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

树木年轮精确地整合和保存了树木在其发育期间生长条件的可测定的环境信息。树木年代学建立在交叉定年的基本原理上，即在多个树木年轮序列之间识别和同步特定的时间模式，以建立绝对定年的树木年轮年表。将统计结果与树木年轮结构所显示的环境因子与树木生长响应之间关系的巩固知识相结合，可作为重建过去环境条件的基础。这种基于树木年轮的研究为气候重建（树木气候学）、人类历史重建（树木考古学）和广泛的生态环境方面做出了重大贡献。在这个跨学科的项目中，我们的目标是实施模式匹配方法来分析年轮密度分布，以解决树木年代学和树木生态学的研究问题。该项目结合了树木生态学小组（specker教授等人）在基于树木年轮的研究方面的专家知识，以及生物信息学小组（Backofen教授等人）在生物信息学应用的校准和聚类方法开发方面的丰富经验。我们的工作目标是开发改进的方法：(1)在聚合多个年轮密度剖面时增强环境信号强度，(2)生成年轮位置的高分辨率时间注释，(3)交叉日期树木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（基于多个间隔的曲线对齐），该方法基于已建立的曲线和序列对齐技术生成信号增强的共识剖面。应用现有的大量年轮密度数据，我们将生成不同树种的空间共识剖面年表。使用附属的准连续点树木密度计数据，通过专用的MICA适配对这些年内密度数据进行时间注释，将得到时间注释的年表。通过利用年内密度剖面信息，这两种年表都被用于建立新的基于对准的交叉测年技术。由于后者的信息内容比例如仅环宽的信息内容更丰富，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，我们将建立一个管道来研究环境因子与树木年轮密度的相关性。最初的研究将侧重于应用概率分配一个位置或地点给一个未定位的树木年轮样本。所有工具和管道都将公开提供，并附有大量文档。

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.