Carolina critters: a collection of camera trap data from wildlife surveys across North Carolina.
Carolina critters: a collection of camera trap data from wildlife surveys across North Carolina.
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卡罗莱纳州小动物：来自北卡罗来纳州野生动物调查的相机陷阱数据集合。
作者：
Monica Lasky;A. Parsons;Stephanie G Schuttler;G. Hess;R. Sutherland;L. Kalies;S. Clark;Colleen Olfenbuttel;J. Matthews;J. Clark;Jordan Siminitz;George Davis;Jonathan Shaw;Casey G. Dukes;Jacob Hill;R. Kays
相机诱捕调查有助于了解动物物种的种群趋势、分布、栖息地偏好、行为、群落动态、活动周期以及物种与环境条件的关联。这一信息具有重要的生态学意义，因为许多物种在当地生态系统中扮演着捕食者、草食性动物、种子传播者和病媒的重要角色。此外，相机陷阱探测到的许多较大的野生动物物种通过狩猎、诱捕或生态旅游具有重要的经济意义。在这里，我们展示了从2009年到2019年美国北卡罗来纳州所有100个县6043个地点的相机陷阱调查数据集。这些数据来自26项调查倡议，包含36种哺乳动物和3种陆生鸟类的215,108项记录。这个庞大的数据集增加了这39种哺乳动物和鸟类的地理分布数据，比全球生物多样性信息设施(GBIF)中北卡罗来纳州的地理分布数据增加了500%。这些数据可用于进行有关物种、种群、群落或生态系统的查询，并产生有关野生动物行为、分布和相互作用的有用信息。没有版权限制。在使用数据发表时，请引用这篇论文。
Camera trap surveys are useful to understand animal species population trends, distribution, habitat preference, behavior, community dynamics, periods of activity, and species associations with environmental conditions. This information is ecologically important since many species play important roles in local ecosystems as predators, herbivores, seed dispersers, and disease vectors. Additionally, many of the larger wildlife species detected by camera traps are economically important through hunting, trapping, or ecotourism. Here we present a data set of camera trap surveys from 6,043 locations across all 100 counties of North Carolina, USA from 2009 to 2019. These data come from 26 survey initiatives and contain 215,108 records of 36 mammal species and three species of terrestrial birds. This large data set increases the geographical distribution data for these 39 mammal and bird species by >500% over what is available for North Carolina in the Global Biodiversity Information Facility (GBIF). These data can be used to conduct inquiries about species, populations, communities, or ecosystems, and to produce useful information on wildlife behavior, distribution, and interactions. There are no copyright restrictions. Please cite this paper when using the data for publication.