Using Landscape Metrics to Characterize Eco Regions

8/2/2019 Using Landscape Metrics to Characterize Eco Regions

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Using Landscape Metrics to Characterize Ecoregions:

Correlations between some landscape metrics, space

and other variables

1. Introduction

Ecological regions, or ecoregions, are areas that exhibit “relative homogeneity in ecosystems” (Omernik

& Bailey, 1997). These regions are widely used to provide a spatial framework for environmental or

natural resources assessment, and research, inventory, monitoring or management of ecosystems

(Omernik J. M., 1995; Bryce, Omernik, & Larsen, 1999; McMahon, et al., 2001). The ecological regions, in

general, have the purpose of exhibiting the patterns in the capacities and potentials of the ecological

systems (Omernik J. M., 2004). During the past century, many different methods have been proposed toidentify and delineate ecoregions (Loveland & Merchant, 2004). Most approaches attempt to

systematically define unique spatial associations of climate, soils, landforms, and vegetation.

Ecoregions are today frequently used to guide decisions about natural resources management;

however, methods for defining and demarcating ecoregions are still in flux. The science of landscape

ecology has matured in parallel with the development of methods to define ecoregions, but there has

been surprising little overlap between the two domains.

The major approaches in the description and classification of the ecoregions in the United States are the

Bailey and Omernik schemes. The Bailey ecoregions (published in 1976 as a map, and described

subsequently in Bailey (1980)) are a result of literature synthesis and limited field testing and evaluation,

and they consist of a classification based on four components maps: vegetation, soil, landform, and

water. This scheme relay on the climate as controlling factor, that is modified by the landforms and

reflected by the vegetation (McMahon, et al., 2001). Bailey ecoregions were adopted for the US Forest

Service National Hierarchical Framework for ecosystem management in 1993 (Bailey, 1995). The

Omernik ecoregions were initially published in 1987, based on the regional patterns of the ecosystems

and their spatial variability. It includes causal and integrative factors, such as climate, soils and geology

(minerals availability), physiography, potential natural vegetation and land use, but with variable

importance of each factor between places (Omernik J. M., 1987). This approach is known as weight of

evidence (Omernik J. M., 2004). Although Omernik ecoregions were conceived initially for water

quantity and quality, subsequent developments turn this ecoregion scheme in the US EPA framework for

ecosystem management (McMahon, et al., 2001).

On the other hand, landscape ecology is the science devoted to study of landscape structure and

pattern, the interactions among the different elements of the landscape, and how these patterns and

interactions change over time. Landscape structure is now known to be critically important as the spatial

relationships among the distinctive elements present, affect the distribution of energy, materials,



mineral nutrients, and species in relation to the sizes, shapes, numbers, kinds, and configurations of the

ecosystems. Thus, landscape ecology focuses on three characteristics of the landscape: structure,

function and change. (Forman & Godron, 1981; Forman & Godron, 1986; Urban, O'Neill, & Shugart,

1987; Turner M. G., 1989; Turner, Gardner, & O’Neill, 2001; Bolliger, Wagner, & Turner, 2007). The

quantification of landscape structure is prerequisite to the study of landscape function and change in

order to relate it to ecological function (Turner M. G., 1989; McGarigal & Marks, 1995).

Therefore the identification of possible landscape patterns in the ecoregions through the use of

landscape ecology theory and metrics is an uncommon application. Moreover it would assess the

possible contribution of landscape ecology to enhance identification, definition, delimitation and

characterization of the ecoregions.

The principal objective of this project was to determine if and how the landscape structure (quantified

by landscape pattern metrics) has a relationship with the spatial distribution of the ecoregions that

could be used for the ecoregionalization process. Three specific questions were asked:

Are the differences in selected landscape pattern metrics used to characterize the ecoregions in

the central United States related to the ecoregions spatial distribution?

Are there direct effects of annual mean daily average temperature and annual mean total

precipitation on landscape metrics, and do these variables account for variation in the landscape

pattern metrics after the effects of space are removed?

Is there residual spatial variation in landscape pattern metrics after accounting for annual mean

daily average temperature and annual mean total precipitation?

2. Materials and Methods

2.1. Study area

The study area was chosen in the central United States, it includes the whole ecoregion denominated

Great Plains (number 9 in the ecoregions of North America level I, delineated by the CEC (1997)), and

the areas adjacent to it in the Eastern Temperate Forest ecological region (number 8), located at the

east side of the Great Plains ecoregion (Figure 1, where red hatch denotes the entire study area). The

digital boundaries of the ecoregions and its definitions were downloaded from the website of the

Western Ecology Division of the U.S. Environmental Protection Agency (US EPA, 2011). The Omernikecoregions level III is considered a good framework for this work, as those are areas of similar

environmental characteristics (soils, geology, natural vegetation, land use, and physiography (Omernik J.

M., 1987; Omernik J. M., 1995)). It allows that principles of landscape ecology established anywhere

within the ecoregion can be reasonably expected to extrapolate across the ecoregion (Omernik J. M.,

1987; O'Neill, et al., 1996). In this area there are 26 ecoregions level III, 9 of which belong to the Eastern

Temperate Forest ecological ecoregion, and 17 to the Great Plains ecoregion.



Figure 1. Ecoregions comprising the study area in the Central Great Plains and Eastern United States.

The National Land Cover Dataset (NLCD) represents the land cover status for the conterminous United

States with a spatial resolution of 30 m. It was developed by the Multi-Resolution Land Characteristics

Consortium (MRLC). The NLCD 2006 dataset (Xian, Homer, & Fry, 2009; U.S. Geological Survey, 2011)

was downloaded from the MRLC web site (MRLC, 2011). This dataset is an update using Landsat imagery

of the 2001 NLCD. The delimitation of the study area in the NLCD map is presented in Figure 2.



Figure 2. Study area delimited in the National land cover dataset (2006)

The NLCD 2006 has 19 land cover classes for the conterminous United States. Three of these land cover

are only found in Alaska. In this case of study, the classes associated with urban and developed areas

(open space, low intensity, medium intensity and high intensity) were collapsed into one. In the sample

blocks only 12 of the 19 land covers are present. The list of those land covers and their corresponding

codes is presented on Table 1.

2.2. Data processing

The characterization of the landscape structure was conducted over selected sample areas. The sample

areas in each ecoregion consist of non-overlapping blocks with dimensions of 45 km by 45 km (purple

squares on Figure 1 and Figure 2). These blocks were positioned in the center of each ecoregion;

although in the cases where one ecoregion has a very irregular form or is divided by another ecoregion,

two sample areas are needed [i.e. ecoregions 25, 26, 27, 29, 40, 42, and 51]. Then, there are 33 sample

areas over the study area.



Table 1. Land covers present in the sample blocks

Code Land cover

11 Open Water

20 Developed

31 Barren Land (Rock/Sand/Clay)

41 Deciduous Forest42 Evergreen Forest

43 Mixed Forest

52 Shrub/Scrub

71 Grassland/Herbaceous

81 Pasture/Hay

82 Cultivated Crops

90 Woody Wetlands

95 Emergent Herbaceous Wetlands

The location of the blocks (sample areas) was selected to obtain at least one block in each Omernik

ecoregion Level III in the study area. The block size was decided as the maximum square that could be

placed in the smallest ecoregion in the study area, to have full-size blocks at the core of each ecoregion

without touching or going beyond the ecoregion boundary. And, it is necessary to avoid placing the

samples close to the ecoregion borders, as it would probably include some pattern related to the

ecotone.

Additionally, using ArcGIS the following data was extracted for each block:

Planar coordinates

Annual mean daily average temperature (Tmean)

Annual mean total precipitation (ppt)

2.3. The landscape metrics

FRAGSTATS 3.3.5 (McGarigal, Cushman, Neel, & Ene, 2002) was used to derive the some landscape

metrics for the blocks. The landscape patches were defined using the patch neighbor rule of 8-cell (it

considers all 8 adjacent cells, including the 4 orthogonal and 4 diagonal neighbors). The class level

metrics and their description are presented on Table 2.

These nine metrics are evaluated for each land cover in the block. It means that to describe one block it

is necessary to have 108 columns (equivalent to 12 times 9). To illustrate the dataset, the Figure 3 shows

the box and whisker plots for each landscape metric given the land covers.



Table 2. Landscape metrics evaluated on the blocks

Name Description

Number of

patches (NP)Number of patches of the same class in the landscape

Total class area

(CA)

Equals the sum of the areas (m2) of all patches of the corresponding patch type

(land cover), divided by 10,000 (to convert to hectares) Mean Patch Area

(AREA_MN)

Equals the sum of the patch area value across all patches of the corresponding

class, of divided by the number of patches of the same class.

Area-weighted

Mean Patch Area

(AREA_AM)

Equals the sum, across all patches of the corresponding patch class, of the patch

area (m2) multiplied by the proportional abundance of the patch.

Area-weighted

mean shape index

(SHAPE_AM)

Equals the sum, across all patches of the corresponding patch class, of the shape

index value multiplied by the proportional abundance of the patch.

The Shape index is equal to the patch perimeter divided by the minimum

perimeter possible for a maximally compact patch (in a square raster format) of

the corresponding patch area.

Area-weighted

mean fractal

dimension

(FRAC_AM)

Equals the sum, across all patches of the corresponding patch type, of the fractaldimension value multiplied by the proportional abundance of the patch.

The Fractal dimension is equal to 2 times the logarithm of patch perimeter (m)

divided by the logarithm of patch area (m2); the perimeter is adjusted to correct

for the raster bias in perimeter.

Mean contiguity

index

(CONTIG_MN)

Equals the sum, across all patches of the corresponding patch class, of the

contiguity index values, divided by the number of patches of the same class.

The Contiguity index is equal to the average contiguity value for the cells in a

patch (i.e., sum of the cell values divided by the total number of pixels in the

patch) minus 1, divided by the sum of the template values (13 in this case) minus

1. (Note: 1 is subtracted from both the numerator and denominator to confine

the index to a range of 1) Mean Euclidean

nearest neighbor

distance

(ENN_MN)

Equals the sum, across all patches of the corresponding patch class, of the

Euclidean nearest neighbor distance values, divided by the number of patches

of the same class.

ENN is equal to the distance (m) to the nearest neighboring patch of the same

type, based on shortest edge-to-edge distance.

Interspersion and

Juxtaposition index

(IJI)

IJI equals minus the sum of the length (m) of each unique edge type involving the

corresponding patch type divided by the total length (m) of edge (m) involving the

same type, multiplied by the logarithm of the same quantity, summed over each

unique edge type; divided by the logarithm of the number of patch types minus 1;

multiplied by 100 (to convert to a percentage).



Figure 3. Box and whisker plots for each landscape metric given the land covers

T o t a l c l a s s a r e a ( C A )

0

50000

100000

150000

200000

11 20 31 41 42 43 52 71 81 82 90 95

N u m b e r o f p a t c h e s ( N P )

0

5000

10000

15000

11 20 31 41 42 43 52 71 81 82 90 95

M e a n P a t c h A r e a ( A R E A_

M N )

0

500

1000

1500

2000

2500

3000

11 20 31 41 42 43 52 71 81 82 90 95

A r e a - w e i g h t e d m e a n P a t c h A r e a ( A R E A_

A M )

0

20000

40000

60000

80000

100000

11 20 31 41 42 43 52 71 81 82 90 95

A r e a - w e i g h t e d m e a n s h a p e i n d e x ( S H A P E_

A M )

0

50

100

150

11 20 31 41 42 43 52 71 81 82 90 95

M e a n c o n t i g u i t y i n d e x ( C O N T I G_

M N )

0.4

0.6

0.8

11 20 31 41 42 43 52 71 81 82 90 95

A r e a - w e i g h t e d m e a n f r a c t a l d i m e n s i o n ( F R A C_

A M )

1.0

1.1

1.2

1.3

1.4

1.5

11 20 31 41 42 43 52 71 81 82 90 95

I n t e r s p e r s i o n a n d J u x t a p o s i t i o n i n d e x ( I J I )

0

20

40

60

80

11 20 31 41 42 43 52 71 81 82 90 95

M e a n E u c l i d e a n n e a r e s t n e i g h b o r d i s t a n c e ( E N N_

M N )

0

5000

10000

15000

20000

25000

11 20 31 41 42 43 52 71 81 82 90 95



2.4. Mantel test and correlograms

Given to nature of the dataset and the questions addressed, a test of association is required to analyze

the data. The Mantel test is widely used by ecologists to explain the distribution of species in terms of

environmental variables, and the spatial configuration. The hypothesis is: the degree of dissimilarity in

one dataset corresponds to the degree of dissimilarity in another independently-derived dataset. It isimportant to have in mind that the standard Mantel test only indicates that a linear relationship exists,

but not the direction of the relationship.

Although in landscape ecology this technique in not commonly used, the basic question in the simple

mantel test “are samples that are environmentally similar also similar in species composition? ...”

(Urban, Goslee, Pierce, & Lookingbill, 2002), can be projected to: are samples that are environmentally

similar also similar in landscape structure? And for the space itself (geographical distances), the question

is: are samples that are close together also similar in landscape structure?

The Partial Mantel test is often used to account for the effects of space in the correlation. But as

demonstrated by Goslee & Urban (2007) the assumption of linearity greatly reduces its effectiveness for

complex spatial patters. Then, the Piecewise Mantel correlograms allows looking at the correlations

between the dissimilarity matrices for each distance class.

The package ecodist package (Goslee & Urban, 2007) implemented in R (R Development Core Team,

2009) were used for these analysis. The dissimilarity matrices were evaluated using the Euclidean

distance (as all the variables are continuous) using the distance() function. For the dissimilarities in the

landscape structure, the landscape metrics were standardized to z-scores prior to computing the

dissimilarity matrix, to account for differences on measurement units. For the other three variables:

Tmean (Annual mean daily average temperature), ppt (Annual mean total precipitation) and Space

(planar coordinates), a dissimilarity matrix was created for each one.

The simple and partial mantel test were evaluated with the function mantel(). They were set to use the

spearman correlation coefficient, because the distributions of dissimilarity matrices are skewed. The

significance of the correlations were assessed using two-sided p-values (null hypothesis: r = 0) obtained

from 10,000 permutations in the dissimilarity matrices.

Three simple Mantel correlations were tested: landscape metrics (LM) and space, LM and Tmean, and

LM and ppt. And also three Partial Mantel correlations were tested: LM and Tmean given space, LM and

ppt given space, and LM and space given Tmean and ppt. The Piecewise Mantel correlograms were

evaluated for the six correlations tested.

3. Results

A simple mantel test was used for addressed the first question: are the differences in selected landscape

pattern metrics in the central United States related to the change in the space?, and the first part of the



second question: are there direct effects of annual mean daily average temperature and annual mean

total precipitation on Landscape metrics? The results are presented on the Table 3. The Piecewise

Mantel correlograms for these three correlations are presented on Figure 4.

Table 3. Results for the simple mantel tests

Correlation tested Mantel r p-valueLM and space 0.29 0.0001

LM and Tmean 0.07 0.30

LM and ppt 0.30 0.0001

Figure 4. Piecewise Mantel correlograms for landscape structure versus space, annual mean daily

average temperature (Tmean) and annual mean total precipitation (ppt)

With the partial mantel correlation were addressed the remaining questions: do these variables account

for variation in the landscape pattern metrics after the effects of space are removed? And is there

500000 1000000 1500000 2000000

- 0 .

4

0 . 2

0 . 8

Landscape metrics against Space

Distance

M a n t e l r

5 10 15

- 0 . 2 0

0 . 0 0

Landscape metrics against Tmean

Difference in Tmean

M a n t e l r

200 400 600 800

- 1 . 0

0 . 0

Landscape metrics against ppt

Diff erence in ppt

M a n t e l r



residual spatial variation in landscape pattern metrics after accounting for annual mean daily average

temperature and annual mean total precipitation? The results are presented on the Table 4. The

Piecewise Mantel correlograms for these three correlations are presented on Figure 5.

Table 4. Results for the partial mantel tests

Correlation tested Mantel r P-valueLM and Tmean given space -0.30 0.0003

LM and ppt given space 0.24 0.001

LM and space given Tmean and ppt 0.33 0.0005

Figure 5. Piecewise Mantel correlograms for landscape structure versus annual mean daily average

temperature (Tmean) and annual mean total precipitation (ppt) given space, and space given annual

mean daily average temperature (Tmean) and annual mean total precipitation (ppt)

5 10 15

- 0 . 2

0 . 2

0 . 6

Landscape metrics against Tmean given Space

Difference in Tmean

M a n t e l r

200 400 600 800 1000

- 1 . 0

- 0 . 2

Landscape metrics against ppt given Space

Difference in ppt

M a n t e l r

500000 1000000 1500000 2000000

- 0 . 2

0 . 4

Landscape metrics against Space given Tmean and ppt

Distance

M a n t e l r



4. Discussion and Conclusion

The first results of this analysis allow concluding that there is a significant correlation between the

landscape structure and the space. When this correlation is evaluated with the piecewise correlogram, it

shows that the correlation is particularly significant at closer distances. It means that blocks that are

close together have a similar landscape structure. And this should be projected to ecoregions (closer

ecoregions have similar landscape structure). But, as it is noticeable on Figure 4, the spatial

autocorrelation do not have a linear spatial pattern.

Neither on the simple mantel test, nor on the piecewise mantel correlogram, were found a significant

correlation between the landscape structure and annual mean daily average temperature (Tmean). On

the contrary, in both tests significant correlations were found between the landscape structure and the

annual mean total precipitation (ppt). With the piecewise correlograms it is possible to say that sites

with similar annual mean total precipitation have similar landscape structures; and sites with very

different annual mean total precipitation have very different landscape structure (negative mantel r).

Also, one could say that this correlation has a closely linear pattern, but this assumption was not testedhere.

When the effects of space are removed on annual mean daily average temperature (Tmean), there is a

significant negative correlation between Tmean and the landscape structure. This result is observable on

the partial mantel test and on the piecewise mantel correlogram for this relationship (landscape

structure and the Tmean given the space). This piecewise correlogram shows that there is a significant

negative correlation relationship among sites with very similar Tmean and a significant positive

correlation among sites with very different Tmean once the effects of space have been removed.

This result is the opposite of what is observed for annual mean total precipitation (ppt). Where there is a

significant correlation between the landscape structure and the ppt after the effects of space are

removed. And this correlation is positive and significant between sites with very similar ppt, and it is also

significant, but negative between sites with very different ppt.

The significant correlation between space and the landscape structure after removing the effects of

tmean and ppt, indicate that there is a residual spatial variation in the landscape metrics not explained

by the spatial co-variation on temp and ppt. But the residual spatial variation in landscape structure

after accounting for Tmean and ppt is only significant for sites that are close together.

The Piecewise Mantel Correlogram is a powerful tool to test relationships between variables at different

scales. Goslee and Urban (2007) stated “when the relationships between variables are not linear thepiecewise removal of spatial variation can be used for a space-free analysis”. And In this case this

analysis was more meaningful than the simple or partial mantel test.



5. Bibliography

Bailey, R. G. (1980). Description of the ecoregions of the United States. N. 1391, 77 pp. U. S. Department

of Agriculture.

Bailey, R. G. (1995, March). Description of the Ecoregions of the United States. Retrieved June 09, 2011,from US Forest Service: http://www.fs.fed.us/land/ecosysmgmt/

Bolliger, J., Wagner, H. H., & Turner, M. G. (2007). Identifying and Quantifying Landscape Patterns in

Space and Time. In F. Kienast, S. Ghosh, & O. Wildi, A changing world: Challenges for Landscape

Research (pp. 177-194). Springer.

Bryce, S. A., Omernik, J. M., & Larsen, D. P. (1999). Ecoregions: A Geographic Framework to Guide Risk

Characterization and Ecosystem Management. Environmental Practice, 1(03), 141-155.

CEC. (1997). Ecological Regions of North America: Toward a Common Perspective. Montreal, Quebec,

Canada: Commission for Environmental Cooperation.

Forman, R. T., & Godron, M. (1986). Landscape ecology. New York: John Wiley & Sons.

Forman, R., & Godron, M. (1981). Patches and Structural Components for a Landscape Ecology.

BioScience, 31(10), 733-740.

Goslee, S., & Urban, D. (2007). The ecodist Package for Dissimilarity-based Analysis of Ecological Data.

Journal of Statistical Software, 22(7).

Loveland, T. R., & Merchant, J. M. (2004). Ecoregions and Ecoregionalization: Geographical and

Ecological Perspectives. Environmental Management, 34(Suppl. 1), S1-S13.

McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: Spatial pattern Analysis Program for Quantifying

Landscape Structure. Portland, OR: U.S. Department of Agriculture,Forest Service, Pacific

Northwest Research Station.

McGarigal, K., Cushman, S. A., Neel, M. C., & Ene, E. (2002). FRAGSTATS: Spatial Pattern Analysis

Program for Categorical Maps. Computer software program produced by the authors at the

University of Massachusetts, Amherst. Retrieved from

http://www.umass.edu/landeco/research/fragstats/fragstats.html

McMahon, G., Gregonis, S. M., Waltman, S. W., Omernik, J. M., Thorson, T. D., Freeouf, J. A., et al.

(2001). Developing a Spatial Framework of Common Ecological Regions for the Conterminous

United States. Environmental Management, 28(3), 293-316.

MRLC. (2011, March 14). NLCD 2006 Provisional Products and Supplementary Layers. Retrieved March

15, 2011, from Multi-Resolution Land Characteristics (MRLC) Consortium:

http://www.mrlc.gov/index.php



Omernik, J. M. (1987). Ecoregions of the Conterminous United States. Annals of the Association of

American Geographers, 77 (1), 118-125.

Omernik, J. M. (1995). Ecoregions: A spatial framework for environmental management. In W. S. Davis,

& T. P. Simon (Eds.), Biological Assessment and Criteria: Tools for Water Resource Planning and

Decision Making (pp. 46-92). Boca Raton, FL: Lewis Publishers.

Omernik, J. M. (2004). Perspectives on the Nature and Definition of Ecological Regions. Environmental

Management, 34(Suppl. 1), S27-S38.

Omernik, J. M., & Bailey, R. G. (1997). Distinguishing between watersheds and ecoregions. Journal of the

American Water Resources Association, 33(5), 935-949.

O'Neill, R. V., Hunsaker, C. T., Timmins, S. P., Jackson, B. L., Jones, K. B., Riitters, K. H., et al. (1996). Scale

problems in reporting landscape pattern at the regional scale. Landscape Ecology, 11(3), 169-

180.

R Development Core Team. (2009). R: A language and environment for statistical computing. Vienna,

Austria.: R Foundation for Statistical Computing.

Turner, M. G. (1989). Landscape Ecology: The Effect of Pattern on Process. Annual Review of Ecology and

Systematics, 20, 171-197.

Turner, M. G., Gardner, R. H., & O’Neill, R. V. (2001). Landscape ecology in theory and practice: pattern

and process. New York, U.S.A.: Springer Verlag.

U.S. Geological Survey. (2011, February 16). NLCD 2006 Land Cover. Sioux Falls, SD, United States: U.S.

Geological Survey.

Urban, D. L., O'Neill, R. V., & Shugart, H. H. (1987). Landscape Ecology: A hierarchical perspective can

help scientists understand spatial patterns. BioScience, 37 (2), 119-127.

Urban, D., Goslee, S., Pierce, K., & Lookingbill, T. (2002). Extending community ecology to landscapes.

Ecoscience, 9(2), 200 - 212.

US EPA. (2011). Level III and IV Ecoregions of the Continental United States. Retrieved January 25, 2011,

from US Environmental Protection Agency - Western Ecology Division:

<http://www.epa.gov/wed/pages/ecoregions.htm>

Xian, G., Homer, C., & Fry, J. (2009). Updating the 2001 National Land Cover Database land cover

classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of

Environment, 113(6), 1133-1147.

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Using Landscape Metrics to Characterize Eco Regions