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PHENOTYPIC VARIATION AMONG ISLAND POPULATIONS OF A
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DAY GECKO, PHELSUMA ORNATA: EVALUATING THE RELATIVE
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CONTRIBUTIONS OF LOCAL ADAPTATION AND GENETIC DRIFT
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Luke J. Harmon1,3 and Richard Gibson2
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Department of Biology, Washington University, St. Louis, MO 63130, USA
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Zoological Society of London, Regent’s Park, London NW1 4RY, UK
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Current address: Biodiversity Research Centre, University of British Columbia,
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Vancouver, BC V6T 1Z4, Canada
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email: [email protected]
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ABSTRACT
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Inter-island variation in morphology plays a key role in speciation and diversification.
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Potentially, both adaptive and nonadaptive processes could result in unique morphologies
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of island populations. Here, we investigate morphological variation among ten
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populations of a day gecko, Phelsuma ornata, and its close relative, P. inexpectata, in the
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Mascarene islands of Mauritius, Réunion, and six associated offshore islands. We first
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reanalyze previous genetic data for this species to demonstrate significant isolation by
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distance among populations of these lizards, with no detectable effect of offshore islands
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on genetic isolation. We find that local populations are distinct both morphologically and
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ecologically. Furthermore, we find significant evidence that both genetic drift and local
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adaptation have influenced patterns of differentiation. Head color patterns are mainly
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influenced by drift, whereas body size is consistently larger on small offshore islands.
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Body shape variation shows some evidence for drift, but differences among populations
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for some variables are too great to be explained by neutral processes alone. These
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differences are not correlated with any measured ecological variable. These data shed
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light on the processes of differentiation in island systems, which likely play an important
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role in the formation of new species.
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Key words: islands, convergent evolution, isolation by distance, Mantel test, random
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skewers, variance-covariance
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INTRODUCTION
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Organisms on islands frequently exhibit unique features compared to their
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mainland relatives (see Grant 1998a and references therein). There are both adaptive and
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nonadaptive explanations for the evolution of these differences (Barton 1996). Island
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species may adaptively evolve different traits as a response to the unique habitats and
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simplified community structure of islands (Schluter 1988b). For example, since island
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ecosystems generally contain fewer species than those on the mainland (MacArthur and
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Wilson 1967), island species might encounter reduced levels of interspecific competition
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and/or predation (Carlquist 1965; MacArthur and Wilson 1967). Alternatively, small
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population sizes and founder effects associated with island invasion may lead to genetic
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drift, such that island forms evolve new traits that are selectively neutral (Mayr 1954).
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Moreover, these two processes are not independent; for example, genetic drift may move
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a population off a local adaptive peak, allowing selection to move the population to a
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different peak in the adaptive landscape (Simpson 1944; Simpson 1953).
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The relative contribution of genetic drift and adaptation to evolutionary
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diversification is a key issue in evolutionary biology that has inspired great debate (e.g.
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Cheetham et al. 1993; Cheetham et al. 1994; Fisher 1958; Kingsolver et al. 2001;
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Lewontin 1974). By providing circumstances that potentially accelerate both of these
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processes, small island systems offer unique opportunities to investigate
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microevolutionary patterns. Furthermore, isolation on islands is often associated with
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speciation (Mayr 1963). Since both genetic drift (Wagner et al. 1994) and adaptation
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(Dieckmann et al. 2004) have been implicated as playing key roles in speciation,
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investigating their relative influence on islands may be informative about the processes
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that generate biodiversity.
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There are several patterns that have been considered signatures of adaptation.
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First, adaptation leads to trait-environment correlations that can then be recognized by
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comparative statistical methods (Wainwright and Reilly 1994). Additionally, adaptation
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can be inferred in cases where island forms repeatedly evolved similar responses when
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confronted with an island environment (Diamond 1991; Gillespie 2004; Losos et al.
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1998). Such convergent evolution suggests that the strength of natural selection on
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islands is enough to overcome any historical contingencies that might otherwise tend to
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make results of each island colonization unique (Pagel 1994; Schluter 1988a). For
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example, flightlessness has evolved repeatedly among birds and insects on isolated
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islands (Carlquist 1974; Diamond 1991; Roff 1990). Finally, character displacement, an
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adaptive response to the presence of competitor species, is a common observation in
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natural settings (Schluter 2000). Phenotypic differences between populations in
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sympatry with competitors and those in allopatry provide some evidence for character
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displacement (Losos 2000).
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Models of genetic drift also make several distinct predictions that can be tested
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using data from natural populations. For example, if the genetic structure among
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populations fits an isolation-by-distance model (Wright 1943), morphological differences
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among populations should be correlated with both genetic and geographic distances
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separating those populations (Lande 1991). A common test for isolation by distance is to
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correlate the genetic difference among populations with the geographic distance
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separating them (Slatkin 1993). Given such a relationship, a significant correlation
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between geographic distance and morphological difference among localities may be
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evidence for the influence of drift on the characters of interest (Clegg et al. 2002).
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However, such a relationship may also reflect adaptation to unmeasured spatially
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autocorrelated factors acting on the traits of interest (Malhotra and Thorpe 1997), and
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thus is only circumstantial evidence for the influence of genetic drift.
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A second type of test for the influence of genetic drift relies on the fact that the
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evolution of phenotypic characters by genetic drift is influenced by genetic covariances
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among traits. Specifically, if the underlying genetic variance-covariance matrix (G
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matrix) is relatively stable over time, then the among population G matrix will mirror the
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within-population G matrix (Lande 1979). If phenotypic variances and covariances
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reflect underlying genetic variances and covariances, as has been shown in a variety of
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cases (Arnold and Phillips 1999; Cheverud 1988; Cheverud 1996; Koots and Gibson
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1996; Roff 1995; Roff 1997; Steppan et al. 2002), then the within- and between-
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population phenotypic variance-covariance (P) matrices will also be correlated under drift
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(Marroig and Cheverud 2004). Since the P matrix is a combination of both genetic and
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environmental variances and covariances among traits, this test effectively assumes that
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phenotypic variation is not influenced by environmental effects, such as phenotypic
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plasticity, maternal effects, and other non-genetic factors (Marroig and Cheverud 2004;
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Steppan 1997).
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Thus, there are a number of ways to statistically test for the influence of selection
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and drift in influencing population differentiation. In this study, we use these tests to
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investigat the patterns of trait variation among populations of the ornate day gecko
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(Phelsuma ornata) and its close relative, the Manapany day gecko (Phelsuma
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inexpectata), in the Mascarene islands. These small, brightly colored lizards that are
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mainly arboreal, surviving on a diet of insects along with pollen and nectar (NyHagen et
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al. 2001; Vinson and Vinson 1969). Phelsuma ornata occurs only on mainland Mauritius
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and surrounding satellite islands. It is most common in coastal regions and is particularly
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abundant in more xeric habitats (Vinson 1976). A closely related species, P. inexpectata,
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occupies a very small area along the SW coast of the nearby island of Réunion (Mertens
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1970), and resembles P. ornata in both morphology and habitat use. Previous genetic
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data (Austin et al. 2004) showed substantial genetic variation among populations of P.
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ornata in Mauritius, with distinct phylogeographic structure among both island and
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mainland localities. Austin et al. (2004) also showed that P. ornata and P. inexpectata
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are genetically distinct sister taxa.
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Previous studies of Phelsuma have shown substantial morphological variation
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among island populations (Gardner 1986; Radtkey 1996). Given the geographic
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population structure within P. ornata, it is possible that local populations of these lizards,
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including those on offshore islands, will differ significantly in their morphological traits.
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In this study, we test two hypotheses about the processes generating such variation for
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three sets of traits: body size, head color pattern, and body shape.
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(1) Geographic variation in morphology will be due to local adaptation.
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This adaptive evolution could manifest itself in several ways. First, certain
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morphological traits may be correlated with measured aspects of lizard habitat use.
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Secondly, natural selection and adaptive evolution may lead to unexpectedly large
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between-population variances and covariances along the principal axes of morphological
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variation. Lastly, adaptation could lead to convergent evolution of the same
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morphological traits, both in communities with the same Phelsuma species composition
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and in those on small offshore islands. The latter assumes that offshore islands share
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similar environments, an assumption we discuss below. Any one or more of these results
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is evidence for the influence of local adaptation in shaping morphological variation.
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(2) Geographic variation in morphology will be due to genetic drift.
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Under the influence of genetic drift, morphological differences among localities will be
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correlated with the geographic distance separating them. Furthermore, patterns of trait
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variance-covariance among populations will be proportional to within-population trait
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variances and covariances.
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METHODS
Study Area
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We collected data on morphology and habitat use of Phelsuma ornata at nine sites
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on Mauritius and nearby islands (Figure 2.1). Six of these sites were small islands off the
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coast of Mauritius: Round Island (RI), Flat Island (FI), Ilot Gabrielle (GAB), Gunner’s
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Quoin (GQ), Ile aux Benetiers (IAB), and Ile aux Aigrettes (IAA). We also included
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three sites in mainland Mauritius: Tamarin mountain (TAM) and Casela Bird Park (CAS)
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on the west coast, and Ylang-Ylang Estate (YY) in the east. We also collected data on
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the closely related species Phelsuma inexpectata at Grand Anse, Réunion, France (RUN),
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an island about the same size as Mauritius located 60 km to the southwest. This species,
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formerly considered a subspecies, shares a recent common ancestor with Mauritian P.
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ornata and is very similar genetically, ecologically, and morphologically (Austin et al.
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2004). Vegetation differed somewhat among localities included in the study. Three of
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the mainland sites (YY, CAS, and RUN) were characterized by highly landscaped park-
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like areas, including an assortment of introduced palm and non-palm trees, such as
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eucalyptus, teak, ravenala, and coconut, while the fourth (TAM) was a xeric habitat near
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a hunting estate in southeastern Mauritius characterized by Acacia trees, Lomatophyllum
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plants, and large boulders. The small outer island habitats (FI, GAB, GQ, IAA, IAB, RI)
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were generally more open than the mainland habitats. Sites also differed in lizard
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community composition, with P. ornata / inexpectata the only species of day gecko at six
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localities (FI, GAB, GQ, TAM, IAA, RUN), occurring in sympatry with P. cepediana at
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two localities (IAB, YY), with P. cepediana and P. guimbeaui at one locality (CAS), and
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with P. guentheri at one locality (RI). Introduced agamids (Calotes versicolor) are
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present in every locality except the northern islands (GQ, FI, GAB, and RI), while
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introduced house geckos (Hemidactylus frenatus) are present at every locality except for
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two (RI and GQ). In general, the small island populations tend to have fewer total lizard
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species than the mainland populations (Arnold 2000).
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Habitat Use
At each site, we walked through suitable habitat searching for adult P. ornata or
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P. inexpectata. When an individual was located, we collected the following microhabitat
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data: perch height (measured or estimated to the nearest 0.5 m), perch diameter (to the
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nearest 1 cm), percent canopy cover (visual estimate), perch type (ground, rock, shrub, or
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tree), and, if the lizard was on a plant, whether it was a palm or a non-palm species.
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There are two non-palm plants in Mauritius that have palm-like features relevant to gecko
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survival, mainly the presence of smooth fronds with narrow, water-containing crevices;
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these plants (Ravenala, Pandanus and Lomatophyllum) were included in the “palm”
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category for the purposes of this study. When lizards were on a tree, the DBH (diameter
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at breast height) of the tree was measured with a measuring tape, and trees were placed
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into one of four categories based on their DBH (< 10 cm, 10 - 20 cm, 20 - 30 cm, > 30
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cm). The substrate that the lizard was perched on was also noted (bark, palm frond, leaf,
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rock, fruit, or burlap-like coconut husk), and categorized as “smooth” or “rough” (rough
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= significant visible texture present, otherwise smooth, all categorizations made by LJH).
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To avoid pseudoreplication of individual lizards, each area in a particular site was only
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searched for lizards in this way once. Data were only collected when the weather was
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sunny or partly cloudy (i.e. the sun was out at least 50% of the time).
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Morphology
We also opportunistically caught adult lizards in each population using a dental
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floss noose. We measured only adult males, as determined by the presence of distinct
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femoral pores and hemipenal bulges. Using a ruler, we took the following morphological
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measurements to the nearest 0.5 mm: snout-vent length (SVL), tail length (intact tails
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only), head length (distance from ear hole to tip of snout), head width at eyes, head width
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at ears, head height at eyes, inter-limb distance (distance from the posterior of the
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insertion of the front limb to the anterior of the insertion of the hind limb), pelvis width,
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humerus length (distance from shoulder to apex of elbow), antebrachium length (distance
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from elbow apex to center of wrist), forefoot length (distance from center of wrist to tip
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of longest toe [IV]), femur length (distance from insertion of hindleg at pelvis to apex of
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knee), crus length (distance from apex of knee to heel), and hindfoot length (distance
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from heel to tip of longest toe [IV]).
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To quantify variation in head color pattern and shape, we took photographs of
each of these measured lizards’ heads from above using a Nikon CoolPix 995 digital
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camera (Nikon USA, Melville, NY). A ruler was included in each photo to provide scale.
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We attempted to take all photographs with the same alignment from directly above the
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lizards head. We then used TPSDig version 1.40 (Rohlf 2004a) to obtain two-
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dimensional coordinates for seven landmarks on the head of each lizard. Four of these
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landmarks were repeated on the right and left side of each head, while three were along
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the midline, for a total of eleven landmarks per photo. Landmarks were defined at the
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following points (Figure 2.2): (1) anterior tip of snout; (2) point along outline of head
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corresponding to center of nostril; (3) anterior-most, (4) distal-most, and (5) posterior-
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most points of green pigment patch on front of snout; (6) point where dorsal edge of
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white / grey supraciliar stripe meets eye and (7) center of anterior end of red supraorbital
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stripe. We removed asymmetry by averaging landmarks from the right and left sides of
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each specimen (Klingenberg et al. 2002). We did this by superimposing each specimen
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with its reflection across the x-y plane using generalized Procrustes analysis (Gower
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1975), and finding the mean of these two shapes using the program Morpheus (Slice
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2000). For this procedure, the four landmarks on each side of the head (2, 4, 6, and 7)
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were matched with the corresponding reflected landmark from the other side of the head,
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while the three landmarks along the midline (1, 3, and 5) were matched with themselves.
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We removed redundant points from the right-hand side of these symmetrical mean
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coordinates, and superimposed the resulting coordinates using generalized Procrustes
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analysis (Gower 1975). From these, we generated partial warp scores for each individual
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using the program tpsRelw version 1.35 (Rohlf 2004b).
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To investigate the error involved in these geometric morphometric measurements,
we took two photos of the heads of each of 14 lizards, repositioning the lizard
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independently each time. We then generated 2d coordinates twice independently for each
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photo, for a total of four sets of coordinates per individual lizard. We used these data to
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calculate the repeatability of partial warp scores for the two different photos with a nested
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ANOVA on each partial warp score, with lizard ID and photograph nested within ID as
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effects. Repeatability was calculated as the sum of squares for the lizard ID effect
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divided by the total sum of squares. Since measurement error was sufficiently small (see
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results), we used one set of coordinates from a single photograph for each lizard in all
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further head color pattern analyses.
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Analyses - differences among localities
We tested for differences among localities in body size, body shape, head color
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pattern, and habitat use. All morphological measurements except head color pattern warp
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axes were log-transformed before analysis. Perch height and percent canopy were
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ln(x+1) transformed, while perch diameter was log-transformed. We tested for allometry
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in the morphological measurements using linear regression of each transformed variable
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on lnSVL. All log-transformed body measurements were significantly correlated with
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lnSVL (all p < 0.05; results not presented). Thus, we created size-independent body
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shape variables by taking the residuals from a linear regression of each log-transformed
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body measurement on lnSVL. Head color pattern partial warp axes represent size-
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corrected estimates of head color pattern change; however, there still could be allometric
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components of head color pattern in these axes, if lizard heads change shape as they
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grow. We tested for allometric components of head color pattern change with linear
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regression on lnSVL. Since some, but not all, partial warp axes were correlated with
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lnSVL (see results), we repeated all analyses using both the raw partial warp scores for
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each individual and residuals from lnSVL.
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We then entered each set of variables into an ANOVA / MANOVA to test for
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differences among localities. Since the size data include some males that have reached
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sexual maturity but not grown to their full size, there is a potential bias due to such males
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being over-represented in certain populations; we accounted for this bias by repeating the
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ANOVA on lnSVL using only the five largest males measured in a particular locality.
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We visualized any significant differences by plotting locality means for the first two
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canonical variates from the MANOVA, or using the data itself in the case of lnSVL. We
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used post-hoc ANOVAs / MANOVAs on each pair of localities to identify all
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significantly different pairs of localities, correcting for multiple comparisons using
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sequential Bonferroni correction.
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To test for differences in each of the categorical habitat use variables among
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localities we used a chi-squared test with significance determined by permutations. This
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was done by permuting the data so that individuals were randomly assigned to localities,
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and using these permuted data sets to construct a null distribution of the chi-squared
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statistic. We generated this distribution from 9999 permutations using R (Team 2004).
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We also carried out a multiple correspondence analysis of all discrete ecological variables
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to create a four-dimensional continuous ecological data space. We assessed differences
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among localities in this space using MANOVA, and visualized differences by plotting
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means of the first two canonical variates for each locality. All of the above analyses were
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repeated excluding P. inexpectata to investigate variation among Mauritian populations
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of P. ornata.
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Analyses - testing predictions of adaptation vs. drift
We generated pairwise difference matrices among all localities for each of the
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data sets (body size, body shape, head color pattern, habitat use). For the body size data
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set, each entry in the matrix represented the difference between the mean lnSVL between
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each pair of localities. For the other data sets (size, head color pattern, body shape, and
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habitat use), we found the centroid for each locality in the full canonical space. We then
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calculated the Mahalonobis distance between each pair of localities in each data space.
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We also created a genetic difference matrix using mitochondrial sequence data for
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Phelsuma ornata from a previous study (Austin et al. 2004). These data included eight of
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the ten sites included in this study (RI, GQ, FI, GAB, IAA, TAM, YY, and RUN). We
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obtained the aligned sequences from (Austin et al. 2004) from GenBank, and used TCS
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(Clement et al. 2000) to construct a haplotype network including all P. ornata /
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inexpectata sequences. We used the resulting network to determine the patristic distance
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between all populations of P. ornata / inexpectata. We also calculated geographic
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distances between all localities included in this study using a map of Mauritius (IGN
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2001). We created a Mauritian island - mainland matrix by assigning each locality to
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either of these two categories (small island: GQ, FI, GAB, RI, IAA, IAB; mainland:
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CAS, TAM, YY; RUN was excluded because it has a large potential area but is separated
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from the population of P. ornata in Mauritius). For this variable, the difference matrix
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contained a “0” if both localities were in the same category, and a “1” otherwise. Finally,
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we created a community difference matrix by calculating the difference in the number of
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sympatric Phelsuma species between each pair of localities (as noted above).
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We first tested the hypothesis that morphological evolution was influenced by
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genetic drift. The genetic data presented by (Austin et al. 2004) appeared to be isolation-
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by-distance; we tested this hypothesis by regressing the genetic difference matrix on the
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geographic distance matrix. Under the hypothesis of isolation by distance, genetic drift
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will lead to differences among localities proportional to their geographic distance from
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each other (Slatkin 1993). Thus, as a test for the effect of genetic drift, we regressed each
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morphological difference matrix on the geographic distance matrix using a Mantel test.
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Genetic drift also predicts a concordance between within- and between-species
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patterns of phenotypic variance-covariance. We tested this in three ways. First, we
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tested for a correlation between pooled within-locality and between-locality phenotypic
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variance-covariance matrices using a Mantel test (Manly 1991). Second, we compared
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the two matrices using a modified random skewers method (Cheverud et al. 1985; Pielou
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1984). For this procedure, we multiplied each matrix by the same random vector and
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calculated the correlation between the two resulting vectors. Repeating this 10000 times,
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we generated a distribution of 10000 correlation values. We generated a p-value for this
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test as p = (number of correlation coefficients that were less than zero) / 10000. If the
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data are compatible with a model of genetic drift, then the two matrices are predicted to
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be correlated by both the Mantel and random skewer method. Third, we compared
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variance of principal axes within and among populations (Marroig and Cheverud 2004).
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To do this, we generated principal component axes from the pooled within-locality
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variance-covariance matrix, projected all the data into this new PCA space, calculated the
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variance within and among populations along each axis, and then regressed these log-
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transformed variances against each other. Under genetic drift, the slope of the regression
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of these variances will be 1 (Ackermann and Cheverud 2002), slopes greater than one
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indicate that the first few PCA axes are more variable among populations than would be
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expected under drift (Marroig and Cheverud 2004).
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Alternatively, if morphological evolution is adaptive, one would expect a
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relationship between morphology and habitat use; thus we first tested for a correlation
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between the morphological difference matrices (SVL, body shape, and head color
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pattern) with the habitat use difference matrix using Mantel tests. We also carried out
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pairwise regressions to test for correlations between individual pairs of morphological
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and habitat use variables. We tested for convergent evolution by correlating each
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morphological data difference matrix with the island-mainland matrix described above,
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as well as with a community matrix describing the difference in the number of sympatric
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Phelsuma species present. Since these populations share historical relationships and may
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have current gene flow between them, they are not independent. It is possible to control
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for this non-independence by using three-way Mantel tests controlling for genetic
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relatedness among localities. Although genetic data are not available for all localities in
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this study, genetic differences were strongly correlated with geographic distance (see
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below), so we instead used three way Mantel tests holding the geographic distance matrix
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constant. All statistics were carried out using R statistical software (Team 2004).
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RESULTS
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mtDNA Differences vs. Geographic Distance
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For localities with sequence data available, average genetic dissimilarity between
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sites was highly correlated with geographic distance (P. inexpectata included: r = 0.97, p
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= 0.0007; excluded, r = 0.63, p = 0.005; Figure 2.3). There was no genetic difference
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associated with island-mainland comparisons when controlling for geographic distance
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(three way Mantel test, genetic distance vs. island-mainland difference matrix; P.
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inexpectata included: r = -0.01, p = 0.5; excluded, r = 0.22, p = 0.2).
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Differences Among Localities - Morphology
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Male lizards differed significantly in body size among sites (all lizards: F9,204 =
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6.1, p < 0.0001; only the five largest males at each site, F9,40 = 17.5, p < 0.0001, Figure
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2.4). Using all measured lizards, eight pairs of localities differed significantly in size,
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while using just the five largest lizards, there were eighteen significant pairwise
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comparisons (Table 2.1). We use these latter SVL means based on the five largest
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individuals for the population means used in the remainder of the analyses.
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All body shape variables were significantly correlated with lnSVL (all p <0.001;
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results not presented); thus residuals from a linear regression on lnSVL were used in all
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remaining analyses. Residual tail length did not differ among sites (ANOVA, F9,42 =
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0.78, p = 0.8); since this measurement was missing from many lizards with regenerated
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or missing tails (162 of 214 individuals), this variable was excluded from all further
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analyses. Lizards differed in body shape among sites (Wilks’ λ = 0.05, p < 0.0001).
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Many pairs of individual localities were also distinct from each other, with a total of 36
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significant pairwise differences between localities, and with the FI population being the
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most distinct (Table 2.1; Figure 2.5). The first two canonical axes derived from the
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MANOVA, which together explain 60% of the total variance in body shape, mainly
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describe variation in the limbs, head, and pelvis, with high values of canonical axis 1
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describing lizards with relatively short front legs, long hind legs, and a small head, and
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large values for axis 2 describing lizards with long front legs, a short interlimb distance,
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and a narrow pelvis (Table 2.2). Univariate ANOVAs showed significant differences
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among localities in all residual variables (all p < 0.05; results not presented).
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Repeatabilities for head color pattern partial warp scores taken from individual
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lizards photographed twice were generally higher than 0.9 (range: 0.76 - 0.98, average
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0.90). Furthermore, partial warp axes with the lowest repeatabilities also tended to have
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smaller total sums of squares, i.e. smaller variance among individuals (results not
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presented). Error associated with different photos of the same individual was generally
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small compared to differences among individuals (average: 6.3% of total variance).
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Thus, we derived partial warp scores (Figure 2.6) from coordinates taken from a single
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photo of each individual for all remaining analyses.
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Five of the ten partial warp axes were significantly correlated with lnSVL (results
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not presented), and thus include allometric components of head color pattern change. For
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this reason, we repeated all head color pattern analyses using residuals from the
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regression of all partial warp axes on lnSVL. Head color pattern varied significantly
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among localities (MANOVA on partial warp axes, Wilks’ λ = 0.037, p < 0.0001;
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residuals, Wilks’ λ = 0.045, p < 0.0001; Figure 2.7). Canonical loadings were similar in
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analyses based on the PW axes and those based on size-removed residuals (Table 2.2).
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The first canonical axis loaded most heavily in opposite directions on PW6 and PW7,
19
representing a narrowing of points on the front of the snout and a broadening of those on
20
the back of the head, while the second described changes along partial warp axes PW1
21
and PW8, representing a compression of points along the midline of the head and, again,
22
a broadening of points on the back of the head (Figure 2.6). Together, these two
23
canonical axes described 80% of the variation in head color pattern (Table 2.2). Most of
17
18
1
the pairwise comparisons between localities were significant (partial warp axes: 29 of 45
2
comparisons significant; residuals, 26 of 45 significant; Table 2.1). Many of these
3
comparisons involved P. inexpectata, which was significantly different from every
4
population of P. ornata. However, differences among populations were also significant
5
when P. inexpectata from Réunion were excluded from the analysis (partial warp axes,
6
Wilks’ λ = 0.11, p < 0.0001; residuals, Wilks’ λ = 0.13, p < 0.0001). Since we deal
7
separately with size changes in the analyses presented here, we use residual head color
8
pattern change axes for the remainder of the analyses described below.
9
There were no significant correlations in the patterns of between-site variation
10
among the morphological data sets included in this study (Mantel tests, SVL - body
11
shape, r = -0.26, p = 0.93; SVL - head color pattern, z = 0.01, p = 0.29; body shape - head
12
color pattern, r = -0.12, p = 0.50). Thus, although each morphological data set differs
13
among sites, patterns of variation among sites vary for the three matrices.
14
Differences among Localities - Habitat Use
15
Lizards differed in habitat use among localities (Wilks’ λ = 0.35, p < 0.0001;
16
Figure 2.8). The first canonical axis, which described 88% of the variation, contrasts
17
lizards living on low, open perches vs. those on high perches with substantial canopy
18
cover (Table 2.2). There were 19 significant pairwise comparisons among sites (Table
19
2.3). Nine of these involved a comparison with Gunner’s Quoin, where the lizards
20
perched on the ground a large proportion of the time. Correspondence analyses of
21
categorical habitat use variables resulted in four axes which explained 52.6% of the total
22
variance, and differed significantly among localities (Wilks’ λ = 0.23, p < 0.0001). Using
23
these axes, there were 34 significant pairwise comparisons among sites (Table 2.3).
18
19
1
Individual ANOVAs and chi-squared tests showed significant differences in all habitat
2
use categories among sites (perch height: F9, 396 = 23.9, p < 0.001; perch diameter: F9, 396
3
= 19.7, p < 0.001; percent canopy: F9, 396 = 34.9, p < 0.001; vegetation type χ2 = 331.0, p
4
< 0.001; palm vs. non-palm χ2 = 196.4, p < 0.001; DBH category χ230 = 70.3, p < 0.001;
5
substrate χ260 = 456.8, p < 0.001; substrate texture χ210 = 66.2, p < 0.001; tree location χ250
6
= 555.6, p < 0.001).
7
We found a significant correlation between differences in continuous and
8
categorical habitat use variables among sites (Mantel test, r = 0.82, p = 0.016), indicating
9
that these two data sets varied similarly among sites. Thus, only the distance matrix
10
based on continuous habitat variables was used in the remainder of analyses; results of
11
analyses using the habitat distance matrix based on categorical variables were similar
12
(results not presented). Habitat use differences among sites were uncorrelated with
13
geographic distance (Mantel test, r = -0.17, p = 0.9).
14
15
Correlates of Morphological Variation
Lizard body size differences among islands were not correlated with geographic
16
distance among localities or differences in habitat use (Table 2.4). Furthermore, body
17
size was not significantly correlated with any individual habitat use variable (Table 2.5).
18
However, there were differences in size between populations of lizards on mainland
19
Mauritius and those on smaller outlying islands (Table 2.4; Figure 2.9). Most significant
20
differences in body size between localities were a mainland-island comparison (all
21
lizards: six of eight significant comparisons; five largest lizards: twelve of eighteen;
22
Table 2.1). Overall, lizards from offshore islands were significantly larger than those
23
from the mainland (mainland: 53.6 ± 5.0 mm, island: 57.5 ± 2.0 mm, F1, 43 = 14.91, p <
19
20
1
0.0004; Figure 2.9; Réunion excluded, see above). Lizard size was also related to the
2
number of other Phelsuma species present in the community, with the largest lizards
3
occurring in communities with only one species of Phelsuma (Table 2.4).
4
Differences among localities in body shape were not significantly correlated with
5
geographic distance, but the between-locality trait variance-covariance matrix was
6
significantly correlated with the pooled within-locality trait variance-covariance matrix
7
(Table 2.4). However, the regression slope of log-transformed PCA variances within and
8
among populations was significantly greater than one (slope ± SE: 1.47 ± 0.19; p[slope ≠
9
1] = 0.03). Differences in body shape were not related to overall differences in habitat
10
use (Table 2.4), and, after correcting for multiple comparisons, no individual traits were
11
significantly correlated with any individual habitat variables (Table 2.5). Finally, body
12
shape did not systematically differ between mainland and small island localities and was
13
not related to the number of sympatric Phelsuma species (Table 2.4).
14
Differences among localities in head color pattern was strongly associated with
15
geographic distance between localities (Figure 2.10, Table 2.4; correlation was still
16
significant when P. inexpectata was excluded: Mantel r = 0.50, p = 0.004). Furthermore,
17
the between-locality trait variance-covariance matrix was highly correlated with the
18
pooled within-locality matrix (Table 2.4). Also, the regression slope of log-transformed
19
PCA variances within and among populations was not significantly different from one
20
(slope ± SE: 1.16 ± 0.15; p [slope ≠ 1] = 0.31). Differences in head color pattern were
21
not associated with habitat use (Table 2.4) nor with any individual habitat use variable
22
(Table 2.5). Finally, head color pattern did not differ between mainland and small island
20
21
1
localities and did not depend on the number of sympatric Phelsuma species present
2
(Table 2.4).
3
DISCUSSION
4
5
Genetic and morphological differences among populations
Geographically structured genetic and phenotypic differences within a species can
6
give insight into the process of differentiation and speciation (Gubitz et al. 2000; Thorpe
7
and Malhotra 1998). In Phelsuma ornata, genetic data suggests that the population
8
structure for these lizards fits an isolation by distance model (Wright 1943). There is
9
substantial variation among populations in morphology and habitat use, with lizards
10
11
differing in body size, body shape, head color pattern and perch microhabitat.
Significant variation among small-island populations has been found in other
12
groups (e.g. (Scott et al. 2003; Wilder and Hollocher 2003), and may be a general feature
13
of isolated or semi-isolated island populations. These small peripheral populations likely
14
play a role in speciation (Grant 1998b). Investigating the causal factors influencing inter-
15
island variation within a species or a group of closely-related species can be informative
16
about the processes driving differentiation and speciation. In the case presented here,
17
even though all measured morphological axes (body size, body shape, and head color
18
pattern) differ among localities, likely explanations for these differences vary.
19
20
Genetic drift and isolation by distance in head color pattern
Under genetic drift in a population with an isolation-by-distance genetic structure,
21
phenotypic differences in a quantitative trait will be correlated with geographic distance
22
(Lande 1991). We found that head color pattern differences among localities reflected
23
such a pattern. Furthermore, the pattern of trait variance and covariance in head
21
22
1
coloration among sites was proportional to the pattern of variance and covariance within
2
populations, again a prediction if the character is influenced by genetic drift (Marroig and
3
Cheverud 2004).
4
This result does not imply that head color pattern has no selective importance in
5
populations of these lizards. Our results are similar to those of a previous study by
6
(Thorpe 1996), who found a significant correlation between presence of blue leg spots
7
and phylogenetic history separating populations of Gallotia galloti in the western Canary
8
islands. In these lizards, blue leg spots are suspected to be involved in conspecific
9
signaling (Thorpe 1996). For day geckos, it is possible that bright coloration might play
10
a role in sexual selection, similar to the role played by coloration in other lizards with
11
conspicuous markings (e.g. (Calsbeek and Sinervo 2002); (Stuart-Fox et al. 2004; Stuart-
12
Fox et al. 2003; Stuart-Fox and Ord 2004). In that case, it might be expected that the
13
particular arrangement of colors on a lizard’s head would be important in terms of
14
attractiveness to mates. If there were differences in these preferences among populations,
15
but those preferences were evolving mainly due to genetic drift, then this could lead to
16
variation among populations like that found in this study. Mate choice experiments using
17
lizards from different localities would be interesting in this context. If lizards show a
18
strong preference for traits from their own population, then this would suggest that sexual
19
selection plays a role in the evolution of these traits.
20
Another alternative possibility is that head color patterns are adaptations to
21
unmeasured habitat variables that are themselves spatially autocorrelated. In other lizard
22
groups, body color pattern has been shown to have an adaptive basis (e.g. (Leal and
23
Fleishman 2002; Rosenblum et al. 2004; Thorpe 2002). Since we were unable to
22
23
1
quantify the hue and intensity of head color patterns in these lizards, many aspects of
2
head coloration were unexplored, and it is possible that other aspects of head color
3
pattern may reflect local adaptation. For example, lizards from more xeric habitats in
4
Mauritius seemed to be duller in coloration than those from more mesic habitats (L.
5
Harmon, pers. obs.). Further work is needed to investigate these differences in greater
6
detail.
7
Making inferences such as these based on comparisons of phenotypic variance-
8
covariance matrices can be problematic if they do not accurately reflect underlying
9
patterns of genetic variance and covariance (Willis et al. 1991). The extent to which G
10
and P are expected to differ is mainly an empirical question (Cheverud 1982; Cheverud
11
1988); most reviews have concluded that G and P are proportional, at least for
12
morphological traits (Cheverud 1988; Cheverud 1996; Koots and Gibson 1996; Roff
13
1995; Roff 1997). Furthermore, differences between these two matrices depend strongly
14
on the sample size used to estimate G, suggesting that at least some of the differences
15
uncovered in the past are due to inadequate sampling and the difficulty in estimating G
16
(Cheverud 1988). For now, using P matrices as a surrogate for G matrices remains a
17
promising approach for species that are highly threatened, cannot be bred easily in the
18
lab, or are otherwise not readily amenable to genetic studies (Arnold and Phillips 1999).
19
Convergent gigantism on small offshore islands
20
Body size also varies among islands, but that variation is not correlated with
21
either geographic distance or any measured environmental variable. However, lizards
22
tend to be larger on small offshore islands compared to those on the mainland. In studies
23
of other reptile species, both insular dwarfism and gigantism have both been encountered
23
24
1
(e.g. Case 1978; (Arnold 2000; Boback 2003; Keogh et al. 2005; Petren and Case 1997).
2
In snakes, several studies have provided evidence that changes in body size on islands are
3
due to differences in the size of available prey (Boback 2003; Keogh et al. 2005). This is
4
also a possible explanation in Phelsuma; (Gardner 1984) suggested a relationship
5
between size and diet in Phelsuma in the Seychelles, with larger species selecting larger
6
prey. Another explanation relies on the idea that consistent environmental differences
7
between mainland and island habitats, such as reduced species diversity, alter the balance
8
between competitive abilities and energetics (Case 1978; Lomolino 1985). This could
9
potentially lead to convergent size changes among island populations. In the case of
10
Phelsuma, this explanation is supported by the observation that offshore Mauritian
11
islands tend to have fewer species (either one, as on IAA, GQ, GAB, FI, or two on IAB
12
and RI). We found that lizard size is also related to the number of sympatric Phelsuma
13
species, with larger individuals found in communities with lower species richness. Since
14
intraspecific aggressive behavior is frequently observed in Phelsuma (L. Harmon, pers.
15
obs.), large body size may give individuals an advantage in intraspecific dominance
16
(Lomolino 1985). This would be relevant if lizard densities were higher and aggressive
17
intraspecific interactions more frequent in island populations compared to those on the
18
mainland. This seems to be the case for several groups of lizards, including P. ornata
19
(Rodda and Dean-Bradley 2002; Rodda et al. 2001).
20
It is also possible that size differences among populations are partially or wholly
21
due to non-genetic factors, such as phenotypic plasticity or selective predation (Barahona
22
et al. 2000). This latter explanation is plausible since there are more potential predators,
23
such as Mauritius kestrels (Falco punctatus), in the more species-rich mainland habitats.
24
25
1
If those predators were selectively capturing larger individuals, then this could lead to a
2
smaller average size in mainland vs. island populations. Of course, this differential
3
selection is also a potential adaptive explanation for differences in body size. Common
4
garden experiments would be necessary to evaluate the relative roles of genetic and
5
environmental factors in determining these body size differences.
6
The relationship between body size and island status became marginally non-
7
significant when controlling for phylogeny. This is because most of the islands sampled
8
are from the north of Mauritius, and are close together geographically and genetically
9
similar; thus, a single increase in body size on one of these island followed by inter-island
10
colonization could result in the pattern seen here. More detailed genetic surveys, as well
11
as better sampling of island populations of P. ornata, would help address this issue.
12
Another confounding variable is the presence of introduced Calotes versicolor in all of
13
the communities except those on the isolated northern islands, where P. ornata are larger.
14
There have been no studies investigating interactions between C. versicolor and P.
15
ornata, although they occupy similar habitats and could interact through competition and
16
/ or predation. This is also a potentially fruitful area of future inquiry.
17
18
Body shape variation among populations
Differences in body shape among localities are more enigmatic. These
19
differences are not correlated with geographic distance nor with any measured ecological
20
habitat use variable. Trait variances and covariances among populations are related to
21
those within populations, suggesting a role for drift; however, this relationship is weak
22
and leaves a large amount of body shape variation unexplained. Furthermore, regression
23
of within- and between- variance of PC axes suggests that along the most variable axes,
25
26
1
there is more variation among populations than would be expected by drift. This could
2
be due to diversifying selection along these main axes, or stabilizing selection along the
3
minor PC axes (Marroig and Cheverud 2004). In the latter case, no ecomorphological
4
correlations would be observed, despite the fact that selection is occurring, because all
5
populations are on the same adaptive peak. In the former case, it is possible that the
6
measured morphological variables are related to other, unmeasured ecological variables.
7
For example, rainfall, temperature, and other environmental variables may play an
8
important role in the ecology of these lizards. A future study investigating correlations
9
between variation in body shape and such environmental variables would be informative.
10
We have shown that populations of Phelsuma ornata and P. inexpectata are
11
morphologically distinct, but that the processes responsible for this variation differ for
12
body size, body shape, and head color pattern. Body size tends to be larger on small
13
offshore islands, suggesting a role for adaptation; in contrast, head color patterns show
14
patterns indicative of the role of genetic drift under an isolation by distance model. Body
15
shape variation remains unexplained, although drift has likely played some role in
16
differentiation. Clearly, phenotypic patterns among populations of these island lizards
17
have been shaped by the interplay of natural selection and genetic drift. This substantial
18
among-population variation in island forms likely plays a key role in the process of
19
diversification during adaptive radiations. Given that the Mascarenes are home to an
20
endemic radiation of these lizards (Austin et al. 2004)(Austin et al. 2004), the patterns
21
seen here may give insight into how speciation proceeds in a limited geographic area.
22
ACKNOWLEDGEMENTS
26
27
1
We would like to thank C. Jones, L. Harmon, V. Tatayah, A. Khadun, C. Gibson,
2
T. Wolff, M. Barry, N. Cole, L. Cole, T. Ross, J.-M. Probst, A. Cheke, K. Freeman,
3
Mauritius National Parks and Conservation, and the Mauritian Wildlife Foundation
4
volunteers for assistance and advice. J. Kolbe, D. Hansen, J. Melville, and N. Cole
5
provided helpful comments on this manuscript. This work was supported by an NSF
6
Dissertation Improvement Grant DEB 0309361.
7
27
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32
33
1
TABLES
2
Table 2.1. Results of paired t-tests / MANOVAS of morphometric variables among
3
localities. P-value corrected for multiple comparisons using sequential Bonferroni
4
correction. lnSVL: below diagonal = all lizards, above diagonal = largest five lizards
5
from each locality. Head color pattern: above diagonal = raw pw scores, below diagonal
6
= residuals from regression on lnSVL. * = p < 0.05, ** = p < 0.01, *** = p < 0.001.
CAS
FI
GAB
GQ
IAA
IAB
RI
RUN
TAM
YY
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
lnSVL
Body Shape
Head Color Pattern
CAS
-
FI
***
-
GAB
***
-
*
-
-
*
***
-
*
***
***
***
*
***
***
***
*
***
***
***
***
*
***
***
**
**
*
***
***
**
***
**
***
***
***
*
7
8
33
GQ
***
-
IAA
***
-
IAB
**
-
RI
***
-
RUN
***
***
-
***
**
-
***
*
-
**
-
*
***
-
***
***
***
**
***
***
-
**
**
**
**
**
-
***
***
***
***
**
**
-
***
***
***
***
***
***
**
***
**
***
*
***
*
**
***
**
*
**
***
*
***
***
***
***
***
*
***
***
***
***
***
***
TAM
***
**
***
*
***
*
*
-
*
**
***
***
***
YY
***
-
***
**
***
*
*
***
***
***
***
-
34
1
Table 2.2. Canonical axes for MANOVAs among localities. Each table includes the
2
correlation between each canonical axes from the MANOVA and the original variables
3
for each data set, and the percentage of total variation explained by that canonical axis.
4
(a) body shape variables.
Variable
F1
F2
F3
H1
H2
H3
HW-EAR
HW-EYE
HH
HL
IL
PW
% variance
explained
Canonical axis
1
2
3
-0.39
0.59
0.08
-0.22
0.80
0.00
-0.58
0.21 -0.34
0.46
0.36 -0.54
0.13
0.09
0.03
0.02
0.19 -0.32
-0.53 -0.10 -0.09
-0.20 -0.04 -0.34
-0.38
0.22 -0.23
-0.03
0.15 -0.55
-0.19 -0.30
0.14
-0.52 -0.42 -0.25
0.32
0.28
0.15
4
-0.15
0.34
-0.47
0.15
-0.13
-0.02
0.07
0.04
-0.20
-0.26
-0.34
0.63
0.11
5
34
5
6
7
0.41
0.10
-0.08
0.43
0.65
0.48
0.41
0.26
0.18
-0.15
0.32
0.20
0.20
-0.05
-0.15
0.03
-0.17
0.28
-0.28
-0.42
0.30
0.12
0.30
0.08
0.44
-0.19
0.13
0.15
-0.24
-0.32
-0.02
-0.40
-0.44
-0.13
0.01
-0.08
8
-0.22
-0.22
-0.07
0.00
-0.51
-0.19
0.14
0.08
0.41
-0.58
-0.07
-0.11
9
-0.04
-0.02
0.03
0.17
0.35
-0.32
-0.10
-0.32
0.44
0.09
-0.31
0.07
0.06
0.04
0.03
0.01
0.00
35
1
Table 2.2 (cont)
2
(b) head color pattern partial warp axes.
Raw
Warp
Axes
% variance
explained
Residual
Warp
Axes
% variance
explained
U1
U2
PW1
PW2
PW3
PW4
PW5
PW6
PW7
PW8
Discriminant
axis
LD1
LD2
-0.35
0.59
0.44 -0.55
-0.83 -0.36
0.46 -0.28
-0.69 -0.19
0.26 -0.57
0.25 -0.48
-0.72
0.27
0.86
0.29
0.03
0.07
LD3
0.22
-0.42
0.19
-0.42
0.34
-0.31
-0.38
0.20
0.07
0.75
LD4
0.38
0.06
-0.17
0.06
-0.11
-0.08
-0.13
-0.25
-0.19
0.14
LD5
0.16
-0.10
0.18
-0.46
0.41
0.00
0.26
-0.11
-0.17
0.00
LD6
0.47
-0.06
-0.19
0.14
0.25
-0.16
0.64
-0.31
-0.05
0.13
LD7
-0.22
-0.19
0.02
0.34
0.22
-0.41
0.14
-0.42
0.12
-0.16
LD8
-0.10
-0.19
-0.11
0.06
0.18
-0.26
0.03
0.14
-0.03
0.60
LD9
0.11
0.48
-0.17
0.42
0.17
0.49
-0.12
-0.07
0.08
-0.01
U1
U2
PW1
PW2
PW3
PW4
PW5
PW6
PW7
PW8
59.5
-0.24
0.35
-0.87
0.40
-0.71
0.18
0.15
-0.68
0.88
0.07
21.5
0.53
-0.53
-0.26
-0.25
-0.13
-0.54
-0.43
0.33
0.22
-0.05
7.8
0.21
-0.51
0.22
-0.47
0.39
-0.38
-0.37
0.27
0.11
0.71
4.9
-0.19
0.11
-0.16
0.44
-0.41
0.02
-0.32
0.14
0.17
0.02
3.9
0.49
-0.14
-0.13
-0.04
0.02
-0.28
-0.21
-0.20
-0.16
0.30
2.0
0.45
-0.05
-0.20
0.18
0.22
-0.18
0.67
-0.31
-0.03
0.11
0.4
-0.30
-0.19
0.03
0.35
0.17
-0.40
0.15
-0.40
0.14
-0.21
0.1
-0.15
-0.26
-0.07
-0.03
0.12
-0.33
0.02
0.18
-0.02
0.56
0.0
0.04
0.38
-0.20
0.44
0.19
0.36
-0.04
-0.06
0.00
0.14
59.3
20.8
8.3
4.5
4.2
2.2
0.4
0.1
0.0
3
35
36
1
Table 2.2 (cont).
2
(c) habitat use
Variable
Perch Height
Perch Diameter
% Canopy
% Variance
Explained
Canonical axis
LD1
LD2
-0.76
0.05
0.63
0.77
-0.85
0.34
87.5
LD3
0.65
0.07
-0.41
9.5
3.0
3
4
36
37
1
Table 2.3. Pairwise differences in habitat use from MANOVA among localities. P-value
2
corrected for multiple comparisons using sequential Bonferroni correction. Above
3
diagonal = continuous habitat use variables, below diagonal = discrete habitat use
4
variables. * = p < 0.05, ** = p < 0.01, *** = p < 0.001.
5
CAS
FI
GAB
GQ
IAA
IAB
RI
RUN
TAM
YY
CAS
-
***
FI
***
***
GAB
***
***
***
**
**
**
*
GQ
***
***
***
***
***
***
***
***
***
IAA
***
***
***
***
*
6
37
IAB
**
***
***
***
*
RI
*
***
***
*
*
RUN
***
***
***
***
***
***
***
-
TAM
**
**
***
***
***
***
***
***
-
YY
*
***
**
***
-
38
1
Table 2.4. Results of Mantel tests of correlations between differences among localities in
2
morphological measurements and explanatory matrices (see text for details). Values
3
represent correlation coefficients (r) from Mantel tests on difference matrices, with
4
significance denoted with the following symbols: † = p < 0.1, * = p < 0.05, ** = p < 0.01,
5
*** = p < 0.001.
Hypothesis test
Geographic distance
Within vs. Between
Locality VCV, Mantel test
Within vs. Between
Locality VCV, Random
Skewers
Habitat use
Island
Island, holding
geographic distance
constant
Community
Morphological data set
SVL
Body shape
0.02
-0.14
Head color pattern
0.95**
-
0.38***
0.70***
-0.08
0.37*
0.63***
-0.19
-0.2
0.66***
-0.17
0.03
0.35†
0.45*
-0.22
-0.1
-0.09
-0.16
6
38
39
1
Table 2.5. Correlation coefficients for regressions of locality means between all pairwise
2
morphological variables and habitat use variables. See text for variable abbreviations. *
3
= p < 0.05 before Bonferroni correction; no correlations were significant after controlling
4
for multiple comparisons.
SVL
F1
F2
F3
H1
H2
H3
HEARS
HEYES
HH
HL
IL
PW
UNIF1
UNIF2
PW1
PW2
PW3
PW4
PW5
PW6
PW7
PW8
PH
-0.24
0.41
0.07
0.01
-0.09
0.40
-0.07
0.27
-0.02
-0.13
-0.43
0.27
-0.17
0.58
-0.46
-0.22
-0.05
-0.14
-0.66*
-0.44
0.15
0.16
0.19
PD
-0.07
-0.50
-0.25
-0.32
-0.19
-0.45
-0.38
-0.56
-0.51
-0.19
0.02
-0.16
-0.16
-0.20
0.14
0.21
-0.25
0.27
0.27
0.20
-0.15
-0.15
0.25
%
canopy
-0.31
0.50
0.21
0.25
-0.27
0.15
-0.24
0.17
-0.11
-0.06
-0.29
0.13
-0.35
0.51
-0.34
-0.11
-0.05
0.03
-0.49
-0.09
0.02
0.04
0.20
%
palm
0.04
0.03
-0.34
0.18
-0.54
-0.19
-0.07
0.63
0.15
0.10
-0.23
0.71*
0.71*
0.24
0.02
-0.26
0.12
-0.21
-0.05
-0.02
-0.06
0.19
0.18
%
smooth
-0.20
0.30
0.07
0.36
-0.16
0.18
0.10
0.71*
0.56
0.34
-0.25
0.30
0.26
0.29
-0.26
-0.21
0.05
-0.16
-0.36
-0.07
0.13
0.19
-0.10
5
6
39
% bark
-0.26
0.39
0.39
-0.18
0.37
0.27
-0.07
-0.44
-0.40
-0.27
-0.14
-0.44
-0.73*
0.13
-0.26
0.12
-0.09
0.11
-0.33
-0.31
0.13
-0.10
0.17
%
trunk
0.26
0.02
-0.19
-0.37
-0.31
0.43
-0.08
-0.11
-0.38
-0.41
-0.31
0.47
-0.09
0.48
-0.15
-0.48
0.09
-0.35
-0.38
-0.29
-0.26
0.39
0.28
% tree
0.05
0.11
0.30
-0.26
0.34
-0.23
0.03
-0.71*
-0.52
-0.38
0.18
-0.48
-0.53
-0.26
0.21
0.15
0.21
0.14
0.20
0.16
-0.17
-0.13
0.25
40
1
FIGURES
2
Figure 2.1. Map of sampling localities for Phelsuma ornata and P. inexpectata included
3
in this study. Abbreviations: RI = Round Island, FI = Flat Island, GAB = Gabrielle
4
Island, GQ = Gunner’s Quoin, IAB = Ile Aux Benetiers, IAA = Ile Aux Aigrettes, CAS =
5
Casela Bird Park, TAM = Tamarin mountain, YY = Ylang-Ylang Estate, RUN =
6
Réunion.
7
8
40
41
1
Figure 2.2. Landmarks on the head of Phelsuma ornata. Locations of landmark points
2
are described in the text.
3
4
41
42
1
Figure 2.3. Relationship between pairwise genetic difference and geographic distance
2
among localities included in this study. Solid points: comparisons within Mauritius and
3
surrounding islands; open points: comparisons between Phelsuma inexpectata in Réunion
4
and populations of P. ornata in Mauritius and surrounding islands. The solid line
5
represents the regression including comparisons involving Phelsuma inexpectata, while
6
the dashed line is the regression calculated excluding these comparisons. Both
7
relationships are significant (see text).
Genetic Differences
50
25
0
0
100
200
Geographic Distance (km)
8
9
42
300
43
Figure 2.4. Differences in lizard size among localities, using only the five largest lizards
2
from each population. See text for locality abbreviations.
45
50
SVL
55
60
1
CAS
3
FI
GAB
GQ
IAA
IAB
RI
RUN
Population
4
43
TAM
YY
44
1
Figure 2.5. Differences in mean lizard body shape among localities. Plot of canonical
2
axis 1 vs. canonical axis 2 from MANOVA on locality. Bars represent standard errors.
3
See text for locality abbreviations.
3
FI
Canonical Axis 2
2
GAB
1
RI
TAM
YY
RUN
0
IAB
CAS
-1
GQ
IAA
-2
-3
-2
-1
0
Canonical Axis 1
4
5
44
1
2
45
1
Figure 2.6. Partial warp axes derived for geometric morphometric analysis of head color
2
pattern. Reference = mean head color pattern of all lizards, all remaining plots represent
3
a thin-plate spline deformation in the positive direction for each relative warp axis.
4
45
46
1
Figure 2.7. Differences in head color pattern among localities. Plot of canonical axis 1
2
vs. canonical axis 2 from MANOVA on locality. Bars represent standard errors. See text
3
for locality abbreviations.
4
2
YY
IAA
RUN
Canonical Axis 2
1
TAM
0
GAB
CAS
IAB
GQ
-1
RI
FI
-2
-3
-3
-2
-1
0
1
2
3
Canonical Axis 1
5
6
46
4
5
6
47
1
Figure 2.8. Differences in habitat use among localities. Plot of canonical axis 1 vs.
2
canonical axis 2 from MANOVA on locality. Bars represent standard errors. See text for
3
locality abbreviations.
6
Canonical Axis 2
TAM
IAB
5
YY
FI
GQ
CAS
RUN RI
IAA
4
GAB
3
-4
-3
-2
-1
0
Canonical Axis 1
4
5
47
1
2
48
1
Figure 2.9. Box plot of differences in body size between localities in mainland Mauritius
2
and nearby offshore islands. Five largest lizards per locality included.
3
4
48
49
1
Figure 2.10. Correlation between differences in head color pattern and geographic
2
distance among localities. Points in the upper right hand corner represent comparisons of
3
populations of Phelsuma ornata in Mauritius and surrounding islands to P. inexpectata in
4
Réunion; the positive relationship between difference in head shape and geographic
5
distance remained when these comparisons were excluded from the analysis (see text).
6
7
49