Andrew J Helmstetter
email: andrew.j.helmstetter@gmail.com

Rosana Zenil-Ferguson

Hervé Sauquet
email: herve.sauquet@gmail.com

Sarah P Otto
email: otto@zoology.ubc.ca

Marcos Méndez
email: marcos.mendez@urjc.es

Mario Vallejo-Marín

Jürg Schönenberger
email: juerg.schoenenberger@univie.ac.a

Concetta Burgarella
email: concetta.burgarella@gmail.com

Bruce Anderson
email: banderso.bruce@gmail.com

Hugo De Boer
email: h.d.boer@nhm.uio.no

Mario Vallejo-Marin
email: mario.vallejo-marin@ebc.uu.se

Sylvain Glémin
email: sylvain.glemin@univ-rennes1.fr

Jos Käfer
email: jos.kafer@cnrs.fr

Trait-dependent diversification in angiosperms: patterns, models and data

Keywords: Macroevolution, flowering plants, speciation, extinction, BiSSE, trait evolution, phylogenetics

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Introduction

Species diversity is unevenly distributed across the tree of life and while substantial research has investigated why some clades are more species-rich than others, many fundamental questions remain unanswered. The causes behind this unevenness can be diverse from catastrophic mass extinctions that decimate diversity [START_REF] Raup | Mass Extinctions in the Marine Fossil Record[END_REF] to key innovations that spur on rapid speciation [START_REF] Hunter | Key innovations and the ecology of macroevolution[END_REF] or ecological factors such as competition that shape species co-existence [START_REF] Drury | Estimating the Effect of Competition on Trait Evolution Using Maximum Likelihood Inference[END_REF][START_REF] Rabosky | Diversity-Dependence, Ecological Speciation, and the Role of Competition in Macroevolution[END_REF]. A greater understanding of the drivers of species diversification can provide insights into the assembly of communities and their phylogenetic structure, the evolution of functional traits that underpin a species' role in its environment, the formation of species interaction networks, and simply how biodiversity has evolved through time [START_REF] Morlon | Phylogenetic approaches for studying diversification (A. Mooers[END_REF]. In practice, phylogenetic diversity metrics can be used as a surrogate for functional diversity [START_REF] Tucker | On the relationship between phylogenetic diversity and trait diversity[END_REF], and both traits and diversification history can help predicting ecological processes, such as extinction threats and naturalization patterns [START_REF] Schmidt | Opposing macroevolutionary and trait-mediated patterns of threat and naturalisation in flowering plants[END_REF].

Traits can themselves affect speciation and extinction because they determine how species interact with their environment and with other species. Morphological and physiological traits can affect species' competitive and colonization abilities, defence against predators, herbivores and parasites; reproductive success, gene flow and adaptation to certain environments. In a wider sense, extrinsic characteristics, such as species' habitat, climatic zone or geographical area can also be considered traits [START_REF] Jablonski | Species Selection: Theory and Data[END_REF]. An important goal of modern macroevolutionary research is to discover which traits actually cause differences in diversification (through ecological processes) rather than being linked to species richness purely by common ancestry (phylogenetic inertia [START_REF] Felsenstein | Phylogenies and the Comparative Method[END_REF]).

This research has been boosted over the last decade by the increasing availability of large phylogenetic trees [START_REF] Jetz | The global diversity of birds in space and time[END_REF][START_REF] Rabosky | An inverse latitudinal gradient in speciation rate for marine fishes[END_REF][START_REF] Smith | Constructing a broadly inclusive seed plant phylogeny[END_REF][START_REF] Upham | Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation (A. J. Tanentzap[END_REF] Accepted manuscript 2019) and the continuing development of a range of statistical models to infer patterns in species diversification and the drivers behind them (see Box 1). The growing amount of empirical knowledge provides an opportunity to synthesise what we know so far about a wide range of ecologically diverse and species-rich clades to try to uncover how traits have driven their diversification. Here we focus on synthesizing results from state-dependent speciation and extinction (SSE) models (Fig. 1) used to investigate trait-dependent diversification (TDD) in flowering plants, or angiosperms. There have been more than 150 such studies, providing an opportunity for an updated perspective on the role different traits have played in angiosperm diversification [START_REF] Vamosi | Macroevolutionary Patterns of Flowering Plant Speciation and Extinction[END_REF]. Angiosperms form a clade of more than 350,000 extant species, making it by far the most species-rich division of plants, even when compared to much older clades [START_REF] Crepet | Darwin's second "abominable mystery": Why are there so many angiosperm species?[END_REF]Sauquet & Magallón, 2018). Almost all of terrestrial life is linked, directly or indirectly, to angiosperms [START_REF] Benton | The Angiosperm Terrestrial Revolution and the origins of modern biodiversity[END_REF] and their success makes them an ideal study group for uncovering the intrinsic traits and extrinsic factors driving their diversification.

Previous work has suggested that the origins of angiosperm diversity can neither be tied to major global events nor the evolution of a single key innovation (Sauquet & Magallón, 2018;[START_REF] Vamosi | Macroevolutionary Patterns of Flowering Plant Speciation and Extinction[END_REF]. Instead various combinations of traits, environment and ecology may have acted to stimulate diversification in different groups [START_REF] Davies | Darwin's abominable mystery: Insights from a supertree of the angiosperms[END_REF][START_REF] Magallón | Angiosperm diversification through time[END_REF], creating a landscape of macroevolutionary dynamics that varies substantially across different angiosperm clades [START_REF] Magallón | Thirty clues to the exceptional diversification of flowering plants[END_REF]. One hypothesis proposes that the traits driving the differences in diversification are a range of vegetative and reproductive characteristics, some of which are unique to angiosperms [START_REF] Stebbins | Flowering Plants: Evolution Above the Species Level[END_REF]. For example, dioecy originated in 890-5000 independent instances [START_REF] Renner | The relative and absolute frequencies of angiosperm sexual systems: Dioecy, monoecy, gynodioecy, and an updated online database[END_REF] but these appear to have led to quite different macroevolutionary dynamics [START_REF] Käfer | Dioecy is associated with higher diversification rates in flowering plants[END_REF][START_REF] Sabath | Dioecy does not consistently accelerate or slow lineage diversification across multiple genera of angiosperms[END_REF][START_REF] Wang | Global distribution and evolutionary transitions of angiosperm sexual systems[END_REF], probably because of their association with other traits [START_REF] Renner | Dioecy and Its Correlates in the Flowering Plants[END_REF][START_REF] Vamosi | Phylogenetic analysis of the ecological correlates of dioecy in angiosperms[END_REF]. However, we do not know how pervasive Accepted manuscript such differences are: although most studies agree that angiosperm diversification is driven by a combination of traits, we know relatively little about which traits are most influential and the extent to which their effects on diversification are consistent across different clades.

In this study we bring together the latest empirical knowledge on angiosperm diversification to compare the effects of traits in different contexts and identify those that have repeatedly stimulated diversification. Limiting our analysis to SSE approaches allows us to directly compare results across studies in a standardized framework, but does leave scope for the influence of known SSE model shortcomings [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF][START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF][START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF].

As a result, we also investigate the relationships between dataset properties (e.g. tree size and sampling fraction), methodological innovation (e.g. hidden states) and the results of published studies, highlighting how biases in our use of SSE models can affect our search for general trends.

To help mitigate these effects we provide a set of best practices including recommendations for result-reporting. Finally, we identify gaps in our current knowledge and suggest how to better incorporate ecology into the study of trait-dependent diversification.

Materials & Methods

Data collection

We collected published studies that cited SSE methods papers [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF][START_REF] Caetano | Hidden state models improve state-dependent diversification approaches, including biogeographical models[END_REF][START_REF] Fitzjohn | Quantitative Traits and Diversification[END_REF][START_REF] Fitzjohn | Diversitree : Comparative phylogenetic analyses of diversification in R: Diversitree[END_REF][START_REF] Freyman | Cladogenetic and Anagenetic Models of Chromosome Number Evolution: A Bayesian Model Averaging Approach[END_REF][START_REF] Goldberg | TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION: TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION[END_REF][START_REF] Goldberg | Phylogenetic Inference of Reciprocal Effects between Geographic Range Evolution and Diversification[END_REF][START_REF] Herrera-Alsina | Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data (M. Alfaro[END_REF][START_REF] Maddison | Estimating a Binary Character's Effect on Speciation and Extinction (T. Oakley[END_REF][START_REF] Magnuson-Ford | Linking the Investigations of Character Evolution and Species Diversification[END_REF][START_REF] Nakov | Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta)[END_REF][START_REF] Rabosky | FiSSE: A simple nonparametric test for the effects of a binary character on lineage diversification rates: TEST FOR TRAIT-DEPENDENT DIVERSIFICATION[END_REF][START_REF] Verboom | Species Selection Regime and Phylogenetic Tree Shape[END_REF] using Google Scholar, last accessed 18th May 2021. To facilitate data collection from papers using SSE models, we developed an R package called 'papieRmache' (https://github.com/ajhelmstetter/papieRmache).

Accepted manuscript

This package has two main purposes (1) to classify papers into different categories based on the frequency of term use in the text and (2) to pull out sections of the main text that contain a keyword or a pair of keywords while highlighting relevant information. We identified the SSE studies on angiosperms by using the keywords 'angiosperm', 'flowering' and 'plant' subsequently validating the subset of papers by hand and removing any studies on groups other than angiosperms. We then collected up to 31 different properties from each paper relating to the trait investigated, the group studied, the phylogenetic tree and the outcome of the SSE model used (see appendix S1), incorporating previously collated information on these properties where available [START_REF] Onstein | Darwin's second 'abominable mystery': Trait flexibility as the innovation leading to angiosperm diversity[END_REF].

In cases where there was uncertainty in how to interpret or collect data from a study we contacted the authors for their assistance and clarification, where possible.

Trait classification

While some sets of character states were the same among studies (e.g. annual vs perennial; diploid vs polyploid), many of them did not overlap. The nature of a trait can be difficult to define so here we use the SSE convention that considers that traits or characters (e.g. pollination)

have two or more states (e.g. biotic pollination, abiotic pollination, hummingbird pollination, etc). We classified traits into different categories to facilitate comparisons among different trait types. At the broadest classification these were intrinsic (traits belonging to the species), extrinsic (environmental or geographic traits), interaction (traits explicitly related to other species), and combination (multiple traits belonging to different categories that were grouped, e.g. species that have small fruits and are found on islands). To allow for analyses at different grouping levels we developed a trait ontology (Table S1) starting at the broadest level as just described and becoming more specific, up to level six.

Accepted manuscript

Data analysis

To examine the effect of particular traits on diversification we used the trait categories defined above and calculated the proportion of models in which TDD was inferred. Studies often consider the effect of a single trait of interest in several different clades, or the effect of several traits on diversification in a single clade of interest. We considered each combination of phylogenetic tree, SSE model type and trait separately here with an outcome of 1 (TDD detected) or 0 (no effect of trait detected). Whether or not TDD was detected was typically based on significance in model comparisons and/or posterior distributions of rates among states. However, if significance was not inferred or reported, we tried to follow the study narrative and statements made in the text. If model comparisons were conducted and reported, only the best-fitting model was considered, unless other models were explicitly referred to in the study. We examined patterns at different levels of trait categorization and among different model types. To facilitate comparison, we mainly consider whether or not a trait has an effect on diversification, irrespective of the direction of the effect, as the direction is only defined at the state level. When both the effect and the absence of an effect of a trait on diversification have been inferred in different models, we consider the effect of the trait 'inconsistent'. Note that inconsistency does not necessarily imply a contradiction, as the differences in the results of SSE models might be caused by differences in statistical power, type I errors, or biological differences between clades, as we will discuss.

Where possible, we extracted net diversification rates (per lineage per million years) associated with each character state. Then, relative differences in net diversification rates were calculated as (r max -r min )/r max (where r max and r min are the net diversification rates of the states with the highest and lowest rates respectively). These were used to represent the magnitude of the effect of a given trait on diversification, while taking into account general variation in diversification rates among clades. Comparisons were then made across the broadest trait categories, levels 1 and 2. For Accepted manuscript ease of interpretation, these analyses were restricted to those models where all net diversification rates were positive. At a narrower scale, we identified eight traits for which there was enough replication to be able to assess whether one character state was consistently inferred to have higher net diversification rates than the other. We ensured these traits had been tested at least five times, in at least two different studies and two different clades.

We examined the relationship between SSE model inferences and several dataset properties: number of tips in the tree, root age, number of genetic markers used to build the tree, sampling fraction (here referring to global sampling fraction unless stated otherwise) and tip bias (here calculated as the number of tips with the most common state divided by the number of tips with rarest state). For each of these we constructed two density plots representing the distributions of values in cases where TDD was, and was not, inferred and compared the overlap between densities.

We also fitted generalized additive models (GAM, [START_REF] Hastie | Generalized Additive Models[END_REF] to the continuous dataset properties with the SSE model result as a binary response variable (TDD vs no effect).

Variables were log-transformed (or arcsine in the case of sampling fraction) to conform better to a normal distribution. Initially, we constructed a GAM using all five variables and assigned the mean of the known values to any missing values. We also assessed each variable individually to determine the shape of each relationship when examined in isolation. In all cases we used smoothing functions (cubic regression splines) and the dimension of the basis used to represent the smooth term was set to k = 5 to avoid overfitting.

Predicting results based on dataset properties

After collecting information from all studies, we found that the outcome of the SSE model, that is, whether TDD was inferred or not, was sometimes associated with the dataset properties.

We therefore attempted to predict SSE model results (inference of TDD vs no effect) from dataset

Accepted manuscript

properties alone, and identify those properties with the largest predictive power. We used all available dataset properties except for highly-specific categorical variables (e.g. trait levels 5-6, clade, family) and those that varied among different states (putative root state, sampling per state, samples per state). We used a machine learning approach, extreme gradient boosting, with the R package 'xgboost' [START_REF] Chen | XGBoost: A Scalable Tree Boosting System[END_REF], a supervised learning approach based on gradient boosting machines. It uses a labelled dataset (the outcome is known) and an ensemble of weak prediction models (e.g. decision trees) whereby new models are added on to existing models per iteration to minimize error. Categorical variables with more than two categories were converted into binary, dummy variables with one-hot encoding (i.e. categorical features in a column are converted into binary variables) to facilitate model building. We trained models on a random selection of 80% of our dataset and tested them on the other 20%. After a parameter optimisation step we repeated this process 500 times to produce a range of accuracy values, the percentage of cases where the real outcome matched the classification, to account for stochasticity in the test and training datasets.

For each iteration we also recovered the relative importance of each variable, which allowed us to determine which dataset properties had the most influence on average. This method is specifically useful when there are many inter-dependent variables, i.e. when the inference of TDD depends on certain combinations of variables rather than major determinants. Given the inter-dependency of variables across the different decision trees, it is difficult to uncover whether a given property generally leads to TDD or not with xgboost. We avoid interpreting the results in this way, focusing on how accurate prediction can be and the types of variables that are most important to the model's predictive ability.

Accepted manuscript

Results

Traits studied and their effects on diversification

We collated information on trait-based diversification from 152 studies using a total of 629 SSE models to study angiosperm diversification. We found that 127 studies were conducted on a single clade, the rest examined diversification patterns across multiple, independent clades that were analysed separately. Variation in breadth of different traits investigated was also observed within studies. In total, 92 studies examined just a single trait (i.e. one level 6 category, see Table S1), while 40 studies looked at diversification patterns in sets of traits that belonged to more than one trait category at the highest level (e.g. extrinsic and intrinsic traits). In terms of taxonomic level, SSE models were most often run on genera or families (Fig. S1) and study clades were relatively evenly-distributed across the angiosperm tree of life (Fig. S2). Studies have focused on 36 out of 64 angiosperm orders, and 81 out of 416 families. As expected, diversification interest is generally proportional to the amount of species diversity in different parts of the angiosperm tree of life.

There was a clear, positive correlation between the number of species in a clade (order, family) and the number of TDD studies applied to species within the clade (Fig. S3,S4).

At the highest level of classification, intrinsic traits (i.e. those belonging to the plant species itself) were tested more often (291 models or 46% of models run) than extrinsic traits (i.e. those related to the species' habitat and geography, 260 models or 41%). Researchers tended to study intrinsic traits relating to reproduction (e.g. flower morphology, fruit morphology, breeding system), traits related to species' biogeography (e.g. biome, geographic range) and vegetative traits (e.g. life form, leaf morphology), investigating less often those traits related to the genome (e.g. ploidy level) or species interactions (e.g. symbiosis, defense or dispersal) (Fig. 2). In general, intrinsic traits were found to be associated with diversification slightly more often than extrinsic traits (59.8% vs Accepted manuscript 52.7%).

Our collation of data showed that results inferred with SSE models were inconsistent at both broad and narrow scales (grey vs coloured portions of bars in Fig. 2). For example, traits such as lifespan [START_REF] Azani | Miocene climate change as a driving force for multiple origins of annual species in Astragalus (Fabaceae, Papilionoideae)[END_REF][START_REF] Drummond | Multiple Continental Radiations and Correlates of Diversification in Lupinus (Leguminosae): Testing for Key Innovation with Incomplete Taxon Sampling[END_REF][START_REF] Salariato | Diversification patterns in the CES clade (Brassicaceae tribes Cremolobeae, Eudemeae, Schizopetaleae) in Andean South America[END_REF][START_REF] Soltis | Phylogenetic relationships and character evolution analysis of Saxifragales using a supermatrix approach[END_REF] and ploidy level [START_REF] Folk | Phylogenetic relationships and character evolution in Heuchera (Saxifragaceae) on the basis of multiple nuclear loci[END_REF][START_REF] Han | Polyploidy promotes species diversification of Allium through ecological shifts[END_REF][START_REF] Landis | Impact of whole-genome duplication events on diversification rates in angiosperms[END_REF][START_REF] Zenil-Ferguson | Interaction among ploidy, breeding system and lineage diversification[END_REF] yielded different results depending on the angiosperm group studied. Among broad trait categories (level 2; see Table S1) that have been tested using >25 models, vegetative traits yielded TDD more often than any other trait type, while pollination traits had the lowest proportion of TDD (Fig. 2a).

Given that we were assessing a range of SSE models, we wondered if using different models led to different patterns in TDD inference. Of those SSE models used frequently (> 30 times), MuSSE, GeoSSE and QuaSSE yielded TDD at high relative frequencies (> 60%) while BiSSE and HiSSE detected TDD less often (∼ 50%; Fig. S5). Models with hidden states will decrease the proportion of false positives so may provide more robust general trends. We compared results from only hidden-state models to those from the entire dataset and found that the proportion of trait-dependent outcomes decreased notably for vegetative, habitat and biogeography traits (Fig. S6). Outcomes for hidden state models remained inconsistent within categories that were tested five times or more, reflecting patterns in the entire dataset. We then took a closer look at results from HiSSE and BiSSE for three commonly-tested, narrow trait categories (level 4) and found mixed patterns. TDD was detected by BiSSE and HiSSE at about the same frequency in traits related to flower morphology (50% vs 42%), BiSSE detected TDD more often in traits related to life form (70% vs 25%) and HiSSE detected TDD more often in traits related to breeding system (75% vs 46%).

We examined eight traits in detail, three of which (lifespan, sexual system and woodiness)

Accepted manuscript

rarely exhibited TDD while in the other five (epiphytism, biome, ploidy, photosynthesis and self-compatibility) TDD was often detected (Fig. S7). We found conflict in which states increased diversification among different models in all traits except epiphyte form, photosynthesis and self-compatibility.

Examining the absolute net diversification rates among states of seven traits (sexual system was not assessed as most rates were relative, i.e. not time-calibrated) we found that patterns across clades reflected those detailed above (Fig. S8). For example, net diversification rates in traits rarely associated with diversification (e.g. woodiness or lifespan) were generally similar among the different states. To understand the effect of major trait categories (levels 1 and 2) on diversification we plotted the distribution of relative differences in net diversification rates for 208 models from 78 studies, grouping rates by trait category (Fig. S9). Generally we found that there was a wide range of relative differences in each trait category but no significant differences among categories.

The evolution of SSE model use and methodological innovation

As SSE models themselves have diversified, the relative frequency of model use has evolved.

We collated data on the types of SSE model used in each study, and plotted their use by year of publication (Fig. 3). BiSSE has remained popular even as newer, more complex models have emerged. Models with multiple states, predominantly MuSSE, have also been commonly used showing that researchers are interested in the effects of more complex traits or trait groups with more than two states. There has also been a consistent focus on using SSE approaches related to geography in models like GeoSSE and GeoHiSSE. When examining the number of studies that use SSE models each year we find a rapid increase since the first study we found that used of BiSSE on angiosperms in 2009 until a conspicuous slowdown and slight drop in 2015 (Fig. 3). This appears to coincide with the publication of a number of influential papers that criticised the propensity of SSE methods for false positives [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF][START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF] and pointed Accepted manuscript out power limitations [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF]. After this, SSE model use continued with a greater variety of models owing to the development of models with hidden states [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF], which have since spread to all aspects of SSE model use (Box 1; Fig. 1) becoming the dominant set of models by 2020 (Fig. 3).

Dataset properties are highly influential

The input data for macroevolutionary studies have grown in size and quality, in parallel with the innovations in the SSE models. For example, we found evidence that trees used with SSE models have gradually grown larger over time (Fig. S10). We then examined the relationship between tree size and whether or not TDD was inferred, regardless of the trait investigated. We found that, in general, TDD was detected less often when trees had smaller numbers of tips (Fig. 4a,S11a).

The number of tips in a tree is important for robustness of SSE model results and guidelines for adequate power were put forward by [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF] who suggested that results from models using trees with fewer than 300 tips should be treated with caution. But has this recommendation shaped SSE model use? The proportion of models run on trees with fewer than 300 tips was initially very high (94% of 139 models for which we found information) in studies published up until 2013.

It then decreased to 57% (277 of 482 models) in studies published from 2014 onwards. Despite this reduction, more than 60 models were run on trees with fewer than 50 tips after 2013.

Tree size and root age are closely linked because trees with larger numbers of tips are generally older (Fig. S12). Indeed, we found that TDD was detected more often when trees with an older root age were used (Fig. 4b,S11b). Regardless of their size or age, trees that more accurately represent the true phylogeny of a group will allow us to more reliably estimate its diversification history. We used information on the total number of molecular markers as a proxy for tree quality.

We found a difference in the distributions indicating that models with trait-dependent outcomes Accepted manuscript usually had better quality trees than those that did not (Fig. 4c,S11c).

Another issue that has been repeatedly brought up in simulation studies is the potential effect of inflated tip bias [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF][START_REF] Maddison | Estimating a Binary Character's Effect on Speciation and Extinction (T. Oakley[END_REF]. Tip bias increases when there is a higher frequency of one state than the others across the tips of the tree. Upon examining the data used with SSE models we found substantial overlap between densities (Fig. 4d) except for extreme values of tip bias where SSE models tended to find no effect of the trait studied (Fig. S11d). Tip ratio bias recommendations were also made by [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF], who cast doubt on inferences made when the ratio exceeds 10:1. Prior to 2014, 85% of SSE models (56 of 66) had suitable tip ratios and this figure remained similar (87%, 314 of 360) for the studies that came after.

Global sampling fraction is the proportion of known species that are present in the tree. If the sampling fraction is low it can drastically affect diversification rate estimation [START_REF] Chang | Estimating Diversification Rates on Incompletely Sampled Phylogenies: Theoretical Concerns and Practical Solutions (T. Buckley[END_REF][START_REF] Fitzjohn | Estimating Trait-Dependent Speciation and Extinction Rates from Incompletely Resolved Phylogenies[END_REF][START_REF] Sun | Estimating rates and patterns of diversification with incomplete sampling: A case study in the rosids[END_REF]. The sampling fraction was explicitly modeled in SSE methods by [START_REF] Fitzjohn | Estimating Trait-Dependent Speciation and Extinction Rates from Incompletely Resolved Phylogenies[END_REF], who recommended that the sampling fraction should be at least 25% to adequately capture diversification dynamics. Approximately 62% of 616 models had sampling fractions greater or equal to 25%. Sampling fractions were initially high but became more varied over time (Fig. S13), which probably reflects easing of assumptions on complete species sampling, but also indicates that high levels of incomplete sampling are not uncommon in recent literature.

Furthermore, we found that models that used trees in which sampling fraction was low (< 50%) generally yielded TDD (Fig. 4e, Fig. S11e). Conversely, high sampling fraction was more often associated with a lack of TDD. Given that the inference of TDD varies with tree size (Fig. 4a), we wondered whether there might also be a relationship between sampling fraction and tree size.

However, upon examination we found only a weak, negative trend where trees with more tips had slightly lower sampling fractions (Fig. S14). We then looked at the relationship between Accepted manuscript sampling fraction and the total number of species in the clade and found a steeper, stronger negative relationship (Fig. S15) meaning that the larger the clade is, the less well-sampled it tends to be. Datasets of small clades with low sampling fraction generally do not exist (as they should not be studied) and large clades with high sampling are currently very rare (Figure S15). These negative trends remain similar regardless of whether TDD is inferred or not.

Better quality data will lead to more robust results, so we compared trends taken from all studies, irrespective of dataset quality, to those from medium quality or better (at least 10% taxon sampling, 100 tips and a maximum tip bias ratio of 20:1) and high-quality datasets only (at least 25% taxon sampling, 300 tips and a maximum tip bias ratio of 10:1; [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF][START_REF] Fitzjohn | Estimating Trait-Dependent Speciation and Extinction Rates from Incompletely Resolved Phylogenies[END_REF]. We found that generally as data quality improves then the proportion of BiSSE models with TDD increases substantially (Fig. S16). Conversely, the proportion of HiSSE models with TDD remains relatively stable regardless of dataset quality. As HiSSE was conceived to leverage the issue of false positive rates in BiSSE, this could indicate that the higher TDD rates in BiSSE and MuSSE are due to false positives. However, we found a relatively low number of studies (20) with datasets that meet all recommendations, so more examples with high quality data are needed to determine whether these trends are robust. We then examined whether dataset quality affected general trends for major trait types (level 2 category). We found that increasing dataset quality had little effect on extrinsic and interaction traits (e.g. biogeography, habitat or pollination) while frequency of TDD for some intrinsic traits such as those related to reproduction tended to increase slightly with dataset quality (Fig. S17), but again the number of studies was low.

How predictable is the inference of trait-dependent diversification?

Empirical results in angiosperms clearly exhibit strong relationships between various dataset properties and whether TDD is inferred by the SSE model. To assess the importance of the Accepted manuscript continuous dataset properties together we fit a GAM including the number of tips, root age, the number of markers, the sampling fraction and the tip bias (Fig. S18). We found that all variables except tree age were significant when predicting SSE model outcome (r 2 = 0.211, see Table S2 for full details).

If we had comprehensive information about studies and their data, including dataset properties, taxonomy and traits, could we predict whether TDD would be inferred? Using a machine learning approach, extreme gradient boosting [START_REF] Chen | XGBoost: A Scalable Tree Boosting System[END_REF], we were able to correctly predict, with approximately 71% accuracy (56-81%, Fig. S19), whether SSE models would infer TDD. The most important features were the information-dense, continuous variables (e.g. number of tips, tip bias, sampling fraction; Fig. 5), further reinforcing earlier observations about their potential influence on SSE model outcomes (Fig. 4, S11, Table S2). Generally, categorical variables, such as those related to the trait studied (e.g. biogeography or fruit morphology) or SSE model used (e.g. BiSSE or HiSSE), played a smaller role in the model's predictive ability (Fig. 5).

Discussion

No consistent drivers of angiosperm diversification

Previous broad-scale empirical studies [START_REF] Davies | Darwin's abominable mystery: Insights from a supertree of the angiosperms[END_REF][START_REF] Hernández-Hernández | Why Are There So Many Flowering Plants? A Multiscale Analysis of Plant Diversification[END_REF][START_REF] Magallón | Angiosperm diversification through time[END_REF] and reviews [START_REF] De Queiroz | Contingent Predictability in Evolution: Key Traits and Diversification[END_REF][START_REF] Donoghue | Key innovations, convergence, and success: Macroevolutionary lessons from plant phylogeny[END_REF][START_REF] Donoghue | Confluence, synnovation, and depauperons in plant diversification[END_REF] have proposed that diversity in different angiosperm groups may have been caused by various combinations of intrinsic and extrinsic traits. In our synthesis of studies using SSE models, we found that while there certainly are particular traits correlated with higher diversification rates, general trends are mostly limited. It may be unrealistic to expect that all angiosperm diversification has been driven by similar responses to a handful of traits but it is Accepted manuscript useful and interesting to investigate the extent to which such traits exist. Indeed, our compilation of the results of 152 studies on TDD in angiosperms indicates that the factors driving angiosperm diversification are more complex than a set of universal drivers. When we compared studies investigating the same traits we found that conclusions generally differed, with some indicating that the trait does have an effect on diversification and others concluding there is no effect. When only considering results from models with hidden states, which should be more reliable, we found that the proportion of TDD changed substantially for some traits, though inconsistency was still common (Figs. 2,S6). As more studies with hidden states are conducted, we will find out whether these trends are general. We note that the inconsistency observed might reflect real trends in the data, or be due to dataset properties (lack of power, model mis-specification). In the following, we will first discuss the biological conclusions of our study, before considering dataset and model properties.

Our analyses and results centered around how traits (e.g. pollination-related traits) rather than the character states of these traits (e.g. bee vs bird pollination) affect diversification. After grouping traits into several levels of categories (Table S1), we found that some types of traits were more often found to affect angiosperm diversification than others. It may come as no surprise that floral traits are among the most investigated and influential (Fig. 2). Indeed, the flower contains the organs needed for sexual reproduction, making it central to the biology, ecology and evolution of angiosperms, and flower characteristics certainly have a large role in determining differences in diversification [START_REF] Vamosi | Macroevolutionary Patterns of Flowering Plant Speciation and Extinction[END_REF]. In particular, reproductive systems are highly variable in angiosperms [START_REF] Barrett | The evolution of plant reproductive systems: How often are transitions irreversible[END_REF] and are again thought to be closely linked to their success [START_REF] Barrett | The comparative biology of pollination and mating in flowering plants[END_REF]. Results from SSE models lend some support to this idea -for example, TDD was commonly inferred when mating system traits were investigated (e.g. self-compatibility; Fig. S7).

Even so, we found that breeding system (the higher level trait classification including all aspects of Accepted manuscript mating and sexual systems) did not yield TDD as often, due to variability in the effects of sexual systems. Other intrinsic traits including those related to photosynthesis, non-floral morphology and the genome have received less attention than traits related to reproduction (Fig. 2). However, they were regularly found to be associated with TDD in the limited number of studies in which they were assessed.

Unfortunately, we could say little about which state was generally advantageous for a given trait because of a lack of overlap among states across the 152 studies. Nevertheless, we were able to examine how particular states affected diversification in eight traits. Five of these demonstrated how different states of the same trait (e.g. woody and herbaceous) can increase diversification in different groups (Fig. S7) while just three traits showed patterns where one state was consistently associated with elevated diversification rates (epiphytism, non-C3 photosynthesis and self-incompatibility).

However, it is difficult to say whether these have truly consistent effects on diversification as they have only been investigated a handful of times in a relatively small proportion of angiosperm species.

Despite some general patterns in those traits that are more often influencing angiosperm diversification, the overarching trend is that the effect of a trait on diversification is clade-dependent.

Therefore, the main question remains open: what drives differences in diversification among angiosperms?

The fact that a definitive answer has yet to be found suggests that it is the complex interplay between trait evolution, biotic interactions and geography that matters. For example, (Hernández-Hernández & Wiens, 2020) identified geography (range size, biome) as an important factor but they could not resolve whether this is a cause or a consequence of differences in diversification. Others have suggested that it is not the presence or absence of a trait that determines the evolutionary success of a clade, but rather the capacity to change [START_REF] Onstein | Darwin's second 'abominable mystery': Trait flexibility as the innovation leading to angiosperm diversity[END_REF][START_REF] Ricklefs | Species Richness Within Families of Flowering Plants[END_REF]. This could partly explain the inconsistency we observe, but again, trait diversity could be both a cause and a consequence of species richness. Furthermore, the choice of clades and traits, as well as the quality

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of the input data, also influence whether or not differences in diversification are detected, and therefore our conclusions.

The importance of evolutionary history and ecological context

Users of models of trait-based diversification face an important challenge -choosing the context in which to conduct analyses. In the simplest scenario, where a trait only evolved once in the study clade, its effect on diversification cannot be tested [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF]) so this should be avoided. At the intermediate scale a trait may have evolved multiple times in closely-related clades but their shared evolutionary history makes them much more similar than distantly-related ones. So, associations between states and rates cannot be interpreted as a general pattern in this limited phylogenetic scope. Broadening the scope of the analysis, by way of either a larger tree, or multiple trees in a meta-analytic framework [START_REF] Sabath | Dioecy does not consistently accelerate or slow lineage diversification across multiple genera of angiosperms[END_REF] can help to reveal these general patterns but leads to different challenges. At larger phylogenetic scales, trees with many taxa are more likely to contain a range of branching patterns where lineage accumulation is faster in some parts of the tree than in others. In older clades there has been more time for macroevolutionary processes to have an impact on the trees we infer and the distribution of traits we observe today.

As we found, larger, older trees more commonly yield TDD (Fig. 4).

Yet at the same time, these trees often contain considerable variation in traits. Many of these traits could have effects on diversification, either contrasting or additive. Thus, the diversification rate of a clade with a certain trait also depends on the presence or absence of other traits. This leads to considerable heterogeneity in large trees that makes analyses more susceptible to false-positive errors caused by factors other than the focal trait (other traits, the environment, time) influencing diversification [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF][START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF]. While hidden-state models help to mitigate this issue, they still assume that any hidden states are categorical and Accepted manuscript have constant transition rates, which does not capture all sources of heterogeneity. Furthermore, they cannot handle all cases of possibly misleading inferences, e.g. when the state impacting diversification has only evolved once or a handful of times [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF][START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF]. These issues make the choice of study clade and how to apply SSE models to it an important consideration (Box 2). One way to deal with the oversimplification of reality inherent to SSE models (reducing diversification rate variation to a few categories) is to adopt a model averaging procedure as suggested by [START_REF] Caetano | Hidden state models improve state-dependent diversification approaches, including biogeographical models[END_REF]. Diversification rate estimates are calculated taking model uncertainty into account, and thus allow a richer interpretation of rate differences than simply whether or not there is a difference in the best fitting model. The estimation of branch-specific diversification rates [START_REF] Maliet | A model with many small shifts for estimating species-specific diversification rates[END_REF] would be another avenue to explore, but to our knowledge this has not yet been adapted to explicitly test for trait-dependent diversification.

It is not only the evolutionary history of the species in the study clade and their traits that is important, but also how these species interact with their environment, and each other. The effect of each trait on diversification should be considered in its own ecological context -a trait may stimulate diversification but does it have the same effect in a temperate vs tropical environment? This ecological context can tell us more about why a trait affects diversification but is insufficiently taken into account as many studies test single traits, are limited to two traits (e.g [START_REF] Zenil-Ferguson | Interaction among ploidy, breeding system and lineage diversification[END_REF], or use hidden states that are difficult to interpret biologically. Thus, to test in what contexts a trait affects diversification, several traits (intrinsic and extrinsic) must be assessed in tandem. Multi-state approaches (ideally including hidden states (e.g. [START_REF] Herrera-Alsina | Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data (M. Alfaro[END_REF]) could be useful for testing for the effect of ecological context (e.g. [START_REF] Onstein | Frugivory-related traits promote speciation of tropical palms[END_REF]). However, current models only allow the investigation of a small number of traits simultaneously before models become intractable due to the large number of parameters. This could be somewhat mitigated by Accepted manuscript careful choice on which parameters, notably transition rates, are allowed to vary, which could also help with biological interpretation if unlikely and impossible transitions between states are limited.

An alternative approach was used by O' [START_REF] Nakov | Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta)[END_REF] who tested the effect of six binary traits by fitting binary models for all trait combinations separately.

Best practices for SSE model use

Though a number of recommendations have been made for accurate inference with SSE models, most empirical studies do not meet them. When using the strict thresholds suggested in the literature we found that just 20 studies (out of 127 for which we retrieved information about each property) involve datasets that meet all strict criteria. We also found that, in some cases, meeting recommendations can affect how often TDD is detected (Fig. S16) and general patterns when trying to determine which traits have been important for angiosperm diversification (Fig. S17) so we encourage users to meet recommendations when possible and to clearly report the properties of their dataset (Box 3). The apparent relationship between sampling fraction and inference of TDD (Fig. 4e) should invite us to be cautious when sampling fractions are low, particularly as better sampled trees yield more accurate estimates of diversification rates [START_REF] Chang | Estimating Diversification Rates on Incompletely Sampled Phylogenies: Theoretical Concerns and Practical Solutions (T. Buckley[END_REF][START_REF] Fitzjohn | Estimating Trait-Dependent Speciation and Extinction Rates from Incompletely Resolved Phylogenies[END_REF]. One explanation may be that as sampling fraction goes down, rate estimates become more reliant on the model rather than the data, though further work is needed to understand why this might favour TDD. Publication bias may also be playing a role. If no TDD is detected in a poorly sampled clade this may be attributed to a lack of power that ultimately prevents publication thereby inflating the number of studies with low sampling that detect TDD.

To clarify these observations, simulation studies should be undertaken to investigate the influence of dataset properties (e.g. sampling fraction) and model inadequacy on SSE model inference simultaneously. While false-positive error rates have been explored for HiSSE (Caetano et al.,

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2018; [START_REF] Rabosky | FiSSE: A simple nonparametric test for the effects of a binary character on lineage diversification rates: TEST FOR TRAIT-DEPENDENT DIVERSIFICATION[END_REF], we still know little about how different dataset properties can affect the model's inferences. A study similar to [START_REF] Davis | Exploring power and parameter estimation of the BiSSE method for analyzing species diversification[END_REF] for HiSSE would be beneficial going forward, perhaps being extended to trees with thousands of tips to match the growing size of datasets. For the moment, the guidelines put forward for BiSSE are probably appropriate starting points to ensure that models are robust, though these may change in the future as the interaction between dataset properties and hidden states is better understood. In the light of these observations, we present recommendations for reporting dataset properties and model results in Box 3, which will help researchers assess the robustness of results, identify their possible limitations and better compare them to other studies. We have focused here on SSE models as they have been applied many times and have themselves been extensively studied. However, our recommendations apply to other diversification models as well because they typically use the same underlying birth-death model, and criticisms such as those in [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF] apply to evolutionary correlation analyses in general, not just SSE models.

Knowledge gaps and future avenues

Our review allowed us to identify clades that are understudied and therefore good focal points for future research to gain a more well-rounded picture of angiosperm macroevolutionary dynamics.

One of the most obvious is Asteraceae, species-rich yet subject to relatively few trait-based diversification studies (Fig. S4), or Alismatales, an order that has more than 4,500 species (Fig. S3) but just a single study on their trait-based diversification [START_REF] Canal | Out of Amazonia and Back again: Historical Biogeography of the Species-Rich Neotropical Genus Philodendron (Araceae)1[END_REF]. In addition, some families with more than 1,000 species, such as Phyllanthaceae or Orobanchaceae have yet to be studied in this way.

High-quality phylogenetic trees are not the only ingredient for SSE studies; trait data also need to be available. We highlight understudied traits related to lifespan, dispersal and symbiosis as Accepted manuscript ripe avenues for future work that have potential to unearth important patterns in TDD. However, apart from a few traits such as geographical range or climatic preferences, gathering high-quality data for several traits in large numbers of species is a time-consuming activity. We encourage the integration of trait data generated from SSE studies (and others) into large, global trait databases such as eFLOWER [START_REF] Sauquet | The ancestral flower of angiosperms and its early diversification[END_REF], TRY [START_REF] Kattge | TRY plant trait database -enhanced coverage and open access[END_REF] or more focused databases (e.g. AusTraits [START_REF] Falster | AusTraits, a curated plant trait database for the Australian flora[END_REF]). These will act as important resources as researchers consider several traits in tandem when testing for context-dependent effects of traits. Most importantly, studies should be conducted on traits where clear hypotheses can be generated about their effect on diversification in the chosen study clade. SSE methods are statistical tools that are used to uncover correlations, and cannot themselves discover causal relationships. By definition, macroevolutionary models try to capture the result of many aggregated small-scale processes in a few high-level parameters. Speciation is an instantaneous split of one branch into two in most macroevolutionary models, although in reality there might be a wide range of different dynamics depending on environmental heterogeneity, biotic interactions, and intrinsic traits (e.g. breeding systems, genomic incompatibilities) [START_REF] Coyne | Speciation[END_REF]. Thus, if a trait is predicted to affect speciation and extinction, high-quality inferences of diversification rates, for which our synthesis provides some guidelines, should be able to detect any signal. However, this signal is only a piece of the puzzle, and the various underlying ecological and genetic processes can also be put to the test. For example, Park et al. ( 2018) compared sister species with contrasted mating systems (selfing vs. outcrossing) and showed that niche breadth tended to decline over time in selfing lineages, in agreement with the dead-end scenario proposed for this trait and detected in macroevolutionary analyses [START_REF] Freyman | Stochastic Character Mapping of State-Dependent Diversification Reveals the Tempo of Evolutionary Decline in Self-Compatible Onagraceae Lineages[END_REF][START_REF] Goldberg | TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION: TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION[END_REF]. Additionally, we can identify traits that have an effect on ecological and genetic mechanisms that control speciation and extinction, such as traits affecting coexistence and niche partitioning [START_REF] Adler | Trait-based tests of coexistence mechanisms[END_REF]) (e.g.

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specific leaf area or seed mass), genetic differentiation between populations or species [START_REF] Gamba | Global patterns of population genetic differentiation in seed plants[END_REF]) (e.g. pollination mode, mating system, growth form) or those associated with commonness and rarity [START_REF] Murray | How plant life-history and ecological traits relate to species rarity and commonness at varying spatial scales[END_REF]) (e.g. seed production). Such traits come with a priori hypotheses and could be ideal candidates for macroevolutionary studies exploring their effect on diversification.

Furthermore, in SSE methods, information about interactions between species is taken into account via the traits that species possess. There is no direct consideration of ecological context [START_REF] Morlon | Phylogenetic approaches for studying diversification (A. Mooers[END_REF], i.e., whether species actually interact, or whether the diversification of a clade in a certain area is influenced by the diversity of other clades that are present there. Recently, an integrated model including trait evolution, species interaction (competition) and diversification has been studied in the context of evolutionary radiation [START_REF] Aristide | Understanding the effect of competition during evolutionary radiations: An integrated model of phenotypic and species diversification[END_REF] and showed that competition can partly decouple trait diversity and species richness. The effect of competition and diversity-dependence in SSE models has also recently been analysed theoretically [START_REF] Kaj | Analysis of diversity-dependent species evolution using concepts in population genetics[END_REF]. However, inference tools based on these more integrated models still remain to be developed. This would require integrating multiple types of data, such as species' distributions and ecological preferences, possibly from different kingdoms (plants, animals) and it requires closer collaboration between modellers, statisticians, data analysts and specialists of the studied taxa.

Conclusions

When bringing together more than a decade of study on TDD in angiosperms, it is the inconsistent effects of traits that stand out, rather than the importance of a particular set of universal drivers. The absence of consistent effects highlights the important role the evolutionary history and ecological context of a clade play in determining how a particular trait affects diversification. Future research will need to focus on how traits interact to influence diversification and identify the Accepted manuscript ecological, genetic and demographic processes that these traits affect with the help of promising new models that are in development. We have only touched the surface of what we can learn about TDD in angiosperms. The production of new datasets that meet recommendations for robust inference, future methodological developments enabling studies at wider scopes and the potential for new discoveries in understudied traits and clades will continue to shape our understanding of angiosperm diversification. Though our study focused on angiosperms, the conclusions we draw about consistency, context dependence and diversification model use apply to studies of TDD across the entire tree of life. State change Speciation and Extinction (ClaSSE; [START_REF] Goldberg | TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION: TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION[END_REF], Fast, intuitive State-dependent Speciation-Extinction (FiSSE; Rabosky and Goldberg, 2017, Geographic State Speciation and Extinction (GeoSSE;Goldberg et al., 2011), Hidden Geographic State Speciation and Extinction (GeoHiSSE;[START_REF] Caetano | Hidden state models improve state-dependent diversification approaches, including biogeographical models[END_REF], Hidden State Speciation and Extinction (HiSSE; [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF], Multi-State Speciation and Extinction (MuSSE;[START_REF] Fitzjohn | Diversitree : Comparative phylogenetic analyses of diversification in R: Diversitree[END_REF], Multicharacter Hidden State Speciation and Extinction (Mu-HiSSE; Nakov et al., 2018), Quantitative State Speciation and Extinction (QuaSSE;[START_REF] Fitzjohn | Quantitative Traits and Diversification[END_REF]Verboom et al., 2020), Several Examined and Concealed States-dependent Speciation and Extinction (SecSSE;[START_REF] Herrera-Alsina | Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data (M. Alfaro[END_REF]. Each box shows the name of the model and the associated citation.

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Models that share similar attributes (e.g. those with hidden states) are colour coded and grouped with boxes. This is not an exhaustive list of SSE models and does not include, for example, models used in epidemiology that allow tips to be sampled at various points in time [START_REF] Scire | Improved multi-type birth-death phylodynamic inference in BEAST 2[END_REF]. S1.

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Box 1: Studying trait-dependent diversification with SSE models

There are many different ways to explore differences in diversification [START_REF] Ricklefs | Estimating diversification rates from phylogenetic information[END_REF]. The simplest way is to quantify species richness, but as species richness is highly dependent on time, either one should only compare equally old clades (sister clade comparisons) or correct for the ages of the clades (e.g. the method-of-moments estimator [START_REF] Magallon | Absolute Diversification Rates in Angiosperm Clades[END_REF]). More information can be obtained by modelling the shape of the phylogenetic tree (see [START_REF] Morlon | Phylogenetic approaches for studying diversification (A. Mooers[END_REF]. Some methods are aimed at identifying shifts in the diversification rate along the phylogenetic tree (e.g. [START_REF] Rabosky | Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees (S.-O. Kolokotronis[END_REF] after which it is up to the user to interpret these shifts and test whether they correlate with species' characteristics. Other more recent methods allow users to estimate diversification rates at the tips of the tree and then to test whether rate differences are associated with species' traits [START_REF] Rabosky | A Robust Semi-Parametric Test for Detecting Trait-Dependent Diversification[END_REF]. Species diversification can be explicitly linked to traits via state-dependent speciation and extinction (SSE) models. This popular family of models is based on birth-death processes where the diversification rates (birth is speciation, and death is extinction) are dependent on character states, and where transition rates between states define how state changes occur. The simplest SSE model is the Binary-State Speciation and Extinction (BiSSE) model [START_REF] Maddison | Estimating a Binary Character's Effect on Speciation and Extinction (T. Oakley[END_REF] that takes as input a phylogenetic tree and state values (0 or 1) for each species in the tree. This allows users to uncover whether lineages with one state diversify faster than those with the other. SSE models can also be used to test whether the transition rates between states in one direction (0 to 1) are faster than the other (1 to 0). A first important extension to BiSSE was the possibility to use incomplete trees [START_REF] Fitzjohn | Estimating Trait-Dependent Speciation and Extinction Rates from Incompletely Resolved Phylogenies[END_REF].

The original model has been extended in various ways to address different types of macroevolutionary questions, as depicted in Fig. 1. ClaSSE [START_REF] Goldberg | TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION: TEMPO AND MODE IN PLANT BREEDING SYSTEM EVOLUTION[END_REF] and BiSSE-ness [START_REF] Magnuson-Ford | Linking the Investigations of Character Evolution and Species Diversification[END_REF] are extensions of BiSSE that include cladogenetic events (speciation simultaneously associated with change in state). ChromoSSE [START_REF] Freyman | Cladogenetic and Anagenetic Models of Chromosome Number Evolution: A Bayesian Model Averaging Approach[END_REF]) is an extension of ClaSSE specially designed to study changes in chromosome numbers such as polyploidization. GeoSSE [START_REF] Goldberg | Phylogenetic Inference of Reciprocal Effects between Geographic Range Evolution and Diversification[END_REF] explicitly models how diversification differs between geographic regions. Other developments include the extension to more than two character states in MuSSE (FitzJohn, 2012), andQuaSSE (FitzJohn, 2010;[START_REF] Verboom | Species Selection Regime and Phylogenetic Tree Shape[END_REF], which can examine the effect of quantitative traits such as mean temperature.

Early SSE models were criticised for their propensity to infer false positives due to model inadequacy: the models were based on the assumption that only the trait of interest would influence diversification, so any kind of heterogeneity would lead to the rejection of the null hypothesis [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF][START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF]. FiSSE [START_REF] Rabosky | FiSSE: A simple nonparametric test for the effects of a binary character on lineage diversification rates: TEST FOR TRAIT-DEPENDENT DIVERSIFICATION[END_REF] is one solution for that problem, but perhaps the most important innovation after the initial wave of SSE models is the introduction of hidden states. These allowed diversification rates to be influenced by the focal traits as well as unobserved factors and provided a new set of more complex null hypotheses: the character independent diversification (CID) models [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF]). Hidden states have been incorporated into most initial SSE methods:

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HiSSE [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF] as an extension to BiSSE, Mu-HiSSE [START_REF] Nakov | Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta)[END_REF], GeoHiSSE 577 [START_REF] Caetano | Hidden state models improve state-dependent diversification approaches, including biogeographical models[END_REF] and SecSSE [START_REF] Herrera-Alsina | Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data (M. Alfaro[END_REF].

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Box 2: Choosing appropriate clades and traits

Given that dataset properties, evolutionary history and ecological context appear to be important for understanding TDD, what strategies can be used to select clades and traits that will produce the most reliable results? Trait choice can be helped by preliminary knowledge of the phylogenetic tree and ancestral state reconstruction, which could be used to ensure that the derived state(s) arose multiple times and that the ratio among different states is not extreme (<10:1). However, exactly how many times a trait needs to evolve to be suitable for study remains a difficult question [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF] that should be further investigated with simulations. With the uncertainties inherent to many studies due to less-than-optimal datasets, we argue there is a large avenue for the investigation of the effect of previously studied traits on diversification using high-quality data and the most powerful methods. Although our expectation is that the results will still be inconsistent due to context dependence, precise knowledge about which traits are most often important, and in which clades, will set the stage for more detailed studies on the causes of inconsistency.

In terms of choosing a clade, it is first important to adhere, as best as possible, to the recommendations for using SSE models e.g. avoid clades much smaller than 300 taxa and focus on those that are well sampled (> 25%). If recommendations cannot be followed, because of natural limitations in clade size, for example, these should be stated clearly as caveats. Working at a large scale, e.g. angiosperm-level analyses, is certainly appealing but creates a range of issues related to confounding factors that current models will find difficult to disentangle. These range from evolutionary history and ecological context, as discussed above, but also other analytical issues such as molecular clock rate variation [START_REF] Shafir | Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates[END_REF]. To better learn about the factors that influence angiosperm diversity, we suggest that studies focus on multiple intermediate-sized clades, i.e. large, well-defined genera, families or tractable orders. However, if these clades are well-sampled they would approach the limit of our current computational feasibility (but see Louca and Pennell, 2020b). Working with many smaller clades may therefore be more feasible in the near future and also yield important insights via the comparison of diversification patterns among many different groups (e.g. [START_REF] Sabath | Dioecy does not consistently accelerate or slow lineage diversification across multiple genera of angiosperms[END_REF], which we think is an acceptable tradeoff for reduced power in standalone analyses.

Focusing on many smaller clades can also help to study rare traits, i.e. those that would lead to strong tip ratio bias (> 10 : 1) in large clades, by selecting a subset of smaller clades with a higher frequency of the trait. Care should be taken that this could potentially bias the results, as those clades could be the ones where the trait is most successful. However, in the case of dioecy, which has an angiosperm-wide frequency of ∼ 5%, this SSE approach applied to ∼ 40 genera by [START_REF] Sabath | Dioecy does not consistently accelerate or slow lineage diversification across multiple genera of angiosperms[END_REF] lead to similar conclusions as angiosperm-wide sister clade comparisons [START_REF] Käfer | Dioecy is associated with higher diversification rates in flowering plants[END_REF]. In general, examining the effect of the same trait in multiple clades would allow researchers to account for the unique and shared aspects of their biology (e.g. through the use of hidden states or trait combinations), and then to combine results (Rabosky

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Box 3: Recommendations for reporting SSE model results

In most studies, at least part of the information we consider crucial for the interpretation of the results was lacking, or it was difficult to access. Collecting data for many properties required us to count from figures or extract statistics from archived raw data, which were not always available. For example, even if the tree has been published in a different study, we recommend that SSE model users clearly and succinctly describe it in their own study, notably the number and nature of the markers used to construct it, the number of tips, the sampling fraction and the age of the root. An estimation of the total number of species in each state would allow users to assess whether there are differences in tip state frequencies or state-specific sampling fraction that could bias results. Knowledge about the root state or assumptions made about it when running the model should also be reported. Surprisingly, the results about diversification and transition rates were often not reported in an easily-accessible and standardized manner, or in some cases, not at all. These should be reported, preferably with confidence metrics around rate estimates e.g. Bayesian credible intervals.

We were unable to extract and use the number of independent origins of each character state. Robust estimates of associations between traits and diversification rates necessitate multiple independent origins (but not too many [START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF]) and corresponding rate changes [START_REF] Maddison | The unsolved challenge to phylogenetic correlation tests for categorical characters[END_REF], so an idea of this value per study, inferred using ancestral state reconstructions, would be useful for interpretation of the robustness of results. This could be done by combining stochastic mapping of traits with an SSE model [START_REF] Freyman | Stochastic Character Mapping of State-Dependent Diversification Reveals the Tempo of Evolutionary Decline in Self-Compatible Onagraceae Lineages[END_REF], though this is generally not available for SSE approaches. Louca and Pennell (2020a) recently pointed out how diversification rate estimation can be susceptible to issues of unidentifiability. Though SSE models are not directly implicated [START_REF] Helmstetter | Pulled Diversification Rates, Lineages-Through-Time Plots and Modern Macroevolutionary Modelling[END_REF], it is known that extinction rate is especially difficult to estimate correctly [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF][START_REF] Maddison | Estimating a Binary Character's Effect on Speciation and Extinction (T. Oakley[END_REF]. One potential way to help 'future proof' analyses from unidentifiability caused by overfitting would be to avoid reporting and assessing speciation and extinction rates separately, focusing instead on compound parameters such as net diversification rate (λ -µ), turnover rate (λ + µ) and extinction fraction (µ/λ) that are typically used in more recent SSE models [START_REF] Beaulieu | Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction[END_REF]. However, testing explicitly whether diversification rates, which can be robustly estimated and are often the focus of the studies, are significantly different (irrespective of the exact values of the speciation and extinction rates) is not often done explicitly. Researchers should thus carefully inspect (and report) the bootstrap, confidence, or credible intervals of the state-dependent diversification rates they obtain in order to conclude whether or not they are really different. To encourage standardized result reporting we propose an initial set of characteristics that should be made available in all future studies using SSE models (Supplementary Data 1).

Figure 1 :

 1 Figure 1: The development of state-dependent speciation and extinction (SSE) models. The original binary-state speciation and extinction model (BiSSE) model (Maddison et al., 2007) is shown at the top of the diagram with all other models depicted below, in the order of their publication. Acronyms are defined as follows: Binary-State Speciation and Extinction-node enhanced state shift (BiSSE-ness; Magnuson-Ford and Otto, 2012), Cladogenetic and Anagenetic Models of Chromosome Number Evolution (ChromoSSE; Freyman and Höhna, 2018), Character-Independent Diversification (CID Beaulieu and O'Meara, 2016), Cladogenetic

Figure 2 :

 2 Figure 2: Stacked barplots showing how often particularly trait types were tested with state-dependent speciation and extinction (SSE) models. Bars are coloured to depict how often Trait-dependent diversification was detected per trait type. If results from multiple SSE models were reported in a single study they were considered separately i.e. each model contributed one result to the totals for each trait category. Two plots are shown, (a) one with relatively broad trait categories (level 2) and (b) one with narrower categories (level 4). An ontology depicting how different trait classification levels are connected can be found in TableS1.

Figure 3 :

 3 Figure 3: A stacked barplot showing the change in state-dependent speciation and extinction (SSE) models used on angiosperm clades over time. Each bar indicates the number of unique SSE model types per study totaled over the publication year. Bars are coloured according to the proportion of each SSE model type published in that year (see legend on the right of the plot). If the same SSE model was used multiple times in a single study it is only counted once (e.g. if BiSSE was used four times in a study published in 2012 this contributes an increase of one to the BiSSE portion of the 2012 bar). The black line shows the total number of studies using SSE models on angiosperms per year. Note that the year 2021 is incomplete.

Figure 4 :

 4 Figure 4: A set of densities depicting the distribution of values for five dataset properties in SSE models that infer trait dependent diversification (coloured densities) and those that do not (grey densities). The dataset properties shown are (a) number of tips in the phylogenetic tree used with the SSE model (data taken from n = 621 models), (b) the age of the tree used with the SSE model (n = 529), (c) the total number of genetic markers (nuclear + plastid + mitochondrial) used to infer the phylogenetic tree used with the SSE model (n = 615), (d) the tip bias, here calculated as the largest tip frequency divided by the smallest (n = 426) and (e) the global sampling fraction (n = 616).

Figure 5 :

 5 Figure 5: A horizontal barplot showing the relative influence of the 20 most important features included in the xgboost model used to predict the outcomes of SSE models, whether or not trait-dependent diversification is inferred, using input data properties and other characteristics of each study. All categorical variables are binary (e.g. 'Intrinsic' provides the information that traits that are intrinsic (1) and not intrinsic (0)). Points are the mean gain values calculated from the 500 iterations that were run. Error bars represent one standard deviation around the mean. Bars are coloured based on the type of variable they represent.

[START_REF] Rabosky | Model Inadequacy and Mistaken Inferences of Trait-Dependent Speciation[END_REF] to uncover broad patterns about the trait of interest.

Acknowledgements

This research is supported by the Fondation pour la Recherche sur la Biodiversité (FRB) through the CESAB project 'DiveRS'. We thank the members of the 'DiveRS' group for the ideas and discussions that led to this manuscript. We thank Nicolas Casajus and Aurore Receveur for advice on data analyses.

//doi.org/10.5061/dryad.8gtht76t2 and the GitHub repository: http:/github.com/ ajhelmstetter/sseReview.

Accepted manuscript

Supplementary Material

A template for result reporting can be found in supplementary data 1. PapieRmache can be found at http:/github.com/ajhelmstetter/papieRmache.