谷歌浏览器插件
订阅小程序
在清言上使用

A ten-year community reporting database reveals rising coyote boldness and associated human concern in Edmonton, Canada

Ecology and Society(2022)

引用 2|浏览9
暂无评分
摘要
In cities throughout North America, sightings of coyotes ( Canis latrans ) have become common. Reports of human-coyote conflict are also rising, as is the public demand for proactive management to prevent negative human-coyote interactions. Effective and proactive management can be informed by the direct observations of community members, who can report their interactions with coyotes and describe the location, time, and context that led to their interactions. To assess the predictors of human-coyote conflict, we used a web-based reporting system to collect 9,134 community-supplied reports of coyotes in Edmonton, Canada, between January 2012 and December 2021. We used a standardized ordinal ranking system to score each report on two indicators of human-coyote conflict: coyote boldness, based on the reported coyote behaviour, and human perceptions about coyotes, determined from the emotions expressed by reporters. We assigned greater conflict scores to behaviours where coyotes followed, approached, charged or contacted pets or people, and to perceptions where reporters expressed fear, worry, concern, discomfort or alarm. Using ordered logistic regression and chi-square tests, we compared conflict scores for each response variable to spatial, temporal and contextual covariates. Our analysis showed that coyotes were bolder in less developed open areas and during the pup rearing season, but human perceptions were most negative in residential areas and during the dispersal season. Reports that mentioned dogs or cats were more likely to describe bolder coyote behaviour, and those that mentioned pets or children had more negative perceptions about coyotes. Coyote boldness and human perceptions both indicated rising human-coyote conflict in Edmonton over the 10 years of reporting. ### Competing Interest Statement The authors have declared no competing interest.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要