Balancing ecological integrity and infrastructure development: Optimizing the UK's contribution to Sustainable Development Goals in sub-Sahara Africa

平衡生态完整性和基础设施发展：优化英国对撒哈拉以南非洲可持续发展目标的贡献

• 批准号: 2136551
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2136551
• 关键词: Balancing; ecological; integrity; infrastructure; development

Studentship strategic priority area:Sustainable use of natural resourcesKeywords:Animal movement, infrastructure development, ecologically informed developmentAbstract: One of the common problems with environmental assessments of large infrastructure projects in Africa is that they often fail to plan for current and future wildlife distributions. The apparent absence of wildlife in an area, however, does not necessarily imply that the area is unsuitable for wildlife. Many wildlife populations are collapsing and/or individuals are only present for short portions of the year (e.g. migratory species). Given the difficulty of directly monitoring all populations in all areas, the only viable alternative is to model distributions as a probabilistic use based on habitat preferences observed intensively elsewhere. This approach forms the basis of this PhD.Specifically, the methodological approach will:1) Capitalize on existing collaborations through Tanzania Wildlife Research Institute (TAWIRI) and the Smithsonian Conservation Biology Institute (SCBI) to collate GPS collar data for multiple African species. Data sources include: Glasgow (Serengeti/Tarangire/Loita Plains wildebeest, zebra, eland, oribi, lions hyena), the SCBI (Chadian scimitar-horned oryx, Laikipia giraffe, Amboseli/Athi-Kaputiei wildebeest, East African elephant) and TAWIRI (wild dogs, elephant, rhino). In addition, GPS data may also be available through an upcoming meeting in South Africa (March 2018) to synthesize African-based collaring studies and includes vultures, kudu, buffalo, elephant, zebra, Sudanese kob. This represents a completely unique cross-taxa, multi-trophic movement dataset, and will ensure that the student begins in a strong position to carry out a successful and impactful project from the onset.2) Movement data will be combined with readily available landscape metrics such as grass greenness (NDVI MODIS), seasonal rainfall and water availability (WorldClim), soil quality (World Harmonized Soil Database) and habitat structure (Sentinel-2) to understand how each species moves in response to environmental cues. The student will apply new classes of state-space movement models (e.g. hidden Markov models) that build on conditional resource selection to capture how environmental covariates account for the variation in the step lengths and turn angles of animal movement trajectories.3) By examining how movement trajectories change near existing anthropogenic features, the student will quantify how infrastructure modifies functional behaviours (e.g. speed, directedness, foraging state), within different environmental contexts.4) Secondly, remotely-sensed data products will be used to develop a multispecies " informed probability of connectedness" (i.e. a multivariate GIS surface of species-specific use, defined by landscape attributes) at regional or national scales which can be used for conservation planning and/or restoration efforts. We will identify existing or planned infrastructure projects through our network of collaborators (several projects in Tanzania/Kenya have already been identified, including the "Serengeti Road" (Hopcraft et al. 2015) and fencing in the Masai Mara). The probabilistic maps can be used within an optimization framework to assess which infrastructure designs or mitigation strategies will provide the greatest service to humans at the least ecological cost.5) Ultimately empirical information will be used to parameterize simulations that will enable managers and policy advisors at WCMC a more informed assessment of the cost-consequence-outcome of alternative infrastructure development scenarios.

学生战略优先领域：自然资源的可持续利用关键词：动物迁徙、基础设施发展、生态知情发展摘要：非洲大型基础设施项目环境评估的常见问题之一是，它们经常未能规划当前和未来的野生动物分布。然而，一个地区表面上没有野生动物，并不一定意味着该地区不适合野生动物。许多野生动物种群正在崩溃和/或个体在一年中只有很短的一段时间存在(例如，迁徙物种)。考虑到直接监测所有地区的所有种群的困难，唯一可行的替代方案是根据在其他地方密集观察到的栖息地偏好，将分布建模为概率使用。具体而言，该方法将：1)利用坦桑尼亚野生动物研究所(TAWIRI)和史密森保护生物学研究所(SCBI)现有的合作，整理多个非洲物种的GPS项圈数据。数据来源包括：格拉斯哥(塞伦盖蒂/塔兰盖尔/洛伊塔平原角马、斑马、兰德、奥里比、狮子鬣狗)、SCBI(乍得弯角羚羊、莱基皮亚长颈鹿、安博塞利/阿西卡普蒂埃角马、东非大象)和TAWIRI(野狗、大象、犀牛)。此外，还可能通过即将在南非举行的一次会议(2018年3月)获得全球定位系统数据，以综合非洲的颈项研究，包括秃鹫、杜鹃、水牛、大象、斑马、苏丹黑头幼崽。这代表了一个完全独特的跨分类群、多营养运动数据集，并将确保学生从一开始就处于有利地位，从一开始就实施一个成功和有影响力的项目。2)运动数据将与现成的景观指标相结合，如草地绿度(NDVI MODIS)、季节性降雨量和水资源可获得性(WorldClim)、土壤质量(世界统一土壤数据库)和栖息地结构(Sentinel-2)，以了解每个物种如何对环境提示做出反应。学生将应用建立在条件资源选择基础上的新的状态空间运动模型(例如，隐马尔可夫模型)，以捕捉环境协变量如何解释动物运动轨迹的步长和转角的变化。3)通过研究运动轨迹如何在现有的人类活动特征附近变化，学生将量化基础设施如何在不同的环境环境中改变功能行为(例如，速度、方向性、觅食状态)。4)其次，遥感数据产品将被用于开发多物种“知情连接概率”(即特定物种使用的多变量地理信息系统表面，在区域或国家范围内)，可用于保护规划和/或恢复工作。我们将通过我们的合作伙伴网络确定现有的或计划的基础设施项目(坦桑尼亚/肯尼亚的几个项目已经确定，包括“塞伦盖蒂公路”(霍普克拉夫特等人。2015年)和马赛马拉的击剑)。概率图可以在优化框架内使用，以评估哪些基础设施设计或缓解策略将以最少的生态成本为人类提供最大的服务。5)最终，经验信息将被用于将模拟参数化，从而使WCMC的管理者和政策顾问能够更明智地评估替代基础设施发展情景的成本-后果-结果。