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Model Description Taxonomic Modeling have clades and or regions spatial saturation. focal taxonomic com- reached the when in not diversity stability to of data of analyses robust measures parative useful diver- be are find proba- can these estimates phylogenetic example but Thus, bilistic rates. and clades description our of heterogeneity related geographic in taxonomic closely major patterns of instabil- the sity richness potential overall, relative identify saturation that, the also the of in We in trend ity bias knowledge. long-term spatial taxonomic the a in esti- of suggesting differences simple approaches, description, regional both a species esti- strong For add to (15–19). find relevant knowledge then we factor taxonomic start- another We of (TE), the taxonomy. mates effort taxonomic (14), formal of of valid Linnaeus of mate trajectory currently with point the of beginning ing model history the description first short-term only and species We using long- rate. the accumulation of species description function a in is year trends given a in scribed aadpsto:Bvledt n aeintm eismdlaeaalbefo Zenodo from available are model series time Bayesian ( and data Bivalve deposition: Data interest. of conflict no declare authors National The A.P., and College; Dartmouth London. Museum, M.M., History Institution; Smithsonian G.H., Reviewers: S.M.E., paper. and the model; wrote description D.J. the and developed P.D.S., P.D.S. data; analyzed S.M.E. data; dardized ( bivalves. unabated marine an the years: at 165 species past animal described the major for newly a rate accrued for has differ- diversity observed that among-clade of group and stability the regional assess in to ences model this use We tion. uhrcnrbtos ...... n ..dsge eerh ..cletdadstan- and collected D.J. research; designed D.J. and P.D.S., S.M.E., contributions: Author doi.org/10.5281/zenodo.159033 uchicago.edu. owo orsodnemyb drse.Eal ei@ciaoeuo djablons@ or [email protected] Email: addressed. be may correspondence whom To doi.org/10.5281/zenodo.159033 oeistruhpoaiitcfrcsso iest levels. diversity of dis- forecasts species probabilistic continued through given coveries estimates patterns large-scale provides in tax- and stability where complete, of most clades is or knowledge regions onomic among identifies incorpo- and approach that space This across description clades. numbers species species of in statisti- uncertainty rate a rates the present to We approach patterns. cal cur- biodiversity of observed robustness tax- the rently and about regions questions among raising rate groups, uneven onomic an at discov- described be and to ered continue species new in and However, macroecology analyses macroevolution. to many biology conservation to from central ranging are fields numbers species of Estimates Significance norBysa iesre oe aalbefo Zenodo from [available model series time Bayesian our In b omte nEouinr ilg,Uiest fChicago, of University Biology, Evolutionary on Committee u oe otcoeyrsmlsta frefs. of that resembles closely most model Our ). . ] h ubro pce de- species of number the )], www.pnas.org/lookup/suppl/doi:10. 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ECOLOGY Including newly discovered deep-sea species changes the rela- tive richness of three well-studied, monophyletic bivalve families. When only considering continental shelf species (water depths of <200 m), true scallops (Pectinidae) are nearly 3 times as diverse as their closest relatives, the mainly tropical thorny oys- ters (Spondylidae) and the cold-water glass scallops (Propeamus- siidae). However, recent deep-sea discoveries (e.g., ref. 32) have more than doubled the number of glass scallops, bringing their diversity much closer to that of their sister clade, the true scal- lops (Fig. 4). Still, even with their apparent taxonomic undersat- uration, we do not predict the glass scallops to surpass or even match the diversity of the mainly continental shelf true scallops for the next 20 and 50 y (Table S2). These probable estimates of clade diversity raise questions about the relationship between each clade’s richness and biolog- ical or environmental factors. At least within these three fami- lies, bathymetric affinity alone appears to be a poor predictor of species richness. Instead, the greater ecological breadth of the true scallops may explain their higher diversity over the more Fig. 3. The 20-y forecast of species richness (noTE model year 2035); fore- restricted ecology of the mostly carnivorous glass scallops and cast values with uncertainty provided in Table S1. Regional richness rank sessile, filter-feeding thorny oysters. Estimating the probability of order is expected to remain stable to 2035, and most of the newly discov- ered species are expected to come from coastlines in the Indo-West Pacific. diversity shifts among clades with continued description of deep- sea species will be paramount for correctly interpreting evolu- tionary patterns. described species and will remain the richest. A mixture of Trop- ical and Temperate coastlines will continue to occupy the mid- Improving Estimates of Species Richness. dle richness ranks, with Polar regions toward the lowest ranks. Alternative estimates of TE. Estimating true TE will require neg- A few regions show nonzero but low probabilities of diversity ative evidence, that is, the failure to recognize new species after rank shift across the 20- and 50-y forecasts (Fig. 3 and Fig. S8). repeated attempts. Combining recent region- and clade-specific These unlikely shifts are mostly confined within climate zones, faunal inventories can offer unparalleled insight into the taxo- implying that the global latitudinal diversity gradient will persist nomic stability and saturation of the taxonomic record. In marine in light of continued species discovery. bivalves, recent rigorous molecular and morphological exam- Forecasts are especially useful in targeted comparisons of ination of a chemosybiotic group (Lucinidae) from Panglao, species richness among regions. For example, an outstanding Philippines, in the Tropical West Pacific confirmed 50 existing question in the geographic patterning of bivalve biodiversity has species and discovered 26 new species (34); a similar treatment been the greater species richness in the Tropical East Pacific of lucinids from Guadeloupe in the TWA confirmed 25 existing (TEP) than in the Tropical West Atlantic (TWA). Paleonto- and 1 new species (35). Despite all of the potential biases con- logical studies have proposed that differential extinction under- flating the results of our model, these observed descriptions are lies this seemingly reversed diversity pattern given the larger precisely the dynamic that our model and other models (36) pre- continental shelf area and greater habitat heterogeneity in the dict for the undiscovered diversity within these two regions. reef-bearing TWA (27, 28). However, the difference is only Trends in biological characteristics. As the clade analysis shows, 66 species, and we should consider the possibility that biases the biological properties of organisms can strongly affect the tim- in taxonomic discovery may bias this interpretation. The TWA ing of the discovery and description of new species (8, 25). The appears to be approaching taxonomic saturation faster than the earliest descriptions within many marine groups are commonly TEP (joint probability βTWA < βTEP = 1; Fig. S4), but the TWA of species with larger body sizes, larger geographic ranges, and has a higher baseline rate of description and may still gain on the diversity of the TEP before reaching saturation (Fig. S3). Assuming trends in description rate remain constant for the next 20 and 50 y, we predict that the diversity of the TWA will get closer to that of the TEP, reducing the difference to 44 Pectinidae Propeamussiidae Spondylidae species [median forecast difference by 2035 and 20 species by S : 241 S: 73 S: 65 β : − 5.4 (− 7.5 , − 3.5) β : 8.4 (3.9 , 12.8) β :−4.2(− 7.2 , − 1.4) 2065 (Table S1)]. The TEP has a 75% probability of remaining β : − 2.5 (− 3.9 , − 1.1) β : 4.4 (0.3 , 8.6) β :−3(− 5.6 , − 0.6) TE TE TE 1000 more diverse over the next 20 y and only a 58% probability over the next 50 y. This closing gap in estimated richness between 500 regions should be considered when analyzing the oceano- Continental Shelf graphic and biological factors that may underlie their diversity (less than 200 m) 0 S : 268 S : 180 S: 67 differences. β : − 4.9 (− 7 , − 2.9) β : 7.9 (5 , 10.7) β : − 3.6 (− 6.5 , − 0.7) β β TE : − 1.7 (− 3.1 , − 0.4) β : 4.8 (2.1 , 7.2) TE : − 2.5 (− 4.9 , − 0.2) TE 1000 Clade Comparisons. The description model and its associated forecasts are also useful tools for comparisons of clade diver- 500 sity. In the marine system, deep-sea exploration has dramatically Number of Species Described

Including Deep Sea 0 (greater than 200 m) elevated our estimates of species diversity in many groups (29), 1750 1850 1950 1750 1850 1950 1750 1850 1950 and we estimate that 43% of marine bivalve species described Year since 2005 were discovered in the deep sea (Fig. S9) (30). Thus, Fig. 4. Description of three closely related clades of marine bivalves exclud- newly discovered species may be concentrated within particular ing and including deep-sea species (phylogeny from ref. 33). Panels are orga- clades, which may challenge the interpretation of many ecolog- nized as in Fig. 2. Despite the doubling of diversity in Propeamussiidae when ical and evolutionary patterns derived from strictly continental including deep-sea species, these glass scallops are not forecast to overtake shelf occurrences (31). the diversity of their more speciose sister clade Pectinidae—the true scallops.

4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1616355114 Edie et al. 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(e.g., (e.g., verified to entirely newly name a difficult for synonymized is 1850) older, year curve an description but reinstate the exist, Systematists on certainly species influence Cryptic their mor- of species. number large cryptic a phologically adding or synonymies like- extensive the of reduces lihood general molecules and This morphology 41). between (22, agreement observed delimitations supports taxonomic largely an lower-level work these molecular of recent and defined morphology, stability been their have by the species bivalve marine into Most curve. description insight qualitative provides the fore- changes subsequent species. undiscovered the curve of and cast description description long-term of the split rates history inferred can Reshaping the grounds throughout description. species molecular of (remove) and synonymize revi- and morphological taxonomic (add) both However, stable. on completely sion tax- is observed the record assumed onomic thus and species accepted currently of probabilistic descriptions. invalid explicit for taxonomically Accounting discovery. and biological species of spatially these model a incorporate directly into 40). to trends (39, remains described being challenge still The are the systematists that con- by eas- conclude most encountered temporally species ily might those a because we range unsaturated, show time, is richness over region geographic observed trend that and increasing within or sizes species stant strong body the taxonomic relatively to of the a closer sizes with if being clade as However, rate a saturation. description or species region in a decline stands interpret model we the As here, 38). (37, occurrences bathymetric shallower oprn h doycaiso ytmseictaxonomic system-specific of idiosyncrasies the Comparing bivalves and mammals, birds, as such groups taxonomic Higher clade particular a within practice taxonomic of history The emdldtedescription the modeled We ev--u iesre rs-aiain[rligfrcs rgn 5) rec- (53), of origin” variant forecast a [“rolling using cross-validation pre- error posterior series forecasting the time the from leave-p-out examined time We in forward distribution. simulating dictive by clades) and (regions Richness. Species Forecasting through discovery taxonomic of 2). patterns (Fig. comparisons observed graphical the the to pos- from these estimates draws compared and terior independent parameter 1,000 each of made distributions We posterior fundamen- fit. marginal patterns—the parame- model observed of the determinant empirically from tal the generated resemble patterns estimates whether ter well determine were and to warm-up) simulations (5,000 each imple- steps indepen- [ 15,000 as Four mixed for Sampler, (52). run U-Turn Stan were language No chains programing dent the probabilistic called the Carlo in mented Monte Hamiltonian of ant per described pri- (doi.org/10.5281/zenodo.159033). species of choice of in and rate formulation, is the description, ors expected trans- model to the Full model year year. from per per the publication parameters the described in estimated for species TE term the of Including offset number of (51). an interpretation year publications as the per year, unique forms named given of species a number of for the number long-term species as the bivalve here and new Finally, defined time describing rate. of TE, description function the incorporated a of we num- is components predicted autoregressive year the short-term per and regression, described long-term this species condi- the Within of autoregressive characterize ber (50). an We regression using series Poisson chain. description tional Markov species in two-state descrip- trend a a temporal of as with probability years occurrence event the consecutive the tion modified modeling of by runs We described long species curves. for zero description allow common to clade distribution—a component and Poisson zero-inflation Zero a regional descrip- (49). under of zero expected distribution having characteristic that years Poisson above individual zero-inflated events of tion a excess an following accommodates year inflation given a in Description. Species Modeling for test the history. against description biases in which S10 ). differences the similar, makes (Fig. more climate–coastline histories climate–coastlines one description than three regional more and and across occur two climate–coastline, to across one species Allowing occur to species endemic of boundaries. are ∼40% climate–coastline species the of with indi- 48% species the Approximately the intersecting each by split for climate–coastlines we occurrences more but vidual We or biotas. one (48), shelf to World of structure species the biogeographic assigned the the of reflect resemble to Ecoregions biogeo- coastline climate–coastlines Marine by Our realms major the (47). in and 2) “realms” Fig. zones, 12 in climate (map geography, turnover graphic coastline of combination standardized 30; taxonomically ref. deep- include from 136 (largely of we species dataset study, sea taxonomic low-resolution clade a cm) from the occurrences (<1 For deep-sea are size Galeommatoidea 46)]. body and bivalves system 30, small [Cyamioidea deep-sea taxonomy (21, exceptionally undersampled as understood of and poorly clades m), have two independent 200 that exclude an to also We be m (45). shelf 0 to continental from acknowledged to intertidal depths widely on at focus (living we study, bivalves richness S1). regional (Dataset (44) the occurrences For georeferenced 62,059 with species valid rently Database. Bivalve Marine Methods and Materials spatial higher resolution. at phylogenetic analyses and necessary quantitative is rigorous more discovery areas building and for highlights research also systematic but continued patterns where biodiversity interpre- our current improves of only rates not tations description richness species Integrating of reserves. forecasts diversity with biological of around knowledge probable and the and assessing ecological in of in quantitative set and in used models a directly evolutionary generates be can richness which species values, short- of into uncertainties forecasts associated term taxo- their and Incorpo- of trends clades. those comparison or rating regions direct geographic among a knowledge provides nomic rate description species h on otro formdlprmtr a siae sn vari- a using estimated was parameters model our of posterior joint The a using “climate–coastlines” termed regions geographic 18 define We R ˆ n oe oei vial rmZenodo from available is code model and Information, Supporting = 5).Mdlaeuc a sesduigpseirpredictive posterior using assessed was adequacy Model (52)]. 1 u aiebvledtbs nlds574cur- 5,744 includes database bivalve marine Our egnrt h ubro pce described species of number the generate We efrcs pce ihesars groups across richness species forecast We aae S2). Dataset NSEryEdition Early PNAS | f6 of 5

ECOLOGY ommended in ref. 4], where we fit the model to incremental time series University of Chicago for discussions, and K. S. Collins and M. Ingalls for from k blocks of p years each starting with 1758–1765. For p = 5, the series revision edits. We thank the following for taxonomic advice, assistance, is 1758–1765, 1758–1770,..., 1758–2010. We estimated the species richness and/or access to collections in their care: M. Aberhan, L. C. Anderson, p years into the future for each block by drawing parameter estimates K. Amano, A. G. Beu, R. Bieler, D. C. Campbell, J. G. Carter, R. von Cosel, J. S. Crampton, E. V. Coan, T. A. Darragh, H. H. Dijkstra, from the model posterior (for the TE model, we used random samples of E. M. Harper, C. S. Hickman, M. Huber, S. Kiel, K. Lam, K. Lamprell, publication counts p years before the end of the time series). We estimated K. A. Lutaenko, N. Malchus, T. Matsubara, P. A. Maxwell, P. M. Mikkelsen, the forecasting error as the difference between the observed and forecast P. Middelfart, N. J. Morris, J. Nagel-Myers, G. Paulay, A. F. Sartori, counts within a forecast window (Fig. S7). F. Scarabino, J. A. Schneider, P. Valentich-Scott, J. T. Smith, J. D. Taylor, J. J. ter Poorten, J. D. Todd, T. R. Waller, A. Waren,´ and F. P. Wesselingh. This ACKNOWLEDGMENTS. We thank G. Hunt, A. Purvis, and M. McPeek for work was supported by National Science Foundation (NSF) and NASA (D.J.) valuable reviews that expanded the breadth of this paper. We thank and NSF Graduate Research Fellowship Program and Doctoral Dissertation K. Roy, M. Foote, S. Huang, and the combined D.J.-Price Lab group at Improvement Grant (S.M.E.).

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