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The geographic range of a species is influenced by past phylogenetic and biogeographic patterns. However, other historical interactions, including the interplay between life history and geography, are also likely involved. Therefore, the range size of a species can be explained on the basis of niche-breadth or dispersal related hypotheses, and previous work on European butterflies suggests that both, under the respective guise of ecological specialisation and colonising ability may apply. In the present study, data from 205 species of butterflies from the Iberian peninsula were processed through multiple regression analyses to test for correlations between geographic range size, life history traits and geographic features of the species distribution types. In addition, the percentage of variance explained by the subsets of variables analyzed in the study, with and without control for phylogenetic effects was tested. Despite a complex pattern of bivariate correlations, we found that larval polyphagy was the single best correlate of range size, followed by dispersal. Models that combined both life history traits and geographic characteristics performed better than models generated independently. The combined variables explained at least 39% of the variance. Bivariate correlations between range size and body size, migratory habits or egg size primarily reflected taxonomic patterning and reciprocal correlations with larval diet breadth and adult phenology. Therefore, aspects of niche breadth i.e. potential larval diet breadth emerged as the most influential determinants of range size. However, the relationships between these types of ecological traits and biogeographic history must still be considered when associations between life history and range size are of interest.
Range size not only represents a proxy for species rarity (IUCN 2001), but also a “parameter” for patterns of diversity (Rosenzweig 1975, 2003, Gaston and Blackburn 1999, He and Legendre 2002, Gaston 2003, Storch et al. 2007). The geographic range of a species is determined by the interaction between habitat suitability and connectivity, species niche requirements and population dynamics (Gregory and Gaston 2000). Regardless of the complexity of the processes involved (Brown 1984, Gaston et al. 1997, 2000, Cowley et al. 2001b, Gilbert and Lechowicz 2004), these interactions operate via the biological properties of individual species. Hence, the relationships should be identifiable as correlations between species range size and ecological features (Brown 1984, 1995, Gaston and Blackburn 1994, Gaston et al. 2000, Diniz-Filho et al. 2005).
Large-scale inter-specific analyses of present species ranges are often hampered by three factors: 1) phylogenetic patterning; 2) geographic turnover in species life histories; and 3) the biogeographic history underlying present ranges. Taxonomic variability and phylogenetic relationships across a species range are routinely addressed (Carrascal et al. 2008, Gove et al. 2009, Calosi et al. 2010), however geographic turnover and biogeographic history remain largely unresolved. First, species life histories exhibit geographic variability across a species range. Consequently, analyses of “mean” trait values measured across geographic gradients may suggest a misleading niche breadth-based explanation (Gaston et al. 2007). A provisional solution is to focus on intermediate scale patterns. Finally, evidence for hidden historical causes in observable ranges can be tested by comparing apparent relationships between range size and ecological factors with simple biogeographic features of the species range, such as the chorotype and range position (as shown by recent work on plant distributions; Weiser et al. 2007, Gove et al. 2009).
Butterflies are highly sensitive to environmental changes, particularly changes impacting vegetation because butterfly larvae are specialised herbivores. Several Lepidopteran life history traits display notable interspecific variation, as well as intraspecific differences along geographic gradients (Nylin 2009). Studies assessing butterfly range size and ecology across species have been reported in temperate areas, most notably the British Isles (Hodgson 1993, Quinn et al. 1998, Dennis et al. 2000, 2004, 2005, Cowley et al. 2001a, b), among others (Hughes 2000, Komonen et al. 2004). Dennis et al. (2004) provides evidence that correlates range size with population density (depending on the geographic scale; Hughes 2000), development time, number of broods per year, adult mobility, and resource use specificity. Furthermore, host plant type is correlated with phenology (Cizek et al. 2006) and adult mobility with larval polyphagy, resource availability and range position (Komonen et al. 2004). However, Dennis et al. (2005) points out that range size and larval host range are associated because of a reciprocal dependence on other life history and resource variables. Therefore, the available evidence suggests a complex pattern of interrelated factors, and those compatible with a dispersal- or niche- related explanation of range size are most relevant.
The role of history in the evolution of the ecological links within the land biotas remains untested, and evidence suggests that the geographic structure of the West-Palaearctic butterfly fauna has been strongly modelled by postglacial events (Dennis et al. 1991, 1998, Schmitt 2007). The present study served to determine how the integration of data on species ranges and ecology might modify former explanations of range size in these insects (Dennis et al. 2004, 2005). In addition, to what degree the correlations among variables were supported from data derived from faunal regions of different climate and physiography. To answer these questions, we tested the relationships between life histories and geographic ranges of Iberian butterflies. Obscure historical patterns, which might be identifiable on a topological basis, were addressed by analyzing relationships between range size and variables that describe species range (with and without controlling for phylogenetic relatedness). We subsequently compared the explanatory power of each of the two subsets of variables (life history and geography), and tested for a relationship between subsets in terms of their shared variance.
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Pairwise analyses found 24 variables significantly correlated with AREA (Table 1) across species means, and 14 across contrasts (Table 1, Fig. 3 and 4). The selected variables are detailed in Table 2 and 3. A maximum of six “life history” variables were selected when this subset of variables was analysed. However, in the combined analyses (life history plus geography), the geographical variables tended to displace the life history traits from the models, with the exception of LPDB (Fig. 3) and, depending on the analysis, SDMN.
Figure 3. Relationships between range size (AREA) and larval diet breadth (LPDB) based on species raw data (A, C) and independent contrasts (B, D). A and B: bivariate plots; C and D: relationships after controlling for effects of other variables with significant effects, based on the residuals of the linear regressions of AREA and LPDB on the remaining variables in the models of Table 2 (column A) and 3 (column A).
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Figure 4. Relationships between range size (AREA) and the concentration of species' ranges in the study relative to Europe (ENDM) based on raw data (A, C) and independent contrasts (B, D). A and B: bivariate plots; C and D: relationships after controlling for the effects of other significant variables (residuals from the linear regressions of AREA and ENDM on the remaining variables, models in Table 2 and 3 for columns A and B, respectively).
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Table 2. Explanation of AREA based on species means and three subsets of independent variables: life history plus geography (A), life-history (B) and geography (C). The figures shown are estimated coefficients and signs (Coef.), Wald statistics and significance levels (P) of the variables selected in each model. Whole model results are shown at the lower part of the table. **=p<0.01, ***=p<0.001, ****=p<0.0001.
|Model: Variable||(A) All variables||(B) Life history||(C) Geography|
| ||Coef.||Wald (P)||Coef.||Wald (P)||Coef.||Wald (P)|
| || || || || || || |
|DF|| ||195|| ||198|| ||197|
|Deviance|| ||193.374|| ||320.105|| ||307.555|
|Deviance/DF|| ||0.992|| ||1.617|| ||1.561|
|Loglikelihood|| ||−303.885|| ||−367.251|| ||−360.975|
|R2|| ||0.820****|| ||0.811****|| ||0.715****|
Table 3. Explanation of AREA based on the independent contrasts and variable selection. Variable subsets: life history plus geography (A), life history (B) and geography (C). The data shown are estimated coefficients and signs (Coef.), Wald statistics and significance levels (P) of the variables selected in each model. Whole model fitting results are shown at the lower part of the table. **=p<0.01, ***=p<0.001, ****=p<0.0001.
|Model:||(A) All variables||(B) Life history||(C) Geography|
|Variable||Coef.||Wald (P)||Coef.||Wald (P)||Coef.||Wald (P)|
| || || || || || || |
|DF|| ||143|| ||144|| ||145|
|Deviance|| ||235.083|| ||240.065|| ||241.998|
|Deviance/DF|| ||1.644|| ||1.667|| ||1.669|
|Loglikelihood|| ||−128.977|| ||−137.855|| ||−136.855|
|R2|| ||0.614****|| ||0.572****|| ||0.268****|
Seven of the geographical variables exhibited significant effects when species means were analysed (ENDM, ALTM, LATM, REGA, REGB, REGC and REGE; Table 2). However, significant effects were not evident when contrasts were analysed, with the exception of ENDM and/or REGD, which remained significant (Table 3, Fig. 4).
Variance partitioning (Table 4) indicated that irrespective of the source of data (species mean values or contrasts), a large part of the explanation of the variance is shared by life history plus geography.
Table 4. Variance partitions among the life history and geographic variables, based on the species values and on the standardised independent contrasts. The subset of variables for each column consisted of those with significant effects in any of the models described in Table 2 (raw data) and Table 3 (contrasts). The figures represent the proportion of variance explained (over 1.000).
| ||Species values||Contrasts|
|Shared (life history+geographic)||0.655||0.390|
|Residual (not explained)||0.139||0.333|
Finally, dispersion (with fixed effects for AREA) was positively correlated with geography (REGD and ENDM) and negatively with larval host type (LHTH i.e. “not feeding on herbaceous plants”), and with “overwintering as a pupa” (OWSP) (Table 5, 6).
Table 5. Bivariate correlations (R) between the geographical dispersion of the species data points not explained by occupancy (residuals of the regression of DISP on AREA) and the potentially explanatory variables, derived from raw data (n=205) and independent contrasts (n=149). ns=p>0.05, *=p<0.05, **=p<0.01, ***=p<0.001, ****=p<0.0001.
|AWL||−0.097 ns||0.131 ns|
|ASXD||0.009 ns||0.083 ns|
|EGGS||−0.040 ns||0.086 ns|
|MIGR||−0.036 ns||0.061 ns|
| || || |
|VOLT||0.061 ns||0.083 ns|
|SDMN||−0.023 ns||0.159 ns|
|OWSA||−0.007 ns||0.028 ns|
|OWSE||0.016 ns||−0.064 ns|
|OWSL||−0.098 ns||0.031 ns|
| || || |
|LPDB||0.061 ns||0.125 ns|
|LHPL||−0.055 ns||−0.031 ns|
| || || |
|EGGB||0.015 ns||0.011 ns|
|MIRO||0.046 ns||0.049 ns|
| || || |
|REGA||−0.020 ns||0.125 ns|
Table 6. Stepwise selection results summarising the relationships between range dispersion (with fixed AREA effects, residuals from the function in Fig. 2) and the remaining variables, based on species raw values and on independent contrasts. The values shown are estimated coefficients and signs (Coef.), Wald statistics, and significance (P) of selected variables. Whole model fitting statistics are indicated at the lower part of the table. *=p<0.05, **=p<0.01, ***=p<0.001, ****=p<0.0001.
| ||Coef.||Wald (P)||Coef.||Wald (P)|
| || || || || |
|DF|| ||200|| ||145|
|Deviance|| ||5.2751|| ||180.325|
|Deviance/DF|| ||0.026|| ||1.244|
|Loglikelihood|| ||84.2684|| ||−222.579|
|R2|| ||0.299****|| ||0.052**|
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The following summarizes the results of our study: 1) significant pairwise correlations between range size and several life history variables were observed; 2) these correlations generally tend to loose strength after controlling for phylogeny; 3) the variables retained by stepwise regression selection were associated with larval host diet breadth, adult phenology, and geographic features of species distribution; and 4) an integral component of the explanation provided by the models was of a mixed (life history and geographic) nature.
Both niche theory and dispersal-related expectations are supported by the bivariate correlations across the species means results generated in this study. Comparatively, widespread species are larger, more sexually dimorphic, and exhibit increased fecundity and dispersal ability. Species with more scattered/patchy distributions tend to show longer periods of adult occurrence, their larvae typically feed on dicotyledonous woody plants, and demonstrate some degree of ant-dependence as well as a restricted Iberian range with low mean latitude and altitude.
The generally (though not universally, Table 1) lower correlations from independent contrasts do not simply reflect the reduced degrees of freedom (as argued by Dennis et al. 2004), but also the different weight of taxonomic patterning across variables (Brooks and McLennan 2002, Diniz-Filho and Torres 2002). Our comparative results were partly hindered by poor phylogenetic resolution and unknown branch lengths, two reasons why (despite the robust contrasts method under such conditions; Martins and Garland 1991, Garland et al. 1992) we concentrated on identifying the sign and strength of the relationships rather than their shape (Quader et al. 2004). Therefore, within these limitations we confidently concluded that the bivariate correlations between range size and relative egg size, dispersive adults, long period of adult occurrence, larval diet breadth and some geographic variables were not taxonomically driven artefacts.
Among the life history variables, multiple selection on independent contrasts supports a causal interpretation of range size in terms of niche breadth (potential or realised larval polyphagy may assist the offspring to thrive in more diverse habitats) and, at least partially, dispersion in the adult “time window”. However, the second variable may actually represent a geographic turnover of species phenologies, as the Iberian lands cover a remarkable array of climate and vegetation types. Phenological variation along a geographic gradient should be higher among populations than within them, therefore the observed correlation may not denote an ability of widespread species to occupy varied habitats, but the degree to which widespread species phenologies are tuned to local conditions. Depending on the methods and variables used to study British butterflies, this explanation is supported by some reports but not others (Hodgson 1993, Dennis et al. 2004, 2005). Correlations between range size and body size or dispersal ability (formerly documented from various taxa, Gaston and Blackburn 1994, Brown 1995, Purvis et al. 2001 and Diniz-Filho et al. 2005, but cf. Hillebrand et al. 2001, Wilkinson 2001, Fernandez and Vrba 2005 or Rundle et al. 2007) are not supported by the most parsimonious interpretation of our results. The relationship between Iberian butterfly range and adult size is taxonomic, range size is weakly related to migratory status, and both relationships break down under multivariate selection protocols. However, we cannot strictly discard alternative explanations of dispersal type, namely because butterfly wing length (which we tested) might not the best surrogate for dispersal ability (as shown for damselflies by Rundle et al. 2007). Furthermore, functional and genetic links among species life history attributes (Stearns 1977, Roff 2002) generally result in complex patterns of cross correlations (see butterfly examples in García-Barros 2000a or Cizek et al. 2006). If distribution patterns are to be evaluated in terms of the realized niche (Austin and Smith 1989, Kockemann et al. 2009) and niche is assessed based on environmental variables (Thuiller et al. 2003), “complex” variables (describing organism-habitat interactions) should perform better in multivariate tests than “proximate” variables (describing features of the organism, or of the habitat, Dennis et al. 2004, 2005). Therefore, the distinction between “causal” and “most parsimonious” solutions is of interest. However, a more exhaustive analysis of the life histories of Iberian butterflies at local or regional levels and comparable studies across different regions is warranted to provide resolution at this scale.
Results of the study suggested that the explanation for range size could be improved by including additional variables to represent processes not explicitly tested. For example, some geographic variables exhibited a high weight and proportion of variance shared with life history data. Range size might be the result of interactions between species life history patterns and geographic history. Comparable conclusions have been drawn from recent work on plant biogeography (Svenning and Skov 2005, Weiser et al. 2007, Gove et al. 2009). Resolving these processes for further analyses is not a simple task, as it requires a comparative approach to the life histories of each of the taxa, done within a phylogeographic framework. The geographic distribution of species is a product of speciation, extinction and the temporal dynamics of its range (Gaston 1998, 2003). Consequently, the range of a species should be examined and explained in terms of the species features, its interactions with the environment (Kean and Barlow 2004) and the historical factors, which likely set limits on other interactions. However, little attention has been drawn to the relationships between different macroecological patterns (Blackburn and Gaston 2001).
Two incidental findings of our work relate to endemism and Rapoport's pattern. The significance of endemism has a practical application in Iberia: Iberian endemics tend to be rare in Iberia (for comparable results from other regions and taxa see Gregory and Blackburn 1998 and Carrascal et al. 2008). Based on our knowledge of butterfly species and their distributions in the study area, most of the butterflies with small ranges in this region are restricted to relatively high elevations on the main mountain chains, which cover a small portion of the peninsula. These species may be particularly sensitive e.g. in terms of global warming and other environmental impacts. For the same reason, the concentration of mountain ranges in the northern half of the peninsula explains the negative pairwise correlations between range size and both latitude and altitude, incongruent with Rapoport's pattern (where ranges should be wider at higher latitudes or altitudes; Rapoport 1975, Lomolino et al. 2006).
In summary, our results identified niche breadth (via larval polyphagy) as primarily correlated with range size, together with interactions between non-explicit historical causes (represented by chorotypes) and life histories. A more thorough “dissection” of the biological correlations of range size, as well as an integrated multi-scale protocol is required before more specific explanations for range size may be achieved.