Estimating phenotypic traits with morphometry from 16 specimens of thallose liverworts of biological soil crusts collected in Southern Sweden and Germany

Samples of were collected in Southern Sweden in September 2022 and Germany in October 2022. The specimens were brought to the lab at IPB in sterile petri dishes and stored for five days in a sample incubator to let plants acclimatize. Plant material was isolated, washed under a light microscope to remove dirt and other residues, filled into Eppendorf tubes and shock-frozen. Voucher specimens were stored in the herbarium Haussknecht Jena (voucher barcodes: JE04010739, JE04010740, JE04010741, JE04010742, JE04010743, JE04010744, JE04010745, JE04010746, JE04010747, JE04010748, JE04010749, JE04010750, JE04010751, JE04010752, JE04010753, JE04010754). For image acquisition, a Zeiss Axio Scope.A1 HAL 50, 6x HD/DIC, M27, 10x/23 microscope with an achromatic-aplanatic 0.9 H D Ph DIC condenser was used for microscopy utilizing the objectives EC Plan Neofluar 2.5x/0.075 M27 (a=8.8mm), Plan-Apochromat 5x/0.16 M27 (a=12.1mm), Plan-Apochromat 10x/0.45 M27 (a=2.1mm), Plan-Apochromat 20x/0.8 M27 (a=0.55mm), and Plan-Apochromat 40x/0.95 Korr M27 (a=0.25mm) using the EC PN and the Fluar 40x/1.30 III and PA 40x/0.95 III filters for DIC. The conversion filter CB3 and the interference filter wideband green were used to improve digital reproduction of colors. The color balance was adjusted in the camera and in software accordingly. For macroscopy and for preparing microscopy slides, a binocular stereo microscope Zeiss Stemi 2000c was used. For macroscopic images, the Venus Optics Laowa 25mm 2.5-5.0x ultra-macro for Canon EF and the Canon EF-RF adapter were used. To acquire digital images, a full-frame, high-resolution camera (Canon EOS RP, 26 megapixel) was used and adapted to the photographic objectives or to the microscopes using binocular phototubes with sliding prism 30°/23 (Axio Scope.A1) and 100:0/0:100 reversed image (Stemi 2000c) using 60-T2 camera adapter for Canon EOS and a Canon EF-RF adapter. To construct images with extended depth-of-field, images were recorded at different focal planes and by attaching the camera to a Cognisys StackShot macro rail fixed on a Novoflex macro stand, and for microscopy by adapting a Cognisys StackShot motor to the fine adjustment of the microscope using two cogged wheels, one small wheel (1 cm diameter) adapted on the motor and one large wheel (8.5 cm diameter) on the fine adjustment of the microscope. The two cogged wheels were coupled with a toothed belt to obtain fine step increments of the stepping motor for high magnifications. A Cognisys StackShot controller was used to control the amount and distance of the stepping motor with the following controller settings: Dist/Rev: 3200 stp, Backlash: 0 steps, # pics: 1, Tsettle: 100.0 ms, Toff: 450.0 ms, Auto Return: yes, Speed: 3000 st/sec, Tlapse: off, Tpulse: 800.0 ms, Tramp: 100 ms, Units: steps, Torque: 6, Hi Precision: Off, LCD Backlight: 10, Mode: Auto-Step using between 25 steps (magnification 1x) and 50 steps (magnification 25x) and 100 steps (magnification 400x) (number of steps depending on aperture settings and effective magnification). Raw images were recorded in CR3-format and pre-processed with Adobe Lightroom Classic (2022 version) where non-destructive image processing such as corrections of the field curvature, removal of chromatic aberration, color balance, increase of contrast and brightness were performed (NELSON 2012). Images were then exported to TIFF-format and any image processing steps were recorded in individual Adobe XMP-files. Multi-focus image fusion was performed on the individual images in the z-stacks using the software Helicon Focus 8.2.9 and by choosing the algorithms depth map and pyramid with different settings of radius (4, 8, 16, 24) and smoothing (2, 4). The best composite image was chosen manually and retained. When composite images contained specimen that were larger than the frame, several images were stitched together using the Photomerge-Reposition function in the software Adobe Photoshop 2023. Images were manually segmented and interfering background removed using the object selection tools. In order to determine the scale, a stage micrometer was photographed separately with any of the objectives and microscope combinations. The scale was calculated per pixel for each combination and scale bars were put post-hoc onto the segmented images. Measurements of morphometric characters were performed manually with ImageJ / Fiji (SCHINDELIN 2012). After setting up the scales for the individual images in the Image-Properties menu, the following morphometric characters were measured: thallus width [µm], thallus length [µm], thallus with violet pigments [0/1], ventral scales [0/1], ventral scales with slime cells [0/1], ventral scales with violet pigments [0/1], ventral scales with hairs [0/1], air pores [0/1], width of ring cells of air pores in adaxial view [µm], height of ring cells p fair pores in cross section [µm], number of ring cells of air pores in cross section [#], width of ring cells of air pores in cross section [µm], height of ring cells of air pores in cross section [µm], width of epidermis cells in cross section [µm], height of epidermis cells in cross section [µm], width of subepidermal cells in cross section [µm], height of subepidermal cells in cross section [µm], width of thallus in cross section [µm], height of thallus in cross section [µm], height of thallus wing in cross section [µm], angle of thallus wing in cross section [°], width of thallus wing in cross section [µm], area of thallus in cross section [µm2]. Lengths and widths were measured using the Measure function from the Analyze menu and saved in CSV files. To automate the measurement of areas, a pixel classification model was first trained using the plugin LabKit (Arzt et al. 2022) by selecting representative background and foreground areas, training and saving the classifier, which was then imported in the plugin StarDist (WEIGERT et al. 2020), which was used to automatically segment the images. Segmented areas were then measured using the Measure function from the Analyze menu and results were saved. CSV files with all individual morphometric measurements of all specimens were joined into one single table and used for subsequent data analyses. Metadata including species name, taxonomic rank information (NCBI-Taxon and GBIF taxonomy identifiers), voucher specimen id, image acquisition date, an object description including the name of the captured phenotypic character(s), the used objective, microscope, and magnification were associated with any raw image based on unique respective file names. Individual file names, name within an image focus stack and name within an image stitching stack were recorded additionally to facilitate subsequent automized image processing in workflows. Python scripts were created to automatize image fusion and image stitching tasks. Raw camera and pre-processed imaging data in CR3 and TIFF format, respectively, were deposited to the BioImage Archive (BioStudies) using the command line IBM Aspera software tool ascp version 3.8.1.161274 to ensure that data has been transmitted without errors. The raw bioimaging data is available under the BioStudies identifier S-BIAD824 (https://www.ebi.ac.uk/biostudies/studies/S-BIAD824). Processed images were converted to the Bio-Formats OME-TIFF format by creating intermediate ZARR-pyramid tiles using the bioformats2raw converter version 0.7.0 and then using the raw2ometiff version 0.5.0 software tool to create the final pyramid images. Processed images and the metadata were first aggregated in a TSV table and then deposited to the Image Data Resource using the software Globus Connect Personal 3.1.6. Version History In March 2025, sequencing results revealed one species to have a different identification in this study. The metadata for "Riccia gougetiana" has been updated to "Riccia ciliifera".

Peters K, Ziegler J, Neumann S

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