Biogeography after Permian/Triassic boundary crisis resulted from a complex combination of functional and dispersal mechanisms.

Pauline Guenser, Marc Leu, Néo Nicollet, Axelle Zacaï, Arnaud Brayard, Nicolas Goudemand, Gilles Escarguel

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Pauline Guenser, Marc Leu, Néo Nicollet, Axelle Zacaï, Arnaud Brayard, et al.. Biogeography after Permian/Triassic boundary crisis resulted from a complex combination of functional and dispersal mechanisms.. Lethaia, 2025, 58 (3), pp.1-24. 10.18261/let.58.3.2 . hal-05308559

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# Biogeography after Permian/Triassic boundary crisis resulted from a complex combination of functional and dispersal mechanisms

PAULINE GUENSER, MARC LEU, NÉO NICOLLET, AXELLE ZACAÏ, ARNAUD BRAYARD, NICOLAS GOUDEMAND AND GILLES ESCARGUEL

LETHAIA

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The Permian/Triassic boundary (PTB) crisis represents one of the most severe mass extinction events, resulting in the extinction of over 80% of marine species. The subsequent biotic recovery during the Early Triassic was characterized by alternating phases of high and low taxonomic richness. This study investigates the macroecological processes that shaped biogeographical patterns during this recovery period by examining two marine nektonic groups: conodonts and ammonoids. Using an extensive database of species occurrences and advanced multivariate and network analyses, we identified distinct biogeographical units (biochores) and evaluated the relative influence of niche and dispersal processes on these patterns. Our results reveal that biogeographical structures evolved from longitudinal differentiation during the Griesbachian to latitudinal differentiation by the Spathian. Conodont biogeography was primarily influenced by functional constraints in the Griesbachian and by a combination of niche and dispersal constraints from the Dienerian to Spathian. In contrast, ammonoid biogeography was primarily dispersal-constrained during the Griesbachian and Spathian, and niche-constrained during the Smithian. This study highlights the importance of integrating macroecological theories to understand recovery dynamics of marine ecosystems after mass extinction events. Our findings suggest a complex interplay of biotic (ecological) and abiotic factors in driving the distribution of biodiversity after the PTB crisis. Conodonts and ammonoids were not driven by the same constraints and it may be due to a difference of sensitivity to abiotic parameters and environmental disturbances. By extending these methodologies to other taxonomic groups and other crises, future research may offer critical insights into the drivers of biodiversity's distribution after a mass extinction.

□ Conodonts, ammonoids, biochore, DNCI, quantitative biogeography, macroecology

Pauline Guenser [pauline.guenser@gmail.com], Gilles Escarguel [gilles.escarguel@univ-lyon1.fr], Université Claude Bernard Lyon 1, LEHNA UMR 5023, CNRS, ENTPE, F-69622, Villeurbanne, France; Marc Leu [marc.leu@pim.uzh.ch], Paläontologisches Institut, Universität Zürich, Karl-Schmid-Strasse 4, 8006 Zürich, Switzerland; Néo Nicollet [neo.nicollet@etu.univ-lyon1.fr], Laboratoire de Géologie de Lyon : Terre, Planètes, Environnement UMR CNRS 5276 (Université Lyon1, ENS, UJM, CNRS) Université Claude Bernard, Lyon 1 Campus de la Doua, bâtiment Géode, R2 2, rue Raphaël Dubois 69622 Villeurbanne Cedex France; Axelle Zacaï [axelle.zacaï@gmail.com], Univ. Lille, CNRS, UMR 8198, Evo-Eco-Paleo, F-59655 Villeneuve d'Ascq, France; Arnaud Brayard [arnaud.brayard@u-bourgogne.fr], Biogéosciences, UMR6282, CNRS, Université Bourgogne Europe, 6 Boulevard Gabriel, 21000 Dijon 59000 Lille, France; Nicolas Goudemand [nico-las.goudemand@ens-lyon.fr], ENS de Lyon, IGFL, CNRS UMR 5242, 46, allée d'Italie 69364 Lyon cedex 07, France; manuscript received on 20/12/2024; manuscript accepted on 02/04/2025; manuscript published on 03/10/2025 in *Lethaia* 58(3).

Past biotic crises deeply impacted spatial distribution of taxa, both during mass extinction events and during subsequent recovery intervals (Parmesan et al. 1999; Erwin 2001; Jablonski 2001; Kocsis et al. 2018). However, the underlying macroecological processes responsible for the evolution of post-crisis biogeographical patterns remain to be deciphered. The Permian/Triassic boundary (PTB) crisis is the mass extinction event that most profoundly impacted the biosphere during the Phanerozoic, with more than

80% of the marine species being wiped out (Raup & Sepkoski 1982; Sepkoski 1986; Stanley 2016). The following 5 myrs, corresponding to the Early Triassic, witnessed a complex biotic recovery. Environmental perturbations such as disruption of the carbon cycle and marine primary productivity with phytoplanktonic blooms (Payne et al. 2004, Galfetti et al. 2007b; Grasby et al. 2019; Wang et al. 2021; Joachimski et al. 2022; Saito et al. 2022; Blattmann et al. 2024; Du et al. 2024), oceanic temperature variations (Sun et al. 2012;
Romano *et al.* 2013; Sedlacek *et al.* 2014; Goudemand *et al.* 2019; Song *et al.* 2019; Stebbins *et al.* 2019), oce-anic stratification and anoxic/euxinic episods (Grasby *et al.* 2013; Takahashi *et al.* 2015; Elrick *et al.* 2017; Huang *et al.* 2017; Song *et al.* 2019; Stebbins *et al.* 2019; Thomazo *et al.* 2019; Zhao *et al.* 2020; Dai *et al.* 2021; Zhang *et al.* 2021), as well as continental weathering (Sedlacek *et al.* 2014) and increased aridity (Bourquin *et al.* 2009; MacLeod *et al.* 2017) occurred through-out that interval, as documented with empirical data (see references above) or inferred via climate model-ling approaches (Winguth *et al.* 2015; Sun *et al.* 2021; Isson *et al.* 2022; Wu *et al.* 2024).

In particular, at the end of the Smithian, that is ca. 2
myr after the PTB (depending on the age model, see
Burgess *et al.* 2014, Widmann 2019, Ogg *et al.* 2020
and Widmann *et al.* 2020), a dramatic event associated
with deadly hot temperatures followed by a marked
cooling through the Smithian/Spathian boundary
(Sun *et al.* 2012; Romano *et al.* 2013; Goudemand *et
al.* 2019; Song *et al.* 2019; Zhao *et al.* 2020; Wang *et
al.* 2021; Zhang *et al.* 2021; Du *et al.* 2022), strongly affec-
ted nektonic organisms such as ammonoids and cono-
donts. The corresponding Early Triassic upheavals of
taxonomic richness have been documented for many
taxa (Yin 1990; Hallam & Wignall 1997; Twitchett
*et al.* 2004; Brayard *et al.* 2006, 2011, 2017; Orchard
2007; Koot 2013; Tintori *et al.* 2014; Haig *et al.* 2015;
Brosse 2017; Foster *et al.* 2017, 2018; Dai *et al.* 2018,
2021; Lai *et al.* 2018; Algeo *et al.* 2019; Lindström
*et al.* 2020; Smith *et al.* 2021; Feng *et al.* 2022; Galasso
*et al.* 2022).

Most authors have proposed abiotic (extrinsic)
parameters as main drivers of the PTB and subse-
quent Early Triassic upheavals (Romano et al. 2013;
Benton & Newell 2014; Song et al. 2014; Wei et al.
2015; Borruel-Abadía et al. 2019; Leu et al. 2019;
Pietsch et al. 2019; Liu et al. 2022; Wu et al. 2024), but
a few have also suggested a role for biotic (intrinsic)
factors (Dai et al. 2018; Isson et al. 2022). While inves-
tigations on taxonomic diversity are commonly found
in the literature, biogeographical studies on the PTB
and Early Triassic intervals remain rare, especially
quantitative ones. So far, most publications focused
on terrestrial taxa such as vertebrates, arthropods and
plants (Skog 2001; Shcherbakov 2008; Ezcurra 2010),
whereas marine fauna were investigated mainly
through molluscs and brachiopods (Chen et al. 2005;
Brayard et al. 2006, 2007; Ke et al. 2016; Kiel 2017;
Echevarría & Ros-Franch 2024). The main described
pattern just after the PTB crisis is cosmopolitanism
(Brayard et al. 2007; Sahney & Benton 2008; Ros
et al. 2011; Button et al. 2017). As for diversity studies,
most authors suggested that biogeographical patterns

after the PTB crisis were driven by abiotic factors, e.g.
sea level (Embry 1988, 1997; Li et al. 2018), oxygen
rate (Feng & Algeo 2014; Dai & Song 2020) or tem-
perature (Liu et al. 2021). Some authors have invoked
biotic factors such as niche partitioning (Pietsch et al.
2019), competition (Erwin 2007; Brosse et al. 2013;
Hautmann et al. 2015; Hofmann et al. 2017) or inva-
sion of vacant ecospaces (Dineen et al. 2019). Yet,
no macroecological investigation was undertaken to
quantitatively test what macroecological process(es)
drove the biogeographical patterns around the PTB.

Over the last decades, two classically opposed perspectives were used to explain the nature and composition of species assemblages: the niche-assembly hypothesis (MacArthur & Wilson 1967; MacArthur 1970; Tilman 1982; Hubbell 2001; Chase & Leibold 2003) and the dispersal-assembly hypothesis (Bell 2001; Hubbell 2001). The former is based on the functional assumption that spatial distribution of species results from organism interactions within communities as the result of overlapping ecological niches (Gause 1934; Hutchinson 1957; Pianka 1974, 1976). The latter is based on the neutral assumption that organisms are functionally equivalent in their individual (per-capita) rates of birth, death, migration and speciation, their spatial distribution being solely due to their random dispersal at the metacommunity level (Hubbell 2001; Gotelli & McGill 2006). Although these two hypotheses are contradictory, it is now considered that they represent the two end-members of a continuous spectrum of assembly processes – a dispersal-niche continuum within which each assemblage is positioned according to the biotic (e.g. species-specific life-history traits) and abiotic (e.g. climate, geography) constraints in presence (Maurer & McGill 2004; Gotelli & McGill 2006; Gravel et al. 2006; Mouillot et al. 2007).

On that basis, we undertake here the first macroecological study of the post-PTB crisis recovery, based on the study of two marine nektonic groups: conodonts and ammonoids. We aim to: (1) characterize their biogeographical patterns (i.e. biochores) and evolution from the Late Permian to the end of the Early Triassic; and (2) decipher the influence of Niche vs. Dispersal processes in the spatial distribution of these two clades. Both conodonts and ammonoids were impacted either by the PTB crisis and/or the Early Triassic environmental disturbances (Orchard 2007, Brayard et al. 2009b). Permian-Triassic ammonoid biogeography was extensively studied in the last decades (Dagys 1988; McGowan 2005; Brayard et al. 2006, 2007, 2015; Dai & Song 2020); and conodonts were mostly studied qualitatively (Clark & Cheng-Yuan 1988; Paull 1988; Paull & Paull 1997;
Lai & Mei 2000; Li et al. 2019), albeit few quantita-
tive investigations exist (Charpentier 1984; Klets
2008). None of these studies deciphered the macro-
ecological processes behind the evidenced biogeo-
graphical patterns of these taxa. To address these two
objectives, we built and analysed an original data-
base of occurrences of Late Permian-Early Triassic
conodont species based on an intensive compilation
of the literature (Guenser et al. submitted to Peer
Community In Palaeontology); for ammonoids, we
used the occurrence dataset from Dai & Song (2020)
updated from Brayard et al. (2007, 2015). Biochores
are characterized by a combination of multivari-
ate analyses (ordination, classification and network
analyses), based on a primary protocol developed in
Brayard et al. (2007). Underlying assembly processes
are deciphered thanks to a recent statistical method
called Dispersal-Niche Continuum Index (Gibert &
Escarguel 2019; Vilmi et al. 2021).

Material and methods

*Material and preparation to multivariate
statistics*

We built an original database of Late Permian to Early
Triassic conodont species occurrences from a com-
pilation of the available literature. The database is
described in detail in Guenser et al. (2025). Permian-
Triassic conodont species are defined based on the P1
elements located posteriormost in their mouth. The
systematics of conodonts evolved through the last dec-
ades thanks to the description of natural assemblages
of multielement apparatuses (Goudemand et al. 2011;
Koike 2016, Sun et al. 2020a, b), allowing to stabilize
the generic classification. However, these apparatuses
are still too few to base the specific classification on
the entire apparatus. Thus, we consider that the dis-
tribution of conodont morphospecies based on their
P1 elements reasonably reflect the distribution of
conodont animals.

Before performing any analysis, we cleaned the
database by discarding: (1) occurrences of species left
in open nomenclature; (2) some species considered as
too poorly constrained (these species are listed as sup-
plementary information with an explanation about
their removal, Supplementary Table S1); and (3) we
kept only occurrences with accurate stratigraphi-
cal position, that is Changhsingian (Late Permian),
Griesbachian, Dienerian, Smithian and Spathian
(Early Triassic). After these steps, the dataset includes
about 9000 occurrences of 244 conodont species in
381 sections.

GPS coordinates of each section were con-
verted into palaeocoordinates with R package
'rgplates' (Kocsis et al. 2023) (Fig. S1). We chose the
'PALEOMAP' model (Scotese & Wright 2018) with
an age of 250 Ma. We noticed that some palaeo-co-
ordinates were not accurate based on the current lit-
erature. Indeed, all sections from Japan were located
next to Primorye (i.e. Eastern Russia) sections in the
north-eastern part of the Tethys Ocean. Japanese
sections are part of different terranes (Mino-Tamba,
Chichibu, Kitakami and Ryoke) with palaeo-locations
that are still difficult to determine. According to the
most recent literature, these terranes were located
closer to the Equator in the middle of the Panthalassa
Ocean (Shi 2006; Algeo et al. 2011; Uno et al. 2012;
Onoue et al. 2017; Vérard 2019). We then forced a
palaeolongitude of 145° and a palaeolatitude of 0°
for all Japanese sections. In addition, two sections
from Bulgaria, Ajvadzikdere and Tranak, were moved
from South-Western Tethys (35°E, 10°S) to North-
Western Tethys (40°E, 15°N). Indeed, these sections
belong to the moesian platform that was located in
the northwestern edge of the Tethys Ocean (Dercourt
et al. 2000; Budurov et al. 2004; Ruban et al. 2007).
Finally two sections from Timor-Leste were moved
from extern Tethys (121.56°E, 2.17°S and 122,01°E,
2.01°S) to south-eastern Pangea, next to Australia
(90°E, 35°S), based on the literature of the last decades
(Wensink & Hartosukohardjo 1990; Villeneuve et al.
2010; Zhang et al. 2023). The list of sections and their
associated coordinates and paleocoordinates is availa-
ble as supplementary information (Table S2, Fig. S1).

All sections were then grouped into regions using
an Unweighted Paired Group Mean Arithmetic
(UPGMA) classification based on geographical (Great
Circle WGS84 ellipsoid distance) distance calculated
from GPS palaeocoordinates. The resulted dendro-
gram is available as supplementary information (Figs
S2-10). We defined 27 regions, each of them compris-
ing between one and 65 sections (Fig. 1A, Table S2).
For each region, mean palaeocoordinates were calcu-
lated from the palaeocoordinates of its corresponding
sections using the R package 'geosphere' (Hijmans
et al. 2016).

The sampling effort of each region (all time inter-
vals included) was assessed using species accumulation
curve (SAC) with the R package iNEXT (Colwell et al.
2012; Chao et al. 2014; Hsieh et al. 2016). The regions
that included less than three sections or less than two
species were removed from this analysis and subsequent
ones as their sampling effort could not be estimated.
These regions are Chulitna, Europe-S, Greenland,
Pamirs, Phrao, Timor, Yiwagou and Zyryanka (Fig. 1A).
The results are available as supplementary information
<figure><img src="image_5.png" /><figcaption>Fig. 1. Palaeo-location of the 27 regions of A) conodont and B) ammonoid taxa defined in this study, respectively. Each region is associated to its number of sampled localities encircled by five arcs, each arc corresponding to a time interval (Changhsingian, Griesbachian, Dienerian, Smithian, or Spathian). A coloured arc means that the region includes at least one locality dated from this time interval; an unfilled arc means that the region does not include any locality dated from this time interval. See the section Material and methods for details about the calculation of the palaeocoordinates. Silhouettes of conodont and ammonoid are public-domain images from the Phytopic website (https://www.phytopic.org/images/5ebc3cb4-379b-40a1-bb8f-bf9dbcad805f/cavusgnathidae, https://www.phytopic.org/images/f35b45c9-8b7b-487a-93a1-c3329b3db8fc/spathites-jeanrogericeras-reveliereanus).</figcaption></figure>

(Fig. S3). The sampling coverage shows that no region was strongly less sampled than the others. Then, an incidence matrix of conodont species in the remaining

regions was built for each of the five time intervals of interest (i.e. Changhsingian, Griesbachian, Dienerian, Smithian and Spathian).
For ammonoids, we used the incidence matrices of genera occurrences from Dai & Song (2020). We selected the matrices that record the presence/absence of ammonoid genera into 27 different regions (i.e. sedimentary basins) from Changhsingian through Spathian (Fig. 1B). Since no palaeocoordinates were associated to the regions, we matched them to the regions defined with the conodont database (Fig. 1B; Table S3). We considered potential presences (quoted ‘1?’) as true. The use of the genus level for ammonoids is justified due to the highly biased species systematics (Brayard et al. 2006, 2007), which would blur global palaeobiogeographical structures. This was also the choice made by other authors for the study of brachiopods, which suffer from the same systematic biases (Shen & Shi 2000).

## Multivariate statistics

Occurrence Ratio Profiles (ORP; Brayard et al. 2006) were first built for each time interval (Fig. 2). An ORP estimates the taxonomic distribution of geographical rarity (sensu Gaston 1994) from a given occurrence matrix. It computes the number of taxa present in 10%-fractions of the sampled regions. Taxa present in less than 20% of the regions are considered ‘endemic’, i.e. geographically rare at the spatial resolution level of the analysed dataset; taxa present in 20-60% of the regions are called structural – i.e. they contribute to biogeographical structuring; taxa present in more than 60% of the regions are considered cosmopolite. For each ORP-value, a 95% confidence interval of the number of taxa is estimated through nonparametric bootstrap – i.e. random resampling with replacement of the available taxa. Then, the empirical ORP is compared to a modelled ORP built through random reshuffling of taxa occurrences among regions (i.e. a lottery model with fixed taxonomic richness into each region; Legendre et al. 1997). Superposition between the empiric and modelled ORPs means that the observed endemicity-cosmopolitanism distribution is explained by a random niche colonisation process. In other words, the biotic and abiotic characteristics of the region do not control its detailed taxonomic composition but only its taxonomic richness as a proxy of the number of colonisable ‘niches’. The ORPs give a first glance of potential biogeographical structuring for each time interval. The method is now available in R language (see Data availability section).

Singletons (i.e. species present in only one region) were then discarded from each incidence matrix to build dissimilarity matrices using the Dice-Sørensen index (Sørensen 1948) (Fig. 2). A combination of three multivariate analyses was performed to define

biochores (i.e. biogeographical sets of regions based on their faunal similarity) for each time interval: Principal Coordinate Analysis (PCoA) (Gower 1966; Legendre & Legendre 2012); UPGMA classification (Sneath & Sokal 1973; Legendre & Legendre 2012); Bootstrap Spanning Network analysis (BSN) (Brayard et al. 2007) (Fig. 2). The PCoA allows to detect gradients of taxonomic similarity – i.e. continuities – between regions, whereas the UPGMA classification allows to detect hierarchically-nested groups of regions – i.e. discontinuities. The BSN is a non-hierarchical clustering technique allowing to detect nested as well as gradual biogeographical structures by directly connecting regions based on the strength and bootstrap support of their taxonomic similarities (Brayard et al. 2007, 2009a). The BSN method is now available on R (see Data availability section).

Based on the combined results of these three multivariate analyses, biochores were proposed for each time interval, and then tested using an Analysis of Similarity (ANOSIM) (Clarke 1993; Legendre & Legendre 2012). When the ANOSIM returns a significant result (p-value<0.05), the taxa responsible for the differentiation of the biochores are identified using a Similarity Percentage analysis (SIMPER; Clarke 1993) (Fig. 2). SIMPER ranks taxa according to their relative contribution to the biochores compositional dissimilarity.

Finally, the assembly process responsible for the biochores differentiation was assessed through the computation of the Dispersal-Niche Continuum Index (DNCI; Gibert & Escarguel 2019, Vilmi et al. 2021; Fig. 2). Based on SIMPER analysis, this method compares the empirical SIMPER profile to three null profiles obtained from three random permutation procedures applied on the empirical occurrence matrix in order to simulate taxa distributions constrained by Dispersal, Niche or both processes. The comparison between empirical and modelled SIMPER profiles ultimately results into a single value, the DNCI, with an associated confidence interval. A significantly negative DNCI indicates that the composition of the assemblages is the first-order result of dispersal constraints acting on the taxa. Conversely, a significantly positive DNCI indicates that the composition of the assemblages is the first-order result of niche constraints regulating ecosystem functioning. Finally, a DNCI value that is not significantly different from zero does not allow the identification of the community assembly process – niche or dispersal – that generated the data analysed, either because of a lack of information, or because the two processes actually co-dominate.
<figure><img src="image_6.png" /><figcaption>Fig. 2. Methodological flowchart showing the biogeographical analyses used in this work, with the Changhsingian stage taken as an example. The stars indicate a repetition in the analytical procedure. See the section Material and methods for details. Abbreviations: ANOSIM – Analysis of Similarity; BSN – Bootstrap Spanning Network; DNCI – Dispersal-Niche Continuum Index; ORP – Occurrence Ratio Profiles; PC – Principal Coordinate; PCoA – Principal Coordinate Analysis; R – Region; S – Species; SIMPER – Similarity Percentage analysis; UPGMA – Unweighted Paired Group Mean Arithmetric.</figcaption></figure>

## Data availability

The material including input and output data, as well as the database and R scripts for conodont and ammonoid data cleaning and analyses are available on GitHub at https://github.com/PaulineGnsr/

Permian-Triassic_assembly-process. Specific R scripts for performing ORP and BSN analyses are available respectively at https://github.com/PaulineGnsr/ORP_analysis.git and https://github.com/PaulineGnsr/BSN_analysis.git.
# Results

## *Endemism and biogeographical structuring*

Conodont ORPs show mostly non-random distributions of the conodont species pointing towards significant biogeographical differentiations (Fig. 3). For all

time intervals, there are more endemic conodont species than predicted by the null model, and less structural species than predicted by the null model, except for the Dienerian for which structural species empirical data crossed the null model (Fig. 3E). For the Smithian, there are more conodont species that occur between 50% and 70% of the regions (i.e. structural

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<figure><img src="image_16.png" /><figcaption>**Fig. 3.** Occurrence Ratio Profiles for each time interval. Conodonts on the left, ammonoids on the right. A, B, Changhsingian. C, D, Griesbachian. E, F, Dienerian. G, H, Smithian. I, J, Spathian. In black, empirical data (mean observed richness with 95% confidence interval based on nonparametric bootstrap resampling); in grey, 95% confidence interval of the null model built from random permutation of the empirical data, Legendre *et al.'s* (1997) lottery model with fixed taxonomic richness into each region. See Figure 1 for silhouette sources.</figcaption></figure>
and cosmopolite species) than expected by the null model (Fig. 3G). Overall, the presence of conodont biochores is therefore supported by a dozen of struc- tural species depending on the interval (between 8 and 40), except for the Dienerian (Fig. 3E) – conodont biochores for this time interval are consequently supposed to be more difficult to define.

Concerning ammonoids, the Changhsingian and Griesbachian ORPs coincide with the null model, pointing towards random biogeographical distribu- tion of the ammonoid taxa at that time (Fig. 3B, D). On the contrary, Dienerian, Smithian and Spathian ORPs show non-random ORPs with more endemic ammonoid taxa than predicted by the null model (Fig. 3F, H, J). For the Dienerian, there are also less ammonoid taxa that occur between 30% and 40% of the regions (i.e. structural taxa) than predicted by the null model (Fig. 3F). For the Smithian and Spathian, there are less ammonoid taxa that occur between 10% and 30% of the regions than predicted by the null model, and more ammonoid taxa that occur between 50% and 60% of the regions than predicted by the null model (Fig. 3H, J). The presence of ammonoid biochores is supported by 5-30 structural taxa for the Smithian and Spathian, and by a couple of structural taxa for the Dienerian. Based on these ORPs, it seems that the definition of ammonoid biochores would probably be not obvious for the Changhsingian and the Griesbachian, and more difficult for the Dienerian than for the Smithian and Spathian.

Conodonts and ammonoids show different patterns of endemicity. There is an increase in the proportion of endemic and structural taxa in ammonoids from the Smithian onwards. On the contrary, conodonts do not show any change of proportion of endemic spe- cies during the Early Triassic. This suggests that the biogeographical patterning (e.g. biochores) of ammo- noids is stronger than that of conodonts during the Smithian and Spathian.

Biochores definition

Conodonts.- For the Changhsingian, the Ellesmere and Adriatic regions are isolated along the first (31.6%) and the second (29.3%) PCoA axes, respectively (Fig. 4A). The five regions of China, Iran, Malaysia, Japan and Pakistan are grouped in a corner of the topology. Himalaya is away from this group along both first and second PCoA axes, but less isolated than Ellesmere and Adriatic regions. These observations on the first two PCoA axes are confirmed by the UPGMA clas- sification, which shows a main cluster made of the same five regions (Fig. 4B). This cluster is actually divided into two sub-clusters: Japan and Malaysia on

one side; Pakistan, China-S and Iran on the other side. Himalaya is branched over the cluster, then Adriatic and finally Ellesmere. The BSN also supports these results as Ellesmere and Adriatic regions share a weak link with only Pakistan and China-S, respectively (bootstrap support BS value <80%; Fig. 4C). The third weakest link is between Himalaya and Malaysia (BS value between 80-90%). The previously identified five-region cluster share links with BS values ≥ 90% (up to 100% between Iran and China-S). It is difficult to define Changhsingian conodont biochores as three regions are isolated from each other and from the rest of the regions, and there is no clear differentiation within the five-region cluster. Nevertheless, based on the UPGMA classification and the BSN, we tenta- tively suggest two Tethyan biochores: (Pakistan; Iran; China) and (Malaysia; Japan) (Fig. 5A). However, these biochores are not unambiguously supported by ANOSIM (p-value = 0.1).

For the Griesbachian, the first PCoA axis (46.1%) exhibits a longitudinal differentiation (Fig. 4D). Indeed, the eastern Panthalassa regions (USA, British Columbia, Ellesmere and Alberta) are restricted to negative values, whereas Tethyan regions (Iran, Adriatic, Pakistan, Malaysia, China-S and Himalaya) are restricted to positive values. The Japan region is situated between those two groups, consistent with its geographical location at the interface of the Tethys and the western margin of the Panthalassa. A second differentiation is observed for Eastern Panthalassa regions along the second PCoA axis (31.7%), distin- guishing two sub-groups: (Ellesmere; Alberta) on one side, and (USA; British Columbia) on the other side. The UPGMA classification is coherent with the topol- ogy given by the first two PCoA axes: the Tethyan regions form a first cluster while the East-Panthalassa regions and Japan are grouped in a second cluster (Fig. 4E). Two sub-groups are also distinguished within this Panthalassa cluster: (Ellesmere; Alberta) and (USA; British Columbia; Japan).

The BSN supports the results from the PCoA and the UPGMA classification (Fig. 4F). Tethyan regions were connected with strong BS (values >90% except for the link between Adriatic and Iran). The regions from Panthalassa are poorly connected as only Ellesmere and Alberta share a link with a BS value >90%. Based on the three analyses, we define a Tethyan biochore (Iran; Adriatic; Malaysia; Pakistan; Himalaya; China) and a Panthalassic biochore (Ellesmere; Alberta; USA- Mexico; Japan; British Columbia) (Fig. 5B). We decided to be conservative and merge the two Panthalassa sub-groups based on PC1 and the first separation of the UPGMA classification. This two-group differen- tiation is supported by ANOSIM (p-value = 0.0016).
<figure><img src="image_17.png" /><figcaption>Fig. 4. First two axes of the PCoA (left column), UPGMA classifications (middle column), and BSN analyses (right column) excluding singletons, based on conodont species occurrences. A-C: Changhsingian; D-F: Griesbachian; G-I: Dienerian; J-L: Smithian; M-O: Spathian. Abbreviations: Adr – Adriatic; Alb – Alberta; Aus – Australia; Bri – British Columbia; Chin – China-S; Ell – Ellesmere; EurE – Europe-E; Him – Himalaya; Jpn – Japan; Lap – Laptev; Mal – Malaysia; NewZ – New-Zealand; PakN – Pakistan-N; Pri – Primorye; Rus – Russia-S; Spi – Spitsbergen; USA – USA-Mexico.</figcaption></figure>

According to the SIMPER analysis, the species most contributing to the biochore differentiation belong to the *Hindeodus* and *Isarcicella* genera (Fig. 6A); they are almost exclusively constrained to the Tethyan biochore.

On the opposite, the distribution of *Clarkina* species is balanced between the two biochores. This means that the longitudinal pattern observed during the Griesbachian results from Tethyan endemic taxa. The
Panthalassic biochore is built by default as there is no conodont taxa endemic to the Panthalassa.

For the Dienerian, the first PCoA axis (61.1%) separates Japan and Adriatic from the rest of the regions (Fig. 4G). Spitsbergen is also isolated along the second PCoA axis (21.0%). The UPGMA classification shows an Adriatic-Japan cluster consistent with the first two PCoA axes (Fig. 4H). The other regions form a second set within which Spitsbergen clusters most distantly. Most connections in the BSN are poorly supported (BS values <90%; Fig. 4I). Japan and Adriatic regions share one of the strongest BSN connections, as well as Himalaya and China, Ellesmere and Alberta, and Himalaya and Ellesmere. Based on these analyses, we define a small equatorial biochore (Adriatic; Japan) and a larger biochore (China; Himalaya; Alberta; Ellesmere; New Zealand; Spitsbergen; USA-Mexico) (Fig. 5C). This biogeographical differentiation is supported by ANOSIM (p-value = 0.0253). As for the Griesbachian, the conodont biochore differentiation is due to an absence of endemic taxa in one of the biochores (Fig. 6B). Here, the equatorial biochore has only two species, *Neospathodus cristagalli* and *Ns. dieneri*, which are also present in the other biochore.

During the Smithian, a latitudinal continuum is observed along the first PCoA axis (35.6%), from the regions close to the Equator to the ones close to the poles (Fig. 4J). Laptev and Spitsbergen regions are isolated from others and restricted to a corner of the PCoA topology. They are also grouped in a cluster in the UPGMA classification (Fig. 4K) and the BSN connects them with a BS value >90% (Fig. 4L). A second small cluster made by Adriatic and Malaysia regions is distinguished in the UPGMA classification. They are indeed restricted to the upper right corner of the PCoA topology, but their BSN connection is poorly supported (BS values <80%). Actually, the most supported links cross the Panthalassa, showing four groups (Australia; British Columbia), (Oman; Alberta), (Himalaya; Japan) and (Primorye; USA). This result highlights long-distance trans-oceanic exchanges between faunas from Tethys and East-Panthalassa. We define two Smithian conodont biochores: a small one (Laptev; Spitsbergen,) and a larger one (USA; British Columbia; Primorye; Australia; Japan; China; Himalaya; Oman; Alberta) (Fig. 5D). This biogeographical differentiation is supported by ANOSIM (p-value = 0.0187). The species most contributing to the biochore building are restricted to the second biochore (Fig. 6C). Only two species, *Borinella buurensis* and *Scythogondolella mosheri*, are shared by Laptev and Spitsbergen, but these are also present in half of the regions of the second biochore.

<figure><img src="image_18.png" /><figcaption>**Fig. 5.** Proposed conodont biochores based on the combination of PCoA, UPGMA classification and BSN results. A: Changhsingian; B: Griesbachian; C: Dienerian; D: Smithian; E: Spathian. Unfilled dots indicate regions that are not included in any biochore. See Figure 1 for silhouette sources.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Fig. 6.** SIMPER analyses for Griesbachian (A), Dienerian (B), Smithian (C) and Spathian (D) conodonts. On the left, contribution of the species to the biochore differentiation, from the most to the less contributive; grey dots indicate the individual contribution of each species, black dots indicate the cumulative contribution of the N first species. On the right, repartition of the species in the biochores, from the most to the less contributive; the colours indicate the biochore. See Figure 1 for silhouette sources. Abbreviations: Ad. - *Aduncodina*; Bo. - *Borinella*; Cb. - *Columbitella*; Ch. - *Chiosella*; Cl. - *Clarkina*; Co. - *Cornudina*; Cs. - *Conservatella*; D. - *Discretella*; Eu. - *Eurygnathodus*; Fo. - *Foliella*; Fu. - *Furnishius*; Gg. - *Gladigondolella*; Gu. - *Guangxidella*; Ha. - *Hadrodontina*; Hi. - *Hindeodus*; Ic. - *Icriospathodus*; Is. - *Isarcicella*; Mag. - *Magnigondolella*; Me. - *Merrillina*; Ncl. - *Neoclarkina*; Ng. - *Neogondolella*; Ns. - *Neospathodus*; Nst. - *Neostrachanognathus*; Nv. - *Novispathodus*; Pc. - *Pachycladina*; Pr. - *Parachirognathus*; Sg. - *Scythogondolella*; Sm. - *Smithodus*; Sp. - *Spathicuspus*; Ss. - *Sweetospathodus*; Ts. - *Triassospathodus*; Ur. - *Urdyella*; Wa. - *Wapitiodus*.</figcaption></figure>
For the Spathian, the first PCoA axis (43.2%) separates clearly the regions in two sets: British Columbia, Russia South, Primorye, Adriatic and Europe East are restricted to positive values; Alberta, USA, Oman, Himalaya, Japan, and China are restricted to negative values (Fig. 4M). These two groups are confirmed by the UPGMA classification (Fig. 4N). The BSN also supports this distinction but the first group shares less connections than the second one (Fig. 4O). Indeed, there are only four links between British Columbia and Russia South, British Columbia and Primorye, British Columbia and Adriatic, and Adriatic and Europe East. Only the connection shared between Adriatic and British Columbia shows a BS value >90%. The regions of the second group are connected with seven links, six of them having a BS value >90%. As for the Smithian, the BSN in particular highlights long-distance connections across the Panthalassa. Two Spathian conodont biochores are thus defined, building a North/South differentiation: (British Columbia; Russia South; Primorye; Adriatic) define a first, higher-latitude biochore; (USA; Oman; Himalaya; Japan; China) define a second, lower-latitude biochore (Fig. 5E). This biogeographical differentiation is supported by ANOSIM (p-value = 0.0089). As for the other time intervals, the species that contribute the most to the biochore building are almost all restricted to the lower-latitude biochore (Fig. 6D). The higher-latitude biochore is thus built by default.

To summarize, the combination of these three multivariate analyses allows to explore both continuous and discrete patterns of faunal (dis)similarities between regions. In particular, the BSN highlights the establishment of strong connections between the Tethys Ocean and Eastern Panthalassa during the Early Triassic. The evolution of the identified biochores through time is consistent with this observation as they first display a longitudinal differentiation and then a North/South differentiation during the Spathian.

**Ammonoids.**- For the Changhsingian, the first axis (PCoA1 = 44.8%) and second axis (PCoA2 = 21.4%) of the PCoA distribute the regions quite homogeneously (Fig. 7A). Only two small groups are visible: Central Iran and Transcaucasia; South Primorye and China-S. The UPGMA classification shows a clustering mostly based on PCoA1 distribution with three clusters: (Salt Range; Greenland), (Japan; China-S; South Primorye), and (Thailand; Central Iran; Trancaucasia) (Fig. 7B). Thus, it supports the two groups defined with the PCoA, but adds a third region to each. The BSN also supports the two groups (Transcaucasia; Central Iran) and (China-S; South Primorye), both showing a high BS (values >90%; Fig. 7C). Thailand is connected to Transcaucasia

with a weak BS (value <80%); Japan is connected to China-S with a medium link (BS value between 80% and 90%) and to South Primorye with a weak link (BS value <80%). Salt Range and Greenland are weakly connected (BS value <80%; Fig. 7C). Based on the three multivariate analyses, we tentatively suggest a southern Tethyan biochore including (Transcaucasia and; Central Iran,) and a northern Tethyan biochore including (China-S; South Primorye and; Japan) (Fig. 8A). However these biochores are not unambiguously supported by ANOSIM (p-value = 0.1).

For the Griesbachian, the first axis of the PCoA (PCoA1 = 40.0%) distinguishes two groups: (South Primorye; Arctic Canada; Spitsbergen; Greenland; Siberia) and (Salt Range; China-S; Himalayas; Oman) (Fig. 7D). Based on PCoA2 (32.9%) the second group can be subdivided into (Salt Range; China-S) and (Himalayas; Oman). The UPGMA classification does not support these three groups, showing only two differing clusters: (Arctic Canada; Greenland; Spitsbergen) and (China-S; South Primorye) (Fig. 7E). The BSN supports (Arctic Canada; Greenland; Spitsbergen) as they share links with 100% BS-values, but not (China-S; South Primorye) (Fig. 7F). All links show BS-values >90% except for the one shared by China-S and Salt Range (BS value <80%) and the one shared by South Primorye and Arctic Canada (BS values between 80% and 90%). Combining the three multivariate analyses, we define a Panthalassic biochore (Arctic Canada; Spitsbergen; Greenland; Siberia) and a Tethyan biochore (China-S; Himalayas; Oman) (Fig. 8B). This Griesbachian ammonoid biogeographical differentiation is supported by ANOSIM (p-value = 0.0286). According to SIMPER analysis, the taxa that contribute most to the biochore building are mostly present in the Panthalassic biochore (Fig. 9A).

For the Dienerian, the combination of PCoA1 (70.1%) and PCoA2 (19.0%) exhibits three couples: (Himalayas; Salt Range), (China-S; South Primorye) and (Nevada; British Columbia) (Fig. 7G). Arctic Canada and Siberia are too distant from each other to be considered as a fourth couple. The UPGMA classification supports these three couples and adds Arctic Canada to (British Columbia; Nevada) (Fig. 7H). It also proposes the clusters (Himalayas; Salt Range) on one side, and (China-S; South Primorye) on the other side. The BSN supports (Himalayas; Salt Range) with a 100% BS value (Fig. 7I). South China and South Primorye share a strong link, as for China-S and Salt Range (BS values >90%). Nevada and British Columbia share a medium link (BS value between 80% and 90%), but Arctic Canada and Siberia share a strong link (BS value >90%). Combining the three multivariate analyses, we define a Tethyan biochore
<figure><img src="image_20.png" /><figcaption>Fig. 7. First two axes of the PCoA (left column), UPGMA classifications (middle column), and BSN analyses (right column) excluding singletons, based on ammonoid genera occurrences. A-C: Changhsingian; D-F: Griesbachian; G-I: Dienerian; J-L: Smithian; M-O: Spathian. Abbreviations: Alb – Albania; Arc – Arctic Canada; Afg – Afghanistan; Ala – Alaska; Bri – British Columbia; Cau – Caucasus; Iran – Central Iran; Gre – Greenland; Him – Himalayas; Jpn – Japan; Mon – Mongolia; Nev – Nevada; Ole – Olenek; Qin – Qinghai; Spi – Spitsbergen; Chin – China-S; Sib – Siberia; Pri – South Primorye; SalR – Salt Range; Thai – Thailand; Tra – Transcaucasia and NW Iran; Tim – Timor; Vie – Vietnam; USA – W-USA; Yug – Yugoslavia.</figcaption></figure>

(Himalayas; Salt Range; China-S; South Primorye) and a Panthalassic biochore (Arctic Canada; Siberia) (Fig. 8C). However, these biochores are marginally or not supported by ANOSIM (p-value = 0.0667).

For the Smithian, PCoA1 (35.8%) isolates Spitsbergen, Arctic Canada, British Columbia and

Olenek into a group, which is supported by the UPGMA classification (Fig. 7J, K). The UPGMA classification also identifies a second cluster (Salt Range; Himalayas; Timor; Vietnam; Oman; Western USA; China-S) which corresponds to the upper left corner of the PCoA with negative PCoA1 and positive PCoA2
<figure><img src="image_21.png" /><figcaption>Fig. 8. Ammonoid biochores based on PCoA, UPGMA classification and BSN. A, Changhsingian. B. Griesbachian. C, Dienerian. D, Smithian. E, Spathian. Unfilled dots indicate regions that are not included in any biochore. See Figure 1 for silhouette sources.</figcaption></figure>

coordinates. The BSN shows that (Spitsbergen; Arctic Canada; British Columbia; Olenek) share strong links, as for links shared by (Salt Range; Himalayas; Timor; Vietnam; Oman; Western USA; China-S) (BS values >90%) (Fig. 7L). Thus, we defined these two groups as northern Panthalassic and southern biochores, respectively (Fig. 8D). This biogeographical differentiation is supported by ANOSIM (p-value = 0.0057). According to SIMPER analysis, the taxa that contribute most to the biochore building are mostly present in the southern biochore (Fig. 9B).

For the Spathian, the combination of PCoA1 (34.8%) and PCoA2 (21.1%) evidences three groups: (Siberia; Olenek; Arctic Canada; British Columbia), (Yugoslavia; Salt Range; Oman; Vietnam), and (South Primorye, Western USA; China-S; Albania; Japan; Timor; Qinghai; Afghanistan; Chios), also supported by the UPGMA classification (Fig. 7M, N). The BSN does not support Siberia into its group as it only shares a weak link with Arctic Canada (BS value <80%; Fig. 7O). Western USA, China-S, Albania, Timor, Afghanistan, Chios share 100% BS-value links. Combining the three multivariate analyses, we thus define three latitudinally-constrained biochores: (Olenek; Arctic Canada; British Columbia) from northern Panthalassa, (Western USA; China-S; Albania; Timor; Afghanistan; Chios) from equatorial Tethys and Panthalassa, and (Salt Range; Oman) from southern Tethys. Vietnam is not included in this last biochore as its link with Salt Range is as strong as the link shared between Salt Range and Afghanistan (Fig. 8E). This biogeographical differentiation is supported by ANOSIM (p-value = 0.0005). According to SIMPER analysis, the taxa that contribute most to the biochore building occur mostly in the equatorial biochore and, to a lesser extent, in the southern Tethys biochore (Fig. 9C-E).

## Assembly process

For conodonts, the DNCI values were calculated from the Griesbachian to the Spathian (Fig. 10). The Changhsingian was not included in the analysis as the corresponding biochores are not supported by ANOSIM. The Griesbachian DNCI is positive whereas the Dienerian, Smithian and Spathian DNCI show negative values; nevertheless, none of these four DNCI values significantly depart from zero. Although not statistically conclusive, these values suggest a stronger relative influence of niche constraints during the Griesbachian compared to the three more recent time intervals, where niche and dispersal constraints may have been more balanced.
<figure><img src="image_22.png" /><figcaption>**Fig. 9.** SIMPER analyses for Griesbachian (A), Smithian (B) and Spathian (C, D, E) ammonoids. See Figure 6 caption for explanation. See Figure 1 for silhouette sources.</figcaption></figure>
For ammonoids, the DNCI values were calculated for the Griesbachian, Smithian and Spathian (Fig. 10). No DNCI was calculated for the Changhsingian and the Dienerian as no significant biochores differentiation was detected. Griesbachian and Spathian DNCI show significantly negative values (around -4 and -2, respectively), whereas the Smithian DNCI shows a positive value (around 1). Dispersal constraints thus have a strong influence on community assembly during the Griesbachian, then functional constraints are relatively stronger during the Smithian, and finally dispersal constraints dominate again during the Spathian, but less strongly than during the Griesbachian.

To conclude, conodonts and ammonoids show different DNCI values and trends throughout the Early Triassic, suggesting that the relative intensity of niche- and dispersal-assembly processes varied differentially during the post-PTB crisis biotic recovery of these two marine clades.

<figure><img src="image_23.png" /><figcaption>Fig. 10. DNCI values for conodonts and ammonoids and for each early Triassic time interval. Black dots and associated horizontal black lines represent mean value and ±2 standard error, respectively.</figcaption></figure>

# Discussion

## Biogeographical patterns beyond cosmopolitanism

At a very large scale, the biogeographical structure of both conodonts and ammonoids evolved from a longitudinal pattern (Tethyan and Panthalassic biochores) to a latitudinal pattern (higher vs. lower latitudes biochores). The spatial distribution of conodonts shows a gradual increase in faunal connections between Tethys and Panthalassa oceans during the Early Triassic. For ammonoids, their spatial distribution was already wide in the Griesbachian, but it

decreased in the Dienerian and increase again in the Smithian and Spathian.

Prior investigations on the distribution of Early Triassic conodonts mainly focused on the biostratigraphical usefulness and applicability of conodont species and the related characterisation of cosmopolitanism for correlation purposes (Clark & Cheng-Yuan 1988; Paull 1988). Some authors investigated whether biogeographical patterns (i.e. provincialism) can be identified or the conodont distribution was constrained by environmental settings (i.e. biofacies) (Paull & Paull 1997; Lai & Mei 2000), and only a couple of studies focused on the quantitative and global biogeography of the Early Triassic conodonts (Charpentier 1984; Klets 2008). Similar to the present methodology, these authors characterized either intervals of global cosmopolitanism or provincialism with dissimilarity indices calculated between regions. Charpentier (1984) compared eastern Panthalassa to Tethys localities at the species level and concluded to a global Early Triassic cosmopolitanism. Klets (2008) worked at the species level and with a substage temporal resolution, comparing 12 *a priori* defined geographical regions. Klets (2008) recognized two biogeographical provinces such as Panboreal and Tethys-Panthalassa superrealms in the Griesbachian. These provinces are related to northeastern Panthalassa and Tethys-southeastern Panthalassa regions of this study. The results of Klets (2008) echoes our results on the isolation of northeastern Panthalassa regions during the Griesbachian. However, we observe an evolution towards a North/South biogeographical differentiation during the Early Triassic, whereas Klets (2008) described a progressive loss of provincialism towards a global cosmopolitanism. Some qualitative studies on conodont biogeography described provincialism patterns based on latitudinal differentiation. Matsuda (1985) focused on Tethyan conodont assemblages and described an alternation between provincialism (during the Griesbachian, early Smithian and early Spathian) and cosmopolitanism (during the late Smithian and late Spathian) throughout the Early Triassic. Matsuda (1985) defined two provinces on the basis of conodont assemblages such as ‘Peri-Gondwana Tethys’ and ‘Tethys’ types, which correspond to southern and northern Tethys biochores of this study. Our results show such a latitudinal differentiation in the Tethys but only during the Spathian, which was qualitatively characterized in Orchard (2022). The results were also characterized by Lai & Mei (2000) and Li *et al.* (2019). The comparison with prior studies is constrained by differences in the included taxa and the geographical dividing. For instance Orchard (2008) described a latitudinal
differentiation during the Smithian for assemblages
from Panthalassa, which corresponds to the local
latitudinal differentiation we also observed in the
Panthalassa during this interval. We undertook the
most detailed biogeographical analysis based on the
most complete and updated dataset on occurrences of
Early Triassic conodont species. Then, it is difficult to
compare properly our results with those from previ-
ous publications, even if they do share similarities.

Previous qualitative studies often described the
distribution of Early Triassic ammonoids as cos-
mopolitan (Dagys 1988; McGowan 2005). Early
Triassic ammonoid biogeography was first quanti-
tatively investigated by Brayard *et al.* (2006, 2007)
using the same protocol as here but with an older
database later updated by Dai & Song (2020). They
described a progressive evolution from cosmopol-
itanism to a latitudinally-restricted distribution,
with a clear latitudinal diversity gradient, during
the Smithian and the Spathian (Brayard *et al.* 2006).
These results are congruent with our multivariate
analyses showing a shift from no biochore and weak
relationships among regions in the Griesbachian,
to a complex, latitudinally-restricted network with
several differentiated biochores during the Smithian
and Spathian interval. Dai & Song (2020) also used
network analyses and additionally observed a sec-
ond phase of cosmopolitanism in the late Smithian,
as initially quantitatively identified by Brayard et
al. (2006), and empirically proposed by e.g. Tozer
(1982) and Dagys (1988). Brayard et al. (2006, 2007,
2015, 2021), Galfetti et al. (2007), Jattiot et al. (2018)
and Dai & Song (2020) concluded that cosmopoli-
tanism was correlated with the end-Permian and the
late Smithian crises and that environmental pertur-
bations play a significant role in this biogeographical
pattern structuring.

Apart from conodonts and ammonoids, quanti-
tative biogeographical studies on other marine taxa
are scarce, mainly resulting from a lack of data in
the Early Triassic fossil record. For example, bryozo-
ans show no Early Triassic biogeographical pattern
because they suffered too much by the end-Permian
crisis and its aftermath (Powers & Pachut 2008).
Other taxa, like bivalves and brachiopods, pres-
ent a cosmopolitan pattern just after the end-Per-
mian mass extinction (i.e. during the Griesbachian:
Chen et al. (2005), Ke et al. (2016), Kiel (2017),
Friesenbichler et al. (2021), Echevarría & Ros-
Franch (2024)). Ophiuroids even benefited from the
end-Permian mass extinction and increased their
spatial distribution during the Early Triassic (Chen
& McNamara 2006). These studies describe the Early
Triassic as either a time of slow recovery or a time

of increase in taxa dispersal resulting in a marked
cosmopolitanism. Both ammonoids and conodonts
display increased connections across the Panthalassa
over the Early Triassic (see the BSN results in
Figs 4, 7), which is in favour of an increase in longitu-
dinal cosmopolitanism (i.e. a longitudinal expansion
of the distribution of taxa). However, the latitudinal
restriction of taxa distribution, especially during the
Smithian and the Spathian works against a global
cosmopolitanism. We suggest that the cosmopolitan-
ism characterized in other taxa results from a large
preservation and sampling bias, and proportionally
low study intensity. In the Early Triassic, conodonts
and ammonoids are of the most abundant, diversi-
fied and globally distributed taxa. Thus, their signif-
icance for biogeographical study are higher than the
other organisms. We are therefore confident about
the robustness of our biogeographical patterns and
we predict that future investigations on other taxa
may differ from cosmopolitanism once their fossil
record is completed and better characterized.

Underlying macroecological processes

While conodonts and ammonoids show rather simi-
lar biogeographical patterns, their respective under-
lying macroecological processes differ. This imply
that conodont and ammonoid distributions were not
constrained by the same drivers and that the end-
Permian and Early Triassic environmental pertur-
bations impacted them in distinct ways. According
to prior studies, conodont biogeography was mainly
explained by abiotic factors such as transgression
events or sea-water temperature (Charpentier 1984;
Clark & Cheng-Yuan 1988; Paull & Paull 1997; Lai &
Mei 2000; Klets 2008; Li et al. 2019). For ammonoids,
Brayard et al. (2006, 2007) hypothesized that the lat-
itudinal biogeographical pattern was built along with
a progressive establishment of a latitudinal gradient
of sea surface temperatures (SST) during the Early
Triassic. Similarly, SST were often evoked to explain
the cosmopolitanism of other taxa just after the end-
Permian extinction (Chen et al. 2005; Chen &
McNamara 2006; Powers & Pachut 2008; Ke et al.
2016; Kiel 2017; Friesenbichler et al. 2021; Echevarría
& Ros-Franch 2024). Yet, based on the DNCI analysis,
we suggest a more complex scenario with shifting mac-
roecological processes throughout the Early Triassic.

During the Griesbachian, conodont biogeogra-
phy was chiefly driven by functional constraints (i.e.
niche-assembly processes; Fig. 10). The end-Permian
mass extinction did not significantly impact cono-
donts, contrary to other marine organisms such as
ammonoids (Orchard 2007). They rather showed a
taxonomic diversification in the immediate aftermath of the PTB crisis, probably implying an ecological niche diversification at that time. The loss of more than 80% of the marine fauna may have allowed conodonts to occupy wider and overlapping niches after the PTB crisis, leading to an increase of conodont interspecific interactions. Incidentally, complex trophic networks, and so biotic interactions, already existed during the Griesbachian (Scheyer et al. 2014). Conversely, ammonoid distribution was mainly constrained by their dispersal potential during the Griesbachian (Fig. 10). We suggest this is directly related to the dramatic effects of the PTB crisis on ammonoids, which encountered at that time, with only a couple of species crossing the PTB, one of their most severe extinction events (Brayard et al. 2009b). We hypothesize here that the biotic interactions between the few surviving taxa were then minimal due to small non-overlapping niches. There is still no significant biochore differentiation in ammonoids during the Dienerian. All available age models (Burgess et al. 2014; Widmann 2019; Ogg et al. 2020), consider this substage as the shortest of the Early Triassic (ca. a few hundred thousand years). It has been historically regarded as very poor in terms of biodiversity (Ke et al. 2016; Brosse 2017; Kiel 2017) but a recent publication describes a Dienerian-dated lagerstätte (Dai et al. 2023). We suggest that the Dienerian suffers from sampling bias partly due to its tiny range and low sedimentary record, which makes it hard to characterize its biogeographical patterns. This is suggested by our Dienerian ORPs whose empirical profiles fit well to their related null profiles, both in ammonoids and conodonts (Fig. 3E, F). From this point of view, the Dienerian conodont biochores, and corresponding interpretations about the assembly processes should be considered with caution.

Our analysis at the substage level implies certain limitations. The Smithian shows complex diversity patterns induced by the crisis that happened during the substage (late Smithian). For instance, Brayard et al. (2006) characterized a latitudinal diversity gradient for ammonoids during the middle Smithian, which disappeared during the late Smithian. Consequently, our average Smithian biogeographical pattern may lack finer-scale observations. Nevertheless, dividing Smithian data into two or three sub-datasets would have precluded the obtention of robust results. During the Smithian, conodont biogeography was more impacted by dispersal constraints, but niche constraints were still present, whereas ammonoid biogeography was dominated by functional constraints (Fig. 10). It has been shown that the latitudinal diversity gradient of ammonoids during the Smithian was linked to the progressive establishment of a

latitudinal gradient of marine temperatures with an abrupt increase during the middle Smithian (Brayard et al. 2004, 2006), but it has not been yet studied nor documented in conodonts. Temperatures might have played a more important role in ammonoid than in conodont distribution. We propose that temperatures potentially first controlled the observed latitudinal biogeographical pattern of ammonoids, which was maintained by complex interspecific interactions thanks to a high taxonomic diversity reached during the Smithian (Brayard et al. 2009b). Restricted habitats has already been shown locally at that time (Jattiot et al. 2018). Conversely, in the aftermath of the late Smithian crisis, ammonoids latitudinal biogeography is explained by their dispersal potential (Fig. 10). They rapidly recovered after the crisis (Brayard et al. 2009b) and a renewed latitudinal distribution is observed during the Spathian (Fig. 8). The successive middle Smithian increase and late Smithian-early Spathian drop in marine temperatures (Goudemand et al. 2019) are associated with a biotic crisis that impacted both ammonoids and conodonts (Brayard et al. 2006, 2009b; Orchard 2007; Stanley 2009). However, the assembly processes differ between the two taxa. Since the latitudinal biogeographical pattern of conodonts is weaker than the one of ammonoids (Fig. 4, Fig. 7) and assuming that conodonts were possibly eurythermic, they likely accommodated a wider range of temperatures than ammonoids, which could explain the relative lesser impact of temperature on their biogeography. Li et al. (2019) already proposed the eurythermy of *Scythogondolella* species based on broad latitudinal ranges and our results fit with this hypothesis.

Because the latitudinal differentiation of Spathian ammonoids (Fig. 8) is associated with a peak of taxonomic richness (Brayard et al. 2009b), we interpret their DNCI value in a different way than for the Griesbachian. First, the recovery after the late Smithian crisis started at the end of the Smithian and the Spathian is the longest Early Triassic substage (ca. 2 myrs according to Widmann 2019 and Widmann et al. 2020), thereby 'allowing' Spathian ammonoids more time to recover (and to display a latitudinal pattern), as compared to the Griesbachian ammonoids (whose substage lasted less than 1 myr). Second, the ammonoid DNCI is higher in the Spathian compared to the Griesbachian (Fig. 10), suggesting that functional constraints played a bigger role during the Spathian than during the Griesbachian, even if smaller than dispersal constraints. Even if ammonoids recovered quickly after a crisis in terms of taxonomic richness, their distribution was mostly controlled by their dispersal ability thanks to favourable abiotic conditions. Conversely, conodont biogeography was controlled
by both functional and dispersal constraints, even after a major crisis such as the late Smithian crisis (Clark 1987; Orchard 2007). They likely kept a high level of interspecific interactions (i.e. wide overlapping niches), which can become predominant if they do not suffer a crisis like it was the case for the PTB one. Finally, conodonts can be considered as more generalist than ammonoids.

## Conclusions

This parallel study of Early Triassic conodont and ammonoid biogeography and macroecology shows that the distribution of both clades evolved from a longitudinal to a latitudinal pattern through this interval. This study improves also our knowledge on the processes controlling these distributions, as we show that the evolution of the distribution of both groups resulted from a complex combination of functional and dispersal constraints. Conodonts were mainly influenced by niche constraints during the Griesbachian, then constantly constrained by both niche and dispersal assembly processes. While they likely occupied wider niches in the immediate aftermath of the PTB crisis, their probable eurythermy also allowed them to recover fast from Early Triassic environmental perturbations. Conversely, the distribution of ammonoids was principally controlled by their dispersal abilities, except during the Smithian, where their record reflects an acme in diversity, a temperature-based biogeographical structuration and marked niche constraints. The stronger latitudinal differentiation of ammonoids, compared to conodonts, especially during the Olenekian, suggests they were more sensitive to temperature than the latter. The contrast between these two marine, nektonic organisms in terms of assembly processes strongly suggests that no single general macroecological process could explain the distribution of marine organisms during the biotic recovery of this major event. We hope this study will foster more quantitative analyses of the biogeographical patterns of other clades and for other crises, in particular planktic and benthic taxa such as ostracods, foraminifera, bivalves or gastropods. Regarding the data we analysed, we will regularly update both databases (conodonts and ammonoids) which will be available online (https://github.com/PaulineGnsr/Permian-Triassic_assembly-process). We invite researchers to participate to these steady updates to keep cutting edge datasets and updated detailed characterisation of biogeographical pattern and subsequent macroecological processes.

## Supplementary Data

*Supplementary info.* Early Triassic Supplementary info

Fig. S1. paleomap sections conodont

Fig. S2. UPGMA classif section conodont

Fig. S3. SAC-SC conodont

Table S1. List of synonymes and discarded species

Table S2. Section paleocoord regions conodont

Table S3. paleocoord regions ammonoids

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