A. Leaché, Jared Grummer, M. Miller, S. Krishnan, M. Fujita, W. Böhme, A. Schmitz, M. Lebreton, I. Ineich, L. Chirio, Caleb OFORI-BOATENG, E. Eniang, E. Greenbaum, Mark‐Oliver Rödel, P. Wagner (2017)
Bayesian inference of species diffusion in the West African Agama agama species group (Reptilia, Agamidae)Systematics and Biodiversity, 15
D. Gonçalves, J. Brito, P. Crochet, P. Geniez, J. Padial, D. Harris (2012)
Phylogeny of North African Agama lizards (Reptilia: Agamidae) and the role of the Sahara desert in vertebrate speciation.Molecular phylogenetics and evolution, 64 3
Mathieu Marmion, Miia Parviainen, M. Luoto, R. Heikkinen, W. Thuiller (2009)
Evaluation of consensus methods in predictive species distribution modellingDiversity and Distributions, 15
V. Nicolas, A. Mataame, P. Crochet, P. Geniez, S. Fahd, A. Ohler (2018)
Phylogeography and ecological niche modeling unravel the evolutionary history of the African green toad, Bufotes boulengeri boulengeri (Amphibia: Bufonidae), through the QuaternaryJournal of Zoological Systematics and Evolutionary Research, 56
E. Rohling, E. Rohling, G. Marino, K. Grant (2015)
Mediterranean climate and oceanography, and the periodic development of anoxic events (sapropels)Earth-Science Reviews, 143
Xia Hua, J. Wiens (2013)
How Does Climate Influence Speciation?The American Naturalist, 182
A. Lansari, M. Vences, Susanne Hauswaldt, R. Hendrix, D. Donaire, Abdellah Bouazza, U. Joger, E. Mouden, T. Slimani (2015)
The Atlas Massif separates a northern and a southern mitochondrial haplotype group of North African water frogs Pelophylax saharicus (Anura: Ranidae) in MoroccoAmphibia-reptilia, 36
M. Stephens, N. Smith, P. Donnelly (2001)
A new statistical method for haplotype reconstruction from population data.American journal of human genetics, 68 4
P. deMenocal, J. Ortiz, T. Guilderson, J. Adkins, M. Sarnthein, Linda Baker, Martha Yarusinsky (2000)
Abrupt onset and termination of the African Humid Period:rapid climate responses to gradual insolation forcingQuaternary Science Reviews, 19
A. Carnaval, M. Hickerson, C. Haddad, M. Rodrigues, C. Moritz (2009)
Stability Predicts Genetic Diversity in the Brazilian Atlantic Forest HotspotScience, 323
Omri Allouche, Asaf Tsoar, R. Kadmon (2006)
Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)Journal of Applied Ecology, 43
Drummond (2012)
Bayesian phylogenetics with BEAUti and the BEAST 1.7Molecular Biology and Evolution, 29
K. Kozak, J. Wiens (2010)
Niche Conservatism Drives Elevational Diversity Patterns in Appalachian SalamandersThe American Naturalist, 176
A. Leaché, P. Wagner, Charles Linkem, W. Böhme, T. Papenfuss, Rebecca Chong, Brian Lavin, A. Bauer, Stuart Nielsen, E. Greenbaum, Mark‐Oliver Rödel, A. Schmitz, M. Lebreton, I. Ineich, L. Chirio, Caleb OFORI-BOATENG, E. Eniang, Sherif Din, Alan Lemmon, F. Burbrink (2014)
A hybrid phylogenetic-phylogenomic approach for species tree estimation in African Agama lizards with applications to biogeography, character evolution, and diversification.Molecular phylogenetics and evolution, 79
C. Merow, Matthew Smith, J. Silander (2013)
A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matterEcography, 36
(2005)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1276 VERY HIGH RESOLUTION INTERPOLATED CLIMATE SURFACES FOR GLOBAL LAND AREAS
J. Wiens, C. Graham (2005)
Niche Conservatism: Integrating Evolution, Ecology, and Conservation BiologyAnnual Review of Ecology, Evolution, and Systematics, 36
T. Herbert, K. Lawrence, A. Tzanova, L. Peterson, R. Caballero-Gill, C. Kelly (2016)
Late Miocene global cooling and the rise of modern ecosystemsNature Geoscience, 9
D. Warren, R. Glor, M. Turelli (2008)
Environmental Niche Equivalency versus Conservatism: Quantitative Approaches to Niche Evolution, 62
Sudhir Kumar, A. Drummond, M. Suchard, Dong Xie, A. Rambaut (2012)
phylogenetics with
F. Ahmadzadeh, Morris Flecks, M. Carretero, W. Böhme, F. Ihlow, P. Kapli, Andreia Miraldo, D. Rödder (2016)
Separate histories in both sides of the Mediterranean: phylogeny and niche evolution of ocellated lizardsJournal of Biogeography, 43
J. Wiens, Caitlin Kuczynski, S. Smith, D. Mulcahy, J. Sites, T. Townsend, T. Reeder (2008)
Branch lengths, support, and congruence: testing the phylogenomic approach with 20 nuclear loci in snakes.Systematic biology, 57 3
Mark Miller, W. Pfeiffer, Terri Schwartz (2011)
The CIPRES science gateway: a community resource for phylogenetic analyses
C. Pook, U. Joger, Nikolaus Stümpel, W. Wüster (2009)
When continents collide: phylogeny, historical biogeography and systematics of the medically important viper genus Echis (Squamata: Serpentes: Viperidae).Molecular phylogenetics and evolution, 53 3
C. Douady, F. Catzeflis, J. Raman, M. Springer, M. Stanhope (2003)
The Sahara as a vicariant agent, and the role of Miocene climatic events, in the diversification of the mammalian order Macroscelidea (elephant shrews)Proceedings of the National Academy of Sciences of the United States of America, 100
J. Elith, M. Kearney, Steven Phillips (2010)
The art of modelling range‐shifting speciesMethods in Ecology and Evolution, 1
Nyári (2010)
Late Pleistocene potential distribution of the North African Sengi or elephant-shrew Elephantulus rozetiAfrican Zoology, 45
F. Martínez‐Freiría, P. Crochet, S. Fahd, P. Geniez, J. Brito, G. Velo‐Antón (2017)
Integrative phylogeographical and ecological analysis reveals multiple Pleistocene refugia for Mediterranean Daboia vipers in north-west AfricaBiological Journal of The Linnean Society, 122
G. Velo‐Antón, J. Parra, G. Parra-Olea, K. Zamudio (2013)
Tracking climate change in a dispersal‐limited species: reduced spatial and genetic connectivity in a montane salamanderMolecular Ecology, 22
B. Swart, K. Tolley, C. Matthee (2009)
Climate change drives speciation in the southern rock agama (Agama atra) in the Cape Floristic Region, South AfricaJournal of Biogeography, 36
M. Evans, Stephen Smith, Rachel Flynn, M. Donoghue (2008)
Climate, Niche Evolution, and Diversification of the “Bird‐Cage” Evening Primroses (Oenothera, Sections Anogra and Kleinia)The American Naturalist, 173
A. Santos, M. Cabezas, A. Tavares, R. Xavier, M. Branco (2016)
tcsBU: a tool to extend TCS network layout and visualizationBioinformatics, 32 4
R. Lanfear, B. Calcott, S. Ho, Stéphane Guindon (2012)
Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses.Molecular biology and evolution, 29 6
J. Anadón, E. Graciá, F. Botella, A. Giménez, S. Fahd, U. Fritz (2015)
Individualistic response to past climate changes: niche differentiation promotes diverging Quaternary range dynamics in the subspecies of Testudo graecaEcography, 38
P. Librado, J. Rozas (2009)
DnaSP v5: a software for comprehensive analysis of DNA polymorphism dataBioinformatics, 25 11
H. Houérou (1997)
Climate, flora and fauna changes in the Sahara over the past 500 million years☆Journal of Arid Environments, 37
J. Wiens, D. Stralberg, D. Jongsomjit, Christine Howell, M. Snyder (2009)
Niches, models, and climate change: Assessing the assumptions and uncertaintiesProceedings of the National Academy of Sciences, 106
Hijmans (2005)
Very high resolution interpolated climate surfaces for global land areasInternational Journal of Climatology, 25
P. Kapli, P. Lymberakis, P. Crochet, P. Geniez, José Brito, M. Almutairi, F. Ahmadzadeh, Andreas Schmitz, Thomas Wilms, N. Pouyani, N. Poulakakis (2015)
Historical biogeography of the lacertid lizard Mesalina in North Africa and the Middle EastJournal of Biogeography, 42
S. Dobrowski (2011)
A climatic basis for microrefugia: the influence of terrain on climateGlobal Change Biology, 17
D. Silvestro, Ingo Michalak (2012)
raxmlGUI: a graphical front-end for RAxMLOrganisms Diversity & Evolution, 12
D. Edwards, J. Keogh, L. Knowles (2012)
Effects of vicariant barriers, habitat stability, population isolation and environmental features on species divergence in the south‐western Australian coastal reptile communityMolecular Ecology, 21
Zhongshi Zhang, G. Ramstein, M. Schuster, Camille Li, C. Contoux, Q. Yan (2014)
Aridification of the Sahara desert caused by Tethys Sea shrinkage during the Late MioceneNature, 513
P. Lemey, A. Rambaut, J. Welch, M. Suchard (2010)
Phylogeography takes a relaxed random walk in continuous space and time.Molecular biology and evolution, 27 8
C. Matthee, A. Flemming (2002)
Population fragmentation in the southern rock agama, Agama atra: more evidence for vicariance in Southern AfricaMolecular Ecology, 11
J. Zachos, M. Pagani, L. Sloan, Ellen Thomas, K. Billups (2001)
Trends, Rhythms, and Aberrations in Global Climate 65 Ma to PresentScience, 292
Daniela Kissling, A. Blach‐Overgaard, Roelof Zwaan, P. Wagner (2016)
Historical colonization and dispersal limitation supplement climate and topography in shaping species richness of African lizards (Reptilia: Agaminae)Scientific Reports, 6
N. Loumbourdis (1986)
The tail of the lizard Agama stellio stellio: Energetics, significance and comments on its regenerationAmphibia-reptilia, 7
J. Wiens, K. Kozak, N. Silva (2013)
DIVERSITY AND NICHE EVOLUTION ALONG ARIDITY GRADIENTS IN NORTH AMERICAN LIZARDS (PHRYNOSOMATIDAE)Evolution, 67
F. Ronquist, M. Teslenko, Paul Mark, Daniel Ayres, A. Darling, S. Höhna, B. Larget, Liang Liu, M. Suchard, J. Huelsenbeck (2012)
MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model SpaceSystematic Biology, 61
L. Lisiecki, M. Raymo (2005)
A Pliocene‐Pleistocene stack of 57 globally distributed benthic δ18O recordsPaleoceanography, 20
Jérémy Migliore, A. Baumel, M. Juin, B. Fady, A. Roig, N. Duong, F. Médail (2013)
Surviving in Mountain Climate Refugia: New Insights from the Genetic Diversity and Structure of the Relict Shrub Myrtus nivellei (Myrtaceae) in the Sahara DesertPLoS ONE, 8
Mónica Arakaki, P. Christin, R. Nyffeler, A. Lendel, U. Eggli, R. Ogburn, E. Spriggs, M. Moore, E. Edwards (2011)
Contemporaneous and recent radiations of the world's major succulent plant lineagesProceedings of the National Academy of Sciences, 108
张荣祖 (1990)
历史生物地新趋势—分衍生物地理学(Vicariance Biogeography), 25
Árpád Nyári, Townsend Peterson&yg, G. Rathbun (2010)
Late Pleistocene Potential Distribution of the North African Sengi or Elephant-Shrew Elephantulus rozeti (Mammalia: Macroscelidea), 45
Joana Veríssimo, Mohamed Znari, H. Stuckas, U. Fritz, Paulo Pereira, J. Teixeira, M. Arculeo, F. Marrone, F. Sacco, M. Naimi, C. Kehlmaier, G. Velo‐Antón (2016)
Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosaBiological Journal of The Linnean Society, 119
E. Arévalo, S. Davis, J. Sites (1994)
Mitochondrial DNA Sequence Divergence and Phylogenetic Relationships among Eight Chromosome Races of the Sceloporus Grammicus Complex (Phrynosomatidae) in Central MexicoSystematic Biology, 43
Carolyn Snyder (2016)
Evolution of global temperature over the past two million yearsNature, 538
Tanja Gernhard (2008)
The conditioned reconstructed process.Journal of theoretical biology, 253 4
K. Tamura, G. Stecher, D. Peterson, A. Filipski, Sudhir Kumar (2013)
MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.Molecular biology and evolution, 30 12
G. Velo‐Antón, F. Martínez‐Freiría, Paulo Pereira, P. Crochet, J. Brito (2018)
Living on the edge: Ecological and genetic connectivity of the spiny‐footed lizard, Acanthodactylus aureus, confirms the Atlantic Sahara desert as a biogeographic corridor and centre of lineage diversificationJournal of Biogeography, 45
Karin Tamar, S. Carranza, R. Sindaco, J. Moravec, J. Trape, Shai Meiri (2016)
Out of Africa: Phylogeny and biogeography of the widespread genus Acanthodactylus (Reptilia: Lacertidae).Molecular phylogenetics and evolution, 103
Prof. Costa (1995)
Behavioural Adaptations of Desert Animals
K. Katoh, D. Standley (2013)
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and UsabilityMolecular Biology and Evolution, 30
L. Granjon, G. Dobigny (2003)
The importance of cytotaxonomy in understanding the biogeography of African rodents: Lake Chad murids as an exampleMammal Review, 33
J. Wiens, D. Ackerly, A. Allen, B. Anacker, Lauren Buckley, H. Cornell, Ellen Damschen, T. Davies, J. Grytnes, S. Harrison, B. Hawkins, R. Holt, C. McCain, P. Stephens (2010)
Niche conservatism as an emerging principle in ecology and conservation biology.Ecology letters, 13 10
T. Shaibi, R. Moritz (2010)
10,000 years in isolation? Honeybees (Apis mellifera) in Saharan oasesConservation Genetics, 11
M. Schuster, P. Duringer, J. Ghienne, P. Vignaud, H. Mackaye, A. Likius, M. Brunet (2006)
The Age of the Sahara DesertScience, 311
T. Cerling, John Harris, B. MacFadden, M. Leakey, J. Quade, V. Eisenmann, J. Ehleringer (1997)
Global vegetation change through the Miocene/Pliocene boundaryNature, 389
J. Gutiérrez‐Rodríguez, A. Barbosa, Í. Martínez‐Solano (2017)
Present and past climatic effects on the current distribution and genetic diversity of the Iberian spadefoot toad (Pelobates cultripes): an integrative approachJournal of Biogeography, 44
S. Carranza, E. Arnold, J. Mateo, P. Geniez (2002)
Relationships and evolution of the North African geckos, Geckonia and Tarentola (Reptilia: Gekkonidae), based on mitochondrial and nuclear DNA sequences.Molecular phylogenetics and evolution, 23 2
Qixin He, D. Edwards, L. Knowles (2013)
INTEGRATIVE TESTING OF HOW ENVIRONMENTS FROM THE PAST TO THE PRESENT SHAPE GENETIC STRUCTURE ACROSS LANDSCAPESEvolution, 67
Prof.Dr. Degen (1996)
Ecophysiology of Small Desert Mammals
Andrew Martin, C. Mcintosh, W. McMillan, S. Palumbi (2004)
THE SIMPLE FOOL’S GUIDE TO PCR
C. Rato, D. Harris, A. Perera, S. Carvalho, M. Carretero, D. Rödder (2015)
A Combination of Divergence and Conservatism in the Niche Evolution of the Moorish Gecko, Tarentola mauritanica (Gekkota: Phyllodactylidae)PLoS ONE, 10
D. Gonçalves, F. Martínez‐Freiría, P. Crochet, P. Geniez, S. Carranza, J. Brito (2018)
The role of climatic cycles and trans-Saharan migration corridors in species diversification: Biogeography of Psammophis schokari group in North Africa.Molecular phylogenetics and evolution, 118
J. Wiens, M. Brandley, T. Reeder (2006)
WHY DOES A TRAIT EVOLVE MULTIPLE TIMES WITHIN A CLADE? REPEATED EVOLUTION OF SNAKELIKE BODY FORM IN SQUAMATE REPTILES, 60
Salvador Carranza, E. Arnold, P. Geniez, J. Roca, J. Mateo (2008)
Radiation, multiple dispersal and parallelism in the skinks, Chalcides and Sphenops (Squamata: Scincidae), with comments on Scincus and Scincopus and the age of the Sahara Desert.Molecular phylogenetics and evolution, 46 3
Mitzy Pepper, M. Fujita, C. Moritz, J. Keogh (2011)
Palaeoclimate change drove diversification among isolated mountain refugia in the Australian arid zoneMolecular Ecology, 20
E. Arnold (1995)
Identifying the effects of history on adaptation: origins of different sand‐diving techniques in lizardsJournal of Zoology, 235
Avise (2000)
10.2307/j.ctv1nzfgj7
C. Badgley, J. Barry, M. Morgan, S. Nelson, A. Behrensmeyer, T. Cerling, D. Pilbeam (2008)
Ecological changes in Miocene mammalian record show impact of prolonged climatic forcingProceedings of the National Academy of Sciences, 105
A. Drummond, S. Ho, M. Phillips, A. Rambaut (2006)
Relaxed Phylogenetics and Dating with ConfidencePLoS Biology, 4
O. Broennimann, M. Fitzpatrick, P. Pearman, B. Petitpierre, L. Pellissier, N. Yoccoz, W. Thuiller, M. Fortin, C. Randin, N. Zimmermann, C. Graham, A. Guisan (2012)
Measuring ecological niche overlap from occurrence and spatial environmental dataGlobal Ecology and Biogeography, 21
M. Clement, D. Posada, K. Crandall (2000)
TCS: a computer program to estimate gene genealogiesMolecular Ecology, 9
Filip Bielejec, A. Rambaut, M. Suchard, P. Lemey (2011)
SPREAD: spatial phylogenetic reconstruction of evolutionary dynamicsBioinformatics, 27
W. Thuiller, Bruno Lafourcade, Robin Engler, M. Araújo (2009)
BIOMOD – a platform for ensemble forecasting of species distributionsEcography, 32
O. Mediannikov, S. Trape, J. Trape (2012)
A Molecular Study of the Genus Agama (Squamata: Agamidae) in West Africa, with Description of Two New Species and a Review of the Taxonomy, Geographic Distribution, and Ecology of Currently Recognized SpeciesRussian Journal of Herpetology, 19
C. Vale, P. Tarroso, João Campos, D. Gonçalves, J. Brito (2012)
Distribution, suitable areas and conservation status of the Boulenger’s agama (Agama boulengeri, Lataste 1886)Amphibia-reptilia, 33
R. Lawson, J. Slowinski, B. Crother, F. Burbrink (2005)
Phylogeny of the Colubroidea (Serpentes): new evidence from mitochondrial and nuclear genes.Molecular phylogenetics and evolution, 37 2
B. Otto‐Bliesner, S. Marshall, J. Overpeck, G. Miller, A. Hu (2006)
Simulating Arctic Climate Warmth and Icefield Retreat in the Last InterglaciationScience, 311
M. Metallinou, J. Červenka, P. Crochet, L. Kratochvíl, T. Wilms, P. Geniez, M. Shobrak, J. Brito, S. Carranza (2015)
Species on the rocks: Systematics and biogeography of the rock-dwelling Ptyodactylus geckos (Squamata: Phyllodactylidae) in North Africa and Arabia.Molecular phylogenetics and evolution, 85
J. Macey, James Schulte, Jonathan Fong, I. Das, T. Papenfuss (2006)
The complete mitochondrial genome of an agamid lizard from the Afro-Asian subfamily agaminae and the phylogenetic position of Bufoniceps and Xenagama.Molecular phylogenetics and evolution, 39 3
F. E., G. Yule (1925)
A Mathematical Theory of Evolution Based on the Conclusions of Dr. J. C. Willis, F.R.S.Journal of the Royal Statistical Society, 88
V. Nicolas, A. Mataame, P. Crochet, P. Geniez, A. Ohler (2015)
Phylogeographic patterns in North African water frog Pelophylax saharicus (Anura: Ranidae)Journal of Zoological Systematics and Evolutionary Research, 53
M. Metallinou, E. Arnold, P. Crochet, P. Geniez, J. Brito, P. Lymberakis, Sherif Din, R. Sindaco, M. Robinson, S. Carranza (2012)
Conquering the Sahara and Arabian deserts: systematics and biogeography of Stenodactylus geckos (Reptilia: Gekkonidae)BMC Evolutionary Biology, 12
Israel Loera, V. Sosa, Stefanie Ickert‐Bond (2012)
Diversification in North American arid lands: niche conservatism, divergence and expansion of habitat explain speciation in the genus Ephedra.Molecular phylogenetics and evolution, 65 2
A. Peterson (2011)
Ecological niche conservatism: a time‐structured review of evidenceJournal of Biogeography, 38
J. Brito, R. Godinho, F. Martínez‐Freiría, J. Pleguezuelos, H. Rebelo, X. Santos, C. Vale, G. Velo‐Antón, Z. Boratyński, S. Carvalho, S. Ferreira, D. Gonçalves, T. Silva, P. Tarroso, João Campos, J. Leite, J. Nogueira, F. Álvares, N. Sillero, A. Sow, S. Fahd, P. Crochet, S. Carranza (2014)
Unravelling biodiversity, evolution and threats to conservation in the Sahara‐SahelBiological Reviews, 89
C. Swezey (2003)
The Role of Climate in the Creation and Destruction of Continental Stratigraphic Records: An Example from the Northern Margin of the Sahara Desert
A. Stamatakis (2014)
RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogeniesBioinformatics, 30
R. Brown, N. Suárez, J. Pestano (2002)
The Atlas mountains as a biogeographical divide in North-West Africa: evidence from mtDNA evolution in the Agamid lizard Agama impalearis.Molecular phylogenetics and evolution, 24 2
Jiajie Zhang, P. Kapli, P. Pavlidis, A. Stamatakis (2013)
A general species delimitation method with applications to phylogenetic placementsBioinformatics, 29
Abstract Diversification events in the Sahara–Sahel have mostly been attributed to regional aridification and subsequent arid–humid fluctuations, through vicariance or adaptation. However, no study has attempted to test these contrasting hypotheses. Here, we assess the importance of aridity-induced vicariance (as opposed to adaptation to new conditions) on diversification processes in North-West African Agama lizards. To test the hypothesis of vicariance as the main driver of diversification, we assessed the occurrence of the following three patterns expected to occur under the proposed scenario: (1) prevalent allopatric or parapatric distributions; (2) allopatric climatic refugia coincident with current distributions; and (3) niche similarity decreasing with increasing phylogenetic distance. We also reconstructed the centre of origin and range expansion dynamics for the Sahelian species to verify the congruence of the genetic signal with the vicariance scenario. All patterns expected from a neutral, non-adaptive niche divergence scenario were present. The diffusion models for the Sahelian species identified similar points of origin, corresponding to the areas of highest genetic diversity, topographic heterogeneity and climatic stability. Other patterns, such as mountain-isolated lineages, also indicate isolation by aridity. Our results support vicariance as the main driver of diversification in NW African Agama at both large and local scales. The importance of southern Mauritania for the conservation of biodiversity and the evolutionary process is highlighted. Agama, aridity, diversification, palaeoclimate, phylogeography, Sahara–Sahel, species distribution modelling, vicariance INTRODUCTION Global cooling and increased aridity in the late Miocene caused a significant increase in global coverage of deserts (Herbert et al., 2016) and created the opportunity and selective pressures for diversification and the consequent major changes in flora and fauna (Cerling et al., 1997; Badgley et al., 2008; Arakaki et al., 2011). Diversification in arid environments is commonly related to adaptation through key innovations [e.g. in cacti (Arakaki et al., 2011) or ice plants (Evans et al., 2009)] and other behavioural, morphological and physiological traits (e.g. Costa, 1995; Degen, 1997) that allow species to occupy different climatic niches and thus diversify or even radiate in arid environments. However, diversification does not occur solely through major adaptations. Some mesic taxa with higher levels of niche conservatism (the tendency to keep the ancestral niche; Wiens et al., 2010) can also diversify in arid regions. Examples range from ecological opportunity, with slow niche divergence and the progressive occupation of newly available (drier) neighbouring conditions (Evans et al., 2009), to allopatric speciation attributable to the vicariant effect of aridification (Pepper et al., 2011). In the Sahara–Sahel ecoregions of North Africa, diversification events have been attributed to the aridification of the region and the subsequent arid–humid fluctuations (Brito et al., 2014). This process started ~15 Mya and culminated with the appearance of the Sahara ~7 Mya (Zachos et al., 2001; Zhang et al., 2014), being followed by shifts between desert- and savannah-like states at regular intervals of 20000–100000 years (Le Houérou, 1997; Brito et al., 2014), which greatly affected species ranges (Brito et al., 2014). Vicariance and ecological adaptation have been suggested as the main processes responsible for diversification, and examples include the Sahara acting as a vicariant agent for mammals (Douady et al., 2003) and reptiles (Gonçalves et al., 2012), the adaptation to newly available arid habitats in geckos (Carranza et al., 2002), species radiations in skinks (Carranza et al., 2008), or a combination of processes (Metallinou et al., 2012). However, in most cases, biogeographical scenarios were proposed based only on the coincidence of divergence times and the periods of climatic cycles (Pliocene and Pleistocene) or empirical correlations (Brito et al., 2014). Using palaeontological data to verify past distributions and suitable climatic conditions would be an ideal solution but unrealistic because obtaining precise data on geological and palaeoecological events or fossils for the region is greatly hindered by stratigraphic discontinuities caused by the erosion associated with cyclic climatic changes (Swezey, 2003). Some studies have implemented projections of past distributions to support the inference of biogeographical scenarios concerning mesic faunal exchange across the desert (Nyári, Peterson & Rathbun, 2010; Gonçalves et al., 2018; Velo-Antón et al., 2018) or Pleistocene climatic refugia (Martínez-Freiría et al., 2017), but they are exceptions. The integration of phylogeographical data and quantitative niche comparisons allows the assessment of niche conservatism and niche divergence as a proxy for the role of climate in diversification (Hua & Wiens, 2013), but, to our knowledge, no study has attempted to test the vicariant hypothesis in the Sahara–Sahel ecoregions. Here we combine dated phylogenies, palaeomodels and ecological niche comparisons to assess the importance of aridity-induced vicariance, as opposed to adaptation to new conditions, for species diversification in North-West Africa, using Agama lizards as a model. Previous studies have suggested aridity-induced vicariance as a major force behind the diversification of this genus in North Africa (Gonçalves et al., 2012). Although several species are distributed in the Mediterranean, Sahel and central Saharan mountains, the North African species do not present obvious interspecific morphological variation that can be related to adaptation to more or less arid habitats, thereby making them a good biogeographical model to assess the influence of humid–arid cycles and the changing landscape in shaping biodiversity patterns in the region (Gonçalves et al., 2012). Agamas are common lizards, found in arid and semi-arid habitats that include rocky outcrops, deserts and forests (Le Berre, 1989; Schleich, Kästle & Kabisch, 1996). Three major branches of the genus occur in North-West Africa, two of which have most of the species within the Sahara–Sahel (Leaché et al., 2014). One branch includes only Agama boulengeri Lataste, 1886, and the other includes most species of the ‘Northern Africa radiation’ (Leaché et al., 2014): Agama impalearis Boettger, 1874; Agama boueti Chabanaud, 1917; and Agama tassiliensis Geniez, Padial & Crochet, 2011. The third branch includes the Agama agama species group (Leaché et al., 2017), distributed along the southern fringes of the Sahel, but most of its species occur south of the study region. If aridity-induced vicariance was the main driver of diversification of mesic taxa, three main patterns are expected. The first pattern is prevalent allopatric or parapatric distributions for species and lineages (Wiley, 1988) attributable to recurrent historical constraints to dispersal linked to humid–arid cycles. If vicariant events were non-existent and ecological divergence and adaptation were predominant, phylogenetically close groups should occur more readily in sympatry (Wiens & Graham, 2005). The second pattern is that of allopatric climatic refugia. Stable climatic areas (potential refugia) should be mostly allopatric and coherent with present distributions (Avise, 2000). Conversely, if allopatry/parapatry was a product of adaptation to different climates (niche divergence), coincidence of climate stability with genetic structure should not be prevalent. The third pattern is niche similarity. If aridity-induced vicariance is prevalent, closely related clades should have similar climatic niches, as opposed to ecological adaptation to different conditions, where closer clades are expected to have more distinct niches (Wiens & Graham, 2005). To test these hypotheses, we compared niches at the intraspecific and interspecific levels. Intraspecific comparisons were focused on A. boulengeri and A. boueti, both occurring at similar latitudes along the West Sahel and the species for which the sampling was more thorough. Considering that the humid–arid cycles mostly translated into north–south movements of the climatic regions, particularly in the southern regions of the desert, it would also be expected that under a climate-driven scenario the species ranges would shift accordingly. To assess the congruence of the genetic signal with this pattern, we estimate the geographical ancestral origin of the lineages and reconstruct range dynamics for the species present in the Sahel–Sahara fringe (A. boueti and A. boulengeri), using genetic diversity measures and continuous diffusion models that integrate genetic and spatial data. MATERIAL AND METHODS Phylogenetic analyses Sampling and study area A total of 718 samples of Agama were available for this study (Fig. 1). Given the uneven spatial distribution of samples, an initial selection of samples best representing the geographical distribution of each species was carried out. For A. impalearis, as the distribution of genetic lineages had already been assessed previously (Brown, Suárez & Pestano, 2002), only a few additional samples were sequenced. A total of 72 were selected for A. boueti, 233 for A. boulengeri, seven for A. impalearis and 11 for A. tassiliensis. The limited sample size of A. tassiliensis was attributable to the difficulty of acquiring samples from the area of occurrence (see Brito et al., 2014). Figure 1. View largeDownload slide Study area, species distribution and presence points of Agama species used in this study. Species occurrence extents obtained from the IUCN are represented as shaded areas. The map inset details the area of the mountains in southern Mauritania. Figure 1. View largeDownload slide Study area, species distribution and presence points of Agama species used in this study. Species occurrence extents obtained from the IUCN are represented as shaded areas. The map inset details the area of the mountains in southern Mauritania. DNA extraction and amplification DNA was extracted from ethanol-preserved tissue using a commercially available kit (Easy-Spin). Amplifications were performed in 10 μL of 2× MyTaq Mix and 0.5 μM of each primer. The PCR conditions were as follows: pre-denaturation at 95 °C (15 min); 40 cycles with denaturing at 95 °C (30 s), annealing range of 48–52 °C (40 s), and extension at 72 °C (45 s); and final extension at 60 °C for 12 min. Some samples required minor adjustments to conditions. Four genes were amplified: ND4(with tRNAs), 16S rRNA, NTF3 and c-mos. For the first three, we used primers from Arevalo, Davis & Sites (1994), Palumbi et al. (1991) and Wiens et al. (2008), respectively. For c-mos, new primers were designed based on S77/S78 of Lawson et al. (2005) and named A77 (5′-AATAGACTGGAAACAGTTGTG-3′) and A78 (5′-CCTTAGGTGTAATTCTCTCACCT-3′). The PCR products were sequenced using cycle sequencing on an automated sequencer. Phylogenetic analyses Additional sequences of Agama species and outgroups were selected based mostly on Leaché et al. (2014) and retrieved from GenBank (Supporting Information, Table S1.1). DNA sequence alignments were inferred with MAFFT v7 (Katoh & Standley, 2013), with default parameters and the Q-INS-i option, then proofread and edited by eye. Coding genes (ND4, NTF3 and c-mos) were translated, and no unexpected stop codons were found. Independent maximum likelihood (ML) trees were inferred for each marker using RAxML v8.1.21 (Stamatakis, 2014), and no topological incongruences were found. Concatenated duplicate haplotypes (i.e. identical across all markers) were removed from the alignment when calculating the phylogenetic trees, in order to decrease computational load. The most appropriate models of molecular evolution and best-fit partitioning scheme were selected using PartitionFinder v.1.1.1 (Lanfear et al., 2012). Settings were: linked branch lengths, BEAST models, Bayesian information criterion model selection, and all partition schemes searched. An initial partition scheme by gene (ND4 and tRNAs separated) was used. Nuclear haplotypes were inferred using PHASE 2.1 (Stephens, Smith & Donnelly, 2001), implemented in DnaSP. PHASE ran for 104 iterations, with a burn-in value of 1000 and a thinning interval of five. Haplotype networks were produced using TCS v1.21 (Clement, Posada & Crandall, 2000), with gaps treated as missing data and otherwise default parameters. Graphical representations were obtained using tcsBU (Santos et al., 2015). Uncorrected p-distances (proportion of nucleotide sites at which two sequences being compared are different) within and among species and lineages were calculated in MEGA6 (Tamura et al., 2013) for each mitochondrial marker. Phylogenies were inferred with Bayesian inference (BI) and ML methods using MrBayes v3.2.6 (Ronquist et al., 2012) and RAxML v8.1.21 through RaxmlGUI 1.5b1 (Silvestro & Michalak, 2012), respectively. Gene partitions were applied according to PartitionFinder results. MrBayes ran for 2 × 107 generations in two independent runs, sampling every 1000 generations. Parameters of sequence evolution (statefreq, revmat, shape and pinvar) were unlinked for all partitions, and the overall rate (ratepr) variable among them. Burn-in was determined using Tracer v1.6 (Rambaut et al., 2014), upon stabilization of log-likelihood, average standard deviation of split frequencies, and ESS for all the parameters. RAxML used the same partition scheme and the GTR + G model (General Time Reversible model with Gamma distributed rate variation among sites), with ten random addition replicates and 1000 thorough bootstrapping replicates. To support lineage delimitation for further analyses, we used the species delimitation method bPTP (species.h-its.org), an update of PTP (Zhang et al., 2013). This was not intended to delimit potential species, but rather to avoid an ad hoc intraspecific lineage delimitation with lineages at different phylogenetic depths. bPTP ran with both ML and BI trees, using mitochondrial markers and removing outgroups. Time calibration In order to perform the time calibration, representatives of each species and supported lineage were selected (considering sequence length) based on the results of the ML and BI phylogenetic analyses (Supporting Information, Fig. S2.2), resulting in a total of 118 sequences, including outgroups (Supporting Information, Table S1.1). We used the same calibration scheme as Leaché et al. (2014), who used the 62.5 Mya Calotes–Phrynocephalus divergence, calculated using 11 fossil calibrations of squamates (Wiens, Brandley & Reeder, 2006), and the 16.4–19.6 Mya Xenagama–Pseudotrapelus divergence, inferred from pairwise sequence divergence in an agamid species (Macey et al., 2006). Analyses were run using BEAST v1.8.3 (Drummond et al., 2012) in the CIPRES gateway (Miller, Pfeiffer & Schwartz, 2011). We performed three independent runs of 5 × 107 generations, sampling every 5000, using unlinked substitution and clock models, and an uncorrelated lognormal relaxed clock (Drummond et al., 2006). We used a Yule speciation tree prior (Yule, 1925; Gernhard, 2008), and treated ambiguities in nuclear sequences as informative sites (setting the option useAmbiguities as ‘true’ in the XML file). Burn-in was determined using Tracer v1.6 (Rambaut et al., 2014). Runs were combined with LogCombiner, and a maximum credibility tree was generated with TreeAnnotator (both in the Beast package). Genetic diversity and spatial diffusion models Sequence and nucleotide diversity measures and demographic statistics were calculated in DnaSP v.5.10.1 (Librado & Rozas, 2009) for all markers. Spatially explicit representations of genetic diversity were produced using a predefined radius search around each sample in order to create pseudo-populations, from which diversity was estimated. The method was described by Veríssimo et al. (2016). The resulting diversity scores could then be spatially interpolated, in this case using the Kriging function in ArcMap. We used a radius of ~100 km, in order to include the isolated samples that would otherwise be ignored for having no neighbours. Continuous diffusion phylogeographical models were produced using BEAST. These models use geographical coordinates of samples as continuous traits to reconstruct the geographical origin and spatially explicit expansion of organisms across a continuous landscape over time, and have already been implemented for predicting the origin of lineages’ ancestors (Veríssimo et al., 2016; Gutiérrez-Rodríguez, Barbosa & Martínez-Solano, 2017; Leaché et al., 2017). Two independent models were generated for A. boueti and A. boulengeri, using all the unique concatenated haplotypes from each species. A Cauchy relaxed random walk (RRW) model (Lemey et al., 2010) and a coalescent constant population size were used as priors. Markov chain Monte Carlo chains were run for 5 × 107 generations, sampling every 5000. Runs were evaluated and processed as above. Output trees were fed into SPREAD (Bielejec et al., 2011) to create a spatial representation of the spread of lineages through time. Ecological niche analyses Presence data To develop ecological niche-based models, a total of 1063 observations (169 A. boueti, 542 A. boulengeri, 228 A. impalearis and 124 A. tassiliensis) with ≤ 1 km resolution (World Geodetic System - WGS, 1984 datum) were collected from fieldwork (N = 889) and museum databases (N = 174). A 50 km buffer around a minimal convex polygon including all samples was used to delimit the study area. All spatial analyses were conducted in ESRI ArcGIS 10. In order to reduce spatial bias in the ecological models attributable to uneven sampling (Merow, Smith & Silander, 2013), localities were removed at random from clusters of species occurrence, forcing a point-free minimal radius of 5 km around each retained presence. The final dataset included 96 presence points for A. boueti, 259 for A. boulengeri, 152 for A. impalearis and 66 for A. tassiliensis. Climatic variables Nineteen variables representing present climatic conditions were downloaded from WorldClim (www.worldclim.org;Hijmans et al., 2005) at 30 arc-s resolution (~1 km × 1 km at the equator). Variables were then clipped to the study area and upscaled to 2.5 arc-min (~5 km × 5 km at the equator). Given that these layers were derived through interpolations and represent macroclimatic conditions, this pixel size allows a reduction in computation time without affecting inference power. Five variables were excluded because of the presence of obvious spatial artefacts. To identify the significantly correlated variables, pairwise correlation among the remaining 14 was calculated using the Band Collection Statistics in ArcGIS and a threshold of R = 0.7. BIO1 + BIO6 and BIO2 + BIO5 were all retained despite slightly higher correlation (R = 0.83 for both pairs), owing to the potential relevance of those variables for restricting the distribution of ectotherms. The final set contained BIO1, BIO2, BIO5, BIO6, BIO7, BIO12 and BIO14 (Table 1). Table 1. Climatic variables used for ecological models and projections, global and regional variation Code Name Units Global Regional BIO1 Annual mean temperature Degrees Celsius −15.5 to 31.9 3.5–30.8 BIO2 Mean diurnal range Degrees Celsius 5.8–20.7 4.5–18.5 BIO5 Maximal temperature of warmest month Degrees Celsius 2.9–48.9 22.6–48.9 BIO6 Minimal temperature of coldest month Degrees Celsius −33.5 to 23 −12.4 to 18.8 BIO7 Temperature annual range Degrees Celsius 11–47.8 10.9–42.8 BIO12 Annual precipitation Millimetres 0–2769 2–1401 BIO14 Precipitation of driest month Millimetres 0–42 0–24 Code Name Units Global Regional BIO1 Annual mean temperature Degrees Celsius −15.5 to 31.9 3.5–30.8 BIO2 Mean diurnal range Degrees Celsius 5.8–20.7 4.5–18.5 BIO5 Maximal temperature of warmest month Degrees Celsius 2.9–48.9 22.6–48.9 BIO6 Minimal temperature of coldest month Degrees Celsius −33.5 to 23 −12.4 to 18.8 BIO7 Temperature annual range Degrees Celsius 11–47.8 10.9–42.8 BIO12 Annual precipitation Millimetres 0–2769 2–1401 BIO14 Precipitation of driest month Millimetres 0–42 0–24 View Large Table 1. Climatic variables used for ecological models and projections, global and regional variation Code Name Units Global Regional BIO1 Annual mean temperature Degrees Celsius −15.5 to 31.9 3.5–30.8 BIO2 Mean diurnal range Degrees Celsius 5.8–20.7 4.5–18.5 BIO5 Maximal temperature of warmest month Degrees Celsius 2.9–48.9 22.6–48.9 BIO6 Minimal temperature of coldest month Degrees Celsius −33.5 to 23 −12.4 to 18.8 BIO7 Temperature annual range Degrees Celsius 11–47.8 10.9–42.8 BIO12 Annual precipitation Millimetres 0–2769 2–1401 BIO14 Precipitation of driest month Millimetres 0–42 0–24 Code Name Units Global Regional BIO1 Annual mean temperature Degrees Celsius −15.5 to 31.9 3.5–30.8 BIO2 Mean diurnal range Degrees Celsius 5.8–20.7 4.5–18.5 BIO5 Maximal temperature of warmest month Degrees Celsius 2.9–48.9 22.6–48.9 BIO6 Minimal temperature of coldest month Degrees Celsius −33.5 to 23 −12.4 to 18.8 BIO7 Temperature annual range Degrees Celsius 11–47.8 10.9–42.8 BIO12 Annual precipitation Millimetres 0–2769 2–1401 BIO14 Precipitation of driest month Millimetres 0–42 0–24 View Large The corresponding variables were downloaded from WorldClim for the middle Holocene [midHol; 6000 years before present (BP); Palaeoclimate Modelling Intercomparison Project Phase III - Coupled Modelling Intercomparison Project Phase V (PMIP3-CMIP5)], Last Glacial Maximum (LGM; ~21 000 years BP; PMIP3-CMIP5) and Last Interglacial (LIG; ~120 000–140 000 years BP; Otto-Bliesner et al., 2006). The LGM variables were at 2.5 arc-min resolution (~5 km × 5 km) and were retrieved for all three global circulation models (GCM) available: CCSM4, MIROC-ESM and MPI-ESM-P. The LIG and midHol variables were at 30 arc-s resolution. For LIG, the GCM was National Center for Atmospheric Research, Community Climate System Model (NCAR-CCSM); for midHol, we obtained the same GCM as for LGM. The LIG and midHol layers were also upscaled to 2.5 arc-min resolution. Although the palaeoclimatic data accounts for only the past 120 000 years, and the duration and intensity of the cycles was variable, the last cycle is likely to be representative of previous cycles throughout the whole Pleistocene, because the direction of temperature changes was the same, and the later cycles had the largest amplitudes (Snyder, 2016). During the Pliocene, geological and palaeontological records indicate that, albeit with varying intensity, similar cycles also took place globally (Lisiecki & Raymo, 2005) and in North Africa (Rohling, Marino & Grant, 2015). Palaeoclimatic modelling Ecological modelling was performed in BIOMOD2 (Thuiller et al., 2016), using two machine learning [artificial neural networks (ANNs) and maximal entropy (MaxEnt)] and two regression-based techniques [generalized additive models (GAMs) and generalized linear models (GLMs)]. This approach aims at reducing uncertainties that may affect a given modelling technique (Wiens et al., 2009). The study area was as described in above (Sampling and study area), which also encompasses the expected variation in Sahara–Sahel extension. Given that the remoteness of the study area precludes extensive sampling, we used random pseudo-absences. To control for potential bias introduced in this step, ten independent sets of 104 pseudo-absences were generated using the ‘disk’ (buffer) function, with 50 km around presence points. Given the large average distances between the presence points used for modelling, the relatively large buffer size should ensure that pseudo-absences correspond to real absences. Ten replicates were run for each technique and pseudo-absence set, in a total of 400 replicates. Presence data for training and testing models were randomly selected for each replicate using a ratio of 70% training to 30% testing. The performance of replicates was evaluated using the true skill statistic (TSS) metric (Allouche, Tsoar & Kadmon, 2006) and a threshold of 0.75. The threshold was selected after visualizing the output, as a compromise between performance, geographical coherence with species distribution and the representativeness of modelling techniques. The consensus model was generated by averaging individual replicates (Marmion et al., 2009), and agreement among replicates was assessed using the standard deviation (Thuiller et al., 2009). Individual model replicates were projected to past climatic conditions (mid-Hol, LGM and LIG). Projections were assessed using clamping masks delimiting areas with environmental conditions outside the current range of climatic conditions (Elith, Kearney & Phillips, 2010). Consensus models and standard deviations were calculated as described above. The consensus models for present and past conditions were then averaged in order to identify climatically stable areas, i.e. potentially persistent areas of occurrence that could serve as refugia through time (Carnaval et al., 2009). Ecological niche comparisons Ecological niches were compared at three different phylogenetic levels, in order to evaluate whether niche divergence followed a Brownian (neutral) motion: intrageneric branch (A. boulengeri vs. the boueti–impalearis–tassiliensis group), species and intraspecific lineage. The intraspecific lineage comparisons were focused on A. boulengeri and A. boueti. We used the same climatic layers as those used in the niche models and the PCA-env approach developed by Broennimann et al. (2012) and updated with functions from the ‘ecospat’ R package (Broennimann, Cola & Guisan, 2016). This method uses a principal component analysis (PCA) to create a two-dimensional representation of climatic space, on which it performs comparisons between pairs of entities, in this case defined by minimal convex polygons encompassing lineage and species distributions. Overlap was measured using the D metric (Warren, Glor & Turelli, 2008), following Broennimann et al. (2012). Both equivalency and similarity tests (Warren et al., 2008) were run with 500 replicates. However, it should be noted that equivalency tests are more restrictive and affected by allopatric ranges and are thus typically less adequate than similarity tests when addressing biogeographical questions (Peterson, 2011). RESULTS Phylogeography A total of 296, 246, 301 and 246 samples were successfully sequenced for 16S (522 bp, aligned), ND4 (699 bp coding portion plus 195 bp of tRNAs), c-mos (570 bp) and NTF3 (669 bp), respectively (Supporting Information, Table S1.1). From those and the previously available data, 341 unique concatenated sequences were kept for the concatenated tree (Supporting Information, Fig. S2.2). PartitionFinder model selection is summarized in the Supporting Information (Table S1.2). The monophyly of all species was confirmed, and no nuclear haplotype sharing was detected among them (Fig. 2; Supporting Information, Fig. S2.1), supporting the phylogenetic relationships described in previous studies. Estimated species crown ages spanned the Pleistocene (A. boueti and A. impalearis), the Pliocene (A. tassiliensis) and the Miocene (A. boulengeri). The bPTP lineage delimitation (Supporting Information, Fig. S2.3) was consistent between the ML and BI trees, recovering two lineages within A. impalearis (‘NW’ and ‘SE’, for intercardinal directions), five within A. boueti [‘C’ (central), ‘W’ and three grouped under ‘boueti E’], three within A. tassiliensis (‘A’, ‘H’ and ‘T’, the initials of Aïr, Hoggar and Tassili mountains) and six within A. boulengeri (‘N’, ‘S’, ‘E’ and ‘M’ in Mali; the two other lineages include three samples in the northern distribution of ‘S’ and are sister lineages to it with a relatively recent split, thus were included in ‘S’). Figure 2. View largeDownload slide Phylogeographic relationships among species and lineages. Left panels show dated phylogenetic trees (condensed in top panel, with two relevant subsections below; for the complete version, see Supporting Information, Fig. S2.1) of combined mitochondrial DNA (ND4 and 16S) and nuclear DNA (c-mos and NT3) data, calculated with BEAST; Bayesian posterior probability (bpp) support values (percentages) are indicated next to the nodes, with asterisks representing 100% support; some support values were omitted to improve clarity; species and lineages (capital letters) are indicated next to the sample codes. Right panels show maps and nuclear haplotype networks representing the distribution and relationships among species (top) and lineages (second and third rows); colours represent species and lineages according to the respective tree. Dashed lines in the maps represent the species’ extent of occurrence. Each circle in the networks represents a different haplotype, and size is proportional to the number of samples sharing that haplotype; the smallest circles along the lines represent mutated positions; the networks in the second and third rows were scaled up from the relevant section of the first row. Figure 2. View largeDownload slide Phylogeographic relationships among species and lineages. Left panels show dated phylogenetic trees (condensed in top panel, with two relevant subsections below; for the complete version, see Supporting Information, Fig. S2.1) of combined mitochondrial DNA (ND4 and 16S) and nuclear DNA (c-mos and NT3) data, calculated with BEAST; Bayesian posterior probability (bpp) support values (percentages) are indicated next to the nodes, with asterisks representing 100% support; some support values were omitted to improve clarity; species and lineages (capital letters) are indicated next to the sample codes. Right panels show maps and nuclear haplotype networks representing the distribution and relationships among species (top) and lineages (second and third rows); colours represent species and lineages according to the respective tree. Dashed lines in the maps represent the species’ extent of occurrence. Each circle in the networks represents a different haplotype, and size is proportional to the number of samples sharing that haplotype; the smallest circles along the lines represent mutated positions; the networks in the second and third rows were scaled up from the relevant section of the first row. The intraspecific lineages share nuclear haplotypes at varying degrees, with the exception of boueti W and tassiliensis A and T for NTF3 (Fig. 2). All intraspecific lineages are seemingly parapatric or allopatric (Fig. 2), in agreement with the first expected scenario. The lineages of A. boulengeri occur in separate mountain systems in Mauritania and nearby Mali. A similar pattern is suggested for A. tassiliensis, with one lineage in each of three central Saharan mountain systems (Tassili N’Ajjer and Hoggar in Algeria, and Aïr in Niger). Agama boueti lineages have a predominantly east–west distribution along the Sahel, although the boueti C lineage reaches from the Senegal River in the south up to Adrar Souttouf in Morocco in the north. All major Sahelian species’ lineages occur in the Assaba–Tagant–Afollé region in Mauritania. Diffusion models identified the same area around Tagant, Assaba and Afollé in Mauritania as the origin of dispersal of the extant diversity of both A. boueti and A. boulengeri. A second, younger possible ancestral area was depicted in Niger for A. boueti. Both species began marked range expansions out of the ancestral area in Mauritania at ~320 thousand years ago, particularly towards the northern parts of their current distribution (Fig. 3). Agama boueti also showed another, more recent expansion towards the West, the current range of the lineage boueti W. The genetic diversity of boueti C and boulengeri N lineages shows signs of recent demographic expansion, although values were not significant (Table 2; Supporting Information, Table S1.3). The spatial interpolation of diversity showed a concordant pattern of steady decrease from the centre towards the borders of both species’ distributions (Fig. 3). Table 2. Summary of genetic diversity and demographic statistics for mitochondrial markers, based on all available samples (including duplicate haplotypes). Uncorrected p-distances among species can be found in Table S1.6. Group 16S L P N h Hd π R2 D FS A. boueti 501/387 26 96 31 0.94 ± 0.011 0.01059 ± 0.00037 0.0744 −0.83499 −14.824 boueti C 500/473 12 46 15 0.916 ± 0.018 0.00414 ± 0.00030 0.0771 −1.0279 −7.694 boueti E 501/390 13 30 11 0.816 ± 0.053 0.00527 ± 0.00527 0.0747 −1.23408 −4.116 boueti W 499/471 8 20 6 0.516 ± 0.132 0.00189 ± 0.00078 0.1143 −2.04091 −2.627 A. boulengeri 504/367 51 243 30 0.876 ± 0.011 0.03426 ± 0.00093 0.1226 1.21511 3.254 boulengeri E 499/393 6 60 6 0.513 ± 0.057 0.00272 ± 0.00031 0.0886 −0.41465 −0.644 boulengeri N 500/389 12 110 14 0.654 ± 0.046 0.00276 ± 0.00027 0.0443 −1.38591 −8.225 boulengeri S 499/474 15 69 11 0.723 ± 0.035 0.00441 ± 0.00073 0.0676 −0.96446 −1.903 A. impalearis 499/462 22 27 18 0.915 ± 0.047 0.01122 ± 0.00112 0.104 −0.47252 −7.527 A. tassiliensis 502/408 17 21 5 0.767 ± 0.067 0.0158 ± 0.00223 0.1896 1.34757 5.535 Group ND4 + tRNAs L P N h Hd π R2 D FS A. boueti 869/640 92 70 41 0.971 ± 0.010 0.02785 ± 0.00167 0.0923 −0.2609 −6.797 boueti C 869/663 39 38 20 0.927 ± 0.028 0.00726 ± 0.00085 0.0565 −1.70358 −7.428 boueti E 867/742 46 16 12 0.958 ± 0.036 0.01613 ± 0.00322 0.1183 −0.5775 −0.932 boueti W 867/811 10 16 9 0.892 ± 0.060 0.00294 ± 0.00042 0.1036 −0.78006 −3.437 A. boulengeri 869/663 175 189 87 0.969 ± 0.006 0.07312 ± 0.00195 0.1377 1.48748 −2.553 boulengeri E 866/751 42 57 25 0.790 ± 0.057 0.01078 ± 0.00092 0.0895 −0.50923 −4.359 boulengeri N 865/666 36 73 30 0.943 ± 0.012 0.00727 ± 0.00045 0.0633 −1.17293 −13.678 boulengeri S 868/765 76 55 30 0.958 ± 0.015 0.01051 ± 0.00191 0.0486 −1.81807 −9.789 A. impalearis 867/785 40 8 6 0.929 ± 0.084 0.02106 ± 0.00470 0.1818 0.3845 1.978 A. tassiliensis 872/658 64 16 8 0.808 ± 0.093 0.03894 ± 0.00615 0.1993 1.31617 6.66 Group 16S L P N h Hd π R2 D FS A. boueti 501/387 26 96 31 0.94 ± 0.011 0.01059 ± 0.00037 0.0744 −0.83499 −14.824 boueti C 500/473 12 46 15 0.916 ± 0.018 0.00414 ± 0.00030 0.0771 −1.0279 −7.694 boueti E 501/390 13 30 11 0.816 ± 0.053 0.00527 ± 0.00527 0.0747 −1.23408 −4.116 boueti W 499/471 8 20 6 0.516 ± 0.132 0.00189 ± 0.00078 0.1143 −2.04091 −2.627 A. boulengeri 504/367 51 243 30 0.876 ± 0.011 0.03426 ± 0.00093 0.1226 1.21511 3.254 boulengeri E 499/393 6 60 6 0.513 ± 0.057 0.00272 ± 0.00031 0.0886 −0.41465 −0.644 boulengeri N 500/389 12 110 14 0.654 ± 0.046 0.00276 ± 0.00027 0.0443 −1.38591 −8.225 boulengeri S 499/474 15 69 11 0.723 ± 0.035 0.00441 ± 0.00073 0.0676 −0.96446 −1.903 A. impalearis 499/462 22 27 18 0.915 ± 0.047 0.01122 ± 0.00112 0.104 −0.47252 −7.527 A. tassiliensis 502/408 17 21 5 0.767 ± 0.067 0.0158 ± 0.00223 0.1896 1.34757 5.535 Group ND4 + tRNAs L P N h Hd π R2 D FS A. boueti 869/640 92 70 41 0.971 ± 0.010 0.02785 ± 0.00167 0.0923 −0.2609 −6.797 boueti C 869/663 39 38 20 0.927 ± 0.028 0.00726 ± 0.00085 0.0565 −1.70358 −7.428 boueti E 867/742 46 16 12 0.958 ± 0.036 0.01613 ± 0.00322 0.1183 −0.5775 −0.932 boueti W 867/811 10 16 9 0.892 ± 0.060 0.00294 ± 0.00042 0.1036 −0.78006 −3.437 A. boulengeri 869/663 175 189 87 0.969 ± 0.006 0.07312 ± 0.00195 0.1377 1.48748 −2.553 boulengeri E 866/751 42 57 25 0.790 ± 0.057 0.01078 ± 0.00092 0.0895 −0.50923 −4.359 boulengeri N 865/666 36 73 30 0.943 ± 0.012 0.00727 ± 0.00045 0.0633 −1.17293 −13.678 boulengeri S 868/765 76 55 30 0.958 ± 0.015 0.01051 ± 0.00191 0.0486 −1.81807 −9.789 A. impalearis 867/785 40 8 6 0.929 ± 0.084 0.02106 ± 0.00470 0.1818 0.3845 1.978 A. tassiliensis 872/658 64 16 8 0.808 ± 0.093 0.03894 ± 0.00615 0.1993 1.31617 6.66 D, Tajima’s D (significant values in bold font); FS, Fu’s FS statistic; h, number of unique haplotypes; Hd, haplotype diversity; L, minimal length, excluding sites with gaps and missing data; N, number of samples or phased sequences; P, number of polymorphic sites, excluding sites with gaps and missing data; π = nucleotide diversity; R2, Ramos-Onsins and Rozas R2 statistic. View Large Table 2. Summary of genetic diversity and demographic statistics for mitochondrial markers, based on all available samples (including duplicate haplotypes). Uncorrected p-distances among species can be found in Table S1.6. Group 16S L P N h Hd π R2 D FS A. boueti 501/387 26 96 31 0.94 ± 0.011 0.01059 ± 0.00037 0.0744 −0.83499 −14.824 boueti C 500/473 12 46 15 0.916 ± 0.018 0.00414 ± 0.00030 0.0771 −1.0279 −7.694 boueti E 501/390 13 30 11 0.816 ± 0.053 0.00527 ± 0.00527 0.0747 −1.23408 −4.116 boueti W 499/471 8 20 6 0.516 ± 0.132 0.00189 ± 0.00078 0.1143 −2.04091 −2.627 A. boulengeri 504/367 51 243 30 0.876 ± 0.011 0.03426 ± 0.00093 0.1226 1.21511 3.254 boulengeri E 499/393 6 60 6 0.513 ± 0.057 0.00272 ± 0.00031 0.0886 −0.41465 −0.644 boulengeri N 500/389 12 110 14 0.654 ± 0.046 0.00276 ± 0.00027 0.0443 −1.38591 −8.225 boulengeri S 499/474 15 69 11 0.723 ± 0.035 0.00441 ± 0.00073 0.0676 −0.96446 −1.903 A. impalearis 499/462 22 27 18 0.915 ± 0.047 0.01122 ± 0.00112 0.104 −0.47252 −7.527 A. tassiliensis 502/408 17 21 5 0.767 ± 0.067 0.0158 ± 0.00223 0.1896 1.34757 5.535 Group ND4 + tRNAs L P N h Hd π R2 D FS A. boueti 869/640 92 70 41 0.971 ± 0.010 0.02785 ± 0.00167 0.0923 −0.2609 −6.797 boueti C 869/663 39 38 20 0.927 ± 0.028 0.00726 ± 0.00085 0.0565 −1.70358 −7.428 boueti E 867/742 46 16 12 0.958 ± 0.036 0.01613 ± 0.00322 0.1183 −0.5775 −0.932 boueti W 867/811 10 16 9 0.892 ± 0.060 0.00294 ± 0.00042 0.1036 −0.78006 −3.437 A. boulengeri 869/663 175 189 87 0.969 ± 0.006 0.07312 ± 0.00195 0.1377 1.48748 −2.553 boulengeri E 866/751 42 57 25 0.790 ± 0.057 0.01078 ± 0.00092 0.0895 −0.50923 −4.359 boulengeri N 865/666 36 73 30 0.943 ± 0.012 0.00727 ± 0.00045 0.0633 −1.17293 −13.678 boulengeri S 868/765 76 55 30 0.958 ± 0.015 0.01051 ± 0.00191 0.0486 −1.81807 −9.789 A. impalearis 867/785 40 8 6 0.929 ± 0.084 0.02106 ± 0.00470 0.1818 0.3845 1.978 A. tassiliensis 872/658 64 16 8 0.808 ± 0.093 0.03894 ± 0.00615 0.1993 1.31617 6.66 Group 16S L P N h Hd π R2 D FS A. boueti 501/387 26 96 31 0.94 ± 0.011 0.01059 ± 0.00037 0.0744 −0.83499 −14.824 boueti C 500/473 12 46 15 0.916 ± 0.018 0.00414 ± 0.00030 0.0771 −1.0279 −7.694 boueti E 501/390 13 30 11 0.816 ± 0.053 0.00527 ± 0.00527 0.0747 −1.23408 −4.116 boueti W 499/471 8 20 6 0.516 ± 0.132 0.00189 ± 0.00078 0.1143 −2.04091 −2.627 A. boulengeri 504/367 51 243 30 0.876 ± 0.011 0.03426 ± 0.00093 0.1226 1.21511 3.254 boulengeri E 499/393 6 60 6 0.513 ± 0.057 0.00272 ± 0.00031 0.0886 −0.41465 −0.644 boulengeri N 500/389 12 110 14 0.654 ± 0.046 0.00276 ± 0.00027 0.0443 −1.38591 −8.225 boulengeri S 499/474 15 69 11 0.723 ± 0.035 0.00441 ± 0.00073 0.0676 −0.96446 −1.903 A. impalearis 499/462 22 27 18 0.915 ± 0.047 0.01122 ± 0.00112 0.104 −0.47252 −7.527 A. tassiliensis 502/408 17 21 5 0.767 ± 0.067 0.0158 ± 0.00223 0.1896 1.34757 5.535 Group ND4 + tRNAs L P N h Hd π R2 D FS A. boueti 869/640 92 70 41 0.971 ± 0.010 0.02785 ± 0.00167 0.0923 −0.2609 −6.797 boueti C 869/663 39 38 20 0.927 ± 0.028 0.00726 ± 0.00085 0.0565 −1.70358 −7.428 boueti E 867/742 46 16 12 0.958 ± 0.036 0.01613 ± 0.00322 0.1183 −0.5775 −0.932 boueti W 867/811 10 16 9 0.892 ± 0.060 0.00294 ± 0.00042 0.1036 −0.78006 −3.437 A. boulengeri 869/663 175 189 87 0.969 ± 0.006 0.07312 ± 0.00195 0.1377 1.48748 −2.553 boulengeri E 866/751 42 57 25 0.790 ± 0.057 0.01078 ± 0.00092 0.0895 −0.50923 −4.359 boulengeri N 865/666 36 73 30 0.943 ± 0.012 0.00727 ± 0.00045 0.0633 −1.17293 −13.678 boulengeri S 868/765 76 55 30 0.958 ± 0.015 0.01051 ± 0.00191 0.0486 −1.81807 −9.789 A. impalearis 867/785 40 8 6 0.929 ± 0.084 0.02106 ± 0.00470 0.1818 0.3845 1.978 A. tassiliensis 872/658 64 16 8 0.808 ± 0.093 0.03894 ± 0.00615 0.1993 1.31617 6.66 D, Tajima’s D (significant values in bold font); FS, Fu’s FS statistic; h, number of unique haplotypes; Hd, haplotype diversity; L, minimal length, excluding sites with gaps and missing data; N, number of samples or phased sequences; P, number of polymorphic sites, excluding sites with gaps and missing data; π = nucleotide diversity; R2, Ramos-Onsins and Rozas R2 statistic. View Large Figure 3. View largeDownload slide Top four lines: continuous spatial diffusion phylogenetic models for A. boulengeri and A. boueti. Only the polygons representing the 80% occurrence confidence interval are shown. Polygon colour represents relative age, the darker being more recent; the estimated origins for each polygon can be found in the raw output, which can be visualized in Google Earth (Supporting Information, Appendix S4). Bottom: distribution of genetic diversity. The colour represent the nucleotide diversity calculated for A. boueti (left) and A. boulengeri (right) based on the mitochondrial markers. Figure 3. View largeDownload slide Top four lines: continuous spatial diffusion phylogenetic models for A. boulengeri and A. boueti. Only the polygons representing the 80% occurrence confidence interval are shown. Polygon colour represents relative age, the darker being more recent; the estimated origins for each polygon can be found in the raw output, which can be visualized in Google Earth (Supporting Information, Appendix S4). Bottom: distribution of genetic diversity. The colour represent the nucleotide diversity calculated for A. boueti (left) and A. boulengeri (right) based on the mitochondrial markers. Palaeomodelling The climatically stable areas (Fig. 4) were generally coincident with the current species’ distributions (Fig. 1), except for A. boulengeri, with the southern and eastern parts of most of its current area of occurrence, the mountains of Mauritania, appearing as less suitable than the neighbouring areas. For intraspecific variability, climate stability is roughly concordant with the present distribution of lineages in the boueti–impalearis–tassiliensis group. Two major highly stable areas can be observed with the same NW–SE disposition as the A. impalearis lineages. For A. boueti, the identified stable areas in SW Mauritania, Senegal River mouth and SE Mauritania are roughly concordant with the C, W and E (in Mauritania) lineages, respectively. In the case of A. tassiliensis, the southern stable area corresponds to the distribution of lineage A, whereas the northern stable area is coincident with lineages T and H, with the cores of Tassili N’Ajjer (T) and Hoggar (H) also having higher stability than the surrounding area. The LIG was predicted to be the least favourable period for all species. The LGM also seems to have been overall less suitable for A. boulengeri (Supporting Information, Fig. S2.4). Model evaluation scores and environmental variable contributions are summarized in the Supporting Information (Table S1.4). Figure 4. View largeDownload slide Stable climatic areas for Agama species, obtained by averaging the occurrence probability for the present and the projections for mid-Holocene, Last Glacial Maximum and Last Interglacial. Warmer colours depict areas with higher stability. Small maps in the upper left corner of each panel represent the standard deviation for the maps in the same relative position. Dashed lines in the A. boulengeri map inset represent the limits of the Mauritanian mountains. Figure 4. View largeDownload slide Stable climatic areas for Agama species, obtained by averaging the occurrence probability for the present and the projections for mid-Holocene, Last Glacial Maximum and Last Interglacial. Warmer colours depict areas with higher stability. Small maps in the upper left corner of each panel represent the standard deviation for the maps in the same relative position. Dashed lines in the A. boulengeri map inset represent the limits of the Mauritanian mountains. Ecological niche comparisons Niche overlap (Table 3) was mostly > 10% for intraspecific lineages (average 11% in A. boueti and 30% in A. boulengeri) and decreased as phylogenetic distance increased (average 3.1% among species, 1% between intrageneric branches). All values were generally low, especially considering that they were weighted by the density of occurrence of each entity within the climatic space, which is likely to be attributable to the allopatry among compared entities. Similarity tests were significant among lineages of A. boulengeri and, although not significant, showed the same tendency for A. boueti lineages, except the E vs. W comparison. The remaining tests were non-significant. Using an extended climatic layer dataset, only comparisons against boueti W were non-significant (Supporting Information, Table S1.5). Tests for significant dissimilarity were all negative (Supporting Information, Table S1.5). Table 3. Niche comparisons at lineage, species and intragenus branch levels Level Comparison (A vs. B) D Equiv. B → A A → B Lineage boue_C vs. boue_E 0.155 0.968 0.082 0.094 boue_C vs. boue_W 0.111 1.000 0.082 0.068 boue_E vs. boue_W 0.058 0.998 0.236 0.230 boul_E vs. boul_N 0.125 1.000 0.046* 0.030* boul_E vs. boul_S 0.579 0.002 0.002* 0.004* boul_N vs. boul_S 0.207 0.002 0.006* 0.008* Species boue vs. boul 0.036 1.000 0.365 0.307 boue vs. imp 0.035 1.000 0.419 0.365 boue vs. tass 0.032 1.000 0.573 0.561 boul vs. imp 0 1.000 1.000 1.000 boul vs. tass 0.029 1.000 0.234 0.214 imp vs. tass 0.055 1.000 0.305 0.355 Branch bit† vs. boul 0.01 1.000 0.070 0.060 Level Comparison (A vs. B) D Equiv. B → A A → B Lineage boue_C vs. boue_E 0.155 0.968 0.082 0.094 boue_C vs. boue_W 0.111 1.000 0.082 0.068 boue_E vs. boue_W 0.058 0.998 0.236 0.230 boul_E vs. boul_N 0.125 1.000 0.046* 0.030* boul_E vs. boul_S 0.579 0.002 0.002* 0.004* boul_N vs. boul_S 0.207 0.002 0.006* 0.008* Species boue vs. boul 0.036 1.000 0.365 0.307 boue vs. imp 0.035 1.000 0.419 0.365 boue vs. tass 0.032 1.000 0.573 0.561 boul vs. imp 0 1.000 1.000 1.000 boul vs. tass 0.029 1.000 0.234 0.214 imp vs. tass 0.055 1.000 0.305 0.355 Branch bit† vs. boul 0.01 1.000 0.070 0.060 B → A and A → B, similarity tests; D, measured niche overlap; Equiv = equivalency test; boue = A. boueti; boul = A. boulengeri; imp = A. impalearis; tass = A. tassiliensis. *Significant (P < 0.05) similarity tests. Equivalency tests are not directly comparable with those calculated using the old methodology (Broennimann et al., 2012; see Discussion). †boueti–impalearis–tassiliensis group. View Large Table 3. Niche comparisons at lineage, species and intragenus branch levels Level Comparison (A vs. B) D Equiv. B → A A → B Lineage boue_C vs. boue_E 0.155 0.968 0.082 0.094 boue_C vs. boue_W 0.111 1.000 0.082 0.068 boue_E vs. boue_W 0.058 0.998 0.236 0.230 boul_E vs. boul_N 0.125 1.000 0.046* 0.030* boul_E vs. boul_S 0.579 0.002 0.002* 0.004* boul_N vs. boul_S 0.207 0.002 0.006* 0.008* Species boue vs. boul 0.036 1.000 0.365 0.307 boue vs. imp 0.035 1.000 0.419 0.365 boue vs. tass 0.032 1.000 0.573 0.561 boul vs. imp 0 1.000 1.000 1.000 boul vs. tass 0.029 1.000 0.234 0.214 imp vs. tass 0.055 1.000 0.305 0.355 Branch bit† vs. boul 0.01 1.000 0.070 0.060 Level Comparison (A vs. B) D Equiv. B → A A → B Lineage boue_C vs. boue_E 0.155 0.968 0.082 0.094 boue_C vs. boue_W 0.111 1.000 0.082 0.068 boue_E vs. boue_W 0.058 0.998 0.236 0.230 boul_E vs. boul_N 0.125 1.000 0.046* 0.030* boul_E vs. boul_S 0.579 0.002 0.002* 0.004* boul_N vs. boul_S 0.207 0.002 0.006* 0.008* Species boue vs. boul 0.036 1.000 0.365 0.307 boue vs. imp 0.035 1.000 0.419 0.365 boue vs. tass 0.032 1.000 0.573 0.561 boul vs. imp 0 1.000 1.000 1.000 boul vs. tass 0.029 1.000 0.234 0.214 imp vs. tass 0.055 1.000 0.305 0.355 Branch bit† vs. boul 0.01 1.000 0.070 0.060 B → A and A → B, similarity tests; D, measured niche overlap; Equiv = equivalency test; boue = A. boueti; boul = A. boulengeri; imp = A. impalearis; tass = A. tassiliensis. *Significant (P < 0.05) similarity tests. Equivalency tests are not directly comparable with those calculated using the old methodology (Broennimann et al., 2012; see Discussion). †boueti–impalearis–tassiliensis group. View Large DISCUSSION All the patterns expected in an aridity-induced vicariance scenario were present, supporting the role of climate in maintaining and probably giving rise to the diversity of the North-African radiation group and A. boulengeri, which most probably occurred as a result of increased and cyclic aridity for the Saharan–Sahelian populations. Diversification in the genus Agama has previously been related to the expansion of savannah habitats in Africa in the late Miocene (Leaché et al., 2014), and phylogeographical patterns in southern Africa have also suggested aridity-induced vicariance as a lead driver of diversification (Matthee & Flemming, 2002; Swart, Tolley & Matthee, 2009). However, adaptation to new habitats or niches is also found in Agama; for instance, the sand-burrowing behaviour of Agama etoshae (Arnold, 1995) or the tail retention as a possible climbing adaptation in Agama lionotus (Loumbourdis, 1986). The mountains in southern Mauritania are identified as a diversity hotspot and predicted climatic refugium for the local Agama species and therefore possibly for other species with similar climatic requirements, which highlights the importance of the region in terms of biodiversity conservation. The apparent ability of Agama from the Sahel–Sahara fringe to survive in that area during major climatic fluctuations emphasizes its pivotal role in the future survival of mesic species in the face of ongoing global warming. Spatial structure of genetic variability The Agaminae subfamily is most likely to have colonized Africa from the Arabian Peninsula through semi-arid corridors, the Sahel region being among them (Kissling et al., 2016) and, as expected from an Afro-tropical group, most of the agama diversity in North Africa is found in the Sahel. The geographical coverage of available genetic information and the number of major intraspecific genetic lineages identified have greatly increased from previous studies (Gonçalves et al., 2012; Mediannikov, Trape & Trape, 2012; Leaché et al., 2014). Species crown ages and lineage split times were all younger than the oldest age of the Sahara (7 Mya; Schuster et al., 2006) and mostly fall within the Pliocene–Pleistocene (A. boulengeri crown age is 6.4 Mya), a pattern shared with other taxa in the region, including reptiles, birds and mammals (Brito et al., 2014) and usually attributed to the aridification and/or climatic cycles. Species and lineages are almost exclusively parapatric or allopatric, which verifies the first expected pattern under the hypothesis of climate-induced vicariance and allopatric diversification. Aridity-induced isolation in sky islands, a pattern also present, for instance, in Myrtus shrubs (Migliore et al., 2013) or Ptyodactylus geckos (Metallinou et al., 2015), is apparent through the one-per-mountain-range distributions of the lineages of A. tassiliensis and A. boulengeri. Evidence of north–south vicariance along the Atlantic coast is found in the north–south separation of sister species A. impalearis and A. boueti, in agreement with previous studies (Gonçalves et al., 2012) and matching the generally recognized role of the Sahara in separating Mediterranean and Sahelian populations (Douady et al., 2003; Brito et al., 2014). Still, the role of the Atlantic coast as a corridor allowing species dispersal across the desert (Brito et al., 2014; Gonçalves et al., 2018) is illustrated by A. impalearis being the only species north of the Sahara and the fact that at the present time the distributions of these two species are very close, almost parapatric. Although genetic analyses revealed no signs of contact between A. impalearis and A. boueti, the difficulty of sampling in the geographical area between both species prevents ruling out possible secondary contacts. A past secondary contact between A. boueti and A. tassiliensis in the Hoggar Mountains, previously hypothesized based on signs of nuclear introgression (Gonçalves et al., 2012), was supported by the palaeo-models (Fig. 4; Supporting Information, Fig. S3). If that was the case, it would contrast with the pattern found in other terrestrial vertebrates, such as the snake Psammophis schokari (Gonçalves et al., 2018) or the anurans Pelophylax saharicus (Nicolas et al., 2015) and Bufotes boulengeri (Nicolas et al., 2018), which seem to have reached the Hoggar Mountains from the north. The phylogeographical pattern recovered for A. boulengeri of deep allopatric mitochondrial lineages with low nuclear diversity and extensively shared haplotypes may indicate climate-mediated range fluctuations and secondary-contact events, but further research is needed to clarify this (Supporting Information, Appendix S3). Climatic refugia The climatically stable areas and areas of predicted present occurrence for species were separated, with large extensions of highly unsuitable regions between them (Fig. 4; Supporting Information, Fig. S2.4), suggesting that climatic conditions did not favour contact. The general correspondence of climatically stable areas to species and lineage distribution in the boueti–impalearis–tassiliensis group is concordant with the vicariance hypothesis, because they indicate potential refugia (Carnaval et al., 2009) whose geographical isolation can lead to allopatric speciation (Avise, 2000). However, in the particular cases of impalearis NW and SE it is more likely to be attributable to a colder and more humid climate in the Atlas Mountains (Fig. 4; Supporting Information, Fig. S2.4) rather than increased aridity, in a similar manner to other taxa in that region, such as Pelophylax frogs, Mauremys pond turtles or Daboia snakes (Lansari et al., 2015; Veríssimo et al., 2016; Martínez-Freiría et al., 2017). For A. boulengeri, the stable climatic areas include sandy areas where the species does not occur at present, which could indicate that only the outskirts of the mountains, rather than the whole massifs, acted as microrefugia not detected by the models, or that there was more exposed rock in the past. It could also be an artefact from a climatic layer dataset that does not include habitat and is therefore insufficient to predict the stable areas for a species occurring almost exclusively on rocky outcrops [see Vale et al., (2012) for topoclimatic models for the present]. The only two other available studies applying multiple climatic phase palaeodistribution modelling in north Africa found contrasting patterns: Martínez-Freiría et al. (2017) found a similar pattern of allopatric potential refugia broadly coherent with lineage distributions of Daboia snakes in Northern Maghreb, whereas Gonçalves et al. (2018) found signs of a continuous coastal climatic refugium along the Atlantic for Psammophis schokari. Although, in the latter case, the stable areas were continuous in terms of climate, habitat suitability was probably interrupted by the basin of the large intermittent (flowing in humid phases) Tamanrasset palaeo-river, which depicts a different example of the impact of Pleistocene climatic cycles on species diversification in the region. Although not using palaeo-modelling, refugia in arid North Africa have been suggested in multiple studies in the last decades. Most of the focus has been on mesic species, with refugia including the mountain systems of central Sahara (Metallinou et al., 2015), inland water bodies of a large scale such as Lake Chad (Granjon & Dobigny, 2003) and a micro-scale such as oases (Shaibi & Moritz, 2010), or the peripheral regions of Sahel and the Mediterranean (Douady et al., 2003). Refugia for aridity-adapted species, which should have suffered range contractions during wet periods (Carranza et al., 2008; Pook et al., 2009; Metallinou et al., 2012; Tamar et al., 2016), are comparatively lacking. In fact, climatic shifts can be so fast and pronounced (DeMenocal et al., 2000) that many species probably need refugia for both humid and arid periods. That is exemplified by the aridity-adapted reptile genus Mesalina, which may survive on the outskirts of the central Saharan mountains in periods of extreme aridity (Kapli et al., 2015). The pattern of refugia is still not well understood in North Africa, but based on examples from other regions, such as the reptile community in south-western Australia, the interaction of climate, physical barriers and range shifts can be complex (Edwards, Keogh & Knowles, 2012). Techniques such as demographic simulations could help to address this subject with more detail and accuracy, as illustrated by a related example concerning the lizard Lerista lineopunctulata (He, Edwards & Knowles, 2013), but proper population sampling and a higher number of loci are needed to pursue such an approach. Range dynamics The mountain regions of southern Mauritania seem to have been the origin of dispersal of extant diversity of A. boueti and A. boulengeri, according to evidence from the diffusion models, interpolation of genetic diversity, and climatic stability. A second, younger possible ancestral area is depicted in Niger for A. boueti, which could indicate quick dispersal or a more ancient widespread distribution in the Sahel, but the sampling gap between Mauritania and Niger precludes further inferences. The diffusion models predicted a recent expansion to the fringes of distribution in Mauritania, a pattern also reflected by lower genetic diversity in those areas (Fig. 3). The marked difference in the expansion signal (Fig. 3, Table 2; Supporting Information, Table S1.3) recovered in the northernmost lineages of A. boueti and A. boulengeri (C and N, respectively), when compared with the southern ones from less arid regions, indicates a relatively recent colonization, starting from the climatically more stable central-southern areas. The niche similarity among lineages suggests that the expansion was not attributable to adaptation to different ecological conditions, and the fact that ‘backwards’ migrations in the diffusion models were predicted only for recent times (osf.io/pdthw/; doi:10.17605/OSF.IO/PDTHW) suggests repeated local extinctions and loss of signal from previous range expansions. Climatic changes in the region can be sudden and pronounced enough to allow it (DeMenocal et al., 2000). The synchronous marked expansions to the north and east in A. boulengeri, and north and west in A. boueti, at ~320 thousand years ago (Fig. 1), followed a glacial termination (Lisiecki & Raymo, 2005), suggesting that these expansions took place in a more humid phase. The stable climatic suitability areas around the southern Mauritanian mountains also stress the importance of the opportunity for elevational displacement for the survival of mesic species as climate fluctuates (Dobrowski, 2011; Velo-Antón et al., 2013). This pattern of apparent lack of adaptation to fluctuating climate indicates niche conservatism (Kozak & Wiens, 2010) and is another indication of the role of climate in the dispersal and subsequent diversification of mesic species. Ecological niche comparisons Observed niche overlap decreased as phylogenetic distance increased, as expected from a Brownian motion of niche diversification and low degrees of ecological adaptation (Wiens & Graham, 2005). It could happen that niche overlap is biased by geographical proximity (Warren et al., 2008), so overlap alone would not be highly significant because we are comparing two Sahelian lineages. However, the randomized niche similarity tests displayed a similar trend; similarity among species was much lower than among lineages. Given the wide geographical scale, the fact that no dissimilarity test was significant (all P > 0.4) again shows support for some conservatism of niche and susceptibility to vicariance. The higher similarity among intrageneric branches (A. boulengeri vs. the rest), even if not significant, does not contradict the vicariance hypothesis, as the divergence between both groups took place well before the significant increase in aridity (Fig. 2; Supporting Information, Fig. S2.1) and is also coherent with a general niche conservatism within the genus and subfamily (Kissling et al., 2016). The significant similarity for all comparisons among lineages of A. boulengeri (Table 3; Supporting Information, Table S1.5) also follows the expected pattern under the hypothesis of vicariance. The A. boueti lineages C and E follow the same pattern, but it is clear that boueti W has a less similar climatic niche compared with the rest. Given no allele sharing in NTF3, this could indicate some level of adaptation or divergence and is compatible with the existence of an undescribed species and the proposal of a ‘boueti’ species complex by other authors based on the morphological and ecological variation within A. boueti (Mediannikov et al., 2012; Leaché et al., 2014). It is worth noting that niche distinction between A. boueti and A. impalearis is much greater than among A. boulengeri lineages of around the same age. Together with the case of boueti W, this could indicate a lower degree of niche conservatism in the boueti–impalearis–tassiliensis group, which could translate into higher adaptation potential to different climates and habitat (Wiens et al., 2010) and help to explain why only one of the groups was able to cross and colonize the Sahara. However, the rock habitat specialization of A. boulengeri is the most likely cause for its geographically restricted occurrence; species of the boueti–impalearis–tassiliensis group occur in soft to hard soil with sparse vegetation and therefore do not require rock connectivity to disperse to different areas. Equivalency tests were mostly non-significant, meaning that the areas where the compared entities occur are more different than expected by chance (except among A. boulengeri lineages). This can be explained by all of the compared areas being allopatric and, in general, having low ecological niche overlap. In addition, equivalency tests are not weighted by the distribution density, meaning that they include all the area within the minimal convex polygon without correcting for actual occupancy (as the similarity tests and niche overlap measurement do); and given the characteristics of the landscape, a significant portion of the area within the minimal convex polygons can be unsuitable for both entities, possibly rendering comparisons meaningless. Previous studies have reported low P-values for equivalency tests (Rato et al., 2015; Ahmadzadeh et al., 2016; Martínez-Freiría et al., 2017; Gonçalves et al., 2018), but the ones obtained here cannot be compared directly with those results, given differences in the way the P-value for equivalency tests is calculated in ‘ecospat’ and in previous scripts (Broennimann et al., 2012). If subject to the previous methodology, the equivalency values obtained here would be close to zero, thus following the same pattern as other published works (Supporting Information, Appendix S4). Most studies addressing niche divergence and conservatism in North Africa are focused in the Mediterranean region (e.g. Anadón et al., 2015; Rato et al., 2015), with examples relating varying levels of niche divergence to patterns of temperature seasonality in the Mediterranean and Atlantic climates (Ahmadzadeh et al., 2016) or linking niche conservatism and allopatric diversification to Pleistocene climatic oscillations (Martínez-Freiría et al., 2017). Diversification in a scenario of climatic niche conservatism has also been described for other arid regions (e.g. Loera, Sosa & Ickert-Bond, 2012), and it might be a common pattern. These examples show that adaptation or niche specialization is not a requirement for diversification in deserts. A similar inference has been reached by Wiens, Kozak & Silva (2013) when comparing niche breaths of phrynosomatid lizards and concluding that high diversity in arid regions was probably attributable to longer time evolving in those habitats. Conclusions This study highlights the importance of climate-induced vicariance in the maintenance of diversity and probably in the diversification of the Agama group and is, to our knowledge, the first study critically evaluating the role of this environmental process in species diversification across the Sahara–Sahel ecoregions. Using Agama species as a model, we have verified the occurrence of the expected patterns for species with a conserved niche under a climatically induced vicariance scenario. Based on the role of the southern Mauritanian mountains as a diversity hotspot and predicted climatic refugia for the local Agama species, we highlight the probable importance of this region for the conservation of local biodiversity and evolutionary processes. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site: Appendix S1. Supplementary tables. Appendix S2. Supplementary figures. Appendix S3. The case of Agama boulengeri. Appendix S4. Notes on equivalency tests. SHARED DATA Supplementary data (DOI: 10.17605/OSF.IO/PDTHW) for the diffusion models can be found at the website of the Open Science Framework (www.osf.io/pdthw). ACKNOWLEDGEMENTS The authors acknowledge BIODESERTS group members for their assistance during fieldwork. P. A. Crochet, P. Geniez and J. M. Padial provided access to samples. This work was funded by the National Geographic Society (CRE 7629-04/8412-08), Mohamed bin Zayed Species Conservation Fund (11052709, 11052707 and 11052499), FCT - Fundação para a Ciência e a Tecnologia (PTDC/BIA-BEC/099934/2008 and PTDC/BIA-BIC/2903/2012), Ministerio de Economía y Competitividad (CGL2015-70390-P) and by European Regional Development Fund through the Operational Programme for Competitiveness Factors, COMPETE (FCOMP-01-0124-FEDER-008917/028276). D.V.G., P.P., G.V.-A. and J.C.B. were supported by FCT (SFRH/BD/78402/2011, PD/BD/128492/2017, IF/01425/2014 and IF/00459/2013, respectively) within QREN-POPH-T4.1 funded by European Science Foundation and Portuguese Ministério da Educação e Ciência. Logistic support for fieldwork was given by Pedro Santos Lda (Trimble GPS), Off Road Power Shop, P. N. Banc d’Arguin (Mauritania), Ministry of Environment of Mauritania, and Faculty of Sciences and Technologies, University of Nouakchott. The authors also thank the two anonymous referees whose helpful comments helped to improve the manuscript. REFERENCES Ahmadzadeh F, Flecks M, Carretero MA, Böhme W, Ihlow F, Kapli P, Miraldo A, Rödder D. 2016. Separate histories in both sides of the Mediterranean: phylogeny and niche evolution of ocellated lizards. Journal of Biogeography 43: 1242– 1253. Google Scholar CrossRef Search ADS Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 1223– 1232. Google Scholar CrossRef Search ADS Anadón JD, Graciá E, Botella F, Giménez A, Fahd F, Fritz U. 2015. Individualistic response to past climate changes: niche differentiation promotes diverging Quaternary range dynamics in the subspecies of Testudo graeca. Ecography 38: 956– 966. Google Scholar CrossRef Search ADS Arakaki M, Christin PA, Nyffeler R, Lendel A, Eggli U, Ogburn RM, Spriggs E, Moore MJ, Edwards EJ 2011. Contemporaneous and recent radiations of the world’s major succulent plant lineages. Proceedings of the National Academy of Sciences of the United States of America 108: 8379– 8384. Google Scholar CrossRef Search ADS Arevalo E, Davis SK, Sites JWJr. 1994. Mitochondrial DNA sequence divergence and phylogenetic relationships among eight chromosome races of the Sceloporus grammicus complex (Phrynosomatidae) in Central Mexico. Systematic Biology 43: 387– 418. Google Scholar CrossRef Search ADS Arnold EN. 1995. Identifying the effects of history on adaptation: origins of different sand‐diving techniques in lizards. Journal of Zoology 235: 351– 388. Google Scholar CrossRef Search ADS Avise JC. 2000. Phylogeography: the histo ry and formation of species . London, UK: Harvard University Press. Badgley C, Barry JC, Morgan ME, Nelson SV, Behrensmeyer AK, Cerling TE, Pilbeam D. 2008. Ecological changes in Miocene mammalian record show impact of prolonged climatic forcing. Proceedings of the National Academy of Sciences of the United States of America 105: 12145– 12149. Google Scholar CrossRef Search ADS Bielejec F, Rambaut A, Suchard MA, Lemey P. 2011. SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics. Bioinformatics 27: 2910– 2912. Google Scholar CrossRef Search ADS Brito JC, Godinho R, Martínez-Freiría F, Pleguezuelos JM, Rebelo H, Santos X, Vale CG, Velo-Antón G, Boratyński Z, Carvalho SB, Ferreira S, Gonçalves DV, Silva TL, Tarroso P, Campos JC, Leite JV, Nogueira J, Alvares F, Sillero N, Sow AS, Fahd S, Crochet PA, Carranza S. 2014. Unravelling biodiversity, evolution and threats to conservation in the Sahara-Sahel. Biological Reviews of the Cambridge Philosophical Society 89: 215– 231. Google Scholar CrossRef Search ADS Broennimann O, Cola V Di, Guisan A. 2016. ecospat v2.1.1: spatial ecology miscellaneous methods. R package . http://www.unil.ch/ecospat/home/menuguid/ecospat-resources/tools.html. Broennimann O, Fitzpatrick MC, Pearman PB, Petitpierre B, Pellissier L, Yoccoz NG, Thuiller W, Fortin M-J, Randin C, Zimmermann NE, Graham CH, Guisan A. 2012. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography 21: 481– 497. Google Scholar CrossRef Search ADS Brown RP, Suárez NM, Pestano J. 2002. The Atlas mountains as a biogeographical divide in North-West Africa: evidence from mtDNA evolution in the Agamid lizard Agama impalearis. Molecular Phylogenetics and Evolution 24: 324– 332. Google Scholar CrossRef Search ADS Carnaval AC, Hickerson MJ, Haddad CF, Rodrigues MT, Moritz C. 2009. Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323: 785– 789. Google Scholar CrossRef Search ADS Carranza S, Arnold EN, Geniez P, Roca J, Mateo JA. 2008. Radiation, multiple dispersal and parallelism in the skinks, Chalcides and Sphenops (Squamata: Scincidae), with comments on Scincus and Scincopus and the age of the Sahara Desert. Molecular Phylogenetics and Evolution 46: 1071– 1094. Google Scholar CrossRef Search ADS Carranza S, Arnold EN, Mateo JA, Geniez P. 2002. Relationships and evolution of the North African geckos, Geckonia and Tarentola (Reptilia: Gekkonidae), based on mitochondrial and nuclear DNA sequences. Molecular Phylogenetics and Evolution 23: 244– 256. Google Scholar CrossRef Search ADS Cerling TE, Harris JM, MacFadden BJ, Leakey MG, Quade J, Eisenmann V, Ehleringer JR. 1997. Global vegetation change through the Miocene/Pliocene boundary. Nature 389: 153– 158. Google Scholar CrossRef Search ADS Clement M, Posada D, Crandall KA. 2000. TCS: a computer program to estimate gene genealogies. Molecular Ecology 9: 1657– 1659. Google Scholar CrossRef Search ADS Costa G. 1995. Behavioural adaptations of desert animals . Berlin, Heidelberg: Springer Berlin Heidelberg. Google Scholar CrossRef Search ADS Degen AA. 1997. Ecophysiology of small desert mammals . Berlin, Heidelberg: Springer Berlin Heidelberg. Google Scholar CrossRef Search ADS DeMenocal P, Ortiz J, Guilderson T, Adkins J, Sarnthein M, Baker L, Yarusinksy M. 2000. Abrupt onset and termination of the African humid period: rapid climate responses to gradual insolation forcing. Quaternary Science Reviews 19: 347– 361. Google Scholar CrossRef Search ADS Dobrowski SZ. 2011. A climatic basis for microrefugia: the influence of terrain on climate. Global Change Biology 17: 1022– 1035. Google Scholar CrossRef Search ADS Douady CJ, Catzeflis F, Raman J, Springer MS, Stanhope MJ. 2003. The Sahara as a vicariant agent, and the role of Miocene climatic events, in the diversification of the mammalian order Macroscelidea (elephant shrews). Proceedings of the National Academy of Sciencesof the United States of America 100: 8325– 8330. Google Scholar CrossRef Search ADS Drummond AJ, Ho SY, Phillips MJ, Rambaut A. 2006. Relaxed phylogenetics and dating with confidence. PLoS Biology 4: e88. Google Scholar CrossRef Search ADS Drummond AJ, Suchard MA, Xie D, Rambaut A. 2012. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Molecular Biology and Evolution 29: 1969– 1973. Google Scholar CrossRef Search ADS Edwards DL, Keogh JS, Knowles LL. 2012. Effects of vicariant barriers, habitat stability, population isolation and environmental features on species divergence in the south-western Australian coastal reptile community. Molecular Ecology 21: 3809– 3822. Google Scholar CrossRef Search ADS Elith J, Kearney M, Phillips S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1: 330– 342. Google Scholar CrossRef Search ADS Evans ME, Smith SA, Flynn RS, Donoghue MJ. 2009. Climate, niche evolution, and diversification of the “bird-cage” evening primroses (Oenothera, sections Anogra and Kleinia). The American Naturalist 173: 225– 240. Google Scholar CrossRef Search ADS Gernhard T. 2008. The conditioned reconstructed process. Journal of Theoretical Biology 253: 769– 778. Google Scholar CrossRef Search ADS Gonçalves DV, Brito JC, Crochet PA, Geniez P, Padial JM, Harris DJ. 2012. Phylogeny of North African Agama lizards (Reptilia: Agamidae) and the role of the Sahara desert in vertebrate speciation. Molecular Phylogenetics and Evolution 64: 582– 591. Google Scholar CrossRef Search ADS Gonçalves DV, Martínez-Freiría F, Crochet PA, Geniez P, Carranza S, Brito JC. 2018. The role of climatic cycles and trans-Saharan migration corridors in species diversification: biogeography of Psammophis schokari group in North Africa. Molecular Phylogenetics and Evolution 118: 64– 74. Google Scholar CrossRef Search ADS Granjon L, Dobigny G. 2003. The importance of cytotaxonomy in understanding the biogeography of African rodents: Lake Chad murids as an example. Mammal Review 33: 77– 91. Google Scholar CrossRef Search ADS Gutiérrez-Rodríguez J, Barbosa AM, Martínez-Solano Í. 2017. Present and past climatic effects on the current distribution and genetic diversity of the Iberian spadefoot toad (Pelobates cultripes): an integrative approach. Journal of Biogeography 44: 245– 258. Google Scholar CrossRef Search ADS He Q, Edwards DL, Knowles LL. 2013. Integrative testing of how environments from the past to the present shape genetic structure across landscapes. Evolution; International Journal of Organic Evolution 67: 3386– 3402. Google Scholar CrossRef Search ADS Herbert TD, Lawrence KT, Tzanova A, Peterson LC, Caballero-Gill R, Kelly CS. 2016. Late Miocene global cooling and the rise of modern ecosystems. Nature Geosci 9: 843– 847. Google Scholar CrossRef Search ADS Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965– 1978. Google Scholar CrossRef Search ADS Hua X, Wiens JJ. 2013. How does climate influence speciation? The American Naturalist 182: 1– 12. Google Scholar CrossRef Search ADS Kapli P, Lymberakis P, Crochet PA, Geniez P, Brito JC, Almutairi M, Ahmadzadeh F, Schmitz A, Wilms T, Pouyani NR, Poulakakis N. 2015. Historical biogeography of the lacertid lizard Mesalina in North Africa and the Middle East. Journal of Biogeography 42: 267– 279. Google Scholar CrossRef Search ADS Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution 30: 772– 780. Google Scholar CrossRef Search ADS Kissling WD, Blach-Overgaard A, Zwaan RE, Wagner P. 2016. Historical colonization and dispersal limitation supplement climate and topography in shaping species richness of African lizards (Reptilia: Agaminae). Scientific Reports 6: 34014. Google Scholar CrossRef Search ADS Kozak KH, Wiens JJ. 2010. Niche conservatism drives elevational diversity patterns in Appalachian salamanders. The American Naturalist 176: 40– 54. Google Scholar CrossRef Search ADS Lanfear R, Calcott B, Ho SY, Guindon S. 2012. Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Molecular Biology and Evolution 29: 1695– 1701. Google Scholar CrossRef Search ADS Lansari A, Vences M, Hauswaldt S, Hendrix R, Donaire D, Bouazza A, Joger U, El Mouden EH, Slimani T. 2015. The Atlas Massif separates a northern and a southern mitochondrial haplotype group of North African water frogs Pelophylax saharicus (Anura: Ranidae) in Morocco. Amphibia-Reptilia 36: 437– 443. Google Scholar CrossRef Search ADS Lawson R, Slowinski JB, Crother BI, Burbrink FT. 2005. Phylogeny of the Colubroidea (Serpentes): new evidence from mitochondrial and nuclear genes. Molecular Phylogenetics and Evolution 37: 581– 601. Google Scholar CrossRef Search ADS Leaché AD, Grummer JA, Miller M, Krishnan S, Fujita MK, Böhme W, Schmitz A, Lebreton M, Ineich I, Chirio L, Ofori-boateng C, Eniang EA, Greenbaum E, Rödel M-O, Wagner P. 2017. Bayesian inference of species diffusion in the West African Agama agama species group (Reptilia, Agamidae). Systematics and Biodiversity 15: 192– 203. Google Scholar CrossRef Search ADS Leaché AD, Wagner P, Linkem CW, Böhme W, Papenfuss TJ, Chong RA, Lavin BR, Bauer AM, Nielsen SV, Greenbaum E, Rödel MO, Schmitz A, LeBreton M, Ineich I, Chirio L, Ofori-Boateng C, Eniang EA, Baha El Din S, Lemmon AR, Burbrink FT. 2014. A hybrid phylogenetic–phylogenomic approach for species tree estimation in African Agama lizards with applications to biogeography, character evolution, and diversification. Molecular Phylogenetics and Evolution 79: 215– 230. Google Scholar CrossRef Search ADS Le Berre M. 1989. Faune du Sahara. 1. Poissons, amphibiens et reptiles . Paris: Editions Chabaud Lechevalier. Le Houérou H. 1997. Climate, flora and fauna changes in the Sahara over the past 500 million years. Journal of Arid Environments 37: 619– 647. Google Scholar CrossRef Search ADS Lemey P, Rambaut A, Welch JJ, Suchard MA. 2010. Phylogeography takes a relaxed random walk in continuous space and time. Molecular Biology and Evolution 27: 1877– 1885. Google Scholar CrossRef Search ADS Librado P, Rozas J. 2009. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451– 1452. Google Scholar CrossRef Search ADS Lisiecki LE, Raymo ME. 2005. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20: 1– 17. Loera I, Sosa V, Ickert-Bond SM. 2012. Diversification in North American arid lands: niche conservatism, divergence and expansion of habitat explain speciation in the genus Ephedra. Molecular Phylogenetics and Evolution 65: 437– 450. Google Scholar CrossRef Search ADS Loumbourdis NS. 1986. The tail of the lizard Agama stellio stellio: energetics, significance and comments on its regeneration. Amphibia Reptilia 7: 167– 170. Google Scholar CrossRef Search ADS Macey JR, Schulte JA2nd, Fong JJ, Das I, Papenfuss TJ. 2006. The complete mitochondrial genome of an agamid lizard from the Afro-Asian subfamily agaminae and the phylogenetic position of Bufoniceps and Xenagama. Molecular Phylogenetics and Evolution 39: 881– 886. Google Scholar CrossRef Search ADS Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions 15: 59– 69. Google Scholar CrossRef Search ADS Martínez-Freiría F, Crochet PA, Fahd S, Geniez P, Brito JC, Velo-Antón G. 2017. Integrative phylogeographical and ecological analysis reveals multiple Pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biological Journal of the Linnean Society 122: 366– 384. Google Scholar CrossRef Search ADS Matthee CA, Flemming AF. 2002. Population fragmentation in the southern rock agama, Agama atra: more evidence for vicariance in Southern Africa. Molecular ecology 11: 465– 71. Google Scholar CrossRef Search ADS Mediannikov O, Trape S, Trape JF. 2012. A molecular study of the genus Agama (Squamata: Agamidae) in West Africa, with description of two new species and a review of the taxonomy, geographic distribution, and ecology of currently recognized species. Russian Journal of Herpetology 19: 115– 142. Merow C, Smith MJ, Silander JAJr. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36: 1058– 1069. Google Scholar CrossRef Search ADS Metallinou M, Arnold EN, Crochet PA, Geniez P, Brito JC, Lymberakis P, Baha El Din S, Sindaco R, Robinson M, Carranza S. 2012. Conquering the Sahara and Arabian deserts: systematics and biogeography of Stenodactylus geckos (Reptilia: Gekkonidae). BMC Evolutionary Biology 12: 258. Google Scholar CrossRef Search ADS Metallinou M, Červenka J, Crochet PA, Kratochvíl L, Wilms T, Geniez P, Shobrak MY, Brito JC, Carranza S. 2015. Species on the rocks: systematics and biogeography of the rock-dwelling Ptyodactylus geckos (Squamata: Phyllodactylidae) in North Africa and Arabia. Molecular Phylogenetics and Evolution 85: 208– 220. Google Scholar CrossRef Search ADS Migliore J, Baumel A, Juin M, Fady B, Roig A, Duong N, Médail F. 2013. Surviving in mountain climate refugia: new insights from the genetic diversity and structure of the relict shrub Myrtus nivellei (Myrtaceae) in the Sahara Desert. PLoS One 8: e73795. Google Scholar CrossRef Search ADS Miller MA, Pfeiffer W, Schwartz T. 2011. The CIPRES science gateway: a community resource for phylogenetic analyses. In: Proceedings of the 2011 TeraGrid Conference on Extreme Digital Discovery - TG ’11. Salt Lake City, Utah: ACM Press New York. Nicolas V, Mataame A, Crochet PA, Geniez P, Fahd S, Ohler A. 2018. Phylogeography and ecological niche modeling unravel the evolutionary history of the African green toad, Bufotes boulengeri boulengeri (Amphibia: Bufonidae), through the Quaternary. Journal of Zoological Systematics and Evolutionary Research : 56: 102– 116. Google Scholar CrossRef Search ADS Nicolas V, Mataame A, Crochet PA, Geniez P, Ohler A. 2015. Phylogeographic patterns in North African water frog Pelophylax saharicus (Anura: Ranidae). Journal of Zoological Systematics and Evolutionary Research 53: 239– 248. Google Scholar CrossRef Search ADS Nyári ÁS, Peterson AT, Rathbun GB. 2010. Late Pleistocene potential distribution of the North African Sengi or elephant-shrew Elephantulus rozeti. African Zoology 45: 330– 339. Google Scholar CrossRef Search ADS Otto-Bliesner BL, Marshall SJ, Overpeck JT, Miller GH, Hu A. 2006. Simulating Arctic climate warmth and icefield retreat in the last interglaciation. Science 311: 1751– 1753. Google Scholar CrossRef Search ADS Palumbi SR, Martin A, Romano S, McMillan WS, Stice S, Grabowski G. 1991. The simple fool’s guide to PCR . Honolulu: University of Hawaii Press. Pepper M, Fujita MK, Moritz C, Keogh JS. 2011. Palaeoclimate change drove diversification among isolated mountain refugia in the Australian arid zone. Molecular Ecology 20: 1529– 1545. Google Scholar CrossRef Search ADS Peterson AT. 2011. Ecological niche conservatism: a time-structured review of evidence. Journal of Biogeography 38: 817– 827. Google Scholar CrossRef Search ADS Pook CE, Joger U, Stümpel N, Wüster W. 2009. When continents collide: phylogeny, historical biogeography and systematics of the medically important viper genus Echis (Squamata: Serpentes: Viperidae). Molecular Phylogenetics and Evolution 53: 792– 807. Google Scholar CrossRef Search ADS Rambaut A, Suchard M, Xie D, Drummond A. 2014. Tracer v1.6 . Available at: http://tree.bio.ed.ac.uk/software/tracer/. Accessed 02 May 2018. Rato C, Harris DJ, Perera A, Carvalho SB, Carretero MA, Rödder D. 2015. A combination of divergence and conservatism in the niche evolution of the Moorish gecko, Tarentola mauritanica (Gekkota: Phyllodactylidae). PLoS One 10: e0127980. Google Scholar CrossRef Search ADS Rohling EJ, Marino G, Grant KM. 2015. Mediterranean climate and oceanography, and the periodic development of anoxic events (sapropels). Earth-Science Reviews 143: 62– 97. Google Scholar CrossRef Search ADS Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP. 2012. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology 61: 539– 542. Google Scholar CrossRef Search ADS Santos AM Dos, Cabezas MP, Tavares AI, Xavier R, Branco M. 2015. TcsBU: a tool to extend TCS network layout and visualization. Bioinformatics 32: 627– 628. Google Scholar CrossRef Search ADS Schleich HH, Kästle W, Kabisch K. 1996. Amphibians and reptiles of North Africa . Koenigstein: Koeltz Scientific Books. Schuster M, Duringer P, Ghienne JF, Vignaud P, Mackaye HT, Likius A, Brunet M. 2006. The age of the Sahara desert. Science 311: 821. Google Scholar CrossRef Search ADS Shaibi T, Moritz RFA. 2010. 10 000 years in isolation? Honeybees (Apis mellifera) in Saharan oases. Conservation Genetics 11: 2085– 2089. Google Scholar CrossRef Search ADS Silvestro D, Michalak I. 2012. RaxmlGUI: a graphical front-end for RAxML. Organisms Diversity and Evolution 12: 335– 337. Google Scholar CrossRef Search ADS Snyder CW. 2016. Evolution of global temperature over the past two million years. Nature 538: 226– 228. Google Scholar CrossRef Search ADS Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30: 1312– 1313. Google Scholar CrossRef Search ADS Stephens M, Smith NJ, Donnelly P. 2001. A new statistical method for haplotype reconstruction from population data. American Journal of Human Genetics 68: 978– 989. Google Scholar CrossRef Search ADS Swart BL, Tolley KA, Matthee CA. 2009. Climate change drives speciation in the southern rock agama (Agama atra) in the Cape Floristic Region, South Africa. Journal of Biogeography 36: 78– 87. Google Scholar CrossRef Search ADS Swezey CS. 2003. The role of climate in the creation and destruction of continental stratigraphic records: an example from the northern margin of the Sahara Desert. In: Cecil CB, Edgar NT, eds. Climate controls on stratigraphy . Special Publications of SEPM (Society for Sedimentary Geology), 207–225. Google Scholar CrossRef Search ADS Tamar K, Carranza S, Sindaco R, Moravec J, Trape JF, Meiri S. 2016. Out of Africa: phylogeny and biogeography of the widespread genus Acanthodactylus (Reptilia: Lacertidae). Molecular Phylogenetics and Evolution 103: 6– 18. Google Scholar CrossRef Search ADS Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. 2013. MEGA6: molecular evolutionary genetics analysis version 6.0. Molecular Biology and Evolution 30: 2725– 2729. Google Scholar CrossRef Search ADS Thuiller W, Georges D, Engler R, Breiner F. 2016. Biomod2: ensemble platform for species distribution modeling . https://CRAN.R-project.org/package=biomod2. Accessed 02 May 2018. Thuiller W, Lafourcade B, Engler R, Araújo MB. 2009. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32: 369– 373. Google Scholar CrossRef Search ADS Vale CG, Tarroso P, Campos JC, Gonçalves DV, Brito JC. 2012. Distribution, suitable areas and conservation status of the Boulenger’s agama (Agama boulengeri, Lataste 1886). Amphibia-Reptilia 33: 526– 532. Google Scholar CrossRef Search ADS Velo-Antón G, Martínez-Freiría F, Pereira P, Crochet PA, Brito JC. 2018. Living on the edge: ecological and genetic connectivity of the spiny-footed lizard, Acanthodactylus aureus, confirms the Atlantic Sahara desert as a biogeographic corridor and centre of lineage diversification. Journal of Biogeography 45: 1031– 1042. Google Scholar CrossRef Search ADS Velo-Antón G, Parra JL, Parra-Olea G, Zamudio KR. 2013. Tracking climate change in a dispersal-limited species: reduced spatial and genetic connectivity in a montane salamander. Molecular Ecology 22: 3261– 3278. Google Scholar CrossRef Search ADS Veríssimo J, Znari M, Stuckas H, Fritz U, Pereira P, Teixeira J, Arculeo M, Marrone F, Sacco F, Naimi M, Kehlmaier C, Velo-Antón G. 2016. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biological Journal of the Linnean Society 119: 943– 959. Google Scholar CrossRef Search ADS Warren DL, Glor RE, Turelli M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution; International Journal of Organic Evolution 62: 2868– 2883. Google Scholar CrossRef Search ADS Wiens JJ, Ackerly DD, Allen AP, Anacker BL, Buckley LB, Cornell HV, Damschen EI, Davies TJ, Grytnes JA, Harrison SP, Hawkins BA, Holt RD, McCain CM, Stephens PR. 2010. Niche conservatism as an emerging principle in ecology and conservation biology. Ecology Letters 13: 1310– 1324. Google Scholar CrossRef Search ADS Wiens JJ, Brandley MC, Reeder TW. 2006. Why does a trait evolve multiple times within a clade? Repeated evolution of snakelike body form in squamate reptiles. Evolution; International Journal of Organic Evolution 60: 123– 141. Wiens JJ, Graham CH. 2005. Niche conservatism: integrating evolution, ecology, and conservation biology. Annual Review of Ecology, Evolution, and Systematics 36: 519– 539. Google Scholar CrossRef Search ADS Wiens JJ, Kozak KH, Silva N. 2013. Diversity and niche evolution along aridity gradients in North American lizards (Phrynosomatidae). Evolution; International Journal of Organic Evolution 67: 1715– 1728. Google Scholar CrossRef Search ADS Wiens JJ, Kuczynski CA, Smith SA, Mulcahy DG, Sites JWJr, Townsend TM, Reeder TW. 2008. Branch lengths, support, and congruence: testing the phylogenomic approach with 20 nuclear loci in snakes. Systematic Biology 57: 420– 431. Google Scholar CrossRef Search ADS Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA. 2009. Niches, models, and climate change: assessing the assumptions and uncertainties. Proceedings of the National Academy of Sciences of the United States of America 106: 19729– 19736. Google Scholar CrossRef Search ADS Wiley EO. 1988. Vicariance biogeography. Annual Review of Ecology and Systematics 19: 513– 542. Google Scholar CrossRef Search ADS Yule GU. 1925. A mathematical theory of evolution based on the conclusions of Dr. J.C. Willis, F.R.S. Journal of the Royal Statistical Society 88: 433– 436. Google Scholar CrossRef Search ADS Zachos J, Pagani M, Sloan L, Thomas E, Billups K. 2001. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292: 686– 693. Google Scholar CrossRef Search ADS Zhang J, Kapli P, Pavlidis P, Stamatakis A. 2013. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29: 2869– 2876. Google Scholar CrossRef Search ADS Zhang Z, Ramstein G, Schuster M, Li C, Contoux C, Yan Q. 2014. Aridification of the Sahara desert caused by Tethys Sea shrinkage during the Late Miocene. Nature 513: 401– 404. Google Scholar CrossRef Search ADS © 2018 The Linnean Society of London, Biological Journal of the Linnean Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Biological Journal of the Linnean Society – Oxford University Press
Published: May 29, 2018
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