鱗翅目翅紋在避敵、溫度適應、以及性擇中皆扮演重要角色；其中，眼斑(eyespot)的複合色彩結構是尤為顯眼的特徵，百年來吸引生物學家探討其功能，以及對物種適存的意義。過去研究指出，眼斑特徵，如數量、面積與顏色對比等，主要用於應付掠食壓力，但非生物因子(如：溫度、雨量與季節性等)可能為其近因(proximate cause)而影響眼斑特徵。然而，多數研究以種內的眼斑變異解釋行為、生理、發生機制與抗掠食者策略，尚無研究探討其巨觀生態特徵，意即在大尺度的掠食壓力與環境梯度下，眼斑是否仍具有跨物種的共同特性，進而反應眼斑適應機制的一般性。本研究蒐集全球開放資料庫The Global Biodiversity Information Facility (GBIF)、 Symbiota Collections of Arthropods Network (SCAN)、 Integrated Digitized Biocollections (iDigBio)與Barcord of Life Data (BOLD)蛺蝶科993種、超過45000筆的數位化標本影像，並利用機器學習中的卷積式類神經網路量化眼斑數量、翅面中最大眼斑面積占比、眼斑總和面積占比、眼斑內顏色對比、眼斑與翅面的顏色對比；再透過階層式分析(Hierarchical partitioning)與結構方程(Structural equation model)廣泛測試溫度、雨量、及其變異度與掠食壓力(以食蟲性鳥類豐富度為指標)等如何解釋全球蛺蝶眼斑特徵。結果顯示，掠食壓力明顯影響眼斑顏色對比，食蟲鳥類豐富度越大，眼斑對比度越強；但對眼斑大小與數量解釋有限。更重要的，大尺度氣候因子主要影響眼斑特徵，溫度季節性、日溫差與最冷季均溫分別解釋眼斑數量、大小和對比的變異，數值越大造成蝶類群聚的眼斑數量越多、對比越強；生產力則可解釋眼斑數量與大小。綜合以上結果，全球蛺蝶科蝶類的眼斑變異主要取決於氣候變異，再來才是掠食壓力；而捕食壓力主要影響眼斑顏色對比度，而非數量或大小。氣候變異對眼斑的影響目前還未有相關機制解釋，本研究提出全球尺度上蛺蝶眼斑特徵隨捕食壓力和氣候因子變化的證據，將為本領域開啟全新的研究視野。

Wing patterns in Lepidopteran play important roles in predation avoidance, thermal adaptation, and sexual selection. Among wing color patterns, the eyespot is a conspicuous trait with a complicated color structure. Eyespots have attracted biologists to explore its functions and fitness for a hundred years. Past studies have pointed out that the eyespot traits, such as number, size, and color contrast, are mainly used to cope with predation pressure. In addition to predation pressure, abiotic factors (such as temperature, precipitation, and seasonality) may affect eyespots as their proximate cause. However, most studies have examined the anti-predator strategy with conspicuous eyespot traits but rarely test the relation between eyespot variation and factors across species at large spatial scale. The eyespot trait variations along predation or environmental gradients are still unknown. This study collected the global open database from The Global Biodiversity Information Facility (GBIF), Symbiota Collections of Arthropods Network (SCAN), Integrated Digitized Biocollections (iDigBio), and Barcorde of Life Data (BOLD) for 993 species in Nymphalidae, more than 45,000 specimen digital images. The convolutional neural network was used to quantify the number of eyespots, the ratio of the max eyespot size, the ratio of total eyespot size, eyespot color contrasts between eyespots and wings, and eyespot color contrasts within eyespots. We then used hierarchical partitioning and structural equation model to examine how temperature, precipitation, productivity, and predation pressure (using the richness of insectivorous birds as an indicator) explain eyespot variation directly and indirectly. We found that predation pressure mainly affected the colour contrast of eyespot, rather than the size and number. Importantly, temperature seasonality, diurnal temperature range, and the mean temperature in the coldest quarter mainly and significantly affected eyespot number, size, and contrast, separately. Our findings indicate that predation pressure effectively selected for high contrast of eyespots across wide variety of Nymphalid butterflies. However, climate and its variation play more important roles in determine eyespot variations at global scale and the underlying mechanisms are to be explored. Our study provides evidence of how eyespots vary with predation pressure and climate factors globally and paves new avenue of related studies in macroecology of eyespot variation.

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