Environmental determinants of geographic butterfly richness pattern in eastern China
- 328 Downloads
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 environmental 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 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.
KeywordsBiogeography Butterfly fauna Energy Heterogeneity Seasonality Species richness
We thank Jan Beck of Universität Basel, and Hong Qian of Illinois State Museum for comments on previous versions of the manuscript. We thank three anonymous reviewers for helpful suggestions. S. Chen thanks his new son, Jiayou Chen, for his encouragement. Financial support from the National Key Technology R&D Program (2012BAC01B08) and the Special Public Science and Technology Research Program for Environmental Protection (201209027) was also acknowledged.
- Allen AP, Gillooly JF, Brown JH (2007) Recasting the species-energy hypothesis: the different roles of kinetic and potential energy in regulating biodiversity. In: Storch D, Marquet PA, Brown JH (eds) Scaling Biodiversity. Cambridge University Press, CambridgeGoogle Scholar
- Bale JS, Masters GJ, Hodkinson ID, Awmack C, Bezemer TM, Brown VK, Butterfield J, Buse A, Coulson JC, Farrar J, Good JEG, Harrington R, Hartley S, Jones TH, Lindroth RL, Press MC, Symrnioudis I, Watt AD, Whittaker JB (2002) Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob Change Biol 8:1–16CrossRefGoogle Scholar
- Bini LM, Diniz-Filho JAF, Rangel TFLVB, Akre TSB, Albaladejo RG, Albuquerque FS, Aparicio A, Araújo MB, Baselga A, Beck J, Bellocq MI, Böhning-Gaese K, Borges PAV, Castro-Parga I, Chey VK, Chown SL, De Marco P Jr, Dobkin DS, Ferrer-Castán D, Field R, Filloy J, Fleishman E, Gómez JF, Hortal J, Iverson JB, Kerr JT, Kissling WD, Kitching IJ, León-Cortés JL, Lobo JM, Montoya D, Morales-Castilla I, Moreno JC, Oberdorff T, Olalla-Ta′rraga MÁ, Pausas JG, Qian H, Rahbek C, Rodríguez MÁ, Rueda M, Ruggiero A, Sackmann P, Sanders NJ, Terribile LC, Vetaas OR, Hawkins BA (2009) Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. Ecography 32:193–204CrossRefGoogle Scholar
- Chou I (2000) Monographia Rhopalocerorum Sinensium, 2nd edn. Henan Science and Technology Press, ZhengzhouGoogle Scholar
- Kissling WD, Field R, Korntheuer H, Heyder U, Böhning-Gaese K (2010) Woody plants and the prediction of climate-change impacts on bird diversity. Proc R Soc B 365:2035–2045Google Scholar
- Legendre P, Legendre L (1998) Numerical Ecology, 2nd, English edn. Elsevier Science BV, AmsterdamGoogle Scholar
- R Development Core Team (2009) R: A language and environment for statistical computing, version 2.12.2. R Foundation for Statistical Computing (online). Available from: http://www.R-project.org