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入侵非本地物种（INNS）已经在南美洲建立。它们影响本地物种、生态系统和生计。许多INNS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，必须遏制它们的传播并降低它们的密度。这必须尽可能经济有效地完成，并考虑：INNS密度应降低多少？这取决于可用于管理的资源，以及重点国际非政府组织的丰富程度与其对人类和生物多样性造成的损害之间的关系。考虑如果现在不采取行动，将来会造成什么样的损害也很重要。种群中的不同个体对传播的贡献不同。因此，针对正确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）。INNS侵入的区域通常很大，空间优先级是必要的。INNS并不是在所有领域都具有同样的破坏性。一些生态系统和人类活动可以承受低密度INNS的存在，而其他生态系统和人类活动则非常脆弱，甚至不能承受低密度INNS。一个例子是在南巴塔哥尼亚的极度濒危的连帽，被引入的美国水貂逼到濒临灭绝。国际旅馆的管理费用也因地域而异，特别是在南美洲，有些地区很难进入，劳动力稀少。另一个重要的考虑因素是INNS是移动的。它们在首次入侵时能够传播，并可以重新入侵它们通过扩散而被清除的地区。如果管理部门能够利用已知的传播模式，这既是一个挑战，也是一个机遇。几十年来，生态学家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，可以通过将移动的INNS从小的、高度吸引人的区域集中地移除来耗尽该INNS，因此成本有效地“吸尘”大得多的区域，或通过有针对性的土地管理，使种子不可能生根，从而遏制植物INNS的传播。我们将设计一个用户，并向利益相关者介绍-这是一个友好的决策工具，我们预计它将在拉丁美洲广泛使用。为了确保我们的方法与拉丁美洲的不同背景相关，我们将与具有较大影响的示例物种合作，并且已经存在数据（入侵松树，女贞和水貂）。我们还将为数据贫乏的松树物种，外来草和食肉黄蜂建模，这些物种影响了巴西，阿根廷和智利的当地社区。我们将找到最有效的战略管理使用先进的计算机模拟考虑物种生态，扩散和干预成本的空间背景下。我们将确定新数据在哪些方面最有效地减少最佳行动方案的不确定性。我们要解决的问题是复杂的，我们将把它嵌入到一个合作的适应性管理过程中，这样管理人员就可以通过面对不同的数据模型来不断提高他们的效率。我们还将通过研究访问、持续合作和研讨会等方式，培训早期职业研究人员和博士生，以此作为在拉丁美洲建设研究能力的一种方式。我们的项目将通过逐步改变有问题的INNS的管理方式，对拉丁美洲的人民和生物多样性产生切实的积极影响。

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.