Ecological niche modelling as an exploratory tool for identifying species limits: an example based...

Ecological niche modelling as an exploratory tool for identifyingspecies limits: an example based on Mexican muroid rodents

D. MARTINEZ-GORDILLO, O. ROJAS-SOTO & A. ESPINOSA DE LOS MONTEROS

Laboratorio de Sistematica Filogenetica, Departamento de Biologıa Evolutiva, Instituto de Ecologıa, Veracruz, Mexico

Introduction

The first objective in Systematics Agenda 2000 is to

discover, describe and inventory global species diversity.

Species are the fundamental units of systematic, ecolog-

ical and evolutionary studies. Accurate delimitation of

species is increasingly important as the species diversity

of the world’s biota is constantly reduced and threatened,

primarily through the destruction of natural habitats

by human activities. However, in stark contrast to

phylogeny reconstruction, there has been relatively little

progress in the statistical methodology of species delim-

itation (see Wiens, 2007 and references within). Laurin

(2008) showed that rank-based nomenclature (i.e.

Linnaean categories) is silent about what a species is.

As a taxonomic rank, species have no simple correspon-

dence to any concept because taxonomic levels are

subjective.

There is an ongoing debate on species concepts, on

how to recognize species and how to infer the limits

between these units. In the biological concept a species is

a reproductive community of populations (reproduc-

tively isolated from others) that occupies a specific niche

in nature (Mayr, 1982). Advocates of the phylogenetic

species concept have claimed that fixed diagnostic

differences are a necessary criterion for species delimita-

tion (Cracraft, 1983). Proponents of the evolutionary

concept state that a species is a single lineage of ancestor-

descendant populations which maintain its identity from

other such lineages and which has its own evolutionary

tendencies and historical fate (Wiley, 1981). For the

cohesion concept a species is the most inclusive group of

organisms having the potential for genetic and ⁄ or

demographic exchangeability (Templeton, 2001). In the

ecological concept a species is a set of organisms exploit-

ing (or adapted to) a single niche (Van Valen, 1976). De

Queiroz (1998) has pointed out that, controversy aside,

alternative species concepts are not fundamentally dif-

ferent given that most of them agree on the fact that

species are evolutionary lineages. The differences are,

instead, based on the characters and the methods used

for establishing the limits of such lineages. Nonetheless,

concepts are operationally equivalent in terms of fixed

differences (e.g. morphological, molecular, ecological,

behavioural). These fixed differences may indicate an

Correspondence: David Martınez-Gordillo, Laboratorio de Sistematica

Filogenetica, Departamento de Biologıa Evolutiva, Instituto de Ecologıa,

A.C., km 2.5 carretera antigua a Coatepec, Xalapa, Veracruz 91070, Mexico.

Tel.: +52 228 842 1800; fax: +52 228 818 7809;

e-mail: [email protected], [email protected]

ª 2 0 0 9 T H E A U T H O R S . J . E V O L . B I O L . 2 3 ( 2 0 1 0 ) 2 5 9 – 2 7 0

J O U R N A L C O M P I L A T I O N ª 2 0 0 9 E U R O P E A N S O C I E T Y F O R E V O L U T I O N A R Y B I O L O G Y 259

Keywords:

ecological niche modelling;

ecological units;

GARP;

historical units;

Muridae;

phylogenetic niche conservatism.

Abstract

Niche conservatism theory suggests that recently diverged sister species share

the same ecological niche. However, if the ecological niche evolves as part of

the speciation process, the ecological pattern could be useful for recognizing

cryptic species. In a broad sense systematists agree that the niche characters

could be used for species differentiation. However, to date such characters

have been ignored. We used the genetic algorithm for rule-set production for

modelling the ecological niche as a means of inferring ecological divergence in

allopatric populations of muroid rodents for which taxonomic identity is

uncertain. Our results show that niche differentiation is significant in most of

the identified phylogroups. The differentiation is likely associated with natural

evolutionary units, which can be identified by applying species concepts based

on phylogenetic and ecological patterns (e.g. phylogenetic, cohesive, evolu-

tionary). Even so, the role of the niche partition within phylogenetic

reconstruction may be a limited one.

doi:10.1111/j.1420-9101.2009.01897.x

absence of gene flow between putative taxa and the

presence of two or more distinct species (Wiens, 2007). It

appears that the challenge is obtaining enough evidence

to establish a clear demarcation in species’ limits.

One approach that has received attention is the use of

ecological niche modelling as a tool for species delimita-

tion (e.g. Graham et al., 2004b; Guisan & Thuiller, 2005;

Stockman & Bond, 2007). Niche is a term describing the

role of a species or population in its ecosystem. The

abiotic or physical environment is part of the ecological

niche because it influences how populations affect, and

are affected by, resources and other organisms. The

different dimensions of the niche include descriptions of

the organism’s life history and habitat. According to the

competitive exclusion principle, two species cannot

occupy the same ecological niche in the same environ-

ment for a long time. There are several definitions of

niche. For data analyses and discussion we followed

Grinnell’s niche concept (i.e. a niche is the set of

environmental conditions within which each species

can survive and maintain its populations without migra-

tion; Grinnell, 1917).

One evolutionary outcome of niche differentiation

among populations could be a shift in ecological charac-

ters to alternative states. Therefore, identifying consistent

differentiation patterns in characters related to the

ecological niche might provide alternative means of

recognizing putatively independent lineages. This way,

historical units could be recognized simultaneously by

differentiation patterns (Cracraft, 1983) and by ecological

processes (Templeton, 2001).

Recently, several studies have used a maximum

entropy algorithm for modelling the ecological niche,

and for making biodiversity decisions (Raxworthy et al.,

2003, 2007; Rissler & Apodaca, 2007; Stockman &

Bond, 2007; Swenson, 2008; Luxbacher & Knouft,

2009). There are currently more than 10 algorithms

for niche delimitation available [e.g. BioClim, genetic

algorithm for rule-set production (GARP), GLM,

MaxEnt, MARS; see Wisz et al., 2008]. From them,

the GARP and the maximum entropy with threshold

features (MaxEnt) are the only ones that have been

extensively used and tested. MaxEnt fits a probability

distribution for occurrence of a species to the set of

pixels across the study region. This probability is

inferred based on the values of those pixels at which

the species has been detected (Phillips et al., 2006).

GARP builds ecological niche models (ENM) based on

nonrandom associations between known occurrence

points for species and sets of digital layers representing

the ecological landscape. The occurrence points are used

for rule generation and model testing. The genetic

algorithm consists of specially defined operators (e.g.

crossover, mutation) that modify the initial rules. After

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

maximize both significance and predictive accuracy),

until a limited set of best rules is retained. The final

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

to produce a potential geographical distribution for the

species under investigation (Peterson et al., 2007). The

quantitative descriptors of model quality have showed

no significant differences between GARP and MaxEnt.

Nonetheless, MaxEnt is very sensitive to data bias (poor

vs. over collected points), whereas GARP does not

produce significant differences even when a minimum

number of occurrence points are used. Additionally,

MaxEnt shows a relative inability to predict into broad

unsampled regions, a condition under which GARP

performs better (Peterson et al., 2007).

Baker & Bradley (2006) concluded that over 2000

new mammal species could be recognized if all the well-

supported phylogroups were acknowledged and given

this taxonomic status. Biodiversity inventories, how-

ever, are limited by the lack of trained systematists,

economic resources and simple accurate methods.

Therefore, the evaluation of alternative methods that

allow fast, efficient approaches for assessing diversity

has become a priority. This is particularly important for

areas with a high number of endemic species. Geo-

graphical isolation, along with restricted gene flow, has

severe effects on niche changes (Graham et al., 2004b;

Wiens & Graham, 2005). This would be expected in

organisms with limited dispersal abilities, and geograph-

ical restriction that have been affected by recent climatic

fluctuations. This is the case for the mountainous

muroid rodents of Mexico (Leon-Paniagua et al.,

2007). Climate cycles during the Pleistocene may have

produced shifts in the ecological niche of their popula-

tions, thus promoting lineage differentiation (Wiens &

Graham, 2005).

The ecological niches of muroid rodents have several

traits that make these rodents suitable for the analysis of

speciation. First, they display rapid differentiation under

genetic drift or selection due to their biological traits (e.g.

high reproductive rate, short generation time, phylopa-

try, low migration rate, low tolerance to environmental

changes; Pagel et al., 1991). Second, muroid rodents have

differentiated lineages within currently recognized spe-

cies that are geographically restricted, due to low gene

flow (i.e. type I phylogenetic pattern; Avise, 2000). Third,

some of the highest speciation rates among vertebrates

have been reported for these rodents (Triant & DeWoody,

2006).

Our main objective was to assess the capacity of ENM

to identify potential independent lineages that may be

considered as different species. This was achieved by

selecting well-supported phylogroups as null hypotheses.

We then compared the congruence between the phylo-

genetic structure of these lineages, with their degree of

niche differentiation. Using ecological niche analysis

together with other data sources (molecular, cytological,

morphological, geographical, etc.) constitutes one of the

first steps towards establishing an integrative taxonomy

(Dayrat, 2005).

260 D. MART INEZ-GORDILLO ET AL.


J O U R N A L C O M P I L A T I O N ª 2 0 0 9 E U R O P E A N S O C I E T Y F O R E V O L U T I O N A R Y B I O L O G Y

Methods

Taxon selection

We selected five currently recognized species of rodents

from the Muridae Family that encompass evolutionarily

distinct lineages [i.e. Baiomys musculus (Merriam, 1892),

B. taylori (Thomas, 1887), Peromyscus furvus Allen &

Chapman, 1897, P. aztecus (Saussure, 1860) and Reith-

rodontomys sumichrasti (Saussure, 1861)]. We selected

these five species based on two criteria. First, the

traditional taxonomy of these species has been

re-evaluated yielding well-supported phylogenies, which

suggest that these lineages may include subunits eligible

for species status (Fig. 1). Details on the phylogeograph-

ical analyses for these species have been published

elsewhere (Harris et al., 2000; Sullivan et al., 2000;

Amman & Bradley, 2004). Second, the distribution of

these species is one of the best known for Mexican

mammals. Locality records for the Northern pygmy

mouse (B. taylori) were obtained from Stickel & Stickel

(1949), Hart (1972), Hunsaker et al. (1959), Packard &

Judd (1968), Hall (1981), Gust & Schmidly (1986),

Killion et al. (1995) and Abuzeineh (2006). The distribu-

tion data for the other species were obtained from

Ceballos & Oliva (2005). Distribution data have been

collected since the early 1900s to the present. Collecting

points represent more than 700 reliable localities.

Voucher specimens are deposited at zoological museums

in Mexico (3) and USA (18).

The Southern pygmy mouse (B. musculus) is consid-

ered to be a species with six subspecies. Five of them

are endemic to Mexico (Hall, 1981). Amman & Bradley

(2004) recovered the phylogeographical pattern of

B. musculus and B. taylori (Fig. 1a). They concluded that

the first species is formed by two well-supported

phylogroups, one distributed along the Trans-Mexican

Volcanic Belt (phylogroup C) and the other endemic to

the south-eastern mountains of Mexico (phylogroup

D). The evidence presented by Amman & Bradley

(2004) suggests that these two phylogroups may be

different species.

With the exception of populations from the mountains

of Tamaulipas and Nuevo Leon in Mexico (referred

hereafter as TN populations), the phylogeography of

B. taylori was analysed by Amman & Bradley (2004). The

genealogy recovered two monophyletic phylogroups.

One is found in Texas (phylogroup A), whereas the

other occurs in the Sierra Madre Occidental (SMO;

phylogroup B). The TN populations are geographically

isolated from the other populations of this species. It is

possible, therefore, that these populations represent an

independent ecological unit from phylogroups A and B.

Hall (1981) considered the Blackish deer mouse (Pero-

myscus furvus) to represent a single species. This species is

endemic to the SMO in Mexico, and its populations

exhibit a discontinuous distribution. Harris et al. (2000),

however, concluded that this species encompasses three

well-supported phylogroups (Fig. 1b).

Carleton (1989) recognized the Aztec mouse (Peromys-

cus aztecus) as a single species with five subspecies.

Sullivan et al. (1997), however, concluded that this was

a species complex with at least two species (P. aztecus and

P. hylocetes). More recently, based on cytochrome-b gene

sequences Sullivan et al. (2000) identified six well-

supported phylogroups within the Aztec mouse complex

(Fig. 1c).

Sumichrast’s harvest mouse (Reithrodontomys sumichr-

asti) has been traditionally considered a single species

(Hall, 1981), but Sullivan et al. (2000) recovered six well-

supported phylogroups, which could represent indepen-

dent historical units (Fig 1d).

R. s. sumichrasti OH

R. s. luteolus E

R. s. nerterus

R. s. luteolus W

R. s. sumichrasti SMO

R. s. dorsalis

B. taylori (phylogroup A)

B. taylori (phylogroup B)

B. musculus (phylogroup C)

B. musculus (phylogroup D)

P. furvus North

P. furvus Center

P. furvus South

P. a. oaxacensis OH

P. a. evides E

P. a. evides W

P. a. aztecus SMO

P. a. hylocetes

P. a. oaxacensis

(c) (d)

(a)(b)100

100

100

100

95

98

98

64

5999

93

6697

99

100

100

100

93

9789

100

89

100

100

100

61

100

92

95

100

91

82

92

Fig. 1 Interrelationship hypotheses for the

species complexes of muroid rodents distrib-

uted in Mexico. (a) Baiomys spp. (Amman &

Bradley, 2004); (b) Peromyscus furvus (Harris

et al., 2000); (c) Peromyscus aztecus (Sullivan

et al., 2000); (d) Reithrodontomys sumichrasti

(Sullivan et al., 2000). Numbers above

branches are bootstrap values as reported

in the original publications.

Niche divergence in Mexican muroid rodents 261



Ecological niche modelling

In this study, niche modelling was carried out with

GARP. This is a software application that includes

inferential tools in an iterative, artificial intelligence-

based approach (Stockwell & Noble, 1992). Occurrence

points are divided evenly into training and test data sets.

GARP works in an iterative process of rule selection,

evaluation, testing and incorporation or rejection. First,

an inferential tool is chosen from a set of possibilities (e.g.

logistic regression, bioclimatic rules), and then is applied

to the training data and a rule developed; a rule is an

IF…THEN statement that describes the conditions under

which a species may be present (or absent). Predictive

accuracy is evaluated based on the test data and an

equivalent number of points sampled randomly from the

study region as a whole. The change in predictive

accuracy from one iteration to the next is used to

evaluate whether a particular rule should be incorpo-

rated into the model. The final model is projected onto

geographical space to produce a digital map of areas with

environmental characteristics suitable for the species

(Hirzel et al., 2002). Detailed assumptions and discussions

on this algorithm have been published elsewhere

(Peterson & Cohoon, 1999; Stockwell & Peters, 1999;

Anderson et al., 2002; Peterson et al., 2007). GARP is

available at http://www.nhm.ku.edu/desktopgarp.

To characterize environments, we used geological

variables from the Hydro-1K dataset (USGS, 2001), and

interpolated climate data set variables from the

WorldClim project (Hijmans et al., 2005). Both data

sets are based on a grid size resolution of 3000 (�1 km2

cell size). Model reconstruction was inferred from 14

ecological (Table 1) and three geological variables (i.e.

Slope, Digital Elevation Model and Compound Topo-

graphic Index). Different results between replicate

modelling runs result from the stochastic nature of

this algorithm. A consensus map, therefore, was

inferred by running 100 replicates (each replicate

consisted of 1000 iterations or until convergence).

Following Anderson (2003), the resulting models were

ranked based on their errors of omission (i.e. sites

where the species is present, but not predicted by the

model) and commission (i.e. sites where the species is

not present, but is predicted by the model), prioritizing

omissions over commissions. Finally, the 10 best

models were stored in ASCII raster format and

imported to the ArcView Ver. 3.2 Geographic Informa-

tion System (GIS; ESRI, 1999) to produce a strict

consensus map (Chefaoui et al., 2005). To corroborate

the predicted distribution, the consensus map was

compared with the geographical limits of the mamma-

lian ecological regions proposed by CONABIO

(Ramırez-Pulido & Castro-Campillo, 1990). This criterion

was used because ecological regions tend to indicate

the distribution limits of the species (Rojas-Soto et al.,

2003). Therefore, only the predicted areas that were

part of ecological regions in which there was at least

one record of the phylogroup (or the most closely

related phylogroup) were accepted.

We used three approaches to evaluate differentiation

in the geographical and ecological space of each species.

First, we estimated an overlap index (OI) between niche

models. The OI is the ratio of the overlap area between

two phylogroups divided by total area occupied by both

phylogroups. In other words, this index is the fraction of

the niche model of one phylogroup predicted by other

phylogroups within the same species. The OI is a measure

Table 1 Structure coefficients for canonical discriminant functions derived from the 14 ecological layers used to generate the ENM.

Variable set

Baiomys

musculus

CF1

Baiomys taylori Peromyscus furvus

Peromyscus

aztecus

Reithrodontomys

sumichrasti

CF1 CF2 CF1 CF2 CF1 CF2 CF1 CF2

Annual Mean Temperature )0.268 0.009 0.363 )0.527 )0.230 0.337 )0.139 0.666 0.182

Mean Diurnal Range )0.338 0.132 )0.117 )0.047 0.037 )0.611 0.463 )0.098 )0.539

Isothermality )0.218 0.364 )0.072 0.497 )0.483 )0.106 0.599 0.485 0.340

Max Temperature Warmest Month )0.408 )0.103 0.251 )0.586 )0.144 0.154 )0.115 0.549 )0.065

Mean Temperature Wettest Quarter )0.226 )0.028 0.436 )0.584 )0.097 0.361 )0.211 0.551 0.141

Mean Temperature Driest Quarter )0.301 0.170 0.173 )0.361 )0.508 0.273 )0.217 0.737 0.138

Mean Temperature Warmest Quarter )0.261 )0.142 0.324 )0.575 )0.124 0.334 0.202 0.573 0.118

Mean Temperature Coldest Quarter )0.286 0.130 0.275 )0.392 )0.416 0.307 )0.050 0.777 0.237

Annual Precipitation 0.380 )0.013 )0.208 )0.282 )0.367 0.344 )0.137 0.282 0.610

Precipitation Wettest Month 0.189 0.105 )0.107 )0.310 )0.385 0.311 )0.242 0.371 0.437

Precipitation Seasonality )0.866 0.558 )0.006 0.285 )0.597 )0.332 )0.264 0.603 )0.660

Precipitation Driest Quarter 0.647 )0.313 )0.268 )0.410 0.033 0.448 )0.086 )0.293 0.683

Precipitation Warmest Quarter 0.136 0.099 )0.150 )0.638 )0.152 0.299 )0.319 0.168 0.359

Precipitation Coldest Quarter 0.584 )0.151 )0.271 )0.190 )0.254 0.438 )0.206 )0.219 0.617

Explained Variance (%) 100.000 99.900 0.100 76.400 23.600 52.700 22.600 51.900 28.600

Bold indicates the highest absolute size of correlation within function.




of the geographical space potentially shared by the two

phylogroups. Second, geographical space differentiation

was also explored by computing the number of records

that each ENM predicted for its own phylogroup and

for the other phylogroups within the same species

(i.e. inter-predictability). Third, to determine whether

phylogroups can be distinguished on the basis of ecolog-

ical space as well as to identify the combination of

ecological characters most important to group member-

ship we performed a discriminant functions analysis.

(a) (b)

(c) (d)

(e)

Phylogroup CPhylogroup D Phylogroup A

Phylogroup BTN population

NorthCenter South

OaxacensisHylocetesAztecus SMOAztecus OHEvides WestEvides East

DorsalisNerterusSumichrasti SMOSumichrasti OHLuteolus WestLuteolus East

Pacific Ocean

Gulf ofMexico

Fig. 2 Summary of Ecological Niche Model predictions for five species complexes based on occurrence data and information for 14 climate

and three geological variables. Black area indicates where niches overlap. (a) Baiomys musculus; (b) B. taylori; (c) Peromyscus furvus;

(d) P. aztecus; (e) Reithrodontomys sumichrasti.




Canonical discriminant functions (CF) were computed by

extracting the values for the ecological variables at each

pixel predicted by the individual ENMs. For these

analyses the phylogroups were designated as the separa-

tion group. All geographical analyses and value extrac-

tions were performed with the GIS ArcView ver. 3.2

computer package. The statistical analyses were per-

formed using SPSS ver. 17 for Mac (SPSS, 2009).

Results

We obtained a total of 20 models that predict the

potential distribution of each phylogroup based on the

environmental variables that set the ecological niche

boundaries. The summary of predictions for these

models for each species is presented in Fig. 2. A

predictability test performed by GARP showed that

the ENM of each phylogroup departed from random

models (P < 0.00001). A linear regression among the

number of locality records per phylogroup vs. the

number of recovered pixels by the ENM was not

significant (r2 = 0.18, P = 0.0649). This indicates there

was no model bias due to insufficient input data.

Finally, the segregation of the potential areas as

inferred by the ENMs fits the proposed hypotheses of

historical independence for each phylogroup (i.e. Harris

et al., 2000; Sullivan et al., 2000; Amman & Bradley,

2004).

Table 2 Approaches used to evaluate

differentiation in the geographical and

ecological space of each species.

PhylogroupOverlap

index (%)

Inter-

predictability

(%) Accuracy (%)*

A B A&B B&A A B

B. musculus C B. musculus D 26.0 24.3 95.7 58.4 75.8

B. taylori A B. taylori B 0.0 0.0 0.0 95.8 99.0

B. taylori A B. taylori TN 1.0 0.0 0.0 95.8 100.0

B. taylori B B. taylori TN 2.0 28.9 0.1 99.0 100.0

P. furvus Center P. furvus North 0.0 0.0 0.0 66.9 95.5

P. furvus Center P. furvus South 16.0 50.0 45.6 66.9 71.8

P. furvus North P. furvus South 0.0 0.0 28.5 95.5 71.8

P. a. aztecus OH P. a. aztecus SMO 7.0 12.5 33.3 58.8 75.5

P. a. aztecus OH P. aztecus evides E 0.0 7.4 33.3 58.8 51.6

P. a. aztecus OH P. aztecus evides W 0.0 0.0 0.0 58.8 68.4

P. a. aztecus OH P. aztecus oaxacensis 1.0 0.0 83.3 58.8 41.8

P. a. aztecus OH P. aztecus hylocetes 0.0 0.0 0.0 58.8 50.4

P. a. aztecus SMO P. aztecus evides E 2.0 0.0 4.1 75.5 51.6

P. a. aztecus SMO P. aztecus evides W 0.0 0.0 0.0 75.5 68.4

P. a. aztecus SMO P. aztecus oaxacensis 5.0 0.0 20.8 75.5 41.8

P. a. aztecus SMO P. aztecus hylocetes 0.0 0.0 0.0 75.5 50.4

P. aztecus evides E P. aztecus evides W 21.0 30.4 18.5 51.6 68.4

P. aztecus evides E P. aztecus oaxacensis 15.0 53.8 40.7 51.6 41.8

P. aztecus evides E P. aztecus hylocetes 7.0 6.7 0.0 51.6 50.4

P. aztecus evides W P. aztecus oaxacensis 16.0 38.4 95.6 68.4 41.8

P. aztecus evides W P. aztecus hylocetes 10.0 8.1 0.0 68.4 50.4

P. aztecus oaxacensis P. aztecus hylocetes 5.0 6.7 0.0 41.8 50.4

R. sumichrasti dorsalis R. sumichrasti luteolus E 15.0 15.1 3.5 42.9 49.4

R. sumichrasti dorsalis R. sumichrasti luteolus W 8.0 0.0 3.5 42.9 52.7

R. sumichrasti dorsalis R. sumichrasti nerterus 3.0 0.0 0.0 42.9 70.5

R. sumichrasti dorsalis R. s. sumichrasti OH 14.0 50.0 3.5 42.9 31.4

R. sumichrasti dorsalis R. s. sumichrasti SMO 5.0 64.7 0.0 42.9 62.4

R. sumichrasti luteolus E R. sumichrasti luteolus W 52.0 53.8 42.4 49.4 52.7

R. sumichrasti luteolus E R. sumichrasti nerterus 5.0 0.0 9.0 49.4 70.5

R. sumichrasti luteolus E R. s. sumichrasti OH 8.0 0.0 9.0 49.4 31.4

R. sumichrasti luteolus E R. s. sumichrasti SMO 1.0 0.0 0.0 49.4 62.4

R. sumichrasti luteolus W R. sumichrasti nerterus 10.0 14.2 0.0 52.7 70.5

R. sumichrasti luteolus W R. s. sumichrasti OH 2.0 0.0 0.0 52.7 31.4

R. sumichrasti luteolus W R. s. sumichrasti SMO 0.0 0.0 0.0 52.7 62.4

R. sumichrasti nerterus R. s. sumichrasti OH 6.0 0.0 10.7 70.5 31.4

R. sumichrasti nerterus R. s. sumichrasti SMO 5.0 0.0 14.2 70.5 62.4

R. s. sumichrasti OH R. s. sumichrasti SMO 15.0 47.0 16.6 31.4 62.4

*Proportion of pixels predicted by the ENMs correctly classified.




On average, OI between the recovered models was

7.6%, though the values for this index vary considerably.

For instance, the average OI estimated for the contrasts

between the phylogroup of R. sumichrasti were lower

than 10%. The highest OI, however, occurred between

the geographical neighbours R. s. luteolus West and

R. s. luteolus East, whose ENM overlap by 52% (Table 2).

Similarly, the niches modelled for each phylogroup had a

low recovery rate for the collecting points of alternative

phylogroups within their lineages (i.e. inter-predictabil-

ity). As for the OI, the inter-predictability between

models was highly variable. The inferred model for

P. a. oaxacensis recovered 29% of its sister group records.

This value was followed by that of P. a. evides West that

recovered 15% of the collection localities of the taxa

encompassed by its sister group. All other contrasts

recovered inter-predictability values below 3%. It is

important to point out that this parameter can be

unbalanced. For example, for M. musculus, phylogroup

C recovered 95% of the known localities for phylogroup

D, whereas the latter only recovered 24% of collecting

points of the former.

The results from the discriminant analysis indicate that

overall phylogroups differed significantly between their

ENMs. For the five species complexes the Wilks’ lambda

(k) scored probability values smaller than 0.001. In most

cases these overall differences are due to radical shifts in

the ecological parameters in the ENMs of the phylo-

groups. For instance, phylogroups C and D of B. musculus

occupy different temperature and precipitation ranges.

Phylogroup C lives in warmer environments than

phylogroup D. The annual mean temperature recorded

in the potential distribution of phylogroup C is 23 �C,

whereas the same factor is 21 �C for the potential

distribution of phylogroup D. In contrast, phylogroup D

inhabits more humid habitats than phylogroup C. The

raw data show that the annual precipitation in the forest

occupied by phylogroup D is 1223 mm, whereas the

precipitation is 1070 mm in the area predicted for

phylogroup C.

The total variation explained by the first two CF ranged

from 75% to 100% (Table 1). The CF for B. taylori and

P. furvus were associated mainly with temperature vari-

ables (i.e. annual mean, isothermality, maximum of the

warmest month, mean of the wettest quarter, mean of

the driest quarter and mean of the warmest quarter).

These variables, however, were positive for B. taylori, and

negative for P. furvus. On the one hand, the differences in

the ENM of R. sumichrasti that were explained by CF1

were associated with three temperature variables (i.e.

annual mean, mean of the driest quarter and mean of the

coldest quarter), whereas the second CF explained 28.6%

and it was associated with a decrease in four precipitation

variables (i.e. annual, precipitation seasonality, precipi-

tation in the driest quarter and precipitation in the

coldest quarter). Finally, the CF for B. musculus and

P. aztecus were associated mainly with two precipitation

components (i.e. annual precipitation, precipitation wet-

test month, precipitation in the coldest quarter and

precipitation in the driest quarter).

Visual inspection of the first and second CF plots shows

a separation between the phylogroups encompassed in

B. taylori (Fig. 3a). The separation along CF 1 between

phylogroups A and B can be explained mainly by

precipitation seasonality. The classification matrix

showed that on average, pixels were assigned to the

0

2.5

–2.5

Canonical function 10 2.5 5–2.5

Can

onic

al fu

nctio

n 2

Pah

PaeE

PaeW

Pao

OHSMO

Canonical function 1

Can

onic

al fu

nctio

n 2

BtABtB

BtTN

0 5–5–10

0

5

(a) (b)

(c) (d)

–5


Can

onic

al fu

nctio

n 2

0 2.5–5

0

5

–5

–2.5

PfC

PfN

PfS


Can

onic

al fu

nctio

n 2

0 2.5 5–2.5–5

0

2.5

–2.5

Rsd

RslE

RslW

Rsn

RssOH

RssSMO

Fig. 3 Plots of canonical discriminant

functions 1 and 2 describing ecological

variation. Big circles represent group

centroids. (a) Baiomys taylori: phylogroup A

(BtA), phylogroup B (BtB), TN populations

(BtTN); (b) Peromyscus furvus: Center (PfC),

North (PfN), South (PfS); (c) P. aztecus: aztecus

OH (OH), aztecus SMO (SMO), evides E

(PaeE), evides W (PaeW), oaxacensis (Pao),

hylocetes (Pah); (d) Reithrodontomys

sumichrasti: dorsalis (Rsd), luteolus E (RslE),

luteolus W (RslW), nerterus (Rsn), sumichrasti

OH (RssOH), sumichrasti SMO (RssSMO).




correct group in 98% of the cases. A similar separation

trend was observed in R. fuscus (Fig. 3b). For this species,

however, precipitation in the warmest quarter explained

the separation along CF 1 between the phylogroup North

and South. Precipitation seasonality, on the other hand,

explained the separation along CF 2 between phylo-

groups Central and South. The classification accuracy

ranged between 95% for phylogroup North and 67% for

phylogroup Central (Table 2). On average, 23% of the

pixels were ambiguously assigned between phylogroups

Central and South; nonetheless, the phylogeny (Fig. 1b)

did not recover these phylogroups as sister taxa. The

amount of data generated for P. aztecus and R. sumichrasti

produced a complex overlap pattern among the disper-

sion clouds of the different phylogroups. In P. aztecus,

however, a separation along CF 1 can be observed for

some of the phylogroups. For instance, P. a. hylocetes does

not overlap with the phylogroup OH, and overlaps

slightly with SMO. The classification matrix shows low

assignation values (< 70%) for most of the data, with

the highest being 76% for the pixels of the phylogroup

OH. The smallest degree of separation is observed in

R. sumichrasti (Fig. 3d). For this species the values

obtained in the classification matrix raged from 71%

for R. s. nerterus to 31% for R. s. sumichrasti OH.

Discussion

In the recent literature, a debate has emerged as to

whether niches are conserved (Wiens & Graham, 2005).

Some authors (Holt & Gaines, 1992; Peterson et al., 1999;

Prinzing et al., 2001; Kozak & Wiens, 2006) have

concluded that the ecological niche evolves at a slower

rate than that at which the speciation process occurs (i.e.

phylogenetic niche conservatism). Closely related spe-

cies, therefore, must share the same ecological parame-

ters. Alternatively, based on complementary evidence

(e.g. morphometric data, colour change regulated by

point mutation and changes in life history traits) other

authors have reached the conclusion that the niche

evolves faster than previously estimated (Losos et al.,

1997; Reznick et al., 1997; Hoekstra et al., 2006; Hendry

et al., 2007). The incompatibility of these two conclusions

may result from comparisons of species exhibiting oppo-

site life strategies (e.g. low vs. high vagility).

The available evidence, however, is not sufficient to

clearly indicate whether this is a common process during

the evolution of lineages. Analyses of potential distribu-

tion patterns might provide further evidence. It is

possible that phylogenetic niche conservatism character-

izes recently diverged sister species. However, niche

differentiation might occur in phylogeographically struc-

tured species with allopatric populations and restricted

gene flow that have been subjected to different ecological

conditions for sufficient periods of time (Avise, 2000).

Apparently, the muroid species used here fit such a

description. Groups with allopatric distribution (i.e.

P. furvus and B. taylori) had a higher degree of ecological

niche differentiation than those with parapatric distribu-

tion (i.e. P. aztecus and R. sumichrasti).

Ecological niche models will help to draw conclusions

about niche conservatism. They may be important in

understanding allopatric speciation, historical biogeogra-

phy, patterns of species richness and community struc-

ture. It is imperative, nonetheless, to have at least an

approximation of the phylogenetic interrelationships for

the species. Conclusions based on non-natural units

would be meaningless. The results presented in this study

corroborate a process of ecological niche differentiation,

one that is correlated with the historical structure of the

phylogroups.

Implications for recognizing species’ limits: themuroid example

The process for the acceptance of new species can be

divided into three phases. First, evolutionary units must

be recognized as putative new species. Second, a formal

description highlighting the characters that differentiate

these units must be provided. Third, a formal name is

erected following the norms and regulations contained in

the codes of nomenclature. The use of ENM deals

exclusively with the first phase (recognition of potential

evolutionary units). One of the problems for species

recognition is that in spite of all the available species

concepts this rank is as subjective as any other taxonomic

rank, and that Linnaean categories are not assigned using

objective criteria (Ereshefsky, 2002; Laurin, 2005). An

empirical way to corroborate the individuality of a

lineage is to evaluate the congruence of data from

independent sources. The ENM can generate comple-

mentary data for inferring patterns of ecological diver-

gence. Therefore, contrasting the information recovered

from ENM and phylogenetic reconstruction is a strategy

of reciprocal illumination for identifying natural units

and perhaps for establishing species’ limits. For instance,

lets us consider the ENM of one species with allopatric

distribution. If the model results in a discontinuous

niche, then each of the areas that encompass collecting

records must be modelled independently. Depending on

the overlap and inter-predictability index, a series of

specimens from those distinctive ecological units would

be used in a phylogenetic analysis to infer if they also

constitute historical lineages. It is after this point that a

taxonomic decision based on phylogenetic criteria (e.g.

diagnosably distinct clades, reciprocal monophyly, line-

age independence) could be made. Our results concur

with the conclusions derived from studies that highlight

the advantages of such an approach (Raxworthy et al.,

2007; Rissler & Apodaca, 2007; Stockman & Bond, 2007;

Swenson, 2008). If two sister lineages can be identified

by their differentiated niche, then that fraction of the

niche is expected to be regulated by autapomorphic

characters and therefore niche differentiation could




constitute taxonomic evidence (sensu Cracraft, 1983). On

the other hand, if the ecological niche has not changed,

symplesiomorphic characters are expected to regulate it.

The GARP was able to identify discrete ecological units

that correspond to phylogroups recognized by indepen-

dent systematics studies as separate historical units (e.g.

Harris et al., 2000; Sullivan et al., 2000; Amman &

Bradley, 2004). The OI between the niches of the

phylogroups encompassed in P. furvus was very small,

and none of the locality records for the North phylogroup

was recovered by the other ENM. These data support the

conclusion that the North phylogroup is no longer an

ecological counterpart to the remaining units. Likewise,

the southernmost populations also showed a significant

ecological differentiation. Templeton (2001) suggests that

a lack of demographic interchangeability due to ecolog-

ical constraints is evidence that those units belong to

different species (i.e. Cohesive Species Concept). Fur-

thermore, the diagnostic molecular characters of the

North phylogroup also provide evidence that this is an

independent species (sensu Phylogenetic Species Concept;

Cracraft, 1983). Based on molecular data, Harris et al.

(2000) concluded that the North phylogroup was defi-

nitely an evolutionarily stable unit, and suggested that

other phylogroups potentially represent alternative spe-

cies. The available data imply that for the northern

populations divergence takes place in an ecological

dimension, whereas for the south populations the diver-

gence is both ecological and geographical.

Sullivan et al. (1997) recommended that the subspecies

P. a. hylocetes should be raised to the species level. Later,

Sullivan et al. (2000) presented a genealogy in which

P. hylocetes was nested within P. aztecus (Fig. 1c). Thus,

the other phylogroups should be raised to the species

level as well. Not only do the phylogroups have fixed

molecular characters, the ENMs also support that these

groups have diverged ecologically.

The ENMs of R. sumichrasti showed ecological separa-

tion among the different phylogroups (Fig. 2e). The lack

of overlap between ENMs suggests that these phylo-

groups have achieved significant niche divergence. Only

the ENMs for R. s. luteolus East and West overlapped

considerably. There is compelling evidence, however,

that these two phylogroups are not sister lineages

(Sullivan et al., 2000). Therefore, such niche overlap

probably represents a symplesiomorphy that has con-

strained the dispersion of these lineages (i.e. phylogenetic

niche conservatism). Sullivan et al. (2000) have sug-

gested that the Isthmus of Tehuantepec could be an

effective barrier for this species, and that the populations

on each side of this barrier might be different species.

The ENMs for B. taylori support the ecological inde-

pendence of phylogroups A and B (Fig. 2b). Amman &

Bradley (2004) reported genetic variability in the cyto-

chrome-b gene of 0.41% for the Texas population

(phylogroup A), which is four times more variable than

phylogroup B. The same authors concluded that the Rio

Grande could be a geographical barrier that has promoted

the isolation and differentiation between the Mexican

and the U.S. populations. The molecular and ecological

evidence support this conclusion.

ENM as an exploratory tool

We are aware that in some cases the ecological niche

seems to make more geographical than phylogenetic

sense. On the one hand, geographically close phylo-

groups exhibited significant ecological divergence reflect-

ing their phylogenetic position. On the other hand, clinal

variation can be discarded because significant ecological

divergence was not restricted to phylogroups placed at

the extremes of the distribution. Rice et al. (2003) found

similar results for niche differentiation and geography.

They analysed the ecological niche of Aphelocoma jays,

and concluded that geographical proximity better

explained niche divergence than did the phylogenetic

interrelationships. Apparently, some plastic characters

that can be easily altered by evolutionary processes

regulate aspects of the ecological niche. Furthermore,

ecological factors are not necessarily intrinsic to the

species. This idea implies that the ecological niche can

play an important role in species differentiation, but that

it is inadequate for phylogenetic reconstruction. At least

some of the characters that form the ecological niche

must change very fast, thus leading to homoplasy

between closely related lineages (Rice et al., 2003).

The genealogical structure and interrelationships for

the TN populations have not been established. Our

results, nonetheless, suggest that they represent an

independent ecological unit (1% OI and 0.1% inter-

predictability). The question of whether the TN popula-

tions represent an independent historical unit requires

corroboration via complete systematic analyses based on

morphological or molecular characters. The ENM sug-

gests that these populations are worthy of such an effort.

Empirical studies (e.g. Graham et al., 2004a; Raxwor-

thy et al., 2007; Rissler & Apodaca, 2007; Stockman &

Bond, 2007) have shown that the use of potential

distribution models is an effective tool to identify differ-

ences in ecological niches. Such analyses are effective for

inferring lineage divergence and segregation. In general,

species are delimited based on one or more qualitative or

quantitative morphological characters that show no

overlap with closely related species. If two species are

consistently distinguished by one or more diagnostic

morphological differences, then presumably there is no

gene flow between them. Wiens & Servedio (2000)

examined the sample sizes needed to have statistical

confidence that a given diagnostic character is truly fixed

(100% frequency) within a species. However, even

allowing for some level of polymorphism in a diagnostic

character (e.g. frequency of 95% rather than 100%),

hundreds or thousands of individuals may still be

required to be reasonably certain that the trait is




diagnostic at the desired level. For exploratory analyses,

therefore, it is more productive to gather a large database

of locality records, than museum specimens for morpho-

logical studies, or samples for DNA sequencing.

The drawbacks

The use of ENM as an exploratory tool for delineating

species among divergent lineages is not without issues

and certain limitations, and consequently, it requires

critical examination. Using mtDNA-based phylogenies as

null hypotheses may be inadequate (e.g. gene-tree ⁄species-tree incongruence, introgression, intracellular

symbionts, matrilineal history and selection). Despite

these criticisms, insights gained through mtDNA markers

over the past 25 years have been immense and recent

comparative phylogeographical studies show obvious

and compelling consistencies between mitochondrial

and nuclear DNA genealogies among disparate taxa

(Zink & Barrowclough, 2008). Nuclear mutation rates

are generally lower than mitochondrial ones (10 : 1

ratio). Therefore, there will be less variation per sequence

for nuclear than for mitochondrial sequences. This results

in nuclear genealogies with low resolution that usually

show no conflict with the mitochondrial trees. One might

have to sequence two to three times as many nuclear

bases as mitochondrial to uncover equivalent numbers of

variable sites and, hence, to produce a tree with equiv-

alent resolution. Furthermore, producing this nuclear

gene tree requires that there be no recombination and

that one can sort the specific sequence that occurs in

each allele (Zink & Barrowclough, 2008 and references

within).

It is possible that some of the differentiation observed

between the niches of two groups is due to phenotypic

plasticity. This is a mechanism that allows organism to

deal with environmental heterogeneity. The role of

phenotypic plasticity on population fitness has been

revised in recent years (Caruso et al., 2006). However, its

impact on adaptive evolution remains controversial.

Adaptive phenotypic plasticity is increasingly demon-

strated for many species (Terblanche & Kleynhans,

2009). Phenotypic plasticity can enhance survival under

adverse conditions, therefore, could be a driver of novel

traits, and potentially of speciation. Nonetheless, addi-

tional research is necessary to test for adaptive pheno-

typic plasticity using a strong inference approach while

controlling for the types of plasticity (e.g. developmental

vs. adult), considering the relevance of the ecological

conditions in the habitat.

The evaluation of ecological interchangeability using

niche-based distribution modelling requires close scru-

tiny. For most algorithms, niche-based distribution

models are viewed as a prediction of the organism’s

realized niche because occurrence localities are drawn

exclusively from the source habitat. Soberon & Peterson

(2005), however, concluded that niche models inferred

by GARP provide an approximation to the species’

fundamental niche. This is based on the fact that the

GARP modifies the initial rules (i.e. ecological parame-

ters) by specially defined genetic operators. The results

are models that have ‘evolved’ and are capable of

predicting distribution zones that do not encompass the

ecological conditions recorder at the input points.

Although overlapping predicted distributions might

support the null hypothesis of ecological inter-

changeability, they do not necessarily rule out noninter-

changeability. An over prediction by the model indicates

that a species’ environmental tolerances are more

extensive than observed and that other factors (e.g.

biotic interactions) may be limiting geographical

range. The ideal of reciprocal transplant and common

environment experimentation is without question

unfeasible for many groups. Moreover, reciprocal trans-

plant experiments are potentially irresponsible and

environmentally unethical under many circumstances.

Thus, further work is needed to design tests of ecological

interchangeability that can be more easily implemented

and are feasible for the rapid biodiversity assessments

required in some conservation situations (Bond &

Stockman, 2008).

The ENMs of B. musculus reciprocally recovered most

of the locality records of the alternative phylogroup.

Peterson et al. (2006) reported similar patterns for nested

ENMs between sister taxa in rodents and other

vertebrates. An explanation for this kind of result is that

the niche of one of the lineages is constrained by

geographical or ecological factors, whereas the niche

of the other species has expanded. Harrison (1998)

concluded that niche differentiation was a secondary

consequence of the process of population divergence.

Consequently, the phylogroups segregated by ENM have

been isolated for a sufficient number of generations to

reach ecological specialization. Consequently, explora-

tory approaches like ENM will produce false negatives for

independent historical units where the sister taxa have

occupied the same niche over time.

The central argument in ‘Ecological’ Species Concepts

(e.g. Van Valen, 1976; Templeton, 2001) is the differen-

tiation of the ecological niche. Even so, there are studies

that demonstrate speciation without ecological diver-

gence (e.g. Peterson et al., 1999; Kozak & Wiens, 2006).

Our proposal differs from those concepts because niche

differentiation between sister taxa is not essential for

speciation. We suggest that niche differentiation is

merely an indication that some lineages might have

followed alternative evolutionary pathways. Analyses

like those presented here can be used to identify

previously undetected ecological variation. As such,

hidden biodiversity within currently recognized species

could be revealed by ENM analysis. Further research into

species with alternative biological traits will allow us to

understand the potential and limitations of ENM for

systematic studies.




Acknowledgments

We thank S. Blanco, G. Ceballos-Gonzalez and G. Oliva

for the collection database. F. Ornelas provide statistical

advice. E. Arellano, A. Guillen, R. Landgrave and M.

Laurin provided comments and suggestions that greatly

improved this manuscript. Special thanks to E. Martınez-

Meyer, C. Munoz and T. Peterson for their advice and

patient assistance with theoretical and methodological

concepts. DMG was supported by a scholarship from the

Mexican government (CONACyT no. 179252).

References

Abuzeineh, A.A. 2006. Landmark morphometric assessment of

the genus Baiomys (Mammalia: Rodentia): geographic varia-

tion, secondary sexual dimorphism, and character displace-

ment in sister species. MSc Thesis, Texas Tech University,

Lubbock, TX.

Amman, B.R. & Bradley, R.D. 2004. Molecular evolution in

Baiomys (Rodentia: Sigmodontinae): evidence for a genetic

subdivision in B. musculus. J. Mammal. 85: 162–166.

Anderson, R.P. 2003. Real vs artefactual absences in species

distribution: test of Oryzomys albigularis (Rodentia: Muridae) in

Venezuela. J. Biogeogr. 30: 591–605.

Anderson, R.P., Gomez-Laverde, M. & Peterson, A.T. 2002.

Geographical distributions of spiny pocket mice in South

America: Insights from predictive models. Global Ecol. Biogeogr.

11: 131–141.

Avise, J.C. 2000. Phylogeography: The History and Formation of

Species. Harvard University Press, Cambridge.

Baker, R.J. & Bradley, R.D. 2006. Speciation in mammals and

the genetic species concept. J. Mammal. 87: 643–662.

Bond, J.E. & Stockman, A.K. 2008. An integrative method for

delimiting cohesion species: finding the population-species

interface in a group of Californian trapdoor spiders with

extreme genetic divergence and geographic structuring. Syst.

Biol. 57: 628–646.

Carleton, M.D. 1989. Systematics and evolution. In: Advances in

the Study of Peromyscus (Rodentia) (G.L. Kirkland Jr & J.N.

Layne, eds), pp. 7–141. Texas Tech University Press, Lubbock.

Caruso, C.M., Maherall, H. & Sherpard, M. 2006. Plasticity of

physiology in Lobelia: testing for adaptation and constraint.

Evolution 60: 980–990.

Ceballos, G. & Oliva, G. 2005. Los mamıferos de Mexico. CONA-

BIO-UNAM, Mexico.

Chefaoui, R.M., Hortal, J. & Lobo, J.M. 2005. Potential distri-

bution modeling, niche characterization and conservation

status assessment using GIS tools: a case study of Iberian Copris

species. Biol. Conserv. 122: 327–338.

Cracraft, J. 1983. Species concept and speciation analysis. Curr.

Ornithol. 1: 159–187.

Dayrat, B. 2005. Towards integrative taxonomy. Biol. J. Linn.

Soc. B 85: 407–415.

De Queiroz, K. 1998. The general lineage concept of species,

species criteria, and the process of speciation: a conceptual

unification and terminological recommendations. In: Endless

Forms: Species and Speciation (D.J. Howard & S.H. Berlocher,

eds), pp. 57–75. Oxford University Press, New York.

Ereshefsky, M. 2002. Linnaean ranks: vestiges of a bygone era.

Phil. Sci. 69: S305–S315.

ESRI (Environmental Systems Research Institute). 1999. ArcView

GIS, V. 3.2. ESRI Inc., Redlands, CA.

Graham, C.H., Ferrer, S., Huettman, F., Moritz, C. & Peterson,

A.T. 2004a. New developments in museum-based informatics

and application in biodiversity analysis. Trends Ecol. Evol. 19:

497–503.

Graham, C.H., Ron, S.R., Santos, J.C., Schneider, C.J. & Moritz,

C. 2004b. Integrating phylogenetics and environmental niche

models to explore speciation mechanisms in dendrobatid

frogs. Evolution 58: 1781–1793.

Grinnell, J. 1917. Field tests of theories concerning distributional

control. Am. Nat. 51: 115–128.

Guisan, A. & Thuiller, W. 2005. Predicting species distribution:

Offering more than simple habitat models. Ecol. Lett. 8: 993–

1009.

Gust, D.H. & Schmidly, D.J. 1986. Small mammal populations

on reclaimed strip-mined areas in Freestone County, Texas.

J. Mammal. 67: 214–217.

Hall, E.R. 1981. The Mammals of North America, Vol. 2. John

Wiley & Sons, New York.

Harris, D., Rogers, D.S. & Sullivan, J. 2000. Phylogeography of

Peromyscus furvus (Rodentia; Muridae) based on cytochrome-b

sequence data. Mol. Ecol. 9: 2129–2135.

Harrison, R.G. 1998. Linking evolutionary pattern and process:

the relevance of species concepts for the study of speciation.

In: Endless Forms: Species and Speciation (D.J. Howard & S.H.

Berlocher, eds), pp. 19–31. Oxford University Press, New York.

Hart, B.J. 1972. Distribution of the pygmy mouse, Baiomys

taylori, in North-Central Texas. Southwest. Nat. 17: 213–214.

Hendry, A.P., Nosil, P. & Rieseberg, L.H. 2007. The speed of

ecological speciation. Funct. Ecol. 21: 455–464.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A.

2005. Very high resolution interpolated climate surfaces for

global land areas. Int. J. Climatol. 25: 1965–1978.

Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. 2002.

Ecological-niche factor analysis: how to compute habitat-

suitability maps without absence data? Ecology 83: 2027–2036.

Hoekstra, H.E., Hirschmann, R.J., Bundey, R.A., Insel, P.A. &

Crossland, J.P. 2006. A single amino acid mutation contributes

to adaptive beach mouse color pattern. Science 313: 101–104.

Holt, R.D. & Gaines, M.S. 1992. Analysis of adaptation in

heterogeneous landscapes: implications for the evolution of

fundamental niches. Evol. Ecol. 6: 433–447.

Hunsaker, D., Raun, G.G. & Swindells, J.E. 1959. Range

expansion of Baiomys taylori in Texas. J. Mammal. 40: 447–448.

Killion, J.M., Grant, W.E. & Vinson, S.B. 1995. Response of

Baiomys taylori to changes in density of imported fire ants.

J. Mammal. 76: 141–147.

Kozak, K.H. & Wiens, J.J. 2006. Does niche conservatism

promote speciation? A case study in North American sala-

manders. Evolution 60: 2604–2621.

Laurin, M. 2005. The advantages of phylogenetic nomenclature

over Linnean nomenclature. In: Animal Names, Vol. 1

(A. Minelli, G. Ortalli & G. Sanga, eds), pp. 67–97. Instituto

Veneto di Scienze, Lettere ed Arti, Venice.

Laurin, M. 2008. The splendid isolation of biological nomencla-

ture. Zool. Scr. 37: 223–233.

Leon-Paniagua, L., Navarro-Siguenza, A.G., Hernandez-Banos,

B.E. & Morales, J.C. 2007. Diversification of the arboreal mice

of the genus Habromys (Rodentia: Cricetidae: Neotominae) in

the Mesoamerican highlands. Mol. Phylogenet. Evol. 42: 653–

664.




Losos, J.B., Warhelt, K.I. & Schoener, T.W. 1997. Adaptive

differentiation following experimental island colonization in

Anolis lizards. Nature 387: 70–73.

Luxbacher, A.M. & Knouft, J.H. 2009. Assessing concurrent

patterns of environmental niche and morphological evolution

among species of horned lizards (Phrynosoma). J. Evol. Biol.

22: 1669–1678.

Mayr, E. 1982. The Growth of Biological Thought. Harvard

University Press, Cambridge.

Packard, R.L. & Judd, F.W. 1968. Comments on some mammals

from western Texas. J. Mammal. 49: 535–538.

Pagel, M.D., May, R.M. & Colline, A.R. 1991. Ecological aspects

of the geographical distribution and diversity of mammalian

species. Am. Nat. 137: 791–815.

Peterson, A.T. & Cohoon, K.C. 1999. Sensitivity of distributional

prediction algorithms to geographic data completeness. Ecol.

Model. 117: 159–164.

Peterson, A.T., Soberon, J. & Sanchez-Cordero, V. 1999. Con-

servatism of ecological niches in evolutionary time. Science

285: 1265–1267.

Peterson, A.T., Papes, M., Reynolds, M.G., Perry, N.D., Hanson,

B., Regnery, R.L., Hutson, C.L., Muizniek, B., Damon, I.K. &

Carroll, D.S. 2006. Native-range ecology and invasive poten-

tial of Cricetomys in North America. J. Mammal. 87: 427–432.

Peterson, A.T., Papes, M. & Eaton, M. 2007. Transferability and

model evaluation in ecological niche modeling: a comparison

of GARP and Maxent. Ecography 30: 550–560.

Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006. Maximum

entropy modeling of species geographic distributions. Ecol.

Modell. 190: 231–259.

Prinzing, A., Durka, W., Klotz, S. & Brandl, R. 2001. The niche of

higher plants: evidence for phylogenetic conservatism. Philos.

T. Roy. Soc. B 268: 2383–2389.

Ramırez-Pulido, J. & Castro-Campillo, A. 1990. Regiones y

provincias mastogeograficas. In: Atlas Nacional de Mexico, Vol. 2

(Instituto de Gerogafıa, eds), Map IV. 8.8. Instituto de Geografıa,

Universidad Nacional Autonoma de Mexico, Mexico.

Raxworthy, J.C., Martinez-Meyer, E., Horning, N., Nussbaum,

R.A., Schneider, G.E., Ortega-Huerta, M.A. & Peterson, A.T.

2003. Predicting distributions of known and unknown reptile

species in Madagascar. Nature 426: 837–841.

Raxworthy, C.J., Ingram, C.M., Rabibisoa, N. & Pearson, R.G.

2007. Applications of ecological niche modeling for species

delimitation: a review and empirical evaluation using day

geckos (Phelsuma) from Madagascar. Syst. Biol. 56: 907–923.

Reznick, D.N., Shaw, F.H., Rodd, F.H. & Shaw, R.G. 1997.

Evaluation of the rate of evolution in natural populations of

guppies (Poecilia reticulata). Science 275: 1934–1937.

Rice, N.H., Martınez-Meyer, E. & Peterson, A.T. 2003. Ecological

niche differentiation in the Aphelocoma jays: a phylogenetic

perspective. Biol. J. Linn. Soc. 80: 369–383.

Rissler, L.J. & Apodaca, J.J. 2007. Adding more ecology into

species delimitation: ecological niche models and phylogeog-

raphy help define cryptic species in the black salamander

(Aneides flavipunctatus). Syst. Biol. 56: 924–942.

Rojas-Soto, O.R., Alcantara-Ayala, O. & Navarro-Siguenza, A.G.

2003. Regionalization of the avifauna of the Baja California

Peninsula, Mexico: a parsimony analysis of endemicity and

distributional modeling approach. J. Biogeogr. 30: 449–461.

Soberon, J. & Peterson, A.T. 2005. Interpretation of models of

fundamental ecological niches and species distributional areas.

Biodivers. Inform. 2: 1–10.

SPSS. 2009. SPSS Base 17.0 for Mac User’s Guide. SPSS, Inc.,

Chicago, IL.

Stickel, L.F. & Stickel, H.S. 1949. A Sigmodon and Baiomys

population in ungrazed and unburned Texas prairie.

J. Mammal. 30: 141–150.

Stockman, A.K. & Bond, J.E. 2007. Delimiting cohesion species:

extreme population structuring and the role of ecological

interchangeability. Mol. Ecol. 16: 3374–3392.

Stockwell, D.R.B. & Noble, I.R. 1992. Induction of sets of rules

from animal distribution data: a robust and informative

method of data analysis. Math. Comput. Simul. 33: 385–390.

Stockwell, D.R.B. & Peters, D.P. 1999. The GARP modelling

system: problems and solutions to automated spatial predic-

tion. Int. J. Geogr. Inf. Sci. 13: 143–158.

Sullivan, J., Markert, J.A. & Kilpatrick, C.W. 1997. Biogeogra-

phy and molecular systematics of the Peromyscus aztecus group.

Syst. Biol. 46: 426–440.

Sullivan, J., Arellano, E. & Rogers, D. 2000. Comparative

phylogeography of Mesoamerican highland rodents: con-

certed versus independent response to past climatic fluctua-

tions. Am. Nat. 155: 755–768.

Swenson, N.G. 2008. The past and future influence of

geographic information systems on hybrid zone, phylo-

geographic and speciation research. J. Evol. Biol. 21: 421–

434.

Templeton, A.R. 2001. Using phylogeographic analyses of gene

trees to test species status and boundaries. Mol. Ecol. 10: 779–

791.

Terblanche, J.S. & Kleynhans, E. 2009. Phenotypic plasticity of

desiccation resistance in Glossina puparia: are there ecotype

constraints on acclimation responses? J. Evol. Biol. 22: 1636–

1648.

Triant, D.A. & DeWoody, J.A. 2006. Accelerated molecular

evolution in Microtus (Rodentia) as assessed via complete

mitochondrial genome sequences. Genetica 128: 95–108.

USGS (United States Geological Survey). 2001. HYDRO 1K:

Elevation Derivative Database. http://edc.usgs.gov/products/

elevation/gtopo30/hydro/namerica.html.

Van Valen, L. 1976. Ecological species, multispecies, and oaks.

Taxon 25: 233–239.

Wiens, J.J. 2007. Species delimitation: new approaches for

discovering diversity. Syst. Biol. 56: 875–878.

Wiens, J.J. & Graham, C.H. 2005. Niche conservatism: integrat-

ing evolution, ecology, and conservation biology. Annu. Rev.

Ecol. Evol. Syst. 36: 519–539.

Wiens, J.J. & Servedio, M.R. 2000. Species delimitation in

systematics: inferring diagnostic differences between species.

P. R. Soc. Lond. B Biol. Sci. 267: 631–636.

Wiley, R.H. 1981. Social structure and individual ontogenies:

problems of description, mechanism, and evolution. In:

Perspectives in Ethology, Vol. 4 (P.P.G. Bateson & P.H. Klopfer,

eds), pp. 105–133. Plenum Press, New York.

Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H.

& Guisan, A. 2008. Effects of sample size on the perfor-

mance of species distribution models. Divers. Distrib. 14: 763–

773.

Zink, R.M. & Barrowclough, G.F. 2008. Mitochondrial DNA

under siege in avian phylogeography. Mol. Ecol. 17: 2107–

2121.

Received 29 April 2008; revised 15 October 2009; accepted 19 October

2009




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