Global climate model comparisons of niche evolution in Turritelline gastropods across the end-Cretaceous mass extinction
Data files
Mar 20, 2025 version files 15 MB
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README.md
51.86 KB
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Supplemental_Information.zip
14.95 MB
Abstract
Paleo-Ecological Niche Modeling (PaleoENM) aims to map the distributions of extinct species using paleo-coordinates of fossils and local environmental data. While General Circulation Models (GCMs) have been widely used to estimate climate conditions in deep time, they have primarily been applied to the terrestrial vertebrate record. Furthermore, variations in paleo-elevation models used in GCM construction can significantly influence the outcomes of PaleoENM. This study addresses two main objectives: (1) to investigate whether changing climatic factors drove niche shifts following the end-Cretaceous mass extinction in the shelly marine invertebrate group, the tower snails (Turritellidae: Turritellinae), and (2) to compare the effects of two different paleo-elevation models on the results of GCM-based predictions of species distribution. Fossil occurrence data from the Maastrichtian and Danian time periods were obtained from the Paleobiology Database, supplemented by museum collections and published literature. Environmental data were extracted from atmosphere-ocean General Circulation Model (GCM) simulations using the HadCM3L model, applying two different sets of paleogeographic and CO2 boundary conditions: Scotese-based and Getech-based. Additional sedimentology and depositional environment data were sourced from the Paleobiology Database (PBDB). We predicted the distributions of Turritellines using the maximum entropy (MaxentMaxEnt) algorithm and performed niche similarity analysis using principal component analysis and kernel density estimation. We found significant differences in the spatial arrangement of suitable habitats between the Maastrichtian and Danian time periods across GCMs. The results also showed that the Getech-based GCM outperformed the Scotese-based GCM in terms of model metrics. Niche overlap across both time periods was high, with niche similarity and equivalency being higher than expected by chance within both GCMs. Our results also suggest that differences in elevation model boundary conditions led to variations in the predicted distribution and niche patterns. This study provides a novel approach to understanding ecological resilience and niche change in invertebrate taxa after mass extinction events. It also explores the robustness of varying GCM boundary conditions on PaleoENM studies and offers a framework for future paleoecological research on fossil invertebrate taxa.
Submitted Materials
- Supplemental_Table_S1.doc: Revised depositional categories derived from depositional environment terms from the PBDB, condensed into 15 categories.
- Supplemental_Table_S2.doc: Finalized thinned occurrence datasets and extracted environmental values for Maxent model building for our Getech model within the Maastrichtian.
- Supplemental_Table_S3.csv: Finalized thinned occurrence datasets and extracted environmental values for Maxent model building for our Getech model within the Danian.
- Supplemental_Table_S4.csv: Finalized thinned occurrence datasets and extracted environmental values for Maxent model building for our Scotese model within the Maastrichtian.
- Supplemental_Table_S5.csv: Finalized thinned occurrence datasets and extracted environmental values for Maxent model building for our Scotese model within the Danian.
- GCM_predictions.qgz: GIS file containing output MaxEnt distribution maps for cartographic manipulation.
- R_Scripts: Folder containing R scripts for running analyses using the Scotese and Getech GCM models.
- Datasets: Folder with data for running analyses using the Scotese and Getech model.
- Datasets_15_Categories: Folder with data for running analyses using the Scotese and Getech model using 15 depositional categories.
- Turritellinae_Occurrences: Folder with data from occurrences of Turritellinae from both the Danian and Maastrichtian periods.
Data Formats
Excel/CSV Files
- .csv: Used for organizing metadata of occurrences used in species distribution modeling. Key columns include:
- species/scientific_name: Species epithet of Turritellinae taxa.
- longitude/lng: Longitude coordinate of occurrence.
- latitude/lat: Latitude coordinate of occurrence.
- Rot_Long: Paleorotated longitude to the Maastrichtian or Danian period.
- Rot_Lat: Paleorotated latitude to the Maastrichtian or Danian period.
- ICS Stage: Geologic stage (Danian/Maastrichtian).
- Lith Code/Lithology1: Lithologic setting (carbonate/siliciclastic).
- Environment: Depositional environment in which Turritellinae taxa were deposited.
- occID: Unique identifier for each occurrence.
- NA: Data not available. Values marked as NA are found in occurrence datasets generated from the PBDB (Paleobiology Database) or GBIF (Global Biodiversity Information Facility), where the information for the occurrences is not available or has not been reported.
Raster Files
- .tif: Raster files used to assess environmental conditions for Turritellinae species.
- .nc: NetCDF (Network Common Data Form) files of environmental conditions from the University of Bristol Paleoclimate Website (link).
Additional Files
- .svg: Scalable Vector Graphics files for MaxEnt output.
- .pdf: Portable Document Format files for MaxEnt output.
- .qgz: Quantum Geographic Information System (QGIS) file containing saved .tif files for mapping and figure creation.
Recommended Software for Data Analysis
- GCM_predictions.qgz: Requires QGIS (download link).
- Scotese_model and Getech_model: Requires R and RStudio (download link).
Usage, Compatibility, and Accessibility
This dataset includes a variety of file types due to the broad scope of analyses conducted (MaxEnt, Ecospat, as well as .csv and .tif manipulation). Most files can be viewed using standard text editing software (e.g., BBedit) and free image editing software (e.g., Inkscape).
All programs, code, and data are publicly available and free to use. Researchers are encouraged to use the original data formats, repeat analyses for reliability and accuracy, and reach out to the corresponding author for further questions or clarifications.
We are committed to supporting users, providing clarity, and fostering future research with this dataset, as well as others related to species distribution modeling.
Descriptions
GCM_predictions.qgz
Supplemental_Table_S1.doc
- PBDB_category: Depositional environmental category from the PBDB.
- new_category (15 categories): New category derived from the PBDB.
- Depositional_Setting category: Binary category (marine/freshwater) derived from the PBDB.
Supplemental_Table_S2.doc
- occID: Unique numeric identifier of occurrence (Note, numbers are discontinuous due to post-thinning)
- x: Longitude of occurrence (in decimal degrees)
- y: Latitude of occurrence (in decimal degrees)
- bathymetry: Bathymetric reading of occurrence (in meters)
- lithology: Lithological category of occurrence assigned to an integer (1 = carbonate, 2 = siliciclastic)
- lithology_factor: Lithological category assigned to occurrence
- mixed_layer: Annual mixed layer depth value of occurrence (in meters)
- monsoon: Monsoon seasonality index value of occurrence (difference in precipitation between the three driest and three wettest months of the year, in millimeters (mm))
- salinity: Annual potential salinity value of occurrence (PSU, practical salinity unit)
- temperature: Annual potential temperature value of occurrence (Celsius)
- environment: Depositional environment category of occurrence assigned to an integer
- environment_factor: Depositional environment category of occurrence
Supplemental_Table_S3.csv: Column names same as Supplemental_Table_S2.doc
Supplemental_Table_S4.csv: - Column names same as Supplemental_Table_S2.doc
Supplemental_Table_S5.csv: - Column names same as Supplemental_Table_S2.doc
Datasets
R_Scripts
- ENM_Paleo_PCA.R: Code for running principal component analysis, ecological similarity, and equivalency analysis using environmental variables and Turritellinae occurrence datasets.
- Paleo_Variable_Averaging.R: Code for averaging environmental data, generating categorical data (lithology/environment), rescaling data, and converting it into .tif format for analysis.
- rgplates.R: Code for paleorotating modern-day Turritellinae fossil occurrences to the Danian and Maastrichtian periods using the Scotese rotation model.
- Turritellinae_Code_Final.R: Code for running MaxEnt analyses, including prediction maps, model statistics, response curves, permutation importance, and MESS maps.
Turritellinae Occurrences
- kiera_Danian.csv: Occurrences of Turritellinae in the Danian period, collected by Kiera D. Crowley (U.S. Coastal Plain).
- Lith code: Lithological category of occurrence
- Long: Longitude of occurrence (in decimal degrees)
- Lat: Latitude of occurrence (in decimal degrees)
- ICS Stage: Geologic stage of occurrence (Danian or Maastrichtian)
- kiera_Maastrichtian.csv: Occurrences of Turritellinae in the Maastrichtian period, collected by Kiera D. Crowley from the literature.
- Lith code: Lithological category of occurrence
- Long: Longitude of occurrence (in decimal degrees)
- Lat: Latitude of occurrence (in decimal degrees)
- ICS Stage: Geologic stage of occurrence (Danian or Maastrichtian)
- Turritellinae_Corrine_Postdoc_Danian.csv: Occurrences of Turritellinae in the Danian period, collected by Corinne Myers.
- Phylum: Phylum of occurrence (Mollusca)
- Class: Class of occurrence (Gastropoda)
- Species: Class of occurrence (Turritella calax)
- Lat: Latitude of occurrence (in decimal degrees)
- Long: Longitude of occurrence (in decimal degrees)
- Stage: Geologic stage of occurrence (Danian or Maastrichtian)
- Group: Geologic Group of occurrence (Midway)
- Formation: Geologic Formation of occurrence (Kincaid)
- State: US State where occurrence was found (Texas)
- Locality: Locality of occurrence (Hwy 7, Kosse)
- Turritellinae_Corrine_Postdoc_Maastrichtian.csv: Occurrences of Turritellinae in the Maastrichtian period, collected by Corinne Myers.
- Phylum: Phylum of occurrence (Mollusca)
- Class: Class of occurrence (Gastropoda)
- Species: Class of occurrence (Turritella calax)
- Lat: Latitude of occurrence (in decimal degrees)
- Long: Longitude of occurrence (in decimal degrees)
- Stage: Geologic stage of occurrence (Danian or Maastrichtian)
- Group: Geologic Group of occurrence (Midway)
- Formation: Geologic Formation of occurrence (Kincaid)
- State: US State where occurrence was found (Texas)
- Locality: Locality of occurrence (Hwy 7, Kosse)
- turritellidae_gbif_Danian.csv: Occurrences from GBIF for the Danian period.
- taxonRank: The rank of the taxonomic entity (e.g., species, genus, family).
- scientificName: The full scientific name of the species, including genus and species (e.g., Panthera leo).
- verbatimScientificName: The exact scientific name as recorded in the original dataset, including any discrepancies.
- verbatimScientificNameAuthorship: The author(s) who described the species, as recorded in the original dataset.
- countryCode: The ISO 3166-1 alpha-2 country code where the occurrence was recorded (e.g., US, CA).
- locality: The detailed locality or place name where the occurrence was observed (e.g., Central Park, New York).
- stateProvince: The state or province where the occurrence was recorded (e.g., Texas, Ontario).
- occurrenceStatus: The status of the occurrence, indicating whether the organism was observed or collected (e.g., present, absent).
- individualCount: The number of individuals observed in the occurrence event.
- publishingOrgKey: The unique identifier for the organization that published the data.
- decimalLatitude: The latitude of the occurrence, in decimal degrees (positive for the northern hemisphere, negative for the southern).
- decimalLongitude: The longitude of the occurrence, in decimal degrees (positive for the eastern hemisphere, negative for the western).
- coordinateUncertaintyInMeters: The uncertainty of the coordinates, in meters.
- coordinatePrecision: The precision of the coordinates provided, indicates the accuracy level.
- elevation: The elevation of the occurrence in meters above sea level.
- elevationAccuracy: The accuracy of the elevation measurement, in meters.
- depth: The depth of the occurrence, typically for marine species, in meters.
- depthAccuracy: The accuracy of the depth measurement, in meters.
- eventDate: The date or date range when the occurrence event took place (e.g., 2025-02-28).
- day: The day of the occurrence event, in numeric format (e.g., 28).
- month: The month of the occurrence event, in numeric format (e.g., 2).
- year: The year of the occurrence event (e.g., 2025).
- taxonKey: A unique identifier for the taxon (species, genus, etc.) in the GBIF database.
- speciesKey: A unique identifier for the species in the GBIF database.
- basisOfRecord: The type of record (e.g., Observation, Specimen, Machine Observation).
- institutionCode: The code representing the institution that holds the record (e.g., Smithsonian, NYU).
- collectionCode: The code representing the collection to which the record belongs (e.g., Mammal Collection).
- catalogNumber: The catalog number assigned to the specimen in the collection (e.g., 12345).
- recordNumber: The unique number assigned to the record in the dataset (e.g., R12345).
- identifiedBy: The person or organization who identified the species.
- dateIdentified: The date when the species was identified.
- license: The licensing terms for the data (e.g., CC-BY, Public Domain).
- rightsHolder: The individual or organization that holds the rights to the data.
- recordedBy: The person or group who recorded the occurrence event.
- typeStatus: The status of the record in terms of being a type specimen or not (e.g., Holotype, Paratype).
- establishmentMeans: The means by which the species came to be established in the location (e.g., native, introduced).
- lastInterpreted: The timestamp of when the data was last interpreted or updated.
- mediaType: The type of media associated with the occurrence (e.g., Photograph, Audio).
- issue: Any issues or warnings related to the data (e.g., Coordinates not matching locality, Taxonomy unresolved).
- turritellidae_gbif_Maastrichtian.csv: Occurrences from GBIF for the Maastrichtian period. Column names same as turritellidae_gbif_Danian.csv
- turritellidae_pbdb_Danian.csv: Occurrences from PBDB for the Danian period.
- turritellidae_pbdb_Danian.csv: Occurrences from PBDB for the Danian period.
- scientific_name: The scientific name of the organism, including genus and species (e.g., Tyrannosaurus rex).
- occurrence_no: A unique identifier for the occurrence record in the PBDB dataset.
- record_type: The type of record (e.g., specimen, observation).
- reid_no: A unique identifier associated with the record, typically an internal number used for reference or identification.
- flags: Flags indicating any special conditions or issues with the record (e.g., questionable data).
- collection_no: The number assigned to the collection in which the occurrence is housed.
- identified_name: The name used for identification of the organism (may differ from the accepted name).
- identified_rank: The taxonomic rank of the identified name (e.g., species, genus).
- identified_no: The number of identified specimens or occurrences in the collection.
- difference: Differences or discrepancies between the identified name and the accepted name.
- accepted_name: The currently accepted scientific name of the organism, often based on the latest taxonomic revision.
- accepted_attr: Attributes related to the accepted name, such as its validity or synonymy.
- accepted_rank: The rank of the accepted name (e.g., species, genus).
- accepted_no: The number of specimens identified under the accepted name.
- early_interval: The earliest time interval in which the organism is believed to have existed.
- late_interval: The latest time interval in which the organism is believed to have existed.
- max_ma: The maximum age of the occurrence in million years (Ma).
- min_ma: The minimum age of the occurrence in million years (Ma).
- ref_author: The author(s) who published the reference for the occurrence.
- ref_pubyr: The year the reference was published.
- reference_no: A unique identifier for the reference used to support the occurrence record.
- phylum: The phylum classification of the organism (e.g., Arthropoda, Chordata).
- class: The class classification of the organism (e.g., Mammalia, Reptilia).
- order: The order classification of the organism (e.g., Carnivora, Saurischia).
- family: The family classification of the organism (e.g., Felidae, Tyrannosauridae).
- genus: The genus classification of the organism (e.g., Panthera, Tyrannosaurus).
- plant_organ: The part of the plant (if applicable) associated with the occurrence (e.g., leaf, stem).
- plant_organ2: A secondary plant organ (if applicable) associated with the occurrence (e.g., flower, root).
- abund_value: The abundance value, indicates the relative abundance of the organism in the occurrence.
- abund_unit: The unit of measurement for abundance (e.g., individuals, specimens).
- lng: The longitude of the occurrence, in decimal degrees (positive for east, negative for west).
- lat: The latitude of the occurrence, in decimal degrees (positive for north, negative for south).
- occurrence_comments: Comments or additional information regarding the occurrence.
- collection_name: The name of the collection in which the occurrence is held (e.g., The Natural History Museum Collection).
- collection_subset: A subset of the collection (if applicable) to which the occurrence belongs.
- collection_aka: Alternative names for the collection, if applicable.
- cc: The country code where the occurrence was recorded (e.g., US, GB).
- state: The state or province where the occurrence was found (e.g., California, Queensland).
- county: The county or region within the state or province where the occurrence was found.
- latlng_basis: The method or basis for the latitude and longitude coordinates (e.g., GPS, map).
- latlng_precision: The precision of the latitude and longitude data (e.g., accurate to 1 meter).
- geogscale: The geographic scale of the occurrence (e.g., local, regional, global).
- geogcomments: Comments regarding the geographic data or location.
- paleomodel: The paleoclimatic or paleoenvironmental model used for the occurrence data.
- paleolng: The paleolongitude of the occurrence in the past, in degrees.
- paleolat: The paleolatitude of the occurrence in the past, in degrees.
- geoplate: The tectonic plate in which the occurrence was located during the time of existence.
- cc: Whether the collection is under conservation or protection (e.g., protected, restricted).
- protected: Indicates whether the occurrence comes from a protected area or has conservation status.
- formation: The geological formation where the occurrence was found.
- stratgroup: The stratigraphic group or grouping to which the formation belongs.
- member: The specific member or subdivision within the formation.
- stratscale: The scale of the stratigraphic unit (e.g., bed, horizon, group).
- zone: The stratigraphic zone of the occurrence, typically based on biozones.
- localsection: The local stratigraphic section where the occurrence was found.
- localbed: The bed within the stratigraphic section where the occurrence was found.
- localorder: The local ordering or layer of the occurrence in the stratigraphy.
- regionalsection: A regional stratigraphic section for the occurrence.
- regionalbed: The regional bed within the stratigraphic section where the occurrence was found.
- regionalorder: The regional order or layer in which the occurrence was found.
- stratcomments: Comments regarding the stratigraphy of the occurrence.
- lithdescript: Description of the lithology (rock types) associated with the occurrence.
- lithology1: The primary lithology (rock type) associated with the occurrence.
- lithadj1: The lithologic adjective associated with the primary lithology (e.g., fine-grained, sandy).
- lithification1: The process by which the primary lithology was lithified (e.g., cementation).
- minor_lithology1: A secondary lithology associated with the occurrence.
- fossilsfrom1: The source of fossils from the primary lithology.
- lithology2: The secondary lithology, if applicable.
- lithadj2: The lithologic adjective associated with secondary lithology.
- lithification2: The process by which the secondary lithology was lithified.
- minor_lithology2: A third, minor lithology associated with the occurrence.
- fossilsfrom2: The source of fossils from the secondary lithology.
- environment: The paleoenvironment where the organism existed (e.g., marine, terrestrial).
- tectonic_setting: The tectonic setting of the occurrence, such as plate boundary or intra-plate region.
- geology_comments: Comments regarding the geology of the area or sample.
- assembl_comps: Composition of the faunal or floral assemblage in the occurrence.
- articulated_parts: The presence of articulated parts in the specimen (e.g., articulated limbs, bones).
- associated_parts: Other associated parts found with the occurrence (e.g., teeth, fragments).
- common_body_parts: The most common body parts found in the occurrence.
- rare_body_parts: Rare body parts found in the occurrence.
- feed_pred_traces: Evidence of feeding or predation traces on the organism.
- artifacts: Any human-made artifacts found alongside the occurrence.
- component_comments: Comments on the different components present in the occurrence (e.g., skeletal, shell).
- pres_mode: The mode of preservation of the occurrence (e.g., fossilized, cast).
- preservation_quality: The quality of preservation (e.g., well-preserved, poorly preserved).
- spatial_resolution: The spatial resolution of the occurrence data (e.g., specific site, regional).
- temporal_resolution: The temporal resolution of the occurrence data (e.g., specific period, broad epoch).
- lagerstatten: Whether the occurrence is from a lagerstätten, a site of exceptional preservation.
- concentration: The concentration of specimens within the occurrence.
- orientation: The orientation of the specimen (e.g., upright, horizontal).
- abund_in_sediment: The abundance of the organism in the sedimentary layer.
- sorting: The sorting of the sedimentary particles in the occurrence (e.g., well-sorted, poorly sorted).
- fragmentation: The degree of fragmentation of the specimen (e.g., complete, fragmented).
- bioerosion: Evidence of bioerosion on the specimen (e.g., marks from burrowing organisms).
- encrustation: Whether the specimen has been encrusted by other organisms (e.g., corals, barnacles).
- preservation_comments: Additional comments about the preservation conditions or quality.
- collection_type: The type of collection (e.g., paleontological, geological).
- collection_methods: Methods used to collect the specimen or occurrence (e.g., excavation, drilling).
- museum: The museum or institution where the collection is housed.
- collection_coverage: The geographical or temporal coverage of the collection.
- collection_size: The size or number of specimens in the collection.
- rock_censused: Whether the rock layers or sections have been censused (recorded) for the occurrence.
- collectors: The individuals or teams who collected the specimens.
- collection_dates: The dates when the specimens were collected.
- collection_comments: Comments related to the collection, such as challenges or special conditions.
- taxonomy_comments: Comments related to the taxonomy of the occurrence (e.g., classification issues).
- taxon_environment: The environmental context of the taxon (e.g., aquatic, terrestrial).
- environment_basis: The basis for identifying the environment (e.g., sediment type, lithology).
- motility: The mobility of the organism (e.g., motile, sessile).
- life_habit: The life habit of the organism (e.g., carnivorous, herbivorous).
- vision: The type of vision the organism had (e.g., eyes, no eyes).
- diet: The dietary habits of the organism (e.g., carnivore, omnivore).
- reproduction: The reproductive mode of the organism (e.g., sexual, asexual).
- ontogeny: The growth and development stages of the organism.
- ecospace_comments: Comments on the ecological space or niche of the organism.
- composition: The composition of the organism’s body or fossil material.
- architecture: The architectural features of the organism (e.g., shell structure, skeletal design).
- thickness: The thickness of relevant fossil layers or materials.
- reinforcement: Whether the organism had structural reinforcement (e.g., armor, exoskeleton).
- turritellidae_pbdb_Maastrichtian.csv: Occurrences from PBDB for the Maastrichtian period. Column names same as turritellidae_pbdb_Danian.csv
- Turritellinae_combined_Danian.csv: Combined occurrences of Turritellinae in the Danian period from all datasets.
-scientific_name: Scientific name of species used in Niche Modeling code (Turritellinae sl assigned just to denote subfamily)
-occID: Unique numeric identifier of occurrence
-Longitude: Longitude of occurrence (in decimal degrees)
-Latitude: Latitude of occurrence (in decimal degrees) - Turritellinae_combined_Maastrichtian.csv: Combined occurrences of Turritellinae in the Maastrichtian period from all datasets. Column names same as Turritellinae_combined_Danian.csv
Getech_model: Folder with data for running analyses using the Getech model.
- bb_getech.csv: Combined thinned occurrences of Turritellinae from the Maastrichtian and Danian used to define the bounding box for niche modeling analysis.
- scientific_name: Scientific name of species used in Niche Modeling code (Turritellinae sl assigned just to denote subfamily)
- occID: Unique numeric identifier of occurrence
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
ENVS_Danian_Getech: Environmental variables (.tif format) used for MaxEnt analysis in the Danian Period.
- bathymetry.tif: Bathymetry (m).
- coldest_season.tif: Coldest sea-surface temperature within a 3-month interval (Celsius).
- environment.tif: Depositional environment (58 categories).
- ice_conc.tif: Ice concentration (percent).
- ice_depth.tif: Ice depth (m).
- lithology.tif: Lithology (carbonate/siliciclastic).
- mixed_layer.tif: Annual mixed layer depth (m).
- monsoon.tif: Monsoon seasonality index.
- salinity.tif: Annual potential salinity (PSU).
- temperature.tif: Annual potential temperature (Celsius).
- warmest_season.tif: Warmest sea-surface temperature within a 3-month interval (Celsius).
ENVS_Maastrichtian_Getech: Same variables as ENVS_Danian_Getech but for the Maastrichtian period.
ENVS_nc_Danian: Raw environmental variables for the Danian period (.nc format) from the University of Bristol Paleoclimate Website.
- bathymetry.nc: Bathymetry (m).
- coldest_season.nc: Coldest sea-surface temperature within a 3-month interval (Celsius).
- ice_conc.nc: Ice concentration (percent).
- ice_depth.nc: Ice depth (m).
- mixed_layer.nc: Annual mixed layer depth (m).
- warmest_season.nc: Warmest sea-surface temperature within a 3-month interval (Celsius).
- monsoon.tif: Monsoon seasonality index.
salinity: Raw environmental variables for the Danian period (.nc format) for annual potential salinity. Layers represent various depths (m) of annual potential salinity (see Paleo_Variable_Averaging.R for averaging variables together)
- teuyG_ocean_salin_5_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 5m
- teuyG_ocean_salin_15_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 15m
- teuyG_ocean_salin_25_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 25m
- teuyG_ocean_salin_35_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 35m
- teuyG_ocean_salin_48_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 48m
- teuyG_ocean_salin_67_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 67m
- teuyG_ocean_salin_96_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 96m
- teuyG_ocean_salin_139_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 139m
- teuyG_ocean_salin_204_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 204m
- teuyG_ocean_salin_301_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 301m
- teuyG_ocean_salin_447_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 447m
- teuyG_ocean_salin_666_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 666m
- teuyG_ocean_salin_996_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 996m
- teuyG_ocean_salin_1501_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 1501m
- teuyG_ocean_salin_2116_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 2116m
- teuyG_ocean_salin_2731_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 2731m
- teuyG_ocean_salin_3347_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 3347m
- teuyG_ocean_salin_3962_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 3962m
- teuyG_ocean_salin_4577_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 4577m
- teuyG_ocean_salin_5192_ann_ftgridded_fsy1.nc: Annual potential salinity (PSU) at 5192m
temperature: Raw environmental variables for the Danian period (.nc format) for annual potential temperature. Layers represent various depths (m) of annual potential temperature (see Paleo_Variable_Averaging.R for averaging variables together)
- teuyG_ocean_temp_5_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 5m
- teuyG_ocean_temp_15_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 15m
- teuyG_ocean_temp_25_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 25m
- teuyG_ocean_temp_35_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 35m
- teuyG_ocean_temp_48_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 48m
- teuyG_ocean_temp_67_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 67m
- teuyG_ocean_temp_96_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 96m
- teuyG_ocean_temp_139_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 139m
- teuyG_ocean_temp_204_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 204m
- teuyG_ocean_temp_301_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 301m
- teuyG_ocean_temp_447_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 447m
- teuyG_ocean_temp_666_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 666m
- teuyG_ocean_temp_996_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 996m
- teuyG_ocean_temp_1501_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 1501m
- teuyG_ocean_temp_2116_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 2116m
- teuyG_ocean_temp_2731_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 2731m
- teuyG_ocean_temp_3347_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 3347m
- teuyG_ocean_temp_3962_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 3962m
- teuyG_ocean_temp_4577_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 4577m
- teuyG_ocean_temp_5192_ann_ftgridded_fsy1.nc: Annual potential temperature (PSU) at 5192m
ENVS_nc_Maastrichtian: Raw environmental variables for the Maastrichtian period (.nc format) from the University of Bristol Paleoclimate Website. NOTE: File formats and layouts are the same as ENVS_nc_Danian
Lithology_datasets_Danian: Marine occurrences within the Danian period from PBDB, assigned to either carbonate or siliciclastic lithologies.
- Lithology_Danian.csv: Raw occurrences of marine specimens from the Danian period with associated ‘lithology1’ category associated with each occurrence.
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- Lithology1: Lithological category of occurrence assigned from the PBDB associated ‘lithology1’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Lithology_Danian_key.csv: Key corresponding to the numeric value assigned to each lithological category (carbonate or siliciclastic) (corresponds to Lithology_Danian.csv)
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- Lithology1: Lithological category of occurrence assigned from the PBDB associated ‘lithology1’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- number: Numeric integer assigned to the lithological category of occurrence
Lithology_datasets_Maastrichtian: Marine occurrences within the Maastrichtian period from PBDB, assigned to either carbonate or siliciclastic lithologies.
- Lithology_Maastrichtian.csv: Raw occurrences of marine specimens from the Maastrichtian period with associated ‘lithology1’ category associated with each occurrence. Column names same as Lithology_Danian.csv
- Lithology_Maastrichtian_key.csv: Key corresponding to the numeric value assigned to each lithological category (carbonate or siliciclastic) (corresponds to Lithology_Maastrichtian.csv). Column names same as Lithology_Danian_key.csv
Substrate_datasets_Danian: Marine-only and Marine + Terrestrial occurrence datasets for the Danian period from PBDB, assigned to different depositional environments.
- Environment_Danian_Marine.csv: Marine-only occurrence dataset.
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional environmental category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Environment_Danian_Marine_Terrestrial.csv: Marine + terrestrial occurrence dataset.
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional environmental category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Environment_Danian_Marine_15_Categories.csv: occurrence dataset of 15 modified depositional categories (Supplemental Table S1)
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional environmental category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Environment_Danian_key.csv: Key corresponding to the numeric value assigned to each depositional environment (corresponds to Environment_Danian_Marine.csv)
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional environmental category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- number: Numeric integer assigned to depositional environment category of occurrence
Substrate_datasets_Maastrichtian: Marine-only and Marine + Terrestrial occurrence datasets for the Maastrichtian period from PBDB, assigned to different depositional environments.
- Environment_Maastrichtian_Marine.csv: Marine-only occurrence dataset. Column names same as Environment_Danian_Marine.csv
- Environment_Maastrichtian_Marine_Terrestrial.csv: Marine + terrestrial occurrence dataset. Column names same as Environment_Danian_Marine_Terrestrial.csv
- Environment_Maastrichtian_Marine_15_Categories.csv: 15 modified depositional categories occurrence dataset (Supplemental Table S1). Column names same as Environment_Danian_Marine_15_Categories.csv
- Environment_Maastrichtian_key.csv: Key corresponding to the numeric value assigned to each depositional environment (corresponds to Environment_Maastrichtian_Marine.csv). Column names same as Environment_Danian_key.csv
Depositional_Setting_Danian: Occurrence datasets for the Danian period from PBDB, assigned to depositional setting (Freshwater/Marine)
- Getech_Setting_Danian.csv: Marine/freshwater occurrence dataset
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional setting category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
Depositional_Setting_Maastrichtian: Occurrence datasets for the Maastrichtian period from PBDB, assigned to depositional setting (Freshwater/Marine)
- Getech_Setting_Maastrichtian.csv: Marine/freshwater occurrence dataset
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- environment: Depositional setting category of occurrence assigned from the PBDB associated ‘environment’ category
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
Occs_Danian_reconstructed: Modern-day and Paleorotated occurrences of Turritellinae specimens in the Danian period (not thinned yet).
- Turritellinae_occs_Danian.csv: csv dataset of Modern-day and Paleorotated occurrences of Turritellinae specimens in the Danian period (used in Turritellinae_Code_Final.R)
- scientific_name: Scientific name of species used in Niche Modeling code (Turritellinae sl assigned just to denote subfamily)
- occID: Unique numeric identifier of occurrence
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
Occs_Maastrichtian_reconstructed: Paleorotated occurrences of Turritellinae specimens in the Maastrichtian period (not thinned yet).
- Turritellinae_occs_Maastrichtian.csv: csv dataset of Modern-day and Paleorotated occurrences of Turritellinae specimens in the Maastrichtian period (used in Turritellinae_Code_Final.R)
- scientific_name: Scientific name of species used in Niche Modeling code (Turritellinae sl assigned just to denote subfamily)
- occID: Unique numeric identifier of occurrence
- Longitude: Longitude of occurrence (in decimal degrees)
- Latitude: Latitude of occurrence (in decimal degrees)
- Rot_Long: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Rot_Lat: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
PCA_Getech: Combined thinned occurrences of Turritellinae from the Maastrichtian and Danian periods for ordination analysis.
- Turritella_total.csv: Note (turritella_Danian corresponds to thinned occurrences from the Danian Period, while turritella_Maastrichtian corresponds to thinned occurrences within the Maastrichtian).
- scientific_name: Scientific name of species used in Niche Modeling code (Turritellinae sl assigned just to denote subfamily)
- Longitude: Paleorotated longitude of occurrence using Getech’s paleorotation model (in decimal degrees)
- Latitude: Paleorotated latitude of occurrence using Getech’s paleorotation model (in decimal degrees)
Scotese_model: all folders, files, and naming schemes are the same as Getech_model, except datasets are generated using the Scotese model of paleorotation
Results: Folder containing results of model output.
Environmental_Layers: Environmental layers used in MaxEnt analysis saved in svg format (generated from Turritellinae_Code_Final.R)
- Env_Layers_getech_Danian: Environmental layers from the Danian period generated using Getech’s model of paleorotation
- Env_Layers_getech_Maastrichtian: Environmental layers from the Maastrichtian period generated using Getech’s model of paleorotation
- Env_Layers_scotese_Danian: Environmental layers from the Danian period generated using Scotese model of paleorotation
- Env_Layers_scotese_Maastrichtian: Environmental layers from the Maastrichtian period generated using Scotese model of paleorotation
Getech: Results from ordination analysis of environmental variables and occurrences of Turritellinae from the Danian and Maastrichtian (generated from ENM_Paleo_PCA.R)
- Correlation circle.svg: Visualizations of the importance of variables within principal component space. The axes represent the principal components, and the plot shows how each variable correlates with those components. Variables that are close to each other on the plot have similar relationships to the components, while those far apart are more distinct.
- equilivancy test.svg: Plot of Niche equivalency test. The equivalency test evaluates whether the niches are equally distributed across the environmental space. A p-value <0.05 in this test suggests that the niches from both time periods are more equivalent than expected by chance
- similarity test.svg: Plot of Niche similarity test. For the similarity test, a p-value <0.05 indicates that the niches from both time periods are less similar than expected by chance, suggesting a lower degree of overlap between their environmental conditions.
- loadings.txt: Loadings from PCA space analysis. Loadings refer to the correlation coefficients (or weights) between the original variables and the principal components (PCs). Essentially, loadings indicate how strongly each original variable contributes to the principal components
- Niche Overlap.svg: Occurrence density grids of principal component space for both the environmental values at each occurrence point and background extent points using a kernel density estimation approach
- overlap.txt: Raw data from niche equivalency and Niche similarity tests. Results of environmental niche overlap analysis, likely comparing the environmental niches of Turritellinae between the Maastrichtian and Danian
- PCA_Space.svg: Principal component space figure of environmental variables of occurrence points + background points of Turritellinae between the Maastrichtian and Danian Periods
Predictions_Danian: MaxEnt suitability map predictions of Turritellinae occurrences from the Danian Period (generated from Turritellinae_Code_Final.R) generated from the optimal model
- getech_Danian_10_omission.tif: Binary MaxEnt map prediction of the distribution of Turritellinae based on 10% omission rate
- getech_Danian_kappa.tif: Binary MaxEnt map prediction of the distribution of Turritellinae based on kappa statistic
- getech_Danian_maxSSS.tif: Binary MaxEnt map prediction of the distribution of Turritellinae based on Maximized Sensitivity + Specificity
- getech_Danian_mean.tif: Binary MaxEnt map prediction of the distribution of Turritellinae based on mean omission rate
- getech_Danian_mtp.tif: Binary MaxEnt map prediction of the distribution of Turritellinae based on minimum training presence (MTP)
- getech_Danian_prediction.tiff: Continuous map prediction of the distribution of Turritellinae based on ‘cloglog’ transformation in which raw values are scaled to 0 - 1
- getech_Danian_MESS_threshold.tif: Multivariate environmental similarity surface (MESS map) based on ‘informed’ MESS analysis, by clamping each environmental variable a priori based on the completeness of response curve (ex: extrapolation can be used if the response curve for an environmental variable reaches 0 or 1 for full modeled behavior).
- getech_Danian_MESS.tif: Multivariate environmental similarity surface (MESS map) without clamping
- getech_Danian_optimal_model.csv: Summary statistics of the optimal model (includes AUC, 10% omission rate, AICc, delta.aicc, and number of coefficient metrics)
- rm: The regularization multiplier used in the MaxEnt model, which controls model complexity and helps prevent overfitting.
- fc: The feature class used in the MaxEnt model (e.g., linear, quadratic, product, threshold). This determines the type of environmental features used in the model.
- tune.args: The tuning arguments or parameters used in the model fitting process, typically representing adjustments made to the model’s settings.
- auc.train: The Area Under the Curve (AUC) value for the training data. It indicates the model’s ability to distinguish between the presence and absence of data during training.
- cbi.train: The Cohen’s kappa-based index (CBI) for the training data. It evaluates the model’s classification performance, with higher values indicating better performance.
- auc.diff.avg: The average difference in AUC between the training and validation datasets, representing the model’s generalization performance.
- auc.diff.sd: The standard deviation of the difference in AUC between the training and validation datasets, providing a measure of variability in model performance.
- auc.val.avg: The average AUC value for the validation dataset, indicating how well the model performs on unseen data.
- auc.val.sd: The standard deviation of the AUC values for the validation dataset, providing a measure of variability in performance on unseen data.
- cbi.val.avg: The average Cohen’s kappa-based index (CBI) for the validation dataset, indicating classification accuracy on unseen data.
- cbi.val.sd: The standard deviation of the CBI values for the validation dataset, providing a measure of variability in model performance on unseen data.
- or.10p.avg: The average omission rate at the 10th percentile of the predicted probability threshold, measuring the proportion of absence points misclassified as presence.
- or.10p.sd: The standard deviation of the omission rate at the 10th percentile, indicating variability in model predictions at this threshold.
- or.mtp.avg: The average omission rate at the maximum test sensitivity plus specificity (MTP) threshold, indicating model performance based on optimal thresholding.
- or.mtp.sd: The standard deviation of the omission rate at the MTP threshold, providing a measure of variability in model performance at this optimal threshold.
- AICc: The corrected Akaike Information Criterion (AICc) value, a measure of model fit that accounts for the number of parameters is used to compare models with different complexities.
- delta.AICc: The difference in AICc between the current model and the best-performing model (the model with the lowest AICc value).
- w.AIC: The Akaike weight (w.AIC) of the model, representing the probability that the model is the best one given the set of candidate models.
- ncoef: The number of coefficients (or parameters) in the model, reflecting the complexity of the model.
- getech_Danian_Depositional_Environment.csv: Suitability scores of depositional environmental variable categories from MaxEnt model (V1 = variable, and p = ‘cloglog’ score, see Environment_Danian_key.csv)
- V1: Depositional environment variable assigned to an integer
- p: Predicted suitability score (cloglog, 0 - 1)
- getech_Danian_Depositional_Lithology.csv: Suitability scores of lithology environmental variable categories from MaxEnt model (V1 = variable, and p = ‘cloglog’ score, see lithology_Danian_key.csv)
-V1: Lithology variable assigned to an integer
-p: Predicted suitability score (cloglog, 0 - 1) - getech_Danian_points.csv: Final thinned occurrence points of Turritellinae used in the MaxEnt model
- x: Longitude of occurrence (in decimal degrees)
- y: Latitude of occurrence (in decimal degrees)
- getech_Danian_importance.csv: Permutation importance of variables from optimal MaxEnt model.
- variable: The environmental variable or predictor used in the MaxEnt model
- percent.contribution: The percentage of the model’s total predictive power that is attributed to each variable. It indicates the importance of each variable in explaining the model’s predictions.
- permutation.importance: The change in model performance (typically measured by AUC or similar metrics) when the values of the given variable are randomly permuted. A larger drop in model performance indicates greater importance of that variable in the model.
- Permutation_importance.svg: Bargraph figure of permutation importance of variables from optimal MaxEnt model.
- response_curve_bathymetry.svg: Graphic figure of response curve of suitability and bathymetry from optimal MaxEnt model
- response_curve_environment.svg: Bargraph figure of the response curve of suitability and depositional environment from optimal MaxEnt model (use Environment_Danian_key.csv to infer categories)
- response_curve_lithology.svg: Bargraph figure of response curve of suitability and lithology from optimal MaxEnt model (use Lithology_Danian_key.csv to infer categories)
- response_curve_mixed_layer.svg: Graphic figure of the response curve of suitability and annual mixed layer depth from optimal MaxEnt model
- response_curve_monsoon.svg: Graphic figure of the response curve of suitability and monsoon seasonality index from optimal MaxEnt model
- response_curve_salinity.svg: Graphic figure of the response curve of suitability and annual potential salinity from optimal MaxEnt model
- response_curve_temperature.svg: Graphic figure of the response curve of suitability and annual potential temperature from optimal MaxEnt model
Predictions_Maastrichtian: MaxEnt suitability map predictions of Turritellinae occurrences from the Maastrichtian Period (generated from Turritellinae_Code_Final.R) generated from the optimal model. NOTE: Format of folders, tables, and graphics same as Predictions_Danian
Scotese: Results from ordination analysis of environmental variables and occurrences of Turritellinae from the Danian and Maastrichtian (generated from ENM_Paleo_PCA.R) NOTE: Format of folders, tables, and graphics same as Getech
Datasets_15_Categories: Datasets containing analyses using 15 depositional categories as well as depositional setting variables. NOTE: Format of folders, tables, and graphics same as Datasets, Getech, Scotese, and Results.
Taxonomy of Turritellinae—Although turritellids are well known from the fossil record, the intrafamilial relationships among turritellids remain an active area of taxonomic research. Turritellidae was divided into five subfamilies, Turritellinae, Protominae, Pareorinae, Turritellopsinae, and Vermiculariinae by Marwick (1957), with Turritellinae including most nominal genera and subgenera. Subsequent analyses, informed by both molecular and morphological data, have found that the Vermiculariinae are well nested within the Turritellinae, which may also be the position of the Protominae (Anderson and Allmon 2024). Many generic and subgeneric names exist in the literature, but very few are used consistently. Recent work, supported by new molecular analyses (Anderson 2018, Anderson and Allmon 2024, Lieberman et al. 1993, Sang et al. 2019) has supported Marwick’s (1957) prioritization of protoconch form, order of appearance of spiral ornamentation, and growth line characters as indicative of taxonomic affinity, in the absence of significant morphological differentiation in the teleoconch (e.g. Vermicularia, Caviturritella) (Friend et al. 2023). However, the comparative lack of well-preserved protoconch material available leaves many species broadly assigned to “Turritella sensu lato” on the basis of plesiomorphic or convergent teleoconch form, making “Turritella” a true “wastepaper basket” genus (Allmon 1996, 2011, Allmon and Cohen 2008, Hendricks et al. 2014, Plotnick and Wagner 2006). Yet, species assigned to Turritella s.l. (given their absence of teleoconch characters that assign them to other subfamilies) are all expected to be encompassed by a monophyletic Turritellinae. Furthermore, extant species within Turritellinae all have similar ecologies as shallow infaunal, suspension (and occasionally deposit) feeding gastropods with the overwhelming majority also having highly similar life histories (Allmon 1988, 2011, Anderson and Allmon 2020). Therefore, we consider the Maastrichtian and Danian members of Turritellinae, a clade first appearing in the Jurassic (Das et al. 2018) and diversifying in the Cretaceous, to be broadly like a modern gastropod genus in terms of species richness, clade age, and ecological similarity among constituent species (Allmon 2011).
Unfortunately, confusion in the literature and large databases like the PBDB is not limited to the proper generic assignments of species. Large numbers of fossil occurrences of “Turritella sp.” do not represent members of monophyletic Turritellinae (or even Turritellidae) but may represent other high-spired gastropods mistakenly identified as “Turritella.” Furthermore, the name “Turritella" has been erroneously applied to entire rock layers such as ‘turritella agate’ when such layers belong to the freshwater gastropod Elimia tenera Hall, 1845 (Allmon and Knight 1993). Therefore, we chose to restrict our analyses to only those occurrences which had species-level identification.
Because of these taxonomic and temporal record uncertainties, we have decided to analyze the subfamily Turritelline as a single taxon. We recognize that ENM techniques are best interpreted at the species-level, however, taxonomic reality and spatiotemporal resolution support a more robust analysis at the clade level (Hendricks et al. 2014).
Occurrence Records—We acquired occurrence records of species belonging to Turritellinae within the Maastrichtian and Danian periods from the Paleobiology Database (PBDB; paleodb.org), Global Biodiversity Information Facility (GBIF; gbif.org, downloaded July 2022), additional occurrences from C.M. (Myers et al. 2013), as well as an exhaustive literature search for the U.S. coastal plain occurrences by W.A. and K.C. We selected genera belonging to Turritellinae based on taxonomic, morphological (Allmon 1988, 2011, Friend et al. 2023, Harzhauser and Landau 2019) and recent molecular phylogenetic evidence from Anderson (2018) and Anderson and Allmon (2024) (Table 1). Phylogenetic evidence suggests a deep split between the majority of turritellid gastropod species, assigned to the subfamily Turritellinae, and the subfamily Pareorinae (Marwick, 1957) which includes the genera Mesalia and Sigmesalia (Anderson 2018, Anderson and Allmon 2024); we omitted both genera from the analysis (Table 1). Molecular evidence also suggests that two other traditional subfamilies, Vermiculariinae Dall 1913 and Protominae Marwick 1957, are nested within the Turritellidae (Anderson and Allmon 2024), however, neither of these has Maastrichtian or Danian fossil records and therefore this inclusion does not affect the analyses. Since the genus Turritella s.s., is nested within Turritellinae, and fossils are commonly misidentified as “Turritella sp.,” we only selected taxa possessing a species epithet (in the ‘accepted name’ category within PBDB), and that could be verified in the literature as being present within the selected time periods. Further occurrence filtering consisted of omitting specimens for which latitude and longitude could not be computed. We binned the time intervals to the Maastrichtian (72.1 - 66 mya) which possessed 2032 occurrences, and the Danian (66-61.6 mya) consisting of 1883 occurrences (Table 2, See Supplemental Information).
Paleogeographic maps of the Phanerozoic predominantly follow the paleo-digital elevation model PALEOMAP, which estimates Earth’s past paleoceanography, the changing area of land, mountains, shallow seas, and deep oceans through time (Scotese 2016, Scotese and Wright 2018). Fossil databases such as the Paleobiology Database (PBDB; paleodb.org) follow PALEOMAP for paleo-rotation of taxa to their estimated place of deposition within the Phanerozoic. The GCM simulations of past climate from (Valdes et al. 2021) have utilized Scotese’s model of paleo-elevation. Paleo-elevation models such as those from the Getech Plc (Getech.com) have also been used in GCM simulations from Lunt et al. (2016) and Farnsworth et al. (2019); these provide alternative reconstructions of Earth’s past climate. However, differences in the paleo-coastlines of each model (particularly epicontinental sea boundaries) may result in the reconstruction of coastal invertebrate occurrences on land instead of in the ocean (Scotese and Wright 2018).
Using the rgplates package v 0.3.2 (Kocsis et al. 2023) in R v. 4.0 (Team 2021), we paleo-rotated both datasets to their respective time periods using Scotese’s PALEOMAP (Scotese 2016) and Getech’s paleo-rotation model provided by Farnsworth et al. (2019). PALEOMAP consists of 120 unique paleo-digital elevation models (PaleoDEMs) representing three-million-year time slices roughly equating to different stratigraphic stages. We paleo-rotated the occurrences of the Maastrichtian to the 69mya time slice, and the Danian to the 66mya time slice. We repeated this process using Getech’s in-house model with the same time slices (Farnsworth et al. 2019) (See Supplemental Information for all paleorotated datasets).
Climate Data—We generated environmental layers using output from the HadCML3 climate model version 4.5 (Valdes et al. 2017), with inputs from the solar luminosity, and both the PALEOMAP paleogeographic atlas (Scotese 2016, Scotese and Wright 2018) (hereafter referred to as the Scotese simulations) and from Getech Plc (Getech.com) (hereafter referred to as the Getech simulations) for the Maastrichtian and Danian periods. For both sets of simulations, we use the GCM “HadCM3LB-M2.1aD,” described in detail in (Valdes et al. 2017), for which surface resolution is 3.75° longitude × 2.75° latitude (grid box size of ~420 × 220 km; at the equator, reducing to ~200 × 280 km at 45° latitude).
The Scotese simulations are similar to the latest Maastrichtian (Map number 22) and Danian (Map number 20) simulations described in Valdes et al. (2021). These are the simulations that prescribe a pCO2 concentration according to Foster et al. (2017). Compared to the Valdes et al. (2021) study, simulations were run for an additional 2,000 years, and with modified atmospheric and ocean physics by applying methods similar to those described in Sagoo et al. (2013), resulting in an improved representation of polar amplification in deep-time climates. In addition, they have islands defined correctly for the purposes of calculating the ocean barotropic stream function, according to Foreman (2005).
The Getech simulations are identical to those described in Farnsworth et al. (2019). The pCO2 concentration for both Maastrichtian and Danian Getech simulations were each 1,120 ppmv, which is within the typical range of uncertainty in Foster et al. (2017) pCO2 data, which itself approximates the actual evolution of pCO2 through time with some uncertainty (see Fig. 1 in (Foster et al. 2017)).
Climate model output variables chosen within the Maastrichtian and Danian for both model sets were: annual mixed layer depth (meters), annual maximum and minimum (warmest and coldest) sea-surface temperature within a 3-month interval (season; Celsius), and monsoon seasonality index (difference in precipitation between the three driest and three wettest months of the year); monsoon seasonality is a proxy for preference of seasonal variability around the tropics. Furthermore, we acquired annual potential temperature (Celsius), and annual salinity (PSU, practical salinity unit) averaged from 5 – 5800m depth (See Valdes et al. (2017) for in-depth definitions of variables). Although modern Turritellines are found predominantly between depths of 2 – 100m (Allmon 2011), occurrences affected by paleo-rotation and placed at deeper depths would be mistakenly removed if layers were restricted to shallow depths including epeiric seas found in North America and Eurasia. Finally, we incorporated bathymetry (meters), derived from the digital elevation models (DEMs) which underlie both the Getech and Scotese GCM simulations. These variables were chosen to encapsulate the abiotic restrictions of modern Turritelline species (Allmon 1988, 1996, 2011, Allmon and Cohen 2008, Allmon and Knight 1993). We rotated all variables from 0 - 360 longitudinal format to -180 - 180 longitudinal format to avoid cutoff at the international date line.
Sedimentology Data—We generated two distinct sedimentological, interpolated datasets derived from lithology and depositional environment proxies at fossil localities within the Maastrichtian and Danian. We downloaded all marine occurrences within the Cretaceous/Paleogene boundary stages from the PBDB and assigned each collection to either carbonate or siliciclastic using the primary lithologic data associated with the occurrence, recorded as ‘lithology1’ field in the collection record (see Hopkins (2014) and Foote (2006) for criterion). Within the PBDB, each lithological term possesses specific definitions so that the collection is consistently described in ways relative to all other collections in the database, allowing for consistent translation to the two lithological categories. We also assigned each collection to different depositional environments recorded from the ‘environment’ field to determine preferred depositional settings for Turritellines within the Maastrichtian and Danian. In total, we used 58 depositional categories. We supplemented the PBDB lithological and environmental datasets with localities from Paleo Reefs (https://www.paleo-reefs.pal.uni-erlangen.de/; (Kiessling et al. 1999)), and the Sedimentary Geochemistry and Paleoenvironments Project (SGP) (https://sgp.stanford.edu/; (Farrell et al. 2021)). We classified all paleo-reef collections as being carbonate, and only selected collections from the SGP belonging to one of the two lithological categories. We categorized paleo-reef collections as ‘reef’ while the SGP only possessed ‘basinal’, ‘fluvial’, ‘inner shelf’, ‘lacustrine’, and ‘outer shelf’ environmental categories, all of which are consistent with the PBDB ‘environment’. Definitions for lithologies and depositional environments can be found at https://paleobiodb.org/public/tips/lithtips.html, and https://paleobiodb.org/public/tips/environtips.html, respectively. It should be noted that depositional environment categories as defined and used in the PBDB are not strictly independent of one another and are defined in a hierarchical fashion. Thus, our analysis provides an example of a very simplified use of this data. In the Supplemental materials, we provide a detailed description of how these categories can be condensed into more directly comparable categories and return to this topic in the Discussion (Supplemental Table S1).
We paleo-rotated the lithological and depositional environment datasets to the Getech and Scotese GCM paleo-rotation models within the Maastrichtian and Danian. We assigned each lithological and environmental category an integer (ex: 1 = carbonate, 2 = siliciclastic) and performed a nearest-neighbor interpolation using the ‘interpNear’ function in the R package terra (Hijmans et al. 2022) to generate categorical raster layers. When lithological or depositional categories vary within a single pixel, the nearest-neighbor interpolation assigns the pixel’s value based on Euclidean distance—selecting the occurrence closest to the pixel’s center. Nearest-neighbor is an efficient method for interpolating categorical data since it preserves original categories, avoids blending (i.e., averaging), and is less computationally intensive (Johnson and Clarke 2021). We resampled the categorical rasters to the same resolution of the GCM layers using the ‘resample’ function in the raster package in R (Hijmans et al. 2021). Climate model, lithological, and depositional environment output variables can be obtained in this dataset.
Ecological Niche Modeling—Before modeling, we spatially thinned occurrences to the resolution of the raster layers to reduce sampling bias, artificial clustering, and subsequent spatial autocorrelation (Veloz 2009) using the spThin package (Aiello-Lammens et al. 2015), which resulted in more even sample sizes of occurrence points across time periods (see Table 2). ENM modeling is more accurate when occurrences are thinned to the resolution of pixels within our raster layers, such that each occurrence gets a singular value of each of the climate and sedimentological values used (Araújo et al. 2019). Similarly, after thinning, only a single species’ occurrence may exist in any single pixel, which is an important step to prevent model over-sampling of the same pixels within the study extent (Aiello-Lammens et al. 2015). Given that the collated occurrence data often contained already-assigned environmental conditions (e.g., lithology and depositional environment in PBDB-based records), we checked whether (1) multiple occurrences within the same pixel had the same environmental designations, and (2) thinning occurrences caused discrepancies between the environmental values derived from our lithology and depositional environment layers and the original PBDB-based record designations. We found no discrepancies between the PBDB environmental designations linked to occurrences within a pixel and the interpolated pixel values from our lithology and depositional environment layers.
Turritellines possess a global distribution (Allmon 2011), thus we chose a study extent to encapsulate the entire preserved geographic range of Turritellinae in both the Maastrichtian and Danian time periods. Furthermore, to include potentially undersampled areas despite dispersal limitations of individual species (Peterson and Soberón 2012), we defined a bounding box buffered by 8.12° (~100,000 m2) as this is the maximum dispersal capability of modern-day Turritellines (‘M’ training region as defined by Soberón and Nakamura (2009)) (Allmon 1996, 2011). From the total region encompassing all thinned occurrences enveloped by the bounding box, we randomly sampled background points for modeling (n=888 for Scotese and n=1842 for Getech study extents); background points differ between niche models due to the number of occurrence points differing between paleorotation models which produced the resultant bounding box. Background points are a random sample of the environmental conditions across our entire study area which help define what is suitable and unsuitable habitat in model output. Our model compares environmental conditions at our presence (occurrence) points to that of our background points to identify which conditions are more strongly associated with the presence of the species (Elith et al. 2011, Phillips 2021, Phillips et al. 2017, Phillips and Dudík 2008). We extracted environmental values from our background points to calculate correlations between variables using the ‘vifcor’ function in the usdm package (Naimi 2017) and filtered out variables with correlation coefficients higher than 0.7. In ENM analyses, the balance of the correlation threshold against the number of environmental variables retained for analysis is an area of active study with coefficients ranging from 0.5 - 0.9 (Graham 2003, Yan et al. 2020). Since fewer variables create underfitting models, we chose a moderate correlation threshold (0.7), which acted as a natural break in which higher thresholds significantly reduced the number of environmental variables retained. We used the same set of variables for subsequent model building, retaining only those that remained after assessing correlations between GCM type and time period.
We used the machine learning algorithm MaxEnt v3.4.4. (Phillips et al. 2017), which remains one of the top-performing algorithms for fitting presence-background ENMs (Valavi et al. 2021). Even though a substantial number of occurrences were removed during thinning, the sample size was large enough to produce robust results (Table 2) (Shcheglovitova and Anderson 2013). At small spatial and taxonomic scales, fewer than 10 occurrences can be sufficient to create highly accurate models (Hernandez et al. 2006, Pearson et al. 2007, van Proosdij et al. 2016). A recent study by Qiao et al. (2017) suggested that the incorporation of occurrences of related species increases model fidelity on undersampled taxa. Nonetheless, the sample size should be considered carefully because the number of occurrence points varies greatly depending on the study system, and too few paleoENM studies have been published to generate a consensus (See Supplemental Information of Myers et al. (2015)). Within ENMs, partitioning data involves splitting the dataset into two subsets: the training dataset is the subset of data used to build the model, while the testing data is used to evaluate the performance of each model iteration (Elith et al. 2011, Phillips 2021, Phillips et al. 2017, Phillips and Dudík 2008). Partitioning reduces the risk of model overfitting and assesses its performance with unseen data. We used the ‘checkerboard2’ strategy of spatial partitioning which divides up our study extent into a grid of spatial cells whereby occurrence points are then alternatively assigned to different partitions (the default setting used here assigns four partitions).
All final models were fitted to the full datasets (training + testing) (Phillips et al. 2017). As the combination of two key complexity settings in MaxEnt models, feature classes and regularization multipliers, can strongly influence model outputs (Radosavljevic and Anderson 2014, Warren and Seifert 2011), we tuned model complexity to find optimal settings. For tuning, in order of increasing complexity, we chose the feature classes linear (L), quadratic (Q), and hinge (H), as well as regularization multipliers 1 through 5 (higher numbers penalize complexity more) (See Supplemental Information for a more detailed explanation of MaxEnt model building). In brief, feature classes determine the shape of the model fit, while regularization multipliers control how much complexity is penalized—this can result in predictor variable coefficients shrinking to 0 and thus dropping out of the model (Phillips and Dudík 2008).
We assessed optimal models using sequential criteria that included threshold-dependent (omission rate) and threshold-independent (AUC) performance metrics. We first filtered models that possessed a delta Akaike Information Criterion (with correction for small sample sizes) less than 2 to compare models between time periods and GCM type (AICc; Warren and Seifert (2011)). We then chose models that possessed the smallest 10-percentile omission rate. If a series of models possessed near-identical 10-percentile omission rates, we chose the simplest model or the one with the fewest non-zero lambda values (model coefficients). For each time and GCM model, we documented variable importance and plotted marginal response curves to better understand the modeled relationships between the predictor variables and the data. We recorded the permutation importance metric output by MaxEnt, which is calculated by randomly permuting the values of all environmental variables but one, building a new model, and then calculating the difference between each model’s training AUC and that of the empirical model (Phillips et al. 2017). Marginal response curves are generated by constraining all predictor variables to their means except for one, then making model predictions along the full range of the focal variable associated with the training data. These curves show the modeled relationship of each variable individually with the occurrence data when all other variables are held constant and are affected by the complexity of the model settings (Phillips et al. 2017).
We calculated a multivariate similarity surface (MESS; Elith et al. (2010)) to detect the degree of similarity in extracted empirical occurrence (presences) environmental values and extracted background environmental values for each time period and GCM type. Increasingly negative values in MESS scores suggest dissimilar environmental conditions (non-analogue environments resulting in model extrapolation), while positive values suggest more similar environmental conditions (interpolation). In the case of our data, we calculated MESS plots by extracting environmental values from occurrence points and projecting them to their respective time period. However, to prevent over-extrapolation, we performed an ‘informed’ MESS analysis, by clamping each environmental variable a priori based on the completeness of the response curve (ex: extrapolation can be used if the response curve for an environmental variable reaches 0 or 1 for full modeled behavior). Code for our informed MESS analysis is provided in our Supplemental Information, which was modified from the ‘mess’ function in the R package dismo (Hijmans et al. 2017).
We made habitat suitability predictions for Turritellinae using our environmental predictor variables. We reclassified continuous prediction output to binary (0 = unsuitable; 1 = suitable), determined by the maximum sum of sensitivity and specificity (MaxSSS) threshold calculated from model evaluation metrics. MaxSSS remains one of the best threshold selection methods for presence/absence data (Liu et al. 2016). We also compared our MaxSSS predictions to those calculated from the 10-percentile omission rate, which is one of the strictest binary thresholds (Phillips et al. 2017). Finally, we generated continuous predictions by transforming our raw MaxEnt predictions to a scale of 0 – 1 to approximate the probability that Turritellinae will be present at a particular location (‘cloglog’ transformation) (Phillips et al. 2017).
Niche Overlap—We compared niche overlap of the Maastrichtian and Danian occurrences using an ordination framework by first reducing dimensionality within the datasets via a Principal Component analysis (PCA). Using the ‘espace_pca’ function in the Wallace v2.0.5 and ade4 package v1.7 in R (Dray and Dufour 2007, Kass et al. 2018), we generated convex hulls of principal component space using the extracted environmental variables from the Turritellinae occurrences within both time periods and plotted with correlation loadings to infer the degree of influence particular environmental variables possess in the distribution of background points within niche space (see Supplemental Information for convex hull reconstructions). Using the ‘espace_occDens’ function in the package ecospat v3.2 (Di Cola et al. 2017) an occurrence density grid was estimated for both the environmental values at each occurrence point and background extent points using a kernel density estimation approach. Niche overlap between occurrence density grids of environmental values at each occurrence point and background points were compared using Schoener’s D (Schoener 1968) using the ‘espace_nicheOv’ function.
Using the ‘ecospat.plot.overlap.test’ function, we conducted niche similarity and equivalency tests to assess the degree of niche differentiation of Turritellinae between time periods. For the similarity test, a null distribution was generated by randomly shifting the occurrences of Turritellinae from one time period (Maastrichtian) within the combined background extent of both time periods, while keeping the niche from the other time period (Danian) fixed. This process is permuted 1000 times to assess whether the observed niche overlap was greater than expected by chance. This process was then repeated by swapping time periods, shifting occurrences from the Danian while keeping the Maastrichtian niche fixed. A p-value < 0.05 indicates that the niches from both time periods are more similar than expected. In contrast, the equivalency test pools the occurrences of Turritellinae from both time periods and randomly assigns them within their combined background extent to assess whether the niches are statistically indistinguishable; this process is also permuted 1000 times. A p-value < 0.05 suggests that the niches are significantly different from each other, indicating they are less equivalent than expected by chance.
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