Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2016)
RESEARCH Asymmetric constraints on limits to PAPER species ranges influence consumer- resource richness over an environmental gradient David Guti errez1*, Roger Vila2 and Robert. J. Wilson3
1Area de Biodiversidad y Conservaci on, ABSTRACT Escuela Superior de Ciencias Experimentales Aim There is little consensus about the relative roles of biotic versus abiotic y Tecnolog ıa, Universidad Rey Juan Carlos, M ostoles, Madrid, ES-28933, Spain, 2Institut factors in setting limits to species distributions or in generating geographical de Biologia Evolutiva (CSIC-Univ. Pompeu patterns of species richness. However, despite the probable importance of host Fabra), Barcelona, ES-08003, Spain, 3College availability in governing the distribution and diversity of consumers, few of Life and Environmental Sciences, studies have simultaneously tested the effects of resource distribution and University of Exeter, Exeter EX4 4PS, UK diversity on consumer ranges and richness patterns.
Location Sierra de Guadarrama, central Spain. Methods We examined the effects of biotic resources, consumer attributes and climate on the ranges and species richness patterns of 43 specialist butterflies at 40 sites over a 1800-m elevational gradient. Evidence for resource use was based on comprehensive field records of oviposition and larval feeding on host plants. Results We show that limitation by either biotic interactions with resources (the distributions and parts eaten of the larval host plants) or intrinsic dispersal ability was stronger at upper than lower elevational range limits for butterflies. Both resource and consumer richness followed a unimodal, humped pattern over the elevational gradient, but host plant richness peaked 300 m lower than butterfly richness. In addition, whereas changes in butterfly species richness were roughly symmetrical around peak richness over the gradient studied, the host plants showed markedly lower species richness at high elevations (> 1750 m). Butterfly species richness increased with host plant resource diversity and relative humidity, with a steeper response to host plant richness in cooler sites (at higher elevations). Main conclusions The results demonstrate the role of bottom-up control by resource availability in limiting consumer distributions and richness. Importantly, resource limitation had increasing relevance towards the coolest parts of environmental gradients and those poorest in resource *Correspondence: David Guti errez, Area de species, with potential consequences for ecological responses to environmental Biodiversidad y Conservacion, Escuela Superior de Ciencias Experimentales y change. Tecnolog ıa, Universidad Rey Juan Carlos, Keywords Tulip an s/n, Mostoles, Madrid, ES-28933, Spain. Consumer–resource interaction, elevational gradient, herbivores, Lepidoptera, E-mail: [email protected] mountain biodiversity, range limits.
VC 2016 John Wiley & Sons Ltd DOI: 10.1111/geb.12510 http://wileyonlinelibrary.com/journal/geb 1 D. Gutierrez et al.
INTRODUCTION such as host use (Rodr ıguez-Castaneda~ et al., 2010). As fur- ther support for bottom-up hypotheses, there should be a The determinants of species geographical ranges are impor- stronger relationship between consumer and resource rich- tant for understanding global diversity patterns, and for ness towards the most resource-limited extremes of environ- modelling and managing the responses of biodiversity to mental gradients. environmental change (Gaston, 2003). It has long been pro- If resources strongly limit consumer ranges, then a mark- posed that some antagonistic biotic interactions (competi- edly positive interspecific relationship between their respec- tion, predation, herbivory, parasitism and disease) are more tive distributions is expected. However, this pattern has likely to impose range limits in relatively species-rich parts of rarely been observed, and the majority of consumers only a species’ distribution, such as at lower latitudes and eleva- occupy a fraction of the distribution of their resource species tions (MacArthur, 1972). Despite these long-standing predic- (Quinn et al., 1998). Variation in life-history characteristics tions, the relative importance of biotic interactions limiting could lead individual data points to depart from this species distributions at opposing ends of ecological gradients expected relationship, but in some cases may provide further remains largely unexplored (Sexton et al., 2009; Louthan support for the limiting effects of resource availability on et al., 2015). Recently, Cahill et al. (2014) suggested that abi- individual consumer distributions (Hopkins et al., 2002). For otic factors are in fact supported more often than biotic instance, species using smaller or more ephemeral resources interactions in setting species warm range limits, in contrast may occupy a smaller fraction of host patches (Rodr ıguez to the widely held classical view stated above. Nevertheless, et al., 1994; Hopkins et al., 2002). On the other hand, abun- many of the studies reviewed focused on competition, and dant or dispersive consumers may occupy a larger fraction of few have considered the role of resource availability in limit- resource patches because of higher rates of host patch coloni- ing distributions (Cahill et al., 2014). In the case of con- zation and reduced rates of local extinction (Hanski, 1999; sumer–resource interactions, two pieces of evidence suggest Hopkins et al., 2002). that biotic factors may increase in importance towards In this study, we test the following hypotheses: (1) species-poor extremes of gradients, such as those at high lati- resource availability limits elevational consumer distributions, tudes and elevations, driven by bottom-up effects of resource with increasing importance towards the resource-species- availability on consumers. First, the ‘resource diversity poor extreme of an environmental gradient, and (2) con- hypothesis’ (Hutchinson, 1959) implies that consumer distri- sumer species richness is accounted for by bottom-up butions and diversity are more limited by biotic resources mechanisms associated with resource constraints. As a model moving down resource diversity gradients (Price et al., 2011). system, we use the specialist butterflies and their host plants Second, recent empirically based models for consumers and of a Mediterranean mountain area in central Spain, where their hosts provide evidence for greater resource limitation of many species exhibit range limits at both high and low eleva- consumer distributions at higher latitudes and elevations, tions (Guti errez Ill an et al., 2010). Butterflies represent an under current conditions (Hanspach et al., 2014), and cli- excellent model for testing the role of biotic interactions in mate warming (Schweiger et al., 2012; Romo et al., 2014). determining range limits because they depend on a limited Much research on geographical gradients in community set of plant species as resources for larval development richness focuses on correlations of richness with environmen- (Hanspach et al., 2014). Highly resolved field observations of tal factors (McCain & Grytnes, 2010). However, such an butterfly abundance and host plant use, and of host plant approach does not allow studies to distinguish between the distributions and climate data collected in situ, enable us to hypotheses that: (1) the environment imposes limits on spe- test: (1) whether host plant distributions directly constrain cies richness independently of species identities (top-down the elevational distributions of butterflies, at both high- and hypotheses; e.g. Brown et al., 2001) versus (2) the environ- low-elevation limits, (2) whether specialist butterfly richness ment constrains individual species’ ranges, and ranges sum is positively related to host plant richness, and (3) whether to yield species richness patterns (bottom-up hypotheses; this relationship varies over the climatic gradients associated Kaufman, 1995). Top-down hypotheses assume that energy with elevation. To explain deviations from the expected pat- or other limiting resources impose a carrying capacity on terns in (1), we test for effects on butterfly distributions of species richness, whereas bottom-up hypotheses assume that resource size (herbaceous versus woody host plants) and per- species richness patterns are generated through mechanisms manence (flower–fruit versus leaf feeders), and of butterfly that modulate the niches of individual species (Boucher- dispersal ability, abundance and climatic tolerance and limits. Lalonde et al., 2014). Although these two groups of hypothe- ses are not mutually exclusive, evidence that individual con- METHODS sumer distributions are constrained by individual resource Study system distributions would support the role of bottom-up hypothe- ses in accounting for consumer richness (Boucher-Lalonde The Sierra de Guadarrama (central Spain) is a mountain range et al., 2014). Surprisingly, this kind of evidence has rarely of approximately 100 km 3 30 km located at 408450 N, been used for this purpose, even when it can be directly 48000 W. This mountain range (maximum elevation 2430 m) is documented by field observations of trophic interactions bordered by two plains, the northern one with a minimum
2 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Elevational limits of butterflies and plants
elevation of c. 700 m and the southern one with a minimum of Aglais urticae c. 400 m. Typical vegetation types are evergreen broadleaf wood- Aglais io Gonepteryx rhamni land (largely Quercus ilex subsp. ballota) below 1000 m, decidu- Vanessa atalanta Nymphalis antiopa High mobility ous woodland (largely Quercus pyrenaica)atc. 1000–1500 m Issoria lathonia Lycaena phlaeas and coniferous woodland (Pinus sylvestris)atc. 1500–2000 m. Argynnis pandora Gonepteryx cleopatra Scrub and open grassland are present at all elevations, including Iphiclides podalirius above 2000 m (Rivas-Mart ınez et al., 1987). Temperature Parnassius apollo decreases with elevation at a rate of c.5.88Ckm21 whereas Lycaena virgaureae Argynnis adippe 21 rainfall increases by c.680mmkm (data for the period Argynnis aglaja Argynnis paphia 1997–2003; Wilson et al., 2005). Argynnis niobe Colias alfacariensis The study system includes 40 sites in and around the Aporia crataegi Limenitis reducta Sierra de Guadarrama, representing open areas occurring in Euphydryas aurinia Medium mobility natural or semi-natural habitat selected on the basis of acces- Melitaea phoebe * Anthocharis euphenoides sibility and to provide a representative sample of all eleva- Zerynthia rumina * Zegris eupheme tions in the region (see Appendix S1 and Fig. S1 in the * Euchloe tagis
Supporting Information). Butterflies were sampled at 34 sites Boloria selene Lycaena alciphron in 2006, and the full set of 40 sites in 2007 and 2008 (eleva- Brenthis daphne * Cyaniris semiargus tional range c. 560–2251 m) using standard methodology * Cupido minimus (Pollard & Yates, 1993) (Appendix S1). Butterfly distributions Polyommatus escheri Lysandra albicans were characterized by three response variables based on data Polyommatus thersites Low mobility Melitaea trivia for 2006–08: prevalence (proportion of sites occupied), maxi- Lycaena bleusei Hamearis lucina mum elevational limit (maximum elevation of sites occu- * Scolitantides panoptes Brenthis hecate pied) and minimum elevational limit (minimum elevation of * Spialia sertorius sites occupied). Satyrium spini Sloperia proto Laeosopis roboris Host plant data Lysandra bellargus
We classified butterfly species according to the trophic spe- 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 cialization of their larvae following Tolman & Lewington Elevation (km) (1997): monophagous (butterflies feeding on plants of a sin- Figure 1 Elevational range for 43 butterfly species (black) and gle species), strictly oligophagous (more than one host plant their host plants (grey). Circles (butterflies) and diamonds (host species but only one host plant genus), oligophagous (host plants) represent the mean elevation of occupied sites. Butterfly plants of various genera from the same family) and polypha- species are classified by mobility (three categories) and ordered gous (host plants of various families). The classification was by their mean elevation (lowest to highest) within mobility adapted to the regional context of the study area, meaning categories. Asterisks indicate species feeding on flowers–fruits. that two species (Cyaniris semiargus and Euphydryas aurinia) The dashed thin lines represent 750 m and 2000 m in elevation classified as polyphagous at a European level were classified used as a reference to exclude those species living at the bottom as strictly oligophagous at a regional level. Given the high and the top of the gradient (see Results for further details). diversity of potential host plants for Iberian butterflies (Garc ıa-Barros et al., 2013), our analyses focus on a final set genera Thymus and Rubus, which were to morphospecies (75 of 43 trophically specialized species: all monophagous, strictly species were identified in total, Table S1). Host plant distri- oligophagous and those oligophagous butterflies feeding on butions were characterized by the same three variables as two host plant genera at most (Appendix S1). In addition to butterfly distributions (prevalence, and maximum and mini- those butterflies identified to genus level (see Appendix S1), mum elevations). two species were excluded from analyses, Favonius quercus,a Fifteen and five butterfly species showed, respectively, canopy-dwelling strictly oligophagous species whose occur- higher maximum or lower minimum elevations than their rence and abundance is probably underestimated by the tran- host plants (Fig. 1, see results below). To test to what extent sect method, and Libythea celtis, a monophagous species with butterfly occurrence beyond the host plant elevational limits no host plant records at the study sites. was the result of under-recording host plants that occurred To examine the distribution and elevational range limits of near transects, we compared host plant distributions based potential larval host plants for specialized butterflies, we on the standard 5-m band against those based on a wider recorded the presence–absence of plant species at the 40 tran- 50-m band for five exemplar butterfly species (Appendix S1). sect sites by carefully following the route of the Butterfly attributes 500 m 3 5 m transect in summer 2008 and spring 2009, with some additional records in 2010. All host plants were Our first main aim was to determine to what extent the identified to species level excepting some taxa from the range size and elevational limits of host plants govern range
Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 3 D. Gutierrez et al. size and limits of their specialist herbivores. We expected maximum and minimum elevation), we performed a stand- positive relationships for elevational range sizes and limits ard generalized least squares (GLS) model (not accounting between butterflies and their larval host plants. We consid- for phylogenetic relationships) and two phylogenetic general- ered six attributes that potentially contributed to possible ized least-squares (PGLS) models using common models for departures of individual species from the expected relation- evolutionary change – Brownian motion and Ornstein– ship: host plant size (herbaceous versus woody host plants), Uhlenbeck models (Butler & King, 2004). PGLS adjusts for the part of the host plant eaten by larvae (flower–fruit versus correlated error structure based on the variance–covariance leaf feeders), butterfly mobility (low, medium or high), but- matrix estimated from the phylogeny. The variance–covari- terfly species abundance; and two measures of the climatic ance structure was selected following the general protocol for breadth and limits of each butterfly’s environmental niche, GLS using the packages ‘nlme’ (Pinheiro et al., 2014; R based on temperature and precipitation data over the Euro- Development Core Team, 2014) and ‘ape’ (Paradis et al., pean range (Schweiger et al., 2014). We represent climatic 2004) (Appendix S1). niche breadth by butterfly range temperature and precipita- Second, to select the model(s) on which inference for each tion SD, and climatic limits by maximum and minimum response variable was based, we fitted with maximum likeli- butterfly range temperature and precipitation (Schweiger hood all possible models that included different combinations et al., 2014; see Appendix S1). of categorical (if applicable) and linear terms of explanatory variables (including butterfly attributes and host plant eleva- Butterfly phylogeny tional range data; Appendix S1) and the selected variance– Traits of related taxa may be similar due to common ancestry covariance structure found during the first step. The model and therefore not statistically independent in comparative confidence set consists of the best model(s) selected from the analyses (Harvey & Pagel, 1991). Ecological traits such as total collection of possible models fulfilling user-specified crite- prevalence and elevational limits are emergent ‘species-level’ ria (Burnham & Anderson, 2002). In our case, the criteria attributes rather than individual traits and are therefore not were (Richards, 2005): (1) select models within six DAICc in themselves heritable in the same way as morphological units of the top-ranked (lowest AICc;AICc being the Akaike traits. However, they may be correlated to phylogeny (Kunin, information criterion corrected for small sample size) model; 2008). To control for potential phylogenetic non- (2) within this set, select only those models which did not independence in the analyses (see below), a phylogenetic tree have simpler, higher-ranking variants (i.e. including a smaller of all study species was constructed (Fig. S2). number of the same explanatory variables), thus avoiding over-parameterized models whilst maintaining a high probabil- Environmental data for species richness analysis ity of selecting the true best model. Following model selection, Butterfly species richness is expected to be influenced by host we used model-averaging to obtain model coefficients based plant richness, but abiotic factors including climate and pro- on the confidence sets. This incorporates model selection ductivity may directly influence consumer richness, and may uncertainty whilst weighting the influence of each model by impose constraints on the extent to which consumer richness the strength of its supporting evidence. Model-averaged coeffi- responds to variation in host richness (McCain & Grytnes, cients were derived by weighting using Akaike weights (AICcw) 2010). For the period 2006–12, hourly air temperature and and averaging coefficients over all models in the confidence set relative humidity were recorded by HOBO H8 Pro temp/RH (i.e. coefficient values set to zero in those models in which a and U23 Pro v2 temp/RH loggers in semi-shaded conditions variable was not included) (Burnham & Anderson, 2002; pack- at each of the 40 sampling sites (Appendix S1). Site tempera- age ‘MuMIn’, Barton, 2012). We explored potential intercorre- ture (8C) and relative humidity (%) were calculated from lations among predictor variables prior to model selection HOBO field data as the average of annual mean temperature (Table S2). Because minimum butterfly range precipitation and relative humidity, respectively, in 2006–08. As a surrogate and maximum butterfly range temperature were highly collin- of productivity, actual evapotranspiration was calculated as ear (rs 520.86, Table S2), models for butterfly maximum ele- the average of annual actual evapotranspiration in 2006–08 vation including temperature as an explanatory variable were (Appendix S1). performed separately from those including precipitation. After identifying the model confidence sets for butterfly Statistical analysis prevalence and maximum and minimum elevations, hier- archical partitioning was performed to evaluate the inde- Cross-species analysis pendent and joint effects of each variable in single models We used the information-theoretic approach (Burnham & containing all predictors (Mac Nally & Walsh, 2004). Stand- Anderson, 2002) to model prevalence and elevational limits ard regression and R2 as the goodness of fit measure were of butterflies following two steps. First, we assessed whether used for hierarchical partitioning calculations. The statistical phylogenetic analysis was necessary by comparing residuals significance of the independent contributions was tested by a from linear models with phylogenetically adjusted linear randomization routine (1000 permutations) based on Z- models. For each response variable (prevalence, and scores (Mac Nally, 2002).
4 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Elevational limits of butterflies and plants
Species richness analysis than their host plants (Fig. 1). Upper elevational limits for butterflies were more likely to exceed those of their host In order to quantify species richness for host resources and plants in high mobility species (6/10 species) than medium butterflies, we counted the number of potential host plant and low mobility species (9/33 species). species and specialist butterflies, respectively, at each site. Ele- vational trends in numbers of species for both host plants Cross-species analysis and butterflies were analysed using quasi-Poisson regression by fitting linear and quadratic models including elevation We tested one non-phylogenetic and two different models of only. Then, more complex models including the number of evolutionary change for butterfly prevalence, and for maxi- host plant species, annual mean temperature, relative humid- mum and minimum elevations (with two model sets for ity and actual evapotranspiration in place of elevation, as maximum elevation excluding alternatively maximum butter- well as interactions between the number of host plant species fly range precipitation and minimum butterfly range temper- and climate and productivity variables, were tested to explain ature). In the four cases, AICc values were the smallest for observed numbers of butterfly species. Linear regression was the non-phylogenetic model and DAICc exceeded two in the used in this case because a potential positive relationship evolutionary models (the Ornstein–Uhlenbeck model did not between the number of butterfly and host plant species was converge for butterfly maximum and minimum elevation) expected. The interaction terms allowed us to test whether (Table S4). This suggested that phylogenetic correction was the relationship between numbers of butterfly and host plant not appropriate (subject to the evolutionary models consid- species varied over climatic gradients (Fleming, 2005). ered) for cross-species analyses. Because annual mean temperature and actual evapotranspira- For butterfly prevalence, the confidence set consisted of tion were highly collinear (rs 520.71; Table S3), models just one model (Table 1), and indicated that butterfly preva- including temperature as an explanatory variable were per- lence increased with increasing host plant prevalence, butter- formed separately from those including actual evapotranspi- fly mobility index (particularly for high mobility) and ration (all the remaining correlations between predictor abundance (Fig. 2). Including an additional interaction term variables had absolute values lower than 0.7; Table S3). for ‘host plant prevalence 3 mobility’ increased the AICc The effect of independent variables on the number of value by 5.72 units relative to the best model (Table 1), sug- butterfly species was examined following the information- gesting a common slope for butterfly–host plant prevalence theoretic approach using the same protocol as for cross- relationships for species differing in mobility. species analyses. Models were ranked by QAICc (Akaike For butterfly maximum elevation (excluding the predictor information criterion for overdispersed data corrected for maximum butterfly range precipitation), the confidence set small sample size, Burnham & Anderson, 2002) for consisted of four models (Table 1). The final averaged model elevational trends and by AICc for the more complex models indicated that butterfly maximum elevation increased with for number of butterfly species. The effect of spatial autocorrela- increasing host plant maximum elevation and increasing but- tion of butterfly data was examined using correlograms and terfly abundance, and decreased for flower–fruit eaters and they suggested that this phenomenon was negligible (Appendix with increasing minimum butterfly range temperature (Figs 2 S1). We also used hierarchical partitioning to evaluate the inde- & S3). Including interaction terms for ‘host plant maximum pendentandjointeffectsofeachvariableonnumberofbutter- elevation 3 host plant part eaten’ and ‘minimum butterfly fly species (main effects only) in single models containing all range temperature 3 host plant part eaten’ increased AICc predictors following the same protocol as above. values by 2.74 and 0.84 units, respectively, relative to a model containing the four main terms with no interactions: this RESULTS suggested a common slope for butterfly–host plant maximum We recorded 64142 individuals from 97 species (plus 4 gen- elevation relationships and butterfly maximum elevation– era not identified to species level) across all 40 sites and 3 minimum range temperature relationships for species differ- years. The 43 study species (specialists, n 5 23780 individu- ing in the part of the host plant eaten. The effect of host als) had on average lesser prevalence, and attained lower plant maximum elevation on butterfly maximum elevation maximum and higher minimum elevations than the remain- could partly arise because herbivore and resource will inevi- ing 54 species (all Mann-Whitney tests, P 5 0.05-0.02). But- tably coincide at the highest elevations in species living near terfly prevalence for the 43 study species ranged from 0.025 the top of the gradient. Excluding the 14 butterfly species (3 species) to 1 (2 species). Butterfly maximum elevation occurring above 2000 m produced a confidence set consisting ranged from 558 m (the lowest site elevation; 1 species) to of two simpler models that explained less variance but main- 2251 m (the highest site elevation; 11 species) (Fig. 1). But- tained the effects of host plant maximum elevation and mini- terfly minimum elevation showed less variability than maxi- mum butterfly range temperature (Table S5). All models for mum elevation, ranging from 558 m (the lowest site butterfly maximum elevation excluding minimum butterfly elevation; 9 species) to 1445 m (1 species). range temperature from the predictor set had higher AICc There were 15 and five butterfly species that showed, values than those excluding maximum butterfly range precip- respectively, higher maximum or lower minimum elevations itation (Tables 1 & S5), suggesting that temperature was
Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 5 D. Gutierrez et al.
Table 1 Confidence sets of regression models for (a) prevalence, (b) maximum elevation (excluding maximum butterfly range precipita- tion from the predictor set), and (c) minimum elevation of butterflies (n 5 43 species in all cases).
2 Model KR AICc DAICc AICcw
(a) Models for butterfly prevalence Host plant prevalence 1 mobility 1 butterfly abundance 6 0.80 234.40 0 1 (b) Models for butterfly maximum elevation Host plant maximum elevation 1 host plant part 1 butterfly abundance 1 minimum 6 0.70 16.57 0 0.36 butterfly range temperature Host plant maximum elevation 1 host plant part 1 minimum butterfly range temperature 5 0.68 16.87 0.30 0.31 Host plant maximum elevation 1 butterfly abundance 1 minimum butterfly range temperature 5 0.68 17.07 0.50 0.28 Host plant maximum elevation 1 minimum butterfly range temperature 4 0.63 20.93 4.37 0.04 (c) Models for butterfly minimum elevation Butterfly abundance 1 maximum butterfly range temperature 4 0.49 217.20 0 1
Parameter estimates (6 adjusted SE) for the model averaged confidence sets are: (a) Butterfly prevalence 520.09 (60.05) 1 0.34 (60.08) host plant prevalence 1 0.09 (60.05) medium mobility 1 0.32 (60.06) high mobi- lity 1 0.29 (60.06) butterfly abundance. (b) Butterfly maximum elevation 5 0.95 (60.18) 1 0.43 (60.11) host plant maximum elevation (km) – 0.17 (60.13) flower–fruits 1 0.14 (60.12) butterfly abundance – 0.05 (60.02) minimum butterfly range temperature. (c) Butterfly minimum elevation 5 4.39 (60.60) – 0.23 (60.07) butterfly abundance – 0.19 (60.03) maximum butterfly range temperature. 2 K, number of parameters (includes a parameter for regression variance); R , coefficient of determination; AICc, Akaike information criterion adjusted for small sample size; DAICc, difference in AICc between the current and ‘best’ model; AICcw, Akaike weight. Host plant part and mobil- ity are categorical variables with ‘leaves’ and ‘low mobility’ as reference levels. more important than precipitation in accounting for butter- respectively), strongly supporting the unimodal pattern. fly upper elevational limits (see also ‘Hierarchical partitioning Based on the model, the predicted number of host plant spe- analyses’ below). cies for the lowest site (558 m) was c. 14 species, with only c. For butterfly minimum elevation, the confidence set con- 2.4 host plant species estimated for the highest site (2251 m). sisted of one model including negative effects of butterfly The number of specialist butterfly species represented on abundance and maximum butterfly range temperature (Table average 37% (range 15–46%) of species in an assemblage 1), indicating that species with greater abundance or greater (excluding taxa identified to genus level), and showed a tolerance of high temperatures reached lower elevations (Fig. unimodal relationship with elevation: 2). When the 19 species occurring below 750 m were excluded, a confidence set of two models that explained no: of butterfly species 5 exp½0:42 ð60:43Þ 1 3:71 ð60:64Þ more variance, maintained the effects of abundance and elevation – 1:32 ð60:23Þ elevation2 maximum butterfly range temperature and also included the effect of butterfly mobility was generated (Table S5). where elevation is in km (DQAICc 5 35.86 and 33.94 for the The results from hierarchical partitioning analyses mostly linear and null models, respectively) (Fig. 3). The number of supported results from the information-theoretic approach butterfly species predicted by the model peaked at an eleva- (Appendix S1, Fig. S4), showing significant effects of host tion c. 300 m higher (1404 m) than the number of host plant plant prevalence, butterfly mobility and abundance on preva- species. lence; and significant effects of host plant and environmental Annual mean temperature was highly negatively correlated niche temperature limits on upper and lower elevational with elevation, whereas annual actual evapotranspiration and limits. mean relative humidity were positively correlated with eleva- tion, but relative humidity showed a decreasing pattern Species richness analysis above 1700 m (Fig. S5). For the more complex model for the The number of host plant species showed a unimodal rela- number of butterfly species, considering the number of host tionship with elevation: plants, temperature and relative humidity, the confidence set consisted of three models (Table 2). The final averaged model no: of host plant species 5 exp½1:05 ð60:48Þ 1 3:83 ð60:77Þ included number of host plant species and annual mean tem- elevation – 1:73 ð60:30Þ elevation2 perature (and their interaction), and annual mean relative humidity as explanatory variables (Table 2). Thus, the mag- where elevation is in km, with a mid-elevational peak in the nitude of the positive relationship between the number of predicted number of species at 1105 m (Fig. 3). This model butterfly and host plant species was largely dependent on had a much smaller QAICc than the linear and the null temperature, with an increasing slope as temperature
(intercept only) models (DQAICc 5 37.87 and 74.13, decreased (Fig. 4). For the alternative model including
6 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Elevational limits of butterflies and plants
1 40 (a) (a) Maximum number of butterfly species
0.8 30 nce
0.6
20 0.4
10 Butterfly prevale 0.2 Number of host plant species 0 0 00.20.40.60.81 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 Host plant prevalence
2.5 (b) 40 (b) Maximum number of host plant species
2 30 vation (km)
1.5 20 aximum ele 1 10 terfly m Number of butterfly species
But 0.5 0.5 1 1.5 2 2.5 0 Host plant maximum elevation (km) 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 Elevation (km) 2.5
m) (c)
k Figure 3 Relationship between (a) number of host plant species and (b) number of butterfly species and elevation. Different on ( 2 symbols in (b) represent sites sampled over 2 (open symbols) and 3 (filled symbols) years. The lines of best fit represent the levati equations in the text, based on quasi-Poisson regression (n 5 40 1.5 sites). Vertical dashed thin lines represent the elevation of maximum predicted number of butterfly (a) and host plant species (b). inimum e
1 ym annual actual evapotranspiration instead of temperature, the
confidence set consisted of three models with higher AICc
Butterfl 0.5 than for the set including temperature, and the final averaged 15 16 17 18 19 model included the three variables with no interactions Maximum butterfly range temperature (°C) (Table S6). The results from hierarchical partitioning analyses (including main effects only) showed that number of host Figure 2 Relationship between (a) butterfly prevalence and host plant prevalence, (b) butterfly maximum elevation and host plant plant species and relative humidity were significantly related maximum elevation, and (c) butterfly minimum elevation and to the number of butterfly species (Fig. S6). maximum butterfly range temperature. Different symbols and lines represent (in a) species differing in mobility (low mobility, open DISCUSSION symbol, dotted line; medium mobility, crossed symbol, dashed line; high mobility, filled symbol, solid line), and (in b) host plant part Our results show that host plants had strong effects on both eaten (leaves, circles, thick line; flowers–fruits, squares, thin line). the species distributions and the richness patterns of special- The lines of best fit represent the equations in Table 1, based on ist butterflies over an elevational gradient, supporting the linear regression applied (in a and c) to species of average hypothesis of bottom-up control of herbivore diversity. The ln(abundance), and (in b) to species of average ln(abundance) and results suggest that consumer richness tracked the environ- minimum butterfly range temperature (n 5 43 species). ment to a large extent through the sum of effects of resource
Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 7 D. Gutierrez et al.
Table 2 Confidence sets of regression models for number of butterfly species including number of host plant species, annual mean tem- perature and annual mean relative humidity as predictor variables (n 5 40 sites).
2 Model KR AICc DAICc AICcw
Number of host plant species 1 annual mean temperature 1 (number of host 5 0.60 230.59 0 0.82 plant species x annual mean temperature) Number of host plant species 1 annual mean temperature 4 0.53 234.51 3.92 0.11 Number of host plant species 1 annual mean relative humidity 4 0.51 235.50 4.91 0.07
Parameter estimates (6 adjusted SE) for the model-averaged confidence set are: Number of butterfly species 5 5.04 (612.47) 1 1.40 (60.57) number of host plant species – 0.04 (60.75) annual mean temperature – 0.09 (60.04) (number of host plant species 3 annual mean temperature) 1 0.04 (60.15) annual mean relative humidity. 2 K, number of parameters (includes a parameter for regression variance); R , coefficient of determination; AICc, Akaike information criterion adjusted for small sample size; DAICc, difference in AICc between the current and ‘best’ model; AICcw, Akaike weight. constraints on the ranges of individual species. Nevertheless, appeared to influence lower and upper elevational range lim- host plant limitations were more important towards the its, respectively; but host plant elevational limits only influ- highest part of the elevational gradient, suggesting that the enced the upper elevational limits of the butterflies. We do effects of consumer–resource interactions were context not have data to test whether the effects of competition and dependent (Meier et al., 2011). predation were stronger at lower elevations, and the biogeo- Host plant distributions imposed limits on butterfly graphically inferred temperature tolerances of species could ranges, but mostly through constraints on upper elevational mask the effects of species interactions on geographical limits (Fig. 2), as inferred using distribution models for a ranges, but our results suggest that biotic interactions are similar system elsewhere in Europe (Hanspach et al., 2014). more important in limiting ranges at cooler than at warmer Estimated maximum and minimum temperature tolerances, parts of species distributions (but see MacArthur, 1972). inferred from the geographical ranges of the study species, These results are consistent with more detailed research on the species Aporia crategi in the same area, which suggested that climatic limitation was the most likely explanation for 40 the lower elevational limit, while the absence of host plants from high elevations set the upper limit (Merrill et al., 2008). Our multispecies approach allowed us to show joint 30 elevational patterns of species richness for consumers and resources (which have rarely been reported before; e.g. Rodr ıguez-Castaneda~ et al., 2010), showing typical peaks in 20 the number of species at medium elevations for both taxa (McCain & Grytnes, 2010). We found a peak in the number of species at c. 1400 m for the specialist butterflies, consistent 10 with the pattern previously shown for the whole species pool (Guti errez Ill an et al., 2010). Nevertheless, two major points
Number of butterfly species emerged when comparing richness patterns of host plants and 0 butterflies. First, host plant species richness was particularly 0 10203040 low at the highest locations (Fig. 3), supporting (along with Number of host plant species the results for individual species) the idea that butterfly eleva- tional ranges were constrained by host plant distributions at Figure 4 Relationship between number of butterfly species and the part of the gradient with the lowest resource diversity, as number of host plant species for 40 sites. For illustrative reported for Himalayan birds (Price et al., 2011). Second, the purposes, different symbols represent 13 sites with annual mean species richness peak for plants was c. 300 m lower than that temperatures below 8 8C (triangles), 13 sites with temperatures for butterflies. Hence there was a mid-elevation section between 8 and 11 8C (diamonds), and 14 sites with temperatures (1100–1400 m) with relatively low numbers of species of but- above 11 8C (circles) sampled over 2 (open symbol) and 3 (filled terflies for the diversity of host plants occurring there. symbol) years. The lines of best fit represent the equations in the Elevational species richness gradients may be influenced by text, based on linear regression applied to the average temperature of sites included in each interval: 6.5 8C (solid line), patterns of human impact, which is usually more intensive at 9.2 8C (dashed line) and 12.4 8C (dotted line). Lines only extend low elevations (Nogu es-Bravo et al., 2008). Although, based over the range of number of host plant species at sites in each on land cover, we estimated that human impact was higher temperature interval. The annual mean relative humidity in the habitat adjacent to the lowest sites; we also found that, averaged for all 40 sites was 71.6%. on average, fairly large areas of natural and semi-natural
8 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Elevational limits of butterflies and plants habitat remain at all elevations (> 90%; Appendix S1), sug- species richness could be influenced by water stress, either gesting that species richness patterns represent robust rela- directly or through effects on the nutritional quality of host tionships of butterfly diversity with host plant species plants (Hawkins & Porter, 2003b; Stefanescu et al., 2011). richness and climatic variables. Our cross-species analyses provide evidence for the role of The relatively congruent elevational pattern of both taxa ecological traits in governing the strength of the relationship resulted in a strongly positive relationship between numbers between the distributions of consumers and their resources. of species of butterflies and of host plants (Fig. 4), support- More dispersive and abundant species were more likely to ing the ‘resource diversity hypothesis’ (Hutchinson, 1959). occupy a larger fraction of their host elevational range, pre- Previous work on consumer assemblages has identified sumably because of higher rates of host patch colonization resource diversity as a strong predictor of species numbers and reduced rates of local extinction (Hanski, 1999; Hopkins (Kissling et al., 2007; Men endez et al., 2007). However, it has et al., 2002). The part of the host plant eaten also affected also been shown that correlations between consumer and butterfly distribution: as expected, species whose juvenile resource diversity can result from both groups responding to stages feed on flowers–fruits had lower upper elevational lim- similar environmental variables and not from a causal inter- its than species feeding on leaves. The more ephemeral avail- relationship (Hawkins & Porter, 2003a). Based on the results ability of flowers and fruits, and their high temporal for the elevational distributions of each butterfly species and variability (Thompson & Gilbert, 2014), may increase the its host plants (see above) and hierarchical partitioning, the chance of asynchrony with consumers, and drive reduced most plausible hypothesis is that the relationship between survival and consequently reduced occupancy relative to leaf numbers of butterfly and host plant species was due to feeders (Rodr ıguez et al., 1994). trophic dependency, and hence consumer richness would Some butterfly species presented elevational ranges that result from bottom-up control. exceeded the distribution of their larval resources (Fig. 1). Once the effects of number of host plant species were There are three potential non-exclusive explanations for this accounted for, butterfly species richness tended to be greater pattern: (1) seasonal elevational migrations, (2) incomplete in cooler sites (Fig. 4) and in sites with higher relative humid- sampling of known host plants, and (3) cryptic species and ity. Butterfly species richness also responded more positively unknown host plant species. (1) The best known case in our to the number of host plant species in cooler sites, corre- study area is Gonepteryx rhamni, which undergoes seasonal sponding to higher-elevation sites in the study system. To our elevational migrations in summer up to 750 m above the knowledge, geographical differences in the strength of the highest elevation of host plants (Guti errez & Wilson, 2014); relationship between consumer and resource diversity have it is possible that similar migratory phenomena explain why until now not been studied over a given environmental gradi- 6/10 of high mobility species showed higher upper eleva- ent. Two potential processes could be responsible for such a tional limits than their host plants. The variance associated pattern (Fleming, 2005): (1) between-site differences in the with seasonal migrations is expected to be partly captured by strength of bottom-up control of animal diversity by plant including butterfly mobility and abundance as explanatory diversity; and (2) between-site differences in the degree of variables in the cross-species analyses. (2) Our tests based on specialization of ecological interactions (Novotn y et al., 2006). comparing host plant elevational ranges based on 5- and 50- Given that our study concentrated on butterflies that were rel- m bands for five exemplar butterfly species suggest that, in ative host plant specialists, it is unlikely that the second pro- some cases, there could have been unrecorded nearby host cess made much contribution to the pattern. This fact, along plants outside the 5-m transect band: this could explain the with the apparently greater effect of host plant distribution in fact that the Frangula-Rhamnus feeding species (Gonepteryx limiting upper than lower limits of butterfly elevational spp. and Satyrium spini) represented three of the five species ranges, suggests that the steepness of the relationship between whose lower elevational limits were lower than that of their consumer and resource richness could be due to differences host plants. (3) Recent studies suggest that cryptic species in the strength of bottom-up control. The most plausible (those overlooked due to their morphological similarity, but explanation is that butterfly richness is tied most closely to sometimes displaying different ecologies including larval host the number of host plant species in locations where other plant taxonomic identity) can be commoner than expected biotic (e.g. host plant nutritional quality, natural enemies, in butterfly taxa. While the incidence of this factor in our habitat connectivity) or abiotic factors (e.g. limits to thermal dataset is most probably minor, it could explain specific cases tolerance or growing season) are least restrictive to coloniza- such as the low mobility species Spialia sertorius, for which tion and survival. Our results suggest that direct (non-host the existence of two deeply diverged mitochondrial lineages related) environmental constraints were strongest in the hot- in the Iberian Peninsula has been documented (Dinc a et al., ter, drier, lower-elevation parts of the study system (Fig. S5), 2015). Points (2) and (3) represent additional sources of var- which also had the lowest values for actual evapotranspira- iance that might influence our model selection process, but tion. Indeed, an alternative model to that using temperature there is no reason to suspect any systematic bias in their inci- suggested that butterfly richness was positively related to dence relative to butterfly species attributes. Nevertheless, the actual evapotranspiration. This observation, combined with fact that some butterfly species occurred at higher elevations the positive effect of relative humidity, suggests that butterfly than their host plants, may potentially contribute to the
Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 9 D. Gutierrez et al. differences observed in host plant and butterfly richness Brown, J.H., Ernest, S.K.M., Parody, J.M. & Haskell, J.P. patterns. (2001) Regulation of diversity: maintenance of species rich- Our results were based on a subset of specialist consumers, ness in changing environments. Oecologia, 126, 321–332. for which resource diversity could be more constraining than Burnham, K.P. & Anderson, D.R. (2002) Model selection and for generalist species (Men endez et al., 2007). It would be multimodel inference: a practical information-theoretic interesting to know whether generalist species show a similar approach, 2nd edn. Springer, New York. pattern, but obtaining the necessary data for polyphagous Butler, M.A. & King, A.A. (2004) Phylogenetic comparative butterflies and their host plants at similar scale and resolu- analysis: a modeling approach for adaptive evolution. The tion would represent a major challenge. We thus advocate American Naturalist, 164, 683–695. wider exploration of consumer–host relationships over eleva- Cahill, A.E., Aiello-Lammens, M.E., Fisher-Reid, M.C., Hua, tional gradients to provide further evidence of the role of X., Karanewsky, C.J., Ryu, H.Y., Sbeglia, G.C., Spagnolo, F., biotic interactions in limiting species distributions and influ- Waldron, J.B. & Wiens, J.J. (2014) Causes of warm-edge encing patterns of diversity. range limits: systematic review, proximate factors and Our study suggests that the effect of resources on con- implications for climate change. Journal of Biogeography, sumer distributions and diversity can be asymmetric over 41, 429–442. environmental gradients, with variation in the strength of Dinc a, V., Montagud, S., Talavera, G., Hern andez-Rold an, J., bottom-up biotic limitation. In this case, resource limitation Munguira, M.L., Garcıa-Barros, E., Hebert, P.D.N. & Vila, showed greater importance towards upper than lower eleva- R. (2015) DNA barcode reference library for Iberian but- terflies enables a continental-scale preview of potential tional limits. Increasing limitation by resource availability at cryptic diversity. Scientific Reports, 5, 12395. the cool range margins of specialist consumers has been Fleming, T.H. (2005) The relationship between species inferred from models of butterfly distributions under current richness of vertebrate mutualists and their food plants in (Hanspach et al., 2014) and future climatic conditions tropical and subtropical communities differs among hemi- (Schweiger et al., 2012; Romo et al., 2014). Here, we provide spheres. Oikos, 111, 556–562. fine-resolution empirical evidence of how host plant use Garc ıa-Barros, E., Munguira, M.L., Stefanescu, C. & Vives already constrains species distributions at cool range margins, Moreno, A. (2013) Fauna Ib erica 37: Lepidoptera Papilio- suggesting that biotic interactions can play an increasing role noidea (ed. by M.A. Ramos, J. Alba, X. Bell es, J. Gos albez, in determining consumer diversity toward the coolest and A. Guerra, E. Macpherson, J. Serrano and X. Templado). most resource-species-poor parts of a geographical gradient. Museo Nacional de Ciencias Naturales, CSIC, Madrid. Gaston, K.J. (2003) The structure and dynamics of geographic ACKNOWLEDGEMENTS ranges. Oxford University Press, Oxford. S. B. D ıez and J. Guti errez Ill an assisted with fieldwork, A. Escu- Guti errez, D. & Wilson, R.J. (2014) Climate conditions and dero and M. de la Cruz helped with identification of host plants, resource availability drive return elevational migrations in C. Stefanescu provided updated data on butterfly mobility and a single-brooded insect. Oecologia, 175, 861–873. R. M. Viejo, R. Heikkinen and one anonymous referee made Guti errez Ill an, J., Guti errez, D. & Wilson, R.J. (2010) Fine- useful comments on the manuscript. The research was funded scale determinants of butterfly species richness and compo- by Universidad Rey Juan Carlos/Comunidad de Madrid (URJC- sition in a mountain region. Journal of Biogeography, 37, CM-2006-CET-0592), the Spanish Ministry of Economy and 1706–1720. Competitiveness (REN2002-12853-E/GLO, CGL2005-06820/ Hanski, I. (1999) Metapopulation ecology. Oxford University BOS, CGL2008-04950/BOS, CGL2011-30259, CGL2013-48277-P Press, Oxford. and CGL2014-57784-P), the British Ecological Society and the Hanspach, J., Schweiger, O., Kuhn,€ I., Plattner, M., Pearman, Royal Society. Research permits were provided by Comunidad de P.B., Zimmermann, N.E. & Settele, J. 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SUPPORTING INFORMATION Table S5 Confidence sets of regression models for butterfly maximum elevation, butterfly maximum elevation of species Additional supporting information may be found in the occurring below 2000 m and butterfly minimum elevation of online version of this article at the publisher’s web-site: species occurring above 750 m. Appendix S1 Supplementary methods and results. Table S6 Confidence sets of regression models for number of Figure S1 Site distribution in 2006–08. butterfly species including number of host plant species, Figure S2 Maximum likelihood reconstruction based on COI annual mean relative humidity and annual actual sequences (658 bp) for the 44 specialist butterfly species evapotranspiration as predictor variables. found in the study system (including Libythea celtis, which was excluded from analysis because host plants were absent from the study sites). BIOSKETCHES Figure S3 Relationship between butterfly maximum elevation and minimum butterfly range temperature. David Guti errez is a senior lecturer in ecology at the Figure S4 The independent and joint contribution of the Universidad Rey Juan Carlos, Spain. He is a specialist environmental variables estimated from hierarchical in metapopulation dynamics of butterflies in frag- partitioning for butterfly prevalence, butterfly maximum mented landscapes, and in the biogeography of insect elevation, butterfly minimum elevation, butterfly maximum communities in mountain systems in the context of elevation for species occurring below 2000 m and butterfly climate change. minimum elevation for species occurring above 750 m. Figure S5 Relationship between elevation and annual mean temperature, annual mean relative humidity and annual Roger Vila is a CSIC Scientist at the Institute of Evo- actual evapotranspiration. lutionary Biology (CSIC-UPF) in Barcelona, Spain, Figure S6 The independent and joint contribution of the where he leads the Butterfly Diversity and Evolution environmental variables estimated from hierarchical Lab. He uses butterflies as a model to study large-scale partitioning for butterfly species richness. biodiversity patterns, spanning from speciation to con- Table S1 Study species with their host plant genera in servation biogeography. Europe and potential host plants in the study area. Table S2 Correlation table of the environmental variables Robert J. Wilson is a senior lecturer at the University included in the cross-species analysis. of Exeter, UK. His research examines the ecological Table S3 Correlation table of the environmental variables effects of climate change and habitat fragmentation, included in the butterfly species richness analysis. with particular focus on the distributions and Table S4 Phylogenetic generalized least-squares models for the relationships between butterfly prevalence, maximum and dynamics of species near their geographical range minimum elevations with the prevalence and elevational margins. limits of their host plants, and host plant and butterfly attributes. Editor: Nick J. B. Isaac
12 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd