Ullmann, T. | Search bioRxiv

bioRxiv2024

TreeCompR: Tree competition indices for inventory data and 3D point clouds

O_LIIn times of more frequent global-change-type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices leave users with the agony of choice, as no single competition index has proven universally superior. C_LIO_LITo help users with the choice and computation of appropriate indices, we present the open-source TreeCompR package, which can handle 3D point clouds in various formats as well as classical forest inventory data and serves as a centralized platform for exploring and comparing different competition indices (CIs). Within a common interface, users can efficiently select the most suitable CI for their specific research questions. The package facilitates the integration of both traditional distance-dependent and novel point cloud-based indices. C_LIO_LITo evaluate the package, we used TreeCompR to quantify the competition situation of 308 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. C_LIO_LIThe compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well-structured output, our package can easily be integrated into existing data-analysis workflows both for 3D point cloud and classical forest inventory data. C_LI

ecology↗

bioRxiv2022

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the "best" ones. However, if only the best results are selectively reported, this may cause over-optimism: the "best" method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes three unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, and differential microbial network analysis. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the "best" method combination to the validation dataset. The results are then compared between discovery and validation data. In all three research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

bioinformatics↗