ecological niche conservatism and pleistocene refugia in the thrush-like mourner, schiffornis sp.,...

ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2007.00258.x

ECOLOGICAL NICHE CONSERVATISM ANDPLEISTOCENE REFUGIA IN THE THRUSH-LIKEMOURNER, SCHIFFORNIS SP., IN THENEOTROPICSA. Townsend Peterson1,2 and Arpad S. Nyari1

1Natural History Museum and Biodiversity Research Center, The University of Kansas, Lawrence, Kansas 660452E-mail: [email protected]

Received March 21, 2007

Accepted August 13, 2007

Recent studies have increasingly implicated deep (pre-Pleistocene) events as key in the vertebrate speciation, downplaying the

importance of more recent (Pleistocene) climatic shifts. This work, however, has been based almost exclusively on evidence from

molecular clock inferences of splitting dates. We present an independent perspective on this question, using ecological niche model

reconstructions of Pleistocene Last Glacial Maximum (LGM) potential distributions for the Thrush-like Mourner (Schiffornis turdina)

complex in the neotropics. LGM distributional patterns reconstructed from the niche models relate significantly to phylogroups

identified in previous molecular systematic analyses. As such, patterns of differentiation and speciation in this complex are con-

sistent with Pleistocene climate and geography, although further testing will be necessary to establish dates of origin firmly and

unambiguously.

KEY WORDS: Climate, ecological niche, Last Glacial Maximum, Pleistocene, speciation.

The age and timing of vertebrate speciation events have been the

subject of extensive debate and analysis in the literature, with re-

cent opinions indicating that most speciation took place prior to

the Pleistocene, and thus suggesting rather old (e.g., >106 years)

speciation events (Klicka and Zink 1997; Drovetski and Ronquist

2003), although some opinions to the contrary have been expressed

(Johnson and Cicero 2004; Weir and Schluter 2004). These con-

clusions, however, have been based exclusively on dating splitting

events via molecular clocks—such time estimates are complicated

by nonuniformity of molecular evolutionary rates and by poor or

scarce calibration of clocks (see, e.g., Britton 2005; Linder et al.

2005; Thorpe et al. 2005; Welch and Bromham 2005; Elango

et al. 2006; Ho and Larson 2006; Peterson 2007). As such, a ma-

jor conclusion in evolutionary biology is approaching the status

of dogma—a strong assertion based on little evidence, in this case

a single source of evidence that has seen considerable debate and

doubt (Ho et al. 2005; Peterson 2007).

The emerging field of ecological niche modeling (ENM) of-

fers an independent perspective on these questions. In particular,

an early pair of papers documented conservative aspects of ecolog-

ical niches across evolutionary time periods (Huntley et al. 1989;

Ricklefs and Latham 1992); detailed, quantitative tests of the hy-

pothesis and numerous applications and explorations (Peterson

et al. 1999; Martınez-Meyer 2002; Graham et al. 2004b; Martınez-

Meyer et al. 2004; Knouft et al. 2006; Martınez-Meyer and Peter-

son 2006; Ruegg et al. 2006; Yesson and Culham 2006) have now

been developed. The result has been the overall picture that ecolog-

ical niche conservatism is common, but far from universal—when

it is tested and confirmed (Peterson et al. 1999; Martınez-Meyer

et al. 2004; Martınez-Meyer and Peterson 2006), however, it offers

considerable potential for prediction and forecasting of biodiver-

sity phenomena (Peterson 2003a; Peterson et al. 2005b; Wiens and

Graham 2005b). Of particular relevance to the question at hand,

ENM can be applied in tandem with paleoclimatic reconstructions

173C© 2007 The Author(s). Journal compilation C© 2007 The Society for the Study of Evolution.Evolution 62-1: 173–183

A. T. PETERSON AND A. S. NYARI

to “retrodict” potential geographic distributions at points in the

past (Hugall et al. 2002; Peterson et al. 2004; Bonaccorso et al.

2006).

Here, we integrate the Pleistocene speciation question with

niche conservatism ideas to develop a novel hypothesis regarding

the Pleistocene speciation question. We use as a test-bed the Schif-

fornis turdina complex, which has been analyzed recently in terms

of molecular phylogeography by one of us (Nyari 2007). This

neotropical frugivorous bird constitutes a complex, long treated as

one overly inclusive species taxon, shows concordant variation in

qualitative vocal (note structure, number of notes, note frequency

range) and mitochondrial molecular characters (ND2, COI, and

cyt b genes), suggesting that six to seven well-supported (i.e.,

high bootstrap support under various manipulations) species can

be recognized within the complex. The molecular studies provide

detailed geographic information on the occurrence of each molec-

ular phylogroup (Nyari 2007). Here, our goals are to (1) examine

the extent of ecological niche conservatism in the group as a whole

(at least in spatial dimensions) using spatial stratification meth-

ods presented previously (Peterson and Holt 2003); (2) integrate

current-climate ENMs with new, fine-scale Last Glacial Maxi-

mum (LGM) Pleistocene climate summaries to estimate a LGM

potential range for the complex; and (3) test the consistency of

molecular phylogroups with Pleistocene refugial distributions—

that is, whether the spatial distribution of Pleistocene refugia has

explanatory power regarding historical patterns of speciation, as

reflected in molecular differences, providing a novel perspective

on the question of vertebrate speciation in the Pleistocene.

MethodsINPUT DATASETS

Occurrence data for the S. turdina group were drawn from

data associated with natural history museum specimens (see

Acknowledgments), for a total of 227 unique occurrences

across the geographic range of the complex, covering all named

forms and all molecular phylogroups. We assigned geographic

coordinates to textual locality descriptions by means of reference

to online gazetteer databases (Alexandria Digital Library Project,

http://middleware.alexandria.ucsb.edu/client/gaz/adl/index.jsp),

achieving a spatial precision of ∼0.1′ of latitude and longitude.

Of these sites, we identified 38 localities that correspond to

samples for which molecular sequence data were included in the

recent molecular analysis (Nyari 2007), which form the basis

of our testing for agreement between molecular and ecological

datasets (see below).

Climate data for the present day (1960–1990) were drawn

from the WorldClim climate archive (Hijmans et al. 2005a). In

particular, in view of the broad latitudinal range of the complex

under analysis and concerns regarding the effects of opposite tim-

ing of seasonality in Northern and Southern hemispheres, we used

a subset of the “bioclimatic” coverages: annual mean temperature,

mean diurnal range, maximum temperature of warmest month,

minimum temperature of coldest month, annual total precipita-

tion, precipitation of wettest month, and precipitation of driest

month. Although some workers preselect a very small suite of

variables prior to analysis (Huntley et al. 1995), we prefer to in-

clude more dimensions, and allow the evolutionary computing

algorithm to seek out important variables and sets of variables for

a given model. All analyses were developed at a spatial resolu-

tion of 0.04◦ to match the approximate spatial accuracy of our

georeferencing.

To summarize Pleistocene LGM climates at 0.04◦ spatial

resolution, we used a new, fine-resolution climate dataset devel-

oped by R. J. Hijmans, as follows. Current climate data from

the WorldClim database were used [http://www.worldclim.org;

(Hijmans et al. 2005b)] as a basis for LGM climate data de-

velopment. For past climates, we obtained general circulation

model (GCM) simulations from two climate models: the Commu-

nity Climate System Model [CCSM, http://www.ccsm.ucar.edu/,

(Kiehl and Gent 2004)] and the Model for Interdisciplinary Re-

search on Climate [MIROC, ver. 3.2; http://www.ccsr.u-tokyo.

ac.jp/∼hasumi/MIROC/]. The original GCM data were down-

loaded from the PMIP2 website (http://www.pmip2.cnrs-gif.fr/).

The GCM data had a spatial resolution of 2.8◦, or roughly

300 × 300 km. Surfaces were created at 0.04◦ spatial resolution

via the following procedure. First, the difference between the

GCM output for LGM and recent, preindustrial, conditions was

calculated. These differences were then interpolated to the 0.04◦

resolution grid using the spline function in ArcInfo (ESRI, Red-

lands, CA) with the tension option. Finally, the interpolated differ-

ences were added to the high-resolution current climate datasets

from WorldClim and LGM bioclimatic coverages created. This

procedure has the dual advantage of producing data at a resolution

that is relevant to the spatial scale of analysis and of calibrating the

simulated climate change data to the actual observed climate data.

ECOLOGICAL NICHE MODELING

We used the Genetic Algorithm for Rule-Set Prediction (GARP)

for generating ecological niche models (Stockwell and Peters

1999); we also generated models for present-day distributions us-

ing Maxent (Phillips et al. 2004, 2006), and results were closely

similar. Given that the behavior of GARP is well-documented in

extrapolative exercises is well known (Peterson 2003a, b; Peter-

son et al. 2005a), whereas that of Maxent has not been explored

sufficiently (Phillips et al. 2006; Peterson et al. 2007), for all sub-

sequent analyses, we used GARP, in its desktop version 1.1.3

(Scachetti-Pereira 2001).

GARP is an evolutionary-computing method that builds eco-

logical niche models based on nonrandom associations between

174 EVOLUTION JANUARY 2008

ECOLOGICAL NICHE CONSERVATISM AND PLEISTOCENE REFUGIA

known occurrence points for species and sets of GIS coverages

describing the ecological landscape. Occurrence data are used by

GARP as follows: 50% of occurrence datapoints are set aside

for an independent test of model quality (extrinsic testing data),

25% are used for developing models (training data), and 25% are

used for tests of model quality internal to GARP (intrinsic test-

ing data). Distributional data are converted to raster layers, and

by random sampling from areas of known presence (training and

intrinsic test data) and areas of “pseudoabsence” (areas lacking

known presences), two datasets are created, each of 1250 points;

these datasets are used for rule generation and model testing, re-

spectively (Stockwell and Peters 1999).

The first rule is created by applying a method chosen ran-

domly from a set of inferential tools (e.g., logistic regression,

bioclimatic rules). The genetic algorithm consists of specially de-

fined operators (e.g., crossover, mutation) that modify the initial

rules, and thus the result are models that have “evolved”—after

each modification, the quality of the rule is tested (to maximize

both significance and predictive accuracy) and a size-limited set

of best rules is retained. Because rules are tested based on inde-

pendent data (intrinsic test data), performance values reflect the

expected performance of the rule, an independent verification that

gives a more reliable estimate of true rule performance. The fi-

nal result is a set of rules that can be projected onto a map to

produce a potential geographic distribution for the species under

investigation.

Following recent best-practices recommendations (Anderson

et al. 2003), we developed 100 replicate random-walk GARP mod-

els, and filtered out 90% based on consideration of error statistics,

as follows. The “best subsets” methodology consists of an initial

filter removing models that omit (omission error = predicting ab-

sence in areas of known presence) heavily based on the extrinsic

testing data, and a second filter based on an index of commis-

sion error (= predicting presence in areas of known absence),

in which models predicting very large and very small areas are

removed from consideration. Specifically, we used a soft omis-

sion threshold of 20%, and a 50% retention based on commission

considerations; the result was 10 “best subsets” models (binary

raster data layers) that were summed to produce a best estimate

of geographic prediction.

ECOLOGICAL NICHE CONSERVATISM

We developed two sets of tests of ecological niche conservatism

across the range of the group using spatial stratification methods—

one, presented elsewhere (Peterson and Holt 2003), splits avail-

able occurrence data simply by their distribution across space,

whereas the other splits occurrence data by their lineage mem-

bership (Nyari 2007). Specifically, in the first tests, we divided

the values of latitude for the 227 unique occurrence points into

quartiles (“bins”; Fig. 2), and tested for predictivity among suites

of points corresponding to these regions. As such, we conducted

an N − 1 jackknife of the four bins, and used each possible set of

three bins to predict the distribution of the species in the fourth

bin, and tested for predictivity better than that expected at ran-

dom in that region. These tests were developed exclusively in the

present-day climate context without inclusion of phylogeographic

information, and so test only the idea that the distinct lineages that

make up the complex occur under a consistent ecological regime

across their range in the neotropics.

Details of the tests of predictive ability and spatial consis-

tency of ecological niches are as follows. Within the test area,

we calculated the proportion of the quartile area predicted present

at each threshold of the GARP model. We also assessed success

in predicting each independent test point at each threshold. We

then calculated a one-tailed cumulative binomial probability as-

sociated with that level of predictive success at that proportional

area predicted present (Anderson et al. 2003). We did not em-

ploy more complex approaches to model validation, such as the

receiver operating characteristic (Fielding and Bell 1997) owing

to concerns regarding emphases of such approaches that do not

necessarily focus on prediction of the entire distributional area

(Anderson et al. 2003). Under the assumption that niche stability

across space will be indicative of potential niche conservatism in

the lineage through time, ecological niche models that passed this

test of predictive ability across broad, unsampled regions in the

present day were then explored further as to their implications in

the Pleistocene LGM.

Second, using information on lineage membership of par-

ticular populations, we developed a similar test of niche con-

servatism. Seven lineages were recovered in detailed molecular

phylogeographic studies of S. turdina (Nyari 2007). We divided

occurrence points based on their respective phylogeographic af-

filiations (see Fig. 1); because only a relatively small number of

localities were genotyped in the molecular studies, we grouped

the remaining occurrence localities based on known morpholog-

ical and plumage breaks corresponding to subspecies boundaries

(Peters 1979; Snow 2004). These seven subsets of the available

occurrence data ranged in sample size 6-76 sites.

We developed GARP models based on all seven possible

sets of six phylogroups, and tested the ability of each repli-

cate model to anticipate the geographic distribution of the sev-

enth phylogroup. In light of the historically limited distribu-

tions of particular lineages, we circumscribed testing areas based

on a buffer around the known occurrences of the testing phy-

logroup with a radius equal to the longest axis of the distribu-

tion of that phylogroup. Within this testing area, we repeated

our calculations of the proportion of area predicted present at

each threshold of the GARP model and success in predict-

ing each independent test point at each threshold, as described

above.

EVOLUTION JANUARY 2008 175


1 2

3

4 5

7

6

71

34562

*

**

**

*

**

*

Guyanan Shield

Mexico to Western Panama

Western Ecuador lowlands

cis-Andean foothills

Amazon Basin headwaters

SE Amazon and Atlantic Forest

Eastern Panama

Figure 1. Map showing the geographic distributions, phylogeographic patterns, and phylogenetic tree reconstructed in recent molecular

systematic studies of the Schiffornis turdina complex (adapted from Nyari 2007). ∗on the tree indicates strong nodal support values of

1.0 in Bayesian posterior probability and >95% in maximum-likelihood bootstrap.

RETRODICTION OF LGM DISTRIBUTIONS

Once predictivity was confirmed in the present day, we devel-

oped an overall model for the present-day distribution and ecol-

ogy of the complex based on the full complement of occurrence

points available for the complex. We used the same protocols for

ENM development as described above, but projected the resulting

best-subsets models onto the LGM climatic coverages described

above. The result was a picture of areas at LGM matching habit-

able present-day climates, or effectively a picture of Pleistocene

refugia for the complex, from the point of view of the present-day

ecological requirements of the species (Rice et al. 2003).

We tested the consistency of geographic extents of molecular

phylogroups with those of Pleistocene refugial distributions. That

is, we asked (statistically) whether the geographic position and

continuity of areas of adequate climate conditions had explana-

tory power above and beyond that expected by chance regarding

distributions of molecular phylogroups. We used a statistical test

that was designed explicitly to consider that the numbers of geo-

referenced and genotyped occurrence points would be relatively

small compared to the overall pool of occurrence data. Specif-

ically, (1) we identified the 38 occurrence points for which the

phylogroup was known, which were the geographic coordinates

of samples included in the earlier molecular study (Nyari 2007).

(2) We reduced that set to the 11 points that occurred within re-

constructed Pleistocene LGM refugia, under the assumption that

a population presently occurring in a Pleistocene LGM refugium

is the same lineage as that which occurred there at LGM; we

here ignore those points not falling into Pleistocene LGM refu-

gia, as they represent putative subsequent expansion and cannot

unambiguously be identified with a particular refugium. (3) We

connected phylogroups and refugia for each sample in linked pairs

(e.g., phylogroup 1 – refugium a), counting the number of such

pairs as an observed value of coincidence. (4) To understand co-

incidence that would be expected were no phylogroup-refugium

association to be present, we randomized refugia with respect to

phylogroups to generate a distribution of 100 null coincidence

values; we then compared the observed value from (3) to this

distribution to obtain a probability value for the comparison.

VISUALIZATION OF LGM BARRIERS TO DISPERSAL

The field of phylogeography has placed considerable weight on

detection of interruptions of gene flow (i.e., barriers) (Avise and

Walker 1998), but has not often been able to characterize those

barriers ecologically. Here, we explore the possibility of visualiz-

ing barriers to gene flow by means of inspection of associations

between LGM climate data and LGM projections of present-day

ENMs. Specifically, as an example, we focused on a disjunction

that apparently formed at LGM between the eastern and western

portions of the Amazon Basin, which was noted in our earlier

analyses of diverse forest taxa (Bonaccorso et al. 2006), as well

as in our present analyses of Schiffornis. Within a transect linking

putative refugia on either side of this barrier, we combined the

ENM prediction with the LGM climate data; the resulting output

grid summarizes all unique combinations of input raster values.

Then, we used simple bivariate plots to visualize environmental

features across this LGM barrier, comparing areas predicted po-

tentially present by all replicate ENMs with areas predicted either

absent or at low levels of suitability (i.e., < 3 of 10 replicate ENMs

predicting present).

ResultsWe first established that the S. turdina assemblage is conservative

in its ecological niche characteristics across its broad geographic



Figure 2. Tests of ecological niche conservatism across the geographic range of the Schiffornis turdina complex. Top panel: quartiles of

latitudinal distribution of known occurrence points of the complex, overlain on the prediction built based on all quartiles except the

northernmost, showing the statistically significant prediction of the distribution of the complex in Mexico and northern Central America.

Occurrence data are shown as dotted circles for training data (i.e., used in model development) and as open squares for testing data (i.e.,

used to test model predictions). Bottom panel: probability values associated with different thresholds of predictions for the four replicate

tests of prediction into one of the regions indicated in the top panel.

distribution. In light of this broad distribution, we considered that

the most difficult challenge for predictivity among portions of the

species’ distribution (Peterson and Holt 2003) would be that of

stratifying the known distribution latitudinally.

The result of the spatial stratification and jackknife procedure

was that two of the four partitions (Fig. 2)—and curiously the 2

(A and D) that are most extreme latitudinally—were predicted

statistically better than random expectations across all prediction

thresholds (all P < 10−5). The other two partitions were also pre-

dicted statistically significantly better than random expectations,

but in one case (partition C) excepting at the lowest (i.e., broadest

area predicted) threshold, and in the other case (partition B) only at

intermediate thresholds. Overall, though, the picture is one of eco-

logical niche conservatism in the group, as distributional patterns



of even the extreme northern and extreme southern populations

are predicted well by the ecological niche characteristics of the

remainder of the species’ distributional area.

Partitioning occurrence data by lineage showed similar re-

sults. Two of the seven lineages (clades 3 and 4 in Fig. 1) had

very small sample sizes (<10 points) of occurrence data, and so

were omitted from analyses. However, of the remaining five lin-

eages, three showed predictivity statistically better than random

expectations at all thresholds (clades 1, 5, and 6; all P < 0.05);

one showed predictivity statistically better than random expecta-

Figure 3. Ecological niche model estimates of present-day and Last Glacial Maximum potential distributional areas, the latter under two

different general circulation model estimates. Also shown are sample points for niche model development (Xs), and sampling localities

for genetic data (squares, with different colors indicating distinct molecular phylogroups).

tions 9 of 10 thresholds (clade 7; P < 0.05); and predictions for

clade 2 were statistically significant (P < 0.05) only for 2 of 10

thresholds. Hence, the predictions resulting from spatial stratifi-

cation by lineage membership showed almost universal predictive

ability, confirming the earlier results of ecological niche stability

across this clade.

We then explored the projection of present-day ENMs to

LGM climates under both the CCSM and MIROC GCM climate

models (Fig. 3). Here, we see that the overall reconstructed dis-

tributional limits of the complex were not dramatically different



at LGM, but that habitable areas were more fragmented and dis-

continuous than at present. In particular, we observed reduced

continuity of the species’ potential distributional area across the

Amazon Basin, with eastern and western sectors of the species’

distribution being isolated by a northwest-to-southeast swath of

less-suitable conditions.

Spatial coincidence of samples belonging to particular molec-

ular phylogroups with particular putative Pleistocene refugia was

close (Fig. 4). For example, among the 11 test points available,

the four samples that were part of the molecular phylogroup

that corresponds to the Guyanan Shield all fell within the one

LGM refugium in that region. Indeed, of the 11 test points avail-

able, in one case a particular refugium included sample local-

ities from two phylogroups, and in one case localities for a

particular phylogroup fell into two distinct refugia. Comparing

this degree of correspondence between phylogroup membership

and refugium against correspondences in 50 randomizations of

refugium with respect to phylogroup, the correspondence between

phylogroup membership and refugium placement is better than

random, so it appears that climate-reconstructed refugia indeed

have significant predictive power regarding phylogroup structure

(P < 0.02).

Finally, we characterized the climatic characteristics of one

LGM barrier between refugia in the eastern and western portions

of the Amazon Basin (Fig. 5). Highly suitable habitats can be ob-

served to be most related to high precipitation through the year,

avoiding coldest minimum temperatures, and avoiding areas that

dry out significantly in any part of the year. These features gen-

0

5

10

15

20

25

5 6 7 8 9 10 Number of categories

Randomized replicates

Observed coincidence

Freq

uen

cy

Phylogroup Refugium

Mexico to WestenPanama (N = 3)

Mexico

Northern Central America

cis-Andean foothills(N = 1)

Western Amazon

Amazon Basin headwaters (N = 3)

Guyanan Shield(N = 4)

Guyanan Shield

Figure 4. Randomization tests assessing correspondence between occurrence points corresponding to samples included in molecular

studies and the phylogroups in which they were placed and ecological niche model reconstructions of putative Pleistocene Last Glacial

Maximum refugia. The diagram inset shows patterns of matching and mismatching of phylogroups and refugia; the histogram shows

numbers of phylogroup–refugium pairs in randomized replicates as compared with the observed value.

erally coincide with the characteristics of the evergreen lowland

rainforest in which this complex is distributed.

DiscussionBy integrating ecological niche characteristics drawn from the en-

vironmental characteristics of known occurrences of the complex

with phylogeographic and phylogenetic information from molec-

ular genetic studies, we can derive a more refined image of driving

forces that led to the distributions and discontinuities among extant

taxa. In this study, we documented significant niche conservatism

over the entire present-day distribution of the complex (Fig. 2),

and showed that, at LGM, the distributional area of the complex

fragmented into several areas corresponding to presumptive Pleis-

tocene refugia (Fig. 3). These areas are separated by less-suitable

areas, with environments that can be identified and characterized

as barriers to gene flow (Fig. 5).

LIMITATIONS

This study is not without its limitations. In particular, here, we bat-

tle with issues of spatial resolution—very narrow barriers (e.g.,

rivers in the Amazon Basin) may simply not be “visible” in our

analyses, even though they may be important to the biogeogra-

phy of the group under question. Recent long-term ecological

studies of Amazonian avifaunas have demonstrated clearly the

effects of habitat fragmentation, even on quite-fine scales, on

the population biology of species such as those analyzed here

(Ferraz et al. 2003). As such, the effects of the coarse resolution



Figure 5. Visualization of ecological and environmental variation across one Pleistocene Last Glacial Maximum barrier as reconstructed

from ecological niche models. The upper left panel shows the area within which visualizations were developed, where the rectangle

encompasses two distinct refugia. The remaining three panels show comparisons of suitable (predictive threshold of 10 of 10 models

predicting presence) versus unsuitable (predictive threshold of < 3 of 10 models predicting presence) in six environmental dimensions.

“Availability” shows conditions of intermediate levels of prediction.

of our LGM projections must be borne in mind in interpretating

results.

An additional complication arises due to the appearance of

nonanalogous climate conditions when ENMs are projected across

major climatic changes. That is, if ENMs are trained in the present,

but climatic conditions in the LGM include sets of conditions

not manifested at present, then modeling approaches will have

unknown or unpredictable behavior in predicting into those ar-

eas (Pearson et al. 2006). Still, given that topographic features

surrounding the Amazon Basin and adjacent to the Mesoamer-

ican portions of the distribution of the S. turdina complex pro-

vide cooler conditions than the complex’s lowland distribution,

these problems are probably of less concern here than for for-

ward projections to still-warmer climates that are likely to appear

over the next century (Pearson and Dawson 2003; Araujo et al.

2005).

Finally, in interpreting these model results, it must be borne

in mind that they provide only static pictures of environmental

suitability across landscapes (Soberon and Peterson 2005). As

such, LGM “refugia” are hypothetical only—if dispersal limita-

tions are sufficient, certain refugia may have been inaccessible to

a species (Araujo and Pearson 2005), effectively leading to dis-

cords between potential and actual distributional areas (Peterson

2003a; Soberon and Peterson 2005). Such complications require

care and thought in interpretation what would appear to be error

in the form of overly broad predicted areas.

NICHE CONSERVATISM

This study adds to a growing body of literature documenting

conservatism of ecological niche characteristics across short-to-

medium periods of evolutionary time (Peterson et al. 1999; An-

dreas et al. 2001; Peterson 2003a), although several examples of



nonconservative ecological niche evolution have also been doc-

umented (Peterson and Holt 2003; Graham et al. 2004a; Knouft

et al. 2006). We do caution, however, based on extensive expe-

rience, about the effects of developing ENMs in overly dimen-

sional environmental spaces and the degree to which this overfit-

ting can produce the appearance of nonconservatism (Fitzpatrick

et al. 2006; Broennimann et al. 2007): regardless of conservatism,

if dimensionality of the environmental space so far outstrips the

sample sizes used to train the ENM, models are unlikely to be able

to predict accurately among areas or across time periods. Regard-

less, niche conservatism has many important implications, and

can provide a key tool in understanding historical biogeography

(Wiens 2004; Wiens and Graham 2005a; Peterson 2006).

PLEISTOCENE SPECIATION

The references cited in the Introduction reflect a strong cur-

rent in beliefs about vertebrate speciation—that Pleistocene cli-

matic fluctuations were not major generators of current vertebrate

species diversity. Rather, most authors argue for pre-Pleistocene

origins for most species (Klicka and Zink 1997; Drovetski and

Ronquist 2003), perhaps owing to broad use of poorly calibrated

molecular clocks in many molecular studies (Ho et al. 2005; Pe-

terson 2007). Although the few molecular dating efforts not based

on such molecular clocks and rather based on coalescent analyses

have indicated younger—Pleistocene—speciation events (Gris-

wold and Baker 2002; Carstens et al. 2005; Jennings and Edwards

2005), this point has not been addressed broadly with independent

sets of evidence, and as such we address it herein.

We present here a very simple test that only begins to shed

light on this question. We identify Pleistocene potential climatic

refugia for species taxa in the S. turdina complex, and show that

they have significant explanatory power regarding what is known

about the spatial distributions of phylogroups within the complex.

Although we do not as yet have data regarding climate patterns

prior to LGM, and as such cannot develop more detailed tests of the

timing of diversification of Schiffornis within the Pleistocene, our

results do show that Pleistocene climate patterns are at least rele-

vant to the question. That is, if Pleistocene fragmentation events

can explain much of the geography of species’ distributions, why

is it necessary to appeal to older climate phenomena, ignoring the

massive, global climate phenomena that characterized the Pleis-

tocene?

Pleistocene climatic fluctuations are known to have occurred

in repeated hot–cold cycles that approached a binary condition,

with extremely short transitions between the two (Dansgaard et al.

1993). As such, our use of LGM climate data quite simply re-

flects the availability of LGM simulations—to our knowledge, no

continent-wide simulations have been developed that allow direct

comparisons among glaciation events within the Pleistocene. Al-

ready, a Last Interglacial (135,000 years before present) dataset is

in preparation (R. J. Hijmans, pers. comm.) for addition to these

analyses, but considerable additional climatic information will be

necessary before we can use niche modeling tools to pin down

dates more precisely than what we have achieved in this article.

Clearly, this question will require much more in the way of

experimentation and exploration, but this analysis can be taken as

a first step toward a more general, broadly based answer. Finally,

here, we explore techniques for visualization of LGM barriers to

gene flow. The ENM approaches allow reconstruction not just of

the pattern of fragmentation of ranges, but also of the ecologi-

cal correlates of range restriction and distributional barriers. This

ecological interpretation of vicariant patterns opens doors to new

insights and new questions that have generally been out of the

realm of possibilities.

ACKNOWLEDGMENTSWe thank M. Papes for her continual help with technical GIS problems,E. Bonaccorso for helpful reflections and insights, and R. Guralnick andE. Waltari for comments on the manuscript. R. J. Hijmans kindly devel-oped and made available the LGM climate datasets. We are grateful tothe following institutions for providing locality data from specimens un-der their care: Louisiana State University Museum of Natural Science;Academy of Natural Sciences, Philadelphia; Field Museum of NaturalHistory; American Museum of Natural History; Museo de Zoologıa, Fac-ultad de Ciencias, Universidad Nacional Autonoma de Mexico; U. S.National Museum of Natural History; and University of Kansas NaturalHistory Museum. Marina Anciaes kindly provided additional georefer-enced localities from the National Museum of Brazil. We acknowledgethe PMIP2/MOTIF data providers and the Laboratoire des Sciences duClimat et de l’Environnement for providing access to the GCM data (datadownloaded on 1 March 2006).

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Associate Editor: K. Crandall


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