Professional Paper 1650–G
On the continental scale, climate is the primary determinant for the overall geographic ranges of vegetation formations (Köppen, 1931; Holdridge, 1947; Trewartha, 1968; Prentice and others, 1992; Bailey, 1998) and of individual plant species (Woodward, 1987; Woodward and Williams, 1987). The accumulation of plant distributional data over the past several decades, coupled with advances in computing and data analysis, has been used in detailed examinations of the relations between present climatic variables and the modern distributions of plant genera (Huntley and others, 1989), species (Iverson and others, 1999; Thompson and others, 1999a; Huang and others, 2008), and pollen assemblages (Williams and others, 2006). Geologic studies reveal that the geographic distributions and abundances of plant species have changed greatly as climate has varied in the past (Huntley and Webb, 1988; Thompson, 1988; Wright and others, 1993; Jackson and Overpeck, 2000; Williams and others, 2004). The relations between present climatic variables and the modern distributions of plant species have provided the basis for the estimation of past climatic conditions from fossil plant remains (Iversen, 1944; Grichuk 1969, 1984; Thompson and others, 1999c; Kühl and others, 2002; Sharpe, 2002).
This volume, in which we explore the climatic variables that may likely control the modern distributions of selected plant taxa for North America, includes substantial revisions and enhancements from previous volumes in this atlas series. These changes include (1) restricting geographic coverage to Canada and the continental United States (collectively referred to as CANUSA), (2) replacing the source dataset for the climatic variables, (3) adding new bioclimatic and monthly temperature and precipitation variables and changing the calculation of the moisture index, (4) digitizing all distribution maps with a more advanced geographic information system (GIS), (5) revising the distribution maps for some species covered in previous volumes, and (6) adding species from arid and semiarid lands in the American Southwest.
This atlas presents information on the relations between present climate and the modern distributions of 757 plant species, groups of related species, and genera from across North America. Included among these are 86 conifer species, 32 conifer groups (such as genera and subgenera), 5 Ephedra species, 599 hardwood species, and 35 hardwood groups. Of the 690 species included here, 148 are new in this volume, 18 have geographic distributions that are remapped from earlier volumes, 3 subsumed other taxa, and 521 have distributions that were redigitized (but not remapped).
In this volume, we provide graphical displays (atlas pages) of the relations between present climatic variables and modern distributions of important trees and shrubs in North America. The graphical displays include (1) maps of geographic distributions of taxa; (2) univariate plots that indicate the presence or absence of the taxon under consideration in relation to single climatic (mean January, mean July, and mean annual temperature; mean January, mean July, and mean annual precipitation) and bioclimatic (growing-degree days, mean temperature of the coldest month, and moisture index) variables; (3) bivariate plots that illustrate the presence or absence of the taxon for July temperature (a proxy for growing-season temperature) versus annual precipitation, January versus July temperature, and January versus July precipitation; (4) complex displays that indicate the presence or absence of the taxon in relation to a combination of three bioclimatic variables (mean temperature of the coldest month [MTCO] in degrees Celsius [°C], mean growing-degree days [on a 5-°C base] [GDD5], and a moisture index); (5) scatterplots of the presence or absence of the taxon in relation to temperature and precipitation for the 12 months of the year; (6) box-and-whisker plots illustrating quartiles and 5 to 95 percent ranges for the taxon in relation to temperature and precipitation for each of the 12 months; and (7) histograms where the width of the bar indicates a specified range of a single climatic and bioclimatic variable, and the height of the bar indicates the percent of the total number of grid points for the taxon that occur within that range (absolute minimum temperature [TMIN], absolute maximum temperature [TMAX], MTCO, mean temperature of the warmest month [MTWA], mean annual temperature [ANNT], mean annual precipitation [ANNP], GDD5, and mean actual evaporation divided by potential evaporation [AE/PE]). In addition to atlas pages, data are presented in tabular form for each climatic and bioclimatic variable so that users can obtain quantitative information without having to estimate the data from the visual displays.
The data presented in previous volumes of this atlas have been used in a range of investigations of relations between present-day plant distributions and climate, reconstructions of past climates from paleobotanical data, and projected potential future changes in the distributions of plants. For example, present-day ecological and forestry studies using these atlas data include explorations of relations of plant species adaptations to climatic variables and other environmental factors on various scales from different areas in the United States in the Northeast (Hickler and others, 2004), Southeast (Fridley and others, 2007), West (Haugo and others, 2010; Littell and others, 2008), and Southwest (Willson and Jackson, 2006).
Atlas data have been used in the development of new methods for estimating climatic variables from botanical and paleobotanical data (Thompson and others, 2008, 2012a,b; Grimm and Denk, 2012) and have provided the basis for reconstructing climatic changes in many parts of North America during the late Quaternary from plant macrofossils (Thompson and others, 1999c; Sharpe, 2002; Hunter and others, 2006; Bartlein and others, 2010) and from fossil pollen assemblages (Willard and others, 2005; Oswald and others, 2007; Lyle and others, 2010). These data have also been used in reconstructing climatic conditions and changes further back in time, including the Miocene in Alaska (Reinink-Smith and Leopold, 2005); the Miocene through Quaternary in Iceland (Denk and others, 2011); the Pliocene through Quaternary in Europe (Svenning, 2003); and the Eocene and Oligocene in North America (Greenwood and others, 2005, 2010) and the North Atlantic (Eldrett and others, 2009). Data from previous volumes of the atlas have been used in the estimation of the potential future ranges of plant species under global-change scenarios in diverse settings, including northern Minnesota (Xu and others, 2007), Yosemite National Park (Lutz and others, 2010), the southwestern deserts (Nataro and others, 2012), and across the western United States (Thompson and others, 1998; Shafer and others, 2001). They have also been used in validation exercises that compare past plant distributions with those simulated based on numerical climate-model simulations of past climates (Bartlein and others, 1998).
The studies listed above provide examples of how these atlas data can be used in different analyses and on different time scales. The inclusion of new monthly and bioclimatic variables in the present volume should expand the range of studies that can profit from these data.
The first five volumes of this atlas (Thompson and others, 1999a,b, 2000, 2006, 2007; hereafter referred to as "previous volumes") employed a climatology for all of North America developed by P.J. Bartlein, of the University of Oregon, based on weather station and other data we compiled for the 30-year climate normal of 1951 to 1980 (Thompson and others, 1999a). Two journal articles and the sixth volume of this atlas (Thompson and others, 2008, 2012a,b) employed a more recently developed climatology for the period 1961 to 1990 based on the global gridded climatic dataset of New and others (2002) and restricted its geographic coverage to north of 25°N. As shown in the maps in figure 1, the present volume restricts the geographic coverage to CANUSA (Canada and the continental United States, including Alaska). The reasons for this northward geographic retraction are (1) our assessment that many of the available plant distribution data in Latin America (defined here as Mexico and Central America only) are not accurately located and (2) our decision to include important new species for which distribution data are only available for CANUSA.
Original Source Data and Interpolation onto the 25-Kilometer CANUSA Grid
We used 1961–1990 (30-year mean) monthly mean temperature in degrees Celsius (°C); monthly total precipitation in millimeters (mm); and monthly mean-percent possible sunshine from the Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002). These variables were interpolated from the CRU CL 2.0 global 10-minute grid to 27,984 points for CANUSA on our 25-kilometer (km) grid using a local regression method to estimate local lapse rates (500-km search radius) for each CRU CL 2.0 dataset variable. Major mountain ranges occur in several parts of CANUSA, especially in the western half of the continent (fig. 1), and climatic variables can change greatly over short distances along steep elevation gradients in these mountainous areas. The local relations between climatic variables and elevation were used to adjust the CRU CL 2.0 climate data to the elevations of the North America 25-km grid points. These adjusted values were interpolated to the 25-km grid points using geographic-distance-weighted bilinear interpolation. In addition, we estimated absolute minimum temperature (TMIN, °C) and absolute maximum temperature (TMAX, °C) from 1951–1980 climate-station data (WeatherDisc Associates, 1989). These data were interpolated to the CRU CL 2.0 10-minute grid and from the CRU CL 2.0 10-minute grid to the 25-km grid using the same lapse-rate-adjusted interpolation method described above.
The interpolated CRU CL 2.0 (1961–1990 [30-year mean]) climate data on the 25-km grid were used to derive three bioclimatic variables: (1) growing-degree days on a 5 °C base (GDD5; Newman, 1980), (2) mean temperature of the coldest month (MTCO, °C) and (3) a moisture index (α), calculated as actual evaporation (AE) divided by potential evaporation (PE) [Note: in our usage, the term evaporation includes evapotranspiration.] The version of α used here is based on the equations of Priestley and Taylor (1972), whereas previous volumes of the atlas used a moisture index based on Thornthwaite and Mather (1955, 1957).
GDD5 values were calculated from estimated daily temperature data created by linear interpolation between successive monthly mean temperature values. For every day of the year, the number of quasi-daily mean temperature degrees greater than 5 °C was summed to produce a mean annual GDD5 value. MTCO was assigned the mean temperature value of the month with the lowest mean temperature. AE was calculated for a generic vegetation type using a soil-moisture accounting method derived from the equilibrium vegetation model BIOME4 (Kaplan and others, 2003) modified to use an eight-layer soil dataset (Global Soil Data Task, 2000) with a more complex routing of water within the soil. PE was calculated according to the method described by Prentice and others (1993). PE represents evaporative demand and is equated with equilibrium evaporation (Prentice and others, 1993).
Correlations Among Climatic Variables
For the CANUSA dataset, the variables related to mean temperatures (ANNT, MTWA, MTCO, and GDD5) are highly correlated among themselves (r ≥0.84). The two variables related to temperature extremes (TMAX and TMIN) are correlated with the mean temperature variables listed above with correlation coefficients of 0.71 and higher but have lower correlation with each other (r = 0.55). Among moisture-related variables, ANNP is positively correlated with ANNT, MTCO, and TMIN (r = 0.49, 0.52, and 0.48, respectively). AE/PE is moderately positively correlated with ANNP (r = 0.42) but has low correlations with temperature variables (<0.15, either positive or negative). The high degree of intercorrelations among many of the climatic variables constrains our ability to attribute the limits of the geographic ranges of plant species to any single climatic variable. In addition, the key limiting factors may not be any single variable but rather the intersection of a certain range of temperatures with a certain range of moisture conditions.
Comparison of Previous and Current Climatologies
As discussed above, there are several major differences between the climatic data used in previous volumes of this atlas and the data employed here, including restricting the geographic domain to CANUSA, changing the base climatology to New and others (2002) and the base period to 1961 to 1990, and changing the equations employed in calculating the moisture index. The scatterplots in figure 2 are based on point-by-point comparisons of the climate values from the previous (1951 to 1980) and new (1961 to 1990) climatologies for the points on the 25-km grid from CANUSA. The histograms below the scatterplots illustrate the differences in the population distributions of grid points on the 25-km grid in relation to climatic variables that are available for both datasets. For MTCO, GDD5, and ANNP, there are strong overall similarities between the old and new climatologies, as indicated by correlation coefficients of 0.96 or greater (fig. 2). MTCO has median and average anomalies of ≤1 °C and the differences are largely unbiased (that is, the proportions of positive and negative anomalies are roughly equal). For GDD5, the anomalies are also small, but there is a bias toward lower GDD5 values in the new climatology for CANUSA (relative to the previous climatology, as seen in the scatterplot). In addition, comparison of the population-distribution histograms shows the many points with high GDD5 in Latin America that were removed when we restricted our domain to CANUSA. ANNP (log ANNP on fig. 2) also has small anomalies and a slight bias toward wetter conditions in the new climatology.
The changes in domain and in the conceptual and computational bases for AE/PE resulted in larger differences here than in the three variables discussed above (fig. 2). The correlation coefficient between the two climatologies is significantly lower (r = 0.72), the anomalies are relatively large, and (for AE/PE) the new climatology has a pronounced negative bias relative to the previous climatology.
Climatic Variables—Geography, Time Scales, and Population Distributions
We present information on the relations between the distributions of individual plant species with climatic variables that may be relevant on different time scales and with different effects among the species presented here. Although the distributions of many species may be influenced by long-term mean conditions of temperature and precipitation, other aspects of climate may be important in determining part or all of the range limits for some species. These aspects include the degree of interannual variability for a given variable, the amplitude of seasonal differences in temperature or precipitation within the year, and rare extreme temperatures that occur over the course of decades.
The accompanying figures provide maps of the distributions of climatic variables across the CANUSA domain, along with histograms that show the population distributions of these variables on the 25-km grid. The mean values for 1961 to 1990 for annual temperature and annual precipitation are mapped in figure 1, accompanied by maps of interannual variability for this time period (standard deviation for ANNT and the coefficient of variation for ANNP). The mean values are also presented for GDD5, AE/PE, the number of months per year above freezing, and the annual percent of possible sunshine. Mean values and interannual variability are shown for monthly temperature in figures 3 and 4, and for monthly precipitation in figures 5 and 6. Precipitation is also presented as the percent of the annual total that falls in a given month (fig. 7). The mean temperatures of the coldest and warmest months (MTCO, MTWA) and the mean precipitation of the driest and wettest months (MPDR, MPWE) are shown in figure 8 along with the extreme temperatures observed over a 30-year period (TMIN, TMAX).
The mean intra-annual range of temperature is depicted on figure 8 as the difference between the mean temperatures of the warmest and coldest months (MTWA minus MTCO), accompanied by a measure of the differences between the coldest and warmest temperatures experienced at a given grid point over the period of record (TMAX minus TMIN). Seasonality of precipitation is expressed in figure 8 as the ratio between the precipitation of the driest and wettest months (MPDR divided by MPWE). The combined effects of seasonality of temperature and of precipitation are shown in figure 9 where the four maps represent the quartiles for intra-annual temperature range, with the quartiles of the seasonality of precipitation shown as shaded colors on each map.
The plant distribution data used in the present volume differ in several aspects from the data used in previous volumes. With the change to the CANUSA domain for analysis, a number of species with distributions entirely (or almost entirely) in Latin America are not included here. For those species from previous volumes that are continued in this volume, there are changes in their coverages on the 25-km grid due to (1) the use of digitizing technology with improved accuracy, (2) the shift to the CANUSA domain (which eliminated grid points in Latin America), and (or) (3) updated taxonomy. We include new species that were not in previous atlas volumes, based on distribution maps that we compiled from published sources and online databases, and we also remapped the distributions of a small proportion of the species that were present in previous volumes.
There are regions of CANUSA with relatively sparse data and complex physiographic settings, such as much of the western Interior United States and Alaska. Consequently, distribution maps of plant species for these areas may be less accurate than those from regions such as eastern North America, where there are more available data and the landscape is more homogeneous. The resolution of the 25-km grid is coarse enough to limit the number of grid points available to depict the differences among locations in areas of high relief, and this may influence our understanding of the relations between climatic variables and the distributions of plant species in mountainous regions.
In our analyses we make two important assumptions. One is that on the geographic scale of our work, the range boundaries of the species being examined were stationary over the last half of the 20th century. We also assume that it is appropriate to compare the species' geographic limits with the climate "normal" for 1961 to 1990. Although we may gain insights by examining this normal, we recognize that it may not adequately reflect the breadth of climatic conditions experienced by woody species over the decades to centuries of their lives.
The analyses and data visualizations in this volume are based on the geographic distributions of plant species (or in a few cases, subspecies; table 1) and of groups of related species that are commensurate with the levels of taxonomic resolution available from the fossil record (table 2). In general, the finer the taxonomic resolution, the more likely it is that there is a circumscribed range of climatic conditions that can be associated with the range limits of the taxon in question. However (and especially when applied to paleoecologic and paleoclimatic studies), there is increased risk that finer taxonomic levels do not accurately reflect the true taxonomic placement of the taxon. The data from which distribution maps were compiled were collected or synthesized at various times over many decades, during which there have been changes in the taxonomy of many of the species covered in this volume. There are many complications caused by disagreements among taxonomic authorities, and by the fission or fusion of preexisting species to form newly defined taxa. We selected species' names that reflect the intent (and taxonomic usage) of original source maps or data, but we also indicate the currently accepted nomenclature and any caveats regarding taxonomic issues (table 1).
Our research interests involve using information from the relations between the modern distributions of plant taxa and climatic variables to infer aspects of past climatic conditions from paleobotanical assemblages. The publications describing these assemblages were published over many decades, and it is frequently the case that taxonomy at the species and genus levels has changed over this period. Some examples include the following: what once was Opuntia acanthocarpa has been split into Cylindropuntia acanthocarpa and Cylindropuntia ganderi; Quercus turbinella has been split into Q. turbinella, Q. cornelius-mulleri, and Q. john-tuckeri; Yucca whipplei was placed in a new genus (Hesperoyucca) and split into two species—H. whipplei and H. newberryi—and the reassignment of several species formerly in the genera Chrysothamnus and Haplopappus to either different existing genera or to new genera (table 1). For paleobotanical studies, there is the additional concern that the associations between living species and fossil specimens becomes more tenuous as one goes further back in time. To address both of these concerns, we created groups of related species at the subgenus, genus, subfamily, or family level (table 2) that can be employed in situations where changing taxonomy or the distance in time between modern and fossil taxa make species-level identifications untenable.
We used the Integrated Taxonomic Information System (ITIS, 2013) as the primary authority on taxonomic issues, supplemented by information from Tropicos.org (2013), eFloras.org (2013) and (for conifers) Farjon (2001). In a few cases, additional information was obtained from the resources listed in tables 3 and 4. The taxonomy of some taxa in this volume is subject to ongoing changes, and it can be expected that some of the names employed here will be revised in the future.
Table 1 lists the species included in this volume and table 2 lists the species that compose the groups. In table 1 we provide the name used in the dominant source of information for a given mapped distribution (original source name), as well as the currently accepted name. We made a few exceptions to avoid confusing inconsistencies among closely related taxa. For example, a previous volume of the atlas (Thompson and others, 1999b) included a cylindrically stemmed cactus commonly called "jumping cholla," for which we used the name "Opuntia fulgida" (following the nomenclature of the source of the distribution map; Little, 1976). Cholla cacti subsequently have been moved from the genus Opuntia to the genus Cylindropuntia (eFloras.org, 2013; Pinkava, 1999). For the present volume, we have added six new species of Cylindropuntia, and to avoid having jumping cholla listed in a different (and now incorrect) genus than the rest of the chollas, we now refer to it as Cylindropuntia fulgida (table 1).
Unlike previous volumes of this atlas series, species (table 1) and groups (table 2) are listed alphabetically by the original source names without separation into conifers, hardwoods, or other higher level taxonomic groupings. These tables are provided as digital spreadsheets so that users can, if desired, sort the lists by currently accepted names or by plant families.
Species Excluded from This Volume
For a variety of reasons, many species presented in previous volumes of this atlas were excluded from the current volume. Twenty-eight of these species are Latin American taxa that do not occur or occur sparsely in CANUSA (Bursera fagaroides, Celtis lindhiemeri, Esenbeckia berlandieri, Fraxinus papilosa, Fremontodendron mexicanum, Lysiloma microphylla, Ostrya chisosensis, Picea chihuahuana, Taxodium mucronatum, Yucca rostrata, and 18 species of Pinus). We also excluded species for which the available range maps do not depict the complete geographic distributions within CANUSA, and this affected plants primarily from the U.S. Forest Service atlases for Alaska (for example, Alnus crispa; Viereck and Little, 1975) and Florida (Little, 1978). In addition, Catalpa bignonioides, C. speciosa, Crataegus chrysocarpa, C. succulenta, and Prunus angustifolia were omitted because the sources of the original distribution maps (Little, 1971, 1976) indicated that the native ranges were uncertain. Improved digitizing of the original distribution maps and other factors reduced the number of grid points for some minor species (Abies bracteata, Betula uber, Cupressus bakeri, C. goveniana, C. guadalupenis, C. macrocarpa, Franklinia alatamaha, Juglans hindsii, Lyonothamnus floribundus, Pinus radiata, P. torreyana, P. washoensis, Populus hinckleyana, Prunus lyonii, Quercus ajoensis, Q. graciliformis, Q. macdonaldii, Q. tomentella, Rhus kearneyi, Taxus floridana, Torreya taxifolia) to zero or one, so we removed them from this volume. Finally, following taxonomic revisions, a few species that were covered in previous volumes of this atlas are now considered to be parts of related species, and in the case of such taxa that we remapped for this volume, we followed the newer taxonomy. The taxa involved are Populus arizonica, which is now part of P. fremontii, Vauquelinia pauciflora, now part of V. californica, the distribution of what was called Yucca carnerosana in the United States is now part of Y. faxoniana, and Yucca torreyi is now part of Y. treculeana).
Species Included in This Volume
In this volume of the atlas, we present data for a total of 690 species (or subspecies; table 1), which are divided into three categories, based on whether they are redigitized from previous volumes (521 species), remapped from previous volumes (18 species), species that subsumed other species (3), or are new species presented for the first time in this volume (148 species). Some species in all three categories have significant portions of their ranges in Latin America, and thus climatic information from the excluded regions is not included in this volume. The remapped and new species were selected to fill-in underrepresented geographic and climatic portions of CANUSA in the arid lands of the southwestern United States. As discussed in the preceding section, this is a region of particular interest because it includes the hottest and driest parts of CANUSA and has strong geographic and elevational climatic gradients, pronounced differences in seasonal temperature and precipitation regimes, and a high degree of interannual variability (especially for precipitation). To survive under these conditions, plant species have developed physical adaptations, such as the ability to store substantial quantities of water or the development of photosynthetic stems in the absence of functional leaves. This region is also the focus of our paleoclimatic reconstructions from botanical remains preserved in ancient packrat (Neotoma spp.) middens, and we selected species for inclusion in this atlas volume based in part on their frequency of occurrence in the U.S. Geological Survey (USGS)/National Oceanic and Atmospheric Administration (NOAA) (2006) North American packrat midden database. However, we did not include other species that are common in the fossil record but are also very widely distributed today and are thus not diagnostic of a reasonably narrow climatic range (examples include Artemisia ludoviciana, Krascheninnikovia lanata, Opuntia polyacantha, and Rhus aromatica).
Nearly all of the distribution maps for the redigitized plant species were published in a series of atlases by the Forest Service of the U.S. Department of Agriculture (Critchfield and Little, 1966; Little, 1971, 1976, 1977, 1978, 1981; Viereck and Little, 1975; hereafter referred to as the USFS atlases). The maps for a few species were originally from (or modified from) other sources (Hultén, Bailey, 1970; 1968; Yang, 1970). It should be recognized that these source maps were published when mapping technology was less developed than it is today and were constructed at very coarse spatial scales (1:10,000,000 and 1:27,000,000 for the USFS atlases). Consequently, users should be cautious when applying any results of analyses based on these maps to questions that require information at a finer spatial scale.
Species with Redigitized Distribution Maps
For two volumes of our atlas (Thompson and others, 1999a,b), we digitized the distribution maps of the plant species using software written in the 1980s and 1990s. GIS software has improved in many aspects since those volumes were published, and all distribution maps for the current volume were redigitized using ArcInfo, which produces more accurate and precise results than the earlier generations of GIS software. Although the effects are usually minor, the change in digitizing software did change the number and coverage of points on the 25-km grid for many species (see discussion below). ArcInfo coverages of distribution maps for species presented in the USFS atlases are available on the internet at http://gec.cr.usgs.gov/data/little/.
Remapped Distributions of Species
There is much information now available on the distributions of plant species in CANUSA that was not available when USFS atlases and other source maps were compiled. Whereas this may not be of great importance for data-rich areas of low relief (such as the eastern United States), it may lead to notable changes in our understanding of species' distributions in other regions. Following our particular research interests, we decided that it was necessary to remap selected species in the arid and semiarid western United States that had been covered in previous atlas volumes. These are Agave utahensis from Benson and Darrow (1981) and the following species from the USFS atlases: Artemisia tridentata, Bursera microphylla, Carnegiea gigantea, Cercidium floridum, Cercidium microphyllum, Fraxinus anomala, Juniperus californica, Nolina bigelovii, Pinus cembroides, Pinus longaeva (included in Little, 1971 as part of Pinus aristata), Pinus quadrifolia, Quercus gambelii, Quercus pungens, Quercus turbinella (now segregated into Q. turbinella, Q. cornelius-mulleri, and Q. john-tuckeri following Nixon and Steele, 1981), Sequoiadendron giganteum, Vauquelinia californica, and Yucca brevifolia. For the remapped taxa, we used the same data sources and methods employed for new species (see discussion below).
Effects of Revising Distribution Maps
To assess the consequences of the changes in climatology, mapping, and digitizing, we compared the population statistics for 490 species that were present in previous volumes as well as this volume (table 5). Large portions of the geographic ranges were lost for 112 species through the exclusion of Latin America (labeled LATIN in table 5). The distributions of the 378 remaining species are almost exclusively within CANUSA, and for 315 of these, the number of grid points remained within 5 percent of the number of grid points from previous volumes (SAME in table 5), 23 species lost more than 5 percent of their original number of grid points (LOST in table 5), and 40 species gained more than 5 percent (GAINED in table 5). With the exception of the LATIN species, the temperature variables JANT (January temperature), JULT (July temperature), and ANNT (annual temperature) remained within ± 0.5 °C of the values presented in previous volumes of the atlas, and GDD5 remained within 85 growing-degree days (table 5). Similarly, for all categories except LATIN, the differences between the old and new datasets are small for JANP (January precipitation), JULP (July precipitation), and ANNP (annual precipitation). The comparison for AE/PE is of questionable value, because we changed the basis for calculating this moisture index. Nevertheless, all differences for AE/PE between the old and new dataset are negative, especially at the higher percentiles of GAINED, LOST, and LATIN.
The species in the LATIN category show the largest differences between the old and new datasets (table 5), with JANT having cooler temperatures across the population spectrum and JULT being warmer at the lower end of the population spectrum. For ANNT, the differences are largest at the highest percentiles, with the largest anomaly (–2 °C) at the 100th percentile. GDD5 is lower across the population spectrum in the new data (table 5), with the largest reductions at the higher percentiles (reaching –618 growing-degree days at the 100th percentile). Despite the large anomalies in the LATIN category, overall the results for the new and old datasets are very similar for all of the temperature and precipitation variables (ALL in table 5). The largest effect here is the overall lower JANT values, but even here the greatest anomaly is only –0.7 °C.
For the purpose of this atlas, it was not necessary to produce highly detailed maps of the geographic distributions of new species. Instead, our aim was simply to determine which points on the 25-km grid are probable locations for a given species to be present. In keeping with our objective of identifying the macroclimatic settings for each species, we took a fairly lenient approach when deciding whether an individual grid point should be coded as present for a given species. For example, we allowed an elevational buffer of at least 100 m above or below the described range of a species under consideration, such that a grid point that fell slightly outside the described range would still be coded as a "presence" for the species.
There are two general categories of approaches for the placement of new species on the 25-km grid. The first includes species within Artemisia (from Beetle, 1960) and Chrysothamnus (as the genus was published by Anderson, 1986) where we used previously published distribution maps without major modifications, except for A. tridentata. For the second category, we placed the species on the 25-km grid based on data compiled from previously published maps and range descriptions in combination with maps of the point locations where specimens were collected or observed. Over the past decade, there has been a rapid expansion of the breadth and amount of data available on the internet for this task. However, this is a mixed blessing because the quality of the data available varied considerably, and careful assessments were required, especially with regard to taxonomic assignments and the accuracy and precision of the specimen's locational information. In addition, previously published maps and data for a given species may differ in many details, and it is sometimes difficult to determine which source (if any) is correct.
It must be recognized that many of the data related to the distributions of new and remapped species were collected prior to the availability of satellite-based global positioning systems, and particularly in the arid and semiarid western United States, the available maps were frequently of very low resolution. As a consequence, we sometimes found specimens whose locations were highly suspect, with the given coordinates sometimes falling within a different State than the purported location. Another potential source of error is the inclusion in the online databases of specimens from gardens, parks, or other nonnative settings. One example of the latter is the occurrence of the southwestern United States species Acacia constricta on ballast piles of ships in Maryland and Virginia (Reed, 1964).
Table 3 lists the primary online resources that were consulted in the compilation of information for mapping new plant species. Online distribution maps include CANUSA-scale sketch maps for the flora of North America (eFloras.org, 2013) and county-level maps (for example, Kartesz [2013] and the PLANTS database [USDA, NRCS, 2013]). The county-level maps are more informative in the eastern United States (where the counties generally are small and individual counties cover relatively homogeneous regions) than in the arid and semiarid western United States (where frequently the counties are large and individual counties may encompass a broad range of environments over steep elevational gradients). There are also online resources that provide the map coordinates (latitude, longitude, and sometimes elevation) for sites where a plant specimen was collected or the presence of a species recorded by a reputable observer. These internet sites include continental- and regional-scale databases (for example, Southwest Environmental Information Network [SEINet, 2013], and Global Biodiversity Information Facility [GBIF, 2013]), State-level databases (especially for California, Oregon, Utah, and Wyoming), and the collections of various herbaria and Federal and State agencies. Regrettably, there are fewer specimen data available for some regions (such as Idaho, Montana, Nevada, and Texas, as well as Federal lands set aside for military or national security activities) and environments (such as high elevations and remote roadless lands).
Printed publications also provide valuable information on the distributions of plant species in the arid and semiarid western United States (table 4). The maps included in "Trees and Shrubs of the Southwestern Deserts" (Benson and Darrow, 1981) provide an important starting point for the creation of new maps for desert species. Mapped specimen data are available for the Sonoran Desert (Turner and others, 1995) and for certain taxonomic categories, including Agave (Gentry, 1982), Cactaceae (Benson, 1982), and Prosopis (Burkart, 1976). Journal articles provided maps or specimen data for a few of the individual taxa under consideration, such as Ambrosia dumosa (Raven and others, 1968), Amsonia (McLaughlin, 1982), Atriplex confertifolia (Sanderson and MacArthur, 2004), and several problematic or revised taxa in the genus Quercus (Nixon, 2002; Nixon and Muller, 1994; Nixon and Steele, 1981; Pavlik and others, 1991; Tucker, 1961).
Floras, theses, and field guides provided key information for regions with few other available data. For example, there are written descriptions of the mountain ranges where individual species occur in Nevada (Kartesz, 1988), New Mexico (Carter, 1997), and Texas (Powell, 1998). Maps of potential natural vegetation (Küchler 1964, 1975, 1985, 1993) and publications by Federal and State agencies, such as the USDA Forest Service Fire Effects Information System (FEIS, 2013), provide other supporting information on species, such as the ecosystems within which they occur.
To place a new species on the 25-km grid, we began by compiling previously published maps, specimen locations, descriptions of range limits, and other available information. The quality of each contributing dataset was assessed, especially with regard to geographic location, elevation, and taxonomy. Most of the new species that we investigated are distributed across regions of high relief, and identification of the elevation range of a species is an important factor in deciding whether individual grid points should be coded as present or absent for the species. Regrettably, as described above, some of the pre-global positioning system (GPS) locations are suspect and may not be reliable indicators of the elevations where a given plant species lives. In cases where we suspected that this problem occurred, we replaced the listed elevation with the elevation for that location indicated by the SRTM30 (Shuttle Radar Topography Mission 30 arc-second) dataset (Farr and others, 2007).
The elevational ranges of widely distributed species vary with latitude and distance from the oceans, and, if possible, this information was taken into account when selecting draft grid points. Following the removal of apparently unreliable information, the remaining data were displayed in GIS software on a computer screen. For widely distributed species, we drew polygons around the areas where the species is recorded as being present and used GIS software to identify which grid points fell within these polygons. In cases of species with very limited distributions, instead of drawing polygons, we selected grid points by comparing the available data with the locational and elevational information associated with candidate grid points. After the draft grid points were selected, we filtered them by comparing their elevations and ecosystem settings with information available for the species in the online and printed resources. It must be noted that this is an imprecise art, and the filters were applied loosely, which seemed appropriate given the uncertainties associated with the data. The filtering procedure was applied generally on a State-by-State basis, with some judgment required to match data from adjacent States.
We assessed the results of our mapping efforts by making bivariate plots of various combinations of climatic variables associated with the filtered grid points. This process allowed us to identify climatic outliers, which we then examined for possible errors in terms of data processing or analysis, and in terms of the quality of associated location, elevation, and other supporting data. Outliers were only removed if we could identify a reason in terms of data quality, processing, or analysis.
Characteristics of the Plant Distribution Data
Species Richness
There are pronounced patterns in the number of woody species per grid point (species richness), both when mapped and when plotted against climatic variables (fig. 10), providing some insights into the macroclimatic factors that are influencing the distributional limits of many species in similar ways. The dataset does have inherent biases, both because the USFS atlases focus largely on commercially important and widely distributed trees and because of the addition of new species tied to our research interests on arid and semiarid lands in the western United States. Despite these biases, there are some strong patterns that we believe reflect the importance of the underlying macroclimatic trends. On the maps there are strong declines in species richness northward in subarctic Canada, corresponding to sharp declines in temperature and GDD5 along the same gradient (fig. 1). Similarly, in eastern CANUSA there is a pronounced decline in species from east to west, corresponding with decreases in precipitation along the same gradient (fig. 1). The correlation coefficients are greater than 0.6 for species richness versus each of the individual variables MTCO, MTWA, GDD5, and ANNP.
On the bivariate plots for species richness in conifers (fig. 10), the highest levels appear to occur at lower temperatures and lower levels of GDD5 than for nonconifers, but the correlation coefficients for conifer species richness are near zero for temperature variables and GDD5. Conifer species richness is more highly correlated with the moisture variables ANNP (correlation coefficient [r] = 0.48), log ANNP (r = 0.54), and AE/PE (r = 0.42). Nonconifer species richness is affected by ANNP (r = 0.57) and more strongly related to GDD5 (r = 0.70), MTWA (r = 0.68), and MTCO (r = 0.65). The overall patterns match those for nonconifers closely, which is not surprising given that there are many more nonconifers than conifers in the dataset. For all species combined, the correlation coefficients are higher than 0.6 between species richness and the individual variables MTCO, MTWA, GDD5, and ANNP. AE/PE has the weakest correlation with overall species richness (r = 0.35). Figure 11 is similar to figure 10, except that the species are categorized by whether they are redigitized, remapped, or new species. Here, it can be seen that the new and remapped species are from regions with generally higher temperature and GDD5 values and lower ANNP and AE/PE values. The highest overall species richness occurs in the regions with the lowest two quartiles of seasonality of temperature and of precipitation (fig. 9). Regions that are beyond the influence of maritime climates, such as the far north, have more pronounced seasonality and fewer species per grid point.
Membership within World Wildlife Fund Ecoregions
The species included in this volume cover a wide range of ecosystems in CANUSA, and our atlas pages and data tables do not provide any information on the ecosystems within which a given species occurs. To address this, we have characterized each species by the number of grid points for which it is present within ecoregions and major habitat types (MHTs, table 6). For this objective we employ the well-described ecoregion system constructed by the World Wildlife Fund (WWF, Ricketts and others, 1999). We follow this system with the single modification of dividing the Temperate Conifer MHT into distinct eastern and western sections.
Displays of Geographic Distributions and Climatic Relations
To facilitate comparisons between the old and new versions of the atlas, we generally follow the language, structure, and examples used in the first volume of the atlas (Thompson and others 1999a). As in the previous volumes, for each taxon there is an atlas page with a map of the occurrences of the taxon on the 25-km grid, coupled with univariate, bivariate, and multivariate displays of presence-absence data of the taxon relative to climatic variables. The layout of the atlas page has been redesigned from previous volumes, with changes including (1) the map now only displays information for CANUSA, (2) the conceptual basis for the moisture index has changed, (3) additional climatic variables were added (TMIN, TMAX, MTWA, and temperature and precipitation data for each of the 12 months of the year), (4) some histograms that were previously displayed in separate figures are now incorporated into the atlas page, with new variables added, and (5) for monthly data there are new bivariate scatterplots and box-and-whisker plots.
The atlas page for a sample species (Pinus edulis, pinyon pine) is shown in figure 12, and in this portrayal of the relations among the modern geographic distribution and climatic variables, each panel (or group of panels) is labeled with a letter that is used to demarcate a portion of the atlas page for the following discussion. The right side of the atlas page is in the same format as in previous volumes of the atlas, and for consistency with those volumes, we maintained the same letter labels for panels a through d. Following the flow of those labels, we continued the letters in a clockwise fashion after d. A consequence of this scheme is that the discussion below does not necessarily proceed in alphabetical order.
Below we provide examples that compare the information provided on the atlas pages among P. edulis and three other pine species from different settings and climatic conditions across CANUSA (figs. 13 through 20), followed by data explorations involving these four and an additional five species of Pinus (fig. 21). To facilitate comparisons with previous results, we compared the same species examined in the introduction for the first volume of this atlas (Thompson and others, 1999a), except that we substituted P. albicaulis for P. engelmannii because the distribution of the latter species lies primarily outside of CANUSA.
Metadata and Distribution Maps
In the upper left corner of each atlas page, the first line in panel g (figs. 12 and 13) provides the name of the taxon as used in this volume, which is based on the name used by the primary source of distribution information listed in table 1. The currently accepted name, which may or may not be the same as the name above, is listed in the second line. Subsequent lines provide primary common names for the taxon, the number of points where the taxon is present on the 25-km grid, and the primary sources consulted in identifying which grid points should be characterized as having the taxon present (see tables 3 and 4 for additional information on sources). There are large differences between the sizes of the geographic distributions of the four sample pine species, which provide a rough guide to the relative breadth of climatic conditions that an individual species can inhabit.
Panel a on the upper left center of each atlas page shows the modern distribution of the taxon on the 25-km grid. Figure 14 illustrates the distributions (taken from the individual atlas pages for these taxa) of four species of pines that inhabit different areas of North America with different climates. Each species is portrayed by depicting the grid points where the taxon is present as red plus signs and omitting those points where the taxon is absent. In the Pacific Northwest, Pinus albicaulis (whitebark pine) has a discontinuous distribution across mountain ranges that are isolated from each other by valleys with steppe or desert vegetation. P. edulis has an even more discontinuous distribution across the hot and dry mountains and plateaus of the American Southwest. In eastern CANUSA, P. clausa (sand pine) is limited to a small region with subtropical climate in Florida, whereas P. banksiana (jack pine) has a very large, continuous distribution in subarctic taiga and northern mixed forest across much of Canada and the northeastern United States.
Relations Between Single Climatic Variables and Species' Distributions
Panel b (figs. 12 and 15) on each atlas page shows univariate plots of the presence and absence of each taxon relative to single climatic variables. Each box contains two lines of points: the upper line (red) illustrates climate values at which the taxon is present, whereas the lower line (black) indicates those where it is absent. Together, these two lines represent all grid points from CANUSA. In most cases, a given plant is absent from many sites where the recording of presences indicate that it should be able to survive under the environmental conditions represented by a single variable. These absences may be the result of a number of factors, including that other climatic and (or) environmental variables, not portrayed in any one univariate view, control the plant's distribution. For example, for the four pine species (fig. 15), the upper three boxes in each panel represent mean annual, January, and July temperature (in °C). The three central boxes represent mean annual, January, and July precipitation (in mm) plotted on a logarithmic scale, which stretches out the distance between points on the lower end of the scale. The lower three boxes represent bioclimatic variables (discussed above) that should be more closely tied to the physiological limits of the plants than are temperature and precipitation (Prentice and others, 1992). The comparison of the four pine species shown in figure 15 illustrates their different adaptations in regard to temperature. Pinus edulis can survive freezing temperatures but does not survive a mean temperature of the coldest month (MTCO) below -10 °C. In the case of P. albicaulis, the points where it occurs on the 25-km grid have values for MTCO that are almost always below 0 °C, and the lower limit of MTCO for this species is several degrees colder than for P. edulis but much warmer than for P. banksiana. Pinus clausa is limited to environments where mean January temperatures are above 10 °C, and mean July temperatures are in a limited range at the very warm part of the scale.
The histograms presented in panel f (figs. 12 and 16) provide visual displays of the relations between distributions of taxa and individual climatic variables. The height of each bar represents the percentage of the total number of occurrences of a taxon relative to the total number of grid cells at that specified range of the given climatic variable. For example, in the upper five variables in panel f for each of the four species in figure 16 (maximum temperature [TMAX], mean temperature of the warmest month [MTWA], minimum temperature [TMIN], mean temperature of the coldest month [MTCO], mean annual temperature [ANNT]), the width of each bar represents a specified range (1 °C) of temperature. The same principle applies to the lower three variables in this panel (growing degree days on a 5-°C base [GDD5], log of mean annual precipitation [ANNP], and mean actual evaporation divided by potential evaporation [AE/PE]), albeit with different units and scaling for each of these variables. Among the taxa in this volume, there is a large range in the number of grid points where the species is present (table 1). Taxa with only a few points may cover a relatively narrow range of a given climatic variable and consequently will have only a small number of very tall histogram bars (for example, MTWA for Pinus clausa [n = 87] in fig. 16). In contrast, taxa with broader distributions and more grid points may have many more (and shorter) histogram bars (for example, MTCO for Pinus banksiana [n = 4,670] in fig. 16). If the vertical scaling is set to the full height of the histogram bars for the taxa with few points, it becomes difficult to discern any detail in the histograms for the more widespread taxa. Consequently, we set the maximum height of histogram bars at ≥50 percent. For example, in the upper five variables in panel f for each of the four species in figure 16 (TMAX, MTWA, TMIN, MTCO, ANNT), the width of each bar represents a specified range (1 °C) of temperature. The same principle applies to the lower three variables in this panel (GDD5, log of ANNP, and AE/PE), albeit with different units and scaling for each of these variables. Each of the four species of pines in figure 16 has a unique combination of temperature variables, with P. clausa having the warmest MTCO, MTWA, and TMIN (but not TMAX). P. banksiana has the lowest MTCO and shares the general lowest TMIN range with P. albicaulis. However, P. banksiana survives at higher levels of MTWA and GDD5 than P. albicaulis; and the larger difference between MTCO and MTWA for P. banksiana reflects the contrast between the northern continental climate of this taiga species with the maritime-influenced temperate conifer forest of P. albicaulis (figs. 8 and 9). The southwestern U.S. species, P. edulis, has warm summers and mild winters and lives under drier conditions than the other three species considered here.
Bivariate Relations Between Climatic Variables and Species' Distributions
Panel c (figs. 12 and 17) uses bivariate plots to illustrate potential interactions among climatic variables in controlling plant species' ranges. Here, gray dots represent where the taxon is absent and red dots where it is present. Red and gray together represent the total climate space of CANUSA as depicted for the particular combination of climatic variables presented. For example, the left box in panel c illustrates the presence (or absence) of the species in relation to mean July temperature (JULT, a proxy for growing-season temperature) and mean annual precipitation. Comparisons among the four pine species in figure 17 show that, in this "climate space," Pinus clausa (which has the fewest number of grid points of the four sample species) inhabits a small area where mean July temperatures are very warm and mean annual rainfall is very high. This constitutes one of the hottest and wettest environments in North America (it may also be that P. clausa is further limited to sandy substrates, as its common name of sand pine implies). The climates associated with the other three species differ in several ways. Among the remaining three species, P. albicaulis is associated with the highest ANNP and coolest JULT; P. banksiana lives under lower ANNP and warmer JULT than P. albicaulis; and P. edulis has the lowest ANNP and warmest JULT.
The center and right scatterplots of panel c (fig. 17) provide some insights into differences in some seasonal aspects of climate among these four species. With regard to temperature, Pinus banksiana can survive under some of the coldest winter conditions in CANUSA but also lives in regions with relatively warm summers. P. albicaulis and P. edulis live in regions with similar winter temperatures that are higher than those for P. banksiana but differ from each other in that P. edulis lives under much warmer summers. P. clausa inhabits regions with still warmer summers and lives under the warmest January temperature (JANT) conditions in CANUSA. January precipitation (JANP) is higher than July precipitation (JULP) for P. albicaulis, whereas the opposite is true for P. clausa. As discussed with regard to other figures and tables below, JULP is greater than JANP for P. edulis, but this pattern is difficult to detect in the style of scatterplot shown in figure 17.
Multivariate Relations Between Climatic Variables and the Distributions of Species
Panel d (figs. 12 and 18) incorporates three bioclimatic variables into a single figure (see Huntley and others, 1995, for a similar presentation of correspondences between climatic variables and tree distributions in Europe). Each of the four boxes within this panel represents a quartile of the moisture conditions in North America, from the driest quarter of the grid cells on the left to the wettest on the right. With the changes in the geographic coverage and calculation of the moisture index described above, the quartile boundaries have changed from the previous volumes, with the most significant difference in the 3rd quartile (which was 0.95 and now is 0.85). With the previous moisture index, a large number of grid points had index values of 1 or close to 1 (fig. 2, AE = PE). This is no longer the case with the new moisture index, which provides a more even spread of points across the upper two quartiles. An example of the effects of this change can be seen in the maps in figure 2 where grid points characterized as being in the fourth quartile of the previous moisture index fall largely in one block of dark blue in temperate and subarctic eastern North America. The fourth quartile with the new index shows a greater diversity of moisture conditions across this area and shows grid points in the fourth quartile extending across Canada and into Alaska.
Within each quartile box, the presence (red) or absence (gray) of each species is plotted relative to GDD5 and MTCO. In the example shown in figure 18, P. albicaulis is present under a wide range of moisture conditions, with grid points in all four AE/PE quartiles (and the fewest in the wettest quartile). It survives under low levels of growing-degree days (GDD5 is generally less than 1,500), but does not live in areas with extremely cold winters (MTCO is -15 °C or warmer). P. banksiana also spans all four AE/PE quartiles but has the fewest of its total points in the driest quartile. This species can live under some of the coldest winter conditions in CANUSA (MTCO is always below freezing and can be as cold as -30 °C), and although it lives under low GDD5 values, these can rise above the values for P. albicaulis. P. edulis is almost entirely restricted to the driest quartile, has warmer winters and higher levels of growing-degree days than for the previous two species (MTCO warmer than -10 °C, GDD5 values as high as 4,000). The remaining species, P. clausa, is not present at grid points in the lowest quartile of the moisture index and lives under the warmest MTCO conditions and highest levels of GDD5 in CANUSA.
Monthly Temperature and Precipitation
For each species, panel e presents 12 scatterplots (one for each month of the year) of temperature versus precipitation for each of the grid points (figs. 12 and 19). As with the previous figures, the presence of the species in the bivariate plots is represented by red dots and absences by gray dots. Taking the presences and absences together, the reader can see the major differences in climatology for CANUSA during the yearly cycle. Temperature has a wider range of expression for the winter months than for the summer because lower latitudes cool less in the winter than high latitudes, producing an enhanced north to south temperature gradient in winter. The highest levels of precipitation also occur in winter months because the strengthened westerlies of that season steer moisture-laden storms into the Pacific Northwest.
For any given month, Pinus albicaulis has a relatively narrow range for temperature (fig. 19) but a wider range for precipitation (except for May and June) than the other three species of pines. This species lives in areas of cool season (November to March) precipitation dominance and moderate summer drought. P. banksiana lives under a wide range of winter temperatures, including some of the coldest in CANUSA, but has a relatively narrow temperature range during the warm season (especially during July and August). This species tolerates a wide range of precipitation amounts during the winter months and a narrower range during the summer-the upper level of precipitation does not change very much through the year, but during the warm months, the drier sites receive more precipitation. The southwestern United States species, P. edulis, is associated with a relatively narrow range of both temperature and precipitation during all months of the year but particularly for July to October. This pine generally lives in areas with summer precipitation maxima associated with the southwestern monsoon and lower levels of precipitation during the cool season leading to the driest conditions from April to June. P. clausa is associated with the warmest and wettest environments in CANUSA, with precipitation at higher levels in the summer than in the winter.
The box-and-whisker plots in panel h provide an additional perspective on monthly relations between climatic variables and plant distributions (figs. 12 and 20). These plots depict the range of variability for temperature (upper box) and precipitation (log scale, lower box) experienced by a species for each month of the year. The vertical extent of each whisker indicates the total range of the climatic variable where the species is considered to be present; the black box indicates the breadth of the interquartile distance (25 to 75 percent of the occurrences of the species), and the black horizontal line indicates the median value. The red box delimits the 5-to 95-percent limits of the climatic variable in relation to the species.
The heights of the 5- to 95-percent ranges on the temperature box and whisker plot for Pinus albicaulis are small and very similar across the months of the year (fig. 20). The whiskers that illustrate the full range of temperature extend much further upward (warmer) than downward relative to the median values. Although the dominance of winter precipitation is evident for P. albicaulis in the box-and-whisker plot, there is much more variability for the winter months than for the months from April to June. The downward tails are long on the precipitation plots for this species suggesting that it may be able to survive under dry conditions; however, the use of the logarithmic scale for this variable may make this more visually pronounced than is warranted.
The temperature box-and-whisker plot for Pinus banksiana shows large swings between summer and winter months for this species that lives in part under some of the most continental climates in CANUSA. For both temperature and precipitation, there is greater variability in the winter months than in the summer, with the least variability in the months from June to August. Pinus edulis lives under warmer temperatures than P. albicaulis, with relatively little difference in the heights of the 5- to 95-percent ranges (red boxes on fig. 20) across the months of the year. This southwestern United States species experiences drought from April through June, followed by a sharp increase in precipitation for the monsoon months of July and August. The degree of precipitation variability changes through the year, with the highest degree of variability during the dry months listed above and the lowest degree of variability during September and October. As mentioned above, Pinus clausa has a small population compared with the other three pine species, and this may affect the height of the box-and-whisker plots in figure 20. Among the four pine species presented here, it has the smallest absolute range of temperature across the months of the year, with a very small range of variability for the high temperatures that prevail from May to September. Compared with the other three pine species, P. clausa experiences little difference in precipitation across the months, and the degree of variability is particularly small for the wettest months (July through September).
Tables of Relations of Plant Distributions to Single Climatic Variables
As in the previous volumes, we provide spreadsheets with data tables for univariate population distributions for each species in relation to each climatic variable. These tables differ from those in previous volumes with the addition of new climatic variables, the change in moisture index, and increased detail on the percentile scale. There are three Excel-workbook files for species and another three for groups, with one file for monthly temperature, another for monthly precipitation, and a third for annual and bioclimatic data (TMIN, MTCO, TMAX, MTWA, ANNT, GDD5, ANNP, and AE/PE).
To obtain these values, we started a counter for each variable at the low end of the climatic or bioclimatic range and then moved up the scale until the first occurrence of the plant was encountered; this was labeled as the "0 percent" point. We then continued to move up the climatic scale until 1 percent of the total number of occurrences of the taxon had been encountered. This value on the climatic scale represents the conditions that correspond with 1 percent of the cumulative occurrences of the species. The same method was then employed to identify 5, 10, 25, 50, 75, 90, 95, 99, and 100 percent of the plant's distribution relative to the chosen climatic variable. Although many of the distributions appear to be Gaussian, many are not, so we did not choose percentage values that would imply Gaussian distributions. The 0- to 100-percent values identify the total range of the taxon in relation to the given climatic or bioclimatic variable; however, given potential errors in modern climate estimation and range maps, users may want to choose 1 to 99 percent, 5 to 95 percent, or 10 to 90 percent as a more realistic representation of the taxon's range. This information may be used by researchers to exclude outliers or to improve paleoclimatic estimates from vegetation assemblages (for example, Thompson and others, 2012a,b). For the 37 species with fewer than 6 grid points (table 1), the data tables provide values for only the 0 and 100th percentiles, with asterisks filling the spreadsheet cells for the values that would be associated with the intermediate percentiles.
Tabular Data for Pinus Edulis
To illustrate the range of information available in these tables, the data associated with a sample species (Pinus edulis, pinyon pine) were compiled from individual data tables (table 7). As an example of how to read this information, the first row of data indicates that this pine lives under mean January temperatures that range from -11.1 °C (0 percent) to 5.3 °C (100 percent), with the median (50 percent) temperature at -2.1 °C. In this example, comparisons of the breadth of climatic estimates for the apparent total range (0 to 100 percent) reveal that they are 1.2 times greater than those for the 1- to 99-percent band and 1.56 times greater than those for the 5- to 95-percent band. This raises the possibility that the total apparent climatic range includes outliers that may represent problems with the original range maps and (or) our estimates of present-day climatic variables.
Comparison of Annual and Monthly Climatic Variables Among Nine Pine Species
In this section we add five more pine species to make a complement of nine species overall, arrayed in two latitudinal transects, one in the west and one in the east (table 8, fig. 21). The nine selected pine species live in different parts of CANUSA, under widely differing climatic conditions, and with large differences in the sizes of their geographic distributions (the latter, as indicated by the number of points [n], where the species is present on the 25-km grid). The four species forming a latitudinal progression in western CANUSA are (in order generally from south to north) Pinus edulis (pinyon pine, n = 364), P. ponderosa (ponderosa pine, n = 912), P. flexilis (limber pine, n = 351), and P. albicaulis (whitebark pine, n = 582). These pines frequently occur in areas of high relief, and their populations live on montane islands of suitable habitat, separated by large areas of desert, steppe, and grassland (The WWF MHTs of Western Coniferous Forests and Xeric Shrublands/Scrublands/Deserts on fig. 1).
The landscape is more subdued (in a topographic sense) in eastern and subarctic CANUSA (see elevation map on fig. 1), and the five pine species in the eastern transect generally have larger contiguous areas of distribution, although substrate preferences (Burns and Honkala, 1990) may have strong influences on their occurrence within regions of suitable climatic conditions. The two southernmost of the eastern five species are located in the WWF Eastern Coniferous Forests ecoregion (fig. 1, table 6). Pinus clausa, (sand pine, n = 87) primarily lives on well-drained sandy soils within a small geographic range in Florida, and P. serotina (pond pine, n = 465), lives slightly farther north on sites with abundant soil moisture across the coastal plains of the Gulf of Mexico and the Atlantic Ocean. Two of the eastern pine species occur in the WWF Broadleaf and Mixed Forests MHT, with P. rigida (pitch pine, n = 714) living on shallow or infertile soils from the northern Appalachian Mountains to the coast of Maine, and P. resinosa (red pine, n = 1,757) living from the edge of the Great Plains grasslands in Minnesota and Manitoba to the Atlantic coast in the Canadian maritime provinces. Pinus banksiana (jack pine, n = 4,670) is the northernmost species in the eastern transect and lives in the WWF Boreal Forest/Taiga and northern portions of the Broadleaf and Mixed Forests MHTs. This boreal species is present at more than 50 times the number of grid points than the subtropical P. clausa.
Table 8 provides comparisons of the median (50 percent) values for the distributions of the nine pine species discussed above relative to each of the climatic variables. The median value represents the midpoint or most likely value for the occurrence of each species and thus provides a good starting point for comparing the climatic tolerances of the taxa. In table 8 and figure 21, the north to south gradients of the western pines (the first four species, in the yellow columns) and of the eastern pines (the last five species, in the blue columns) are clearly evident in the MTCO, MTWA, and GDD5 data. The ANNP and AE/PE data indicate that these species live under diverse moisture regimes, which do not form simple latitudinal gradients. Among the nine pines, MTCO is above freezing only for two southeastern species (P. serotina and P. clausa). The lowest and highest median MTCO values among the nine species are -20.8 °C for P. banksiana and 15.5 °C for P. clausa, a range of 36.3 °C. This range is much lower for MTWA, with a difference of 14.5 °C between the lowest and highest median values (13.2 °C for P. albicaulis versus 27.7 °C for P. clausa). The bioclimatic variable GDD5 integrates energy inputs on an annual basis, and by this measure there are large differences among the nine species. Although the overall trend in GDD5 resembles that for MTWA, it may be amplified by differences in cloudiness (see "annual percent possible sunshine" on fig. 1); the species with the lowest median GDD5 (P. albicaulis, n= 735) lives in the cloudy Pacific Northwest, whereas that with the highest (P. clausa, n = 6,345) lives in sunny Florida.
For moisture, the lowest median ANNP values are for the species that live primarily in the western Interior of the United States (P. edulis [365 mm], P. flexilis [475 mm], and P. ponderosa [518 mm]). The remaining western species (P. albicaulis, 875 mm) lives farther north and closer to the Pacific Ocean. In eastern CANUSA, the taiga species P. banksiana has the lowest ANNP (593 mm), whereas the three pines that live on the Atlantic and Gulf of Mexico coastal plains (P. rigida, P. serotina, and P. clausa) all have median ANNP of more than 1,100 mm. AE/PE, a measure of the balance between available moisture (related to ANNP) and evaporation (related to temperature, cloudiness, and other factors), provides a different perspective on the water budgets of these species. Although P. clausa has the highest ANNP of the nine pines (1,290 mm), it lives in a southern hot sunny region with high evaporation and high GDD5 (6,345), and its median AE/PE is only 0.72. Conversely, P. banksiana, which lives in a northern cloudy area with much lower GDD5 (1,080) has a slightly higher median AE/PE value (0.79) than P. clausa, even though its ANNP is lower. All of the western species have lower median values for AE/PE (0.34 to 0.66) than any of the eastern species (0.79 to 0.92).
In addition to differences in annual climatic variables, the pine species live under differing seasonal regimes of temperature and precipitation. The differences between the median temperatures for MTCO and MTWA and ranges of median monthly temperatures are of similar amplitudes among the western pines (≈22 ° to 24 °C, table 8, figure 21). In contrast, the amplitudes vary largely among the eastern pines, from ≈12 °C for P. clausa to ≈37 °C for P. banksiana. With regard to the seasonality of precipitation, in the west P. albicaulis receives more moisture in the winter months, whereas P. edulis has a dominance of precipitation in July and August (table 8, fig. 21). There is a large difference between the median precipitation of the driest and wettest months for P. edulis (MPDR/MPWE = 0.31) with the driest month (June) receiving less than one-third of the rainfall of the wettest month (August). In the east, P. rigida has the least difference between the wettest and driest months (MPDR/MPWE = 0.71), whereas the northernmost and southernmost species (P. banksiana and P. clausa) have dry months in winter receiving only about a quarter of the precipitation of the wet summer months (MPDR/MPWE ≤0.29).
Seasonal Precipitation and Distribution of Southwestern United States Species
As discussed in the section on new and revised distribution maps for species, we concentrated on plants from the southwestern United States, an arid and semiarid region with a high degree of spatial and temporal variability in precipitation (figs. 6, 8, and 9). There is a strong gradient from winter to summer precipitation dominance from west to east across this region, and we selected three species to illustrate the differences in climatic conditions experienced by plants adapted to different locations along this gradient (fig. 22; see data tables and atlas pages). The westernmost species, Juniperus californica, has the highest median ANNP (417 mm) of the three species. It grows as a shrub or small tree in woodlands and along the upper margins of the deserts in eastern California and adjacent southern Nevada and northwestern Arizona. J. californica has evolved structural and physiological traits that make it exceptionally well adapted to surviving extended droughts (Willson and others, 2008). It lives under a Mediterranean-type climate with a dominance of precipitation falling in the months from November through March (fig. 22). There is a steady decline in median precipitation through April and May at the sites where it grows leading into intense drought during the hottest part of the year (June through August). Precipitation increases through September and October, returning to high levels by November. This juniper's adaptations to this strong seasonal precipitation cycle include having a short growing season during the spring (following the winter rains during the warming period of the spring), becoming dormant during the summer drought, and persisting in this state until the following spring (Alfieri and Kemp, 1983).
Moving eastward, Carnegiea gigantea (saguaro), a hallmark species of the Sonoran Desert (table 6) in southwestern Arizona and adjacent California, has the lowest median annual precipitation of the three species (214 mm) and lives under warm winters and extremely hot summers. This columnar cactus lives under a biseasonal precipitation regime where relatively high precipitation from December through March is followed by increasing aridity leading into June, the month of greatest drought (fig. 22). This drought is relieved by a second precipitation maximum related to the North America monsoon from July into September, which is followed by a lesser drought in October and November. In addition to this strong intraseasonal pattern in rainfall, there is also a high degree of interannual variability in precipitation for this desert species (fig. 1). Saguaros utilize several adaptations to survive extremely hot conditions with episodic and highly variable rainfall (Nobel and Loik, 1999; Turner and others, 1995), including having vertical ribs and accordion-like folds that provide the capability to expand and contract the diameter of its stem as water availability changes, photosynthetic stems that release less water than leaves, and a broad and shallow root system that captures water even from short-lived or low-intensity rainfall events.
The easternmost of the three plants, Flourensia cernua (tar bush), is a key species of the Chihuahuan Desert (table 6). The seasonal precipitation pattern associated with its distribution is in many aspects a mirror image of that for Juniperus californica. F. cernua experiences a dry season from November through March or April (fig. 22), followed by a general wetting trend through June, leading into a precipitation maximum from July into September, and then progressively drier conditions through October and November. This plant is generally drought-deciduous during the winter months and physiologically active during the wet summer period (Smith and others, 1997). It has several adaptive mechanisms that may help it survive under precipitation events associated with the summer monsoon, including a root system set to capture water from intense local storms (Gibbens and Lenz, 2001), an ability to funnel rainfall captured by its crown down to its roots (Mauchamp and Janeau, 1993), and establishment in geomorphic settings where it can capture dispersed, thin, overland flows associated with cloudbursts (sheetflow; Montaña and others, 1990).
Climatic Variables and Plant Distributions
Examination of the figures and tables in this atlas will convince most readers that climate is an important element in the distribution of North American plant taxa, certainly on a continental scale at least. On finer scales, competition among species, soil conditions, and other environmental factors strongly influence the actual distributions of plant species. As climate is constantly changing, it is highly likely that many species are currently adjusting their ranges, and thus the data in this atlas may underestimate the climatic tolerances of some taxa. It is also probable that some species could prosper in areas far from their native habitats, as planted Douglas-fir trees (native to western North America) do in parts of northeastern North America today. However, intervening natural barriers to immigration, such as the dry climate of the Great Plains, have prevented species such as Douglas-fir , from colonizing these far-away habitats.
We thank Jeremy Havens and David Smith for their work on this volume. Daniel Muhs and Joan Fitzpatrick provided helpful reviews. The authors wish to express their appreciation of the support provided by Debra A. Willard and the Research and Development Program of the USGS Climate and Land Use Mission Area.
We thank the following individuals for their work in digitizing or other aspects of the data production for previous volumes: Larissa Agbulos, Jenny Buchner, Carol Ann Chapmann, Bev D'Amato, Laurie DeMarco, Eric Dorsett, Eric Fisher, Jeff Honke, Bev Lipsitz, Colby Loucks, David Olsen, Marketa McGuire, David McIntyre, Gary Selner, Randy Schumann, Peter Schweitzer, Sharon Smith, Dick Taylor, Paco Van Sistine, and Ollie Williams. The previous volumes were much improved by reviews provided by Tom Ager, Paul Carrara, Steve Jackson, Dan Muhs, and Jack Williams; and by editorial and graphics work by Tom Judkins, Mary Kidd, Rick Scott, and Carol Quesenberry. Elliott Spiker and the USGS Global Change and Climate History Program funded much of the work on previous volumes.
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