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Abiotic factors & productivity as predictors of land cover
Michael P. Simanonok
INTRODUCTION
Large scale spatial studies, at their core investigate the causes of landscape configuration
and with land cover being the main variable to consider in regard to conservation, understanding
the predictors driving landscape configuration and land cover are vital for conservation and the
maintenance of biodiversity (Weins 2002). Furthermore, the inherent natural properties and
production of terrestrial systems can drive the degree of human land use (Zhao et al. 2011).
Protected areas, such as national parks, can be particularly vulnerable due to a marked increase
in land use around the parks as well as an increase in the intensity of that use (Hansen et al.
2011). Thus, an increased understanding of what predictors determine land cover can help to
identify those areas most highly at risk, as well as provide basal ecological knowledge of the
determinants of natural land covers.
Landscape scale spatial analysis of gross primary productivity (GPP) and net primary
productivity (NPP) has become incredibly available and relatively simple to process, which
allows us to ask ecologically important questions regarding relationships between ecological
systems and their use (Running et al. 2004, Zhao et al. 2005). NPP and GPP are both inherently
tied to precipitation, temperature, as well as elevation (Zhang et al. 2009), thus these become
variables of interest.
Similar work has focused primarily on land use/cover change (Parmenter et al. 2003) ¸
and has not explicitly examined if these variables can be used to directly predict land cover
without considering previous land use. The question addressed herein, is what are the biotic
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(NPP & GPP) and abiotic (precipitation, temperature, elevation) factors important in determining
land use and land cover, and what is the relative importance of each?
METHODS
Study Area
The area of analysis is the federally protected national parks within the state of Montana
(Fig. 1); primarily composed of Glacier National Park, Yellowstone National Park, and Bighorn
Canyon National Recreation Area (Table 1). Data for national parks were obtained from the
Montana Natural Resource Information System. The rationale for the selection of protected areas
is that they ideally have seen relatively little change in land use over the last ~50 years (the
relevant range of our datasets) and therefore should provide a good measurement tool for how
these biotic and abiotic factors determine land cover.
Data
These analyses require data on precipitation, temperature, elevation, NPP, GPP, and land
cover. Precipitation, temperature, and elevation data were all obtained from Worldclim.org, and
full methods regarding the generation of these data are described by Hijmans et al. (2005). All
three of these layers are at ~1km2 resolution (30 arc second grids), and the precipitation and
temperature data covered a period from 1950-2000. Monthly temperature averages from that
time period for federally protected areas in Montana were averaged together to create a single
mean temperature, and this was also done for the precipitation data.
Estimates of NPP and GPP were derived from Moderate Resolution Imaging
Spectroradiometer (MODIS) imagery (Oak Ridge National Laboratory Distributed Active
Archive Center), also at 1km2 resolution. These data are generated by two NASA satellites
(Terra & Aqua) that provide spectral images of the entire surface of the Earth every 1-2 days.
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Algorithms are then run on the imaging data to provide the estimated values for NPP and GPP
(Running et al. 2004, Zhao et al. 2005).
Land cover data were obtained from the Multi-Resolution Land Characteristics
Consortium. The National Land Cover Dataset (Fry et al. 2006) has the entire conterminous
United States classified into 16 different land cover classes (Fig. 2 & Table 2) at 30m2 resolution.
The data are generated from the Landsat Enhanced Thematic Mapper+ from satellite data
obtained in 2006, and full methods are described by Fry et al. (2006). Using ArcMap (ESRI
2011), these data were scaled up to 1km2 resolution, so as to match the other datasets, using a
resampling tool based on the composition of the nearest neighbor cells. This upscaling results in
a loss of information, however given the nature of the research question as well as the fact that
all other layers are at a higher resolution this resampling was found to be unavoidable.
Data Manipulation
The above layers (temperature, precipitation, elevation, NPP, GPP, & land cover) were
imported into ArcMap (ESRI 2011) and clipped for the boundaries of the federally protected
areas of Montana. The raster data were then converted into a single shapefile of point data, using
the Extract Multi Values tool. After points of “no data” were removed, this resulted in 6,924
individual spatial points for the national parks, with every point including a measurement for
elevation, temperature, precipitation, GPP, & NPP.
Analysis
The attribute table of the point data containing all variables was exported and all analyses
were performed using R (R Development Core Team 2008). A regression analysis was modeled
for each individual variable as predictors of land cover. Note that since a high degree of
colinearity exists between these variables each regression was run separately; a full model
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utilizing all of these potential predictors at once would not be appropriate. As a supplement for
the regression analyses, an Akaike information criterion (AIC) was built comparing these
individual models (Akaike 1974). This was done as an attempt to help enhance the more specific
questions such as the relative importance of each climatic variable, and the appropriateness of
GPP vs. NPP.
RESULTS
Regression analyses showed strong evidence for correlation of all three climate factors
(elevation, precipitation, & temperature) with land cover type (Table 3). The p values for all
three variables were so infinitesimally small that ascribing relative importance from this analysis
would not be appropriate; the strong degree of colinearity between these variables is likely
responsible for the similar results.
Higher elevations tended to be associated with barren land and shrub/scrub land covers,
with lower elevations being primarily associated with evergreen forests (Fig. 3). For
precipitation, areas of high precipitation appeared to be associated with shrub/scrub cover as
well, with forested areas showing lower precipitation (Fig. 4). Areas of higher temperature were
visibly linked to evergreen and mixed deciduous forests, with those areas of colder temperature
being more likely to have barren or more herbaceous cover (Fig. 5)
Interestingly, neither GPP nor NPP were found to be important predictors of land cover
(Table 3). Graphically, these results make sense, as areas with generally homogeneous trends for
productivity show markedly heterogeneous land cover assemblages (Figs. 6 & 7).
The AIC results for comparing the individual models did find differences in the
predictive ability of the climate variables, and found precipitation to be the best fit, with
elevation and temperature having relatively little difference between them (Table 4). For the
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GPP/NPP comparison, GPP was a slightly better fit but not convincingly different, and it was a
much poorer fit than any of the climate-related factors (Table 4).
DISCUSSION
What these results suggest is that while climatic factors may be determinants for
productivity there is no observed association between productivity and land cover. Furthermore,
it is likely that climate; precipitation in particular, can be a strong predictor for land cover. It is
also possible that there are other factors not considered in this study that may well be able to
more accurately predict land cover. With precipitation being the most important predictor found
in this study, it is surprising that productivity metrics did not show strong patterns with land
cover, as water is the most limiting factor for productivity in terrestrial systems (Running et al.
2004).
As this study investigated land cover for federally protected areas, it is necessary to be
aware of the implications for management. Given the ability to identify land cover based on
spatial analysis of already existing data, it is feasible that habitat types of high concern could be
easily identified and areas for future conservation be easily selected without significant time
spent in the field. Similar work has been performed, such as predicting changes in land use
intensity through already existing land cover models (Lambin et al. 2000).
Another important consideration is the global necessity for understanding these processes
in light of anthropogenic disturbance such as climate change. A major goal of data collection
programs such as MODIS is for the purpose of learning about the biological processes of
productivity and how climate is related; specifically investigating how ecosystems are driven and
changed by humans, their innate biotic processes, and climatic determinates (Janetos & Justice
2000). Being able to predict how climate and production determine land cover provides valuable
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utility in making specific predictions from broad climatic models, such as the Intergovernmental
Panel on Climate Change’s report (IPCC 2007).
Other recent broad scale spatial studies have investigated the relationship between
productivity and diversity (Huston & Wolverton 2009, Belote et al. 2011) and it would be
interesting to see if considering productivity and diversity of plant communities in tandem would
provide accurate prediction of land cover. It has been shown that highly productive areas
generally have highly diverse biotic communities (Huston & Wolverton 2009), and it could be
expected that patch richness would show a similar response. The next step in addressing these
questions would be to apply a similar conceptual methodology over a larger spatial area. This
project, in particular, was limited in the scale of area studied due to time and computational
constraints, and it would be of interest to see the results for larger areas. It is possible that at
broader spatial scales, or for other systems, these patterns and relationships could differ.
REFERENCES
Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on
Automatic Control 19(6): 716–723.
Belote, RT, Prisley, S, Jones, RH, Fitzpatrick, M, de Beurs, K. 2011. Forest productivity and tree
diversity relationships depend on ecological context within mid-Atlantic and Appalachian
forests (USA). Forest Ecology and Management 261: 1315-1324.
ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research
Institute.
Fry, J, Xian, G, Jin, S, Dewitz, J, Homer, C, Yang, L, Barnes, C, Herold, N, and Wickham,
J. 2011. Completion of the 2006 National Land Cover Database for the Conterminous
United States, PE&RS 77(9): 858-864.
Janetos, AC, & Justice, CO. 2000. Land cover and global productivity: a measurement strategy
for the NASA programme. International Journal of Remote Sensing 21(6-7): 1491-1512.
6
Hansen, AJ, Davis, CR, Piekielek, N, Gross, J, Theobald, DM, Goetz, S, Melton, F, DeFries, R.
2011. Delineating the ecosystems containing protected areas for monitoring and
management. BioScience 61: 363-373.
Hijmans, RJ, Cameron, SE, Parra, JL, Jones, PG, Jarvis, A. 2005. Very high resolution
interpolated climate surfaces for global land areas. International Journal of Climatology
25: 1965-1978.
Huston, MA, Wolverton, S. 2009. The global distribution of net primary production: resolving
the paradox. Ecological Monographs 79(3): 343-377.
IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group
I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
[Solomon, S, Qin, D, Manning, M, Chen, Z, Marquis, M, Averyt, KB, Tignor, M, &
Miller, HL (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA.
Lambin, EF, Rounsevell, MDA, Geist, HJ. 2000. Are agricultural land-use models able to predict
changes in land-use intensity? Agriculture, Ecosystems and Environment 82: 321-331.
Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). 2011.
MODIS subsetted land products, Collection 5. Available on-line
[http://daac.ornl.gov/MODIS/modis.html] from ORNL DAAC, Oak Ridge, Tennessee,
U.S.A.
Parmenter, AW, Hansen, A, Kennedy, RE, Cohen, W, Langner, U, Lawrence, R, Maxwell, B,
Gallant, A, Aspinall, R. 2003. Land use and land cover change in the greater Yellowstone
ecosystem: 1975-1995. Ecological Applications 13(3): 687-703.
R Development Core Team (2008). R: A language and environment for statistical computing. R
Foundation for Statistical Computing,Vienna, Austria.
Running, SW, Nemani, RR, Heinsch, FA, Zhao, MS, Reeves, M, Hashimoto, H. 2004. A
continuous satellite-derived measure of global terrestrial primary production.
BioScience 54(6): 547-560.
Wiens, JA. 2002. Central concepts and issues of landscape ecology. In: Applying Landscape
Ecology in Biological Conservation Ed. K. Gutzwiller.
Zhang, Y, Xu, M, Chen, H, Adams, J. 2009. Global pattern of NPP to GPP ratio derived from
MODIS data: effects of ecosystem type, geographical location and climate. Global
Ecology and Biogeography 18: 280-290.
Zhao, MS, Heinsch, FA, Nemani, RR, Running, SW. 2005. Improvements of the MODIS
terrestrial gross and net primary production global data set. Remote Sensing of
Environment 95(2): 164-176.
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Zhao, MS, Running, S, Helnsch, FA, Nemanl, R. 2011. MODIS-Derived Terrestrial Primary
Production. In: B. Ramachandran et al. (eds.), Land Remote Sensing and Global
Environmental Change, Remote Sensing and Digital Image Processing 11: 635-660.
APPENDIX
Tables
Table 1: Federally protected areas within the state of Montana that were used for this analysis
AREA
788194.5313
162275.3927
6449239.778
2715201.713
2403445.497
667420.3536
7749125.513
4075969323
108489779.8
640134515.2
PERIMETER
3958.46712
1806.804131
14127.06924
7287.912661
8940.00619
4083.871139
18125.18955
358956.2274
125496.0042
342100.3342
NAME
Bear Paw Battlefield
Fort Union Trading Post National Historic Site
Grant-Kohrs Ranch National Historic Site
Big Hole National Battlefield
Little Bighorn Battlefield National Monument
Little Bighorn Battlefield National Monument
Bighorn Canyon National Recreation Area
Glacier National Park
Bighorn Canyon National Recreation Area
Yellowstone National Park
Table 2: Land cover classes as defined by the Multi-Resolution Land Characteristics
Consortium. Available at www.mrlc.gov
8
ACRES
194
40
1593
670
593
164
1914
1007158
26807
158175
Table 3: Results of regression analysis; see Appendix: R Code for further information.
Predictor
F stat
Elevation
21.99
Temperature 70.05
Precipitation 336
GPP
2.6
NPP
2.34
df
1,6922
1,6922
1,6922
1,6922
1,6922
p
2.79E-06
<2.2e-16
<2.2e-16
0.11
0.13
Table 4: Results for AIC analysis; see Appendix: R Code for further information.
Predictor
Elevation
Temperature
Precipitation
GPP
NPP
df
3
3
3
3
3
AIC
50870.39
50822.63
50564.20
50889.76
50890.02
Delta
306.1882
258.4293
0
325.5538
325.8117
Figures
Figure 1: Study area; federally protected areas within Montana.
Figure 2: Land cover of study area. See Table 2 for legend.
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Figure 3: Elevation for the study area; white areas indicate higher elevation.
Range = 913 to 3167 meters.
Figure 4: Mean annual precipitation from 1950-2000 for the study area; white areas indicate
greater precipitation. Range = 22.6 to 68.9 millimeters.
Figure 5: Mean annual temperature from 1950-2000 for the study area; white areas indicate
warmer temperatures. Range = -4.4 to 9.6 °C.
Figure 6: Gross Primary Productivity for the study area; white areas indicate higher productivity.
Range = 52 to 1087 kg C / m2
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Figure 7: Net Primary Productivity for the study area; white areas indicate higher productivity.
Range = 42 to 740 kg C / m2
R Code
> summary(alt)
Call:
lm(formula = lcnpsre ~ altnps, data = finaltable)
Residuals:
Min
1Q
-43.929 -2.610
Median
-1.999
3Q
7.442
Max
51.698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.310244
0.499020 92.802 < 2e-16
altnps
-0.001285
0.000274 -4.689 2.79e-06
Residual standard error: 9.528 on 6922 degrees of freedom
Multiple R-squared: 0.003167, Adjusted R-squared: 0.003023
F-statistic: 21.99 on 1 and 6922 DF, p-value: 2.794e-06
> summary(temp)
Call:
lm(formula = lcnpsre ~ GRID_CODE, data = finaltable)
Residuals:
Min
1Q
-43.642 -2.924
Median
-2.029
3Q
7.148
Max
51.916
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.04197
0.16441 261.79
<2e-16
GRID_CODE
0.05038
0.00602
8.37
<2e-16
Residual standard error: 9.495 on 6922 degrees of freedom
Multiple R-squared: 0.01002, Adjusted R-squared: 0.009876
F-statistic: 70.05 on 1 and 6922 DF, p-value: < 2.2e-16
> summary(prec)
Call:
lm(formula = lcnpsre ~ precnps, data = finaltable)
Residuals:
Min
1Q
-44.633 -2.915
Median
-1.284
3Q
6.708
Max
51.716
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Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.05687
1.15250
56.45
<2e-16
precnps
-0.37648
0.02054 -18.33
<2e-16
Residual standard error: 9.32 on 6922 degrees of freedom
Multiple R-squared: 0.04629, Adjusted R-squared: 0.04615
F-statistic:
336 on 1 and 6922 DF, p-value: < 2.2e-16
> summary(gpp)
Call:
lm(formula = lcnpsre ~ gppre, data = finaltable)
Residuals:
Min
1Q
-44.239 -2.236
Median
-2.055
3Q
7.807
Max
51.035
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.354e+01 3.270e-01 133.136
<2e-16
gppre
8.733e-04 5.422e-04
1.611
0.107
Residual standard error: 9.542 on 6922 degrees of freedom
Multiple R-squared: 0.0003747, Adjusted R-squared: 0.0002303
F-statistic: 2.595 on 1 and 6922 DF, p-value: 0.1072
> summary(npp)
Call:
lm(formula = lcnpsre ~ nppre, data = finaltable)
Residuals:
Min
1Q
-43.982 -2.227
Median
-2.048
3Q
7.832
Max
51.028
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.351e+01 3.594e-01 121.072
<2e-16
nppre
1.346e-03 8.806e-04
1.529
0.126
Residual standard error: 9.542 on 6922 degrees of freedom
Multiple R-squared: 0.0003375, Adjusted R-squared: 0.0001931
F-statistic: 2.337 on 1 and 6922 DF, p-value: 0.1264
> AICc(alt,temp,prec,gpp,npp)
df
AIC
AICc
Delta
weight
ER
Model 1 3 50870.39 50870.39 306.1882 3.251415e-67 3.075584e+66
Model 2 3 50822.63 50822.63 258.4293 7.634689e-57 1.309811e+56
Model 3 3 50564.20 50564.20
0.0000 1.000000e+00 1.000000e+00
Model 4 3 50889.75 50889.76 325.5538 2.027172e-71 4.932981e+70
Model 5 3 50890.01 50890.02 325.8117 1.781894e-71 5.612007e+70
> AICc(gpp,npp)
df
AIC
AICc
Delta
weight
ER
Model 1 3 50889.75 50889.76 0.0000000 0.5321966 1.00000
Model 2 3 50890.01 50890.02 0.2579299 0.4678034 1.13765
> AICc(alt,temp,prec)
df
AIC
AICc
Delta
weight
ER
Model 1 3 50870.39 50870.39 306.1882 3.251415e-67 3.075584e+66
Model 2 3 50822.63 50822.63 258.4293 7.634689e-57 1.309811e+56
Model 3 3 50564.20 50564.20
0.0000 1.000000e+00 1.000000e+00
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