Predicting the unpredictable: the role of eco-evolutionary experience in species interactions across species and habitats.

外来入侵物种(IAS)、活动范围的转移、恢复本地物种的重新入侵和重新野生是目前和未来世界生物群重新分布的主要驱动因素(例如Seebens等人)。2017年)。因此，我们在预测新物种相互作用的结果并了解它们对生物多样性的影响的能力方面面临着前所未有的挑战。该项目将在预测一系列物种和生境的生态和进化背景下的物种相互作用结果方面取得最新进展。相互作用的物种之间的生态进化经验(EEE)的程度正在成为一个主要的结果预测因子，例如入侵后的物种替换(例如Penk等人)。2017年)。事实上，预测IAS生态影响的指标的最新进展与EEE驱动结果的假设是一致的，例如，由于入侵捕食者，本地动物数量急剧减少(Dick等人)。以及恢复本地捕食者对改良生态系统的影响。在这里，我们建议开发一个预测框架，允许预测从原住民重新克隆到全新的相互作用物种对的情景下物种相互作用的结果。这将基于在预测跨分类群和营养类群的竞争性和捕食性相互作用的结果方面显示出巨大希望的指标的组合(Dick等人。2017年)。特别是，将经典的“功能反应”(资源摄取率)和数值反应(例如丰度、繁殖)结合到相对影响势(RIP)指标中，可以预测入侵物种影响的程度，并有望在物种的EEE范围内实现更广泛的物种相互作用(Dick等人)。2017年)。此外，通过将气候变化和寄生虫/疾病等各种上下文相关性纳入RIP指标，可以实现进一步的预测潜力。因此，我们建议利用北爱尔兰和苏格兰的各种研究系统来应对这一总体挑战：(1)在单个研究系统中测试我们的指标的预测能力，纳入上下文相关性，并将小规模数据与生态系统范围的影响进行协调；(2)整理现有/新的研究系统数据，以元分析测试一般性。例如，最近庞托-里海物种在爱尔兰北部的入侵导致了几乎没有生态进化历史的分类群之间的新的相互作用，而苏格兰的松貂、苍鹰和秃鹰的范围转移导致了物种相互作用的重新建立。这一范围的EEE提供了丰富的系统来源，以测试不同生境、分类组、营养水平和预测的环境变化的具体结果和方法学的一般性，并提供上下文相关性。

Invasive alien species (IAS), range shifts, re-invasions by recovering native species and rewilding are among the major drivers of the current and future redistributions of the world's biota (e.g. Seebens et al. 2017). We thus face unprecedented challenges in our ability to predict the outcomes of new species interactions and understand their impact on biodiversity. This project will develop recent advances in predictions of species interaction outcomes in ecological and evolutionary contexts across a range of species and habitats. The degree of eco-evolutionary experience (EEE) among interacting species is emerging as a major predictor of outcomes, such as species replacements after invasion (e.g. Penk et al. 2017). Indeed, recent advances in metrics that predict the ecological impacts of IAS are consistent with the hypothesis that EEE drives outcomes, such as dramatic decreases in native fauna due to invasive predators (Dick et al. 2017) and the impact of recovering native predators on modified ecosystems. Here, we propose to develop a predictive framework that allows forecasting of the outcomes of species interactions under scenarios ranging from recolonisations by natives to completely novel pairs of interacting species. This will be based on combinations of metrics that show great promise in predicting outcomes of competitive and predatory interactions across taxa and trophic groups (Dick et al. 2017). In particular, combining the classical "functional response" (resource uptake rate) and numerical response (e.g. abundance, reproduction) into the Relative Impact Potential (RIP) metric has led to prediction of the degree of invasive species impacts and has promise for wider species interactions across the spectrum of species' EEE (Dick et al. 2017). Further, by incorporating various context-dependencies such as climate change and parasitism/disease into the RIP metric, further predictive potential can be achieved. We thus propose to utilise various study systems in Northern Ireland and Scotland to address this overall challenge by: (1) testing our metrics for predictive power in individual study systems, incorporating context-dependencies and reconciling small scale data with ecosystem wide impacts and (2) collating existing/new study systems data to test generality with meta-analyses. For example, recent invasions in N. Ireland by Ponto-Caspian species have resulted in novel interactions among taxa with little eco-evolutionary history, while range shifts of e.g. pine marten, goshawk and buzzard in Scotland are resulting the re-establishment of species interactions. This range of EEE provides a rich source of systems to test specific outcomes and generality of the methodology across habitats, taxonomic groups, trophic levels and predicted environmental change that provides context-dependencies.