Designing landscapes that are robust to climate change

背景：为了减轻气候变化和栖息地丧失的威胁，一个物种未来的气候范围需要包含足够的合适的栖息地，可以从当前的栖息地获得。政策制定者和保护管理者需要模型来帮助他们设计有效和连接的栖息地网络。这些模型需要在非常广泛的候选景观中预测种群的命运，但当前的模拟模型忠实地捕捉到了空间上明确的基于个体的种群动态，对于这一目的来说太慢了。其他能够足够快速地评估景观的方法，包括我们自己的Condatis软件，都不是基于种群动态，并且缺乏经验验证。目标：1.开发新的、计算高效的种群和集合种群模型，用于空间明确的栖息地网络上的持久性和范围扩展。2.开发快速评估景观变化对持久性和范围的影响的方法，这些方法与保护目标的关系尚不清楚；而且几乎没有经验证据表明它们推荐最佳栖息地配置。及时性：政策制定者和保护从业者现在需要这类工具：我们的项目将确保景观规划中的决策得到最好的生态知识的支持。我们将使用一种新的数学框架(Cornell等人，2019)，它比模拟更有效地计算空间显式人口模型的预测。我们还将利用JH的Condatis软件(Hodgson等人，2015)的最新进展，该软件展示了如何有效地计算景观修改的影响。扩展3.针对英国鳞翅目物种观察到的范围移动，经验验证了模型。新颖性：这个项目将克服当前支持这种景观规模决策的主要弱点：它们不是基于潜在的种群动态模型；它们基于的指标与保护目标的关系尚不清楚；几乎没有经验证据表明它们推荐最佳栖息地配置。

Background: To mitigate the threats of climate change and habitat loss, a species' future climatic range needs to contain enough suitable habitat that can be accessed from current habitat. Policy makers and conservation managers need models to help them design effective and connected habitat networks. These models need to predict the fate of populations in a very wide range of candidate landscapes, but current simulation models that faithfully capture spatially explicit individual-based population dynamics are too slow for this purpose. Alternative methods that can evaluate landscapes rapidly enough, including our own Condatis software, are not based on population dynamics and lack empirical validation.Objectives: 1. Develop novel, computationally efficient, population and metapopulation models for persistence and range expansion on a spatially explicit habitat network. 2. Develop methods for rapidly evaluating the effect of landscape changes on persistence and range whose relationship to conservation targets are unclear; and there is scant empirical evidence that they recommend the best habitat configurations. Timeliness: Policy makers and conservation practitioners need tools of this type now: our project will ensure that decisions made in landscape planning are supported by the best ecological knowledge. We will use a new mathematical framework (Cornell et al 2019) which computes predictions for spatially-explicit population models orders of magnitude more efficiently than simulation. We will also exploit the recent advances in JH's Condatis software (Hodgson et al, 2015), which show how to compute efficiently the effect of modifications in the landscape.expansion 3. Empirically validate the models against observed range shifting in UK Lepidoptera species. Novelty: This project will overcome the key weaknesses underlying current tools to support this sort of landscape-scale decision, specifically: they are not based on an underlying population dynamics model; they are based on metrics whose relationship to conservation targets are unclear; and there is scant empirical evidence that they recommend the best habitat configurations.