DOI: 10.5061/dryad.w9ghx3g17
title: Global climate model comparisons of niche evolution in Turritelline gastropods across the end-Cretaceous mass extinction

description: 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.
description: <em>Taxonomy of Turritellinae—</em>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. <em>Vermicularia</em>,
 <em>Caviturritella</em>) (Friend et al. 2023). However, the
 comparative lack of well-preserved protoconch material available leaves
 many species broadly assigned to “<em>Turritella sensu
 lato</em>” on the basis of plesiomorphic or convergent teleoconch
 form, making “<em>Turritella</em>” a true “wastepaper basket”
 genus (Allmon 1996, 2011, Allmon and Cohen 2008, Hendricks et al. 2014,
 Plotnick and Wagner 2006). Yet, species assigned to <em>Turritella
 s.l.</em> (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 “<em>Turritella</em>
 sp.” do not represent members of monophyletic Turritellinae (or even
 Turritellidae) but may represent other high-spired gastropods mistakenly
 identified as “<em>Turritella.</em>”  Furthermore, the name
 “<em>Turritella" </em>has been erroneously applied to
 entire rock layers such as ‘turritella agate’ when such layers belong to
 the freshwater gastropod <em>Elimia tenera </em>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).
 <em>Occurrence Records—</em>We acquired occurrence
 records of species belonging to Turritellinae within the Maastrichtian and
 Danian periods<em> </em>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
 <em>Mesalia </em>and <em>Sigmesalia</em> (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
 <em>Turritella s.s., </em>is nested within Turritellinae, and
 fossils are commonly misidentified as “<em>Turritella
 </em>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 <em>rgplates</em> 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). <em>Climate
 Data—</em>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
 pCO<sub>2</sub> 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) pCO<sub>2</sub> data, which itself
 approximates the actual evolution of pCO<sub>2</sub> 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<em>
 </em>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. <em>Sedimentology
 Data—</em>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 <em>terra</em> (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 <em>raster </em>package in R (Hijmans et al. 2021). Climate model, lithological, and depositional environment output variables can be obtained in this dataset. <em>Ecological Niche Modeling—</em>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 <em>spThin </em>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 m<sup>2</sup>) 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 <em>usdm </em>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 <em>dismo</em> (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). <em>Niche Overlap—</em>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 <em>Wallace</em> v2.0.5 and <em>ade4</em> 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 <em>ecospat</em> 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. 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description: # Global climate model comparisons of niche evolution in Turritelline
 gastropods across the end-Cretaceous mass extinction --- ## 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](https://www.paleo.bristol.ac.uk/ummodel/scripts/papers/?)). ###
 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](https://qgis.org/download/)). * **Scotese_model** and
 **Getech_model**: Requires R and RStudio ([download
 link](https://posit.co/download/rstudio-desktop/)). --- ## 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](https://www.barebones.com/products/bbedit/index.html)) and free
 image editing software (e.g., [Inkscape](https://inkscape.org/)). 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 &lt;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 &lt;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**.