Ecological Applications, 24(4), 2014, pp. 791–811� 2014 by the Ecological Society of America

Geographic coincidence of richness, mass, conservation value,and response to climate of U.S. land birds

RALPH GRUNDEL,1,7 KRYSTALYNN J. FROHNAPPLE,1 DAVID N. ZAYA,1,2 GARY A. GLOWACKI,1,3

CHELSEA J. WEISKERGER,4,5 TAMATHA A. PATTERSON,4,6 AND NOEL B. PAVLOVIC1

1U.S. Geological Survey, Great Lakes Science Center, 1100 N. Mineral Springs Rd., Porter, Indiana 46304 USA2Illinois Natural History Survey, 1816 S. Oak Street, Champaign, Illinois 61820 USA

3Lake County Forest Preserves, 1899 West Winchester Road, Libertyville, Illinois 60048 USA4National Park Service, Indiana Dunes National Lakeshore, 1100 N. Mineral Springs Road, Porter, Indiana 46304 USA

5Michigan State University, Department of Civil and Environmental Engineering, c/o U.S. Geological Survey,1100 N. Mineral Springs Rd., Porter, Indiana 46304 USA

6University of Notre Dame, Department of Biological Sciences, c/o U.S. Geological Survey, 1100 N. Mineral Springs Rd.,Porter, Indiana 46304 USA

Abstract. Distributional patterns across the United States of five avian communitybreeding-season characteristics—community biomass, richness, constituent species’ vulnera-bility to extirpation, percentage of constituent species’ global abundance present in thecommunity (conservation index, CI), and the community’s position along the ecologicalgradient underlying species composition (principal curve ordination score, PC)—weredescribed, their covariation was analyzed, and projected effects of climate change on thecharacteristics and their covariation were modeled. Higher values of biomass, richness, and CIwere generally preferred from a conservation perspective. However, higher values of thesecharacteristics often did not coincide geographically; thus regions of the United States woulddiffer in their value for conservation depending on which characteristic was chosen for settingconservation priorities. For instance, correlation patterns between characteristics differedamong Landscape Conservation Cooperatives. Among the five characteristics, communityrichness and the ecological gradient underlying community composition (PC) had the highestcorrelations with longitude, with richness declining from east to west across the contiguousUnited States. The ecological gradient underlying composition exhibited a demarcation nearthe 100th meridian, separating the contiguous United States grossly into two similar-sizedavian ecological provinces. The combined score (CS), a measure of species’ threat of decline orextirpation, exhibited the strongest latitudinal pattern, declining from south to north. Over;75% of the lower United States, projected changes in June temperature and precipitation toyear 2080 were associated with decreased averaged values of richness, biomass, and CI,implying decreased conservation value for birds. The two ecological provinces demarcatednear the 100th meridian diverged from each other, with projected changes in Junetemperatures and precipitation from the year 2000 to 2080 suggesting increased ecologicaldissimilarity between the eastern and western halves of the lower United States with changingclimate. Anticipated climate-related changes in the five characteristics by 2080 were moreweakly correlated with latitude or longitude then the responses themselves, indicating lessdistinct geographic patterns of characteristic change than in the characteristics themselves.Climate changes projected for 2080 included geographic shifts in avian biomass, CS, and PCvalues, a moderate overall decline in CI, and general decline in species richness per site.

Key words: avian biomass; avian community characteristics; climate change; combined score;conservation index; conservation metrics; conservation value; covariation; geographic coincidence; landbirds; Landscape Conservation Cooperatives; richness.

INTRODUCTION

Characteristics of animal or plant communities, such

as diversity, can serve as a metric for setting manage-

ment and conservation goals for landscapes that the

communities inhabit and for prioritizing areas for

protection status. However, choosing a specific metric

as the measure of management or conservation success

can be difficult because multiple characteristics of the

managed community may represent suitable goals

(Margules and Pressey 2000, Fleishman et al. 2006,

Francis and Goodman 2010, Sewall et al. 2011). If it is

possible to enhance or maintain several desirable

characteristics concurrently at a given location, man-

agement goal-setting or site prioritization for conserva-

tion, conceptually, is easier. However, management

Manuscript received 21 May 2012; revised 30 August 2013;accepted 11 September 2013; final version received 4 October2013. Corresponding Editor: S. Oyler-McCance.

7 E-mail: rgrundel@usgs.gov

791

actions that improve the condition of one desirable

community characteristic can degrade another desirable

characteristic (Grundel and Pavlovic 2008), or desirable

characteristics may not coincide in space (Orme et al.

2005). Community characteristics are often inadequately

documented, so we do not know how these character-

istics covary (Francis and Goodman 2010) and, hence,

we do not understand the conservation value of a

landscape or what trade-offs might arise as a result of a

particular management course or an ecosystem pertur-

bation.

Our lack of understanding of how potentially

important community conservation attributes covary is

true even for well-studied taxa, such as land birds in the

United States for which detailed, large-scale conserva-

tion plans exist (Rich et al. 2004). Studies have examined

how various environmental attributes (elevation, pro-

ductivity, habitat composition) relate to avian commu-

nity characteristics, including richness (Hawkins et al.

2003, Lawler et al. 2004, Phillips et al. 2010) and

abundance (McFarland et al. 2012), but generally not to

patterns of coincidence of multiple characteristics (Law-

ler et al. 2003). Similarly, although effects of scale on

spatial patterns of avian community characteristics have

been investigated, comparisons of scale effects on

covariation of characteristics are limited (Pearson and

Carroll 1999, Rahbek 2004, Belmaker and Jetz 2011).

The minimal goal of biota conservation is the

preservation of species in a management region or

restoration of species that have been lost. Achieving that

goal means retaining species in large enough numbers

that typically occurring extremes of population fluctu-

ations do not cause extirpations. The magnitude of the

contribution that a given area makes to that conserva-

tion goal is related to how many species reside in the

area, how abundant species are, and how well the least

common species are maintained. Probably most com-

monly, that contribution is expressed by the area’s

species richness (Fleishman et al. 2006). Although

conservation-planning goals are often expressed in terms

of number of species (richness, biodiversity), other goals

may be set. Species abundance, maintenance of historic

habitat structure or historic ecological processes, reten-

tion of endemic species, helping threatened species, and

complementarity of characteristics of conservation

importance are examples of alternative conservation

goals (Margules and Pressey 2000, Redford et al. 2013).

Despite the popularity of richness as a metric of

conservation priority, or an indicator of conservation

success, it does not provide definitive information on

rarity or conservation value of those species. We might

hypothesize that increasing the number of species in an

area will also increase the likelihood of threatened

species being present. In a previous local study of the

relationship between richness and prevalence of threat-

ened bird species, we hypothesized that landscape

compositions that favored high diversity would also be

favorable to threatened bird species (Grundel and

Pavlovic 2008). However, diversity and favorability for

threatened species were negatively correlated, such that

management to maximize a landscape’s benefit to the

most threatened species was not compatible with

management to maximize species richness. Similarly,

global hotspots for richness are not consistently global

hotspots for threatened species (Williams et al. 1996,

Orme et al. 2005), and areas of high richness are often

inhabited mainly by common species (McKinney 2002).

Such findings emphasize the importance to conservation

planning of understanding the spatial congruence of

different aspects of conservation value to improve our

ability to understand the complementarity of these

aspects of conservation value (Margules and Pressey

2000). Complementarity implies that desirable charac-

teristics of conservation importance coincide at some

spatial resolution.

Here we examine the spatial congruence of five

potential characteristics of conservation value associated

with U.S. bird communities. We investigate the state of

these characteristics and their covariation, not whether

this state can be readily improved and not about

mechanisms of how changing one characteristic causes

another characteristic to change (Grundel and Pavlovic

2008). Thus, this analysis informs us of expectations of

co-occurrence of desirable levels of characteristics of

conservation importance, high richness and high abun-

dance, for example. This analysis examines a snapshot in

time of these characteristics and is, therefore, a static

analysis. However, as a means of understanding the

resilience to change, and likely trajectories, of the

characteristics, we look at one important agent—climate

change—that may alter these characteristics ubiquitous-

ly and systematically in coming decades (Matthews et al.

2011, Martin and Maron 2012). Although the effects of

climate change on many bird species have been

estimated and aggregated across communities (Mat-

thews et al. 2011), we have not yet estimated how climate

change might affect patterns of covariation among

community characteristics and, hence, might affect

possible future trade-offs among these characteristics.

Toward the goal of understanding how multiple

characteristics that are of conservation value for bird

communities covary in space, and how that covariation

might be affected by changing climate, we examine five

characteristics that should be important for preserving

bird species in areas across the contiguous United States.

These characteristics are bird abundance, community

richness, presence of threatened bird species in a

community, frequency of landscape use by bird species,

and maintenance of the ecological gradient along which

birds in the United States are distributed. These

characteristics can serve as conservation endpoints

individually or in concert. To improve our understand-

ing of how these avian community characteristics vary

geographically today and might vary in a future of

changing climate, we document (1) how these five

community characteristics vary across the contiguous

RALPH GRUNDEL ET AL.792 Ecological ApplicationsVol. 24, No. 4

United States, (2) how they covary across the country,

and (3) how changing climate might affect bothdistribution and covariation of these characteristics.

METHODS

Community characteristics

We examined five characteristics associated with

breeding-season land bird communities in the contigu-ous United States (Table 1; details in Appendix A). For

these five characteristics, our goal was to describepatterns of spatial variation to ascertain the coincidence

of the characteristics. The characteristics were chosen torepresent different ways in which locations were

contributing to retention of species: abundance ofspecies, number of species, presence of threatened or

rare species, and ecological attributes of landscapes.The five characteristics were: (1) total mass of birds in

the community (MASS) as an indicator of abundance(mass and number of birds observed per Breeding Bird

Survey (BBS) route were highly correlated [r¼ 0.83] andyielded similar analytical results, so we used mass, rather

than number of birds, to help account for variations inthe size of bird species); (2) richness, the number of birdspecies in a location (RICH); (3) the combined score

(CS), a measure of how threatened the most threatenedspecies in a community were (Panjabi et al. 2005); (4) a

conservation index (CI) that asked ‘‘what percentage ofa species’ global population was observed along a given

BBS route?’’ and was, therefore, a measure of howimportant that route was to the species in global

perspective (Grundel and Pavlovic 2008); (5) a location’sapos; position along the ecological gradient underlying

community composition (PC), which can indicatewhether some community compositions or combina-

tions of ecological characteristics (e.g., elevation, cli-mate, productivity) are associated with specific

conservation characteristics.These avian community characteristics were derived

from data compiled by the U.S. Breeding Bird Surveyfor the lower 48 states of the (contiguous) United States

(Sauer et al. 2005). The BBS is a roadside survey ofbreeding birds. More than 3000 survey routes arelocated across the United States. Each route used in

this study consisted of 50 stops that were visited onceduring the breeding season, most commonly in June.

Birds observed or heard within 400 m of each point wererecorded during a 3-minute period, resulting in a total

area surveyed of ;25 km2. Stops were separated fromeach other by at least 0.8 km. We used data from BBS

routes surveyed between 1997 and 2004, an 8-yearinterval that spanned the 2001 National Land Cover

Database, NLCD (Homer et al. 2004), one of thepredictor sets used in this study. Routes with complete

50-stop data from at least six of the eight years wereconsidered for selection. We refer to data and results

from 1997–2004 as ‘‘Current.’’ North America has beendivided into Bird Conservation Regions (BCR), large

areas whose ecological differentiation is thought to be

important for birds (Panjabi et al. 2005). From 1729

available BBS routes with 6–8 years of completed

surveys, we attempted to select and allocate routes per

BCR in proportion to the region’s proportional area of

the contiguous United States (BCR region’s percentage

of U.S. area3 1729), thereby selecting a similar number

of routes per km2 of each of the 30 BCR regions in the

contiguous United States. Because some regions con-

tained few routes (range 8–193 BBS routes per BCR), it

was not possible to distribute the routes equally per unit

area among BCRs without eliminating most available

routes. Therefore, from the 1729 routes, we used all BBS

routes within regions (n¼18) with insufficient number of

routes available to meet the proportionality criterion

and in the remaining BCR regions (n ¼ 12), we

proportionately selected routes randomly from those

available in a BCR. In total, 46.5 6 6 (mean 6 SE)

routes were selected per BCR (range 8–118 per BCR).

Because this study examined the effects of land

characteristics on bird communities, we eliminated water

birds from the data set. We also eliminated nocturnal

species (owls, nightjars) that typically are not well

sampled in the BBS. After these adjustments, we used

data from 335 bird species observed on 1396 BBS routes

(Fig. 1).

For each route, we averaged the counts for each

species across years, producing a 1396 route 3 335

average species count matrix used as the basis of most

analyses in this study. The total mass of birds observed

per route (MASS, kilograms per route per year) was

estimated by multiplying each bird species’ average

yearly counts per route by the species’ published

estimated average mass (Dunning 2008) and summing

over all species present on a route.

Some species present along a BBS route would not be

detected on a given survey (Boulinier et al. 1998), so

methods have been created to account for undetected

species when calculating richness per route (RICH). We

selected Chao’s abundance coverage estimator (ACE),

as implemented in EstimateS (Colwell 2009), based on

favorable comparisons to other estimators in simulation

and field studies of richness estimation, and ACE’s

relative insensitivity to sampling grain size (Hortal et al.

2006, Reese 2012). As with many richness estimators,

ACE estimates the number of undetected species to add

to the number of observed species to give an overall

estimate of richness. The number of undetected species is

derived from the number of infrequently observed

species, which for ACE was defined as species observed

10 or fewer times (Chao and Lee 1992, Chao et al. 2005).

For each BBS route, we estimated bird species richness

per route using ACE for each of the 6–8 years of data,

and then averaged those yearly richness values to give a

mean yearly route bird species richness (RICH).

For each route, we calculated a measure of how

threatened the most threatened species were, using

individual species’ continental combined scores (CCS)

(Panjabi et al. 2005). The CCS calculation process used

June 2014 793COINCIDENCE OF CONSERVATION MEASURES

BBS data, information on species’ ranges, and expert

opinion to estimate components of threat of species

extirpation, including population size, distribution,

threats on breeding and nonbreeding ranges, and

population trends, and combined those factors into a

single score ranging from 4 (least threatened) to 20

(most threatened). The CCS was designed to help

conservation practitioners understand the overall level

of threat for individual species. We averaged CCS for

the five species with the highest CCS (most threatened

species) to produce a combined score index (CS) for a

BBS route. We used CS as an indicator of a route’s

contribution to conserving the most threatened species.

We selected five species to represent the number of

species that might typically receive heightened attention

in a conservation planning situation.

The fourth avian community characteristic examined

was the conservation index (CI) (Grundel and Pavlovic

2008), which was derived from estimates of global

population sizes of U.S. land birds (Panjabi et al. 2005,

Thogmartin et al. 2006). These global population

estimates were based on BBS count data adjusted to

take into account (1) that each bird observed during a

BBS survey was usually a member of a pair, (2) diurnal

variation in detectability of species, and (3) an estimated

radius over which a species was detected. These density

estimates, combined with estimates of area occupied by

the species, were used to yield an estimate of the species

global population size. We used these same three

adjustments to produce a density estimate for each bird

species observed along a BBS route (birds detected/ha).

For each species, those route density estimates (bird

densityi Eq. 1) were divided by the bird’s estimated

global population size, yielding an estimated proportion

of the global population of a species present on 1 ha of

the route. We summed that proportion for all species

present along a route to produce that route’s conserva-

tion index (CI):

conservation index ¼X335

i¼1

ðbird densityi=PSGiÞ ð1Þ

where PSGi is the estimated global population size of

bird species i. The CI tended to increase when globally

rarer species were detected along a route, because each

observed individual of a rare species contributed more to

the CI than each individual of a common species

TABLE 1. Importance (Imp., mean 6 SE) of 22 variables in predicting five avian community characteristics averaged over boostedregression tree (BRT), random forest (RF), multiple adaptive regression splines (MARS), and ordinary least squares (OLS)models.

Predictor

MASS RICH CS CI

Imp.(%) rS

Imp.(%) rS

Imp.(%) rS

Imp.(%) rS

June temperature 7.7 6 1.1 0.38 8.1 6 1.4 �0.11 14.8 6 2.4 0.24 6.1 6 1.1 0.02June precipitation 3.1 6 1.0 0.40 4.4 6 1.7 0.34 6.5 6 2.1 0.15 7.9 6 1.3 �0.08Elevation 12.9 6 2.2 �0.51 5.0 6 1.0 �0.26 5.1 6 1.2 �0.19 3.9 6 1.2 �0.02Slope 7.9 6 2.0 �0.48 3.7 6 1.1 0.14 3.9 6 1.0 0.06 2.5 6 0.9 0.23Gross primary productivity 8.2 6 2.0 0.28 6.8 6 0.7 0.55 8.4 6 2.1 0.33 10.0 6 2.5 0.27Canopy cover 3.6 6 1.5 �0.07 21.1 6 7.9 0.65 9.8 6 1.8 0.31 7.7 6 0.8 0.38Forest fragmentation (natural) 2.8 6 0.9 �0.26 1.7 6 1.1 �0.40 6.1 6 1.5 �0.08 12.5 6 4.4 0.17Forest fragmentation (human) 5.1 6 1.2 0.55 3.3 6 2.2 0.37 2.9 6 0.8 0.04 5.3 6 1.4 �0.25Open water 1.5 6 0.5 0.8 6 0.5 1.4 6 0.6 2.0 6 0.4Developed land 1.6 6 0.5 1.4 6 0.5 3.8 6 1.4 1.2 6 0.7Barren 1.8 6 0.7 1.5 6 0.6 1.3 6 0.5 2.1 6 0.7Deciduous forest 2.5 6 1.0 14.8 6 4.4 4.5 6 2.5 3.2 6 0.7Evergreen forest 7.5 6 2.6 3.7 6 0.6 2.1 6 0.8 7.0 6 1.5Mixed forest 2.5 6 1.3 1.9 6 0.9 2.2 6 0.9 0.7 6 0.5Scrubland 4.1 6 2.2 4.2 6 1.0 5.5 6 1.6 5.2 6 0.9Grassland 4.5 6 0.7 2.2 6 1.1 4.9 6 1.9 3.6 6 1.4Pasture 5.6 6 0.9 2.0 6 0.6 1.7 6 0.6 1.8 6 1.0Cropland 9.3 6 1.0 2.0 6 0.9 5.3 6 1.3 8.5 6 1.2Wooded wetlands 1.0 6 0.6 2.5 6 1.3 1.5 6 0.9 1.6 6 0.8Emergent wetlands 1.6 6 0.6 1.0 6 0.6 1.6 6 0.6 2.6 6 1.0Human density 2.6 6 1.4 3.6 6 1.2 4.5 6 0.5 2.0 6 1.1Land cover diversity 2.7 6 1.3 0.29 4.3 6 2.7 0.47 2.1 6 0.9 0.20 2.8 6 0.7 0.12Developed land cover 0.33 0.22 0.07 �0.12Agricultural land cover 0.56 0.12 �0.11 �0.40

Notes: The community characteristics are community biomass (MASS), species richness (RICH), conservation index (CI, thepercentage of constituent species’ global abundance in the community); combined score (CS, species’ threat of decline orextirpation); and the principal curve ordination score (PC, the community’s position along the ecological gradient underlyingspecies composition). Importance values per model sum to 100 for each predictor; higher values indicate greater importance. Forthe 11 variables used in the final geographically weighted regression (GWR) model, Spearman rank correlation (rS) is shown.

� 55% of species variation was explained by the principal curve ordination of the species count BBS route data (square-root-transformed).

� BRT model importance (%) for 11 predictors selected for GWR, with the OLS standardized regression coefficient (b 6 SE).*** OLS regression coefficient significant at P , 0.001.

RALPH GRUNDEL ET AL.794 Ecological ApplicationsVol. 24, No. 4

(Grundel and Pavlovic 2008). The CI was an indicator of

how globally important a route was to the overall avian

community in terms of landscape use by the birds, with a

caveat that presence of a bird in an area did not

necessarily prove that the area is quality habitat for the

species (Bock and Jones 2004).

Finally, we characterized the ecological gradient

underlying avian community composition using princi-

pal curve (PC) ordination (De’ath 1999). De’ath (1999)

noted that ordination of sites by their species compo-

sition typically has two goals: finding an ecological

gradient that influences species composition and de-

scribing the similarity of sites in their species composi-

tion. However, a given ordination technique is typically

better at characterizing only one of these goals. Principal

curve ordination emphasizes discovery of the ecological

gradient underlying species composition. Thus, sites

with similar PC ordination scores would share similar

key ecological characteristics, key because these charac-

teristics are strongly related to species composition. Sites

with similar PC ordination scores would also have

similar species composition, although sites can have

similar species composition but dissimilar PC scores.

For instance, imagine a bird community consisting of

two bird species whose abundance varies along an

elevation gradient, with both species abundant at middle

elevations but not present at low and high elevations. In

a successful PC ordination, low-elevation sites will have

similar PC ordination scores near one end of the PC

FIG. 1. Location, across the contiguous United States, of Breeding Bird Survey (BBS) routes (n¼ 1396) used as data sourcesand Landscape Conservation Cooperative (LCC) regions: (1) North Pacific; (2) California; (3) Great Basin; (4) Great Northern; (5)Desert; (6) Southern Rockies; (7) Plains and Prairie Potholes; (8) Great Plains; (9) Gulf Coast Prairie; (10) Eastern Tallgrass Prairieand Big Rivers; (11) Gulf Coastal Plains and Ozarks; (12) Upper Midwest and Great Lakes; (13) Appalachian; (14) PeninsularFlorida; (15) South Atlantic; (16) North Atlantic. LCCs are numbered in order (from west to east) of the longitude of the centroidsof their area within the contiguous United States.

TABLE 1. Extended.

PC� PC�

Imp.(%) rS

Imp.(%)

OLSb 6 SE

11.1 6 2.5 �0.44 10.5 �0.006 6 0.008***28.4 6 7.9 �0.77 43.0 �0.100 6 0.007***4.4 6 0.4 0.72 6.2 �0.046 6 0.008***1.4 6 0.7 0.42 1.3 0.006 6 0.0068.4 6 3.1 �0.76 7.0 �0.112 6 0.010***5.9 6 1.8 �0.52 5.6 �0.049 6 0.009***2.2 6 0.4 (11) 0.50 3.1 �0.003 6 0.00513.6 6 2.3 �0.69 21.1 �0.129 6 0.007***0.5 6 0.40.6 6 0.41.1 6 0.72.0 6 1.01.1 6 0.80.5 6 0.45.7 6 0.73.1 6 0.81.9 6 0.62.4 6 1.31.5 6 0.80.7 6 0.61.9 6 1.21.6 6 0.9 �0.49 0.7 �0.018 6 0.005

�0.39 3.1 �0.003 6 0.005�0.35 0.7 �0.006 6 0.008

June 2014 795COINCIDENCE OF CONSERVATION MEASURES

score range, high-elevation sites will have similar scores

to each other at the other end of the range, and mid-

elevation sites will have mid-range PC scores, despite

low- and high-elevation sites having similar community

composition. Therefore, the PC ordination process

ordinates sites by their community composition, yet

captures the underlying ecological gradient. If we map

sites as points in a multidimensional space whose axes

are defined by abundances of species present at the sites,

a principal curve is a smooth, one-dimensional curve

that passes through those points in a manner that

minimizes the distance from the points to the curve. The

curve is scaled to a length of 1, with each site given a

score based on where along the curve its point is closest.

Therefore, our 1396 BBS routes will each have a PC

score between 0 and 1. Ordinations of simulated and real

ecological data have shown that the one-dimensional

principal curve is often more effective in explaining

variation in community composition than higher dimen-

sion ordinations using other ordination techniques

(De’ath 1999). That a one-dimensional gradient can

effectively explain species composition allows us to

represent ecological gradients underlying community

composition more simply on a map or via correlation

with other aspects of community composition and value,

such as richness. A complex combination of variables

might characterize this underlying ecological gradient,

even though the gradient itself was represented in a

single dimension as a position along a curve. Species

counts were square-root-transformed prior to ordina-

tion to decrease the influence of the most abundant

species on ordination results.

In selecting these characteristics, we assumed for

MASS, RICH, and CI that higher values indicated

increased conservation value of the communities,

although there were exceptions, such as abundant pest

species contributing to high MASS. Moreover, conser-

vation value is a nuanced concept. For example, if

global bird population sizes increased, CI in an area

would tend to decline unless bird populations in that

area increased at a faster rate than did the global

populations. Nonetheless, even if CI declined while bird

populations in that area increased, CI would remain a

valid conservation value indicator because CI’s funda-

mental premise—that the contribution of an area to the

global conservation situation is an important indicator

of whether to prioritize that area for conservation—is

still valid. In this example, other metrics, such as MASS,

would capture the population increases, illustrating

how, in this paper, we will be evaluating trade-offs and

spatial congruence among metrics that capture different

aspects of conservation value. There are at least two

aspects of conservation value assessed in this study: the

status of the area, whether it is in good ecological

condition, and the conservation priority of the area

(whether we might want to put our conservation efforts

into one area and not another). Thus, an area can have a

high conservation value because it has preferred values

of conservation parameters—high species richness, for

example. Or, an area can have a high conservation

priority even if the conservation value parameter is more

ambiguous or even representative of undesirable eco-

logical conditions. Higher CS scores, for example, can

represent a priority conservation state, in that such

higher scores indicate presence of threatened species in

an area, potentially making that area a conservation

priority for managers. However, high CS scores also

represent a poor ecological scenario: local presence of

species that are in decline and facing possible extirpation

globally. Additionally, over time, increased CS within an

area could indicate either that new conditions were

conducive to residence by more threatened species or

that new conditions increased the threat level for

existing species. Therefore, CS scores were examined

to indicate which areas were inhabited by more

threatened species and were of special concern, but

whether projected changes in CS were beneficial or

detrimental to those species cannot be simply stated.

Nonetheless, we assumed that high CS scores for an area

indicated conservation priority and, therefore, poten-

tially conflicted with prioritizing the site based on

conservation value as indicated by other characteristics

of the site, such as MASS or RICH. Positive correlations

between all six pairings of MASS, RICH, CS, and CI

were informative from a conservation perspective

because such correlations indicated potential spatial

congruence of states of the four characteristics that

represented either good ecological status (e.g., high

richness or mass) or conservation priority (e.g., high

CS).

As discussed for the calculation of RICH, differences

in detection of species among BBS routes can affect the

calculated values of each of the five responses (Boulinier

et al. 1998). In Appendix B, we examined how

accounting for detection differences might affect the

five responses and the correlations among these respons-

es that are the focus of analyses in this paper. We found

that these correlations were not substantially changed by

modification of the responses to account for detection

differences among species.

Predictors of avian community composition

We examined the relationship between 22 possible

predictor variables (Appendix A; Table 1) and the five

avian community characteristics. Predictors were select-

ed to represent climate, topography, productivity, and

land cover characteristics of the landscape, with the

restriction that Pearson correlation between any two

predictors in the set was ,0.75. We modeled the

relationship of those predictors to the avian community

characteristics and used the subsequent models to

estimate the spatial distribution of the five characteris-

tics over the contiguous United States. For the modeling

data set, we averaged predictor values either within 400

m of the BBS route line (for land cover variables), or

along the route line as a line-weighted mean (for all

RALPH GRUNDEL ET AL.796 Ecological ApplicationsVol. 24, No. 4

other variables; Beyer 2004). For subsequently project-

ing the model to the national scale, we estimated

predictors and characteristics within 73 7 km grid cells,

asking what we might expect to find along a BBS route

within such a grid cell. This resolution, 49 km2, is on the

order of the area of a small national park in the United

States and is similar to the area examined during a BBS

survey.

Because June and annual temperatures were highly

correlated and gave similar results in models, we used

only temperatures and precipitation around the survey

time (typically June) as climatic indicators (Table 1;

Appendix A). Elevation and slope were topographic

variables. Productivity measures included canopy cover

and improved estimates of gross primary productivity

(GPP) derived from the MODIS 17 satellite product

(Zhao et al. 2005). GPP was not calculated for highly

developed areas, water bodies, and certain barren

landscapes, so avian community characteristic projec-

tions were not made to those landscapes. Two measures

of forest fragmentation, percentage fragmentation due

to natural and human causes, were used (Wade et al.

2003, Wade 2006). Percentages of 12 NLCD land cover

classes (some aggregated across several NLCD classes)

present along routes were also used as predictors.

Human population density was highly correlated with

non-open developed land cover, so we selected human

population density, rather than non-open developed

land, as a predictor. Finally, we calculated land cover

diversity (Hansen et al. 2011) as eH0

, where H0 is

Shannon-Wiener diversity (Hill 1973) calculated based

on the proportions of the 12 land cover types found

along a route or within a 49-km2 cell.

Modeling approach for community characteristic

predictions

We modeled the relationship between the predictors

and each of the five community characteristics for the

1396 BBS routes and then used those models for two

objectives: to predict how the characteristics varied

across the lower United States in areas outside of the

1396 routes and to predict how climate change might

affect these characteristics across the United States.

Prior to modeling, we examined residual distribution

from ordinary least squares (OLS) regression models for

the five characteristics and log-transformed MASS and

CI to eliminate significant heteroscedasticity of residu-

als. We initially calculated relationships between the 22

predictors and the characteristics from the 1396 routes

using four modeling techniques: boosted regression trees

(BRT) and random forests (RF) (Cutler et al. 2007,

Elith et al. 2008, Freeman and Frescino 2011, Hijmans

et al. 2012), multiple adaptive regression splines

(MARS) (Milborrow 2011), and OLS. Predictor impor-

tance represents how model fit improved with the

predictor in the model rather than not in the model.

We estimated the relative importance (importances

scaled to total to 100% for predictors in an analysis)

of each predictor variable in predicting each of the five

characteristics and then averaged the relative impor-

tances across the four modeling techniques (Gromping

2006, Freeman and Frescino 2011, Milborrow 2011)

(Table 1). We used those averaged importances, and

considerations of collinearity, to be discussed, to select a

subset of 11 variables as predictors that produced high

model fits.

Moran’s I statistic is a measure of spatial autocorre-

lation ranging from�1 to 1, indicating whether values of

a variable tend to be clustered (I near 1), dispersed (I

near �1), or randomly distributed in space (I near 0)

(Rangel et al. 2010). When calculated at different

distances from a point, in a spatial correlogram,

Moran’s I can inform us over what distances values of

a variable tended to be similar. Presence of autocorre-

lation in spatial data could also affect the independence

of observations, such as the observations from BBS

routes. Having found significant autocorrelation, usual-

ly at distances up to several hundred kilometers (Bahn et

al. 2006), we selected geographically weighted regression

(GWR) to model the five characteristics for the 1396

BBS routes and then used those models to make

characteristic predictions across the United States.

GWR is a local regression technique in which the

regression coefficients are allowed to vary spatially,

rather than producing a single, global estimate of the

coefficients as in OLS (Fotheringham et al. 2002). The

local coefficient was determined at a point in space

based on data from nearby points, with the weight given

to each nearby point declining with distance from the

focal point. The local nature of the model produced a

better model fit and was influenced by local character-

istics, helping to alleviate the effects of spatial autocor-

relation on coefficient errors. GWR better predicted

characteristic range extremes derived from the existing

BBS data than did the other modeling techniques (BRT,

RF, MARS, OLS) for this data set (Tables 1 and 2). For

GWR, we used a bi-square weighting scheme and an

adaptive kernel, which meant that the size of the

neighborhood in which local data were gathered was

allowed to vary to account for neighborhoods with

relatively more or less data present (Bivand et al. 2011).

Prior to GWR modeling, OLS variance inflation factors

(VIF) were examined to assess possible multicollinearity

among predictors, using a standard of VIF . 7.5 to

indicate significant multicollinearity (ESRI 2011). VIF

scores indicated that canopy cover and several of the

land cover classes were collinear. Based on the most

important predictors found from BRT, RF, MARS, and

OLS models (Table 1) and patterns of collinearity, we

retained canopy cover, combined cropland and pasture

into one agriculture cover category, combined all

development categories into a single developed cover

category, and eliminated population density as a

predictor. This left 11 predictors for the final GWR

modeling: June temperature and precipitation, canopy

cover, agricultural cover, developed cover, GPP, eleva-

June 2014 797COINCIDENCE OF CONSERVATION MEASURES

tion, slope, land cover diversity, and natural- and

human-associated forest fragmentation.

We used GWR to predict the five characteristics

across 120 806 49-km2 grid cells covering the contiguous

United States. These predicted Current (1997–2004)

characteristics represented the anticipated avian com-

munity characteristics for a hypothetical BBS route that

existed within a grid cell. The prediction process

proceeded by first calculating a GWR model relating

the characteristics from the 1396 routes to the Current

predictor values from those routes and then applying

that model to the predictor values associated with the

countrywide grid cells.

Analysis of community ordination results indicated

that climatic variables (June temperature and precipita-

tion) were significantly correlated with community

composition. Because we wished to examine how the

five characteristics might change in time, and given the

importance of these climatic factors in predicting

community composition and the anticipated widespread

effects of changing climate, we modeled how climate

change might alter the characteristics. To examine

possible effects of climate change on the five character-

istics, we substituted predictor data associated with

future climate scenarios during the prediction phase.

Specifically, we substituted June temperature and

precipitation projections for 2080 in place of mean

1997–2004 June temperature and precipitation. These

late 21st century June temperature and precipitation

data were obtained from the ClimateWizard website

(available online)8 (Girvetz et al. 2009), based on a mid-

range scenario of future carbon dioxide emissions

(SRES Scenario A1B; Core Writing Team et al. 2008).

For a given emissions scenario, projected June mean

temperature and precipitation values were available

from 16 different General Circulation Climate (GCM)

models through the ClimateWizard. We selected values

that represented the middle of the range of these 16

values for temperature projections for the medium

(A1B) emissions scenario. One analysis done with these

data was to calculate the percentage of the grid cells in

which characteristic values were projected to decrease

from Current to 2080. For this analysis, we also

recalculated models based on projected extremes in

June temperature, along with June precipitation projec-

tions associated with those temperature extremes, to

illustrate how GCM projection variability might affect

results. The low extreme represented the GCM model

with the lowest temperature projections from emissions

Scenario B1 (low carbon emissions), while the high

extreme was the GCM model with the highest temper-

ature projections from emissions Scenario A2 (high

carbon emissions). Climate projections, as well as some

other predictors used in this paper, become more

uncertain as spatial scale decreases (Kerr 2011).

Therefore, larger scale trends observed from the models

will generally be more reliable than specific predictions

made at the smallest resolution level, 49 km2.

We used mid-range 2080 temperature and precipita-

tion projections in three ways (Climate Models 1, 2, and

3) to estimate how the five avian community character-

istics might be affected by differences between Current

and 2080 temperatures and precipitation. First (Climate

1), we calculated a GWR model for the 1396 routes,

based on the 11 predictors that included Current June

temperature and precipitation. We then used that model

to predict Current characteristic values across the U.S.

grid cells, based on the 11 predictors including Current

June temperature and precipitation. Finally, we estimat-

ed 2080 characteristic values in the grid cells based on

the same 11 predictors, except that we substituted mid-

range 2080 June temperature and precipitation for

Current June temperature and precipitation. The differ-

ence between 2080 and Current characteristic estimates

for each of the grid cells was divided by the range

(maximum minus minimum) of Current characteristic

values to give a percentage characteristic change from

Current to 2080. This represented characteristic change,

relative to the Current range, assuming that the nine

predictors (besides June temperature and precipitation)

were not affected by the changes in June temperature

and precipitation. In fact, some of these predictors (e.g.,

elevation, slope) would not tend to change with climate

change, while others (GPP and land covers, for example)

might.

Second (Climate 2), we followed the same protocol as

in Climate 1 of modeling the 1396 route characteristics

with Current data, then applying that model to Current

climate data and then to 2080 climate data across the

grid cells. However, for Climate 2, we modeled the five

characteristics with June temperature and precipitation

as the only predictors. This assumed that temperature

and precipitation were adequate predictors of the five

characteristics. Again, change from Current to 2080 was

expressed as a percentage of Current range.

TABLE 2. Model fits (R2 values) for prediction of five aviancommunity characteristics by landscape variables (see Table1) averaged over boosted regression tree (BRT), randomforest (RF), multiple adaptive regression splines (MARS),and ordinary least squares (OLS) models.

Model MASS RICH CS CI PC

BRT� 0.61 0.77 0.40 0.50 0.93RF� 0.61 0.77 0.45 0.48 0.94MARS� 0.52 0.71 0.30 0.38 0.86OLS� 0.51 0.60 0.25 0.36 0.80GWR� 0.68 0.79 0.57 0.59 0.93

� Cross-validated R2 for model including all 22 predictors(not including developed and agricultural land covers).

� Geographically weighted regression R2 based on 11predictors (June temperature, June precipitation, elevation,slope, GPP, canopy, natural fragmentation, human-causedfragmentation, land cover diversity, agricultural land cover,developed land cover). All GWR models were significant (Ftest) at P , 0.001.

8 www.climatewizard.org

RALPH GRUNDEL ET AL.798 Ecological ApplicationsVol. 24, No. 4

Third (Climate 3), we simplified the 11-predictor

model by including, besides June temperature and

precipitation, only predictors that would not change

with climate (elevation and slope), plus two predictors

possibly affected by climate: GPP and the percentage of

a route covered by the most common land cover type

(MaxHab), regardless of that land cover type. For

Climate 3, we needed to estimate Current and 2080

values of GPP and MaxHab as inputs into the model

during the prediction phase. We did this, using GWR,

by modeling GPP or MaxHab as a function of Current

June temperature and precipitation across the 1396

routes and then using that model to predict GPP and

MaxHab for Current (using Current June temperature

and precipitation as inputs) or 2080 (using mid-range

2080 June temperature and precipitation as inputs)

across the grid cells. Finally, as with the other two

climate effect models, we modeled characteristic values

for the 1396 routes based on route slope, elevation,

GPP, MaxHab, and June temperature and precipitation,

and then used that model to project Current and 2080

characteristic values based on grid cell values of slope,

aspect, and estimated Current or 2080 June temperature,

precipitation, GPP, and MaxHab values. As before, we

calculated characteristic change from Current to 2080 as

a percentage of the Current characteristic range across

the grid cells.

We also examined how the predictors might affect

bird community composition by performing a nonmetric

multidimensional scaling (NMS) ordination (McCune

and Mefford 2011) on the route3 species count (square-

root-transformed) matrix and then correlated the

subsequent ordination axes with the predictors. Al-

though NMS and PC both ordinated the route3 species

matrix, their outputs differed somewhat in their

emphases. As noted, principal curve ordination produc-

es a one-dimensional result that describes the ecological

gradient underlying the compositional matrix. NMS can

produce a multidimensional result (in this study a two-

dimensional result) that emphasizes compositional

similarity among routes.

We examined relationships between pairs of commu-

nity characteristics (see Table 3) using Spearman rank

correlations (SPSS 2004). To explore potential nonlinear

relationships, we also regressed each characteristic pair

using MARS (multiple adaptive regression spines), a

flexible regression technique that readily illustrates

nonlinearities in relationships between two variables

(Milborrow 2011). Unlike correlation, which is symmet-

rical, the regression of variable 1 on variable 2 does not

necessarily yield the same relationships as regression of

variable 2 on variable 1. Therefore, we calculated both

regressions for each characteristic pair and reported the

result with the higher fit (higher R2).

Finally, we reported results on characteristic congru-

ence in space both across the entire contiguous United

States and by Landscape Conservation Cooperatives,

LCC (Austen 2011). The LCCs are conservation science

partnerships between governmental and nongovernmen-

tal institutions that support conservation planning at

landscape scales. Sixteen LCCs cover the contiguous

United States. LCCs were principally defined on the

basis of Bird Conservation Regions (BCR) with some

modifications (Austen 2011); see Fig. 1. Examining

results nationally and by LCC regions provided insight

into possible effects of spatial scale on findings and put

results into a regional, as well as national, conservation

context.

RESULTS

Predictors of community composition and spatial scale

of characteristic variation

Principal curve ordination of the BBS species data

(square-root-transformed) accounted for 55% of the

species variation. Of the 11 predictors selected for

analyses, June precipitation (BRT importance score

43.0%), human-associated forest fragmentation (21.1%),

June temperature (10.5%), GPP (7.0%), elevation

TABLE 3. (A) Pearson correlations among five avian community characteristics; between characteristics and latitude andlongitude; and with changes (D) in characteristics associated with a shift from Current to 2080 climate conditions averaged acrossthe three climate models (Fig. 5). (B) Pearson correlations among average percentage changes (D) of characteristics associatedwith a shift from Current to 2080 climate conditions; and between climate-associated percentage changes and latitude andlongitude.

Characteristic or change MASS RICH CS CI Latitude Longitude D

A) Characteristic

MASS �0.19 0.36 �0.01RICH 0.12 �0.06 0.62 �0.26CS 0.08 0.35 �0.52 0.01 �0.12CI 0.02 0.16 0.59 �0.27 �0.15 0.20PC �0.38 �0.69 �0.12 0.07 0.30 �0.74 0.31

B) Change (D) DMASS DRICH DCS DCI Latitude Longitude

DMASS 0.41 0.14DRICH 0.25 0.02 �0.25DCS �0.06 0.59 �0.22 �0.14DCI �0.15 0.31 0.39 �0.33 0.03DPC �0.19 �0.02 �0.04 0.38 �0.41 �0.28

June 2014 799COINCIDENCE OF CONSERVATION MEASURES

(6.2%), and canopy cover (5.6%) made the highest

relative contributions (importance scores) to explaining

PC in BRT models (Table 1, last column). Each of these

most important predictors had negative OLS regression

coefficients, suggesting that the PC scores declined

across the BBS sites as precipitation, fragmentation,

temperature, GPP, elevation, and canopy cover in-

creased. Because these are OLS regression coefficients,

the relationship of one predictor to the PC score is after

controlling for the effects of the other predictors. We

repeated the ordination using NMS ordination, which

yielded two ordination axes (NMS axis 1, R2¼0.48; axis

2 R2¼ 0.37). Predictor importances were similar for the

NMS ordination and for the principal curve ordination

(see Appendix C for details). The principal curve scores

were most strongly associated (highest absolute Pearson

correlation) with BBS counts of common Eastern U.S.

birds, such as Red-bellied Woodpecker (Melanerpes

carolinus), Northern Cardinal (Cardinalis cardinalis),

Blue Jay (Cyanocitta cristata), Downy Woodpecker

(Picoides pubescens), Tufted Titmouse (Baeolophus

bicolor), and American Crow (Corvus brachyrhynchos)

(see Plate 1). A complete list of correlations between PC

scores and bird species counts on the BBS is shown in

Appendix C.

Moran’s I, relativized by its maximum value, was

positive for several hundred kilometers for several of the

characteristics (Fig. 2). PC exhibited a high degree of

clustering of similar values (high Moran’s I, 0.6–0.8)

over distances up to ;1200 km, RICH and MASS

exhibited moderate clustering (Moran’s I 0.3–0.5) of

similar values up to ;1000 km, and CS and CI exhibited

relatively weak clustering of similar values at distances

of ,250 km.

For the medium A1B emissions scenario and medium

GCMmodel values, June temperature during 1997–2004

(20.08 6 4.68C, mean 6 SD) was significantly lower than

2080 projected June temperature (23.78 6 4.88C) and

1997–2004 June precipitation was significantly greater

(83.3 6 50.4 mm, mean 6 SD) than 2080 precipitation

(73.2 6 40.6 mm) (paired t test for each, P , 0.0001)

across the 49-km2 grid cells used in this study (Table 4).

Projected 2080 June temperatures were higher in 99.5%

of the cells than in 1997–2004 and projected 2080 June

precipitation levels were lower in 66.9% of the grid cells.

Prediction of bird community characteristics

Model fits to the five characteristics were similar

across the four preliminary modeling techniques tested

(Tables 1 and 2). The strongest predictor (highest mean

relative importance) of MASS was a topographic

variable (elevation); for RICH the strongest predictor

was canopy cover; for CS and PC it was a climate

variable (June temperature and June precipitation,

respectively); and for CI it was natural fragmentation

and GPP. GWR is a local regression technique and

produced a regression coefficient for the relationship

between predictors and responses for each of the 1396

BBS route neighborhoods. Therefore, the relationship

between predictors and responses in the GWR models

could not be summarized by a single coefficient.

However, for the 11 predictors used in the GWR

models, we provided Spearman rank correlations to

indicate the general global direction of the relationships

FIG. 2. Moran’s I, divided by its maximum possible score, as a function of distance between BBS survey points, for five aviancommunity breeding season characteristics: community biomass (MASS), species richness (RICH), conservation index (CI, thepercentage of constituent species’ global abundance in the community); combined score (CS, species’ threat of decline orextirpation); and the principal curve ordination score (PC, the community’s position along the ecological gradient underlyingspecies composition).

RALPH GRUNDEL ET AL.800 Ecological ApplicationsVol. 24, No. 4

(Table 1). For the predictors with the highest impor-

tance values, MASS increased with decreasing elevation

and increasing GPP and agricultural land cover; RICH

increased with increasing canopy cover and decreasing

June temperature; CS increased with increasing canopy

cover and June temperature; CI increased with increas-

ing fragmentation of forests due to natural causes and

increasing GPP; and PC increased with decreasing June

temperature and precipitation and human-associated

forest fragmentation.

Across modeling techniques, CS and CI had the

lowest model fits, with R2 values of 0.57 and 0.59 for

GWR models (Table 2). PC models had the highest

GWR R2 (0.93), but the PC ordination itself explained

55% of species variation so that the predictors in the

GWR model explained ;51% of the species variation

(93% of 55%) across the BBS routes.

Spatial pattern and congruence of community

characteristics

Among pairs of the five community characteristic

variables, only RICH–PC, and CS–CI had absolute

correlation coefficients .0.5 across the contiguous

United States (Table 3A). Even after accounting for

possible nonlinearity in paired relationships, only

RICH–PC and CS–CI had model fits (R2) . 0.5

(multiple adaptive regression splines model; Milborrow

2011); see Appendix D for details.

A few correlation pairings, CS–CI, RICH–CS,

RICH–CI, exhibited consistent positive or negative

correlations across LCCs, but correlations between most

pairs of characteristics varied in sign across LCCs (Fig.

3). Variations in PC ordination scores within an LCC or

across the United States represent extant differences in

community composition or the underlying ecological

gradient associated with those community differences.

Therefore, strength of correlation of PC with the four

characteristics informs us whether the extant variation

in community composition or ecological gradient was

systematically associated with different states of MASS,

RICH, CS, or CI. The absolute value of the correlation,

rather than the direction of the correlation, tells us

whether a systematic relationship existed. Of the four

characteristics other than PC, RICH had the highest

overall absolute correlation with the ecological gradient

represented by PC (Table 3A), had the most LCCs with

relatively high (e.g., .0.5) absolute correlation with PC,

and exhibited more of a geographic pattern in its PC

correlations as seen by the clustering of high absolute

values in the geographically ordered LCCs in Fig. 3.

Positive correlations among the six pairings of the

four non-PC characteristics were potentially helpful

from a conservation perspective because desirable states

(high richness and presence of threatened species, for

example) coincide. Examining Fig. 3 by LCC, rather

than by characteristic pairs, showed that the frequency

of such positive correlations differed among the LCCs.

Specifically, among the 16 LCCs, the North Atlantic and

Desert LCCs had the most significant, positive correla-

tions among the six possible correlated pairs ofcharacteristics, and the Gulf Coastal Plains and Ozarks

LCC and Plains and Prairie Pothole LCC had the fewest(Table 5). The Appalachian and South Atlantic LCCs

had the highest averaged value of MASS, RICH, and CI(Fig. 4F).

CS exhibited the strongest (highest correlation)latitudinal gradient (Table 3A). PC and RICH had the

strongest longitudinal gradients. Characteristics ana-lyzed by LCC region showed different patterns of

geographic variation (Fig. 4). For example, when LCCswere arranged in approximate west to east order, MASS

and RICH and the averaged MASS, RICH, and CI weregenerally higher in the east, CS and CI were variable

longitudinally, and PC declined west to east. Highestvalues of MASS occurred in the Plains and Potholes

LCC and Great Plains LCC where lowest values ofRICH occurred. CI was much higher in California thanin other LCCs.

Changes in characteristics from Current to 2080 werenot strongly correlated with value of the characteristic

(i.e., higher (or lower) values of the characteristic werenot associated with larger (or smaller) climate-related

changes from Current to 2080; Table 3A, last column).Climate-related changes in characteristics were not

strongly correlated with latitude or longitude, withclimate related changes in Mass and PC having the

highest absolute correlation with latitude (r ¼ 0.41).Among pairs of characteristics, climate-related changes

in CS and Rich were the most strongly correlated (r ¼0.59) (Table 3B).

Geographic distribution of the five characteristics andthe projected 2080 percentage change compared to the

Current range, for the three Climate models, weresummarized in Fig. 5 and in Table 4 (enlarged versions

of maps in Figs. 5 and 6 are available in Appendix E).Higher values of MASS, RICH, and CI were assumed

to be desirable conservation attributes of bird commu-nities in landscapes. Fig. 6 (left) showed the distribution

of the Current average of these three characteristics,

TABLE 4. Percentage of grid cells (mean 6 SD, n ¼ 120 806cells) for three climate models in which the characteristicvalue is predicted to decline from Current to 2080.

Characteristic

Decrease (%), by climate scenario

Low Medium High

MASS 45 6 8 49 6 21 46 6 21RICH 78 6 11 89 6 8 67 6 5CS 57 6 4 48 6 8 35 6 5CI 61 6 12 61 6 9 43 6 4PC 48 6 7 46 6 4 40 6 3Temperature 21.3 6 4.9 23.7 6 4.8 26.5 6 5.1Precipitation 56.3 6 32.7 73.2 6 40.6 103.0 6 56.0

Notes: Shown are Low (Scenario B1, Lowest Model);Medium (SRES emission scenario A1B, Ensemble Average);and High (Scenario A2, Highest Model) scenarios (http://www.climatewizard.org/). Temperature is mean June temperature(8C); precipitation is mean June precipitation (mm).

June 2014 801COINCIDENCE OF CONSERVATION MEASURES

after scaling each characteristic’s value in a cell to the

percentage of their respective ranges (0 for lowest to 100

for highest range value across cells) and averaging the

three scaled values per cell. Fig. 6 (right) showed the

predicted change in averaged MASS, RICH, and CI

(calculated as a percentage of characteristics’ range as in

Fig. 5), based on climate changes projected from

Current to 2080. Percentage changes were averaged

across Climate 1, 2, and 3 models, for each of MASS,

RICH, and CI, and then averaged across those three

FIG. 3. Spearman rank correlations (rS) between community characteristic pairs (defined in Fig. 2), by Landscape ConservationCooperative (LCC) region. LCC number codes are as in Fig. 1. All correlations were significant at P , 0.001, except as indicated byX. Sample sizes (n) for each LCC are given in Table 5. The horizontal line shows rS across all grid cells.

RALPH GRUNDEL ET AL.802 Ecological ApplicationsVol. 24, No. 4

TABLE 5. Number of positive Spearman correlations (rS) between MASS, RICH, CS, and CI pairings, out of a maximum of six,significant at different minimum levels of rS; percentage change in averaged MASS, RICH, and CI (%MRC) from Current to2080 associated with projected climate change; and number of grid cells (n) per Landscape Conservation Cooperative (LCC)region.

LCC

rS

%MRC n0 0.4 0.6

North Pacific 5 3 1 �1.2 3 002California 5 2 2 �2.8 3 005Desert 6 5 3 �3.5 7 695Great Basin 4 3 3 �3.9 8 394Southern Rockies 5 3 1 �4.8 7 932Great Northern 3 1 1 �4.1 11 517Great Plains 5 4 2 �3.7 12 099Plains and Prairie Potholes 2 2 1 �1.1 13 811Gulf Coast Prairie 3 2 1 �5.0 8 420Eastern Tallgrass Prairie and Big Rivers 5 3 3 �6.7 5 400Gulf Coastal Plains and Ozarks 2 1 1 �14.7 10 587Peninsular Florida 6 2 2 �1.5 1 155South Atlantic 3 2 1 �5.2 5 140Appalachian 3 2 1 �7.4 9 165Upper Midwest and Great Lakes 4 3 1 �13.0 8 975North Atlantic 6 5 3 �3.2 4 509All 5 1 1 �5.6 120 806

Note: A rS value of 0 means that the correlation was statistically significant (at the P , 0.001 level), regardless of magnitude ofthe correlation.

FIG. 4. (A–E) Means (SE values are small and obscured by symbols) of five community characteristics for the 16 LCC regions.Bird community biomass and species richness were measured per BBS route per year; because CI values are very low, they areshown multiplied by 105. (F) Means (shown multiplied by 102) of average of MASS, RICH, and CI, after standardization of thethree variables as a fraction of their respective ranges, for the 16 LCC regions. Dotted lines represent mean values of responsesacross all grid cells.

June 2014 803COINCIDENCE OF CONSERVATION MEASURES

FIG. 5. Distribution of five avian community characteristics across the United States: bird mass (kg per BBS route per year),richness (number of species per BBS route), high combined scores (CS), conservation index (CI; values shown have been multipliedby 105), and principal curve (PC) scores. (A) Current (1997–2004) characteristic scores across the lower 48 states. (B–D) Predictedpercentage change (color codes in the large key), as a percentage of the Current characteristic range, from Current to 2080predictions. Projections are based on Climate 1 (change predictions based on current values for non-climate variables) for (B);Climate 2 (change predictions based on temperature and precipitation only) for (C); and Climate 3 (Future model: changepredictions based on predictors including projected future gross primary productivity and MaxHab, the percentage of a routecovered by the most common land cover type) for (D). To the right of each map in (B–D) is the adjusted geographically weightedregression (GWR) model R2 value.

RALPH GRUNDEL ET AL.804 Ecological ApplicationsVol. 24, No. 4

characteristics. This averaged predicted value of MASS,

RICH, and CI decreased in 75.2% of cells from Current

to 2080. The average predicted MASS, RICH, and CI

declined from Current to 2080 in each LCC, with the

greatest percentage declines in the Gulf Coastal Plains

and Ozarks LCC and Upper Midwest and Great Lakes

LCC (Table 5).

DISCUSSION

We analyzed distributional patterns of five avian

breeding-season community characteristics across the

United States, described covariation of these character-

istics, and documented possible effects of projected

climate change on the characteristics and their covari-

ation. Because any of these community characteristics

could potentially serve as a basis for conservation

planning, documenting how they covary helps us to

understand national- and regional-scale trade-offs in

selecting one, or more, of these characteristics as the

basis for planning, and also aids in identifying areas of

particular conservation value on that national or

regional scale (Lawler et al. 2003). Although one of

these characteristics, species richness, is perhaps the

most widely used measure of conservation value of

landscapes, richness by itself may be insufficient to

characterize the ability of conservation areas to main-

tain ecological functions (Fleishman et al. 2006, Redford

et al. 2013). The same conclusion may hold for other

individual community characteristics, such as abun-

dance, used to measure habitat quality or conservation

value (Skagen and Yackel Adams 2011). Therefore,

understanding patterns of variation of multiple mea-

sures of conservation value can provide a more realistic

guide to conservation prioritization.

In what contexts might the conclusions from this

study be applied? The five bird community characteris-

tics that we examined describe the contribution that an

area (in this study a 49-km2 grid cell) makes to the

FIG. 5. Continued.

June 2014 805COINCIDENCE OF CONSERVATION MEASURES

fundamental objective of retention of bird species.

Ultimately, we might investigate whether overall reten-

tion of species across an area of concern is more highly

related to one characteristic (richness, abundance, or

management attention to the least common species, for

example) than to others. Here, however, using these

analyses we can better understand whether a conserva-

tion management decision is likely to be either–or,

richness or abundance, abundance or rare species, in

different locales within the contiguous United States. If

the four characteristics do not coincide in a desirable

way, then managers have to consider whether to

emphasize increasing bird abundance, helping threat-

ened species, or boosting richness, in pursuing the

fundamental objective of retaining species. We empha-

size that the analyses are largely static: they do not

comment on whether an area should, or could, have

more species, for example, just whether areas of extant

high richness coincide with areas of extant high mass,

for instance.

The information on coincidence can help managers to

make decisions on selecting an area for conservation or

can provide perspective on what combinations of

desirable characteristics coincide over different spatial

scales. We found that the characteristics changed across

the landscape at different rates. The ecological gradient

underlying community composition (PC) changed rela-

tively little for distances up to ;1200 km. Values of mass

(MASS) and richness (RICH) were less clustered

spatially than PC and over shorter distances, ;1000

km. The combined score (CS) and conservation index

(CI) changed most rapidly and randomly across the

landscape. For the manager, these differences in spatial

autocorrelation suggest that as the area of planning

increases, values of some of these metrics will stay at

more consistent levels than for other metrics. Although a

typical manager will not be responsible for areas

hundreds of kilometers wide, understanding variability

over larger areas is relevant to the contribution that a

smaller unit makes to conservation across the larger

unit. The rates of spatial change just noted suggest that

management planning related to threatened and rarer

species, represented by the CS and CI scores, might

increase the needed size of a preserve to capture the high

end of the CS and CI, given the lower rate of clustering

of CS and CI scores compared to richness, for example.

For four characteristics examined—abundance of

species in the community (MASS), richness (RICH),

level of threats to the most threatened species in the

community (CS), and importance of an area to the

community of species present (CI)—higher values

generally would be preferred from the perspective of

conservation of species. Geographic coincidence of high

values therefore means that managers would not be

faced with considering trade-offs among desirable

characteristics. High positive correlations between these

characteristics are an indication that geographic coinci-

dence of high extant values was present within the

geographic domain considered. Such a correlational

analysis does not tell us whether extant values can be

increased; that is a separate management consideration.

The five community characteristics did not strongly

coincide on a national scale (Table 3). Among the five

characteristics, PC–RICH and CI–CS had the highest

absolute correlations (r . 0.5). This suggests that

richness often changed as the ecological gradient

underlying community composition changed. Similarly,

CI, a globally integrated measure of landscape use by

birds, was positively related to the presence of threat-

ened, and often rare, species whose occurrence in a

community was indicated by higher CS (Grundel and

Pavlovic 2008). When a rare species uses a landscape,

the landscape’s conservation importance increases, as

indicated by a higher CI score.

However, magnitudes of the conservation character-

istics did not consistently coincide geographically. For

example, neither bird community richness and mass, nor

richness and the conservation index, were highly

correlated (r , 0.2), so regions of the United States

often differed in their conservation value depending on

which metric we chose to represent conservation value

(Fig. 5, Current). The Appalachian LCC, for instance,

had the highest mean richness (Fig. 4) but, relative to

the other LCCs, average MASS, CS, and CI. Across the

Appalachian LCC, RICH was not highly correlated

with MASS or CI. Under the assumption that higher

values of MASS, RICH, CS, and CI are positively

related to species retention, positive correlations be-

tween pairs of the four characteristics could benefit

conservation efforts by showing the feasibility that

improvement of one characteristic could be associated

with improvement of a second characteristic at a

location. Nationwide, only one positive correlation,

out of six possible correlations among the four (non-PC)

characteristics, occurred at moderate levels of correla-

tion (.0.4); see Table 5. However, when the relation-

ships were examined at the regional level of LCC, results

were variable. LCCs differed in the frequency with

which these four characteristics were positively correlat-

ed. For example, the North Atlantic and the Desert

LCCs had the highest frequency of significant positive

correlations, with five of six pairs being positively

correlated at rS . 0.4 vs. one of six for the Gulf Coastal

Plains and Ozarks and the Great Northern LCCs (Table

5). Managers working in the first two LCCs could have

a heightened expectation that conservation sites could

benefit species retention in several ways (abundance,

richness, helping threatened species), whereas managers

in the second two LCCs would have a lower expectation

for that benefit and might have to give greater

consideration to managing separate units of their

preserves for different aspects of conservation value.

As noted, the main intent of this study was to examine

existing, or static, relationships among the five charac-

teristics. However, we modeled possible changes in the

five characteristics related to climate change to explore

RALPH GRUNDEL ET AL.806 Ecological ApplicationsVol. 24, No. 4

sensitivity of these characteristics to environmental

change and to understand the temporal trajectory of

these metrics in the face of a likely ubiquitous agent of

change. For approximately 75% of the area of the

contiguous United States, projected changes in June

temperature and precipitation from the year 2000 to

2080 were associated with an averaged decrease of

MASS, RICH, and CI, where decreased values are

linked to lower conservation value of sites for birds (Fig.

6B). A mean decline in this aggregate conservation value

metric was predicted for every LCC (Table 5). The

greatest climate-related decreases in this averaged mass,

richness, and conservation index were across the

Southeast (e.g., Gulf Coastal Plains and Ozarks LCC)

and upper Midwest United States (Upper Midwest and

Great Lakes LCC) (Table 5, Fig. 6). Although each

LCC was predicted to experience an average decline in

this metric, there were areas experiencing increases. The

greatest increases, a desirable conservation outcome,

were across parts of the upper Great Plains region in the

central United States straddling the boundary between

the Great Plains LCC and the Eastern Tallgrass Prairie

and Big Rivers LCC (Figs. 1 and 6). We examined where

across the United States high values of MASS, RICH,

and CI coincided (Fig. 6A). Values tended to be higher

in the eastern half of the country, especially in an area

that included much of the Ohio River basin and the

Appalachian Mountains (e.g., Appalachian LCC, South

Atlantic LCC), and along the southwest U.S. coast

(California LCC) (Fig. 4f ). These regions, therefore,

may be especially important for breeding bird conser-

vation.

The five community characteristics included metrics

that emphasize the importance of rare species in making

conservation decisions (CS and, in part, CI) and others

that emphasize common species (MASS). Although rare

species are often the focus of conservation action, it is

the total amount (number or mass) of organisms within

a community that is often most related to how a

community affects its resident ecosystem (Lyons et al.

2005). Common species often make up a larger

percentage of the total community abundance or

biomass than rarer species. Looking at total community

mass, therefore, is one way to understand the ecosystem

services that birds provide with an emphasis on the role

played by common species, even though we do not know

whether the common species play a disproportionately

large role compared to the rarer species (Gaston 2010,

Redford et al. 2013).

In part, the lack of strong correlations among the five

characteristics is a reflection of differences in latitudinal

and longitudinal patterns of characteristic variation

(Table 3). Strongest longitudinal patterns (highest

correlation with longitude) were observed for RICH,

which declined from east to west across the lower United

States, and the ecological gradient that underlies

community composition, as represented by principal

curve scores (PC). As was the case for PC, avian

community compositional differences expressed in NMS

ordination results were more strongly arrayed along a

longitudinal gradient (see Appendix C for details). CS, a

measure of a species’ threat of decline, exhibited the

strongest latitudinal pattern, decreasing from south to

north as a general trend (Fig. 5, Table 3A). The

ecological gradient underlying composition (PC) exhib-

FIG. 6. (A) Average of MASS, RICH, and CI, each as a percentage of their range. (B) Average percentage increase or decreaseof MASS, RICH, and CI from Current to 2080 based on the values averaged from Climate models 1, 2, and 3. Values for each gridcell shown (n¼ 120 806) are averaged values of MASS, RICH, and CI, after standardization of the three variables as a fraction oftheir respective ranges.

June 2014 807COINCIDENCE OF CONSERVATION MEASURES

ited a sharp demarcation near the 100th meridian,

separating the United States grossly into two similar-sized avian ecological provinces, although latitudinal

gradients in PC were also apparent, especially in theeastern half of the country.

Predicted changes in the five characteristics withchanges in temperature and precipitation projected for

2080 were not highly correlated with latitude orlongitude (Table 3B). The three models of how climate

change might affect the characteristics (Climate 1,Climate 2, and Climate 3) produced similar geographic

patterns for predicted characteristic changes, althoughexpected percentages of change differed among the

models (Fig. 5). In general, Climate 2, which onlyincorporated temperature and precipitation into the

model of climate-associated change, predicted thelargest changes. Because this model ignored concomi-

tant changes in landscape characteristics, such as in land

cover and gross primary productivity, its predictions are

inherently less plausible. Despite such differences amongmodels, between Current and 2080 dates, the three

models predicted (1) MASS declines in the South andincreases in the North of the lower United States,

especially increases in the upper Midwest and Northeast;(2) greatest RICH declines in the Southeast and western

Great Lakes states; (3) increases in the species’ threatscore, CS, in most of the Southwest and West across

models and declines across much of the northern row ofU.S. states; (4) increases in the conservation index (CI)

in the East and parts of the Southwest and declinesacross the upper Midwest and northern tier of Western

states; and (5) climate-related divergence of the ecolog-ical gradient underlying community composition, with

principal curve scores west of the 100th meridianincreasing and to the east decreasing, furthering the

longitudinal separation of the gradient and making the

PLATE 1. Differences in land bird community composition across the U.S. were most highly correlated with counts of commoneastern U.S. bird species such as (clockwise from upper left) Tufted Titmouse (Baeolophus bicolor), Red-bellied Woodpecker(Melanerpes carolinus), Northern Cardinal (Cardinalis cardinalis), and Downy Woodpecker (Picoides pubescens). Photo credits: R.Grundel.

RALPH GRUNDEL ET AL.808 Ecological ApplicationsVol. 24, No. 4

two gross ecological provinces more unlike each other.

The ecological divergence with predicted climate chang-

es was especially pronounced around the state of Texas,

where principal curve scores diverged strongly from

scores in the eastern half of the United States.

Across climate scenarios, slightly more than half of

the U.S. grid cells exhibited projected increases in MASS

from Current to 2080 (Table 4). RICH decreased in

most cells, especially under the medium emissions

scenario. PC values increased in most cells, with greater

increases under higher projected 2080 temperatures.

Combined score (CS) and conservation index (CI) went

from most cells exhibiting decreases to a majority

increasing as projected 2080 temperatures increased.

The changes in CI suggest an overall decline in

conservation value of the U.S. avian landscape associ-

ated with late-21st century low-to-moderate climate

changes, but an increase in conservation value under the

greatest projected 2080 temperature increases. CS

increased in most cells under that highest emissions

scenario, suggesting an increase in threatened or rare

species or the level of threat to, or rarity of, species.

Because the CI tends to increase as the frequency of

globally rare species in a location increases, the CI

increase, a generally desirable community characteristic,

may arise because of the increase in rare species with

increasing temperatures.

Temperature and water balance previously have been

related to avian richness patterns at large scales (richness

aggregated at 200 km or more), perhaps through their

effect on plant productivity (Hawkins et al. 2003,

Phillips et al. 2010). June temperature and precipitation

were generally among the most important predictors of

the five avian community characteristics (Table 1), but

were especially important as components of the ecolog-

ical gradient underlying community composition and as

predictors of community composition itself (see Appen-

dix C for details). There were exceptions to climatic

variables being the strongest predictors of the five

characteristics. For example, elevation was the most

important predictor of MASS. Nonetheless, the consis-

tent importance of temperature and precipitation, in

contrast to relatively low importance of land cover type,

underlines the likelihood that climate changes will have

relatively strong effects on avian community character-

istics, especially because birds are highly mobile and are

therefore unlikely to be dispersal limited.

We previously proposed the conservation index as a

measure of conservation value of landscapes (Grundel

and Pavlovic 2008). The CI reflects current avian

landscape use patterns and, as such, does not provide

historic perspective on what intact native landscapes

might have once supported. Nonetheless, the CI is an

index that combines information on avian abundance,

richness, and rarity in a way that higher values indicate a

greater presence of more birds and species, especially

globally rarer species. The index had highest values,

corresponding to highest conservation value, across much

of California and southern Texas, in the southern

Appalachian Mountains and Atlantic Coastal Plain,

and through parts of the Southwest (Figs. 4 and 5).

Lowest values were through the heavily agricultural

landscape of the Midwest. Prior study of hypothetical

landscapes in the Midwest noted a negative correlation

between CI and richness, such that the oft-stated

conservation goal of managing landscapes to maintain

or increase species richness while promoting use by

threatened species might be difficult to achieve (Grundel

and Pavlovic 2008). Across the United States, CI and

RICH were not negatively correlated, but were not highly

correlated, (Table 3A; r¼ 0.16), suggesting that areas of

high breeding bird species richness and high CI would be

found, but not consistently. However, for half (n¼ 8) of

the LCCs in the contiguous United States, CI and RICH

were correlated at rS . 0.5 (Fig. 3), indicating that in

many areas of the country, locations occur where

regionally high diversity and significant use by rarer

species coincide. On an LCC basis, the highest CI

coincided with a below-average RICH (California

LCC), whereas the LCC with the highest RICH

coincided with average CI (Appalachian LCC) (Figs. 4

and 5). Throughout much of the Appalachians and the

Southeast, high values of richness and combined score co-

occurred, indicating regions of high species richness with

many species having high threat scores (Fig. 5, Current).

Resource managers making on-the-ground decisions

concerning management actions and goal setting are often

aware of the myriad factors affecting species retention and

population robustness. What might be even more

challenging is gaining perspective on whether achieving

high diversity and high use by threatened species, where

‘‘high’’ can be defined nationally, regionally, or locally, is a

common occurrence in their region. When resource

managers are asked to describe the ultimate goal of their

management, responses often include maintenance or

increase of biodiversity, especially of native or endemic

species, improving conditions for threatened species, and

maintaining or improving ecosystem function or services

that ecosystems provide for human society (Margules and

Pressey 2000, Fleishman et al. 2006). Several management

challenges arise from this multiplicity of goals, including

acknowledging that multiple potential goals exist, quan-

tifying multiple goal parameters, and understanding that

when there are multiple desirable goals, patterns of

covariation of the goals often mean that not all of those

goals can be concurrently achieved. Priorities have to be

set, elevating the importance of one goal over another.

Our objective in this study was to examine how a series of

five avian community breeding-season characteristics

varied and covaried geographically and how one pervasive

agent of change, climate, may alter variation patterns in

the future. The results of this analysis inform us, on a

national and regional scale, of the trade-offs likely to be

present in goal setting. A particular manager’s perspective

is often limited to a much smaller area than the national

or regional scale that we examined. As scale decreases, the

June 2014 809COINCIDENCE OF CONSERVATION MEASURES

nature of the trade-offs found on a national scale can

change. However, as was illustrated by several examples inthis paper, regional patterns of coincidence of conserva-

tion value metrics can inform the local practitioner of therole that their preserve might play in the regionalconservation scheme. Armed with insights, such as the

presence or absence of favored relationships betweenconservation value parameters, the local practitioner

might see that most preserves in a region will contributesimilar attributes—high mass and high richness maybe—

to the conservation scheme, whereas in another region thelocal practitioner might see that some preserves contribute

one attribute—high mass or high richness. This insightshould help to set the local goals. Thus, the conclusions

reached here are to be taken as investigating anational- and regional-scale perspective on prioritizinggoal parameters for conservation or management decision

making. Local decisions on management for avianconservation can consider this broader perspective when

selecting measures of conservation value for settingmanagement goals.

ACKNOWLEDGMENTS

The analyses in the paper are based on a variety of publiclyavailable data products and analytical tools that are acknowl-edged with references in the text. These surveys, databases,models, and other data sources and programs for statisticalanalyses are available because of the work of many scientistsand volunteers who have labored thousands of hours on theseproducts. We are grateful for their efforts. Jean Adams assistedwith R coding. We acknowledge Wayne Thogmartin, MichaelRunge, and two anonymous reviewers for comments thatclarified the manuscript and improved the scope of analyses.Any use of trade, product, or firm names is for descriptivepurposes only and does not imply endorsement by the U.S.Government. This article is Contribution 1786 of the U.S.Geological Survey Great Lakes Science Center.

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SUPPLEMENTAL MATERIAL

Appendix A

Avian community characteristics and possible predictors of those characteristics, along with variable descriptive statistics anddata sources (Ecological Archives A024-046-A1).

Appendix B

Possible effects of differences in species detection on results (Ecological Archives A024-046-A2).

Appendix C

NMS ordination of bird counts (square-root transformed) from 1396 BBS routes (Ecological Archives A024-046-A3).

Appendix D

Relationships between community characteristic pairs modeled by multiple adaptive regression splines (MARS) (EcologicalArchives A024-046-A4).

Appendix E

Enlarged versions of maps from Figs. 5 and 6 (Ecological Archives A024-046-A5).

June 2014 811COINCIDENCE OF CONSERVATION MEASURES