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Ecology, 91(4), 2010, pp. 1140–1151 Ó 2010 by the Ecological Society of America

Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands HELEN M. POULOS1,2 2

AND

ANN E. CAMP1

1 Yale School of Forestry and Environmental Studies, New Haven, Connecticut 06511 USA Environmental Studies Program, Wesleyan University, Middletown, Connecticut 06459 USA

Abstract. The abundance and distribution of species reflect how the niche requirements of species and the dynamics of populations interact with spatial and temporal variation in the environment. This study investigated the influence of geographical variation in environmental site conditions on tree dominance and diversity patterns in three topographically dissected mountain ranges in west Texas, USA, and northern Mexico. We measured tree abundance and basal area using a systematic sampling design across the forested areas of three mountain ranges and related these data to a suite of environmental parameters derived from field and digital elevation model data. We employed cluster analysis, classification and regression trees (CART), and rarefaction to identify (1) the dominant forest cover types across the three study sites and (2) environmental influences on tree distribution and diversity patterns. Elevation, topographic position, and incident solar radiation were the major influences on tree dominance and diversity. Mesic valley bottoms hosted high-diversity vegetation types, while hotter and drier mid-slopes and ridgetops supported lower tree diversity. Valley bottoms and other topographic positions shared few species, indicating high species turnover at the landscape scale. Mountain ranges with high topographic complexity also had higher species richness, suggesting that geographical variability in environmental conditions was a major influence on tree diversity. This study stressed the importance of landscape- and regional-scale topographic variability as a key factor controlling vegetation pattern and diversity in southwestern North America. Key words: diversity; environmental gradients; landscape ecology; Madrean evergreen woodlands; Sky Islands, southwestern North America; vegetation pattern.

INTRODUCTION Understanding how environmental conditions and ecosystem processes influence the abundance, distribution, and diversity of vegetation has been a central problem of plant ecology and biogeography since the phytogeographical work of Von Humboldt (1805). The abundance and distribution of species reflect the consequences of the ways in which the niche requirements of species and the dynamics of populations interact with spatial and temporal variation in the environment (Whittaker 1965, Whittaker and Niering 1965, Hannawalt and Whittaker 1976, Christensen and Peet 1984, Allen et al. 1991, Urban et al. 2000). While we know that environmental variability affects species diversity according to positive environmental heterogeneity–diversity relationships (Whittaker 1972, Huston 1979, Tilman 1982, Keddy 1990), few studies have identified which topographic settings support the highest species richness at landscape and regional scales. Ordination methods for examining species–environment relationships have a long history in plant Manuscript received 30 September 2008; revised 7 July 2009; accepted 13 July 2009. Corresponding Editor: C. Galen. 1 E-mail: [email protected]

community ecology (ter Braak and Prentice 1988, Legendre and Legendre 1998). Most ordination techniques assume symmetric, unimodal response functions when in reality the mechanisms controlling plant distributions can take a variety of nonlinear forms (Austin 1980, 1999, 2002, Okansen and Minchin 2002). Recent advances in data mining and simulation-randomization-based statistical tests improve upon traditional gradient analysis approaches for examining species–environment and diversity relationships by facilitating robust statistical tests of vegetation pattern. Classification and regression trees (CART) represent an improvement over traditional ordination techniques because they are nonparametric and provide thresholds for differentiating the site requirements of different species (De’ath and Fabricius 2000). Our goal was to employ newly available statistical techniques to characterize the influence of topography on tree species composition and diversity across three mountain ranges of southwestern North America. While several studies have provided snapshots of the influence of topography on plant distribution patterns in individual mountains in the northern Sierra Madre Occidental in Arizona (i.e., Whittaker and Niering 1965, 1968, Wentworth 1981, Huebner and Vankat 2003), no one has examined these relationships across multiple moun-

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tain ranges in either of the two Sierra Madre Ranges that occur from southern Mexico to Arizona and New Mexico in the United States. We were specifically interested in quantifying tree dominance and diversity patterns with respect to topography along a short chain of three mountain ranges in the Chihuahuan Desert Borderlands (CDB) of the northern Sierra Madre Oriental. While most prior research of this type has focused on one scale of study, our intensive sampling across the entire forested area of the three sites permitted an investigation into the manner in which a variety of abiotic factors influenced tree dominance and diversity at local, landscape, and regional scales. METHODS Study area description The Sky Island forests of the arid American Southwest and northern Mexico are mountain island archipelagos. Current forests are Pleistocene relicts, and their distributions are the product of species migrations from lowlands to uplands during early Holocene warming (VanDevender and Spaulding 1979). Upland Sky Islands are bounded at lower elevations by deserts dominated by shrub and succulent desert flora, where tree establishment and growth is inhibited due to high temperatures and moisture-limited conditions. The three protected areas included in this study were the Davis Mountains Preserve of The Nature Conservancy (DM), Big Bend National Park (BB), and the Maderas del Carmen Protected Area (MC). They are located in the northern edge of the Sierra Madre Oriental, which begins in New Mexico, USA, and continues southward 1350 km to the states of Puebla and Quere´taro in Mexico. DM and BB are located in west Texas, USA, while MC is in the northern portion of the state of Coahuila, Mexico. Forming the southeastern edge of the Basin and Range Geographic Province, these mountains represent an ecological transition zone, sharing biological affinities with flora of the Rocky Mountains and the Sierra Madre Ranges (Muldavin 2002). The mountains are volcanic and consist mainly of extrusive igneous rock. They originated 35–39 million years ago in the same Oligocene orogeny that formed most of the Front Range of the Rocky Mountains. The development of the Sierra Madre Oriental during the Oligocene epoch of the Cenozoic made it the first cordillera to develop in northern Mexico and provided generalized geographic diversity, which in turn led to habitat diversity (Ferrusquı´ a-Villafranca et al. 2005). Soils of the three study areas are a mixture of mollisols and entisols. They are composed of moderately deep gravelly loam, which is well drained and non-calcareous (Carter 1928, USDA Soil Conservation Service 1977). Runoff is medium to rapid. Available water capacity is low. High-elevation forests (above 1600 m elevation) in this region are composed of pi˜non–juniper, oak, pine–

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oak, and mixed-conifer woodlands. Pi˜non–juniper woodland is the dominant low- to mid-elevation forest type across all three study areas. This vegetation type is dominated by Pinus cembroides Zucc., Juniperus deppeana E. Von Steudal, Quercus grisea Liebmann, Q. emoryi Leibmann, and J. flaccida Schltdl. Pine–oak forests dominate middle to high elevations of the three sites. These forests are widespread across MC, but they are restricted to valley bottoms in the other two sites. Pine– oak forests are primarily composed of P. ponderosa Lawson and oak associates. In MC, southwestern white pine (P. strobiformis Engelm.) is also a major component of this forest type. Oak woodlands exist in BB and MC across a range of elevations, and their species composition varies both by elevation and local site conditions. Mixed conifer forests cover middle and high elevations in MC, and they are dominated by Abies coahuilensis Johnston, Pseudotsuga menziesii Mirb., and Cupressus arizonica Greene. Pseudotsuga menziesii and C. arizonica also have limited populations in Boot Canyon in BB, but they are absent from DM. Taxonomy follows Nixon (2002) for the Mexican oaks and Powell (1998) for all other species. The modern climate is arid, characterized by cool winters and warm summers. Precipitation is distributed bimodally in late summer and winter, with the majority of precipitation falling during summer storms as part of the North American Monsoon System. No climatic data exist for the Sierra del Carmen. However, mean annual precipitation ranges from 20 to 180 cm in the uplands of the Sierra La Encantada and Sierra Santa Rosa, which abut the Sierra del Carmen to the south. Mean annual temperature ranges from 128C in January to 188C in July (CONABIO 2006). Mean annual precipitation for the Chisos Basin in BB is 70 cm (range 32–135 cm). Mean January precipitation is 1.5 cm (range 0–2.5 cm) and is 8.0 cm (range 0.2–20.5 cm) in July. Mean monthly minimum temperatures are 1.88C in January and 17.08C in July. Maximum temperatures are 14.18C in January and 29.18C in July. Mean annual precipitation is 40 cm (range 25–140 cm) in Ft. Davis, Texas, just outside DM. Mean January precipitation in DM is 1.2 cm (range 0– 8.1 cm) and is 7.1 cm (range 0.4–23 cm) in July during the monsoon. Mean monthly minimum temperatures in Ft. Davis are 0.08C in January and 17.78C in July, while mean monthly maximum temperatures are 15.58C in January and 32.68C in July. Field methods Six hundred permanent field plots (200 at each site) were established using a systematic sampling grid to stratify sample plots at 600-m intervals across each study area at the intersection of grid lines. The center of each plot was marked with reinforcing bar, and the spatial location of the plot center was recorded using a handheld global positioning system (;10-m spatial resolution).

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Vegetation within each plot was sampled using nested, circular, fixed-area plots that were 10 m in radius for all trees 5 cm diameter at breast height (dbh, measured at 1.3 m above the ground surface). The species, dbh, total height, height at the base of the live crown, and the live crown ratio (ratio of live crown height to total tree height) were recorded for all trees (n ¼ 17748). Seedlings (trees , 5 cm dbh) were tallied by species using nested 5m radius plots. Plot areas were corrected for slope upon return from the field following Husch et al. (2003). We took hemispherical photographs at the center point of each vegetation plot as measures of incident and diffuse solar radiation. Photos were taken with a Nikon Coolpix 900 digital camera with Coolpix 900 fish-eye lens (Nikon, Tokyo, Japan) mounted on a self-leveling tripod positioned 1 m above the ground under cloudy sky conditions in the morning. The incident sky factor (ISF) and diffuse sky factor (DSF) of each plot were calculated using HemiView canopy analysis software version 2.1 (Delta-T Devices, Cambridge, UK). Major vegetation types Groups of plots with similar woody plant composition were identified using cluster analysis. We clustered species’ importance values, which were calculated as the sum of the relative density and the relative basal area (BA) of each species (0–200 range) to identify the dominant vegetation types in the three study sites. We used Ward’s method and relative Euclidean distance as the similarity measure. Ward’s method minimizes the within-group variance relative to among-group variance. Differences in the species importance values among groups were determined using a multiple response permutation test (MRPP). Indicator species analysis (Dufrˆene and Legendre 1997) with Euclidean distances as the distance metric was used to determine the significant indicator species in each vegetation type. Indicator species analysis combines information on the concentration of species abundance in a particular group and the faithfulness of occurrence of a species, where a perfect indicator always appears in that group (McCune and Grace 2002). The significance of indicator values was tested using a Monte Carlo randomization approach. Cluster analysis, MRPP, and indicator species analysis were performed with PC-Ord software (McCune and Mefford 1999). Tree species diversity Species’ count data for each plot were used in the diversity analysis following Whittaker (1972). Species richness (Sobs), Simpson diversity (s), and Shannon diversity (H 0 ) were used as measures of alpha diversity (a) for each vegetation type, along with two nonparametric incidence-based species richness estimators, Chao2 and Jack2 (e.g., Whittaker 1972, Magurran 2004). Chao2 and Jack2 are robust sample-based species richness estimators that estimate species richness in a species pool using maximum likelihood methods (Chaz-

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don et al. 1998). Chao2 is a richness estimator that emphasizes the importance of species that occur only as singletons and doubletons in species richness estimation, while Jack2 is a second-order jackknife estimator of species richness (Magurran 2004). Formulas and descriptions of their performance can be found in Chao (1987) and Chazdon et al. (1998). We constructed sample-based rarefaction curves (species accumulation as a function of occurrence) with 95% confidence intervals (Colwell et al. 2004) to assess sampling completeness and to compare differences in species richness among vegetation types using EstimateS software version 7.5 (Colwell 2005). Rarefaction differs from classical species–area curves (e.g., MacArthur and Wilson 1967), which plot the cumulative number of species recorded (S ) as a function of sampling effort (n). Instead, rarefaction plots the total number of individuals counted with repeated random sampling against the total number of species found in those samplings (Colwell and Coddington 1994). Sample-based rarefaction permits comparison of species richness (Sobs) among groups with different sample sizes using a Monte Carlo randomization procedure (Gotelli and Colwell 2001). Chao2 and Jack2 were also computed as functions of the sample accumulation level (Chazdon et al. 1998) to reduce the bias that under-sampling may impose on total species richness estimates. Beta and gamma diversity were calculated to identify species turnover among vegetation types and regionalscale differences in diversity among the three study sites. Variation in b diversity among the vegetation types was identified using Jaccard and Sørenson similarity indices that were modified and scaled by Chao et al. (2005) to accommodate for sample size differences among vegetation types. Gamma diversity was estimated and compared among sites using all of the plots at each site to quantify landscape-scale species richness. Gamma diversity was calculated by constructing rarefaction curves for all samples and all species in DM, BB, and MC. Topographic influences on species composition and diversity Environmental influences on woody plant species composition and richness were identified using classification and regression trees (CART) (Breiman et al. 1984). We used a set of 16 topographic variables (Table 1) to (1) identify the major environmental influences on the distributions of the vegetation types identified from cluster analysis and (2) determine what topographic settings supported high vs. low diversity. The topographic variables were derived from a combination of field measurements and landscape metrics derived from digital elevation models (DEMs) (USGS 2005). Elevation, aspect, slope, topographic relative moisture index (TRMI), incident sky factor (ISF), and diffuse sky factor (DSF) were calculated from field measurements, while the remainder of the topographic data were

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TABLE 1. Environmental variables used for quantifying tree species–environment and diversity–environment relationships using classification and regression trees (CART). Variable or code Elevation N aspect S aspect Slope Down neigh Down elev ISF DSF TRMI PRR Shade relief Topo pos 150 Topo pos 450 Topo config Relative elevation Landform Sediment transport WI Network index Flow direction Flow accumulation

Definition meters above sea level cosine transformation of aspect (degrees) (Beers et al. 1966) 1.0 (southwest) to 1.0 northeast sine transformation of aspect (degrees) (Beers et al. 1966) 1.0 (southwest) to 1.0 northeast maximum rate of change in elevation between a cell and its eight surrounding neighbors number of immediately surrounding neighbors that are downslope from the cell downslope elevation change incident sky factor calculated from hemispherical photographs using Hemiview Canopy Analysis software, version 2.1 (Delta-T Devices, Cambridge, UK) diffuse sky factor calculated from hemispherical photographs using Hemiview Canopy Analysis software, version 2.1 topographic relative moisture index ¼ slope aspect, pitch, slope position, and configuration that ranges from 0 (xeric) to 60 (Parker 1982) cumulative potential relative radiation based on hourly solar position, topography, and topographic shading (Pierce et al. 2005) hill shade or hypothetical topographic illumination (Burrough and McDonnell 1988) topographic position, calculated as the difference between a cell’s elevation and the mean elevation of cells within a 150-m radius topographic position, calculated as the difference between a cell’s elevation and the mean elevation of cells within a 450-m radius topographic configuration ranging from concave to convex, calculated using the spatial analyst function in ArcMap 9.1 difference in elevation between a cell and its eight surrounding neighbors landform type derived from Terrain Analysis System software (Lindsay 2005) based on Pennock et al. (1987); landform types include (1) convergent foot slope, (2) divergent foot slope, (3) convergent shoulder, (4) divergent shoulder, (5) convergent back slope, (6) divergent back slope, and (7) level sediment transport capacity index ¼ (As/22.13)0.6 3 (sin S/0.0896)1.3 where As is the specific catchment area and S is the local slope wetness index (Beven and Kirkby 1979) derived from Terrain Analysis System software (Lindsay 2005) defined as WI ¼ ln (As/tanS ) minimum wetness index value along a flow path (Lane et al. 2004); this value defines when a cell with a zero or negative saturation deficit is connected to the drainage network flow direction from ArcHydro extension in ArcMap 9.1 and a 30-m digital elevation model flow accumulation from ArcHydro extension in ArcMap 9.1 and a 30-m digital elevation model

extracted from the DEMs. The environmental influences on vegetation distributions were quantified using a classification tree approach, whereas the relationship between local site conditions and H 0 and s diversity were determined using regression trees. All classification and regression tree analyses were performed using the R statistical language (R Development Core Team 2007). Decision trees are useful for grouping or distinguishing two or more known classes of observations based on a suite of predictor variables. They are ideal for ecological data sets because they are nonparametric, can handle categorical variables, and are robust to outliers (De’ath and Fabricius 2000). Furthermore, their output is easy to interpret and the decision rules created by the trees can be linked to environmental processes across landscapes. Classification and regression are performed using the same recursive partitioning algorithm that iteratively subsets the data into successively more homogeneous groups through a series of binary splits (Venables and Ripley 1994). At each partition, the model computes an estimate of the within-partition heterogeneity or ‘‘impurity’’ of the partitions or a goodness-of-split criterion. We used CART in this study as a means of parsing the vegetation types and diversity indexes according to the variation in the environmental data set. This allowed us to identify the key environmental variables influencing the distribution of each

forest cover type, and it assisted us in identifying the environmental settings that support high tree diversity. The fits of both the species–environment and topography–diversity models were evaluated by examining the cost complexity parameter that measured how well the explanatory landscape metric variables separated the data. RESULTS Vegetation types We identified nine dominant forest vegetation types across the three mountain ranges: gray oak, gallery forest, Emory oak, Graves oak, pi˜non pine, oak–pi˜non– juniper, ponderosa–southwestern white pine, cypress– fir, and alligator juniper (Table 2, Appendix A). Species composition differed significantly by vegetation type (MRPP, P , 0.0001), and the names of the vegetation cover types were assigned using the names of species with high indicator values from indicator species analysis. The Davis Mountains contained four vegetation types including pi˜non pine, oak–pi˜non–juniper and ponderosa–southwestern white pine. Big Bend National Park hosted Graves oak, Emory oak, pi˜non pine, oak–pi˜non– juniper, and alligator juniper forests. The largest variety of vegetation types occurred in MC, including gray oak,

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TABLE 2. Mean environmental attributes for each vegetation type in sample plots in the Davis Mountains Preserve of The Nature Conservancy, Big Bend National Park, and the Maderas del Carmen Protected Area, located in the northern edge of the Sierra Madre Oriental, USA and Mexico.

Attribute

Gray oak

Gallery forest

Graves oak

Emory oak

Pi˜non pine

Oak– pi˜non– juniper

n 60 72 18 20 147 106 Elevation (m) 1917 1906 1834 1657 2037 2021 Slope (8) 20 23 22 12 20 17 ISF 0.573 0.333 0.381 0.585 0.555 0.568 DSF 0.625 0.367 0.406 0.632 0.589 0.597 S aspect 0.071 0.194 0.224 0.157 0.072 0.010 N aspect 0.035 0.145 0.282 0.219 0.101 0.048 TRMI 29 34 39 34 25 29 PRR 18 158 18 266 15 689 17 963 18 652 19 417 Network index 5.3 4.1 7.0 7.4 5.4 5.2 Relative elevation 96.9 93.9 98.2 97.3 97.1 96.2 Shade relief 0.48 0.45 0.48 0.49 0.52 0.48 Topo pos 450 1.7 16.6 52.3 22.3 11.9 0.9 Topo pos 150 1.0 0.7 16.2 4.7 0.9 1.0 Wetness index 6.47 5.89 7.15 7.54 6.05 5.87 Topo config 0.55 0.37 1.98 0.54 0.41 0.22 Down neighbor 3 4 3 2 4 4 Down elev 8 16 5 4 10 12 Flow accumulation 48 48 62 337 14 32 Flow direction 30 23 53 38 31 27 Sediment transport 13.5 24.3 9.6 6.4 13.8 16.3 Grass (%) 45 41 26 50 52 59 Shrub (%) 34 16 26 46 23 26 Soil (%) 33 34 40 45 41 49

Ponderosa pine– southwest Arizona Alligator white pine cypress–fir juniper 59 2364 18 0.349 0.399 0.149 0.065 34 18 018 4.1 97.3 0.51 3.0 0.4 6.07 0.16 4 13 13 36 21.6 37 5 26

21 97 2376 1924 19 12 0.282 0.597 0.312 0.620 0.156 0.074 0.009 0.080 31 31 17 771 19 718 4.7 5.6 97.5 95.3 0.54 0.49 4.6 0.7 2.5 1.2 5.95 6.48 0.41 0.05 4 4 13 7 25 36 32 31 16.3 11.5 32 66 11 21 38 49

Note: See Table 1 for an explanation of attribute abbreviations.

gallery forest, pi˜non pine, oak–pi˜non–juniper, alligator juniper, ponderosa–southwestern white pine, and cypress–fir vegetation types. The cypress–fir vegetation type was unique to MC, although Arizona cypress was present in a few restricted sites in BB. Within a vegetation type, the overall species composition differed among sites. Gray oak, pi˜non pine, and and oak–pi˜non–juniper forests in BB contained a juniper complex (Juniperus deppeana, J. flaccida, and J. pinchotii ) not found in the other two sites. While these juniper species also occurred to some extent in MC, J. flaccida and J. pinchotii primarily occurred in BB. The pi˜non pine, alligator juniper, and oak–pi˜non–juniper forests in MC had a greater diversity of oak species than the other two sites. In MC, these forest types contained mixtures of Quercus grisea, Q. arizonica, Q. mohriana, and Q. laceyi. The primary oak species in these vegetation types at the other two sites was Q. grisea. Species–environment associations The nine vegetation types varied significantly according to local environmental conditions (MRPP, P , 0.001; Table 2, Fig. 1, Appendix B). Gallery forests dominated shady, low-elevation valley bottoms. Ponderosa–southwestern white pine forests occupied drier, high-elevation sites. Graves oak existed on mesic midslopes. Pi˜non pine was found across a range of topographic settings, as it was the most common cover type in the study area. However, it was most common at middle elevations on upper slopes and ridge tops with

high solar radiation when considering the entire elevation gradient. At MC it was a low- to mid-elevation cover type, while at BB and DM, pi˜non pine dominated middle to upper elevations. Gray oak forests covered steep, dry sites with low shade relief, while Emory oak forests dominated flatter, dry sites at low elevations. Oak–pi˜non–juniper forests existed on flat, mid-elevation, dry toe slopes. Alligator juniper dominated the exposed, upper slopes of lower elevations. Alpha diversity Vegetation types on mesic sites had higher species richness (more individual species) than dry-site vegetation types (Fig. 2). For example, while gallery, Emory oak, and alligator juniper forests all dominated low elevations, mesic gallery forests had significantly higher species richness than all other vegetation types. Correspondingly, low-elevation dry-site cover types (Emory oak and alligator juniper forests) had the lowest species richness. Sampling efficiency was high for all three sites (Table 3, Appendix C), although it was highest for dominant vegetation types. Rare vegetation types found in only one of the protected areas (cypress–fir, Graves oak, and Emory oak) had lower sampling efficiency. Beta diversity The Chao-Jaccard and Chao-Sørenson similarity indices revealed that mesophytic (Graves oak, gallery forest, ponderosa–southwetsern white pine, and cypress–fir) and xerophytic (gray oak, alligator juniper,

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FIG. 1. Decision tree rules for the vegetation–environment relationships in the Davis Mountains Preserve of The Nature Conservancy (DM), Big Bend National Park (BB), and the Maderas del Carmen Protected Area (MC), located in the northern edge of the Sierra Madre Oriental, USA and Mexico. Vegetation type abbreviations are: AJ, alligator juniper; CF, cypress–fir; EO, Emory oak; GF, gallery forest; GO, gray oak; GRO, Graves oak; OPJ, oak–pi˜non–juniper; PP, pi˜non pine; and PSW, ponderosa– southwestern white pine. See Table 1 for explanations of attribute abbreviations.

oak–pi˜non–juniper, and pi˜non pine) vegetation types had high b diversity (shared few species) (Table 4). Xerophytic vegetation types shared many species, as did mesophytic groups, indicating low b diversity among vegetation types that grew under similar site conditions.

Gamma diversity Landscape-scale species richness was highest in MC, followed in order by BB, and DM (Fig. 3, Appendix C). The MC had significantly higher species richness than the other two sites. The Shannon diversity index and s

FIG. 2. (A) Sample-based rarefaction curve for species richness (Sobs) of woody plants for each vegetation type. (B) Rarefied species number and 95% confidence intervals (error bars) for vegetation types identified by cluster analysis of species importance values of plots in the study area. See Fig. 1 for an explanation of vegetation type abbreviations.

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TABLE 3. Jack2 and Chao2 species richness, singletons, doubletons, sampling efficiency, and Shannon and Simpson tree diversity for the Maderas del Carmen, Big Bend, and the Davis Mountains and the percentage of the expected collected. ACE

Jack2

Chao2

Obs Obs Obs Species Value (%) Value (%) Value (%) Singletons Doubletons Shannon Simpson obs

Species Gray oak Mesic woodland Graves oak Emory oak Pi˜non pine Oak–pi˜non–juniper Ponderosa–southwest white pine Cypress–fir Alligator juniper

25 40 22 10 28 23 27 25 19

26 42 25 12 31 24 28 31 21

97 94 91 79 91 97 95 78 92

27 46 59 18 83 36 31 41 22

91 87 38 55 34 64 88 61 85

32 52 39 16 49 39 36 40 25

78 76 57 60 56 59 75 61 75

2 4 3 2 4 2 3 7 2

2 5 5 1 7 3 1 1 3

1.7 2.8 2.0 1.2 1.5 1.4 2.0 2.2 1.3

3.0 11.9 3.8 2.1 3.0 3.0 4.7 6.0 2.6

Notes: Abbreviations are: ACE, abundance-based coverage estimator; Obs, percentage of species observed for each diversity estimator; Chao2, total species richness estimator (Chao 1984); Jack2, second-order incidence-based species richness estimator; singletons, single species occurrences; doubletons, species with only two individuals in the pooled samples; Shannon, mean Shannon diversity among simulations; Simpson, mean inverse Simpson diversity among runs.

were also higher in MC than the other two sites. Sampling efficiency was adequate for the three sites, suggesting that an ample sample size was used for diversity estimation. Topographic influences on diversity Shannon diversity was highest in shady valley bottoms (Fig. 4). Wetter mid-slopes and ridge tops had intermediate diversity. Hot, dry sites with high solar radiation supported low tree diversity. The s diversity analysis revealed similar results. Simpson diversity was highest in valley bottoms, and it was lowest on hot, dry mid-slopes and ridge tops. DISCUSSION This research demonstrates the influence of landscape- and regional-scale environmental variability on

tree species dominance and diversity patterns in the CDB. At the landscape scale, more favorable sites (i.e., wetter, cooler valley bottoms with deeper soils) supported mesophytic, high-diversity vegetation types, while more extreme sites (hotter, drier mid-slopes and ridge tops) were characterized by lower tree diversity. At the regional scale, more topographically complex mountains (BB and MC) supported a greater diversity of vegetation types and total species richness. Environmental influences on forest pattern Environmental variation across landscapes is a wellknown influence on tree species composition and distributions. This study builds upon prior research in individual mountain ranges in the Southwest that identified elevation and soil moisture as important

TABLE 4. Chao-Jaccard and Chao-Sørenson similarity in woody plant species composition among vegetation types in the study region.

Vegetation type

Gray Gallery Graves Emory Pi˜non Oak–pi˜non– oak forest oak oak pine juniper

Ponderosa– southwest Cypress– Alligator white pine fir juniper

Chao-Jaccard Gray oak x Gallery forest 0.772 Graves oak 0.84 Emory oak 0.90 Pi˜non pine 0.96 Oak–pi˜non–juniper 0.99 Ponderosa–southwest white pine 0.64 Cypress–fir 0.56 Alligator juniper 0.98

x 0.66 0.31 0.81 0.82 0.89 0.93 0.79

x 0.75 0.87 0.94 0.34 0.59 0.98

x 0.95 0.90 0.06 0.06 0.95

x 0.99 0.83 0.61 0.98

x 0.57 0.91 1.00

x 0.98 0.67

x 0.72

x

Chao-Sørenson Gray oak x Gallery forest 0.87 Graves oak 0.91 Emory oak 0.95 Pi˜non pine 0.98 Oak–pi˜non–juniper 0.99 Ponderosa–southwest white pine 0.78 Cypress–fir 0.72 Alligator juniper 0.99

x 0.79 0.47 0.89 0.90 0.94 0.96 0.88

x 0.86 0.93 0.97 0.50 0.74 0.99

x 0.97 0.95 0.12 0.12 0.97

x 0.99 0.91 0.76 0.99

x 0.73 0.95 1.00

x 0.99 0.80

x 0.84

x

Note: For an explanation of the similarity indices, see Methods: Tree species diversity.

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FIG. 3. Gamma (c) diversity with 95% confidence intervals for the Davis Mountains Preserve of The Nature Conservancy (DM), Big Bend National Park (BB), and the Maderas del Carmen Protected Area (MC). Gamma diversity is landscape diversity, i.e., total diversity over a large area.

FIG. 4. Decision tree predicting (A) Shannon diversity (H 0 ) and (B) Simpson diversity (s) as functions of environmental variables. See Table 1 for explanations of attribute abbreviations.

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influences on species sorting patterns (Whittaker and Niering 1965, 1968, Wentworth 1981, Barton 1994, Poulos et al. 2007b) by demonstrating that these variables are key controls on regional-scale tree distributions. In addition to elevation and soil moisture, our study found that solar radiation and topographic position also contributed to tree diversity and dominance in CDB forests. These variables are associated with elevation and moisture, but they further refine the influences of light regimes and topography on vegetation patterns. Temperature decreases while soil moisture increases with elevation due to the environmental lapse rate (Tranquillini 1979). This is why more mesophytic vegetation types such as cypress–fir and ponderosa– southwestern white pine forests occurred at higher elevations and xerophytic forest types including Emory oak, gray oak, and alligator juniper dominated lower elevations. Topographic position also affects forest microclimates because incident solar radiation varies across the surface of a slope. Lower slopes and valley bottoms experience the least amount of solar radiation because radiation is often reflected upwards to mid-slopes and because of higher shade relief from surrounding slopes (Barry 1992). This further explains the occurrence of mesophytic vegetation types such as gallery forest in valley bottoms. Mid-slopes had the highest incident solar radiation due to direct incoming radiation and the reflection of radiation off other topographic positions. Extremely drought-tolerant species (i.e., Juniperus deppeana, Pinus cembroides, and Quercus grisea) dominated these hotter and drier sites. The harshest growing conditions in the three study sites occurred at low elevations, mid-slopes, and ridgetops that had high temperatures and solar radiation. Those sites were colonized by the most drought-tolerant species (i.e., J. deppeana, P. cembroides, Q. laceyi, and Q. grisea), while wetter, cooler sites were occupied by drought-avoiding species (i.e., P. ponderosa, P. strobiformis, Q. sideroxyla, Q. rugosa, Abies coahuilensis, and Cupressus arizonica). Junipers are known to be some of the most drought-tolerant tree species in the Southwest (Padien and Lajtha 1992), which may explain why J. deppeana dominated lower elevations that were characterized by shallow soils, low soil moisture, and high solar radiation. Likewise, upland and valley bottom pines in the Chiricahua Mountains in Arizona were much less drought tolerant than lowland pines, but were better competitors for light, the major growth-limiting factor in closed-canopy forests (Barton 1993, Barton and Teeri 1993). The differentiation of dry- and wet-site vegetation types according to elevation was probably related to the differential drought tolerance of trees across the elevational gradient. Several prior studies have demonstrated the role of drought tolerance in the niche differentiation of junipers and oaks across southwestern North

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America (Padien and Lajtha 1992, Lajtha and Getz 1993, Poulos et al. 2007a, 2008), suggesting that plant– water relations may play a crucial role in determining tree distribution patterns across desert landscapes. Site generalists had wide ecological amplitudes that allowed their survival across a range of topographic settings. For example, pin˜ on pine woodlands were distributed across the entire elevation gradient of our study region. In a drought experiment in DM on P. cembroides, this species exhibited variable needle morphology and drought responses in accordance with its distribution at low, intermediate, or high elevations (Poulos and Berlyn 2007). This suggests that the wide distribution of site generalists such as P. cembroides is the result of these species’ capacities to adapt to a variety of moisture and radiation regimes. Comparisons of tree species richness with other parts of western North America The high tree diversity in mesophytic vegetation types in this study was similar to the Rocky Mountains, where species richness is related to soil moisture (Peet 1978, Allen et al. 1991). In other parts of the Southwest, species richness is correlated with topographic complexity and forest physiognomy (Whittaker and Niering 1965, Poulos et al. 2007b). As in our study region, wetter portions of the Rockies had the highest species richness (Peet 1978). In contrast, species richness showed an opposite pattern in southeastern Arizona, where it was highest in drought-tolerant, short-stature vegetation types (pi˜non pine and juniper) that spanned a wide range of elevations and topographic settings. This suggests that while species richness in CDBs is closely tied to soil moisture and light, topographic complexity has a stronger influence on diversity in Arizona. The variability of species richness across landscapes of the Southwest signals the need for site-specific biodiversity management in this eco-region. Species turnover Not only do mesophytic vegetation types have high diversity, but they also contain tree species that were found nowhere else on the landscape. The mesic valley bottoms that support high a and b diversity represent relatively small proportions of the landscape, making them a high conservation priority for maintaining biodiversity in this fragile desert ecosystem. The high b diversity on extreme vs. favorable sites also suggests that drought-tolerant vegetation types represent unique components of the landscape. The high species turnover among vegetation types across the landscape indicates that environmental heterogeneity is an important element responsible for both a and b diversity patterns in this study region. The b diversity patterns in DM, BB, and MC are similar to previous work in Sky Island forests in Arizona that have high species turnover among vegetation types and across environmental gradients (Whittaker and

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Neiring 1965, Poulos et al. 2007b). The rapid turnover in local environmental conditions with topography in Sky Island Mountains is probably the mechanism responsible for the species compositional differences among vegetation types that are differentially distributed across the landscape. Regional diversity patterns The c diversity comparisons showed that the largest, most southerly preserve had the highest species richness in this eco-region. While our sample size was small (n ¼ 3), several potential explanations exist for this pattern: (1) the effect of larger mountain ranges on species richness, (2) the proximity of each mountain range to the Sierra Madre Oriental mother range, or (3) the influences of topographic complexity on diversity. Larger mountains provide a wider range of potential habitats for species colonization, which may explain the higher species richness in MC. This explanation is based on the species–area relationship in which species richness increases as a function of area (Williams 1943, Connor and McCoy 1979). Larger preserves can expect greater diversity in species composition, while smaller preserves cannot realistically expect to encapsulate all of the diversity that exists in a region. Island biogeography theory also suggests that the closer an island to the mainland, the higher the number of species’ colonizations (MacArthur and Wilson 1967). The closer location of MC and BB to the Sierra Madre Oriental proper may also be a possible explanation for their higher species richness relative to DM. Mountains with more broken topography may also provide a wider diversity of habitats for plant colonization. Gamma diversity appeared to increase in the two more rugged mountain ranges in our study, which is consistent with the positive relationship between environmental heterogeneity and species richness observed by others (Ricklefs 1987, Myers et al. 2000, Sarr et al. 2005). The higher c diversity in MC was potentially positively influenced by topographic complexity that resulted in more potential sites for plant colonization. Poulos et al. (2007b) found the same pattern in the Chiricahua National Monument, where species richness was highest in vegetation types that spanned a range of topographic settings. However, future research that investigates the contribution of topographic variability to tree diversity across the entire chain of mountains in the Sierra Madre Oriental range could provide a more general understanding of how topography shapes diversity in this region. Conclusion The results from this research highlight the close relationship between landscape structure and tree dominance and diversity patterns in DM, BB, and MC. The Sky Island evergreen woodlands of the Sierra Madres are thought to be biodiversity hot spots in an otherwise desert landscape. Until now, little data existed

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to quantify biodiversity in these ecosystems (but see Whittaker and Niering 1965, Poulos et al. 2007b). This study fills knowledge gaps about the species richness and distribution of biodiversity in a little-studied part of the Southwest, and it highlights the importance of studying plant diversity patterns across a range of spatial scales as a method for understanding the influence of environmental variability on vegetation pattern. ACKNOWLEDGMENTS We thank Richard Gatewood of the National Park Service, John Karges and Colin Shakelford of The Nature Conservancy, and Bonnie and Billy Pat McKinney of the Cemex Corporation for their logistical support of this project. We also thank Harold Slater, Mark Kurowski, Kyla Dahlin, Mila-Dunbar Irwin, Sarette Arsenault, Akasha Faist, and Darren Wallis for their assistance in the field. Funding for this research was graciously provided by the Joint Fire Sciences Program of the U.S. Department of the Interior (number 03-3-3-13), the Yale Center for International and Areas Studies, the Yale Institute for Biospheric Studies, and the Mellon Foundation. LITERATURE CITED Allen, R. B., R. K. Peet, and W. L. Baker. 1991. Gradient analysis of latitudinal variation in southern Rocky Mountain Forests. Journal of Biogeography 18:123–139. Austin, M. P. 1980. Searching for a model for use in vegetation analysis. Vegetatio 42:11–21. Austin, M. P. 1999. A silent clash of paradigms: some inconsistencies in community ecology. Oikos 86:170–178. Austin, M. P. 2002. Spatial pattern prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157:101–118. Barry, R. B. 1992. Mountain weather and climate. Methuen, London, UK. Barton, A. M. 1993. Factors controlling plant distributions: drought competition and fire in montane pines in Arizona. Ecological Monographs 63:367–397. Barton, A. M. 1994. Gradient analysis of relationships among fire, environment and vegetation in a southwestern USA mountain range. Bulletin of the Torrey Botanical Club 121: 251–265. Barton, A. M., and J. A. Teeri. 1993. The ecology of elevational positions in plants: drought resistance in five montane pine species in southeastern Arizona. American Journal of Botany 80:15–25. Beers, T. W., P. E. Dress, and L. C. Wensel. 1966. Aspect transformation in site productivity research. Journal of Forestry 64:691–692. Beven, K. J., and M. J. Kirkby. 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24:43–69. Breiman, L., J. H. Freidman, R. A. Olshen, and C. J. Stone. 1984. Classification and regression trees. Wadsworth, Belmont, California, USA. Burrough, P. A., and R. A. McDonnell. 1998. Principles of geographical information systems. Oxford University Press, London, UK. Carter, W. T. 1928. Soil survey (reconnaissance) of the TransPecos area, Texas. Bulletin of the University of Texas Soil Service 35:1–66. Chao, A. 1984. Non-parametric estimation of the number of classes in a population. Scandinavian Journal of Statistics 11: 265–270. Chao, A. 1987. Estimating the population size for capture– recapture data with unequal catchability. Biometrics 43:783– 791. Chao, A., R. L. Chazdon, R. K. Colwell, and T. J. Shen. 2005. A new statistical approach for assessing compositional

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APPENDIX A Indicator species for each vegetation type identified by cluster analysis of vegetation plots (Ecological Archives E091-081-A1).

APPENDIX B Dominant species and environmental characteristics of the nine vegetation types identified in the study (Ecological Archives E091-081-A2).

APPENDIX C Mean species per plot and tree diversity estimates for each vegetation type in the study region and percentage of the observed collected for each rarefied diversity estimator (Ecological Archives E091-081-A3).