臺灣博碩士論文加值系統

生物的體色除了警戒、求偶、擬態以外，亦有調節體溫的功能，巨觀尺度的體色變異隱含其生理適應機制。尤其，外溫動物體表與環境的熱交換速率直接影響體溫及活動時間，攸關其適存度。與體色變異有關的巨觀生理機制包括－熱黑化假說（Thermal melanism hypothesis），該假說認為黑化的物種具有較快的熱輻射吸熱速率。體型亦被認為會影響物種的身體溫度，大體型物種其對流散熱較慢，因此與小體型外溫動物相比，大體型外溫動物可以達到更高身體溫度。體型體色取捨假說（Body size colouration trade-off）進一步指出，在低溫環境下，大型外溫動物吸熱較慢，尤其需要黑化以提升吸熱速度，小型外溫動物本具有較快的吸熱能力，反而可以將體色投資在其他功能，因此可觀察到體型增加、體色越深。而能適應更冷的環境。除了溫度外，顏色亦可能受到其他環境因子影響，如UV、寄生蟲、和乾旱壓力(Desiccation)。過去研究分別指出體型、體色在外溫動物的體溫調節上應扮演重要的角色。近年雖有許多研究討論體色在大尺度下的趨勢，且推測體溫調節是主要影響體色深淺在環境梯度上的分布，但體型及體色鮮少被同時納入討論。本研究以臺灣的蝴蝶為材料，探討溫度、雨量、輻射與體型在影響外溫動物體色深淺的解釋量，以及溫度、雨量、輻射、體型對體色深淺的直接與間接影響。並進一步藉由加熱實驗來檢測體型與體色實際上對蝴蝶體溫調節能力的影響。分析結果顯示，體型為主要影響群聚體色深淺的因子，平均體型較大的群聚其平均體色也較深。環境因子對體色的影響皆遠低於體型。輻射為次要影響群聚體色深淺的因子，且主要是藉由體型間接影響群聚體色深淺。加熱實驗亦指出蝶類標本的體溫調控能力同時受其體型與體色深淺影響，且在黑化在不同體型的蝶類中扮演不同的角色，對體型相對較小的蝶類而言，黑化會提升可超過環境的最高溫度（maximum excess temperature），但對其吸散熱速率沒有明顯影響。對體型相對較大的蝶類而言，黑化可提升蝶類的吸散熱速率，但不影響可超過環境的最高溫度。本研究推測對通常體型較熱帶小的溫帶蝶類而言，黑化協助達到更高的體溫，在較寒冷的地區較具有優勢，而體型較溫帶大的熱帶蝶類則可以在不改變最高溫的情況下藉由黑化來獲得較快的散熱速率以避免過熱。因此在探討外溫動物的體色變異時應將體型一同納入分析已得到更加全面的結果。

Species color has multiple ecological functions, such as camouflage, signaling, UV protection, and thermoregulation. Color patterns are crucial to understanding macrophysiological adaptation. Ectotherms require heat source from the environment to keep body temperature high enough for locomotion, so the rate of heat transfer is important for ectotherms’ survival. Several hypotheses related to macrophysiological adaptation has been proposed to explain the variation of ectotherms’ color lightness. Thermal melanism hypothesis suggests that darker ectotherms are favored in the cold area, due to higher solar absorbing rate. In addition to thermoregulation, ectotherms having dark color also provide a better protection against ultraviolet radiation, pathogen, and desiccation. Body size also proposed to be related to ectotherms heat transfer, large body size has a lower surface-volume ratio and thus have lower heating and cooling rate. Although the thermal melanism hypothesis obtained widely empirical supports, body size, which also important for ectotherms’ thermoregulation, was seldom considered simultaneously. We investigated the direct and indirect effect of temperature, radiation, precipitation, and ectotherms body size on the variation of color lightness by applying Taiwanese butterfly surveys from 1993 to 2014. We further conducted a heating experiment to investigate the relationship between body size, color and thermal properties (maximum excess temperature; heating (cooling) rate. Results show body size is the dominant factor in explaining the color lightness variation of butterflies. Radiation affects butterflies color lightness variation indirectly through body size. The result of the heating experiment shows that butterflies color lightness and body size affect thermoregulation ability simultaneously. When body size is small, having darker color has little difference in heating (cooling) rate but reaching higher maximum excess temperature than light color. When body size is large, having darker color has little difference in maximum excess temperature but having faster heating (cooling) rate than light color. The result gives explanations of why temperate butterflies, normally with small body size, with dark color would be favored in cold environment, and tropical ectotherms, normally have larger body size, can also have dark color. We suggest that body size should be considered simultaneously in explaining ectotherms’ color lightness to give a more comprehensive prediction.

合格證明 i
中文摘要 ii
ABSTRACT iii
致謝 iv
CONTENTS v
List of tables vii
List of figures viii
List of appendix table ix
List of appendix figures x
1. INTRODUCTION 1
1.1. Macrophysiology 1
1.2. Morphological traits and thermoregulation 1
1.2.1. Color lightness and thermoregulation 1
1.2.2. Body size and thermoregulation 2
1.2.3. Body size-coloration trade-off 3
1.3. Ultraviolet (UV) resistance 3
1.4. Precipitation and lightness 3
1.5. Aims 4
2. MATERIALS AND METHODS 5
2.1. Butterfly surveys 5
2.2. Morphological traits 5
2.3. Combine morphological traits and butterfly surveys 5
2.4. Environment variables 6
2.5. Experimental design 6
2.6. Statistical analysis 7
2.6.1. Assemblage color lightness and assemblage body area 7
2.6.2. Generalized additive model – calculating species preferred environment 7
2.6.3. Hierarchical partitioning 7
2.6.4. Multimodal inference 7
2.6.5. Structural equation modeling 8
2.6.6. Curve estimation 8
2.6.7. Relationship between maximum excess temperature and body area, color lightness 8
2.6.8. Relationship between heating(cooling) rate and body area, color lightness 8
3. RESULTS 10
3.1. Data description 10
3.1.1. Result of GAM—species preferred environment factors 10
3.2. Hierarchical partitioning 10
3.3. Multimodal inference 11
3.3.1. Assemblage comparison 11
3.3.2. Interspecies comparison 11
3.4. Structural equation modeling 11
3.5. Thermal experiment 12
3.5.1. Maximum excess temperature and body area, color lightness 12
3.5.2. Heating(cooling) rate 12
4. DISCUSSION 13
5. CONCLUSION 14
6. REFERENCE 15
7. TABLES 18
8. FIGURES 23
9. TABLE IN APPENDIX 35
10. FIGURES IN APPENDIX 36

姜信宏。2018。蝴蝶色斑與構造如何參與體溫調控。科技部大專生研究計畫，計畫編號：106-2813-C-006-144-B。
徐堉峰 (2013)。臺灣蝴蝶圖鑑(上)弄蝶、鳳蝶、粉蝶。臺灣：晨星。
徐堉峰 (2013)。臺灣蝴蝶圖鑑(中)灰蝶。臺灣：晨星。
徐堉峰 (2013)。臺灣蝴蝶圖鑑(下)蛺蝶。臺灣：晨星。
楊耀隆、方懷聖、林斯正。1994。臺灣中部地區昆蟲資源調查(2/5)。 83 特生-動-05。106-134
楊耀隆。1999。臺灣中部地區蝴蝶資源。特有生物研究保育中心。10(1):28-48
臺灣氣候變遷推估與資訊平台計畫 (TAIWAN Climate Change Projection ＆ Information Platform, TCCIP http://tccip.ncdr.nat.gov.tw/)
潘學儀。2015。氣候變遷與亞熱帶蝶類分布位移。國立成功大學生命科學研究所碩士論文。
Arbuckle, J. L. (2016). Amos (Version 24.0) [Computer Program]. Chicago: IBM SPSS.
Ashton, K. G., ＆ Feldman, C. R. (2003). Bergmann's rule in nonavian reptiles: turtles follow it, lizards and snakes reverse it. Evolution, 57(5), 1151-1163.
Atkinson, D. (1994). Temperature and organism size – a biological law for ectotherms? Advanced Ecological Restoration, 25, 1–58.
Bastide, H., Yassin, A., Johanning, E. J., ＆ Pool, J. E. (2014). Pigmentation in Drosophila melanogaster reaches its maximum in Ethiopia and correlates most strongly with ultra-violet radiation in sub-Saharan Africa. BMC Evolutionary Biology, 14(179).
Beckmann, M., Václavík, T., Manceur, A. M., Šprtová, L., von Wehrden, H., Welk, E., ＆ Cord, A. F. (2014). glUV: a global UV-B radiation data set for macroecological studies. Methods in Ecology and Evolution, 5(4), 372–383.
Bishop, T. R., Robertson, M. P., Gibb, H., van Rensburg, B. J., Braschler, B., Chown, S. L., Foord, S. H., Munyai, T. C., Okey, I., Tshivhandekano, P. G., Werenkraut, V., ＆ Parr, C. L. (2016). Ant assemblages have darker and larger members in cold environments. Global Ecology and Biogeography, 25(12), 1489–1499.
Bogert, C. M. (1949). Thermoregulation in reptiles, a factor in evolution. Evolution, 3(3), 195–211.
Buckley, L. B., Hurlbert, A. H., ＆ Jetz, W. (2012). Broad-scale ecological implications of ectothermy and endothermy in changing environments. Global Ecology and Biogeography, 21(9), 873–885.
Chown, S. L., Gaston, K. J., ＆ Robinson, D. (2004). Macrophysiology: large-scale patterns in physiological. Functional Ecology, 18, 159–167.
Chris Walsh and Ralph Mac Nally (2013). ‘hier.part’: Hierarchical Partitioning. R package version 1.0-4.
Clench, H. K. (1966). Behavioral Thermoregulation in Butterflies. Ecology, 47(6), 1021–1034.
Clusella-Trullas, S., Terblanche, J. S., Blackburn, T. M., ＆ Chown, S. L. (2008). Testing the thermal melanism hypothesis: A macrophysiological approach. Functional Ecology, 22(2), 232–238.
Clusella-Trullas, S., van Wyk, J. H., ＆ Spotila, J. R. (2007). Thermal melanism in ectotherms. Journal of Thermal Biology, 32(5), 235–245.
Diamond, S. E., ＆ Kingsolver, J. G. (2010). Environmental Dependence of Thermal Reaction Norms: Host Plant Quality Can Reverse the Temperature‐Size Rule. The American Naturalist, 175(1), 1–10.
Fick, S. E., ＆ Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315.
Gates, D. M. (2012). Biophysical ecology. Courier Corporation.
Hair, J. F. Jr., Anderson, R. E., Tatham, R. L. ＆ Black, W. C. (1995). Multivariate Data Analysis (3rd ed). New York: Macmillan.
Hawkins, B. A., ＆ Lawton, J. H. (1995). Latitudinal gradients in butterfly body sizes: is there a general pattern? Oecologia, 102(1), 31-36.
Kalmus, H. (1941). Physiology and ecology of cuticle colour in insects. Nature, 148, 428–431.
Kearney, M., ＆ Porter, W. (2009). Mechanistic niche modeling: combining physiological and spatial data to predict species’ ranges. Ecology letters, 12(4), 334-350.
Lea, J. M. D., Walker, S. L., Kerley, G. I. H., Jackson, J., Matevich, S. C., ＆Shultz, S. (2018). Non-invasive physiological markers demonstrate link between habitat quality, adult sex ratio and poor population growth rate in a vulnerable species, the Cape mountain zebra. Functional Ecology, 32(2), 300–312.
Ortonne, J.-P. (2002). Photoprotective properties of skin melanin. The British Journal of Dermatology, 146 Suppl, 7–10.
Rajpurohit, S., Parkash, R., ＆Ramniwas, S. (2008). Body melanization and its adaptive role in thermoregulation and tolerance against desiccating conditions in drosophilids. Entomological Research, 38(1), 49–60.
Rawlins, J. E. (1980). Thermoregulation by the black swallowtail butterfly, Papilio polyxenes (Lepidoptera: Papilionidae).Ecology,61(2), 345-357.
R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Reilly, J. R., Hajek, A. E., Liebhold, A. M., ＆ Plymale, R. (2014). Impact of Entomophaga maimaiga (Entomophthorales: Entomophthoraceae) on Outbreak Gypsy Moth Populations (Lepidoptera: Erebidae): The Role of Weather. Environmental Entomology, 43(3), 632–641.
Satorra, A., ＆ Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis.
Schweiger, A. H., ＆ Beierkuhnlein, C. (2016). Size dependency in colour patterns of Western Palearctic carabids. Ecography, 39(9), 846–857.
Stevenson, R. D. (1985). The relative importance of behavioral and physiological adjustments controlling body temperature in terrestrial ectotherms. The American Naturalist, 126(3), 362-386.
Talloen, A. W., Dyck, H.Van, Lens, L., ＆Url, S. (2004). The Cost of Melanization : Butterfly Wing Coloration under Environmental Stress. Evolution, 58(2), 360–366.
Lea, J. M. D., Walker, S. L., Kerley, G. I. H., Jackson, J., Matevich, S. C., ＆Shultz, S. (2018). Non-invasive physiological markers demonstrate link between habitat quality, adult sex ratio and poor population growth rate in a vulnerable species, the Cape mountain zebra. Functional Ecology, 32(2), 300–312.
Wang, Z. P., Liu, R. F., Wang, A. R., Du, L. L., ＆ Deng, X. M. (2008). Phototoxic effect of UVR on wild type, ebony and yellow mutants of Drosophila melanogaster: Life Span, fertility, courtship and biochemical aspects. Science in China, Series C: Life Sciences, 51(10), 885–893.
Watt, W. B. (1968). Adaptive significance of pigment polymorphisms in Colias butterflies. I. Variation of melanin pigment in relation to thermoregulation. Evolution, 22(3), 437–458.
Willmer, P. G., ＆ Unwin, D. M. (1981). Field analyses of insect heat budgets: Reflectance, size and heating rates. Oecologia, 50(2), 250–255.
Wilson, K., Cotter, S. C., Reeson, A. F., Pell, J. K., ＆ Cotteer, S. C. (2001). Melanization and disease resistance in insects. Ecology Letters, 4(6), 637–649.
Zeuss, D., Brandl, R., Brändle, M., Rahbek, C., ＆ Brunzel, S. (2014). Global warming favours light-coloured insects in Europe. Nature Communications, 5, 3874.