Montpellier 2 University, France University of the Aegean, Greece

Master 2 Ecology & Biodiversity - Research project Specialty : Biodiversity & Evolution Focus : BIODIV “Biodiversity conservation”

2011-2012

First steps in a project integrating phylogeny, climate, geological history and dispersion dynamics, to explain the phylogeography of the genus Batrachoseps .

Julian WITTISCHE

Internship supervisor: Pr. em. David B. Wake

Museum of Vertebrate Zoology Department of Integrative Biology University of California, Berkeley First steps in a project integrating phylogeny, climate, geological history and dispersion dynamics, to explain the phylogeography of the genus Batrachoseps .

Julian Wittische

University of California, Berkeley, Departement of Integrative Biology, Museum of Vertebrate Zoology, Berkeley CA, USA [email protected]

2 ABSTRACT The slender salamanders genus, Batrachoseps (Plethodontidae), is the most speciose group of caudate amphibians in the western nearctic zone. All clades and even species within these groups show a marked phylogeographic structure, although there is often a discrepancy between mtDNA and nDNA (allozymes) datasets. Batrachoseps species are morphologically cryptic and ecologically very similar. Slender salamander species do not merge when a parapatry occurs and sympatry is limited especially within clades, so that they seem to replace each other spatially. Several elements give insight about the diversification processes of this genus and the non-adaptive radiation hypothesis, where non-ecological speciation occurs, seems likely. To all appearances isolation periods following fragmentation could have have been amply long to create a divergence, great enough to prevent populations from merging and efficiently hybridizing. However the non-adaptive hypothesis has not been studied yet from an environmental niche point of view. Methods from invasive species niche studies are adapted and ordination techniques are used in order to compare and illustrate the environmental niches of slender salamanders based on several climatic variables. Through these comparisons, predictions made by the non-adaptive hypothesis are tested with an increasing complexity and rigor in the methodology. The biological significance of these environmental niche metrics and tests is discussed within a conceptual context and hypotheses about the reasons why our first hypothesis could be true while leading to our results are formulated. Finally an experimental ecology perspective is given and the integration of this study in a wider project is highlighted.

Key words: southwestern North America, lungless salamanders, biogeography, niche, species formation, ordination statistics

3 CONTENTS

Introduction...... 5

Materials and Methods...... 7

1. Phylogeny: sister species and their divergence time...... 7 2. Georeferences and climatic data...... 8 3. First method to calculate climatic overlap of the species temperature regime and associated statistical analyses...... 8 4. Second method: addition of precipitation variables and associated multivariate analyses...... 9 5. Third method: adaptation of Broennimann et al, 2012 to the comparison of the niches of two species...... 10

Results...... 10

1. Thermal regime analysis on 12 months...... 10 2. Convex hulls and BCA...... 11 2. Broennimann et al. Framework...... 11

Discussion...... 12

References...... 17

List of figures...... 21

Tables...... 22

Figures...... 25

Appendix...... 33

Acknowledgements...... 77

4 INTRODUCTION

Patterns in species formation bring a lot of conceptual issues (Williams, 1992; Wake, 2009). The arbitrary level of genetic or morphological divergence used to delimit species is one of them. Biologists always had trouble writing a stable and robust definition of what a species is, although some elements of the concept are shared by most of them (Mayden, 1997; de Queiroz, 1999, 2005, 2007). The main issue is not defining the properties of taxonomic categories but rather to find and describe divergence and eventually split local evolutionary lineages (Vences and Wake, 2007). One can notice numerous examples of diversification bursts in the animal kingdom: they can be produced by physical separation creating divergence by isolation or by adaptive speciation while maintaining opportunity for genetic interaction. Non-adaptive radiations are common and there is a possibility that current ecologically differentiated sympatric species have been the result of such radiation. Also radiations may include elements from both types of speciation (Rundell and Price, 2009). Phylogeographical analysis by its integration of population genetics and historical biogeography is a great tool to identify population-level evolutionary units which can then be used to sort out hypotheses about the formation of species. Both the richness in phylogenetic data and the increasing knowledge of the distribution of species give phylogeography a great power to explain patterns of biological diversity. A lot of the species formation history can be inferred from studying geographic variation both within and between species (Irwin, 2012). All phylogeographical hypotheses one may develop, should respect a few key rules: the vast majority of species have geographic populations with a sometimes great genetic structure and are very likely to show an intraspecific phylogenetic tree (Avise, 1987), dispersal ability (intrinsic and extrinsic) is a major element to understand phylogeography and much of the variance in monophyletic groups with great genetic gaps or lack of phylogeographical structure in a species can be explained by their dispersal history (Fumihito et al., 1996).

In bygone days, individuals were diagnosed as belonging to a species by their morphology. However since the discovery of DNA and the ever-increasing use of genetic data, many phenotypically cryptic lineages have been discovered. Previously misapprehended biodiversity was here, defying science and holding back a lot of information. The first to describe one Batrachoseps species, Batrachoseps attenuatus, was Johann Friedrich von Eschscholtz, famous for his two expeditions and for being the first to describe Enteropneusta (Eschscholtz, 1833). Even after a detailed morphological analysis from Hendrickson in 1954, there was still only one species recognized in a huge part of California. Studies have regularly added species in the genus by discovery or splitting of older taxa (Cope, 1865; Cope, 1869; Camp, 1915; Bishop, 1937,Brame and Murray, 1968; Marlow et al., 1979; Wake, 1996; Jockusch et al., 1998; Jockusch et al, 2001; Wake et al., 2002; Jockusch et al., 2012). Each new taxonomy enabled scientists to have a better understanding of the biodiversity in the genus Batrachoseps , which is now the most species-rich salamander clade in western north America. Slender salamanders can be found along the Pacific coast of North America (Fig.2) from the Cascade Mountains of Oregon (USA) to the Sierra San Pedro Mártir in Baja California (Mexico). There are some disjoint interior ranges from the Cascade Mountains of northern Oregon through the Sierra Nevada and Inyo mountain systems in California to the Sierra San Pedro Mártir near the southern end of its range in Baja California. Coastal populations of the subgenus Batrachoseps are very diverse taxonomically and include at least partially three of the four groups of the subgenus Batrachoseps , the relictus group being only in the Sierra Nevada system. The radiation of Batrachoseps is likely to result from the complicated geological history of

5 California (Yanev, 1980; Jockusch et al., 2001). Thus the phylogeny is strongly linked with the geological history of this part of the world. The monophyly of the whole genus is well supported by both morphology and molecular data (Jackman et al., 1997). All species share the following morphological traits: a projectile tongue attached to the jaw by an elongated genioglossus muscle four digits on the hind feet (usually salamenders have five), a large dorsal fonticuli cranii in the skull (Wake, 1966; Lombard and Wake, 1986; Jackman et al., 1997). The oldest phylogenetic split is the one between the two subgenera Plethopsis and Batrachoseps (Jockusch and Wake, 2002). The monophyly of each of the two subgroups is also very well supported but only by molecular data (Jackman et al., 1997; Parra-Olea, 1999). The most perceptible morphological differences are found in Plethopsis , the three species of the subgenus are large and robust looking relatively to the species of the subgenus Batrachoseps . However two species of the subgenus Batrachoseps (pacificus and stebbinsi ) are large and also robust (Marlow et al., 1979).Their skeleton has been examined and symplesiomorphy has been found within the Plethospsis subgenus and three synapomorphies have been reported in the Batrachoseps subgenus: loss of prefrontal bones, reduction or loss of the preorbital processes of the vomer and fusion of the praemaxillae in adults (Wake, 1989; Jackman et al., 1997). Few details are known about their ecology but as far as major outlines of their ecology are concerned they are all similar The species of the subgenus Batrachoseps have apparently specialized in a fossorial life, at least partially. Their morphology has changed to a more elongated type with reduced limbs. 13 out of 22 described species have rather restricted ranges (Fig.2), especially the species from the Sierra Nevada but also Batrachoseps pacificus, for example, which is endemic to the northern Channel Islands. It is worth noting that it is very uncommon that salamander species on islands have their own species status because usually they are of the same species as the adjacent mainland (Vences and Wake, 1997). Species, on the Pacific coast or in the Sierra Nevada are distributed in a parapatric fashion (Fig.2). Overall the subgenus Plethopsis is situated on the outer limits of the range of the other species with Batrachoseps campi and Batrachoseps robustus being eastern than the other Sierra Nevada species, and Batrachoseps wrighti being the northernmost species. The species seem to have been displaced with geological events with the exception of Batrachoseps nigriventris which crosses the San Andreas fault. This species is also showing a zone of sympatry which is not the rule in the genus (Fig.2). Moreover the few zones of sympatry are limited in size. One hypothesis to explain the phylogeography of Batrachoseps was set by Jockusch et al. (2001), focusing on the pacificus group redefined in Jockusch and Wake (2002). The geological history of California have been rich in land moving events (Norris and Webb, 1990) and a reconstruction of coastal California has been done by Hall (2002). This allowed Jockusch et al. (2002) to develop a complete scenario of phylogenetic split linked with geological events such as the northwestward movement and fragmentation of the Salinia plate (see Appendix Fig.A1), which is very likely to have created vicariance or the Central Valley embayment which has blocked most of the species from dispersing towards its adjacent northern parts (Wake, 2006). A study using physiographic features in order to explain phylogeography within one Batrachoseps species has also been done (Martínez-Solano et al., 2007). Some characteristics of Batrachoseps species are not to be forgotten: their low dispersal (Hendrickson, 1954; Cunningham, 1960; Maiorana, 1974; Jockusch and Wake, 2002), their high degree of philopatry and their small home range size. A typical home range size would be no more than a few meters wide, maximum (Hendrickson, 1954; Cunningham, 1960; Stebbins and Cohen, 1995; Smith and Green, 2005 ). Concerning their philopatry, male and female are likely to present a differential dispersal, the female usually staying closer to their original birth place (Miller, Haig and Wagner, 2005). All of those ecological characteristics combined with the low vagility of the individuals and the old age of Batrachoseps and the intense geological activity of the region may have resulted in

6 the deep genetic differences across small distances (Jockusch & Wake, 2002; Martínez-Solano et al., 2007; Martínez-Solano & Lawson, 2009).There are very strong discordances between the phylogeographic patterns of different sets of markers (Wake & Jockusch, 2000; Jockusch & Wake, 2002), see (Martínez-Solano et al., 2012) for a very detailed and recent example of discrepancy between mitochondrial DNA and nuclear DNA. Mitochondrial DNA thanks to the rapid pace of its nucleotides substitution and to the very nature of its transmission have been provided one of the first tools for strong phylogenetic inferences at a microevolutionary scale, within species. Using molecular data derived from different kinds of DNA markers may sometimes give insight about processes responsible for the patterns of species formation (Avise, 2000). Nuclear DNA data is gathered by allozyme analysis.

The aim of this paper is to expose the environmental influence on the biogeography of Batrachoseps. This study is designed to be a starting point of a bigger project which will involve developing dispersion model for different types of DNA and integrating in a chronologically and geographically explicit way the geological history of Oregon, California and Baja California. The objectives are to use climatic variables to test whether the non-adaptive hypothesis of Batrachoseps radiation which has for example been developed in Wake (2006) is confirmed, from an environmental niche point of view. The main expectation of this hypothesis is that sister species will have less divergent environmental niches because their splitting event is more recent than other species pairs. Indeed if the speciation is non-adaptive then the divergence of niches should be associated with time since split only, not external ecological parameters.

MATERIALS AND METHODS

1. Phylogeny: sister species and their divergence time The phylogeny used in this paper (Fig.1) is mainly drawn from the molecular analyses from a series of papers trying to solve it with different methods and based on mtDNA, nDNA or a combination of both (Jockusch et al., 2001; Jockusch and Wake, 2002, Jockusch et al., 2012; Martínez-Solano et al., 2012). The taxonomy which was in place before the description of B. altasierrae and B. bramei (Jockusch et al., 2012) was used, because the data sources were not up to date. For the nigriventris group ( Jockusch and Wake, 2002), two clades were considered (northern and southern) to draw the tree to avoid a complete parapatry of the group, but the data of the whole species was used in the analysis to avoid any taxonomical bias. The first and fourth nodes of the tree are supported by the same three out of the four methods used in the creation of its phylogeny: single most likely tree found in the likelyhood analysis under HKY + I + G model, strict consensus of 352 trees identified in maximum parsimony analyses using equal weights and finally single best tree (length = 57.519) found in least squares analysis using LogDet distances and including rate variation. The parapatry of B. stebbinsi, B. gregarius, and the group formed by B. simatus and the southern clade of B. nirgriventris is probably the best option considering the very different phylogenies of this subgroup given by the different methods. About the Plethopsis subgenus three of the methods agree in B. campi being closer to B. robustus than to B. wrighti . The final choice made concerning the phylogeny is the position of the relictus and nigriventris groups relatively to pacificus , was to consider nigriventris as the closest one based on Jockusch and Wake (2002) and D.B. Wake expertise (see a phylogenetic alternative to this position in Jockusch et al., 2001). Sister species were defined according to a simple comprehensive phylogeny of the complete

7 genus, that was assembled for the first time in this paper. They were chosen to cover most of the geographic distribution of the genus. Additionally at least one pair per phylogenetic group was chosen in order to cover the whole tree. The only group that was not part of the sister species pairs was Batrachoseps attenuatus because it is phylogenetically distinct and constitute a group by itself ( Yanev, 1980; Jockusch and Wake, 2002; Martínez-Solano et al., 2007). Although it would have been interesting to have a non-biased subset of sister species, phylogenetically and geographically, a mix of true sister species relatively to the systematic sense was used, from well supported nodes in the literature cited above (i.e. B. minor-B. incognitus, B. kawia-B. relictus, B. major-B. pacificus, B. campi-B. robustus ) and non-strict sister species. Those are carrying an interest for us because their phylogeny may change with future analysis or because different methods gave different trees and even if a choice was made to draw the tree, alternatives were still considered (e.g. for B. campi-B. wrighti ). Although Batrachoseps aridus was considered, its number of specimen is too low for some analysis so B. major-B. pacificus was picked too. A list of all sister species can be found in Table 1.

2. Georeferences and climatic data A total of 24167 georeferenced specimen data was used, including their species name and their coordinates, downloaded from the ARCTOS information system (http://arctos.database.museum/ ). All of the data was associated with the Museum of Vertebrate Zoology previous studies. A check for 0 and NA values, for duplicates was made and finally the final localities were mapped to check for obvious georeferencing errors. For each specimen climatic data from the WORDCLIM database (Hijmans et al., 2005) and from a data-set with monthly temperature data, with 30s arc spatial resolution were extracted. The extractions were made thanks to ArcGIS v.10 (ESRI, Redland, CA). The first set of variables which have been used for the first method are the monthly minima and maxima calculated calculated from records from the 1950-2000 period. The second set of data used for the multivariate analysis are the 19 BIOCLIM variables. Ambiant temperature are likely to be a good indicator of the body temperatures in plethodontid salamanders (Feder and Lynch,1982; Feder et al., 1982). For the third series of analysis raw data was downloaded from 4 tiles at 30s arc from WORLDCLIM and used R to create a data-frame used in the analysis. For the absence locations between Batrachoseps minor and Batrachoseps incognitus used in the first method, a layer of 21 localities was created with ArcGIS v.10 in an approximate uniform grid both parallel to the coast, to carry the same coast-inland pattern as the ranges, and between the two ranges of the species (from Amphibia Web).

3. First method to calculate climatic overlap of the species temperature regime and associated statistical analyses In order to estimate the width of the temperature regime for each species each month, first means of variables were created for each species and then the minimum temperatures were subtracted from maximum temperatures. Then to calculate an overlap value, the method is the same as in Kozak & Wiens (2007). Differences in sample size can lead to many bias and to check if sample size had an effect on temperature regime width, a linear regression was made but the effect is not significant ( p -value = 0.792). The calculation was although applied on all species pairs in order to see where would be the overlap values of the sister species pairs in the distribution of overlap values for all species pairs. A Wilcoxon test was done in order to answer this question. The minima, maxima and means of sister pairs specifically and of all possible overlap values were found.

8 Then a check whether the distributions of values for each species were following a Gaussian distribution was made. It was not the case so it was decided to do a Kruskal-Wallis test and none of variables would be considered similar between all species by the test (all p-values < 2.2*10 -16 ). Consequently a matrix of p-values of successive Wilcoxon tests to compare the means of all species,was created. A selection of the pairs of species which Wilcoxon test would show a p-value bigger than 0.05 was done, in order to compare those pairs to the sister species pairs defined earlier. The next step was comparing the standard deviation of species with standard deviation of a sample of same size (88 which is the smallest sample size without taking into account B. aridus ) of a mix of 44 values from the same species and 44 values from a random species per variable. Batrachoseps aridus was taken off this analysis because of its low sample size. The mean of the two kind of standard deviation on 500 iterations before applying a Student test to the data, was recorder. Another test was created, in a similar thought, to see if amount of overlap between species could be compared with the overlap of parts of ranges within one species (random subset of the entire localities list and also geographically comprehensive subsets). About the comprehensive subsets, they include a sample of the most peripheric unique localities (e.g. 40 northernmost vs 40 southernmost same for East/West). The last part of this series of tests was to check if two species could be climatically closer to the absence locations between their ranges than to themselves as suggested in Kozak & Wiens (2007). In order to do this, 5 localities were sampled in the absence locations and the same method was used as for two species. Thus it was possible to count how many times out of 500 iterations one or both of the species would be closer to the absence locations than to themselves for any random month. 100 repeats were made to be able to calculate the average number.

4. Second method: addition of precipitation variables and associated multivariate analyses This method used Principal Component Analysis to illustrate and quantify the overlap of the climatic niches of sister species in a multidimensional environmental space (Broennimann et al., 2007; Gallagher et al., 2010). The first steps were to reduce the 19 variables to the first two axes of the PCA and then to conduct a between-class analysis as done in Gallagher et al, (2010). This was done mainly to identify which combination of variables were behind the difference in the environmental niches of all the species. After this PCAs were done to be able to illustrate and to quantify the environmental niche overlap between the sister species and also the thermally close species pairs which were defined thanks to the first method in order to see if the addition of the precipitation variables and a bigger diversity in temperature variables led to different results. Then a between class analysis was conducted. It performs a particular case of a principal component analysis with respect to instrumental variables, in which there is only one instrumental variable, and it is a factor. Species name is an observable variable which is a possible source of variation so it was used as a factor. The BCA tested the magnitude and significance of the occurrence clouds, yielding a between-class inertia ratio which was further tested using 500 Monte Carlo randomizations. In order to have an efficient illustration that enables us to use the PCAs' result to make graphic interpretation of the overlap between the niches, a convex hull algorithm was implemented. A convex hull is the minimal convex set containing all the points in an Euclidian plane. We should consider the convex hulls as illustration of environmental spaces of the considered species. The minimum convex polygons that encompass all the occurrence points for each species were used to classify species into 4 groups adapted from the method in Gallagher et al., (2010) - complete dislocation, small overlap, big overlap, near-complete overlap/subset - then the number of these groups were recorded for each category of species pair found in Table 1. Then a mean of BCA inertia ratios was also done for each category and overall the 190 possible pairs.

9 5. Third method: adaptation of Broennimann et al, 2012 to the comparison of the niches of two species Broennimann et al, defined a framework to compare environmental niches. They use three steps: calculation of the density of occurrences and of environmental factors along the axes of a multivariate analysis, then a measurement of niche overlap (Schoener, 1970) and finally statistical tests of niche similarity and equivalency as described in Warren et al (2008). The importance of their framework resides in the fact that they manage to avoid the influence of the frequency of different climatic conditions that occur across a region on the species occurrences. Otherwise the estimation of niche overlap would be underestimated. Indeed the differences measured through the calculation of niche overlaps could be due to differences in environmental characteristics of the study area rather than real differences between species (Broennimann et al, 2012). Using the framework described also allows independence of resolution and sampling effort in the transformation of geographical occurences into a multivariate environmental space. I adapted their methods for the comparison of the niches of two related species instead of the native and exotic niche of an invasive species. As shown in their paper a PCA calibrated on the entire environmental space of the two study areas (PCA-env) and not only on the occurences(PCA-occ), is the most accurate ordination technique to detect the quantity of niche overlap. Moreover it does not carry significant biases. The B. luciae - B. gavilanensis pair was used with all methods to check if the test results and the overlap values would significantly change. The similarity test is designed to compare the quantity of overlap between the niches of the two species and the quantity of overlap between the “observed” niche of one species (that is why two similarity tests are run for each considered pair of species), and random niches selected by changing the density of occurrences of the second species among available conditions throughout the study area. This test gives insight about the possibility of the two niches being similar by chance. If the “observed” overlap between the two niches is in the higher 5% of the simulated overlaps, the two niches are more similar to each other than would expected by chance. The principle behind the niche equivalency is different : it is rather self- explanatory in that this test is designed to compare the niches of two species to check if one can consider them equivalent, if occurrences could be associated with any of the two species without significant impact. If the “observed” overlap is in outside of 95% of the simulated overlap values, then equivalency is accepted. The B. incognitus – B. minor pair did not have a sufficient number of localities to be used in this framework.

RESULTS

1. Thermal regime analysis on 12 months Thanks to the box plot (Fig.3) one can have an idea of the thermal regime of the species used in this analysis. The hypothesis laid down predicted that sister species should have a closer thermal regime than random species pairs but as you can see on Fig.4 it is not obvious. Indeed the Wilcoxon test cannot confirm the hypothesis very signifanctly ( W = 1078, p-value = 0.04552). Sister species tend to have higher overlap values than random species. With all possible species pairs the mean of overlap values is 8.70, the minimum is 2.84 ( B. major - B. wrighti ) and the maximum is 11.67 (B. minor - B. gavilanensis ). The mean of overlap values for sister species pairs is 10.02, the minimum is 7.46 ( B. relictus - B. kawia ) and the maximum is 11.43 ( B. minor - B. incognitus ).The next step was to compare the different species thermal regimes and it ended up with 13 pairs of species that were considered as thermally close species (Table 1).

10 The standard variation rises clearly when comparing a sample of values of one species with a sample from varied species even when most of the sample is from the same species (p-value< 2.2e-16). When random subsets of a same species are compared, overlap values are generally high (more than 10.5) even in species with species with big ranges (e.g. Batrachoseps attenuatus ). Due to a lack of localities the North-South and East-West subset are biased and give overestimated overlap values. However in one case ( Batrachoseps major ) the North and South subsets seem have a significantly lower overlap value (9.29) Also compared, were the overlaps of parts of ranges within one species. Batrachoseps minor and Batrachoseps incognitus were used . On average these two species are not thermally closer to the absence locations between them, than to each other. Yet they are in around 10% of the iterations (47.76 out of 500). The sum of overlap for Batrachoseps incognitus – Batrachoseps minor, Batrachoseps minor – absence locations and Batrachoseps incognitus – absence locations are respectively: 11.42971, 10.7154 and 10.73016.

2. Convex hulls and BCA On Fig.5 we can distinguish two zones where there are several overlaps or at least a concentration of centroids of the diverse convex hulls. The position of the name is an indicator of the position of the centroids. In the first group we can see B. incognitus, B. minor, B. luciae, B. gavilanensis, B. pacificus, B. nigriventris, B. major, B. attenuatus. Several species seems to be a link between the zones even if they seem closer to the first group: B. attenuatus, B.major, B. nigriventris. The second group is composed of: B. diabolicus, B. kawia, B. stebbinsi, B. regius, B. relictus, B. gabrieli, B. simatus, B. aridus, B. gregarius. Examining this figure , we can also note the clear disjunction of the environmental spaces of B. campi, B. wrighti and B. robustus , the latter being closer to a dense overlap zone. Also note that two first there is a steep decrease in the variance explained after the two first principal components. We can put the variables in three groups of correlated variables: variables of precipitation, variables of temperature of warm or dry months and variables of temperature of cold or wet months. The annual mean temperature (variable 1) is not comprehensively related with any group, and is between the two groups of temperature variables and is negatively correlated to the precipitation variables group. Examination of the PCAs' results (Table 2 and 3; Fig.5 and 5) and their illustration assessed through convex hulls encompassing occurrence clouds in the PCA bi-plots and, as in Fig.6, shows that all patterns are described, but that the most common among all possible pairs is the complete dislocation of their environmental spaces. As far as categories of special species pairs are concerned, geographically close categories of species pairs are the one where the most situations of subsets or near-complete overlap are found (Table 2), as expected. Between-class inertia ratios range from to 0.005 ( B. gregarius – B.kawia ) and 0.839 ( B. diabolicus -B. wrighti ). The mean of all values is 0.350 and the standard deviation is 0.219 On Fig. 4 the convex hulls show that the biggest distances between centroids are B. wrighti with B. pacificus and B. aridus . The values shown by the BCA inertia ratio values were always confirmed by the Monte Carlo randomization in which all species pairs were shown to occupy significantly different climatic space (threshold: 0.05). The first three plots of Fig.6 are derived from sister species pairs.

3. Broennimann et al. framework Only two sister species pairs had D values above 0.5 - 50% of the maximum overlap - as

11 seen in Table 3. Although the B. luciae – B. gavilanensis pair (Fig.7) is also one of the two species pairs with an accepted niche equivalency hypothesis, the other one ( B. relictus – B. kawia ) has a D value of 0.49. Two sister species pairs could not be considered to have similar niches: B. campi – B. wrighti and B. simatus – B. nigriventris . Two ordination techniques did not give the same conclusions as the PCA-env (Table 3). The ENFA (Ecological Niche Factor Analysis; Hirzer et al, 2002) did not lead to B. luciae – B. gavilanensis having equivalent niches, but the similarity test conclusion stays the same. The between-group analysis (Dolédec & Chessel, 1987) did not lead to the same results as the PCA-env at all, but it is because of the a priori groups used in this adaptation of the framework are equivalent, thus this analysis is not useful because it cannot detect niche differences in this situation.

DISCUSSION

Niche conservatism, resulting from a non-adaptive speciation, was expected in Batrachoseps , especially within sugenera. When trying to group species pairs from monthly data over a year, only 3 pairs are among the sister species category too, over 13 pairs created by this methodology (Table 1). More similarity between the sister and the thermally close species category was expected. Sister species tend to have higher overlap values on average but it is not a strong trend. The mean of overlap values for sister species is 1 unit below the mean of overlap values of temperate sister species described in Kozak and Wiens, 2007. Under our hypothesis, allopatric sister species (which could have merged since speciation, see Jockusch and Wake, 2002) should predict that those species have greater temperature overlap with each other than they do with absence locations between them. While this is the case for B. minor and B. incognitus, the difference of overlap is not as substantial as in other studies on North American salamanders, (Kozak & Wiens, 2006; Kozak and Wiens, 2007) probably because they focus on allopatric speciation between species that are found at higher altitude. Indeed the lowland part between them is intrinsically different, which explains their higher differences in overlap values. It is very interesting that the two subsets (North-South) of B. major which were considered as different species during the calculations of the sum of overlap are found to have a relatively low overlap as it is less than the mean of overlap value for sister species. Non-monophyly of mtDNA in B. major has been found, with two lineages (northern and southern) that are more closely related to other species in the genus than to each other, but this division was not apparent in nuclear DNA (Martínez-Solano et al., 2012). Indeed a mitochondrial DNA tree show that the northern clade of B. major is more closely related to B. minor and that the southern clade is more closely related to B. pacificus and B. aridus . Moreover there is a lack of evidence concerning gene flow between the two clades. mtDNA suggests there is evidence to support splitting B. major into two species and the differences in their thermal regime shown here add some power to this but it is not to be forgotten that there is a severe lack of morphological variation between the two clades and that there is a monophyly according to allozyme data. The fact that B. minor and B. incognitus have higher overlap values between them than with absence locations is also interesting and gives strength to the non-adaptive hypothesis of the formation of species. Indeed the non-adaptive hypothesis of formation of species in Batrachoseps involves allopatric fragmentation of species ancestral range, may not involve as much climatic change in the geographic space between populations as in other species of North American salamanders (i.e. some Plethodon species, see Kozak and Wiens, 2006) notably due to a behavioral

12 trait which will be described later. So this would add information about the lower nonetheless close value of B. minor and B. incognitus with the associated absence locations compared to the overlap value of the two species.

The two zones distinguished on Fig.5 and described in the results are in fact corresponding to the two major zones of occurrences and diversity of Batrachoseps species. The first zone corresponds to the coastal and insular populations of Batrachoseps . It is remarkable that we have an important overlap of the environmental spaces and yet along the coast, species clearly replace each other. Some geographic characteristics are reflected in Fig.5 as the inclusion of the ranges of B. minor and B. incognitus in B. nigriventris's range, which is also true geographically. However much of the overlap seen thanks to the convex hulls is not found geographically as seen on Fig.7. This is an important element underlining the role of interspecific competition in explaining Batrachoseps distribution. Sympatry exists but is limited probably due this mechanism. The only example of sympatry within a Batrachoseps phylogenetic is B. stebbinsi and B.nigriventris . This sympatry can be understood by the two species being a little bit different with B.nigriventris being smaller, slenderer and more generalist habitat-wise (Wake, 2006). Yet this morphological and egological difference is an exception in the genus. The second zone corresponds to the inland populations – specifically in the Sierra Nevada – of the Batrachoseps subgenus. Indeed the Plethopsis subgenus, as seen on Fig.5, has disjoint convex hulls for species within itself, and only B. robustus is close to one of the two zones described above. The disposition of the Plethopsis species' environmental spaces, illustrates through convex hulls, is in agreement with our hypothesis. The subgenera Plethopsis and Batrachoseps constitute the first split in the phylogeny of the genus (see Fig.1), and the three species of Plethopsis have had lots of time to differentiate from each other and from the Batrachoseps subgenus. So even if the niche has not been that conserved, species with a longer divergence time seem to have differentiated more. The Plethopsis species are closer geographically, environmentally and phylogenetically too, to the Sierra Nevada Batrachoseps phylogenetic group. The species that were described as being part of or at least linking the two zones of dense overlap, are in fact species with wide range. B.attenuatus actually has a part of its geographic range, disjointed and a part of its range is in inland central California(see Fig.7). The addition of precipitation variables was important, e.g. to have a more complete view of B. wrighti' s niche which is actually very different, thing that was not obvious with the first method. This species's environmental space is different notably because of higher precipitation in its range, it is the northernmost species. Yet its environmental space is closer to its closest relatives ( B. robustus and B. campi ) than to B. attenuatus which is by far the closer geographically, which also is agreeing with our hypothesis. BCA inertia ratio value can be interpreted in an easy way, the value multiplied by 100 give the percentage of the total variance explained by the unique discriminating variable used, and this variable was the belonging to one species. So for example, in the second graph of Fig.6, 43.9% of the total variance among the points of the environmental space, can be explained by their belonging to the two species, B. campi and B. relictus, showing a complete and large gap between their convex hulls . The higher the % is the less overlap there is between the environmental space. One may observed as in the first graph of Fig.6, a rather low BCA inertia ratio value but still a complete disjunction of the convex hulls. This is why graphic interpration through the use of convex hulls should always be paired with a BCA analysis. Indeed a low inertia value does not always mean an overlap of the environmental spaces of the two species. Moreover the two following graphs of Fig.6 show us how a similar BCA inertia ratio value (around 0.076) can lead to two different situations when the environmental spaces are illustrated. In this case the similar value describes a situation of low overlap ( B. gregarius and B. stebbinsi) as well as a situation a nearly complete overlap (B. attenuatus and B. gavilanensis ). Amazingly, the species pairs created through the first method

13 constitute the category with the higher BCA inertia ratio value (Table 2). This discrepancy with the results of the first method can be explained by the differences in the variable used, notably the addition of precipitation variables but also by the methodological tools used. Nevertheless it seems obvious looking at the correlation circle of Fig.5, that temperature variables alone cannot efficiently distinguish some of the species environmentally. In Table 2, you can also notice that sister species is the category with the second highest mean BCA inertia ratio value, whereas sister species should, according to our hypothesis, show a high overlap. It is not the case with 5 complete dislocations out of their environmental space out of 8 pairs and none with complete overlap. Yet the geographical influence on this results is hard to describe. By comparing the next two category in Table 2, our hypothesis, lato sensu , would expect that the closer (phylogenetically) the species are, the higher overlap they would show. It is not obvious with the graphic interpretation, but when the BCA inertia ratio values are taken into account, the species which are geographically close and within the same phylogenetic group have on average a value 50% inferior to the value of species with adjacent ranges but in different phylogenetic groups. Alhough sister species pairs do not show as much niche conservatism as expected, when comparing geographically close species pairs, those which are close phylogenetically tend to have had less niche divergence. It is not to be forgotten that sister species pairs do have a mean value (0.279) significantly lower than the mean on all possible species pairs (0.350).

However it is hard to get a test of our hypothesis which is clear of biases. Thanks to the use of kernel smoothers and a correction of differences in environmental availability (Broennimann et al, 2012), the biases related to geographical dimension and relative abundance of different environments, are avoided. The second method was not corrected for environmental availability so it is necessary to further the analysis by using the framework described in this paper.

The third method leads to comparable results as the second method for several species pairs (Table 3). However the B. relictus – B. kawia pair which showed a small overlap graphically, yet with a rather low BCA inertia ratio value, has two not only similar but also equivalent niches. The B. simatus – B. nigriventris pair which had a relatively small BCA inertia ratio value as well as two disjunct but very close environmental spaces in the second method's results, is not considered to have similar niches by the third method results.You can also see in Table 3, that two species pairs, i.e. B. pacificus – B. major and B. robustus – B. wrighti , were put in the complete discordance category with notably a high BCA inertia ratio value for the latter pair. However, the third method with its corrective nature, reveals that those pairs have species niches that are more similar to each other than expected by chance (within each pair). The fact that B. luciae and B. gavilanensis (see Fig.7) have their niches considered equivalent and also the highest D value is not surprising because several facts convey clues about the proximity of the two species; there could have been a gene flow (nDNA) through a secondary contact. Indeed in addition to the two species being sisters, the genetic distances between populations close to the border between the ranges are lower on average than the ones between populations farther from this zone Moreover, according to allozyme data several populations from the parapatry region are found in the other species cluster along the first dimension of a multidimensional scaling space, based on the Nei genetic distance (Jockusch et al, 2001). The D index (Schoener, 1970) does not seem to be a reliable predictor of the conclusions of the two types of tests. Indeed as seen in Table 3, B. pacificus – B. major which has a lower D value than B. campi – B. wrighti , has a p-value < 0.05 for both similarity tests whereas the latter pair has not. Again, another reason why D should not be considered alone is the fact that B. gregarius – B. stebbinsi which has a higher D value than the B. relictus – B. kawia pair does not end up having

14 equivalent niches after the test, as the latter pair. For the same reason the BCA value of the second method does not seem to be a good predictor also for the tests conclusions. Neither does the graphic interpretation semi-quantitative categories. The technical reason why the between-group analysis did not led to similar results as the PCA-env ones, has been given in the result part. However why the ENFA did not lead to an acceptation of the equivalency of the niches of B. luciae – B. gavilanensis is less obvious. It is known that ENFA potentially leads to high errors (Broennimann et al , 2012) but it is unlikely to be biased and it should, contrary to what it gave here, give a higher similarity to the niches of the two considered species because some variance is lost in the process of making the two axes. The abnormal results obtained here could be explained by the a priori hypotheses and the specific conceptual characteristics of this analysis (Hirzel et al, 2002) which may not be adequate for the type and structure of data used in this paper.

Observing this clear structure in the geography of the genus without being able to give a clear confirmation or rejection of our hypothesis does not mean that a non-adpative radiation did not occur. Even if the analysis did not send a powerful message, it is still possible to make hypotheses about why they did not whereas we strongly expected that they did. Let us briefly review the conceptual characteristics behind the word “niche” which could give insight about our analysis and results. The term and concept was introduced into science by Joseph Grinnell (1917) in a paper about the ecology of a New World passerine, although the term had been used before (Grinnell, 1910). The niche as he described it, was the sum of the habitat requirements of a species to survive and reproduce. Elton (1927) also described a fundamentally similar concept of niche, yet more centered on a role in a biocenose, than an habitat. Hutchinson (1957) described the niche as a multidimensional hyperspace in which a species could maintain or expand. It is a more formal and also more species-centered conceptualization of a niche. In his paper Hutchinson also develops two sub-concepts the fundamental niche and the realized niche, the former being an expression of the abiotic parameters required by a species and the latter being a projection of this fundamental niche with the possibility of negative interactions with competitors for example. Finally another important sub-concept was the potential niche described by Jackson and Overpeck (2000), as being the intersection between the functional niche and the available, existing combinations of requirements, in the considered spatio-temporal frame. Therefore the fundamental niche described by Hutchinson is always bigger than the potential niche which is in turn axiomatically bigger than the realized niche, lato sensu , because we would need to know whether the populations where the individuals were collected, are source population (Pulliam, 2000; Soberon, 2007). This is however very likely given the low dispersal ability and ecology of those salamanders. In order to have an idea of the asymptotic fundamental niche, one should do experiments and/or apply biophysical models (Kearney and Porter, 2009; Porter and Kearney, 2009). A species might for example be unable to expand and use its entire potential climatic niche because of geographical (e.g. dispersal barriers) or historic (e.g. anthropogenic alteration of the habitat). To get back to our results, an important thing to realize is whether the differences of realized niches illustrated by the different methods with more or less biases are due to differences in the potential climatic niches or are simply explained by intragenus interspecific competitive exclusion. This principle often credited to Gause had clearly been established by Grinnell (1917). For example, looking at Fig.6, we could make the hypothesis that the potential niche of B. simatus is disjunct from the potential niche of B. nigriventris or we could imagine that both species would have exactly the same potential climatic niche but by interspecific competitive exclusion they would express only a part of it which is what we see on the graph. We could even go further by hypothesizing that all 20 species (and even the now 22 species) share the exact same fundamental and potential niche, and they all express disjointed parts of this genus or subgenus potential niche.

15 The very similar ecological traits in all Batrachoseps species could also point out to the importance of the phenotypic plasticity and/or genetic factors in defining the fundamental climatic niche. As far as the main features of their ecology, including ecophysiology, are concerned they are all very similar. Batrachoseps spend the vast majority of their lives underground, thus removing themselves from the stresses of daily life such as interactions with other species, as they largely avoid predators and there is no evidence of competition with other species (Wake, pers. comm.), and daily moisture, humidity and temperature variations. Slender salamanders can modify their behavior. As an example B. major , in Baja Californa occurs in an area where rainfall rarely exceeds 100 mm per year whereas B. attenuatus at the northern end of its range is found in areas that receive 3000 mm per year. There is no evidence of ecological divergence among the major clades with respect to ecology. There are substantial differences in ecology between B. major and B. wrighti which are sister taxa for example if we consider their habitat for example. One is in wet forests of the cool mountains of northern Oregon and the other is in the driest parts of California in hot mountains, but climate follows the geography and the species seem to mitigate their environments by their ecological behaviors and they may actually be very similar in ecology if one can discount the possibly irrelevant environmental part of their ecology, which they avoid by their partially fossorial lifestyle.

In order to test whether sister species, first, and then all species share the same fundamental niche, manipulative experiments of species' climatic tolerances could be done. For example a translocation experiment between B. luciae and B. gavilanensis would give us information about whether the equivalency of their climatic niche shown in this paper is enough to assure the same survival and reproduction in the translocated populations. Similar kind of experiments on sister species pair which are very far from each other in the environmental space defined in this paper and the geography (e.g. B. robustus and B. wrighti ) would be even more powerful to test our hypothesis. Experiments in semi-controlled environments (such as the “Metatron”, located in Caumont, France), where individuals can move from units to units with different environments, would be very interesting to see how much different Batrachoseps species would disperse and if they would rather disperse to find a preferred environment or just stay and counter any unfavorable conditions by their submergent behavior. If translocations experiments would be successful in showing that most Batrachoseps species share a similar climatic fundamental close, then translocation could be a very interesting conservation option for species such as B. campi and B. aridus which are endangered (AmphibiaWeb) as well as for some other Batrachoseps species which are vulnerable. Understanding the processes which influenced the diversification of Batrachoseps species could also help in the comprehension oft he important radiation undergone by its sister group, Bolitoglossa , which is the most speciose genus of salamanders.

Overall, the results discussed in this paper suggest considerable environmental niche divergence yet no clear confirmation whether it is more a fundamental or realized niche difference, therefore no confirmation of all the elements that suggest a non-adaptive radiation. If the species do have adapted to different environments, then it seems more likely that the ecological divergence that might have taken place was subsequent to a non-adaptive radiation. If the fundamental niche within Batrachoseps can be shown to be the same, then modeling the dispersal of mtDNA and nDNA would require a lot less parameters and its integration with the complex geological history of California could well explain the current phylogeography of the genus by itself.

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19 assumptions. Proc Natl Acad Sci USA 106:19644–19650. Stebbins, R.C., Cohen, N.W., 1995. A Natural History of Amphibians. Princeton University Press, Princeton, NJ. Vences, M., Wake, D.B. 2007. Speciation, species boundaries and phylogeography of amphibians. Amphibian biology, 7,1997, 2613–2671. Wake, D.B. 1966. Comparative osteology and evolution of the lungless salamanders, family Plethodontidae. Memoirs of the Southern California Academy of Science 4, 1–111. Wake, D.B., 1989. Phylogenetic implications of ontogenetic data, in: David, B., Dommergues, J-L., Chaline, J., Laurin, B., (Eds.), Ontogenèse et Évolution. Geobios, Mémoire Spécial, 12, pp. 369–378. Wake, D.B., 1996. A new species of Batrachoseps (Amphibia: Plethodontidae) from the San Gabriel Mountains, southern California. Contrib. Sci., Nat. Hist. Mus. Los Angeles Co. 463:1-12. Wake, D.B., 2006. Problems with species: patterns and processes of species formation in salamanders. Annals Missouri Botanical Garden 93, 8-23. Wake, D.B., 2009. What Salamanders Have Taught Us About Evolution. Annual Review of Ecology, Evolution, and Systematics, 40,1, 333–352. Wake, D.B., Hadly, E.A., Ackerly, D.D., 2009. Biogeography, changing climates, and niche evolution: Biogeography, changing climates, and niche evolution. PNAS 106, 19631–6. Wake, D.B., Jockusch, L.E., 2000. Detecting species borders using diverse data sets. Examples from plethodontid salamanders in California, pp. 95-119. ,in: Bruce, R.C., Jaeger, R.G., Houck, L.D. (Eds.). The Biology of Plethodontid Salamanders. Kluwer Academic/Plenum Publishers, New York. Wake, D.B., Yanev, K.P., Hansen, R.W., 2002. A new species of slender salamander, genus Batrachoseps, from the southern Sierra Nevada of California. Copeia 2002:1016-1028. Warren, D.L., Glor, R.E., Turelli, M., 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868–2883. Yanev, K.P., 1980. Biogeography and distribution of three parapatric salamander species in coastal and borderland California., in: Power, D.M. (Eds), The California Islands: Proceedings of a Multidisciplinary Symposium , Santa Barbara Museum of Natural History, Santa Barbara, California, pp. 531–550.

20 LIST OF FIGURES

Fig.1: Phylogenetic tree of the genus Batrachoseps used in this paper. It is a simplified synthesis of the literature, before the last taxonomic change. Fig.2: Distribution of the species of Batrachoseps in California. Fig.3: Box plot on thermal values of a year for all species. The width of the boxes represent the sampling size. Fig.4:Distribution of overlap values of sister species (in red), and all species pairs (in blue). Fig. 4: Convex hulls on PCA's results illustrating the environmental space of all species. Fig.6: Comparisons of the climatic niches of four species pairs, based on a PCA conducted on 19 bioclimatic variables. Fig.7:Niche of B atrachoseps luciae and Batrachoseps gavilanensis in in climatic space – example of a principal component analysis (PCA-env). The two panels at the top represent the niche of the species along the two first axes of the PCA. Grey shading shows the density of the occurrences of the species by cell. The solid and dashed contour lines illustrate, respectively, 100% and 50% of the available (background) environment. The contribution of the climatic variables on the two axes of the PCA and the percentage of inertia explained by the two axes is also shown. Histograms show the observed niche overlap D between the two ranges (bars with a diamond) and simulated niche overlaps (grey bars) on which tests of niche equivalency , niche similarities are calculated from 100 iterations. The significance of the tests is shown (ns, non-significant; ***P < 0.001)

21 Table 1 : List of species pairs defined and used in the article

Climatically close species pairs Sister species pairs defined by the first method B.pacificus - B.major B.stebbinsi - B.attenuatus B.luciae - B.gavilanensis B.stebbinsi - B.diabolicus B.stebbinsi - B.gregarius B.stebbinsi - B.gregarius B.relictus - B.kawia B.stebbinsi - B.minor B.campi - B.wrighti B.diabolicus - B.gregarius B.wrighti - B.robustus B.diabolicus - B.nigriventris B.simatus - B.nigriventris B.aridus - B.major B.minor - B.incognitus B.aridus - B.simatus B.major - aridus B.campi - B.relictus B.minor - B.gavilanensis B.regius - B.incognitus B.pacificus - B.kawia B.wrighti - B.robustus Geographically and phylogenetically Geographically close and phylognetically close (within group) species distant (between group) species pairs without a large sympatric zone pairs without a large sympatric zone B.minor - B.gavilanensis B.diabolicus - B.gregarius B.incognitus - B.gavilanensis B.relictus - B.gregarius B.incognitus - B.luciae B.kawia - B.gregarius B.gavilanensis - B.luciae B.regius - B.gregarius B.gabrieli - B.major B.relictus - B.robustus B.aridus - B.major B.simatus - B.robustus B.regius - B.kawia B.relictus - B.kawia B.stebbinsi - B.simatus B.minor - B.incognitus B.gregarius - B.simatus Species with sympatry between Species with sympatry within phylogenetic phylogenetic groups group B.nigriventris - B.minor B.nigriventris - B.stebbinsi B.nigriventris - B.incognitus B.attenuatus - B.gavilanensis B.nigriventris - B.gavilanensis B.nigriventris - B.pacificus B.nigriventris - B.major B.attenuatus – B.diabolicus

22 Table 2:. Pattern of climatic niche overlap in each category defined in Table 1, the mean and standard variation of between-class analysis inertia ratio were also calculated.

Geographically Geographically Sympatry Thermallly Sympatry within Climate niche Sister and close but not between close one phylogenetic overlap pattern species phylogenetically phylogenetically phylogenetic (method 1) group close close groups Complete 5 7 1 1 0 0 dislocations Small overlap 2 1 1 1 0 0 Big overlap 1 0 5 2 1 1 Near-complete overlap or subset 0 3 3 2 6 0 situation Mean of BCA 0.274 0.3 0.103 0.207 0.07 0.103 inertia ratios Standard deviation of BCA 0.177 0.238 0.079 0.235 0.048 NA inertia ratios

23 Table 3: BCA inertia value, semi-quantitative category, Schoener's D value, and p-values from the equivalency and similarity tests for each considered sister species.

Semi-quantitative Equivalency test p- Similarity test p- Similarity test p- Pair BCA inertia ratio value Schoener's D value category value value (1->2) value (2->1) B. incognitus – 0.444 Complete dislocation NA NA NA NA B.minor B. pacificus – B. 0.217 Complete dislocation 0.073 0.0198 0.0198 0.0198 major B. luciae – B. 0.126 Big overlap 0.781 0.5900 0.0198 0.0198 gavilanensis B. stebbinsi – B. 0.075 Small overlap 0.512 0.0198 0.0198 0.0396 gregarius B. kawia – B. 0.229 Small overlap 0.49 0.1188 0.0198 0.0198 relictus B. campi – B. 0.539 Complete dislocation 0.092 0.0198 0.4158 0.4158 wrighti B. robustus – B. 0.445 Complete dislocation 0.485 0.0396 0.0198 0.0198 wrighti B. simatus – B. 0.116 Complete dislocation 0.06 0.0198 0.6139 0.49505 nigriventris

24 Fig.1: Phylogenetic tree of the genus Batrachoseps used in this paper. It is a simplified synthesis of the literature, before the last taxonomic change.

25 Fig.2: Distribution of the species of Batrachoseps in California, taken from (Wake, 2006). The photographs are of specimens of the four species comprising the central coastal cluster of the pacificus clade (photos by M. Garcia-Paris & D. B. Wake). Authorities for species are provided in the text. Batrachoseps major is found southern, in Baja California (Mexico) and Batrachoseps wrigthi is found northern, in Oregon (USA).

26 Fig.3: Boxplot on thermal values of a year for all species. The width of the boxes represent the sampling size.

27 Fig.4:Distribution of overlap values of sister species (in red), and all species pairs (in blue). There are 190 combinations of species pairs (20 choose 2).

28 Temperature variables (cold or wet monthes)

Precipitation variables Temperature variables (hot or dry monthes) Fig. 4: Convex hulls on PCA's results illustrating the environmental space of all species. The scree plot give us information of the variance explained by the two principal components (here PC1: 44.14% and PC2: 32.52%). The correlation circle give us an idea of which variables are correlated with the axes.

29 Fig.6: Comparisons of the climatic niches of four species pairs, based on a PCA conducted on 19 bioclimatic variables. These graphs represent the range of patterns of climate niches found across the 190 possible species pairs.

30 Fig.7: Niche of B atrachoseps luciae and Batrachoseps gavilanensis in in climatic space – example of a principal component analysis (PCA-env). The two panels at the top represent the niche of the species along the two first axes of the PCA. Grey shading shows the density of the occurrences of the species by cell. The solid and dashed contour lines illustrate, respectively, 100% and 50% of the available (background) environment. The contribution of the climatic variables on the two axes of the PCA and the percentage of inertia explained by the two axes is also shown. Histograms show the observed niche overlap D between the two ranges (bars with a diamond) and simulated niche overlaps (gray bars) on which tests of niche equivalency , niche similarities are calculated from 100 iterations. The significance of the tests is shown (ns, non-significant; ***P < 0.001).

31 APPENDIX Table A1: Overlap values for all species pairs aridus attenuatus campi diabolicus gabrieli gavilanensis aridus attenuatus 8,6728373 campi 6,56755622 7,82222764 diabolicus 10,3341663 9,68586492 8,15256467 gabrieli 7,99517019 9,28515939 10,3711265 9,69076182 gavilanensis 9,3824302 10,4583152 8,59209904 10,1954232 9,93392454 gregarius 10,1171009 9,63032558 8,26347292 11,5053042 9,79343224 10,1203967 incognitus 8,74178306 10,6109 8,38219767 9,52191858 9,58115534 11,3149939 kawia 10,6522536 9,4632236 7,67494701 11,5153117 9,2031031 9,97302344 luciae 8,18235937 10,7032063 7,45770559 8,87027911 8,80000683 10,5906612 major 9,91696299 9,07910407 6,27053094 9,22383816 7,80415068 9,41085978 minor 9,33265044 10,4581733 8,35971466 9,89108391 9,66967155 11,6670606 nigriventris 9,53193043 10,952884 7,85022892 9,86624898 9,27638277 11,0624207 pacificus 8,12383546 9,81290365 6,12396339 8,19717309 7,56377257 9,35857627 regius 8,5115263 9,18277988 9,8245679 10,2076481 11,2738453 9,83708961 relictus 6,32750481 7,98890043 10,847912 8,00962575 10,3628637 8,80585807 robustus 4,1823208 5,72069889 9,78633182 5,82465601 8,15444005 6,80397354 simatus 11,3622351 8,88140423 6,90761034 10,8518022 8,39377051 9,5716446 stebbinsi 9,8070869 9,55048374 8,4179994 11,1355215 10,004387 10,0727763 wrighti 3,92152142 5,12612686 9,38848962 5,54858135 8,06257733 6,45789814 gregarius incognitus kawia luciae major minor gregarius incognitus 9,42791087 kawia 11,4345044 9,23600192 luciae 8,74238585 10,8923039 8,54285481 major 9,25068122 8,67169585 9,43383084 8,41455019 minor 9,80806275 11,4297092 9,66535171 10,6715581 9,43211326 nigriventris 9,76954373 10,6471712 9,70189064 10,5760416 9,97679622 11,2488783 pacificus 8,02572656 9,56257458 7,96756751 10,1548733 9,57209559 9,58264855 regius 10,3410704 9,3000464 9,74418697 8,52658961 8,09773409 9,50571009 relictus 8,08643271 8,8267008 7,45738434 7,90689176 5,83958559 8,66997535 robustus 5,86484568 7,17994819 5,20786497 6,26146351 3,40656442 6,75830775 simatus 10,6086119 8,79299948 11,1631456 8,13381919 9,608421 9,35347755 stebbinsi 11,4460666 9,40139526 11,1172168 8,61011844 9,13199362 9,72773693 wrighti 5,66087006 6,89969175 4,97085126 5,75748782 2,8436427 6,40481304 nigriventris pacificus regius relictus robustus simatus stebbinsi nigriventris pacificus 10,106815 regius 9,24183164 7,4563116 relictus 7,90051217 6,27738158 9,71038771 robustus 5,61570625 4,24205312 7,44707939 9,71303877 simatus 9,45639341 7,79009189 8,92857482 6,65176376 4,42207969 stebbinsi 9,66813844 7,82381087 10,5718678 8,19274062 5,85745437 10,2677581 wrighti 5,16162828 3,60199431 7,32857377 9,66082278 10,8488822 4,189796 5,6272077632 Figure A1: Historical biogeography of the Batrachoseps pacificus clade in southern and central California, taken from (Wake, 2006). This scenario is based on geological reconstructions by Hall (2002) and the phylogenetic hypotheses of Jockusch et al. (2002) and Jockusch and Wake (2002).

33 BIOCLIM

Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in ecological niche modeling (e.g., BIOCLIM, GARP). The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year).

They are coded as follows:

BIO1 = Annual Mean Temperature BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 = Isothermality (BIO2/BIO7) (* 100) BIO4 = Temperature Seasonality (standard deviation *100) BIO5 = Max Temperature of Warmest Month BIO6 = Min Temperature of Coldest Month BIO7 = Temperature Annual Range (BIO5-BIO6) BIO8 = Mean Temperature of Wettest Quarter BIO9 = Mean Temperature of Driest Quarter BIO10 = Mean Temperature of Warmest Quarter BIO11 = Mean Temperature of Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter

This scheme follows that of ANUCLIM, except that for temperature seasonality the standard deviation was used because a coefficient of variation does not make sense with temperatures between -1 and 1).

34 R SCRIPT

A specific server was built by the author with the contribution and advice of computer technicians (Intel core i5 dual core ® , 12Gb RAM ddr3 kingston ® , 3.6GHz + Intel core i3 dual core ® , 8Gb RAM ddr3 kingston ® , 3.2GHz) . For more details about the computation time and the system requirements especially for the third analysis please contact the author.

A clearer, more didactic and fully interactive version of the code will be available soon. For any urgent need of explanations about the code or help, the author remains entirely at your disposal.

A code creating 3D convex hulls in a motion to further the illustration and the graphic interpretation has been started. Please contact the author for any suggestions, or to get the functional piece of the code and/or pseudocode.

R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

Packages used (without all dependencies): BIOMOD, ade4, adehabitat, sp, gam, MASS, mvtnorm, gbm, dismo, raster, maptools, vioplot, lmtest, Hmisc

SCRIPT FOR THE FIRST METHOD #Objective:Calculate temperature overlap between sister species of Batrachoseps #First, let's read table of monthly min and max temps for all points rm(list= ls()); setwd("C:/Users/User/Desktop/Analyse 1"); batraclim<-read.table(file="ClimateData1.0.txt", sep="\t", header=TRUE);

#Let's load and read our file with species and coordinates: batra_geo<-read.csv(file="Batrachoseps georeferences 4.csv", sep=";",header=TRUE); #Let's make a map of the occurrence localities of Batrachoseps species: library(maptools); data(wrld_simpl); plot(wrld_simpl, xlim=c(-130,-110), ylim=c(20,47), axes=TRUE, col='orange'); #Let's restore the box around the map box(); #Let's plot points

35 points(batra_geo[,2], batra_geo[,3], col='green', pch=20, cex=0.75) #let's plot points again to add a border, for better visibility points(batra_geo[,2], batra_geo[,3], col='dark green', cex=0.75)

#Let's calculate mean values for each month for each species batrameans<-aggregate(batraclim, by=list(batraclim[,1]), FUN='mean');

#Let's make a vector of species names to use for row names batrasp<-c('aridus', 'attenuatus', 'campi', 'diabolicus', 'gabrieli', 'gavilanensis', 'gregarius', 'incognitus', 'kawia', 'luciae', 'major', 'minor', 'nigriventris', 'pacificus', 'regius', 'relictus', 'robustus', 'simatus', 'stebbinsi', 'wrighti');

#Assign species names to rows row.names(batrameans)<-batrasp; cat('Mean values of monthly min and max temps for each species:', '\n'); head(batrameans);

#Let's reorganize the months chronologically batrameans<-batrameans[,c(16,12,11,10,9,8,7,6,5,15,14,13,28,24,23,22,21,20,19,18,17,27,26,25)];

#Calculate temperature range for each month for each species range<-mat.or.vec(20, 12); for (i in 1:12){ #This is if we have the tmax first in "batrameans" range[,i]<-batrameans[,i+12]-batrameans[,i]; } row.names(range)<-row.names(batrameans);

pair1<-c('incognitus', 'minor'); pair2<-c('pacificus', 'major'); pair3<-c('luciae', 'gavilanensis');

36 pair4<-c('gregarius', 'stebbinsi'); pair5<-c('relictus', 'kawia'); pair6<-c('campi', 'wrighti'); pair7<-c('wrighti', 'robustus'); pair8<-c('simatus', 'nigriventris'); pairsSP<-c(pair1, pair2, pair3, pair4, pair5, pair6, pair7, pair8);

#Calculate overlap for all species pairs ##Here I apply a script only for sister species sumoverlapBatraSP<-rep(0,8); for (j in 1:8){ for (i in 1:12){

upper<-min(batrameans[pairsSP[(j*2-1)],i+12], batrameans[pairsSP[j*2],i+12]);

lower<-max(batrameans[pairsSP[(j*2-1)],i], batrameans[pairsSP[j*2],i]);

osubi<-upper-lower;

overlap<-0.5*(osubi/range[pairsSP[(j*2-1)],i]+osubi/range[pairsSP[j*2],i]);

sumoverlapBatraSP[j]<-sumoverlapBatraSP[j]+overlap;

}

} sumoverlapBatraSP;

##Here I want to use all species to make pairs to check the influence of the phylogeny: sumoverlapBatra<-mat.or.vec(20,20); for (i in 1:20){ for (j in 1:20){ for (k in 1:12){

37 upper<-min(batrameans[batrasp[i],k+12], batrameans[batrasp[j],k+12]);

lower<-max(batrameans[batrasp[i],k], batrameans[batrasp[j],k]);

osubi<-upper-lower;

overlap<-0.5*(osubi/range[i,k]+osubi/range[j,k]);

sumoverlapBatra[i,j]<-sumoverlapBatra[i,j]+overlap;

}

}

} row.names(sumoverlapBatra)<-batrasp; colnames(sumoverlapBatra)<-batrasp; head(sumoverlapBatra);

#Here I get rid of the duplicates in the matrice by replacing values by - 1: sumoverlapBatraN<-sumoverlapBatra for (i in 1:20){ for (j in 1:20){ if (i<=j){ sumoverlapBatraN[i,j]<--1;

} } } head(sumoverlapBatraN); mean(sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)]); a<-min(sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)]); b<-max(sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)]); for (i in 1:20){

38 for (j in 1:20){ if (sumoverlapBatraN[i,j]==a){ cat("Species pair with lowest overlap value"); cat("\n"); cat(colnames(sumoverlapBatraN)[i]); cat("\n"); cat(rownames(sumoverlapBatraN)[j]); cat("\n"); } else if (sumoverlapBatraN[i,j]==b){ cat("Species pair with highest overlap value"); cat("\n"); cat(colnames(sumoverlapBatraN)[i]); cat("\n"); cat(rownames(sumoverlapBatraN)[j]); cat("\n"); } } }

################################################################################ ######## #Histogram of overlap values: hist(sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)],border="green", col="light blue",breaks=50, xlab="",ylab="",xlim=c(0,12),axes=FALSE,main=""); hist(sumoverlapBatraSP, col="red",breaks=25, add=TRUE); axis(side=1,at=seq(1,12,by=0.5),pos=0); axis(side=2,at=seq(0,17,by=1),pos=1); mtext(side=2,text="Frequencies",line=0.000001,cex=1, font=4); mtext(side=1,text="Overlap values",line=2.5,cex=1, font=4);

#Range distribution of all the species: TD_aridus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps aridus"),4:27]));

39 TD_attenuatus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps attenuatus"),4:27])); TD_campi<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps campi"),4:27])); TD_diabolicus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps diabolicus"),4:27])); TD_gabrieli<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps gabrieli"),4:27])); TD_gavilanensis<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps gavilanensis"),4:27])); TD_gregarius<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps gregarius"),4:27])); TD_incognitus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps incognitus"),4:27])); TD_kawia<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps kawia"),4:27])); TD_luciae<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps luciae"),4:27])); TD_major<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps major"),4:27])); TD_minor<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps minor"),4:27])); TD_nigriventris<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps nigriventris"),4:27])); TD_pacificus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps pacificus"),4:27])); TD_regius<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps regius"),4:27])); TD_relictus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps relictus"),4:27])); TD_robustus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps robustus"),4:27])); TD_simatus<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps simatus"),4:27])); TD_stebbinsi<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps stebbinsi"),4:27])); TD_wrighti<-c(as.matrix(batraclim[which(batraclim[,1]=="Batrachoseps wrighti"),4:27])); par(mar=c(5,5,5,5)); boxplot(TD_aridus,TD_attenuatus,TD_campi,TD_diabolicus,TD_gabrieli,TD_gavilanensis, TD_gregarius,TD_incognitus,TD_kawia,TD_luciae,TD_major,TD_minor,TD_nigriventris, TD_pacificus,TD_regius,TD_relictus,TD_robustus,TD_simatus,TD_stebbinsi,TD_wrighti, range=0,varwidth=TRUE,notch=TRUE,col="light blue",las=3,ylab="",xlab=""); axis(1, at = seq(20), labels = batrasp, tick = TRUE, lwd = 2, las = 3, cex.axis = 0.8); abline(a=0,b=0,col="blue", lty="dotted") abline(a=26,b=0,col="red", lty="dotted") mtext(side=1,text="Species",line=1,cex=1, font=4,las=2); mtext(side=2,text="Temperature (°C)",line=2.5,cex=1, font=4);

library(vioplot); vioplot(TD_aridus,TD_attenuatus,TD_campi,TD_diabolicus,TD_gabrieli,TD_gavilanensis,

40 TD_gregarius,TD_incognitus,TD_kawia,TD_luciae,TD_major,TD_minor,TD_nigriventris, TD_pacificus,TD_regius,TD_relictus,TD_robustus,TD_simatus,TD_stebbinsi,TD_wrighti); list_batra<-c("Batrachoseps aridus","Batrachoseps attenuatus","Batrachoseps campi", "Batrachoseps diabolicus","Batrachoseps gabrieli","Batrachoseps gavilanensis", "Batrachoseps gregarius","Batrachoseps incognitus","Batrachoseps kawia", "Batrachoseps luciae","Batrachoseps major","Batrachoseps minor", "Batrachoseps nigriventris","Batrachoseps pacificus","Batrachoseps regius", "Batrachoseps relictus","Batrachoseps robustus","Batrachoseps simatus", "Batrachoseps stebbinsi","Batrachoseps wrighti");

#function to ease the use of mixed raw data for each species rawmix<-function(x){ return(c(as.matrix(batraclim[which(batraclim[,1]==list_batra[x]),4:27]))) }

#Check if the differences in sample size can lead to the underestimation of the widths of the temperature regimes for small samples: temperature_width<-mat.or.vec(20,1); for (i in 1:20){ temperature_width[i]<-max(rawmix(i))-min(rawmix(i)); } temperature_width; num_local<-mat.or.vec(20,1); for (i in 1:20){ num_local[i]<-sum(batraclim[,1]==list_batra[i]); } num_local; sample_size_problem<-lm(temperature_width~num_local); plot(temperature_width~num_local, col="blue"); abline(model, col="red");

41 lm.res <- residuals(sample_size_problem); hist(lm.res); shapiro.test(lm.res); library(lmtest); hmctest(sample_size_problem); dwtest(sample_size_problem); summary(sample_size_problem); #No problem !!!

#Kruskal-Wallis: (non prar --> see Shapiro tests ) for (i in 1:24){ npanova<-kruskal.test(batraclim[,i+3]~batraclim[,1]); print(npanova); } var.species<-rep(0,20); for (i in 1:20){ var.species[i]<-var(rawmix(i)); } var.species var.species<-rbind(batrasp,var.species); mean.species<-rep(0,20); for (i in 1:20){ mean.species[i]<-mean(rawmix(i)); } mean.species

#COMPARE THE MEANS AND DISTRIBUTONS OF THE RAW DATA FOR EACH SPECIES PAIR pairA<-c('Batrachoseps minor','Batrachoseps incognitus'); pairB<-c('Batrachoseps pacificus', 'Batrachoseps major'); pairC<-c('Batrachoseps luciae', 'Batrachoseps gavilanensis');

42 pairD<-c('Batrachoseps stebbinsi','Batrachoseps gregarius'); pairE<-c('Batrachoseps relictus', 'Batrachoseps kawia'); pairF<-c('Batrachoseps wrighti','Batrachoseps campi'); pairG<-c('Batrachoseps wrighti','Batrachoseps robustus'); pairH<-c('Batrachoseps simatus', 'Batrachoseps nigriventris');

PAIRS<-c(pairA,pairB,pairC,pairD,pairE,pairF,pairG,pairH);

# for (i in 1:20){ if (length(rawmix(i))>5000){ print(shapiro.test(sample(rawmix(i),5000,replace=F))); }else{ print(shapiro.test(rawmix(i))); } }

#OUCH!! Ok let's do a non parametric tests #Means: mean.test<-mat.or.vec(20,20); for (i in 1:20){ for (j in 1:20){ if (i

43 for (i in 1:20){ for (j in 1:20){ if (mean.test[i,j]>0.05){ print(col[j]); print(row[i]); cat("\n\n\n"); } } } climatic_sister_species<-mat.or.vec(2,14); list_col<-"SUH"; list_row<-"UP?"; for (i in 1:20){ for (j in 1:20){ if (mean.test[i,j]>0.05){ list_col<-c(list_col,col[j]); list_row<-c(list_row,row[i]); } } } climatic_sister_species[1,]<-list_col; climatic_sister_species[2,]<-list_row; climatic_sister_species; genetic_sister_species<-mat.or.vec(2,8); for (i in c(1,3,5,7,9,11,13,15)){ genetic_sister_species[1,(i+1)/2]<-pairsSP[i]; } for (i in c(2,4,6,8,10,12,14,16)){ genetic_sister_species[2,i/2]<-pairsSP[i]; } genetic_sister_species; climatic_sister_species;

44 #Do sister species have a greater overlap than random species pairs? shapiro.test(sumoverlapBatraSP); shapiro.test(sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)]); wilcox.test(sumoverlapBatraSP,sumoverlapBatraN[which(sumoverlapBatraN[,]!=-1)]);

#function to ease the use of month data for each species monthdata.min.or.max<-function(x){ return(c(as.matrix(batraclim[which(batraclim[,1]==list_batra[x]),4]))) }

#Analysis of the standard devation #I took off aridus because the sampling size is too weak. all.sd<-0; all.sdmix<-0; for (k in 1:500){

sd.species<-rep(0,20); for (i in 2:20){ sd.species[i]<-sd(sample(monthdata.min.or.max(i),88)); } sd.species; sd.species<-sd.species[-1];

#Now I want to mix several species to see if the standard deviation is changing

sdmix.species<-rep(0,20); for (i in 2:20){ pick1<-trunc(runif(1,min=2,max=20)); sdmix.species[i]<- sd(c(sample(monthdata.min.or.max(i),44),sample(monthdata.min.or.max(pick1),44))); } sdmix.species; sdmix.species<-sdmix.species[-1];

45 all.sd<-c(all.sd,mean(sd.species)); all.sdmix<-c(all.sdmix,mean(sdmix.species)); } all.sd<-all.sd[-1]; all.sdmix<-all.sdmix[-1]; mean.of.sd.mean<-mean(all.sd); mean.of.sdmix.mean<-mean(all.sdmix); mean.of.sd.mean; mean.of.sdmix.mean; shapiro.test(all.sd); shapiro.test(all.sdmix); t.test(all.sd,all.sdmix); mean(sumoverlapBatraSP); #rm(list= ls()); setwd("C:/Users/User/Desktop/Analyse 1") batraclim<-read.table(file="ClimateData1.0.txt", sep="\t", header=TRUE);

#Let's load and read our file with species and coordinates: batra_geo<-read.csv(file="Batrachoseps georeferences 4.csv", sep=";",header=TRUE); loc.minor<-batra_geo[batra_geo[,1]=="Batrachoseps minor",]; loc.inco<-batra_geo[batra_geo[,1]=="Batrachoseps incognitus",];

#Let's make a map of the occurrence localities of our two Batrachoseps species: library(maptools); data(wrld_simpl); plot(wrld_simpl, xlim=c(-125,-115), ylim=c(30,40), axes=TRUE, col='white');

46 #Let's restore the box around the map box();

#Let's plot points of species and absences points between their range points(loc.minor[,2], loc.minor[,3], col='green', pch=20, cex=0.75); points(loc.inco[,2], loc.inco[,3], col='blue', pch=20, cex=0.75); loc.absences<-read.csv(file="extrabsences.csv", sep=",",header=TRUE); loc.absences<-loc.absences[,c(1:2,15:26,3:14)]; loc.absences[,3:26]<-loc.absences[,3:26]/10; points(loc.absences[,1], loc.absences[,2], col='red', pch=20, cex=0.75);

#Now let us pick a month to compare overlap between species loc and absences: #Function to ease the use of mixed raw data for each species #variable->input column number of the variable you are interested according to batrameans structure #species-> input with "" and only the second term of the species name e.g. aridus #Need to have run Batrachoseps_ClimateComparison before

#Function for comparison by month between species and absences: month_comp_abs<-function(species,month){ upper<-min(batrameans[species,month+12], mean(loc.absences[,month+14]));

lower<-max(batrameans[species,month], mean(loc.absences[,month+2]));

osubi<-upper-lower;

overlap<-0.5*(osubi/range[species,month]+osubi/(max(sample(loc.absences[,month+14],5))- min(sample(loc.absences[,month+2],5))));

return(overlap); }

47 #Function for comparison by month between two species month_comp_species<-function(species1,species2,month){ upper<-min(batrameans[species1,month+12], batrameans[species2,month+12]);

lower<-max(batrameans[species1,month], batrameans[species2,month]);

osubi<-upper-lower;

overlap<-0.5*(osubi/range[species1,month]+osubi/range[species2,month]);

return(overlap); }

month_comp_abs("minor",5); month_comp_abs("incognitus",5); month_comp_species("minor","incognitus",5);

#Sum of overlaps:

#minor sumoverlap_minor_inco<-0; for (i in 1:12){ sumoverlap_minor_inco<-sumoverlap_minor_inco+month_comp_species("minor","incognitus",i); } sumoverlap_minor_inco; sumoverlap_minor_abs<-0; for (i in 1:12){ sumoverlap_minor_abs<-sumoverlap_minor_abs+month_comp_abs("minor",i); }

48 sumoverlap_minor_abs; sumoverlap_inco_abs<-0; for (i in 1:12){ sumoverlap_inco_abs<-sumoverlap_inco_abs+month_comp_abs("incognitus",i); } sumoverlap_inco_abs;

#By sampling a few localities in the absences data, how many times are the species env niche closer to each other than with the absences data countcount<-0; for (k in 1:100){ countsup<-0; for (i in 1:500){ rdmonth<-runif(1,4,10); if (month_comp_abs("minor",rdmonth)>month_comp_species("minor","incognitus",rdmonth)| month_comp_abs("incognitus",rdmonth)>month_comp_species("minor","incognitus",rdmonth)){ countsup<-countsup+1; } } countsup; countcount<-c(countcount,countsup); } countcount<-countcount[-1]; countcount; mean(countcount);

#Now I want to check if the overlap between species can be compared with the overlap of parts of ranges within one species #Let us choose one species for example: B. nigriventris -> we used it in several sister species pair so I think it is good idea: plot.new(); plot(wrld_simpl, xlim=c(-125,-115), ylim=c(30,40), axes=TRUE, col='white');

49 #Let's restore the box around the map box(); loc.nigri<-batra_geo[batra_geo[,1]=="Batrachoseps nigriventris",];

#Let's plot points of species and absences points between their range points(loc.nigri[,2], loc.nigri[,3], col='black', pch=20, cex=0.75);

#RANDOM comp_within_species_random<-function(species_name,number_of_samples){ nigri<-batraclim[batraclim[,1]==species_name,]; nigri<-nigri[,c(15,11,10,9,8,7,6,5,4,14,13,12,27,23,22,21,20,19,18,17,16,26,25,24)];

a<-nigri[sample(1:dim(nigri)[1],number_of_samples),]; b<-nigri[sample(1:dim(nigri)[1],number_of_samples),]; rangeNigri<-function(x){ y<-numeric(12); for (i in 1:12){ y[i]<-mean(x[,i+12])-mean(x[,i]);

} return(y); } sumoverlapNigri<-0; for (k in 1:12){

upper<-min(mean(a[,k+12]),mean(b[,k+12]));

50 lower<-max(mean(a[,k]),mean(b[,k]));

osubi<-upper-lower;

overlap<-0.5*(osubi/rangeNigri(a)[k]+osubi/rangeNigri(b)[k]);

sumoverlapNigri<-sumoverlapNigri+overlap;

} return(sumoverlapNigri); }

#GEOGRAPHICALLY COMPREHENSIVE comp_within_species_comprehensive_NS<-function(species_name,number_of_samples){

nigri<-batraclim[batraclim[,1]==species_name,]; nigri<-nigri[,c(2,3,15,11,10,9,8,7,6,5,4,14,13,12,27,23,22,21,20,19,18,17,16,26,25,24)];

north<-nigri[tail(unique(nigri[,2]),number_of_samples),]; south<-nigri[head(unique(nigri[,2]),number_of_samples),];

north<-north[,-c(1,2)]; south<-south[,-c(1,2)];

rangeNigri<-function(x){ y<-numeric(12); for (i in 1:12){ y[i]<-mean(x[,i+12])-mean(x[,i]);

} return(y); }

51

sumoverlapNigri<-0;

for (k in 1:12){

upper<-min(mean(north[,k+12]),mean(south[,k+12]));

lower<-max(mean(north[,k]),mean(south[,k]));

osubi<-upper-lower;

overlap<-0.5*(osubi/rangeNigri(north)[k]+osubi/rangeNigri(south)[k]);

sumoverlapNigri<-sumoverlapNigri+overlap;

}

return(sumoverlapNigri); } comp_within_species_comprehensive_EW<-function(species_name,number_of_samples){

nigri<-batraclim[batraclim[,1]==species_name,]; nigri<-nigri[,c(2,3,15,11,10,9,8,7,6,5,4,14,13,12,27,23,22,21,20,19,18,17,16,26,25,24)];

west<-nigri[tail(unique(nigri[,1]),number_of_samples),]; east<-nigri[head(unique(nigri[,1]),number_of_samples),];

west<-west[,-c(1,2)]; east<-east[,-c(1,2)];

rangeNigri<-function(x){ y<-numeric(12); for (i in 1:12){

52 y[i]<-mean(x[,i+12])-mean(x[,i]);

} return(y); }

sumoverlapNigri<-0;

for (k in 1:12){

upper<-min(mean(west[,k+12]),mean(east[,k+12]));

lower<-max(mean(west[,k]),mean(east[,k]));

osubi<-upper-lower;

overlap<-0.5*(osubi/rangeNigri(west)[k]+osubi/rangeNigri(east)[k]);

sumoverlapNigri<-sumoverlapNigri+overlap;

}

return(sumoverlapNigri); } comp_within_species_random("Batrachoseps attenuatus" , 10); comp_within_species_comprehensive_NS("Batrachoseps nigriventris" , 10); comp_within_species_comprehensive_EW("Batrachoseps nigriventris" , 10);

all_comp<-mat.or.vec(3,19); colnames(all_comp)<-batrasp[2:20]; rownames(all_comp)<-c("random","north-south","east-west"); all_comp; for (i in 1:19){

53 all_comp[1,i]<-comp_within_species_random(list_batra[i+1],5); all_comp[2,i]<-comp_within_species_comprehensive_NS(list_batra[i+1],5); all_comp[3,i]<-comp_within_species_comprehensive_EW(list_batra[i+1],5); }

SCRIPT FOR THE SECOND METHOD*

#Script to run PCAs, monte carlo simulations and graph climate niches setwd("C:/Users/User/Desktop/Analyse 2"); library(Hmisc); library(ade4);

#Import data batrabio<-read.table(file="Bioclimfull1.txt", sep="\t", header=TRUE); list_batra<-c("Batrachoseps aridus","Batrachoseps attenuatus","Batrachoseps campi", "Batrachoseps diabolicus","Batrachoseps gabrieli","Batrachoseps gavilanensis", "Batrachoseps gregarius","Batrachoseps incognitus","Batrachoseps kawia", "Batrachoseps luciae","Batrachoseps major","Batrachoseps minor", "Batrachoseps nigriventris","Batrachoseps pacificus","Batrachoseps regius", "Batrachoseps relictus","Batrachoseps robustus","Batrachoseps simatus", "Batrachoseps stebbinsi","Batrachoseps wrighti"); batrasp<-c('aridus', 'attenuatus', 'campi', 'diabolicus', 'gabrieli', 'gavilanensis', 'gregarius', 'incognitus', 'kawia', 'luciae', 'major', 'minor', 'nigriventris', 'pacificus', 'regius', 'relictus', 'robustus', 'simatus', 'stebbinsi', 'wrighti'); pair1<-c('Batrachoseps incognitus', 'Batrachoseps minor'); pair2<-c('Batrachoseps pacificus', 'Batrachoseps major'); pair3<-c('Batrachoseps luciae', 'Batrachoseps gavilanensis');

54 pair4<-c('Batrachoseps gregarius', 'Batrachoseps stebbinsi'); pair5<-c('Batrachoseps relictus', 'Batrachoseps kawia'); pair6<-c('Batrachoseps campi', 'Batrachoseps wrighti'); pair7<-c('Batrachoseps wrighti', 'Batrachosepsist robustus'); pair8<-c('Batrachoseps simatus', 'Batrachoseps nigriventris'); pairsSP<-c(pair1, pair2, pair3, pair4, pair5, pair6, pair7, pair8);

################################### ###################################

scatterutil.chull.Julian.style<-function (x, y, fac, optchull = c(0.25, 0.5, 0.75, 1), col = rep(1, length(levels(fac)))) { if (!is.factor(fac)) return(invisible()) if (length(x) != length(fac)) return(invisible()) if (length(y) != length(fac)) return(invisible()) for (i in 1:nlevels(fac)) { x1 <- x[fac == levels(fac)[i]] y1 <- y[fac == levels(fac)[i]] long <- length(x1) longinit <- long cref <- 1 repeat { if (long < 3) break if (cref == 0) break num <- chull(x1, y1) x2 <- x1[num]

55 y2 <- y1[num] taux <- long/longinit if ((taux <= cref) & (cref == 1)) { cref <- 0.75 if (any(optchull == 1)) polygon(x2, y2, lty = 1, border = col[i],lwd=3); } if ((taux <= cref) & (cref == 0.75)) { if (any(optchull == 0.75)) polygon(x2, y2, lty = 5, border = col[i]) cref <- 0.5 } if ((taux <= cref) & (cref == 0.5)) { if (any(optchull == 0.5)) polygon(x2, y2, lty = 3, border = col[i]) cref <- 0.25 } if ((taux <= cref) & (cref == 0.25)) { if (any(optchull == 0.25)) polygon(x2, y2, lty = 2, border = col[i]) cref <- 0 } x1 <- x1[-num] y1 <- y1[-num] long <- length(x1) } } }

############### ############### s.chull.Julian.style<-function (dfxy, fac, xax = 1, yax = 2, optchull = c(0.25, 0.5, 0.75, 1), label = levels(fac), clabel = 1, cpoint = 0, col

56 = rep(1, length(levels (fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0, 0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE) { palette(rainbow(20)); dfxy <- data.frame(dfxy) opar <- par(mar = par("mar")) par(mar = c(0.1, 0.1, 0.1, 0.1)) on.exit(par(opar)) coo <- scatterutil.base(dfxy = dfxy, xax = xax, yax = yax, xlim = xlim, ylim = ylim, grid = grid, addaxes = addaxes, cgrid = cgrid, include.origin = include.origin, origin = origin, sub = sub, csub = csub, possub = possub, pixmap = pixmap, contour = contour, area = area, add.plot = add.plot) scatterutil.chull.Julian.style(coo$x, coo$y, fac, optchull = optchull, col = col) if (cpoint > 0) for (i in 1:nlevels(fac)) { points(coo$x[fac == levels(fac)[i]], coo$y[fac == levels(fac)[i]], pch = 20, cex = par("cex") * cpoint, col = col[i]) } if (clabel > 0) { coox <- tapply(coo$x, fac, mean) cooy <- tapply(coo$y, fac, mean) scatterutil.eti(coox, cooy, label, clabel, coul = col,boxes=FALSE) } box() invisible(match.call()) }

###############################

57 ############################### s.class.Julian.style<-function (dfxy, fac, wt = rep(1, length(fac)), xax = 1, yax = 2, cstar = 1, cellipse = 1.5, axesell = TRUE, label = levels(fac), clabel = 1, cpoint = 1, pch = 20, col = rep(1, length(levels(fac))), xlim = NULL, ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0, 0), include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, add.plot = FALSE) { palette(rainbow(20)); f1 <- function(cl) { n <- length(cl) cl <- as.factor(cl) x <- matrix(0, n, length(levels(cl))) x[(1:n) + n * (unclass(cl) - 1)] <- 1 dimnames(x) <- list(names(cl), levels(cl)) data.frame(x) } opar <- par(mar = par("mar")) par(mar = c(0.1, 0.1, 0.1, 0.1)) on.exit(par(opar)) dfxy <- data.frame(dfxy) if (!is.data.frame(dfxy)) stop("Non convenient selection for dfxy") if (any(is.na(dfxy))) stop("NA non implemented") if (!is.factor(fac)) stop("factor expected for fac") dfdistri <- f1(fac) * wt coul = col w1 <- unlist(lapply(dfdistri, sum)) dfdistri <- t(t(dfdistri)/w1) coox <- as.matrix(t(dfdistri)) %*% dfxy[, xax] cooy <- as.matrix(t(dfdistri)) %*% dfxy[, yax]

58 if (nrow(dfxy) != nrow(dfdistri)) stop(paste("Non equal row numbers", nrow(dfxy), nrow(dfdistri))) coo <- scatterutil.base(dfxy = dfxy, xax = xax, yax = yax, xlim = xlim, ylim = ylim, grid = grid, addaxes = addaxes, cgrid = cgrid, include.origin = include.origin, origin = origin, sub = sub, csub = csub, possub = possub, pixmap = pixmap, contour = contour, area = area, add.plot = add.plot) if (cpoint > 0) for (i in 1:ncol(dfdistri)) { pch <- rep(pch, length = nrow(dfxy)) points(coo$x[dfdistri[, i] > 0], coo$y[dfdistri[, i] > 0], pch = pch[dfdistri[, i] > 0], cex = par("cex") * cpoint, col = coul[i]) } if (cstar > 0) for (i in 1:ncol(dfdistri)) { scatterutil.star(coo$x, coo$y, dfdistri[, i], cstar = cstar, coul[i]) } if (cellipse > 0) for (i in 1:ncol(dfdistri)) { scatterutil.ellipse(coo$x, coo$y, dfdistri[, i], cellipse = cellipse, axesell = axesell, coul[i]) } if (clabel > 0) scatterutil.eti(coox, cooy, label, clabel, coul = col,boxes=FALSE) box() invisible(match.call()) }

######################## ######################## s.corcircle.Julian.style<-function (dfxy, xax = 1, yax = 2, label = row.names(df), clabel = 1, grid = FALSE, sub = "", csub = 1, possub = "bottomleft", cgrid = 0,

59 fullcircle = TRUE, box = FALSE, add.plot = FALSE) { palette(rainbow(20)); arrow1 <- function(x0, y0, x1, y1, len = 0.1, ang = 15, lty = 1, edge) { d0 <- sqrt((x0 - x1)^2 + (y0 - y1)^2) if (d0 < 1e-07) return(invisible()) segments(x0, y0, x1, y1, lty = lty) h <- strheight("A", cex = par("cex")) if (d0 > 2 * h) { x0 <- x1 - h * (x1 - x0)/d0 y0 <- y1 - h * (y1 - y0)/d0 if (edge) arrows(x0, y0, x1, y1, ang = ang, len = len, lty = 1) } } scatterutil.circ <- function(cgrid, h, grid) { cc <- seq(from = -1, to = 1, by = h) col <- "lightgray" if (grid) { for (i in 1:(length(cc))) { x <- cc[i] a1 <- sqrt(1 - x * x) a2 <- (-a1) segments(x, a1, x, a2, col = col) segments(a1, x, a2, x, col = col) } } symbols(0, 0, circles = 1, inches = FALSE, add = TRUE) segments(-1, 0, 1, 0) segments(0, -1, 0, 1) if (cgrid <= 0 | !grid) return(invisible())

60 cha <- paste("d = ", h, sep = "") cex0 <- par("cex") * cgrid xh <- strwidth(cha, cex = cex0) yh <- strheight(cha, cex = cex0) + strheight(" ", cex = cex0)/2 x0 <- strwidth(" ", cex = cex0) y0 <- strheight(" ", cex = cex0)/2 x1 <- par("usr")[2] y1 <- par("usr")[4] rect(x1 - x0, y1 - y0, x1 - xh - x0, y1 - yh - y0, col = "white", border = 0) text(x1 - xh/2 - x0/2, y1 - yh/2 - y0/2, cha, cex = cex0) } origin <- c(0,0) df <- data.frame(dfxy) if (!is.data.frame(df)) stop("Non convenient selection for df") if ((xax < 1) || (xax > ncol(df))) stop("Non convenient selection for xax") if ((yax < 1) || (yax > ncol(df))) stop("Non convenient selection for yax") x <- df[, xax] y <- df[, yax] if (add.plot) { for (i in 1:length(x)) arrow1(0, 0, x[i], y[i], len = 0.1, ang = 15, edge = TRUE) if (clabel > 0) scatterutil.eti.circ(x, y, label, clabel) return(invisible()) } opar <- par(mar = par("mar")) on.exit(par(opar)) par(mar = c(0.1, 0.1, 0.1, 0.1)) x1 <- x y1 <- y x1 <- c(x1, -0.01, +0.01)

61 y1 <- c(y1, -0.01, +0.01) if (fullcircle) { x1 <- c(x1, -1, 1) y1 <- c(y1, -1, 1) } x1 <- c(x1 - diff(range(x1)/20), x1 + diff(range(x1))/20) y1 <- c(y1 - diff(range(y1)/20), y1 + diff(range(y1))/20) plot(x1, y1, type = "n", ylab = "", asp = 1, xaxt = "n", yaxt = "n", frame.plot = FALSE) scatterutil.circ(cgrid = cgrid, h = 0.2, grid = grid) for (i in 1:length(x)) arrow1(0, 0, x[i], y[i], len = 0.1, ang = 15, edge = TRUE) if (clabel > 0) scatterutil.eti.circ(x, y, label, clabel, origin,boxes=FALSE) if (csub > 0) scatterutil.sub(sub, csub, possub) if (box) box() invisible(match.call()) }

################################################################# ################################################################# batrabio<-read.table(file="Bioclimfull1.txt", sep="\t", header=TRUE); batrabio<-batrabio[,c(1,4:22)];#dataframe avec toutes les variables bioclim et les noms d'espèces

CVHULL<-function(species1,species2){

batrapair<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,];

batrapair[,1]<-factor(batrapair[,1]);

batrapair<-unique(batrapair[,]);

62 fac<-batrapair$SCIENTIFIC_NAME;

par(mar=c(0,0,0,0));

pca.ade4<-dudi.pca(batrapair[,2:20], center=T, scale=T, scannf=F); 2;

gr8<-s.chull.Julian.style(pca.ade4$li,fac,clabel=2,cpoint=.65,col=c("red","blue"),grid=F); }

#################################################################### ####################################################################

#Between class analysis betweenclass<-function(species1,species2){ batrasis<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; batrasis[,1]<-factor(batrasis[,1]); batrasis<-unique(batrasis[,]); facBCA<-batrasis$SCIENTIFIC_NAME; par(mar=c(0,0,0,0)); pca.BCA.ade4<-dudi.pca(batrasis[,2:20], center=T, scale=T, scannf=F); between<-bca(pca.BCA.ade4,facBCA, scannf = TRUE); 1; return(between$ratio); }

########################################################################## ##########################################################################

#PCA on all species to evaluate important variables at the genus level: par(mar=c(3,3,3,3)); batrabio2<-read.table(file="Bioclimfull1.txt", sep="\t", header=TRUE); batrabio2<-batrabio2[,c(1,4:22)];

63 batrabio2<-unique(batrabio2[,]); pca.all.ade4<-dudi.pca(batrabio2[,2:20], center=T, scale=T, scannf=T); 2 batrabio2[,1]<-factor(batrabio2[,1]); fac2<-batrabio2$SCIENTIFIC_NAME; gr8<- s.chull.Julian.style(pca.all.ade4$li,fac2,label=batrasp,clabel=2,cpoint=.65,col=c(2,1,6,4:5,8,7,3,9:11 ,17,19,14:16,12,18,13,19,20),cgrid=0,grid=F); eigen<-pca.all.ade4$eig; cat("axis1 = ",round(eigen[1]/sum(eigen)*100,2),"%","axis2 = ",round(eigen[2]/sum(eigen)*100,2),"%"); bca.value<-bca(pca.ade4.666,fac.all.in.1, scannf = F, nf=1)$ratio; screeplot(pca.all.ade4); s.corcircle.Julian.style(pca.all.ade4$co,clabel=1.6,label=1:19); s.label(pca.all.ade4$li); s.class.Julian.style(pca.all.ade4$li, fac2, xlim=c(-8,12),cstar=1, cellipse=2, clabel=0.5, ylim=c(- 3,3),col=c(1:20));

## Run Monte Carlo with minor and incognitus min_inco<-batrabio[batrabio$SCIENTIFIC_NAME=='Batrachoseps minor'| batrabio$SCIENTIFIC_NAME=='Batrachoseps incognitus',]; real_ratioMI<-betweenclass('Batrachoseps incognitus', 'Batrachoseps minor');

min_inco[,1]<-factor(min_inco[,1]); min_inco<-unique(min_inco[,]); par(mar=c(2,2,2,2)); pca.ade4.min.inco<-dudi.pca(min_inco[,2:20], center=T, scale=T, scannf=F); ratio_random<-matrix(0, ncol=2, nrow=500); ratio_random[,1]<-seq(1:500); for (i in 1:500){ rand<-sample(min_inco$SCIENTIFIC_NAME);

64 #fac.rand<-factor(rand); ratio_random[i,2]<- bca(pca.ade4.min.inco, as.factor(rand), scannf = F, nf=1)$ratio; } sum(ratio_random[,2]>=real_ratioMI); hist(ratio_random[,2], breaks=100, freq = T, col=3, xlab="", xlim=c(0,real_ratioMI+0.1), main = "Random ratios vs observed one"); abline(v=real_ratioMI, col=2,lwd=5);

## Run Monte Carlo with luciae and gavilanensis luc_gav<-batrabio[batrabio$SCIENTIFIC_NAME=='Batrachoseps luciae'| batrabio$SCIENTIFIC_NAME=='Batrachoseps gavilanensis',]; real_ratioLG<-betweenclass('Batrachoseps luciae', 'Batrachoseps gavilanensis'); 1 luc_gav[,1]<-factor(luc_gav[,1]); luc_gav<-unique(luc_gav[,]); par(mar=c(2,2,2,2)); pca.ade4.luc.gav<-dudi.pca(luc_gav[,2:20], center=T, scale=T, scannf=F); ratio_random<-matrix(0, ncol=2, nrow=500); ratio_random[,1]<-seq(1:500); for (i in 1:500){ rand<-sample(luc_gav$SCIENTIFIC_NAME); #fac.rand<-factor(rand); ratio_random[i,2]<- bca(pca.ade4.luc.gav, as.factor(rand), scannf = F, nf=1)$ratio; } sum(ratio_random[,2]>=real_ratioLG); hist(ratio_random[,2], breaks=100, freq = T, col=6, xlab="", xlim=c(0,real_ratioLG+0.02), main = "Random ratios vs observed one"); abline(v=real_ratioLG, col=5,lwd=5);

################################################################################ ####

65 ############################################################################# ##################################################################### ######################################################### ############################################### ###################################### ########################### ################# ######### #### #

#Function to get all information on one graph (FIRST set up the screen as detailed later ) tout<-function(species1,species2){

## The first plot should be located in screen 1: screen(1); CVHULL(species1, species2); all.in.1<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; all.in.1[,1]<-factor(all.in.1[,1]); all.in.1<-unique(all.in.1[,]); fac.all.in.1<-all.in.1$SCIENTIFIC_NAME; pca.ade4.666<-dudi.pca(all.in.1[,2:20], center=T, scale=T, scannf=F); bca.value<-bca(pca.ade4.666,fac.all.in.1, scannf = F, nf=1)$ratio; text(8,-5,labels="BCA ratio value"); text(13,-5,labels=round(bca.value,5)); screen(4); s.corcircle.Julian.style(pca.ade4.666$co,clabel=0.8,label=1:19,box=T); screen(3); stuff<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; real_ratioSTUFF<-betweenclass(species1,species2); 1 stuff[,1]<-factor(stuff[,1]);

66 stuff<-unique(stuff[,]); pca.ade4.stuff<-dudi.pca(stuff[,2:20], center=T, scale=T, scannf=F); ratio_random<-matrix(0, ncol=2, nrow=500); ratio_random[,1]<-seq(1:500); for (i in 1:500){ rand<-sample(stuff$SCIENTIFIC_NAME); #fac.rand<-factor(rand); ratio_random[i,2]<- bca(pca.ade4.stuff, as.factor(rand), scannf = F, nf=1)$ratio; } sum(ratio_random[,2]>=real_ratioSTUFF); screen(3); par(mar=c(2,2,2,2)); hist(ratio_random[,2], breaks=100, freq = T, col=6, xlab="", xlim=c(0,real_ratioSTUFF+0.1),main=""); abline(v=real_ratioSTUFF, col=5,lwd=5);

}

#List of intersting species pair:

#SISTER SPECIES DEFINED IN THE METHODS

'Batrachoseps incognitus', 'Batrachoseps minor' 'Batrachoseps pacificus', 'Batrachoseps major' 'Batrachoseps luciae', 'Batrachoseps gavilanensis' 'Batrachoseps gregarius', 'Batrachoseps stebbinsi' 'Batrachoseps relictus', 'Batrachoseps kawia' 'Batrachoseps campi', 'Batrachoseps wrighti' 'Batrachoseps robustus', 'Batrachoseps wrighti' 'Batrachoseps simatus', 'Batrachoseps nigriventris'

67 #CLIMATICALLY CLOSE SPECIES FOUND BY THE FIRST ANALYSIS #( different method and data!!) #(without major-aridus!!)

"Batrachoseps stebbinsi","Batrachoseps attenuatus" "Batrachoseps relictus","Batrachoseps campi" "Batrachoseps gregarius","Batrachoseps diabolicus" "Batrachoseps nigriventris","Batrachoseps diabolicus" "Batrachoseps stebbinsi","Batrachoseps diabolicus" "Batrachoseps minor","Batrachoseps gavilanensis" "Batrachoseps stebbinsi","Batrachoseps gregarius" "Batrachoseps regius","Batrachoseps incognitus" "Batrachoseps pacificus","Batrachoseps kawia" "Batrachoseps stebbinsi","Batrachoseps minor" "Batrachoseps robustus","Batrachoseps wrighti"

#GEOGRAPHICALLY AND PHYLOGENETICALLY CLOSE (WITHIN GROUP) #SPECIES PAIRS WITHOUT A LARGE SYMPATRIC ZONE:

#Example of the northern group of the pacificus group: "Batrachoseps minor","Batrachoseps gavilanensis" "Batrachoseps incognitus","Batrachoseps gavilanensis" "Batrachoseps incognitus","Batrachoseps luciae" "Batrachoseps gavilanensis","Batrachoseps luciae"

#Example of gabrieli and major: "Batrachoseps gabrieli","Batrachoseps major"

#Example of aridus and major: "Batrachoseps aridus","Batrachoseps major"

#Example of the Sierra Nevada species: #relictus group "Batrachoseps regius","Batrachoseps kawia"

68 "Batrachoseps relictus","Batrachoseps kawia" #nigriventris group "Batrachoseps stebbinsi","Batrachoseps simatus" "Batrachoseps gregarius","Batrachoseps simatus"

#GEOGRAPHICALLY AND PHYLOGENETICALLY DISTANT (BETWEEN GROUP) #SPECIES PAIRS WITHOUT A SYMPATRIC ZONE:

#Sierra Nevada: "Batrachoseps diabolicus","Batrachoseps gregarius" "Batrachoseps relictus","Batrachoseps gregarius" "Batrachoseps kawia","Batrachoseps gregarius" "Batrachoseps regius","Batrachoseps gregarius" "Batrachoseps relictus","Batrachoseps robustus" "Batrachoseps simatus","Batrachoseps robustus"

#SPECIES WITH SYMPATRY BETWEEN PHYLOGENETIC GROUPS:

"Batrachoseps nigriventris","Batrachoseps minor"#patterns and processes "Batrachoseps nigriventris","Batrachoseps incognitus"#same "Batrachoseps attenuatus","Batrachoseps gavilanensis" "Batrachoseps nigriventris","Batrachoseps gavilanensis" "Batrachoseps nigriventris","Batrachoseps pacificus" "Batrachoseps nigriventris","Batrachoseps major" "Batrachoseps attenuatus","Batrachoseps diabolicus"

#SPECIES WITH SYMPATRY WITHIN PHYLOGENETIC GROUP:

"Batrachoseps nigriventris","Batrachoseps stebbinsi"

############################################ ############################################

#Before using the tout() function:

69 close.screen( all = TRUE ); get( getOption( "device" ) )(); ## Split the screen into two rows and one column, defining screens 1 and 2. split.screen( figs = c( 2, 1 ) ); ## Split screen 2 into one row and three columns, defining screens 3 and 4. split.screen( figs = c( 1, 2 ), screen = 2 );

#COMMENT MESURER OVERLAP RELATIVEMENT AUX SURFACES DES DEUX CONVEX HULLS #... sister_species_pairs<-c('Batrachoseps incognitus', 'Batrachoseps minor', 'Batrachoseps pacificus', 'Batrachoseps major', 'Batrachoseps luciae', 'Batrachoseps gavilanensis', 'Batrachoseps gregarius', 'Batrachoseps stebbinsi', 'Batrachoseps relictus', 'Batrachoseps kawia', 'Batrachoseps campi', 'Batrachoseps wrighti', 'Batrachoseps robustus', 'Batrachoseps wrighti', 'Batrachoseps simatus', 'Batrachoseps nigriventris');

bca_ratio_SS<-mat.or.vec(3,length(sister_species_pairs)/2); for (i in 1:length(sister_species_pairs)/2){ bca_ratio_SS[1,i]<-sister_species_pairs[2*i-1]; bca_ratio_SS[2,i]<-sister_species_pairs[2*i]; bca_ratio_SS[3,i]<-TRUC POUR CALCULER L OVERLAP RELATIF A LA SURFACE DES CONVEX HULLS (sister_species_pairs[2*i-1],sister_species_pairs[2*i]); 1 }

70 #Fonction pour faire les graph gra<-function(species1,species2){ CVHULL(species1, species2); all.in.1<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; all.in.1[,1]<-factor(all.in.1[,1]); all.in.1<-unique(all.in.1[,]); fac.all.in.1<-all.in.1$SCIENTIFIC_NAME; pca.ade4.666<-dudi.pca(all.in.1[,2:20], center=T, scale=T, scannf=F); bca.value<-bca(pca.ade4.666,fac.all.in.1, scannf = F, nf=1)$ratio; text(5,-5,labels=round(bca.value,5),cex=2); cat(bca.value); }

#####################################" #MOYENNE DES BCA INERTIA RATIO POUR CHACUNE DES CATEGORIES sister<-c(0.444,0.217,0.126,0.075,0.229,0.539,0.445,0.116); thermally_close<-c(0.049,0.439,0.042,0.204,0.327,0.011,0.075,0.422,0.670,0.618,0.445); geo_phy_close<-c(0.011,0.041,0.055,0.126,0.250,0.072,0.051,0.229,0.107,0.085); geo_not_phy<-c(0.042,0.208,0.005,0.035,0.262,0.645); symp_between<-c(0.007,0.023,0.078,0.052,0.128,0.130,0.078); symp_within<-c(0.103); mean(sister); mean(thermally_close); mean(geo_phy_close); mean(geo_not_phy); mean(symp_between); mean(symp_within); sd(sister); sd(thermally_close); sd(geo_phy_close); sd(geo_not_phy); sd(symp_between);

71 sd(symp_within); all.bca.values<-0; for (i in 1:19){ for (j in (i+1):20){ species1<-list_batra[i]; species2<-list_batra[j]; all.in.1<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; all.in.1[,1]<-factor(all.in.1[,1]); all.in.1<-unique(all.in.1[,]); fac.all.in.1<-all.in.1$SCIENTIFIC_NAME; pca.ade4.666<-dudi.pca(all.in.1[,2:20], center=T, scale=T, scannf=F); bca.value<-bca(pca.ade4.666,fac.all.in.1, scannf = F, nf=1)$ratio; all.bca.values<-c(all.bca.values,bca.value); } } all.bca.values<-all.bca.values[-1]; length(all.bca.values); mean(all.bca.values); sd(all.bca.values); min(all.bca.values); max(all.bca.values); a<-min(all.bca.values); b<-max(all.bca.values); which(all.bca.values[]==a); which(all.bca.values[]==b); for (i in 1:19){ for (j in (i+1):20){ species1<-list_batra[i]; species2<-list_batra[j]; all.in.1<-batrabio[batrabio$SCIENTIFIC_NAME==species1| batrabio$SCIENTIFIC_NAME==species2,]; all.in.1[,1]<-factor(all.in.1[,1]); all.in.1<-unique(all.in.1[,]);

72 fac.all.in.1<-all.in.1$SCIENTIFIC_NAME; pca.ade4.666<-dudi.pca(all.in.1[,2:20], center=T, scale=T, scannf=F); bca.value<-bca(pca.ade4.666,fac.all.in.1, scannf = F, nf=1)$ratio; all.bca.values<-c(all.bca.values,bca.value); if (bca.value==b){ cat("Species pair with highest bca value"); cat("\n"); cat(list_batra[i]); cat("\n"); cat(list_batra[j]); } else if (bca.value==a){ cat("Species pair with lowest bca value"); cat("\n"); cat(list_batra[i]); cat("\n"); cat(list_batra[j]); cat("\n"); cat("\n"); } } } hist(all.bca.values);

SCRIPT FOR THE THIRD METHOD As the script from Broennimann was used with minor changes associated with our specific analysis, computer sytem and data characteristics and as it is available, only the formatting code of the beginning will be displayed here. Of course the full code is available upon request to [email protected] rm(list= ls()); setwd("C:/Users/Julian/Desktop/Analyse 3");

################################################################################ ################# ############################## load functions and packages ######################################

73 ################################################################################ ################# source("niche.overlap.functions.R"); source("occ.prep.functions.R"); library(BIOMOD); library(ade4); library(adehabitat); library(sp); library(gam); library(MASS); library(mvtnorm); library(gbm); library(dismo); library(raster);

#Import data (peut faire avec getData) #Set the working directory to the path that contains your .bil files setwd("C:/Users/Julian/Desktop/Analyse 3/wc0.5"); #Create a list of .bil files that exist in the working directory files<-list.files(pattern='\\.bil$'); #Vars is a vector of bioclim variable numbers vars<-sort(unique(as.numeric(gsub('^bio([0-9]+)_.*', '\\1',files)))); #For each of vars, create raster object for each tile and merge ##Grids will be a list of rasters, each of which is the merged tiles for a BC var grids<-sapply(vars, function(x) { patt<-paste('bio', x, '_', sep='') tiles<-files[grep(patt, files)] merged<-eval(parse(text=paste('merge(',toString(paste('raster(',tiles, ')', sep='"')), ')',sep=''))) }) #Give the list elements names names(grids)<-paste('bio',vars,sep='');

74 #Check if anything went wrong head(grids); head(grids$bio1); head(grids$bio19);

#Combine all list elements into a stack s<-stack(grids); #Put values in a dataframe #d<-as.data.frame(s); #xy <- xyFromCell(s, 1:ncell(s)) #Problème de mémoire #memory.limit(); #memory.limit(size =4000);

## 12Go of RAM are not enough to use cbind on d and xy so crop is needed !!!! e <- extent(-124.43,-115.5,30.07,45.55); s.crop<-crop(s,e); d.crop<-as.data.frame(s.crop); xy.crop <- xyFromCell(s.crop, 1:ncell(s.crop)); df<-cbind(xy.crop,d.crop); head(df); colnames(df)<- c("x","y","X1","X2","X3","X4","X5","X6","X7","X8","X9","X10","X11","X12","X13","X14","X1 5","X16","X17","X18","X19"); head(df);

#Load climate variable for all site of the study area 1 (column names should be x,y,X1,X2,...,Xn) clim1<-na.exclude(df);

#Load climate variable for all site of the study area 1 (column names should be x,y,X1,X2,...,Xn) clim2<-na.exclude(df);

#Global climate for both ranges clim12<-rbind(clim1,clim2);

75 setwd("C:/Users/Julian/Desktop/Analyse 3"); batrabio<-read.table(file="Bioclimfull1.txt", sep="\t", header=TRUE); species1<-"Batrachoseps pacificus"; species2<-"Batrachoseps major"; batrasp1<-batrabio[batrabio$SCIENTIFIC_NAME==species1,2:3]; batrasp2<-batrabio[batrabio$SCIENTIFIC_NAME==species2,2:3]; colnames(batrasp1)<-c("x","y"); colnames(batrasp2)<-c("x","y"); head(batrasp1); head(batrasp2); #Loading occurrence sites for the species (column names should be x,y) #WARNING: the script is not the same because I use one area and two species !!!!!!!!!!!! occ.sp.aggr.sp1<-na.exclude(batrasp1); occ.sp.aggr.sp2<-na.exclude(batrasp2); #Remove occurrences closer than a minimum distance to each other (remove aggregation). Setting min.dist=0 will remove no occurrence. occ.sp1.NR<-occ.desaggragation(df=occ.sp.aggr.sp1,colxy=1:2,min.dist=0.05,plot=F); occ.sp2.NR<-occ.desaggragation(df=occ.sp.aggr.sp2,colxy=1:2,min.dist=0.05,plot=F);

#Create sp occurrence dataset by adding climate variables from the global climate datasets #Resolution should be the resolution of the climate data grid occ.sp1<- na.exclude(sample.sp.globvar(dfsp=occ.sp1.NR,colspxy=1:2,colspkept=NULL,dfvar=clim1,colvar xy=1:2,colvar="all",resolution=0.008333333)); occ.sp2<- na.exclude(sample.sp.globvar(dfsp=occ.sp2.NR,colspxy=1:2,colspkept=NULL,dfvar=clim2,colvar xy=1:2,colvar="all",resolution=0.008333333));

76 ACKNOLEDGEMENTS

The author is especially grateful to David B. Wake for his constant encouragement and advices, to Michelle Koo for her GIS, logistic help and friendship, to Sean Rovito for his helpful critics and for introducing the author to some bibliography, to Carol Spencer for her work as a herpetology curator and her work to update ARCTOS, to the whole Amphibia Web team for accepting me and giving me responsibilities, to Lydia Smith and Elyse freitas for laboratory training, to Fabien Leprieur for helping me with the bibliography and the methods, to Romain Le Guen for software help, to Stéphane Tomas and Billy Dunaway for hardware help, to Theodore Papenfuss for his advices, and finally to Dan Rabinsky for introducing the author to scientific programming and enabling a quick skill progress. Numerous other individuals contributed to the database and the life of the Museum of Vertebrate Zoology and should also be thanked. Funding was provided by the Langedoc-Roussillon region (France) and therefore all people contributing to this funding should receive all the author's gratitude.

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