythe samecasesthat were usedto compute it is
biased in favor of better discrimination. Even allowing for this bias, the discrimination
betweengroups is impressive.
The misclassified stations provide a clue to the validity of the map units. For
example, most of the misclassifiedstations are relatively near the dryfhumid temperate
boundary. This result can be interpreted to mean that the cores of the regional units are
valid but the boundaries, in terms of hydrologic productivity, may need some
adjustment. This interpretation would not be possible if the misclassifiedstations had
beenscattered throughout the groups.
The misclassifiedstations also serveto confirm the existenceof finer subdivisions.
For example, Bailey's (1976) map shows the dry westernside of the humid temperate
..
R. G. Bailey
247
---1
rd ,
.
) ("'"\"
~. .
46
.
.Group
I
.Group
2
.
.
.
..
.
.
-)
50
0
I
0
500 miles
,I
SOOkm
Figure 2. Predicted group membership from discriminant analysis,hydrologic bench-mark stations.
easternboundary of the subhumid prairie.
,
ecodomain in central USA distinguished as subhumid prairie (Figure 2). Four of the
sevenmisclassifiedstations (Nos 27, 36, 46, and 50) are located in this region.
Discriminant analysisseemsto be a particularly appropriate method for testing the
validity of land units at all scalesof interest. In this study,the method was applied to the
broadest level of ecological generalization. The next level of testing suggestedas
worthwhile is at more detailed l~vels of the hierarchy than was possible with the low
resolution data available for this study. Through multi-level application of this method,
a completeanalysis of the ecoregionmap may be done. Other measuresof productivity,
suchas net primary productivity, needto be used for testing.
The method differs from previous use of multivariate approacheswith land units
(Radloff and Betters, 1978; Laut and Paine, 1982;Briggs and France, 1983)which use
clusteranalysis of grid units to provide the initial map units. The approach taken in this
study usesmultivariate discriminant analysis to test and validate map units initially
recognizedand delineated by theoretical considerations.
The author wishesto thank G. L. Thompson and G. E. Brink for help with the retrieval of the
WATSTORE data, and R. M. King for help with the statisticalanalysis.J. M. Omernik and R. J.
Olson reviewed the manuscript.
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