PALAIOS, 2016, v. 31, 525–532 Research Article DOI: http://dx.doi.org/10.2110/palo.2015.088 PYCHNO: A CORE-IMAGE QUANTITATIVE ICHNOLOGY LOGGING SOFTWARE ERIC R. TIMMER, MURRAY K. GINGRAS, AND JOHN-PAUL ZONNEVELD Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada, T6E 2E3 email: ric@ualberta.ca ABSTRACT: Collecting and analyzing semi-quantitative ichnological parameters such as size-diversity index and bioturbation intensity, can increase the resolution of paleoenvironmental analyses. Specialized software, PyCHNO, was designed to ease, improve, and standardize current ichnological data collection techniques. With PyCHNO, ichnological data derived from burrow diameter measurements, trace fossil identification (ichnogenus level), and bioturbation index are collected at a user-defined scale, and size-diversity index is calculated from these measurements. Data collected in PyCHNO, including size-diversity index, maximum burrow diameter, trace fossil diversity, trace fossil taxon abundance, and bioturbation index is easily exported as text files or plotted as PDF logs. INTRODUCTION Process ichnology is a relatively new trace fossil analysis method that stems from both ichnofabric and ichnofacies analysis. In a process ichnology framework, trace fossils are interpreted via a combination of qualitative and semi-quantitative metrics in order to interpret physico- chemical stresses experienced by infauna (Gingras et al. 2011). Two measurements used in process ichnology as proxies for the amount of stress experienced by infauna as reflected by preserved trace fossils are bioturbation index (BI) and size-diversity index (SDI). To encourage the use of these parameters for process ichnology workflows, a specialized ichnology data collection software package, PyCHNO, has been designed to minimize collection errors and decrease data collection time. The volume and resolution of data that can be collected with this software has hitherto been impossible to collect in a time-efficient manner. A further goal of this software is to standardize the data collection workflow and basic data averaging techniques for ichnological analyses. Therefore, PyCHNO can be used for any ichnological applications that require detailed trace fossil data collection. The name ‘‘PyCHNO’’ is derived from a combination of Python (programming language) and ichnology. The following work demonstrates how PyCHNO can be used to quickly and effectively gather a large amount of ichnological information from core photographs. The software development, features, and general workflow of PyCHNO are outlined in the following sections. The development sections review the goals of developing quantitative image- based ichnological data collection software. The features section outlines the various data collection/characterization techniques that have been implemented in PyCHNO. A typical data collection workflow is also presented. PyCHNO (along with a demonstration video) is provided and available for free download as Online Supplemental Material. An in-depth demonstration of the application of PyCHNO collected data for interpreting the spatial and temporal variability in paleoenvironmental stresses is presented in Timmer et al. (this volume). INDICES USED IN PYCHNO Bioturbation index (BI) is a qualitative assessment of the proportion of the original sediment that was bioturbated (Reineck 1967; Taylor and Goldring 1993). The most common scheme involves assigning a number between 0 (no bioturbation) and 6 (complete biogenic sediment homogenization) to describe the degree of bioturbation in a given interval (Fig. 1). A few workers have attempted to measure bioturbation intensity with image analysis workflows (Honeycutt and Plotnick 2008; Dorador et al. 2014). The methods in Dorador et al. (2014) require commercial software and are inefficient for whole-core descriptions as they are time- consuming. Honeycutt and Plotnick’s (2008) work, although groundbreak- ing, is not appropriate for describing the BI of an entire sedimentary core’s image. These techniques require significant image processing and user interaction to gather quality data and are thus time-consuming. For this reason, the BI scheme sensu Reineck (1967) and Taylor and Goldring (1993) is adopted for the PyCHNO software. Size-diversity index (SDI) is calculated as the product of ichnogenera diversity and maximum causative burrow diameter. The maximum burrow diameter is a proxy for the size of the largest burrowing organism at a given time. The underlying assumption is that the largest burrow diameter within a given interval is the most indicative of environmental conditions. Size-diversity index can be useful as a proxy for salinity and dissolved oxygen stress (Wignall and Myers 1988; Hauck et al. 2009). Because of this, SDI is especially useful for paleoenvironmental interpretations in marginal-marine depositional systems. Users recording SDI data should be aware of the possible pit-falls of measuring burrow diameters from 2D planes, as trace fossils typically occur in 3D (McIlroy 2004). Using conventional SDI collection workflows, the worker must measure the maximum burrow diameter of most trace fossils within a given depth interval, find the maximum causative burrow diameter from those measurements, identify the ichnogenera present within that interval, and tabulate all of this data manually. Collecting SDI data is time-consuming, which effectively acts as a limiting factor on the adoption of SDI in ichnological analysis. Depth averaging SDI measurements over a set interval (e.g., 75 cm for a core sleeve), also limits the accuracy of SDI. This is especially important if the SDI data are input in a geostatistical grid. In a geostatistical model, the SDI is ‘‘upscaled’’ or averaged into a grid, which may have different vertical cell thicknesses (e.g., 1 m) compared to the collected SDI data measurement increment (e.g., 75 cm). If the SDI data are already averaged over a specified interval (e.g., 75 cm), and then further averaged (e.g., 1 m increments) in order to match grid cell sizes, resolution is greatly reduced. Published Online: November 2016 Copyright Ó 2016, SEPM (Society for Sedimentary Geology) 0883-1351/16/031-525/$03.00