Is the Blue-Spotted Phenotype More Widespread in the Eastern Slow Worm Anguis Colchica (nordmann, 1840) Than the Western Slow Worm Anguis Fragilis Linnaeus, 1758 and Does It Correlate with the Male Body Size? A Case Study from Central Europe
Jagiellonian Univ | NATRIX Herpetol Assoc | Univ Wroclaw | Polish Acad Sci | Univ Zielona Gora
Abstract
The blue-spotted phenotype in a slow worm can be considered as an alternative colour morph or a secondary sexual characteristic. This phenotype is known to entail an elevated predation risk; thus, its continuous presence in a population must be balanced by additional and positive fitness consequences. In this study, we show that blue-spotted males are characterised by a greater snout-vent length (SVL) than typical specimens. Importantly, the SVL of blue-spotted males reaches the magnitude of the average female size. This indicates that the presence of blue spots may involve a correlated positive effect on growth and body size. The greater body size of the blue-spotted males could enhance their survival and mating success, and thus facilitate the continued presence of a high fraction of this morph within the population. In addition, we found that the blue-spotted phenotype is more common in the eastern than the western slow worm, and the proposed fitness consequences of the blue-spotted phenotype might enhance its tendency to spread in the eastern Anguis lineage.
MoreTranslated text
Key words
colour polymorphism,condition,divergence,sexual dimorphism
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1991
被引用17 | 浏览
1981
被引用149 | 浏览
2004
被引用142 | 浏览
2004
被引用11 | 浏览
1997
被引用16 | 浏览
2007
被引用46 | 浏览
1999
被引用91 | 浏览
2004
被引用79 | 浏览
2008
被引用62 | 浏览
2014
被引用35 | 浏览
1999
被引用37 | 浏览
2017
被引用24 | 浏览
1967
被引用13 | 浏览
2021
被引用7 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest