Understanding and utilising plant-animal interactions to facilitate forest regeneration in degraded landscapes

该项目将采用准实验方法和空间模型，测试自然和人为因素对以油棕生产为主的广阔地区（北方婆罗洲）天然林恢复成功的影响。东南亚是全球土地利用和土地覆盖变化率最高的地区之一，65%的陆地表面现在被人类改造的热带森林覆盖。与单一木材或油棕种植园不同，采伐的森林通常保留了大部分特有的生物多样性和生态系统功能。如果管理得当，它们在保护生物多样性和固碳方面具有巨大潜力。这个博士项目将通过确定在采伐森林生态系统中树木重建的生物，环境和人为限制来解决围绕这种适当管理的关键不确定性。使用现场，桌面和温室为基础的技术相结合，博士生将研究人类管理的作用（如减少影响伐木，藤本植物去除），生物过程（扩散，竞争，草食动物，菌根）和非生物因素（地形，小气候，土壤特性，景观连通性）在调节树木的建立和生长在马来西亚婆罗洲沙捞越的Murum地区。我们希望博士生使用温室研究，操纵性田间实验，遥感和先进的“大数据”空间建模的混合。这些领域的世界专家将提供全面培训。与我们的合作伙伴合作，博士将能够将这些发现纳入管理计划，详细说明为未来成功的森林恢复活动铺平道路的行动。

Using a quasi-experimental approach and spatial modelling, this project will test the influence of natural and anthropogenic factors on the success of natural forest restoration in an expansive landscape dominated by oil palm production (Northern Borneo). Southeast Asia has experienced some of the highest rates of land-use and land cover change globally, with 65% of the terrestrial land surface now covered by human-modified tropical forests .Unlike monoculture timber or oil palm plantations, logged forests often retain a large proportion of their endemic biodiversity and ecosystem functions. If managed appropriately, they hold tremendous potential for biodiversity conservation and carbon sequestration. This PhD project will resolve key uncertainties surrounding this appropriate management by identifying the biological, environmental and anthropogenic constraints on tree re-establishment in logged forest ecosystems. Using a combination of field-, desk-and greenhouse-based techniques, the PhD student will examine the role of human management (such as reduced impact logging, liana removal), biotic processes (dispersal, competition, herbivory, mycorrhizas) and abiotic factors(topography, microclimate, soil properties, landscape connectivity) in regulating tree establishment and growth in the Murum region in Sarawak, Malaysian Borneo. We expect the PhD student to use a blend of greenhouse studies, manipulative field experiments, remote sensing and advanced, 'big data' spatial modelling. Full training will be provided from world experts in these fields. Working with our CASE Partner, the PhD will be able to feed those findings into management plans detailing actions that pave the way for successful forest restoration activities in the future.