DOI: 10.5061/dryad.vmcvdnd2v
title: Comparative Spatial Paleoecology: Assessing Niche Competition between Eocene North American Multituberculates and Rodents Regarding Forest Resources to Elucidate the Cause of Multituberculate Extinction

description: Multituberculate extinction is often cited as a classic case of
 competitive exclusion, coinciding with the first rodent arrivals in the
 late Paleocene. Analyzing 124 North American multituberculate last
 occurrence records during the Eocene from 56 to 34 million years ago, this
 study aimed to differentiate Eocene multituberculate and coeval rodent
 floral associations through geographic spatial analysis to understand
 niche overlap between the two groups.  If competitive exclusion
 with rodents was a factor in multituberculate extinction, both
 multituberculates and rodents would be predicted to share similar forest
 habitat preferences and have competed for similar ecological niches
 regarding their forest associations. Using spatial analysis, this study
 found that Eocene rodents and multituberculates did not overlap in their
 forest associations. The findings indicate that multituberculates were
 unique in inhabiting a specific type of ancient forest habitat, favoring
 forests composed of Metasequoia, Glyptostrobus, and Alnus, and thus
 thrived in wetter northern temperate forest communities during the Eocene.
 Metasequoia and Glyptostrobus declined significantly in North America
 during the later Cenozoic, coinciding with multituberculate decline and
 extinction as the global climate shifted toward colder and drier climates
 around the Eocene-Oligocene boundary. In contrast, the success of rodents
 is attributed to their much broader forest affinity. These preferences
 align with the widespread distribution of rodents today, contributing to
 their modern success. The absence of any similar reconstructed forest
 habitat preferences between rodents and multituberculates suggests that
 changing forest structure, rather than competitive exclusion, drove
 multituberculate extinction.
description: <strong>How was this dataset
 collected?</strong> The dataset consists of
 latitude and longitude data for Eocene North American fossil sites. It
 includes multituberculates (Neoplagiaulacidae and Neolitomus) and six
 rodent families (Ischyromyidae, Cylindrodontidae, Sciuravidae,
 Eutypomyidae, Eomyidae, and Protoptychidae) as defined in McKenna and Bell
 (1997). Data sources included primary literature, the Paleobiology
 Database, and various museum collections. Each site’s location was
 verified and adjusted using Google Earth to the nearest outcrop of rock.
 Data on fossil plants were also collected and included in this study.
 Where available, each occurrence was referenced with a PaleobioDB number.
 The PaleobioDB database is licensed under a CC0 International License.
 Specific references for each occurrence are stored in the MAMMAL_REF and
 PLANT_REF subfolders. <strong>How has this
 dataset been processed?</strong> To analyze the
 spatial relationships between mammal and plant fossil groups, four ranked
 parameters were calculated for each comparison. The necessary data,
 including point and polygon shapes, is located in the
 DATA_ANALYSIS/GIS_FILES folder. This folder contains a file named
 Project-Multituberculates.qgs, which loads all relevant data, including
 the outlines of the world's coastlines and a 2x2 grid used for model
 counts. Centroid and polygon shape files of the point data were also
 processed. Details on how the four parameters were calculated are
 available in the README.txt file. For calculating the EDRF parameter,
 Python scripts and necessary data are provided in the
 EDRF-Program&Data folder within the DATA_ANALYSIS.zip
 folder. Null model values for each parameter are
 determined using scripts and data in the NULL_MODEL.zip folder, with
 methods detailed in the Methods.txt file, including instructions for
 running the scripts in the STOCHASTIC and PROBABILISTIC
 subfolders. The GRID_MODEL.zip folder includes tallies
 and modeled occurrences for the Chi-Square test, with results reported in
 a separate file. Tallies for each grid were made using the mammal and
 plant data in the GIS_FILES folder. Tables and
 appendices summarizing the conclusions from the data analysis are also
 included.  
description: # Comparative Spatial Paleoecology: Assessing Niche Competition between
 Eocene North American Multituberculates and Rodents Regarding Forest
 Resources to Elucidate the Cause of Multituberculate Extinction
 [https://doi.org/10.5061/dryad.vmcvdnd2v](https://doi.org/10.5061/dryad.vmcvdnd2v) ## Description of the data and file structure # Data Repository Overview This README provides an overview of the data files and analysis included in this repository. Navigate through the sections below for detailed information about each part of the dataset and methodology. This readme file also includes lists of all associated files. * [1. Collection of Raw Data](#1-collection-of-raw-data) * [2. Data Analysis](#2-data-analysis) * [3. Null Model](#3-null-model) * [4. Grid Model](#4-grid-model) * [5. Results](#5-results) ## 1. Collection of Raw Data This section discusses how the raw data was acquired from various sources including published literature, databases, and museum collections. ## 2. Data Analysis Explanation of the data analysis process used for evaluating spatial relationships between mammal and plant groups, including the calculation of four ranked parameters. Includes all GIS files, scripts and data to carry out the four parameter analyses described in the paper. ## 3. Null Model Details on the development of two types of null models used to assess the randomness of the spatial distribution of data points. This includes both stochastic and probabilistic approaches, include methods, mathematical formulas, and python scripts, with associated data for randomization. ## 4. Grid Model The grid model approach used for the Chi-Squared statistical analysis of the completeness of the data. This involves mapping Eocene fossil localities across North America to examine fossil preservation biases. Including tallies of fossil localities for each 2x2 grid in the GIS files, and results of the statistical test calculations. See Appendix 1 for results. ## 5. Results Presentation of the study results, including tables and appendices that list the rankings and analyses of the data. These files are referenced in the publication, and summarize the conclusions of the data analysis. --- # 1. Collection of Raw Data The latitude and longitude data of Eocene North American fossil sites for multituberculates (Neoplagiaulacidae and Neolitomus) and six Eocene rodent families (Ischyromyidae, Cylindrodontidae, Sciuravidae, Eutypomyidae, Eomyidae, and Protoptychidae, (as defined in McKenna and Bell, 1997) were gathered from primary literature, online databases (such as the Paleobiology Database), and various museum collections. Each locality was verified using Google Earth and adjusted to the nearest outcrop of rock. In addition locality information for various fossil plants were also collected from the literature, and included in the study. If known the PaleobioDB reference number is give for each occurrence. The Paleobiology Database is licensed under a CC0 International License. References for the source of information for each specific occurrences can be found the MAMMAL_REF and PLANT_REF folders. REF.zip \|- Data/ \| |- MAMMAL_REF \| | | - REFERENCES_Cylindrodontidae.csv \| | | - REFERENCES_Eomyidae.csv \| | | - REFERENCES_Eutypomyidae.csv \| | | - REFERENCES_Ischyromyidae.csv \| | | - REFERENCES_Multituberculate.csv \| | | - REFERENCES_Protoptychidae.csv \| | | - REFERENCES_Sciuravidae.csv \| |- PLANT_REF \| | | - REFERENCES_Abies.csv \| | | - REFERENCES_Acer.csv \| | | - REFERENCES_Araliaceae.csv \| | | - REFERENCES_Cinnamomum.csv \| | | - REFERENCES_Fabaceae.csv \| | | - REFERENCES_Fagaceae.csv \| | | - REFERENCES_Glyptostrobus.csv \| | | - REFERENCES_Lauraceae.csv \| | | - REFERENCES_Liquidambar.csv \| | | - REFERENCES_Macginitiea.csv \| | | - REFERENCES_Metasequoia.csv \| | | - REFERENCES_Moraceae.csv \| | | - REFERENCES_Myrtaceae.csv \| | | - REFERENCES_Platanus.csv \| | | - REFERENCES_Populus.csv \| | | - REFERENCES_Rhus.csv \| | | - REFERENCES_Rosaceae.csv \| | | - REFERENCES_Salix.csv \| | | - REFERENCES_Ulmaceae.csv --- # 2. Data Analysis To evaluate the spatial relationships between mammal group and plant groups, four ranked parameters were calculated for each mammal-plant comparison, using the points and polygon shapes that can be found in the GIS_FILES folder. Open Project-Multitebercualtes.qgs will open the GIS file containing all localities of each mammal and plant group, an outline of the world's coastline, as well as a Grid-2x2 to be used in the counts for the grid model. The ANOVA-statistical Analysis is output files for distance matrixes, while the Data-Tables contain all the linked data GIS Data files, centroid points, and each polygon shape used in the analysis. ## Number of Occurrences in the Polygon (NOP) The first parameter used in the study is called the Number of Occurrences in the Polygon (NOP), and it simply measures the percentage of fossil plant occurrences within each mammal polygon shape. To calculate this percentage, the total number of plant occurrences within a mammal polygon is divided by the total known fossil occurrences of that plant group, then multiplied by 100. This calculation also considers instances where plant and mammal fossils are found together at the same locality. This analysis was done using the GIS_FILES. ## Centroid Ellipsoid Distance (CED) The second parameter analyzed is the centroid ellipsoid distance (CED), which measures the ellipsoidal distance between the centroids (geometric barycenters) of the plant and mammal polygons. Using vector analysis in QGIS, the geometric barycenter for each polygon was calculated, and the distance between the centroid points of the mammal and plant polygons was measured in kilometers. A lower CED value indicates that the polygons are closer together. The CED primarily measures accuracy (rather than precision) in the spatial overlap of the data. This metric reflects the similarity of biomes in terms of general geographic occurrence patterns, with lower CED values suggesting a similar geographic distribution concerning latitude, climate, and other environmental factors for both mammal and plant groups. CED values were ranked from smallest to largest. Each centroid position can be found in the GIS_FILE folder. ## Non-Overlap Area (NOLA) The third parameter, termed NOLA (Non-Overlap of Areas), quantifies the non-overlapping regions between mammal and plant polygons. If the mammal polygon is entirely enclosed by the fossil plant polygon, the NOLA value is 0. Lower NOLA values indicate that plant polygons encompass the entire known geographic range of the mammals. Conversely, fossil plants in a different climatic zone from the mammals' geographic range result in high NOLA values, with 100% indicating complete non-overlap of the two polygons, or in other words avoidance. Thus, a high NOLA parameter suggests a lack of close associations between the mammal and plant groups, hence NOLA values are ranked from smallest to largest. Data used for this calculation can be found in the folder GIS_FILES. ## Ellipsoid Distance Ratio Function (EDRF) The fourth and final parameter simply compares two things and finds a ratio: the average distances between similar data points (plant-to-plant or mammal-to-mammal distances) and the distances between different data points (plant-to-mammal distances). This comparison helps clarify the spatial relationships between these two sets of data points. This was done using the formula in the published paper. To accomplish this calculation, a computer program was written in python to quickly calculate this ratio value from all the distances. (see Ellipsoid-Distance-Ratio-Function.py). This file requires data to be organized into two sets of data. The data tables used for this analysis can be found in the EDRF-Program&Data folder. Each data file is organized by an abbreviation of the plant group and mammal group. Files are also available to compare mammal groups to other mammal groups. Instructions for using the Ellipsoid-Distance-Ratio-Function.py python script are as follows: This script was first written by Benjamin Burger on October 3rd 2023 It can run using Python version 3.11, with pandas and math libraries. The script will prompt you to run a formatted file of data saved as a .csv (comma delimited file) This script looks at a data set of fossil localities saved in a .csv file the .csv file should have the following headings, separated by commas id,latitude,longitude,type id is a unique number beginning with 1 latitude and longitude are listed in decimal format type is either A or B type A is the plant locality data points type B is the mammal locality data points the program will compare the distances of similar A-A and B-B and opposing A-B types and find the ratio X/Y where X is the MEAN value of A-B distances, and Y is the MEAN value of A-A and B-B distances Any value below 1.00 is considered significant overlap in the cluster of data. The higher the value above 1.00 the greater the dissimilarity between the two data point clusters Values below 1.00 differ based on the sample size, with a theoretical lowest value of 0.00 with 1 shared data point. This analysis also considers multiple points from the same locality, so you can look at localities with multiple specimens or multiple species from the same locality id is a unique number series beginning with 1 latitude and longitude are in decimal format A theoretical value of 1.0 indicates a perfectly balanced ratio of the two sets of distances. The higher the ratio above 1.0 the more independently clustered the two data sets are, and the weaker the plant to mammal association. In contrast the lower the ratio is below 1.0, the smaller the sample size and less certainty there is within the ratio. For example, a ratio of 0 would indicate all the mammal and plant pairing were found in a single location, if both the mammal and plant fossils were found at only two locations together the ratio would be 0.5. The absolute value of the EDRF was subtracted from 1 so that the top ranked pairing was the value closest to 1.0. When you run the script with an associated data file, it will return a EDRF value. The program and associated data can be found in the EDRF_PROGRAM&DATA folder in the DATA_ANALYSIS.zip folder. Here is the list of files available in the DATA_ANALYSIS folder. Just open Project-Multituberculates.qgs using QGIS (which can be downloaded from the QGIS website: [https://qgis.org](https://qgis.org) and distributed under a GNU open source license) and you can then view all the files in the GIS_FILES folder, including shape and database files. DATA_ANALYSIS.zip \|- GIS_FILES/ \| | - Project-Multituberculates.qgs \| | - ne_10m_coastline \| | - Grid-2x2-ne.gpkg \| | - Grid-2x2-ne.gpkg-shm \| | - Grid-2x2-ne.gpkg-wal \| | - Data-Tables/ \| | | - Abies_Centroid.cpg \| | | - Abies_Centroid.dbf \| | | - Abies_Centroid.prj \| | | - Abies_Centroid.shp \| | | - Abies_Centroid.shx \| | | - Abies_Polygon.cpg \| | | - Abies_Polygon.dbf \| | | - Abies_Polygon.prj \| | | - Abies_Polygon.shp \| | | - Abies_Polygon.shx \| | | - Abies2.csv \| | | - Acer.csv \| | | - Acer_Centroid.cpg \| | | - Acer_Centroid.dbf \| | | - Acer_Centroid.prj \| | | - Acer_Centroid.shp \| | | - Acer_Centroid.shx \| | | - Acer_Polygon.cpg \| | | - Acer_Polygon.dbf \| | | - Acer_Polygon.prj \| | | - Acer_Polygon.shp \| | | - Acer_Polygon.shx \| | | - Alnus.csv \| | | - Alnus_Centroid.cpg \| | | - Alnus_Centroid.dbf \| | | - Alnus_Centroid.prj \| | | - Alnus_Centroid.shp \| | | - Alnus_Centroid.shx \| | | - Alnus_Polygon.cpg \| | | - Alnus_Polygon.dbf \| | | - Alnus_Polygon.prj \| | | - Alnus_Polygon.shp \| | | - Alnus_Polygon.shx \| | | - Araliaceae.csv \| | | - Araliaceae_Centroid.cpg \| | | - Araliaceae_Centroid.dbf \| | | - Araliaceae_Centroid.prj \| | | - Araliaceae_Centroid.shp \| | | - Araliaceae_Centroid.shx \| | | - Araliaceae_Polygon.cpg \| | | - Araliaceae_Polygon.dbf \| | | - Araliaceae_Polygon.prj \| | | - Araliaceae_Polygon.shp \| | | - Araliaceae_Polygon.shx \| | | - Carya.csv \| | | - Carya_Centroid.cpg \| | | - Carya_Centroid.dbf \| | | - Carya_Centroid.prj \| | | - Carya_Centroid.shp \| | | - Carya_Centroid.shx \| | | - Carya_Polygon.cpg \| | | - Carya_Polygon.dbf \| | | - Carya_Polygon.prj \| | | - Carya_Polygon.shp \| | | - Carya_Polygon.shx \| | | - Cinnamomum.csv \| | | - Cinnamomum_Centroid.cpg \| | | - Cinnamomum_Centroid.dbf \| | | - Cinnamomum_Centroid.prj \| | | - Cinnamomum_Centroid.shp \| | | - Cinnamomum_Centroid.shx \| | | - Cinnamomum_Polygon.cpg \| | | - Cinnamomum_Polygon.dbf \| | | - Cinnamomum_Polygon.prj \| | | - Cinnamomum_Polygon.shp \| | | - Cinnamomum_Polygon.shx \| | | - Cornus.csv \| | | - Cornus_Centroid.cpg \| | | - Cornus_Centroid.dbf \| | | - Cornus_Centroid.prj \| | | - Cornus_Centroid.shp \| | | - Cornus_Centroid.shx \| | | - Cornus_Polygon.cpg \| | | - Cornus_Polygon.dbf \| | | - Cornus_Polygon.prj \| | | - Cornus_Polygon.shp \| | | - Cornus_Polygon.shx \| | | - Cylindrodontidae.csv \| | | - Cylindrodontidae_Centroid.cpg \| | | - Cylindrodontidae_Centroid.dbf \| | | - Cylindrodontidae_Centroid.prj \| | | - Cylindrodontidae_Centroid.shp \| | | - Cylindrodontidae_Centroid.shx \| | | - Cylindrodontidae_Polygon.cpg \| | | - Cylindrodontidae_Polygon.dbf \| | | - Cylindrodontidae_Polygon.prj \| | | - Cylindrodontidae_Polygon.shp \| | | - Cylindrodontidae_Polygon.shx \| | | - Eomyidae.csv \| | | - Eomyidae_Centroid.cpg \| | | - Eomyidae_Centroid.dbf \| | | - Eomyidae_Centroid.prj \| | | - Eomyidae_Centroid.shp \| | | - Eomyidae_Centroid.shx \| | | - Eomyidae_Polygon.cpg \| | | - Eomyidae_Polygon.dbf \| | | - Eomyidae_Polygon.prj \| | | - Eomyidae_Polygon.shp \| | | - Eomyidae_Polygon.shx \| | | - Eutypomyidae.csv \| | | - Eutypomyidae_Centroid.cpg \| | | - Eutypomyidae_Centroid.dbf \| | | - Eutypomyidae_Centroid.prj \| | | - Eutypomyidae_Centroid.shp \| | | - Eutypomyidae_Centroid.shx \| | | - Eutypomyidae_Polygon.cpg \| | | - Eutypomyidae_Polygon.dbf \| | | - Eutypomyidae_Polygon.prj \| | | - Eutypomyidae_Polygon.shp \| | | - Eutypomyidae_Polygon.shx \| | | - Fabaceae.csv \| | | - Fabaceae_Centroid.cpg \| | | - Fabaceae_Centroid.dbf \| | | - Fabaceae_Centroid.prj \| | | - Fabaceae_Centroid.shp \| | | - Fabaceae_Centroid.shx \| | | - Fabaceae_Polygon.cpg \| | | - Fabaceae_Polygon.dbf \| | | - Fabaceae_Polygon.prj \| | | - Fabaceae_Polygon.shp \| | | - Fabaceae_Polygon.shx \| | | - Fagaceae.csv \| | | - Fagaceae_Centroid.cpg \| | | - Fagaceae_Centroid.dbf \| | | - Fagaceae_Centroid.prj \| | | - Fagaceae_Centroid.shp \| | | - Fagaceae_Centroid.shx \| | | - Fagaceae_Polygon.cpg \| | | - Fagaceae_Polygon.dbf \| | | - Fagaceae_Polygon.prj \| | | - Fagaceae_Polygon.shp \| | | - Fagaceae_Polygon.shx \| | | - Fagaceae.cpg \| | | - Fagaceae.dbf \| | | - Fagaceae.prj \| | | - Fagaceae.shp \| | | - Fagaceae.shx \| | | - Ficus.csv \| | | - Ficus_Centroid.cpg \| | | - Ficus_Centroid.dbf \| | | - Ficus_Centroid.prj \| | | - Ficus_Centroid.shp \| | | - Ficus_Centroid.shx \| | | - Ficus_Polygon.cpg \| | | - Ficus_Polygon.dbf \| | | - Ficus_Polygon.prj \| | | - Ficus_Polygon.shp \| | | - Ficus_Polygon.shx \| | | - Ginkgo.csv \| | | - Glyptostrobus-list \| | | - Gyptostrobus_Polygon.cpg \| | | - Gyptostrobus_Polygon.dbf \| | | - Gyptostrobus_Polygon.prj \| | | - Gyptostrobus_Polygon.shp \| | | - Gyptostrobus_Polygon.shx \| | | - Gyptrostrobus_Centroid.cpg \| | | - Gyptrostrobus_Centroid.dbf \| | | - Gyptrostrobus_Centroid.prj \| | | - Gyptrostrobus_Centroid.shp \| | | - Gyptrostrobus_Centroid.shx \| | | - Ischyromidae-Area.cpg \| | | - Ischyromidae-Area.dbf \| | | - Ischyromidae-Area.prj \| | | - Ischyromidae-Area.shp \| | | - Ischyromidae-Area.shx \| | | - Ischyromyidae.csv \| | | - Ischyromyidae_Centroid.cpg \| | | - Ischyromyidae_Centroid.dbf \| | | - Ischyromyidae_Centroid.prj \| | | - Ischyromyidae_Centroid.shp \| | | - Ischyromyidae_Centroid.shx \| | | - Ischyromyidae_Polygon.cpg \| | | - Ischyromyidae_Polygon.dbf \| | | - Ischyromyidae_Polygon.prj \| | | - Ischyromyidae_Polygon.shp \| | | - Ischyromyidae_Polygon.shx \| | | - Juglans.csv \| | | - Juglans_Centroid.cpg \| | | - Juglans_Centroid.dbf \| | | - Juglans_Centroid.prj \| | | - Juglans_Centroid.shp \| | | - Juglans_Centroid.shx \| | | - Juglans_Polygon-2.cpg \| | | - Juglans_Polygon-2.dbf \| | | - Juglans_Polygon-2.prj \| | | - Juglans_Polygon-2.shp \| | | - Juglans_Polygon-2.shx \| | | - Juglans_Polygon.cpg \| | | - Juglans_Polygon.dbf \| | | - Juglans_Polygon.prj \| | | - Juglans_Polygon.shp \| | | - Juglans_Polygon.shx \| | | - Lauraceae.csv \| | | - Lauraceae_Centroid.cpg \| | | - Lauraceae_Centroid.dbf \| | | - Lauraceae_Centroid.prj \| | | - Lauraceae_Centroid.shp \| | | - Lauraceae_Centroid.shx \| | | - Lauraceae_Polygon.gpkg \| | | - Liquidamaber_Polygon.cpg \| | | - Liquidamaber_Polygon.dbf \| | | - Liquidamaber_Polygon.prj \| | | - Liquidamaber_Polygon.shp \| | | - Liquidamaber_Polygon.shx \| | | - Liquidambar.csv \| | | - Liquidambar_Centroid.cpg \| | | - Liquidambar_Centroid.dbf \| | | - Liquidambar_Centroid.prj \| | | - Liquidambar_Centroid.shp \| | | - Liquidambar_Centroid.shx \| | | - Macginitiea.csv \| | | - Macginitiea_Centroid.cpg \| | | - Macginitiea_Centroid.dbf \| | | - Macginitiea_Centroid.prj \| | | - Macginitiea_Centroid.shp \| | | - Macginitiea_Centroid.shx \| | | - Macginitiea_Polygon.cpg \| | | - Macginitiea_Polygon.dbf \| | | - Macginitiea_Polygon.prj \| | | - Macginitiea_Polygon.shp \| | | - Macginitiea_Polygon.shx \| | | - Metasequoia.csv \| | | - Metasequoia_Centroid.cpg \| | | - Metasequoia_Centroid.dbf \| | | - Metasequoia_Centroid.prj \| | | - Metasequoia_Centroid.shp \| | | - Metasequoia_Centroid.shx \| | | - Metasequoia_Polygon.cpg \| | | - Metasequoia_Polygon.dbf \| | | - Metasequoia_Polygon.prj \| | | - Metasequoia_Polygon.shp \| | | - Metasequoia_Polygon.shx \| | | - Metasequoia.cvs \| | | - Moraceae.csv \| | | - Moraceae_Centroid.cpg \| | | - Moraceae_Centroid.dbf \| | | - Moraceae_Centroid.prj \| | | - Moraceae_Centroid.shp \| | | - Moraceae_Centroid.shx \| | | - Moraceae_Polygon.cpg \| | | - Moraceae_Polygon.dbf \| | | - Moraceae_Polygon.prj \| | | - Moraceae_Polygon.shp \| | | - Moraceae_Polygon.shx \| | | - Multituberculate.csv \| | | - Multituberculate_Centroid.cpg \| | | - Multituberculate_Centroid.dbf \| | | - Multituberculate_Centroid.prj \| | | - Multituberculate_Centroid.shp \| | | - Multituberculate_Centroid.shx \| | | - Multituberculate_Polygon.cpg \| | | - Multituberculate_Polygon.dbf \| | | - Multituberculate_Polygon.prj \| | | - Multituberculate_Polygon.shp \| | | - Multituberculate_Polygon.shx \| | | - Myrtaceae.csv \| | | - Myrtaceae_Centroid.cpg \| | | - Myrtaceae_Centroid.dbf \| | | - Myrtaceae_Centroid.prj \| | | - Myrtaceae_Centroid.shp \| | | - Myrtaceae_Centroid.shx \| | | - Myrtaceae_Polygon.cpg \| | | - Myrtaceae_Polygon.dbf \| | | - Myrtaceae_Polygon.prj \| | | - Myrtaceae_Polygon.shp \| | | - Myrtaceae_Polygon.shx \| | | - Picea.csv \| | | - Picea_Centroid.cpg \| | | - Picea_Centroid.dbf \| | | - Picea_Centroid.prj \| | | - Picea_Centroid.shp \| | | - Picea_Centroid.shx \| | | - Picea_Polygon.cpg \| | | - Picea_Polygon.dbf \| | | - Picea_Polygon.prj \| | | - Picea_Polygon.shp \| | | - Picea_Polygon.shx \| | | - Pinus.csv \| | | - Pinus_Centroid.cpg \| | | - Pinus_Centroid.dbf \| | | - Pinus_Centroid.prj \| | | - Pinus_Centroid.shp \| | | - Pinus_Centroid.shx \| | | - Pinus_Polygon.cpg \| | | - Pinus_Polygon.dbf \| | | - Pinus_Polygon.prj \| | | - Pinus_Polygon.shp \| | | - Pinus_Polygon.shx \| | | - Platanus.csv \| | | - Platanus_Centroid.cpg \| | | - Platanus_Centroid.dbf \| | | - Platanus_Centroid.prj \| | | - Platanus_Centroid.shp \| | | - Platanus_Centroid.shx \| | | - Platanus_Polygon.cpg \| | | - Platanus_Polygon.dbf \| | | - Platanus_Polygon.prj \| | | - Platanus_Polygon.shp \| | | - Platanus_Polygon.shx \| | | - Platycarya.csv \| | | - Platycarya_Centroid.cpg \| | | - Platycarya_Centroid.dbf \| | | - Platycarya_Centroid.prj \| | | - Platycarya_Centroid.shp \| | | - Platycarya_Centroid.shx \| | | - Platycarya_Polygon.cpg \| | | - Platycarya_Polygon.dbf \| | | - Platycarya_Polygon.prj \| | | - Platycarya_Polygon.shp \| | | - Platycarya_Polygon.shx \| | | - Populus.csv \| | | - Populus_Centroid.cpg \| | | - Populus_Centroid.dbf \| | | - Populus_Centroid.prj \| | | - Populus_Centroid.shp \| | | - Populus_Centroid.shx \| | | - Populus_Polygon.cpg \| | | - Populus_Polygon.dbf \| | | - Populus_Polygon.prj \| | | - Populus_Polygon.shp \| | | - Populus_Polygon.shx \| | | - Protoptychidae.csv \| | | - Protoptychidae_Centroid.cpg \| | | - Protoptychidae_Centroid.dbf \| | | - Protoptychidae_Centroid.prj \| | | - Protoptychidae_Centroid.shp \| | | - Protoptychidae_Centroid.shx \| | | - Protoptychidae_Polygon.cpg \| | | - Protoptychidae_Polygon.dbf \| | | - Protoptychidae_Polygon.prj \| | | - Protoptychidae_Polygon.shp \| | | - Protoptychidae_Polygon.shx \| | | - Rhus.csv \| | | - Rhus_Centroid.cpg \| | | - Rhus_Centroid.dbf \| | | - Rhus_Centroid.prj \| | | - Rhus_Centroid.shp \| | | - Rhus_Centroid.shx \| | | - Rhus_Polygon.cpg \| | | - Rhus_Polygon.dbf \| | | - Rhus_Polygon.prj \| | | - Rhus_Polygon.shp \| | | - Rhus_Polygon.shx \| | | - Rosaceae.csv \| | | - Rosaceae_Centroid.cpg \| | | - Rosaceae_Centroid.dbf \| | | - Rosaceae_Centroid.prj \| | | - Rosaceae_Centroid.shp \| | | - Rosaceae_Centroid.shx \| | | - Rosaceae_Polygon.cpg \| | | - Rosaceae_Polygon.dbf \| | | - Rosaceae_Polygon.prj \| | | - Rosaceae_Polygon.shp \| | | - Rosaceae_Polygon.shx \| | | - Salix.csv \| | | - Salix_Centroid.cpg \| | | - Salix_Centroid.dbf \| | | - Salix_Centroid.prj \| | | - Salix_Centroid.shp \| | | - Salix_Centroid.shx \| | | - Salix_Polygon.cpg \| | | - Salix_Polygon.dbf \| | | - Salix_Polygon.prj \| | | - Salix_Polygon.shp \| | | - Salix_Polygon.shx \| | | - Sciuravidae.csv \| | | - Sciuravidae_Centroid.cpg \| | | - Sciuravidae_Centroid.dbf \| | | - Sciuravidae_Centroid.prj \| | | - Sciuravidae_Centroid.shp \| | | - Sciuravidae_Centroid.shx \| | | - Sciuravidae_Polygon.cpg \| | | - Sciuravidae_Polygon.dbf \| | | - Sciuravidae_Polygon.prj \| | | - Sciuravidae_Polygon.shp \| | | - Sciuravidae_Polygon.shx \| | | - Sequoia.csv \| | | - Sequoia_Centroid.cpg \| | | - Sequoia_Centroid.dbf \| | | - Sequoia_Centroid.prj \| | | - Sequoia_Centroid.shp \| | | - Sequoia_Centroid.shx \| | | - Sequoia_Polygon.cpg \| | | - Sequoia_Polygon.dbf \| | | - Sequoia_Polygon.prj \| | | - Sequoia_Polygon.shp \| | | - Sequoia_Polygon.shx \| | | - Taxodium.csv \| | | - Taxodium_Centroid.cpg \| | | - Taxodium_Centroid.dbf \| | | - Taxodium_Centroid.prj \| | | - Taxodium_Centroid.shp \| | | - Taxodium_Centroid.shx \| | | - Taxodium_Polygon.cpg \| | | - Taxodium_Polygon.dbf \| | | - Taxodium_Polygon.prj \| | | - Taxodium_Polygon.shp \| | | - Taxodium_Polygon.shx \| | | - Ulmaceae.csv \| | | - Ulmaceae_Centroid.cpg \| | | - Ulmaceae_Centroid.dbf \| | | - Ulmaceae_Centroid.prj \| | | - Ulmaceae_Centroid.shp \| | | - Ulmaceae_Centroid.shx \| | | - Ulmaceae_Polygon.cpg \| | | - Ulmaceae_Polygon.dbf \| | | - Ulmaceae_Polygon.prj \| | | - Ulmaceae_Polygon.shp \| | | - Ulmaceae_Polygon.shx \| | - ANOVA-Statistical Analysis \| | | - Distance-matrix-isch-meta.csv \| | | - Distance-matrix-Isch-Metasequ.csv \| | | - Distance-matrix-isch-ulma-2.csv \| | | - Distance-Matrix-Isch-Ulma.csv \| | | - Multi-Meta-Distance-Matrix.csv \| | | - Multi-Ulma-Distance-Matrix.csv \|- EDRF_PROGRAM&DATA/ \| | - Ellipsoid-Distance-Ratio-Function.py \| | - A-B-Mammal-Plant Files/ \| | | - ABIE-CYLI.csv \| | | - ABIE-EOMY.csv \| | | - ABIE-EUTY.csv \| | | - ABIE-ISCH.csv \| | | - ABIE-MULT.csv \| | | - ABIE-PROT.csv \| | | - ABIE-SCIU.csv \| | | - ACER-CYLI.csv \| | | - ACER-EOMY.csv \| | | - ACER-EUTY.csv \| | | - ACER-ISCH.csv \| | | - ACER-MULT.csv \| | | - ACER-PROT.csv \| | | - ACER-SCIU.csv \| | | - ALNU-CYLI.csv \| | | - ALNU-EOMY.csv \| | | - ALNU-EUTY.csv \| | | - ALNU-ISCH.csv \| | | - ALNU-MULT.csv \| | | - ALNU-PROT.csv \| | | - ALNU-SCIU.csv \| | | - ARAL-CYLI.csv \| | | - ARAL-EOMY.csv \| | | - ARAL-EUTY.csv \| | | - ARAL-ISCH.csv \| | | - ARAL-MULT.csv \| | | - ARAL-PROT.csv \| | | - ARAL-SCIU.csv \| | | - CARY-CYLI.csv \| | | - CARY-EOMY.csv \| | | - CARY-EUTY.csv \| | | - CARY-ISCH.csv \| | | - CARY-MULT.csv \| | | - CARY-PROT.csv \| | | - CARY-SCIU.csv \| | | - CINN-CYLI.csv \| | | - CINN-EOMY.csv \| | | - CINN-EUTY.csv \| | | - CINN-ISCH.csv \| | | - CINN-MULT.csv \| | | - CINN-PROT.csv \| | | - CINN-SCIU.csv \| | | - CORN-CYLI.csv \| | | - CORN-EOMY.csv \| | | - CORN-EUTY.csv \| | | - CORN-ISCH.csv \| | | - CORN-MULT.csv \| | | - CORN-PROT.csv \| | | - CORN-SCIU.csv \| | | - FABA-CYLI.csv \| | | - FABA-EOMY.csv \| | | - FABA-EUTY.csv \| | | - FABA-ISCH.csv \| | | - FABA-MULT.csv \| | | - FABA-PROT.csv \| | | - FABA-SCIU.csv \| | | - FAGA-CYLI.csv \| | | - FAGA-EOMY.csv \| | | - FAGA-EUTY.csv \| | | - FAGA-ISCH.csv \| | | - FAGA-MULT.csv \| | | - FAGA-PROT.csv \| | | - FAGA-SCIU.csv \| | | - FICU-CYLI.csv \| | | - FICU-EOMY.csv \| | | - FICU-EUTY.csv \| | | - FICU-ISCH.csv \| | | - FICU-MULT.csv \| | | - FICU-PROT.csv \| | | - FICU-SCIU.csv \| | | - GINK-CYLI.csv \| | | - GINK-EOMY.csv \| | | - GINK-EUTY.csv \| | | - GINK-ISCH.csv \| | | - GINK-MULT.csv \| | | - GINK-PROT.csv \| | | - GINK-SCIU.csv \| | | - GLYP-CYLI.csv \| | | - GLYP-EOMY.csv \| | | - GLYP-EUTY.csv \| | | - GLYP-ISCH.csv \| | | - GLYP-MULT.csv \| | | - GLYP-PROT.csv \| | | - GLYP-SCIU.csv \| | | - JUGL-CYLI.csv \| | | - JUGL-EOMY.csv \| | | - JUGL-EUTY.csv \| | | - JUGL-ISCH.csv \| | | - JUGL-MULT.csv \| | | - JUGL-PROT.csv \| | | - JUGL-SCIU.csv \| | | - LAUR_ISCH.csv \| | | - LAUR-CYLI.csv \| | | - LAUR-EOMY.csv \| | | - LAUR-EUTY.csv \| | | - LAUR-MULT.csv \| | | - LAUR-PROT.csv \| | | - LAUR-SCIU.csv \| | | - LIQU-CYLI.csv \| | | - LIQU-EOMY.csv \| | | - LIQU-EUTY.csv \| | | - LIQU-ISCH.csv \| | | - LIQU-MULT.csv \| | | - LIQU-PROT.csv \| | | - LIQU-SCIU.csv \| | | - MACG-CYLI.csv \| | | - MACG-EOMY.csv \| | | - MACG-EUTY.csv \| | | - MACG-ISCH.csv \| | | - MACG-MULT.csv \| | | - MACG-PROT.csv \| | | - MACG-SCIU.csv \| | | - META-CYLI.csv \| | | - META-EOMY.csv \| | | - META-EUTY.csv \| | | - META-ISCH.csv \| | | - META-MULT.csv \| | | - META-PROT.csv \| | | - META-SCIU.csv \| | | - MORA-CYLI.csv \| | | - MORA-EOMY.csv \| | | - MORA-EUTY.csv \| | | - MORA-ISCH.csv \| | | - MORA-MULT.csv \| | | - MORA-PROT.csv \| | | - MORA-SCIU.csv \| | | - MYRT-CYLI.csv \| | | - MYRT-EOMY.csv \| | | - MYRT-EUTY.csv \| | | - MYRT-ISCH.csv \| | | - MYRT-MULT.csv \| | | - MYRT-PROT.csv \| | | - MYRT-SCIU.csv \| | | - PICE-CYLI.csv \| | | - PICE-EOMY.csv \| | | - PICE-EUTY.csv \| | | - PICE-ISCH.csv \| | | - PICE-MULT.csv \| | | - PICE-PROT.csv \| | | - PICE-SCIU.csv \| | | - PINU-CYLI.csv \| | | - PINU-EOMY.csv \| | | - PINU-EUTY.csv \| | | - PINU-ISCH.csv \| | | - PINU-MULT.csv \| | | - PINU-PROT.csv \| | | - PINU-SCIU.csv \| | | - PLAT-CYLI.csv \| | | - PLAT-EOMY.csv \| | | - PLAT-EUTY.csv \| | | - PLAT-ISCH.csv \| | | - PLAT-MULT.csv \| | | - PLAT-PROT.csv \| | | - PLAT-SCIU.csv \| | | - PLATY-CYLI.csv \| | | - PLATY-EOMY.csv \| | | - PLATY-EUTY.csv \| | | - PLATY-ISCH.csv \| | | - PLATY-MULT.csv \| | | - PLATY-PROT.csv \| | | - PLATY-SCIU.csv \| | | - POPU-CYLI.csv \| | | - POPU-EOMY.csv \| | | - POPU-EUTY.csv \| | | - POPU-ISCH.csv \| | | - POPU-MULT.csv \| | | - POPU-PROT.csv \| | | - POPU-SCIU.csv \| | | - RHUS-CYLI.csv \| | | - RHUS-EOMY.csv \| | | - RHUS-EUTY.csv \| | | - RHUS-ISCH.csv \| | | - RHUS-MULT.csv \| | | - RHUS-PROT.csv \| | | - RHUS-SCIU.csv \| | | - ROSA-CYLI.csv \| | | - ROSA-EOMY.csv \| | | - ROSA-EUTY.csv \| | | - ROSA-ISCH.csv \| | | - ROSA-MULT.csv \| | | - ROSA-PROT.csv \| | | - ROSA-SCIU.csv \| | | - SALI-CYLI.csv \| | | - SALI-EOMY.csv \| | | - SALI-EUTY.csv \| | | - SALI-ISCH.csv \| | | - SALI-MULT.csv \| | | - SALI-PROT.csv \| | | - SALI-SCIU.csv \| | | - SEQU-CYLI.csv \| | | - SEQU-EOMY.csv \| | | - SEQU-EUTY.csv \| | | - SEQU-ISCH.csv \| | | - SEQU-MULT.csv \| | | - SEQU-PROT.csv \| | | - SEQU-SCIU.csv \| | | - TAXO-CYLI.csv \| | | - TAXO-EOMY.csv \| | | - TAXO-EUTY.csv \| | | - TAXO-ISCH.csv \| | | - TAXO-MULT.csv \| | | - TAXO-PROT.csv \| | | - TAXO-SCIU.csv \| | | - ULMA-CYLI.csv \| | | - ULMA-EOMY.csv \| | | - ULMA-EUTY.csv \| | | - ULMA-ISCH.csv \| | | - ULMA-MULT.csv \| | | - ULMA-PROT.csv \| | | - ULMA-SCIU.csv \| | - Mammal-Mammal \| | | - MULT-CYLI.csv \| | | - MULT-EOMY.csv \| | | - MULT-EUTY.csv \| | | - MULT-ISCH.csv \| | | - MULT-PROT.csv \| | | - MULT-SCIU.csv --- # 3. Null Model Details of the null models can be found in the folder NULL_MODEL. For each of these four parameters, two null models were also developed to determine whether the paired spatial data points are distributed randomly. The first null model assumes a stochastic distribution of the paired spatial data points over a unit area. Stochastic models do not rely on data but are mathematical calculations that define a pure random distribution of the four parameters. These mathematical models are discussed in the subfolder STOCHASTIC. The second null model assumes a probabilistic distribution, where the spatial data points are randomized. Details are discussed in the subfolder PROBABILISTIC. To run the probabilistic scripts a data table with all the locality data can be found in this subfolder called ALL_LOCALITY_DATA_LIST.csv This is a list of all observed points to use for randomization in Monte Carlo analysis for the probabilistic null model analysis. NULL_MODEL.zip \|- Methods.txt \|- STOCHASTIC/ \| |- Stochastic_methods.odt \| |- NOLA-circle-overlap.py \| |- NOLA-circles-random.py \| |- NOLA-polygon-20-random-points.py \| |- NOLA-polygon-Random-fourteen.py \| |- NOLA-polygon-Random-Monte-Carlo.py \| |- NOLA-polygon-Random-six.py \| |- NOP-Null-Model.py \|- PROBABILISTIC/ \| |- Probabilistic_methods.rtf \| |- ALL_LOCALITY_DATA_LIST.csv \| |- CED-distance-between-points.py \| |- CED-distance-between-random-points.py \| |- EDRF-Selected-random-text.py \| |- NOLA-ALL-PROB.py \| |- NOP-ALL-PROB.py --- # 4. Grid Model To evaluate the correlation of fossil preservation bias between the number of fossil localities in a region and the observed occurrences for each fossil mammal group, an additional statistical analysis was conducted, using a 2 x 2-degree latitude and longitude reference grid to map all known Eocene fossil localities in North America within each referenced grid cell. Based on the total number of fossil localities in each grid, a model was used to predict the expected number of fossil occurrences given the known prevalence of fossil localities within the grid. The observed and expected numbers (using the fossil preservation model) were compared using a chi-square test of significance. If the P-values are at or near 0 for a group (p &lt; .05), the null hypothesis can be rejected, indicating that the spatial distribution of fossil occurrences are independent of the sampling density. The file containing locality counts and predictions can be found in the folder GRID_MODEL under the file Grid-Preservation-Decay-Curve. The results of the chi-test for each fossil group is found in the table GRID-Chi-Sq-Results.csv The grids and fossil localities can be found in the GIS_ANALYSIS folder. GRID_MODEL.zip \| - GRID-Preservation-Decay-Curve.csv \| - GRID-Chi-Sq-Results.csv --- # 5. Results Results of the study are presented in two sets of files. If you need just a summary of the results of the spatial analysis these files will show the results for each comparison. Tables 1-7 are the results comparing each mammal group with various fossil plant group across the four measured parameters, and their ranking of each measured parameter and final total ranking. Data marked with a superscript S is within the stochastic null model range, while data marked with a superscript P is within the probabilistic null model range. Table 8 compares mammal groups with each other. Appendix 1 is Statistical chi-sq test comparison of preservation bias between modeled fossil occurrences (based on fossil locality density) and actual occurrences for each fossil group studied. The P values correspond to 158 degrees of freedom, representing 158 grid cells in North America that contain Eocene terrestrial fossil localities, n is the number of unique occurrences. Appendix 2A-F. Raw unranked results of the four parameters of spatial association or avoidance of each plant group with respect to each mammal group. Results fall within the expected stochastic (s) or probabilistic (p) null model range. Note this data is the same as in the Tables, but lacks any ranking and scoring of the data. These result tables are uploaded without a subfolder to facility easy use in finding results of the study, and because they are referenced in the associated paper. Appendix-1.rft Appendix-2A.rft Appendix-2B.rft Appendix-2C.rft Appendix-2D.rft Appendix-2E.rft Appendix-2F.rft Table-1.rft Table-2.rft Table-3.rft Table-4.rft Table-5.rft Table-6.rft Table-7.rft Table-8.rft #### List of plant abbreviations: | List of Plant Fossil | Common Names | | Seed/Fruit Type | | :------------------- | :------------------------------------------------------------ | :---- | :--------------- | | Abies | Fir Tree | ABIE | Cone-like | | Acer | Maple | ACER | Samara | | Alnus | Alder Tree | ALNU | Cone-like | | Araliaceae | Ginseng family | ARAL | Spiky-Fruit | | Carya | Hickory | CARY | Nut-Like | | Catalpa | Catalpa trees | CATA | Bean-like | | Cinnamomum | Cinnamon Tree | CINN | Berry-Like | | Cornus | Dogwood  | CORN | Berry-Like | | Fabaceae | Lugumes | FABA | Bean-like | | Fagaceae | Chestnut, Acorn, Oak | FAGA | Acorn & Pod-like | | Ficus | Fig Tree (only Ficus) | FICU | Fruit | | Ginkgo | Ginkgo | GINK | Berry-Like | | Glyptostrobus | Chinese swamp cypress | GLYP | Cone-like | | Juglans | Walnut | JUGL | Nut-Like | | Lauraceae | Various Laurel Trees | LAUR | Bean-like | | Liquidambar | Sweetgum | LIQU | Cone-Like | | Macginitiea | Extinct Sycamore | MACG | Cotton-Like | | Metasequoia | Dawn Redwood | META | Cone-like | | Moraceae | Mulberry (includes Ficus) | MORA | Berry-like | | Myrtaceae | Guava | MYRT | Fruit | | Picea | Spruce | PICE | Cone-like | | Pinus | Pine | PINU | Cone-like | | Platanus | Sycamore | PLAT | Cotton-Like | | Platycarya | Cone Nut Tree | PLATY | Cone-like | | Populus | Popular trees/aspen/cottonwood | POPU | Cotton-like | | Rhus | Sumac | RHUS | Berry-like | | Rosaceae | Rose, service berry, apple, hawthorn, plum, cherry, raspberry | ROSA | Fruit | | Salix | Willow | SALI | Cotton-like | | Sequoia | Redwood | SEQU | Cone-like | | Taxodium | Swamp cypress | TAXO | Cone-like | | Ulmaceae | Elm Tree | ULMA | Samara | ## Code/software QGIS version 3.32.0-Lima. [https://qgis.org/](https://qgis.org/) Open file: Project-DATA_ANALYSIS.zip /GIS_FILES/Multituberculates.qgs Python 3.11.5 with tcl/tk 8.6.12 [https://www.python.org/](https://www.python.org/) Files: DATA_ANALYSIS.zip /DRF_PROGRAM&DATA/Ellipsoid-Distance-Ratio-Function.py Note that you will be prompted to open a data file. Data files for this program are found in the following directories /EDRF_PROGRAM&DATA/A-B-Mammal-Plant Files/ /EDRF_PROGRAM&DATA/Mammal-Mammal/ NULL_MODEL.zip /STOCHASTIC/NOLA-circle-overlap.py /STOCHASTIC/NOLA-circles-random.py /STOCHASTIC/NOLA-polygon-20-random-points.py /STOCHASTIC/NOLA-polygon-Random-fourteen.py /STOCHASTIC/NOLA-polygon-Random-Monte-Carlo.py /STOCHASTIC/NOLA-polygon-Random-six.py /STOCHASTIC/NOP-Null-Model.py /PROBABILISTIC/CED-distance-between-points.py /PROBABILISTIC/CED-distance-between-random-points.py /PROBABILISTIC/EDRF-Selected-random-text.py /PROBABILISTIC/NOLA-ALL-PROB.py /PROBABILISTIC/NOP-ALL-PROB.py To run the PROBABILISTIC scripts you need to have the data file within the same directory as the script. /PROBABILISTIC/ALL_LOCALITY_DATA_LIST.csv \## Access information Raw location data is also publicly accessible from the PaleobioDB website: [https://paleobiodb.org](https://paleobiodb.org)