ATP - Automated Intra-Annual Tree-Ring Profiling for Dendroecological Research

树木年轮准确地整合和保存了有关树木在发育期间的生长条件的可更新的环境信息。树轮年代学建立在交叉测年的基本原理上，即识别和同步多个树轮序列之间的特定时间模式，以开发绝对过时的树轮年表。综合统计结果和关于树木年轮结构所显示的环境因素与树木生长反应之间关系的巩固知识，是重建过去环境条件的基础。这种基于树轮的研究对气候重建(树轮气候学)、人类历史重建(树轮考古学)和环境的广泛生态方面都有重要的贡献。在这个跨学科的项目中，我们的目标是实现模式匹配的方法来分析年内树轮密度分布的树的年际和树生态学的研究问题。该项目加入了树枝生态学小组的专家知识(斯派克教授等人)。在基于树轮的研究中，拥有生物信息学小组的丰富经验(Backofen教授等人)关于生物信息学应用的比对和聚类方法开发。我们的活动旨在开发改进的方法(1)在聚合多个年轮密度剖面时增强环境信号强度，(2)为年轮内的树木年轮位置生成高分辨率的时间注释，(3)交叉更新年轮序列并建立树木年轮年表。为此，我们将扩展和改进我们的MICA方法(基于多区间的曲线比对)，该方法在已有的曲线和序列比对技术的基础上生成信号增强的共识轮廓。应用现有的大量年内树轮密度数据，我们将生成不同树种的空间一致剖面年表。使用Appendant准连续点树形计数据，通过专门的MICA适配对这些年内密度数据进行时间注释将产生时间注释的年表。这两种年表都被用来通过利用年内密度剖面信息来建立基于排列的新的交叉测年技术。由于后者的信息量比环宽要大，这些新方法将需要更少的样品材料，同时提高测年精度。最后，我们将建立一条管道，以调查环境因素与树轮密度剖面的相关性。最初的研究将集中在以概率方式将一个地点或地点分配给未定位的树轮样本的应用。所有工具和管道都将公之于众，并附有广泛的文件。

Tree-rings integrate and conserve precisely datable environmental information on the growing conditions of trees during the time of their development. Dendrochronology builds on the basic principle of cross-dating, i.e. the identification and synchronization of time specific patterns between multiple tree-ring sequences to develop absolutely dated tree-ring chronologies. The integration of statistical findings and consolidated knowledge about the relations between environmental factors and the tree growth response as manifested in the tree-ring structures serve as the basis for the reconstruction of past environmental conditions. Such tree-ring based studies provide significant contributions to the fields of climate reconstruction (dendroclimatology), reconstruction of human history (dendroarchaeology) and to a wide range of ecological aspects of the environment.Within this interdisciplinary project, we aim at implementing methods of pattern matching to the analysis of intra-annual tree-ring density profiles for dendrochronological and dendroecological research questions. The project joins the expert knowledge of the Dendroecology group (Prof. Spiecker et al.) in tree-ring based research with the extensive experience of the Bioinformatics group (Prof. Backofen et al.) concerning alignment and clustering method development for bioinformatics applications. Our activities are targeted towards the development of improved methods (1) to enhance environmental signal strength when aggregating multiple intra-annual tree-ring density profiles, (2) to generate high-resolution temporal annotations for intra-annual tree-ring positions, (3) to cross-date tree-ring sequences and build tree-ring chronologies.To this end, we will extend and improve our MICA approach (Multiple Interval-based Curve Alignment), which generates signal-enhanced consensus profiles building on established curve and sequence alignment techniques. Applied on the available extensive intra-annual tree-ring density data, we will generate spatial consensus profile chronologies for various tree species. Using appendant quasi-continuous point dendrometer data, temporal annotation of these intra-annual density data via dedicated MICA adaptions will result in time-annotated chronologies. Both types of chronologies are used to establish new alignment-based cross-dating techniques through the exploitation of intra-annual density profile information. Due to the extended information content of the latter compared to e.g. just ring-width, these new methods will require less sample material while enhancing dating precision. Finally, we will establish a pipeline to investigate the correlation of environmental factors with tree-ring density profiles. An initial study will focus on the application to probabilistically allocate a location or site to an un-located tree-ring sample. All tools and pipelines will be made publicly available and accompanied with extensive documentation.