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Rapid Endangered Species Assessment: A novel approach to improve extinction risk assessments in poorly known species.

机译:快速的濒危物种评估:一种新颖的方法,可以改善鲜为人知的物种的灭绝风险评估。

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摘要

The number of endangered species is rapidly increasing while paucity of adequate information and resources delays establishment of conservation actions. The IUCN's listing system is insufficient to determine conservation priorities and many species lack information even to be evaluated (i.e., "data deficient"). Here I proposed and tested the Rapid Endangered Species Assessment approach, combining methods including distribution modeling, landscape/habitat availability analysis and an evaluation of population spatial structure to improve information about species of concern and make future extinction risk assessments more attainable. I used the Pfrimer's Parakeet (Pyrrhura pfrimeri) an endemic Brazilian species, as a case study. I modeled and validated the potential distribution of the species, known to occur in association with the tropical dry forests in central Brazil, using the Maxent method. The model predicted potential occurrence in three regions in central Brazil. Field surveys used to validate model found the species at 17 sites, all located in the dry forests of the Parana River Basin. Modeling results set boundaries for the analysis of loss and fragmentation of the species' habitat. Satellite imagery and remote sensing techniques were used to estimate deforestation trends over a large spatiotemporal scale (31 years). Results indicated a 66.3% decrease in forest extent, average annual deforestation rate of 2.1% and significant increase in fragmentation, suggesting that these forests may disappear in less than 20 years if current deforestation rates persist. The habitat availability analysis set boundaries for the investigation of spatial arrangement of local populations of Pfrimer's Parakeets, which was carried out by collecting information on abundance, densities estimates, species home range, and habitat use. Twenty individuals were monitored with radio transmitters and home range estimates averaged between 195.7 ha (fixed kernel) and 261.8 ha (minimum convex polygons). Habitat use was estimated with behavioral observations of birds flying through a mosaic of forests and pastures. I found no evidence that the species used open areas farther than 300m from the forest edge. Systematic line transects and observations of parakeets per time effort produced estimates of population densities at 11.7 individuals/km 2, indicating strong variation in abundance indices among areas, suggesting a population decline. The Rapid Endangered Species Assessment represents a broad and integrative approach carried out over a short term and was successfully used to produce relevant data about a poorly known species. Thus, I propose the use of this approach as a minimally ideal yet scientifically viable method to generate information for extinction risk assessment of species.
机译:濒危物种的数量正在迅速增加,而缺乏足够的信息和资源却延迟了保护行动的建立。 IUCN的清单系统不足以确定保护的优先次序,许多物种甚至缺乏评估信息(即“数据不足”)。在这里,我提出并测试了“快速濒危物种评估”方法,结合了分布模型,景观/栖息地可利用性分析和种群空间结构评估等方法,以改善有关关注物种的信息,并使将来的灭绝风险评估更加可行。作为案例研究,我使用了巴西特有物种Pfrimer's Parkeet(Pyrrhura pfrimeri)。我使用Maxent方法对已知与巴西中部热带干旱森林相关的物种的潜在分布进行了建模和验证。该模型预测了巴西中部三个地区的潜在发生。用于验证模型的野外调查在17个地点发现了该物种,它们全部位于巴拉那河盆地的干旱森林中。建模结果为分析物种栖息地的丧失和破碎化设置了边界。卫星图像和遥感技术被用来估算大时空范围(31年)内的毁林趋势。结果表明,森林面积减少了66.3%,年平均森林砍伐率为2.1%,碎片化程度显着增加,表明如果持续保持当前的森林砍伐率,这些森林可能会在不到20年的时间内消失。栖息地可利用性分析为研究普弗里默长尾小鹦鹉的当地种群的空间分布设置了边界,这是通过收集有关丰度,密度估计,物种居所范围和栖息地使用的信息来进行的。用无线电发射机监视了20个人,平均房屋范围估计在195.7公顷(固定内核)和261.8公顷(最小凸多边形)之间。栖息地的使用是通过观察鸟类飞过一片森林和牧场的行为来估计的。我没有发现任何证据表明该物种在距森林边缘300m以上的空旷地区使用。系统的横断面和每只鹦鹉的每次努力观察得出的人口密度估计为11.7个人/平方公里2,表明各个区域之间的丰度指数变化很大,这表明人口正在减少。快速濒危物种评估代表了在短期内进行的广泛而综合的方法,并已成功地用于生成有关鲜为人知的物种的相关数据。因此,我建议将此方法用作产生理想物种灭绝风险评估信息的最不理想但科学可行的方法。

著录项

  • 作者

    Bianchi, Carlos A.;

  • 作者单位

    Oregon State University.;

  • 授予单位 Oregon State University.;
  • 学科 Agriculture Wildlife Conservation.;Biology Conservation.;Biology Ecology.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 194 p.
  • 总页数 194
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:50

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