five

Data from: Abiotic and biotic predictors of macroecological patterns in bird and butterfly coloration|生态学数据集|动物颜色数据集

收藏
DataONE2017-10-30 更新2024-06-26 收录
生态学
动物颜色
下载链接:
https://search.dataone.org/view/null
下载链接
链接失效反馈
资源简介:
Animal colour phenotypes are invariably influenced by both their biotic community and the abiotic environments. A host of hypotheses have been proposed for how variables such as solar radiation, habitat shadiness, primary productivity, temperature, rainfall and community diversity might affect animal colour traits. However, while individual factors have been linked to colouration in specific contexts, little is known about which factors are most important across broad taxonomic and geographic scales. Using data collected from 570 species of birds and 424 species of butterflies from Australia which inhabit an area spanning a latitudinal range of 35 degrees and covering deserts, tropical and temperate forests, savannas and heathlands, we test multiple hypotheses from the colouration literature and assess their relative importance. We show that bird and butterfly species exhibit more reflective and less saturated colours in better-lit environments, a pattern that is robust across an array of variables expected to influence the intensity or quality of ambient light in an environment. Both taxa display more diverse colours in regions with greater net primary production and longer growing seasons. Models that included variables related to energy inputs and resources in ecosystems have better explanatory power for bird and butterfly colouration overall than do models that included community diversity metrics. However, the diversity of the bird community in an environment was the single most powerful predictor of colour pattern variation in both birds and butterflies. We observed strong similarities across taxa in the covariance between colour and environmental factors, suggesting the presence of fundamental macro-ecological drivers of visual appearance across disparate taxa.
创建时间:
2017-10-30
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

Google Scholar

Google Scholar是一个学术搜索引擎,旨在检索学术文献、论文、书籍、摘要和文章等。它涵盖了广泛的学科领域,包括自然科学、社会科学、艺术和人文学科。用户可以通过关键词搜索、作者姓名、出版物名称等方式查找相关学术资源。

scholar.google.com 收录

网易云音乐数据集

该数据集包含了网易云音乐平台上的歌手信息、歌曲信息和歌单信息,数据通过爬虫技术获取并整理成CSV格式,用于音乐数据挖掘和推荐系统构建。

github 收录

Tropicos

Tropicos是一个全球植物名称数据库,包含超过130万种植物的名称、分类信息、分布数据、图像和参考文献。该数据库由密苏里植物园维护,旨在为植物学家、生态学家和相关领域的研究人员提供全面的植物信息。

www.tropicos.org 收录

CHARLS

中国健康与养老追踪调查(CHARLS)数据集,旨在收集反映中国45岁及以上中老年人家庭和个人的高质量微观数据,用以分析人口老龄化问题,内容包括健康状况、经济状况、家庭结构和社会支持等。

charls.pku.edu.cn 收录

TCIA

TCIA(The Cancer Imaging Archive)是一个公开的癌症影像数据集,包含多种癌症类型的医学影像数据,如CT、MRI、PET等。这些数据通常与临床和病理信息相结合,用于癌症研究和临床试验。

www.cancerimagingarchive.net 收录