Assessing Ponderosa Pine (Pinus ponderosa ...

Assessing Ponderosa Pine (Pinus ponderosa) Suitable Habitat throughout Arizona in Response to Future Climate Models by Matthew P. Peters

A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Masters of Science

Approved April 2011 by the Graduate Supervisory Committee: Ward Brady, Chair Douglas Green Abdessamad Tridane

ARIZONA STATE UNIVERSITY May 2011

ABSTRACT The species distribution model DISTRIB was used to model and map potential suitable habitat of ponderosa pine throughout Arizona under current and six future climate scenarios. Importance Values for each climate scenario were estimated from 24 predictor variables consisting of climate, elevation, soil, and vegetation data within a 4 km grid cell. Two emission scenarios, (A2 (high concentration) and B1 (low concentration)) and three climate models (the Parallel Climate Model, the Geophysical Fluid Dynamics Laboratory, and the HadleyCM3) were used to capture the potential variability among future climates and provide a range of responses from ponderosa pine. Summary tables for federal and state managed lands show the potential change in suitable habitat under the different climate scenarios; while an analysis of three elevational regions explores the potential shift of habitat upslope. According to the climate scenarios, mean annual temperature in Arizona could increase by 3.5% while annual precipitation could decrease by 36% over this century. Results of the DISTRIB model indicate that in response to the projected changes in climate, suitable habitat for ponderosa pine could increase by 13% throughout the state under the HadleyCM3 high scenario or lose 1.1% under the average of the three low scenarios. However, the spatial variability of climate changes will result in gains and losses among the ecoregions and federally and state managed lands. Therefore, alternative practices may need to be considered to limit the loss of suitable habitat in areas identified by the models.

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DEDICATION To my Wife, Mother, and Father for their endless support.

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ACKNOWLEDGMENTS I am indebted to those who have collected and generated data that is publically available, and to my colleagues at the Forest Service Northern Research Station for their use and support of the DISTRIB model.

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TABLE OF CONTENT Page LIST OF TABLES …………………………………………………………..... vi LIST OF FIGURES …………………………………………………………... vii LIST OF ACRONYMS …………………….………………………………… viii CHAPTER 1

INTRODUCTION ……………………………………………………. 1

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LITERATURE REVIEW …………………………………………….. 2

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HABITAT CHARACTERISTICS …………………………………… 4

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METHODOLOGY …………………………………………………… 5 Overview of DISTRIB ……………………………………………..... 6 Climate ……………………………………………………………..... 9 Potential Evapotranspiration ……………………………………....… 9 Elevation …………………………………………………………….. 10 Landcover ………………………………………………………….... 11 Soil Properties ……………………………………………….………. 12 Forest Inventory and Analysis ……………………………….…….... 13 Model Assessment ……………………………………….….………. 15 Summary Statistics ……………………………………….…….….... 16 White Mountain-San Francisco Peaks Ecoregion ………….……....... 18

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DATA ANALYSES AND RESULTS ………………………………... 20 Model Assessment ………………………………………………..….. 20 Summary Statistics ……………………………………………...…… 22 iv

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White Mountain-San Francisco Peaks Ecoregion …………………... 25 6

DISCUSSION …………………………………………….………...... 27

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CONCLUSION …………………………………………………….… 30

REFERENCES ……………………………………………………………….. 32 BIOGRAPHICAL SKETCH …………………………………………………. 35

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LIST OF TABLES Table

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Predictor Variables ……………………………………………. 8

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NLCD Categories ……………………………………………... 12

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Summary of Managed Lands …………………………………. 18

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Summary of Climate Models ………………….……………… 20

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LIST OF FIGURES Figure

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Distribution and Study Area ………………………...…………… 5

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DISTRIB model overview ……………………………………….. 7

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Model Summary ……………………………………..……….….. 14

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White Mountain – San Francisco Peaks Ecoregion ………….…... 19

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Current Model Agreement ……………………………….……..... 21

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Difference Maps ………………………………………….…….... 24

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LIST OF ACRONYMS BT

Bagging Trees

DEM

Digital Elevation Model

FIA

Forest Inventory and Analysis

GCM

Global Circulation Model

GFDL

Geophysical Fluid Dynamics Laboratory

GIS

Geographical Information System

Hadley

Hadley Center

IV

Importance Value

NED

National Elevation Dataset

NLCD

National Landcover Classification Data

NRCS

National Resource Conservation Service

PCM

Parallel Climate Model

RF

Random forest

RTA

Regression Tree Analysis

SDM

Species Distribution Model

STATSGO

State Soil Geographic

SSURGO

Soil Survey Geographic

USGS

United States Geological Survey

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Chapter 1 INTRODUCTION Simulated future climate models indicate that the American Southwest could become drier and warmer by the end of the century. Kennedy et al. (2010) provides evidence that suggest Global Climate Models for the end of the century could represent realistic scenarios of future climate. Species Distribution Models (SDM) can be used to explore the influence of climatic changes on suitable habitat and provide information to resource managers for decision making. The DISTRIB model developed by the U.S. Forest Service to examine potential changes in suitable habitat for 134 eastern U.S. tree species was applied to ponderosa pine (Pinus ponderosa) habitat throughout Arizona. Modeled current suitable habitat is compared to simulations where climate variables were swapped with future projections to examine the potential changes to suitable habitat under six scenarios. Using the potential future projections of suitable habitat, summaries for federally and state managed lands were compiled to examine gains and losses to suitable habitat.

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Chapter 2 LITERATURE REVIEW Climate models (current and future) along with inventory data have been used to examine distributions for many tree species (Rehfeldt et al. 2006, Iverson et al. 2008a). Under future climate and CO2 emission scenarios, the American Southwest is projected to become hotter and drier by 2100. In response to altered climate patterns, tree species could experience shifts in their ranges as the distribution of suitable habitat changes. Species distribution models (SDMs) that utilize advanced data mining techniques have shown promising results when evaluated with current range maps (Prasad et al. 2006, Rehfeldt et al. 2006). However, the models developed by Prasad et al. (2006) and Iverson et al. (2008a) for the eastern U.S. differ from those developed by Rehfeldt et al. (2006) for the western U.S., even though both groups use a Random Forest classification approach. Prasad et al. (2006) and Iverson et al. (2008a) use the DISTRIB model with four categories of predictor variables (climate, elevation, land use, and soil) and species relative abundance values (Importance Values, IV) to model potential suitable habitat. The DISTRIB model creates regression trees using three methods (Bagging Trees, Random Forest, and Regression Tree Analysis) to partition the data and predict IVs based on groupings of predictor variables. In comparison, Rehfeldt et al. (2006) include only climate variables and species presence-absence data to model bioclimate profiles using only a Random Forest approach. While these two groups have contributed to much of the research on the potential responses of trees to climate 2

change in the U.S., our aim is to layout the procedures to model ponderosa pine suitable habitat in Arizona under low and high CO2 emission scenarios at a finer resolution with fine-scale elevation and soil data using the DISTRIB model described by Prasad et al. (2006). Then, summary tables for federal lands and ecoregions within Arizona will be used to describe the potential changes from six climate scenarios.

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Chapter 3 HABITAT CHARACTERISTICS Arizona is located in the American Southwest where elevation ranges from 13 to 3847 m, in 2000 the mean annual temperature was 16.1 °C, and annual precipitation was 771.4 mm on average (PRISM Climate Group 2004). According to three climate models and two CO2 emission scenarios, annual temperatures could increase 2 to 22% while precipitation has been projected to decrease by 36 to 48%. The general trend in Arizona is a warmer and drier climate by the end of the current century. Within its range, ponderosa pine inhabits elevations from sea level to 3050 m, and mean annual temperature range from 5° to 10° C, with summer temperatures (July-August) between 17° and 21° C (Oliver and Ryker 1990). In Arizona, growing season precipitation (May-August) can reach 205 mm (Oliver and Ryker 1990). Ponderosa pines are found on soil orders including Entisols, Inceptisols, Mollisols, Alfisols, and Ultisols throughout Arizona, and soil pH can range from 4.9 - 9.1, but is generally 6.0 – 7.0 (Oliver and Ryker 1990). It is often found on south and east facing slopes at higher elevations, and is generally more drought tolerant than Douglas-fir (Pseudotsuga menziesii ) and white fir (Abies concolor) (Schubert 1974).

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Chapter 4 METHODOLOGY Following methods developed and described by Iverson and Prasad (1998) and Iverson et al. (2008a), the DISTRIB model was applied to ponderosa pine present in Arizona and portions of western New Mexico (Figure 1). While the procedures for running DISTRIB’s regression models were not changed, the list of predictor variables (Table 1) and spatial resolution (4 km) differed from that of Iverson et al. (2008a).

Figure 1. Distribution of ponderosa pine mapped by Little (1971), with study area.

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4.1 Overview of DISTRIB The empirical-statistical model DISTRIB (Iverson and Prasad 1998, Prasad et al. 2006, Iverson et al. 2008a) employs three regression tree analyses to model species distributions based on environmental predictor variables and Importance Values (IV; relative abundance) of a species. The regression tree models were run using R statistical software (R Development Core Team 2010) version 2.12.0 and included Random Forest, Bagging Trees, and Regression Tree Analysis. Each model processes the data differently and therefore is better suited for interpretation (RTA), reliability assessment (BT), and prediction (RF) (Iverson et al. 2008a). Regression Tree Analysis (RTA) recursively partitions the data into successively smaller groups with binary splits based on a single predictor variable to generate a regression tree (Prasad et al. 2006). Mapping the distribution of predictor variables at the terminal nodes provides a sense of the spatial distribution of IVs for a particular predictor. This can be used to assess the overall distribution of the predictors driving the model and provide insight for interpreting the results. Bagging Tree (BT) generates 30 regression trees with bootstrapping the data to reduce the variance of the output error (Prasad et al. 2006). The bootstrap resampling uses 63% of the original data (“in-bag”), while replicating the other 37% for a full sample for each regression tree; the portion excluded is termed the

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“out-of-bag” data (Prasad et al. 2006). Results from BT can be used to assess the stability and consistency of predictor variables across the 30 regression trees. Agreement between the regression trees can be used to assess the model’s reliability and provide a measure for evaluation. Random Forest (RF), like BT, draws many bootstrapped samples (1000), but each regression tree is generated from a randomized subset of predictors (Prasad et al. 2006). Thus random forest is named for its randomization and many regression trees. Since a randomized subset of predictor variables are used to create many regression trees, this method reduces the chance of over fitting data that may be correlated, and thus should be used to predict or interpolate values.

Figure 2. Overview of DISTRIB regression tree ensemble approach.

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Table 1. Predictor variables used for current and future ponderosa pine habitat modeling. Italic names had the greatest contribution and were used in the final model. Climatea,b TAVG TJAN TJUL TGROW TJANJULDIF PPT PPTGROW PET PETGROW PPTNGROW PETNGROW GRPPTPETDIF NGRPPTPETDIF

Mean annual temperature (°C) Mean January temperature (°C) Mean July temperature (°C) Mean May-September temperature (°C) Mean difference between July and January temperature (°C) Annual precipitation (cm) Mean May-September precipitation (cm) Mean annual potential evapotranspiration (cm) Mean May-September potential evapotranspiration (cm) Mean October-April precipitation (cm) Mean October-April potential evapotranspiration (cm) Mean May-September precipitation - potential evapotranspiration (cm) Mean October-April precipitation - potential evapotranspiration (cm)

Elevationc ELV_CV ELV_MAX ELV_MEAN ELV_MIN ELV_RANGE SLOPE

Elevation coefficient of variation Maximum elevation (m) Average elevation (m) Minimum elevation (m) Range of elevation (m) Mean slope (% )

Soil Ordersd ALFISOLS ARIDISOLS ENTISOLS HISTOSOLS INCEPTISOLS MOLLISOLS VERTISOLS

Alfisol (%) Aridisol (%) Entisol (%) Histosol (%) Inceptisol (%) Mollisol (%) Vertisol (%)

Soil Propertiesd BD CLAY FPROD KFFACT KSAT OM PH ROCKDEP NO10 NO200 TAWC

Soil bulk density (g/cm³) Percent clay (