Oversea dispersal by speckled rattlesnakes has been confirmed by molecular data directly (a single sample from Cabeza de Caballo had a mainland mtDNA haplotype, but an island-endemic nuclear genome), and by the sister-group relationship between the populations on Ángel de la Guarda and Cabeza de Caballo islands, which have never shared a terrestrial connection. Our D-statistic analyses indicate that periodic trans-marine dispersal and introgression from Ángel de la Guarda into the islands of Los Angeles Bay (and likely the peninsular mainland) best explains the discordant patterns observed between mtDNA and nDNA data. This dispersal scenario is also supported by prevailing surface currents in the Gulf of California, which form a counterclockwise gyre moving toward the south and west along the Baja California coast during spring and summer active seasons [ 32 ]. Oversea dispersal may be a consequence of the great tidal amplitudes in the central Gulf (> 4m during spring tides; [ 29 ]) coupled with the apparent proclivity of insular speckled rattlesnakes for foraging in intertidal zones ([ 33 ], JMM personal observation). It is plausible that snakes seek refuge in crevices and rock cavities in intertidal zones during low tides, and are then occasionally swept out to sea as refugia become inundated during high tides. Such events can explain the episodic influx of genes from Ángel de la Guarda Island rattlesnakes to populations on islands in Los Angeles Bay and the adjacent peninsular mainland.

Rattlesnake populations from the land-bridge islands of Los Angeles Bay (Smith, Piojo, and Cabeza de Caballo) and the large deep-water island of Ángel de la Guarda (~12 km northeast of Los Angeles Bay across the Ballenas Channel) also exhibit mito-nuclear discordance. Ángel de la Guarda is an ancient island (~2 my [ 31 ]), on which the rattlesnake population has had sufficient time to diverge from the mainland. In contrast, the islands in Los Angeles Bay shared a connection with the peninsular mainland within the past 10 ky [ 32 ], and should be closely related to mainland samples of C. pyrrhus. In general, this is the pattern we inferred from mitochondrial DNA, except for the population from Cabeza de Caballo Island, which forms a deeply divergent clade sister to C. angelensis. However, the ML analysis of nuclear SNP data indicates that weakly supported branches leading to populations in Los Angeles Bay and Ángel de la Guarda may render C. pyrrhus paraphyletic with respect to C. mitchellii ( Fig 4 ).

Although clustering patterns were congruent for purposes of species delimitation, mitochondrial and nuclear datasets were discordant in estimates of phylogenetic relationships at deep levels in the phylogeny for C. mitchellii as well as for insular populations in the central Baja California region corresponding to C. pyrrhus and C. angelensis. In C. mitchellii, deep coalescence results in two divergent mitochondrial clades that render this taxon non-monophyletic with respect to C. pyrrhus + C. angelensis, and possibly C. stephensi ( Fig 3 ); however, the species was inferred as monophyletic from the ML analysis of nuclear SNP data and comprised a single phenotypic cluster ( Fig 4 ). These observations suggest that past barriers to gene flow between deep mitochondrial clades of C. mitchellii were either porous or transient enough not to affect strongly the nuclear genome. Deep mitochondrial subdivisions may develop in species with low vagility in the absence of complete barriers to gene flow [ 27 , 28 ]; thus, a specific vicariant event need not be invoked. Furthermore, the long and narrow peninsula of Baja California may enhance demographic effects on mitochondrial phylogeographic structure by linearizing the geographic arrangement of metapopulations, which would restrict zones of contact, and hence gene flow, between subpopulations (in general, terrestrial vertebrates in peninsular Baja California have extensive phylogeographic structure in mitochondria [ 29 , 30 ]).

Climatic Data and Species Delimitation

The justification for using climatic data in species delimitation is based on the idea that distinct species occupy distinct ecological niches, a widespread interpretation of Van Valen’s [34] proposals concerning the nature of species. We do not deny that ecological traits may play an important role in speciation; however, our results from ordination and model-based clustering of multiple datasets demonstrated that climatic data do not have the inherent taxonomic signal that is evident with genetic and phenotypic data, calling into question the use of climatic data in species delimitation. The only previous application of climatic data to species delimitation methods that algorithmically delineate groups likewise did not result in the discovery of meaningful clusters, in this instance based on congruence of assignment with traditional taxonomic criteria [35]; however, these authors did not question the validity of using climatic data for delimiting species. While the reason for these conflicting results is straightforward, i.e., climate develops and changes through non-evolutionary mechanisms, the implications are underappreciated for practical systematics. Our argument is simple: (i) climate is generated by non-evolutionary processes; (ii), ordination and clustering of climatic data will not delineate biological entities, but rather prevailing precipitation and temperature regimes; (iii) thus, climatic data do not possess inherent taxonomic signal; (iv) therefore, climatic data are irrelevant for delimiting species entities using ordination and clustering methods. If these propositions are correct, then the interpretation of divergence in underlying eco-physiological traits does not follow from the ordination of climatic data, even though divergence in ecological traits may be reflected in some climate clusters.

Particularly noteworthy was the failure of the climatic dataset to identify the deeply divergent population from Ángel de la Guarda Island, whereas all other datasets unambiguously delineated this population as a distinct group. Crotalus angelensis apparently has been evolving in isolation since Ángel de la Guarda Island was sheared from the peninsular mainland approximately 2 mya by tectonic rifting [31], but this vicariant event is intractable on the basis of climate, because the island lies in an exceptionally arid and relatively uniform climatic zone that includes most of the northeastern peninsula and its nearshore islands. Most of the six climate clusters spanned multiple species boundaries and attest to the problems with delineating species boundaries based on climate. For example, the distribution of C. pyrrhus as delineated by intrinsic trait data encompasses all six climate groups, at least marginally, while C. mitchellii and C. angelensis are found exclusively within subregions of single climate clusters. This is not to say that climate clusters are biologically uninformative. For example, the parapatric boundary between C. mitchellii and C. pyrrhus in the midpeninsular region is the only species boundary that also corresponds to a boundary between climate clusters, and indicates a possible role of climate in the divergence of these species, or in the current maintenance of species boundaries. Additionally, the application of distribution modeling techniques may provide important information on niche similarity among closely related species, or on which climatic variables best explain distributions of different species, and could thereby indicate ecological axes along which divergence may have occurred [36]. However, we emphasize that this information is useful only in a post-species delimitation context and does not provide additional evidence for species boundaries. For example, simply because the boundary between C. mitchellii and C. pyrrhus coincides with a shift in climate, the evidence for species boundaries is not stronger for this pair of taxa than it is for the boundary between C. tigris and C. pyrrhus, which does not correspond with a distinctive shift in climate.

In addition to the issue of taxonomic signal as it pertains to species delimitation and assignment, there is a broader issue of the general relationship between intrinsic physiological tolerance and climate. Although a species obviously cannot maintain populations outside a specific range of climatic conditions, it remains unclear theoretically what type of object is modeled by the most frequently used set of climatic variables (derivatives of temperature and precipitation), and its connection to underlying intrinsic traits of the organisms with which they are associated [37]. When treated in aggregate (i.e., in a multivariate space) the use of climatic data as a surrogate for an intrinsic biological trait seems intuitive, with population- and species-specific means, variances, and other parameters that could be plotted and conceptualized as environmental tolerance. But when considered at the level of individual climatic variables, for example “Mean Temperature of Driest Quarter,” they are nonsensical as proxies for such traits. Climatic envelopes should be interpreted as a multivariate property of the environment of species, rather than as an intrinsic trait of an organism or species, or as a proxy for such a trait. Environmental tolerance arises from myriad intrinsic and extrinsic properties that vary in importance from species to species, and range from enzymatic structure and function, to behavioral thermoregulation, to evaporative transpiration. A multitude of different traits may be involved, which could result in the same outcome regarding environmental tolerance as represented by different species’ climatic envelopes, none of which is distinguishable using climatic data. Thus, measurements generated from climatic data differ from many other emergent properties of populations, such as summary statistics of allele frequencies, because there is no direct homology between intrinsic properties of organisms and climate.

Because climate is a property of locations rather than of organisms, we contend that climatic variables do not reflect attributes of evolving genetic and phenotypic systems, and are therefore invalid surrogates for intrinsic eco-physiological traits. Although this proposition may seem obvious, the frequent use of climatic data to infer evolutionary divergence in species delimitation has prompted us to use a reductio ad absurdum argument with an empirical example to demonstrate that climate clusters do not possess inherent taxonomic signal. Our study focused specifically on clustering and ordination rather than on correlative SDMs per se, and while these methods involve different analytical procedures, in context of species delimitation both make assertions of evolutionary divergence from patterns based on climatic data exclusively, generally without mention of alternative explanations (for example, statistical differences in climatic envelopes between parapatric or allopatric species can be explained simply by the geography of climate rather than through divergence in intrinsic traits [38, 39]). However, most studies that use climatic data for species delimitation actually incorporate these datasets only after candidate species have been identified and specimens assigned by other means. Correlative SDMs are phenomenological regression models, and require that individual locality records be correctly assigned to species a priori for results to have interpretive validity (i.e., model structure and functional forms are dependent on parameterization of models with data; these models perform poorly when distribution and taxonomy are uncertain or incomplete [40, 41]). Insofar as species delimitation means to define or establish hypotheses of species boundaries, the use of SDMs to delimit or validate species involves circular reasoning; therefore, in practice these analyses are used mostly to describe the climatic envelopes of species. However, it is clear from previous arguments provided by researchers of species delimitation that in general climatic data (used either in ordination or in SDMs) are incorrectly perceived as providing evidence for species boundaries [7–9, 42, 43].