权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Predicting invasion probabilities of introduced non-native freshwater fishes according to climate change and management

根据气候变化和管理预测引进的非本地淡水鱼类的入侵概率

基本信息

批准号：
2064193
负责人：
金额：
--
依托单位：
Bournemouth University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2064193
关键词：
Predicting invasion probabilities introduced non

项目摘要

Anthropogenic induced climate change is resulting in warming temperatures and changes inprecipitation patterns. Whilst the scale of future temperature increases is emissiondependent, warming of up to 2oC is considered inevitable. A further aspect of global changeis introduced non-native species (INNS). Whilst only a small proportion of INNS developinvasions, the impacts on biodiversity can be irreversible.In temperate areas, the probabilities of many INNS to establish, spread and impact (i.e.invade) are predicted to increase under climate change. INNS management is assisted whenpredictions indicate the high-risk species for priority actions. Predictions of INNSdistributions are based on species distribution models that predict environmental suitabilitybased on abiotic data (e.g. climate). INNS impacts are predicted by empirical and modellingmethods. These approaches cannot, however, integrate how altered abiotic and bioticconditions affect emergent relationships between INNS and native communities, or predicthow management interventions can reduce invasion probabilities.By contrast, Agent Based Models (ABMs) explicitly consider the system components (agents)and attempt to understand how system properties emerge from interactions betweenagents and their environment. ABMs are increasingly powerful tools for exploring ecologicalinteractions in changing environments and within spatial and regulatory contexts. They havenot, however, been applied to understanding how climate change and managementinterventions interact to affect the invasion probabilities of INNS.Thus, using river basins in England as model areas, freshwater fishes as model INNS and theEnvironment Agency as Case Partner, the PhD develops novel ABMs to predict current andfuture INNS invasion probabilities according to climate change projections, hydrologicalconnectivity and management interventions. The ABMs will predict future INNSdistributions and impacts within and between English river basins, and over a range ofclimate and management scenarios. Objectives (O) will:O1 Select model INNS and generate data on current and future species-climaterelationships, species-species interactions, and identify appropriate managementinterventions;O2 Develop an initial ABM for predicting the dispersal and impact of an INNS within Englishriver basins according to current climate conditions and hydrological connectivity, and nomanagement interventions;O3 For each model INNS, parameterise the initial ABM to produce species-specific finalABMs to predict current INNS invasion probabilities within and between river basins; andO4 Use the final ABMs to predict how climate change and management interventions willinfluence future invasion probabilities of INNS in England.In O1, horizon scanning selects three model INNS that are not yet invasive in England andrepresent different life history strategies (intense r-selected to k). Data for modelparameterisation are then developed for each model INNS using field, laboratory andliterature approaches. An initial ABM model is developed in O2 in RANGESHIFTER usingrelevant spatial, ecological, hydrological and bioclimatic data. The final ABMs are producedin O3 by integrating parameters and management interventions from O1 with the ABMfrom O2. They are used predictively in O4 by integrating climate change projections withmanagement interventions to generate knowledge relevant for natural resourcemanagement.

人为引起的气候变化正在导致气温变暖和降水模式的变化。虽然未来气温上升的幅度取决于排放，但升温高达20摄氏度被认为是不可避免的。全球变化的另一个方面是引入非本地物种（INNS）。虽然只有一小部分INNS开发入侵，但对生物多样性的影响可能是不可逆转的。在温带地区，在气候变化的影响下，许多INNS建立、传播和影响（即入侵）的可能性预计会增加。当预测表明需要优先采取行动的高风险物种时，有助于INNS管理。inns分布的预测基于基于非生物数据（如气候）预测环境适应性的物种分布模型。利用经验和模型方法预测了INNS的影响。然而，这些方法不能整合改变的非生物和生物条件如何影响INNS与土著社区之间的紧急关系，或预测管理干预措施可以减少入侵概率。相比之下，基于智能体的模型（ABMs）明确地考虑系统组件（智能体），并试图理解系统属性是如何从智能体与其环境之间的交互中产生的。在不断变化的环境以及空间和监管背景下，ABMs正在成为探索生态相互作用的强大工具。然而，它们还没有被应用于理解气候变化和管理干预如何相互作用来影响INNS的入侵概率。因此，博士使用英格兰的河流流域作为模型区域，淡水鱼作为模型INNS，环境署作为案例合作伙伴，开发了新的ABMs，根据气候变化预测，水文连通性和管理干预来预测当前和未来的INNS入侵概率。ABMs将预测未来英国河流流域内部和流域之间的inns分布和影响，以及一系列气候和管理情景。目标(O)将：1选择模型INNS并生成当前和未来物种-气候关系、物种-物种相互作用的数据，并确定适当的管理干预措施；O2根据当前的气候条件、水文连通性和管理干预措施，制定一个初步的ABM，用于预测INNS在英国河流流域内的扩散和影响；对于每个模型INNS，参数化初始ABM以产生特定物种的最终ABM，以预测当前INNS在流域内和流域间的入侵概率；使用最终的ABMs预测气候变化和管理干预将如何影响INNS在英格兰的未来入侵概率。在O1中，水平扫描选择了三个模型INNS，它们在英格兰尚未侵入，代表不同的生活史策略（强度r选择到k）。然后使用现场、实验室和文献方法为每个模型INNS开发模型参数化数据。利用相关的空间、生态、水文和生物气候数据，在rangshifter中建立了O2初始ABM模型。最终的abm是在O3中通过将O1的参数和管理干预与O2的abm相结合而产生的。通过将气候变化预测与管理干预相结合，从而产生与自然资源管理相关的知识，在O4中对它们进行了预测性使用。