Estimating Future Distribution Probabilities of Southern Red Oak and Water... in the Southeastern United States under a Changing Climate

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Introduction

Oak (Quercus) is the most prevalent tree genus in the

United States (Fei et al 2011). Oaks play an critical role in ecosystems by producing masts and providing habitat for many species. Also, oaks play an important role in economics because of their valuable and high quality timber. Southern red oak (Q. falcata) is one of the more common upland southern oaks, while water oak (Q. nigra) is commonly found along southeastern water courses and bottomland (Walters and Yawney 2004). Both belong to the red oak species group and have relatively high growingstock volume among oak species, particularly in the southeastern area (Moser et al. 2006). Forest management practices, non-native species invasion, introduced pests and pathogens, and climate change may cause significant declines in oak abundance, but the trend of oak decline is not universal among species across a spatial domain and the complex causes have not been clearly revealed (Fei et al. 2011; McShea et al. 2007).

Because species distribution and climate have a strong link (Woodward, 1987), the climatic envelope modeling technique was applied to determine the significant climatic factors for southern red oak and water oak presence among a set of climatic variables (general trends, extreme values, and variation, Table 2) and to estimate future distribution probabilities due to a projected climate change scenario.

Methods

 Climate Envelope Modeling (CEM):

Characterizing the current distribution of species in geographic space;

Modeling the climatic niche in ecological space

Projecting back into geographic space with future climatic conditions.

 Data source:

Climate data (1970-2070): the monthly climate data provided by Western Kentucky University. The data are presented at 10km resolution.

 1970-2009: reanalyzed climate data

 2010-2070: WRF model (IPCC A1B)

Occurrence plots were extracted from the USDA

Forest Service’s FIA database (v5.1).

Table1. Counts of the occurrence for the two oak species

Species southern red oak water oak

Presence plots

12447

8787

Absence plots

41253

44883

TEMPLATE DESIGN © 2008 www.PosterPresentations.com

Estimating Future Distribution Probabilities of Southern Red Oak and Water Oak in the Southeastern United States under a Changing Climate

Zhen Sui

1

, Zhaofei Fan

1

, Xingang Fan

2

,

and Martin A. Spetich

3

1. Department of Forestry, Mississippi State University, Starkville, MS; 2. Department of Geography and Geology, Western Kentucky University, Bowling Green, KY;

3. US Forest Service Southern Research Station, Hot Springs, AR

Results

Future climate was projected based on IPCC A1B emission scenario. The tendency of 19 bioclimatic variables

(predictors) is shown in Figure 1.

10 (9) and 14 (13) climatic variables significantly affected the occurrence of southern red oak and water oak, respectively, at the level of 0.05 (0.01) according to GLM

approach (Table 2).

Figure 1. The tendency of 19 bioclimatic variables (predictors)

Table 2. Estimates of significant climatic variables by GLM

Description

BIO1

BIO2

BIO3

BIO4

BIO5

BIO6

BIO7

Annual Mean Temperature

Mean Diurnal Range

Isothermality (BIO2/BIO7) (*100)

Temperature Seasonality

Max Temperature of Warmest Month

Min Temperature of Coldest Month

Temperature Annual Range (BIO5-BIO6)

BIO8

BIO9

Mean Temperature of Wettest Quarter

Mean Temperature of Driest Quarter

BIO10 Mean Temperature of Warmest Quarter

BIO11 Mean Temperature of Coldest Quarter

BIO12 Annual Precipitation

BIO13 Precipitation of Wettest Month

BIO14 Precipitation of Driest Month

BIO15 Precipitation Seasonality

BIO16 Precipitation of Wettest Quarter

BIO17 Precipitation of Driest Quarter

BIO18 Precipitation of Warmest Quarter

BIO19 Precipitation of Coldest Quarter

Significant level:

α = 0.01** and α = 0.05*

Southern red oak

-5.2453**

0.6760**

-0.0707*

-0.0536

-6.3543

9.3155

9.2098

-0.1399**

-0.0097

-0.3153

3.1245**

0.0063**

0.0199**

-0.0072

-0.1986**

-0.0105**

-0.0017

-0.0002

-0.0090**

Water oak

-4.2109**

-0.6940**

0.1932**

-0.5533*

1874.7301

-1870.4743

-1870.6877

-0.0480**

0.1926**

-0.4934

-0.0845

-0.0038**

0.0114*

-0.0434**

-0.0981**

-0.0145**

0.0326**

0.0120**

0.0033**

Results

12447 and 8787 plots with southern red oak and water oak occurrence, respectively, were detected from forest inventory data after 1990 (Figure 2). Most of these plots were located within their native range (Little 1970).

The future distribution maps were projected by three climate envelope models for 2010-2020, 2021-2050, and

2051-2070 (Figure 3 and Figure 4) .

Figure 2. The native range of southern red oak and water oak associated with FIA occurrence plots

Figure 3. Projected distribution probability of southern red oak

Figure 4. Projected distribution probability of water oak

Conclusion and Discussion

Two sets of climatic variables are significantly correlated with the occurrence of southern red oak and water oak, respectively. However, the projected ranges were not visually extended outside the native ranges among different CEMs significantly.

Generally, our results indicate that southern red oak

(SRO) will still achieve high occupied probability along the

Piedmont in MS and AL. SRO has relatively high colonization probability so it could move 10-30 km northward from the current distribution (Iverson et al.

2004). However, Fei et al. (2011) reported that SRO lost

59% of its abundance in its native range, especially in the

Coastal Plain, during 1980 - 2008. As for water oak (WO), our results showed it will remain prevalent in the Coastal

Plain, even though the predicted probability is not spatially even. It coincided with previous results that WO universally increased 83% of its abundance in native range from 1980 to 2008 (Fei et al. 2011).

This study demonstrated the species-climate relationship of SRO and WO and projected their potential distribution shift due to a changing climate. However, the climate-based models still have generic limitations in considering biotic factors, such as competition, predation, parasitism, and mutualism (Pearson and Dawson, 2003).

Thus, it’s necessary to compare these results with other climate-based studies and local scale experiments to verify our understanding.

References

1. Fei, S., N. Kong, K.C. Steiner, W.K. Moser and E.B. Steiner. 2011. Change in oak abundance in the eastern United States from 1980 to 2008. Forest Ecology and Management. 262: 1370-1377.

2. Iverson, L.R., M.W. Schwartz and A.M. Prasad. 2004. How fast and far might tree species migrate in the eastern United States due to climate change?

Global Ecology and Biogeography. 13: 209-219.

3. Little Jr, E.L. 1971. Atlas of United States trees. Volume 1. Conifers and important hardwoods. Miscellaneous publication 1146. US Department of

Agriculture, Forest Service, Washington, DC.

4. McShea, W.J., W.M. Healy, P. Devers, T. Fearer, F.H. Koch, D. Stauffer et al.

2007. Forestry matters: decline of oaks will impact wildlife in hardwood forests.

The Journal of Wildlife Management. 71: 1717-1728.

5. Moser, W.K., M. Hansen, W. McWilliams and R. Sheffield. 2006. Oak composition and structure in the eastern United States. Fire in Eastern Oak

Forests: Delivering Science to Land Managers: 15-17.

6. Pearson, R.G. and T.P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?

Global Ecology and Biogeography. 12: 361-371.

7. Peterson, A.T. 2003. Predicting the geography of species' invasions via ecological niche modeling. Quarterly Review of Biology. 78: 419-433.

8. Walters, R. and H. Yawney. 2004. Silvics Manual: Volume 2: Hardwoods.

United States Department of Agriculture, Forest Service, Washington, DC.

9. Woodward, F.I. 1987. Climate and plant distribution. Cambridge University

Press.

Acknowledgement

This study is supported by the USDA Forest Service’s National

Forest Health Monitoring (FHM) Program through the joint venture agreement (10-JV-11330134-048) with the USDA Forest Service

Southern Research Station.

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