ORI GIN AL PA PER

Environmental determinants of geographic butterflyrichness pattern in eastern China

Shengbin Chen • Lingfeng Mao • Jinlong Zhang • Kexin Zhou •

Jixi Gao

Received: 13 June 2013 / Revised: 27 February 2014 / Accepted: 7 March 2014� Springer Science+Business Media Dordrecht 2014

Abstract A long-standing task for ecologists and biogeographers is to reveal the

underlying mechanisms accounting for the geographic pattern of species diversity. The

number of hypotheses to explain geographic variation in species diversity has increased

dramatically during the past half century. The oldest and the most popular one is envi-

ronmental determination. However, seasonality, the intra-annual variability in climate

variables has been rarely related to species richness. In this study, we assessed the relative

importance of three environmental hypotheses: energy, seasonality and heterogeneity in

explaining species richness pattern of butterflies in Eastern China. In addition, we also

examined how environmental variables affect the relationship between species richness of

butterflies and seed plants at geographic scale. All the environmental factors significantly

affected butterfly richness, except sampling area and coefficient of variation of mean

monthly precipitation. Energy and seasonality hypotheses explained comparable variation

in butterfly richness (42.3 vs. 39.3 %), higher than that of heterogeneity hypothesis

(25.9 %). Variation partitioning indicated that the independent effect of seasonality was

Communicated by Peter J. T. White.

Electronic supplementary material The online version of this article (doi:10.1007/s10531-014-0676-8)contains supplementary material, which is available to authorized users.

S. Chen � K. Zhou � J. Gao (&)Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection,No. 8 Jiangwangmiao Street, Xuanwu District, Nanjing 210042, Chinae-mail: gjx@nies.org

S. Chene-mail: chainpin@126.com

L. MaoDepartment of Plant Biology, Michigan State University, East Lansing, MI 48824, USA

J. ZhangFlora Conservation Department, Kadoorie Farm and Botanic Garden, Lam Kam Road, Tai Po,NT, Hong Kong SAR

123

Biodivers ConservDOI 10.1007/s10531-014-0676-8

much lower (0.0 %) than that of energy (5.5 %) and heterogeneity (6.3 %). However,

seasonality performed better in explaining butterfly richness in topographically complex

areas, reducing spatial autocorrelation in butterfly richness, and more strongly affect the

association between butterflies and seed plants. The positive relationship between seed

plant richness and butterfly richness was most likely the result of environmental variables

(especially seasonality) influencing them in parallel. Insufficient sampling may partly

explain the low explanatory power of environmental model (52.1 %) for geographic

butterfly richness pattern. Our results have important implications for predicting the

response of butterfly diversity to climate change.

Keywords Biogeography � Butterfly fauna � Energy � Heterogeneity � Seasonality �Species richness

Introduction

Understanding the underlying mechanisms shaping the geographic pattern of species

diversity has been a long-standing task for ecologists and biogeographers. This is

increasingly critical not only for biodiversity conservation but also for predicting the

response of species diversity to global change (Kerr et al. 2007). The number of

hypotheses to explain geographic variation in species diversity has increased dramatically

during the past half century (Willig et al. 2003; Field et al. 2009). While different

hypotheses refer to ecological, biogeographic, or evolutionary processes, they all predict

a prominent role of environmental variables (energy, seasonality and heterogeneity) in

explaining geographic species richness patterns (Willig et al. 2003; Field et al. 2009;

Tello and Stevens 2010). In addition, plant diversity is often regarded as the direct driver

of animal richness, especially for herbivores and frugivores (Kissling et al. 2007; Kissling

et al. 2010; Zhang et al. 2013).

The roles of energy and heterogeneity in determining geographic species richness

patterns have been extensively examined (Evans et al. 2005; Field et al. 2009). Energy can

be grouped into two categories, kinetic and potential energy, which potentially control

species number in different ways (Clarke and Gaston 2006; Allen et al. 2007). Kinetic

energy, which directly reflects fluxes of solar radiation, is often represented by measures of

temperature and potential evapotranspiration (Evans et al. 2005; Clarke and Gaston 2006;

Allen et al. 2007), while potential energy directly or indirectly relates to the rate of

conversion of solar radiation to reduced carbon compounds through photosynthesis, and

can be reflected by actual evapotranspiration (Allen et al. 2007). Many studies have found

strong positive relationships between species richness of various taxa and energy-related

variables (Hawkins et al. 2003; Field et al. 2009). The heterogeneity hypothesis assumes

that habitats with complex spatial structure may provide more niches and more diverse

ways of exploiting resources, or that they increase speciation rates and thus species

richness (Ruggiero and Hawkins 2008). Heterogeneity often represents spatial variation of

energy availability at local scales (Tello and Stevens 2010).

Seasonality, the intra-annual variability in climate variables (Williams and Middleton

2008; Ackerly et al. 2010; Carrara and Vazquez 2010), has been rarely related to species

richness and the results were controversial (Kerr 1999; Badgley and Fox 2000; Ruggiero

and Kitzberger 2004; Qian 2008; Carrara and Vazquez 2010; Tello et al. 2010).

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Seasonality may have two effects on biodiversity, depending on the characteristics of

specific organisms and on the scale at which ecological and evolutionary processes happen.

On the one hand, seasonality could increase species diversity if species specialize and

occupy different temporal niches (Chesson and Huntly 1997). On the other hand, sea-

sonality could lead to low species diversity because species must evolve broad niches to

survive in highly seasonal habitats, which may make the community more readily saturated

(Tello and Stevens 2010). Our poor understanding on how seasonality controls biodiversity

present a major challenge for predicting the future of biodiversity in a changing climate.

Therefore, more researches are required to elucidate the effect of seasonality on species

diversity of various taxa.

Butterflies, one of the best-known invertebrate taxa worldwide, are an important

component of biodiversity and ecosystem structure. Climatic conditions strongly con-

strain the metabolism and behavior of butterflies; therefore, they are particularly suitable

for examining species richness-environment relationships (White and Kerr 2007) and

predicting potential effect of future climatic change on species distributions (Fleishman

2010). Several studies have found weak direct relationship between butterfly richness and

plant richness at different geographical scales (Hawkins and Porter 2003a, b; Menendez

et al. 2007; Kitahara et al. 2008). However, these analyses were generally conducted at

medium geographic extent and the results were conflicting. So far, almost all geographic

scale studies on butterfly diversity have been carried out in Europe or North America. A

geographic broadening of investigated regions may benefit the aim of finding general

patterns.

The butterfly fauna of China, with 12 families, 375 genera and[1300 species, is more

diverse than that of Europe or North America (Chou 2000). Most species occur in Eastern

China, a topographically complex region that is affected by monsoon climate (Chou 2000).

The local butterfly fauna in many areas (such as nature reserves, national parks and

counties) of east China has been intensively inventoried during the past decades. In the

present study, we used a large dataset on butterfly species richness to test the following two

hypotheses: (1) seasonality plays an important role in explaining geographic variation in

butterfly richness, together with energy and heterogeneity, and (2) seed plant richness

contributes substantially to the geographic pattern of butterfly richness, besides environ-

mental variables.

Materials and methods

Species richness data

We compiled 58 local butterfly surveys from journal papers and books that contained all of

the following information: survey area, topography, location, seed plant richness and

butterfly sampling effort (Online Appendix 1). Twenty-seven localities had data on number

of specimens, 53 localities had data on number of survey years, and 22 localities had both

of them. There was a significant correlation between these two measures of sampling

efforts (r = 0.696, p \ 0.001 for ln-transformed variables). Although all localities have

been extensively surveyed with the aim of providing complete species lists for butterflies,

there was great variation in sampling effort (i.e., 300–5600 specimens, 1–12 years for

survey).

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Environmental data

We used mean annual temperature (TEM), annual precipitation (PRE), annual actual

evapotranpiration (AET), and annual potential evapotranspiration (PET) as energy vari-

ables. Standard deviation of mean monthly temperature (TEMsd), coefficient of variation

of mean monthly precipitation (PREcv), coefficient of variation of mean monthly actual

evapotranpiration (AETcv), and coefficient of variation of mean monthly potential

evapotranspiration (PETcv) were regarded as variables reflecting seasonality. Coefficient

of variation can be misleading for variables including both positive and negative values

and meaningful for variables containing only positive values. Since some localities have

monthly temperature\0 �C, we used standard deviation for temperature and coefficient of

variation for the rest variables. Elevational range (ELER, maximum elevation—minimum

elevation) represents heterogeneity because climate usually changes systematically along

altitudinal gradients. We also regarded sampling area (AREA) to reflect heterogeneity

because larger area may cover more diverse climatic conditions and habitats. The average

area of each locality was 426 km2 (±713 SE). Elevational range and sampling area were

significantly correlated (r = 0.422, p = 0.003 for ln-transformed variables).

Data for temperature and precipitation were obtained from WorldClim (Hijmans et al.

2005). Data for the evapotranspiration variables stemmed from the Global Evapotranspi-

ration and Water Balance Data Sets developed by Ahn and Tateishi (1994) and Tateishi

and Ahn (1996). Climatic data were assembled for the 58 localities according to their

geographical midpoints.

Statistical analysis

First, we tested the normality of all variables, and ln-transformed butterfly richness (BR),

seed plant richness (SPR), elevational range, sampling area, number of specimens, and

number of survey years to obtain normal distributions. We used multiple ordinary least

squares regression with linear and quadratic terms to examine the effects of each putative

predictor and each set of variables (energy, seasonality, and heterogeneity) on butterfly

richness. We used adjusted R2 to estimate explained variance. We examined the statistical

significance of the correlations and regressions based on corrected degrees of freedom,

calculated using Dutilleul’s (1993) modified t test.

There were strong correlations between putative predictors (Table S1). To resolve this

problem before modeling analysis, we eliminated the putative predictor with the largest

variance inflation factor each time until all the selected predictors’ variance inflation factor

\5 (Quinn and Keough 2002). We repeated this procedure separately for the three sets of

variables, i.e. energy variables (TEM, PRE, AET and PET), seasonality variables (TEMsd,

PREcv, AETcv and PETcv), and heterogeneity variables (ELER and AREA). For each set

of variables, we used Akaike’s information criterion (AIC) to rank all possible models and

selected the model with the minimum AIC as the best one.

We used variation partitioning approach (Heikkinen et al. 2005) to decompose the

variation in butterfly richness among the three sets of predictors (energy, seasonality and

heterogeneity). Here, variation partitioning lead to eight fractions: pure effect of energy,

seasonality, and heterogeneity; combined variation due to joint effects of energy and

seasonality, energy and heterogeneity, seasonality and heterogeneity, combined effects of

the three groups of explanatory variables, and finally unexplained variation. We used

‘‘permutation of the residuals’’ approach to test the significance of the independent effect

of energy, seasonality and heterogeneity (Legendre and Legendre 1998).

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To examine whether seed plant richness provide additional explanation for butterfly

richness, besides environmental variables, we repeated the above regression analyses for

seed plant richness, using energy, seasonality and heterogeneity variables as putative

predictors. Then, we performed residual regression to examine the relationship between

butterfly richness and seed plant richness when the effects of environmental factors were

gradually removed.

Spatial autocorrelation in macroecological species richness data may result in inflation

of type I error and bias the estimation of coefficients in statistical modeling (Diniz-Filho

et al. 2003; Bini et al. 2009). We used Moran’s I of butterfly richness and residuals of

ordinary least squares regression models at different distances to test whether spatial

autocorrelation was present. The significance of Moran’s I was determined by permutation

approach. Moran’s I coefficient indicated that residuals of energy and heterogeneity

models had significant spatial autocorrelation at least at the shortest distance (Table S2).

Therefore, we used simultaneous autoregressive models to repeat multiple regression

analyses, and compared the results from both non-spatial ordinary least squares regression

and simultaneous autoregressive models (Table S3). To determine whether sampling effort

affect local butterfly richness, we regressed the residual butterfly richness of environmental

model against ln-transformed number of specimens and survey years. The data analyses

were performed in R (R Development Core Team 2009) and ‘Spatial Analysis in Mac-

roecology’ (SAM) (Rangel et al. 2010).

Results

Butterfly richness in eastern China showed evident latitudinal gradient (Fig. S1), averaged

141 and varied from 37 to 376 (Fig. 1). All environmental factors, except sampling area

and coefficient of variation of mean monthly precipitation, significantly affected butterfly

richness in single-predictor models, but their effects differed in some degree. Butterfly

richness generally increased with energy and heterogeneity variables, while decreased with

seasonality variables (Table 1).

Multiple ordinary least squares regression analyses showed that, energy model

(PET ? PET2) explained 42.3 % of the variation in butterfly richness, slightly higher than

that of seasonality model (TEMsd) (39.3 %), and much higher than that of heterogeneity

model (ELER) (25.9 %). The environmental model combining all the selected variables

explained 52.1 % of the variation in butterfly richness (Table 2). Simultaneous autore-

gressive models yielded similar results to multiple ordinary least squares regression

models; therefore, the effects of spatial autocorrelation on the results of our analyses are

negligible (Table 2, Table S3). Variance partitioning analysis showed that energy

hypothesis had significant independent effect (5.5 %), much higher than that of seasonality

(0.0 %). Heterogeneity had the highest independent effect (6.3 %) among these three sets

of variables. There were significant positive relationships between elevational range and

residual butterfly richness of energy and seasonality models (Fig. 3).

Correlograms of Moran’s I of butterfly richness, and residuals from energy, seasonality

and heterogeneity models indicated that energy and seasonality removed most positive

spatial autocorrelation at short distances and negative spatial autocorrelation at long dis-

tances, while heterogeneity reduced positive spatial autocorrelation at intermediate dis-

tances but left considerable spatial autocorrelation at short and long distances (Fig. 4a;

Table S2). Spatial autocorrelation in residuals of environmental model at each distance was

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not significant (p [ 0.05; Fig. 4b; Table S2), suggesting that spatial autocorrelation in

model residuals was negligible.

There was a highly significant correlation between butterfly richness and seed plant

richness (Fig. 5a). However, the correlation between residuals of these two variables

decreased at different degree when the influences of environment (energy, seasonality and

heterogeneity) were removed (Fig. 5b, c, d). When all environmental effects were parti-

tioned out, there was no significant correlation between the residuals of species richness of

Fig. 1 Spatial locations of 58 butterfly faunas analyzed in this study

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butterflies and seed plants (Fig. 5e). There were significant linear correlations between ln-

transformed number of specimens or survey years and residual butterfly richness left by

environmental model (Fig. 6).

Discussion

Using inventory data on 58 local butterfly fauna in Eastern China, we demonstrated that

there are strong relationships between butterfly richness and environmental factors (energy,

seasonality, and heterogeneity) and seed plant richness. By using variation partitioning

method to explicitly determine the unique and joint effects of each set of environmental

variables, and using residual regression to examine the association between butterfly

richness and seed plant richness, we found that (1) the variation explained by seasonality in

butterfly richness was comparable to that of energy, much higher than that of heterogeneity

and (2) butterfly richness showed significant correlation with seed plant richness, due to

their similar responses to environmental variables.

Seasonality

Our analyses showed that the explanatory power of seasonality for butterfly richness was

just slightly lower than energy, but much higher than heterogeneity (Table 2). This partly

Table 1 Regression coefficients from single predictor ordinary least squares regression

Variable Linear Quadratic

Energy variables

TEM (?)0.285* 0.321*

PET (?)0.333* 0.423*

PRE (?)0.191* 0.223*

AET (?)0.321* 0.346*

Seasonality variables

TEMsd (-)0.393* 0.404*

PETcv (-)0.346* 0.368*

PREcv (-)0.145 0.146

AETcv (-)0.211* 0.227*

Heterogeneity variables

AREA (?)0.032 0.024

ELER (?)0.259** 0.265**

AREA, ELER and Species richness was ln-transformed. p-values were calculated after accounting forspatial autocorrelation using Dutilleul (1993)’s method

TEM mean annual temperature, PRE annual precipitation, AET annual actual evapotranpiration, PET annualpotential evapotranspiration, TEMsd standard deviation of mean monthly temperature, PREcv coefficient ofvariation of mean monthly precipitation, AETcv coefficient of variation of mean monthly actual evapo-tranpiration, PETcv coefficient of variation of mean monthly potential evapotranspiration, ELER elevationalrange, AREA sampling area

** p \ 0.01; * p \ 0.05

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indicated that seasonality played an important role in explaining geographic variation in

butterfly richness, together with energy and heterogeneity. Studies that considered sea-

sonality as a predictor of species richness yielded confounding results. Some studies found

seasonality to be good predictors of species richness (e.g. mammals: Andrews and O’Brien

2000; Badgley and Fox 2000; Tello and Stevens 2010; Carrara and Vazquez 2010; birds:

Qian 2008; Williams and Middleton 2008; Qian et al. 2009; Carrara and Vazquez 2010),

while others found species richness was not strongly influenced by seasonality (e.g. insects:

Kerr 1999; Schuldt and Assmann 2009; herptiles: Qian et al. 2007). These controversial

results could potentially be explained by the discrepancy in their dependency on external

energy supply and ability to maintain body temperature (i.e., endotherms vs. ectotherms).

Since most terrestrial ectotherms depend heavily on external energy input to maintain their

metabolism, the bottleneck for their survival and persistence may be availability, other than

temporal variation of energy. However, for endotherms, which can maintain thermal

homeostasis, the bottleneck may convert from energy to seasonality. Consistent with this,

the independent effect of seasonality is null for butterfly richness in our study (Fig. 2), and

seasonality variables were not selected in the best fitting environmental model for

amphibians and reptiles in China (Qian et al. 2007) and for carabid beetles in the western

Palaearctic area (Schuldt and Assmann 2009). However, we must notice that energy and

seasonality variables were calculated from the same basic data in our study. Therefore, it

was not surprising that energy and seasonality would overlap in their capacity to explain

butterfly richness (Fig. 2).

There were also other evidences highlighting the importance of seasonality for

explaining geographic butterfly richness pattern. First, elevatioanl range showed higher

positive correlation with the residual butterfly richness of energy model than that of sea-

sonality model (Fig. 3). This indicated that although both sets of variables have compa-

rable explanatory powers, seasonality performed better in explaining spatial variation in

butterfly richness in topographically complex areas than energy did. Second, Moran’s

I correlograms showed that seasonality did better in reducing spatial autocorrelation in

residuals of butterfly richness at the shortest distance than energy and heterogeneity did

(Fig. 4a; Table S2). Third, when the influence of seasonality was removed, the correlation

between residual richness of butterfly and seed plant was weaker than that of energy and

heterogeneity models (Fig. 5). This indicated that the role of seasonality in shaping the

Seasonality

a = 0.0

b = 5.5 c = 6.3f = 0.0

d = 2.5e = 19.7

g = 17.1

undetermined variation47.9

Energy Heterogeneity

Fig. 2 Results of variationpartitioning for butterfly richnessin terms of the proportion ofvariation explained (%).Variation of species richness isexplained by three sets ofvariables: seasonality, energy,and heterogeneity, and theunexplained variation; a, b, and,c are unique effects ofseasonality, energy andheterogeneity, respectively; whiled, e, f and g indicates their jointeffects

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association between butterfly richness and seed plant richness was more important than

that of energy and heterogeneity.

Seed plant richness

There was significant correlation between butterfly species richness and seed plant richness

when the influence of environmental variables was included. However, when we removed

the impacts of environmental variables by fitting all environmental variables, the residual

richness of butterfly and seed plants showed no significant correlation (Fig. 5). This

indicated that there was no direct association between butterfly richness and seed plant

richness, and the correlation between them simply stemmed from their similar responses to

environmental variables (Hawkins and Porter 2003b).

The may be two reasons accounting for the indirect association between butterfly

richness and seed plant richness. First, the geographic species richness pattern is primarily

determined by few common species (Lennon et al. 2004), and for butterflies, the distri-

bution of habitat generalists was more likely influenced by climate variables than that of

specialists (Menendez et al. 2007). Second, only a small fraction of seed plants can be used

by butterflies as nectars for adults and hostplants for larvae (Chou 2000). That is, richness

of total plant richness was a rough agency of resource availability. Therefore, for butter-

flies, it is more likely to find direct association between the diversity of specialists and that

of nectars or host plants (Kitahara et al. 2008).

Ln-transformed elevarional range5 6 7 8 9

Res

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Res

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r 2 = 0.228p < 0.001

r 2 = 0.075p = 0.022

a

b

Fig. 3 The relationshipsbetween ln-transformedelevational range and residualbutterfly richness (BR) of energymodel (a), and seasonality model(b)

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Energy and heterogeneity

Multiple ordinary least squares regression and variation partitioning showed that, energy

was the most important factor explaining the variance in butterfly richness, in accordance

with previous analyses (Hawkins and Porter 2003a, b; Hawkins et al. 2003; White and Kerr

2007). Energy-related variables could influence butterfly richness through several physi-

ological, behavioral and evolutionary processes. Higher temperature not only allows but-

terflies to invest more time in foraging (Turner et al. 1987; Boggs and Murphy 1997), but

also influences egg hatching, growth rate, development and survival (Gotthardt et al. 2000;

Bale et al. 2002) which may result in more generations during one growth season.

Although the explanatory power of heterogeneity model was lower than those of energy

and seasonality, it had the largest unique component (6.3 %) in variation partitioning

(Fig. 2). This indicated that heterogeneity was a complimentary factor of climatic vari-

ables. This has also been shown by previous studies in which heterogeneity variables were

frequently selected in best fitting environmental models for butterfly richness (Kerr et al.

2001; Hawkins and Porter 2003a, b; White and Kerr 2007). The highest pure effect of

Distance (km)500 1000 1500 2000 2500

Mo

ran

's I

Mo

ran

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-1.0

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-0.5

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Butterfly richnessResiduals of energy modelResiduals of seasonality modelResiduals of heterogeneity model

a

b

Fig. 4 Moran’s I correlograms of butterfly richness and residuals. a butterfly richness and residuals ofenergy model, seasonality model, and heterogeneity model; b butterfly richness and residual ofenvironmental model

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heterogeneity in shaping the geographic butterfly richness pattern may be due to strong

niche separation along altitudinal gradients in local butterfly faunas, irrespective of cli-

matic conditions (Chou 2000).

Sampling effort

The insufficient sampling may partly account for the low fraction of variance explained by

environmental variables in our analysis (but see Kerr et al. 2001; Hawkins and Porter

2003a, b). The number of specimen is the best measure of sampling effort (Willott 2001).

The number of survey years is a rough estimation of sampling effort. The significant linear

correlation between number of specimen or survey years and residuals left by

Residual-SPR-1.0 -0.5 0.0 0.5 1.0

Res

idu

al-B

R

-1.0

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0.0

0.5

1.0

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3.0

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5.0

5.5

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r2 = 0.378p < 0.001

a

b

r2 = 0.011p = 0.186

Residual-SPR

-1.0 -0.5 0.0 0.5 1.0

Res

idu

al-B

R

-1.0

-0.5

0.0

0.5

1.0

Residual-SPR

-1.0 -0.5 0.0 0.5 1.0

Res

idu

al-B

R

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5Residual-SPR

-1.0 -0.5 0.0 0.5 1.0

Res

idu

al-B

R

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

r2 = 0.176p < 0.001

b c

d e

r2 = 0.181p < 0.001

r2 = 0.115p < 0.005

Fig. 5 Relationships between butterfly species richness (BR) and seed plant richness (SPR). Influence ofenvironmental variables was included or removed by fitting environmental variables prior to drawing thescatter plots. a Influence of environmental variables included; b Influence of energy removed; c Influence ofseasonality removed; d Influence of heterogeneity removed; e Influence of energy, seasonality, andheterogeneity removed

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environmental model (Fig. 6a, b) indicated that, some local butterfly fauna are underes-

timated (Chou 2000). However, these factors that reduced the explanatory power of

environmental model may not bias the analytical results, because spatial richness pattern is

mainly determined by common species (Lennon et al. 2004), which are seldom overlooked

during field surveys (Gaston et al. 1995).

Acknowledgments We thank Jan Beck of Universitat Basel, and Hong Qian of Illinois State Museum forcomments on previous versions of the manuscript. We thank three anonymous reviewers for helpful sug-gestions. S. Chen thanks his new son, Jiayou Chen, for his encouragement. Financial support from theNational Key Technology R&D Program (2012BAC01B08) and the Special Public Science and TechnologyResearch Program for Environmental Protection (201209027) was also acknowledged.

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Ln-transformed number of specimen5 6 7 8 9 10

Res

idu

al-B

R

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Ln-transformed number of survey years0 1 2 3

Res

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al-B

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-0.8

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-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

a

b

r 2 = 0.387p < 0.001

r 2 = 0.273p < 0.005

Fig. 6 Relationships betweenresidual butterfly species richness(BR) of environmental model anda ln-transformed number ofspecimen; b ln-transformednumber of survey years

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