Also known as the beaked whale family, beaked whale
family of mammals
Ziphiidae is a family of deep-diving toothed whales found in oceans around the world, known for their ability to descend to extreme depths in search of squid and fish. These whales are scientifically important for understanding how mammals adapt to extreme marine environments, though they remain relatively poorly studied due to the difficulty of observing them in their deep-ocean habitats.
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beaked whales
Ziphiidae
FAMILY
剑吻鲸科(學名Ziphiidae),又稱喙鲸科,在鲸目中是最不為人所知的一群動物。喙鯨(Beaked Whale)這個詞最早見於古挪威語“nebhval”,意為“有喙的鯨”,包含本科所有的成員,而其中瓶鼻鯨屬(Hyperoodon)與貝喙鯨屬(Berardius)這2屬的喙鯨有時又合稱為瓶鼻鯨(bottlenose whales)。喙鯨相當難以觀察,其中數種尚未發現活體,僅少數物種有較深入的研究資料。關於喙鯨的分類一直是暫時性與未定,常有新種發表或舊種的改正,現生種共計有6屬20餘種。 喙鯨的共同特徵最明顯的就是嘴喙,在比例上比擁有長嘴喙的海豚來得短,但較其他鯨類都來得長。所有喙鯨在喉嚨部位均有一對呈V字型的深溝,此溝槽有時又稱為“喉腹摺”,但與鬚鯨科成員擁有的喉腹摺在構造上大不相同,一般所說的喉腹摺是指後者。喙鯨的胸鰭小,趾骨短而手臂部分(撓骨與尺骨)相對較長;部分種類擁有稱之為“胸鰭窩”(flipper pocket)的構造,在胸鰭內側體壁處有輕微的凹陷,使胸鰭可以往內縮,可能是為了減少阻力,但由於外觀上極不顯著,只有擱淺或死亡個體才能察覺其存在。尾鰭占相當大的比例,中央通常沒有凹刻(少數種類有輕微的凹陷)。辨別各種喙鯨的方法最重要的是牙齒的大小、形狀,與著生在下顎的位置。大部分種類僅保有1對牙齒,只有成年雄鯨的牙齒會發育到突出牙床,某些種類其成年雄鯨的牙齒在嘴巴閉上時會外露,至於雌鯨與未成年雄鯨是看不到牙齒的。成年雄鯨身上常有細長的傷痕,在全身呈十字形交叉分布,由此判斷牙齒可能是用來打鬥的武器。 一般而言,喙鯨是深層的潛水者,很少接近海面,最主要的發現深度在至少300公尺深的地方。大多數種類似乎以海洋中層或深海的槍烏賊與魚類為食。
via GBIF
~22 min read
All known beaked whales as of 2007 Beaked whales are members of the cetacean family Ziphiidae, noted as being one of the least-known groups of mammals because of their deep-sea habitat, reclusive behavior and apparent low abundance. Only three or four of the 24 existing species are reasonably well-known. Baird's beaked whales and Cuvier's beaked whales were subject to commercial exploitation, off the coast of Japan, while the northern bottlenose whale was extensively hunted in the northern part of the North Atlantic in the late 19th and early 20th centuries.
Reports emerged in late 2020 of the possible discovery of a new beaked whale species off the coast of Mexico, the taxonomy of which had not been determined as of December 2020.
via Wikidata · CC0
via Wikidata sitelinks · CC0
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).