Annotated checklist of Albanian butterflies (Lepidoptera, Papilionoidea and Hesperioidea)

Verovnik, Rudi; Popović, Miloš

doi:10.3897/zookeys.323.5684

Cited by 14 publications

(9 citation statements)

References 3 publications

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“…Apart from its type-locality (South Greece, Peloponnese), it has been recorded in different parts of Central and Northern Greece (Brown 1976a, Wakeham-Dawson and Spurdens 1994, Wakeham-Dawson 1998, Pamperis 2009), from a few areas in south Macedonia (Kolev and van der Poorten 1997, Melovski and Bozhinovska 2014) and from some localities in south-west Bulgaria and one isolated place in the central part of the country (Abadjiev 2001, Kolev 1994, Kolev and van der Poorten 1997). Tshikolovets (2011) and Eckweiler and Bozano (2016) show its distribution extending into Albania, although the species has not been recorded from this country in recent surveys (Verovnik and Popović 2013). Verovnik et al (2015) recorded it in Bosnia and Herzegovina.…”

Section: Distribution Areasmentioning

confidence: 99%

Karyosystematics and molecular taxonomy of the anomalous blue butterflies (Lepidoptera, Lycaenidae) from the Balkan Peninsula

Vishnevskaya¹,

Saifitdinova²,

Lukhtanov³

2016

CCG

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The Balkan Peninsula represents one of the hottest biodiversity spots in Europe. However, the invertebrate fauna of this region is still insufficiently investigated, even in respect of such well-studied organisms as Lepidoptera. Here we use a combination of chromosomal, molecular and morphological markers to rearrange the group of so-called anomalous blue butterflies (also known as ‘brown complex’ of the subgenus Agrodiaetus Hübner, [1822] and as the Polyommatus (Agrodiaetus) admetus (Esper, 1783) species group) and to reveal its cryptic taxonomic structure. We demonstrate that Polyommatus aroaniensis (Brown, 1976) is not as widespread in the Balkans as was previously thought. In fact, it has a dot-like distribution range restricted to the Peloponnese Peninsula in South Greece. Polyommatus orphicus Kolev, 2005 is not as closely related to the Turkish species Polyommatus dantchenkoi (Lukhtanov & Wiemers, 2003) as was supposed earlier. Instead, it is a Balkan endemic represented by two subspecies: Polyommatus orphicus orphicus (Bulgaria) and Polyommatus orphicus eleniae Coutsis & De Prins, 2005 (Northern Greece). Polyommatus ripartii (Freyer, 1830) is represented in the Balkans by an endemic subspecies Polyommatus ripartii pelopi. The traditionally recognized Polyommatus admetus (Esper, 1783) is shown to be a heterogeneous complex and is divided into Polyommatus admetus sensu stricto (the Balkans and west Turkey) and Polyommatus yeranyani (Dantchenko & Lukhtanov, 2005) (east Turkey, Armenia, Azerbaijan and Iran). Polyommatus nephohiptamenos (Brown & Coutsis, 1978) is confirmed to be a species with a dot-like distribution range in Northern Greece. Finally, from Central Greece (Timfristos and Parnassos mountains) we describe Polyommatus timfristos Lukhtanov, Vishnevskaya & Shapoval, sp. n. which differs by its haploid chromosome number (n=38) from the closely related and morphologically similar Polyommatus aroaniensis (n=47-48) and Polyommatus orphicus (n=41-42). We provide chromosomal evidence for three separate south Balkan Pleistocene refugia (Peloponnesse, Central Greece and Northern Greece/South Bulgaria) and stress the biogeographic importance of Central Greece as a place of diversification. Then we argue that the data obtained have direct implications for butterfly karyology, taxonomy, biogeography and conservation.

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Section: Distribution Areasmentioning

confidence: 99%

Karyosystematics and molecular taxonomy of the anomalous blue butterflies (Lepidoptera, Lycaenidae) from the Balkan Peninsula

Vishnevskaya¹,

Saifitdinova²,

Lukhtanov³

2016

CCG

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“…Studies of butterflies in Albania increased in the last several years and resulted in findings of several new species for the country. According to Verovnik & Popović (2013a) checklist comprises 196 butterfly species. Šašić et al (2015) added two more species.…”

Section: Introductionmentioning

confidence: 99%

New records of rare butterflies (Lepidoptera: Papilionoidea) in Albania

Nahirnić-Beshkova¹,

Beshkov²,

Jakšić³

2022

Hist. nat. bulg.

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“…In addition, we also create a new large-scale butter y (namely Butter y-200) dataset that also covers four-level categories for multi-granularity image recognition. Currently, this dataset consists of 200 prevalent species of butter ies, which are grouped into 116 genera, 23 sub-families, and 5 families according to the insect taxonomy [34,38]. It contains 25,279 images in total and at least 30 images per species.…”

Section: Introductionmentioning

confidence: 99%

“…It contains 25,279 images in total and at least 30 images per species. It's worth noting that these category hierarchies can be obtained from the literature of taxonomy [33,38] or directly retrieved from Wikipedia conveniently, thus the methods of embedding this structured information can be easily adapted to various domains.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

Chen

Gao

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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Object categories inherently form a hierarchy with di erent levels of concept abstraction, especially for ne-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across di erent levels, which can e ectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of negrained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of di erent levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn ner-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butter y dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors.

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Annotated checklist of Albanian butterflies (Lepidoptera, Papilionoidea and Hesperioidea)

Cited by 14 publications

References 3 publications

Karyosystematics and molecular taxonomy of the anomalous blue butterflies (Lepidoptera, Lycaenidae) from the Balkan Peninsula

Karyosystematics and molecular taxonomy of the anomalous blue butterflies (Lepidoptera, Lycaenidae) from the Balkan Peninsula

New records of rare butterflies (Lepidoptera: Papilionoidea) in Albania

Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

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