Optimising the long term management of invasive species affecting biodiversity and the rural economy using adaptive management

大量具有高度破坏性的外来入侵物种(Inn)已经在南美洲建立起来。它们影响本地物种、生态系统和生计。许多客栈现在如此普遍，以至于根除不是一种选择。从长远来看，在那些不采取行动是不可接受的地区，必须遏制它们的蔓延，降低它们的密度。这必须尽可能地具有成本效益，并考虑：客栈密度应该降低多少？这取决于可用于管理的资源，以及焦点客栈的丰富性与其对人类和生物多样性造成的损害之间的关系。考虑到如果现在不采取行动，将来会造成什么损害，这也很重要。如何才能达到预期的减少呢？一个种群中不同的个体对传播的贡献不同。因此，针对合适的年龄段或在不同的季节采取行动，应该根据物种的生物学特性(例如，大松树比小松树产生更多的种子)。客栈侵占的区域往往很大，有必要对空间进行优先排序。客栈并不是在所有领域都具有同等的破坏性。一些生态系统和人类活动可以承受低密度客栈的存在，而另一些生态系统和人类活动非常脆弱，甚至无法容忍低密度客栈的存在。一个例子是南巴塔哥尼亚的极度濒危的白头水貂，由于引进了美国水貂，它几乎濒临灭绝。客栈的管理成本在空间上也各不相同，特别是在南美洲，那里的一些地区很难进入，劳动力稀少。另一个重要的考虑是，客栈是流动的。它们能够在第一次入侵时传播，并可以再次入侵通过扩散而被驱逐的地区。如果管理层能够利用已知的传播模式，这既是一个挑战，也是一个机会。生态学家几十年来一直在详细研究扩散动力学，但很少使用这些知识来设计有效的管理干预措施。例如，可以通过集中地将移动客栈从一个非常有吸引力的小区域中移走来耗尽它，从而以经济高效的方式对更大的区域进行吸尘，或者可以通过有空间目标的土地管理使种子不可能建立来遏制植物客栈的蔓延。我们将设计并向利益相关者介绍一种用户友好的决策工具，我们预计该工具将在拉丁美洲广泛使用。为了确保我们的方法适用于拉丁美洲的不同环境，我们将使用具有较大影响且已有数据的物种实例(入侵松树、女贞和水貂)。我们还将为数据贫乏的松树物种、外来草本植物和肉食性黄蜂等影响巴西、阿根廷和智利当地社区的可信情景建模。我们将使用复杂的计算机模拟，在空间环境中考虑物种生态、扩散和干预成本，找到最有效的战略管理。我们将确定哪些新数据将最有效地减少最佳行动方案的不确定性。我们解决的问题是复杂的，我们将把它嵌入到合作适应管理的过程中，以便管理者通过面对不同的数据模型来不断提高他们的效率。我们还将利用我们的项目作为在拉丁美洲建设研究能力的一种方式，通过研究访问、持续合作和研讨会的方式培训早期职业研究人员和博士生。我们的项目将通过对有问题的客栈的管理带来阶段性变化，对拉丁美洲的人和生物多样性产生切实、积极和直接的影响。

A large number of highly damaging invasive non-native species (INNS) have become established in South America. They affect native species, ecosystems and livelihoods. Many INNS are now so widespread that eradication is not an option. Their spread must be contained and their density reduced, in the long-term, in those areas where taking no action is not acceptable. This must be done as cost effectively as possible, and consider:By how much should INNS density be reduced? This depends on the resources available for management and on the relationship between the abundance of the focal INNS and the harm it causes to people and biodiversity. Considering what harm would be caused in the future if no action was taken now is also important.How should the desired reduction be achieved? Different individuals in a population contribute differently to spread. Thus, targeting the right age classes or acting in different seasons should be informed by the biology of the species (e.g. large pines produce more seeds than small ones).Where should the species be reduced? The areas invaded by INNS are often vast and spatial prioritisation is necessary. INNS are not equally damaging in all areas. Some ecosystems and human activities can withstand low density INNS presence, while others are so vulnerable they cannot tolerate even low INNS density. An example is the critically endangered hooded grebe in Austral Patagonia, driven to near extinction by the introduced American mink. The cost of managing INNS also varies spatially, especially in South America, where some areas are very difficult to access and the workforce is sparse. A further important consideration is that INNS are mobile. They have been able to spread when they first invaded, and can re-invade areas from which they have been removed through dispersal. This is both a challenge and an opportunity if management can exploit known patterns of spread. Ecologists have been studying dispersal dynamics in detail for decades, but have rarely used this knowledge to design effective management interventions. For instance, it may be possible to deplete a mobile INNS by intensively removing it from a small, highly attractive area, hence cost-effectively "vacuuming" a much larger area, or the spread of a plant INNS may be contained by making the establishment of seeds unlikely through spatially targeted land management.We will design and introduce to stakeholders a user-friendly decision tool that we expect will become widely used in Latin America. To make sure our approach is relevant for different contexts in Latin America, we will work with example species that have large impacts, and for which data already exist (invasive pines, privet, and mink). We will also model plausible scenarios for data-poor pine species, exotic grasses and carnivorous wasps, which impact local communities in Brazil, Argentina and Chile. We will find the most effective strategic management using sophisticated computer simulations considering species ecology, dispersal and intervention costs in a spatial context. We will identify where new data would most effectively reduce uncertainty on the best course of action. The problem we tackle is complex, and we will embed it in a process of co-operative adaptive management, so that managers continually improve their effectiveness by confronting different models to data. We will also use our project as a way to build research capacity in Latin America, by training early career researchers and PhD students by means of research visits, continuous collaboration and workshops.Our project will have a tangible positive and immediate impact on people and biodiversity in Latin America by delivering a step-change in the management of problematic INNS.