dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we apply the independent species sampling formula (7) on the same datasets. We follow the approach of  Etienne (2007)  and reduce the number of parameters to estimate by assuming that I ' = I for all '. Moreover, we eliminate the sampling efforts q ' by setting the expected sample size equal to the observed sample size for each local community '. As a result, the likelihood has to be maximized over two parameters only (h and I). Dataset Model / I m LL DLL BCI ZSC 595Á1 61Á61 0Á0029 À311Á92 À3Á20 ISA 595Á2 61Á81 0Á0029 À321Á11 À3Á41 Korup ZSC ∞ 49Á52 0Á0020 À318Á67 À1Á63 ISA ∞ 49Á61 0Á0020 À327Á75 À1Á66 Pasoh ZSC 1528 263Á4 0Á0098 À363Á75 À4Á37 ISA 1527 264Á0 0Á0098 À372Á49 À4Á58 Sinharaja ZSC 927Á6 32Á42 0Á0019 À252Á88 +0Á05 ISA 950Á1 32Á35 0Á0019 À261Á97 +0Á03 Yasuni ZSC 10 980 197Á0 0Á0111 À306Á75 À9Á60 ISA 11 130 196Á9 0Á0111 À314Á88 À9Á68 Lambir ZSC 2500 372Á5 0Á0111 À402Á32 À15Á94 ISA 2500 372Á9 0Á0111 À411Á08 À16Á15 Dataset Model h ¼ m0 þ m1JM 1 À m1 a ¼ m0 1Àm1 I m LL DLL BCI nDL ZSC 34Á97 0 ∞ 1 À318Á85 0 nDL ISA 35Á06 0 ∞ 1 À327Á97 0 DL ZSC 38Á32 0Á1203 1049 0Á0466 À308Á19 0Á54 DL ISA 37Á33 0Á1354 960Á2 0 Á0428 À317Á01 0Á69 Korup nDL ZSC 44Á54 0Á0289 ∞ 1 À318Á31 0Á36 nDL ISA 44Á19 0Á0303 ∞ 1 À327Á35 0Á40 DL ZSC 13Á87 0Á4326 1046 0Á0408 À306Á82 10Á22 DL ISA 12Á99 0Á4420 996Á8 0 Á0390 À315Á38 10Á71 Pasoh nDL ZSC 126Á4 0 ∞ 1 À392Á51 0 nDL ISA 126Á7 0 ∞ 1 À401Á20 0 DL ZSC 184Á2 0 Á0361 2192 0Á0763 À359Á31 0Á07 DL ISA 183Á0 0 Á0447 2081 0Á0727 À367Á80 0Á11 Sinharaja nDL ZSC 25Á63 0 ∞ 1 À253Á78 0 nDL ISA 25Á73 0 ∞ 1 À262Á82 0 DL ZSC 12Á72 0Á5123 145Á3 0 Á0085 À252Á13 1Á19 DL ISA 11Á77 0Á5270 138Á8 0 Á0081 À260Á59 1Á42 Yasuni nDL ZSC 178Á3 0 ∞ 1 À307Á58 0 nDL ISA 178Á6 0 ∞ 1 À315Á68 0 DL ZSC 61Á86 0Á5272 1117 0Á0598 À278Á88 18Á27 DL ISA 60Á39 0Á5324 1098 0Á0589 À286Á54 18Á66 Lambir nDL ZSC 195Á0 0 ∞ 1 À437Á89 0 nDL ISA 195Á3 0 ∞ 1 À446Á57 0 DL ZSC 245Á5 0 Á1161 2546 0Á0713 À385Á20 1Á18 DL ISA 244Á3 0 Á1202 2503 0Á0702 À393Á65 We find very good agreement between the estimates obtained with the zero-sum constraint and those obtained with the independent species assumption (Table  4 ).
M U L T I P L E G U I L D S Recently, we derived the zero-sum sampling formula for a single sample of two dispersal guilds with a metacommunity governed by point-mutation speciation  (Janzen, Haegeman & Etienne 2015 ). As we were interested in detecting guild differences in dispersal rate, we assumed that the two guilds have the same distribution of relative abundances in the metacommunity, but no species in common. Here we apply the multipleguilds sampling formula (11) of the independent species approach to the dataset studied by Janzen,  Haegeman & Etienne (2015). Importantly, the assumption that the guild metacommunities do not differ can be implemented in different ways. The zero-sum approach of Janzen,  Haegeman & Etienne (2015)  assumed that the two guilds have the same speciation rates, and hence, the same metacommunity diversity h (denoted by 'sS', which stands for same speciation rate).
Many forms of density dependence can be incorporated in the independent species framework. We assume that the per capita birth rate is proportional to 1 À a k and that the per capita death rate is constant. This leads to positive density dependence for 0 < a < 1 and negative density dependence for a < 0. In Appendix S6 we show that the sampling probability PðkjxÞ then becomes, Table  4. Fits for multiple samples. From the abundance data of three Panamian forest plots, we constructed eleven datasets, each consisting of three samples (one full dataset, and ten reduced datasets; see  Etienne (2007)  for details). We computed the maximum-likelihood fits for two model variants. The first variant, ZSC, imposes the zero-sum constraint (results taken from  Etienne 2007)  Sampling formula for community data 1513 PðkjxÞ ¼ IxðIxÀaÞ k ð1ÀqÞ ÀIxþa IxÀa q k k! if k ! 1 IxÀa ð1ÀqÞ ÀIxþa IxÀa if k ¼ 0.
L A R G E D A T A S E T S Even if the zero-sum sampling formula is available, its evaluation becomes often cumbersome for large datasets. We have argued above that the independent species sampling formula is easier to evaluate. To further support this statement, we consider Hubbell's neutral model (point-mutation speciation and dispersal-limited sampling). For a fixed set of parameter values (metacommunity diversity h = 50 and dispersal number I = 1000), we generate sample data for sample sizes ranging from J = 10 3 to J = 10 6. This can be easily done within the independent species framework, because the abundance frequencies are independent Poisson random variables, see eqn (1). For each of the generated samples, we fit the model parameters, using maximum likelihood, once with the zero-sum sampling formula and once with the independent species sampling formula.
In contrast, the independent species computation time remains below a few minutes for sample size J up to 10 6. As an illustration, we fit Hubbell's model to an extended dataset of the BCI tropical forest plot, which includes all trees with dbh (diameter at breast height) above 1 cm (rather than trees with dbh above 10 cm). Due to the large sample size (J % 2Á3 9 10 5 ), we were not able to evaluate the zero-sum likelihood on our computer. Likelihood maximization using the independent species approach did not pose any problem (see Table  S3 ).
P R E S E N C E -A B S E N C E D A T A We can apply our approach also to datasets where only species occurrences were scored in multiple sites, i. e. presence-absence data. We consider L samples. We introduce the presence- P ' ðo ' ¼ 1jxÞ ¼ P ' ðk ' ! 1jxÞ ¼ 1 À P ' ðk ' ¼ 0jxÞ ¼ 1 À ð1 À q ' Þ I'x :.
Discussion We have provided a framework to compute, under the independent species assumption, a sampling formula for all mainland-island(s) models for which we can specify the metacommunity abundance density q(x) and the local sampling probability PðkjxÞ. The computational complexity of the sampling formula reduces to the evaluation of onedimensional integrals of the form R PðkjxÞqðxÞdx. Because the integrands are often sharply peaked, the numerical evaluation of these integrals can be challenging. We include a dedicated integration algorithm in the R package SADISA (which stands for Species Abundance Distributions under the Independent Species Assumption). Currently, the package implements the sampling formulas only for the analyses presented in the paper. However, it is relatively straightforward to use the methods implemented in the package for other community models.
The independent species framework allows us to fit a broad set of neutral community models. This set is much broader than the models with zero-sum sampling formulas, for which our approach is often (much) more efficient. The framework can be applied to larger datasets (higher abundances, more species, more samples) and to relative abundance and presence-absence data. The only requirement is the specification of the metacommunity abundance density q(x)which depends on the speciation processand the local sampling probability PðkjxÞwhich depends on the local demographic dynamics. Even in cases where the independent species sampling formulas are approximate, such as the random-fission and the per-species speciation models, the parameter estimates are almost indistinguishable from the zero-sum results.
The approach is not restricted to neutral scenarios, as illustrated by our examples of density dependence and species-dependent parameters. Independent-species models can be easily simulated, because the abundance frequencies are independent Poisson random variables (see Appendix S1). Simulated datasets are useful to explore   3. Computational complexity of zero-sum and independent species likelihood maximization. We generated samples of different size for the neutral community model with point-mutation speciation (h = 50) and dispersal limitation (I = 1000), and estimated the model parameters, using the zero-sum (red dots) and independent species (green dots) sampling formula. Computation time scales consistently with sample size J: proportional to J 2 for the zero-sum approach (red line) and proportional to ffiffi ffi J p for the independent species approach (green line).
We have presented a derivation for several of these functions, which can serve as a template for other community models. Once the functions q(x) and PðkjxÞ have been specified, we can apply the independent species formalism to evaluate the sampling formula and to determine the maximumlikehood parameters. The R package SADISA includes a step-by-step demonstration for single-sample and multiplesamples examples. Reliable inference of community processes from abundance data is well-known to be very challenging. While the independent species approach drastically simplifies the evaluation of the likelihood function, it evidently does not resolve fundamental issues of fitting community models to abundance data. For example, in Hubbell's neutral model, very large samples are required to distinguish between cases with high regional diversity and low dispersal and cases with low regional diversity and high dispersal (see the ridge of high likelihood in Fig.
1 ). Community structure is the result of the interplay between several processes, both at local and regional scales, which are often difficult to tell apart using abundance data alone  (McGill et al. 2007; Al Hammal et al. 2015). These issues are as problematic for the independent species as for the zero-sum approach. Therefore, the independent species sampling formulas must not be applied blindly, but should be combined with techniques to evaluate the reliability of the maximum-likelihood estimates. When applying the sampling formulas in practice, it is important to assess the estimation bias of the model parameters. A common approach consists in simulating many times the community model with the estimated parameter values, and determining the maximum-likelihood parameters for each of the simulated datasets, which are then compared to the simulation values.
The zero-sum and independent species model variants present the same parameter estimation biases. However, the evaluation of these biases is more efficient for independent species models, because they are particularly easy to simulate. Simulated datasets are also used to test whether the fitted model can satisfactorily reproduce the empirical data  (Etienne 2007; Jabot & Chave 2011). The flexibility of the independent species assumption allows us to construct new hypothesis tests on a wide range of community processes. However, the reliability of such tests should be carefully assessed. For example, we repeatedly used the tropical forest data to illustrate our sampling formulas. Each of these sampling formulas deals with one or two community processes (including dispersal limitation, different speciation mechanisms, and density dependence), and we determined for each process separately whether it is supported by the data (using Akaike information criterion).
Acknowledgements We thank four anonymous reviewers and  S. Dray  for insightful comments, and the  Center for Tropical Forest Science for data collection. Financial support was provided by the  French National Research Agency (ANR)  through the  TULIP Laboratory of Excellence  (to B. H. , grant number  ANR-10-LABX-41 ), by the  Netherlands Organization for Scientific Research (NWO)  (VICI grant number  865. 13. 003 ) through VIDI and  VICI grants  to R. S. E. , and by the bilateral  French-Dutch Van Gogh programme  (to B. H. and R. S. E. ).
Parameters studied The first parameter was the number of snail populations for each habitat type and each indicator species. The differences between the numbers recorded for the different plant species within the same habitat type were analyzed using Fisher's exact test. The fisher. multcomp function of the RVAideMemoire package (R Core Team, 2016) was used as a post hoc test to perform pairwise comparisons. The other two parameters were the surface area of each habitat and the number of overwintering snails per m 2 of habitat. Individual values noted for either parameter were averaged and standard deviations were established, taking into account the indicator plant. Normality of these values was analyzed using a Shapiro-Wilk normality test  (Shapiro and Wilk, 1965). As the distributions of these values were not normal, a Kruskal-Wallis test was used to establish levels of significance.
Estimating and dating phylogeny A time-calibrated phylogeny for species within the subgenus Mendacibombus was estimated with a Bayesian analysis of four genes using BEAST  (version 1. 8. 2, accessed 2015: beast. bio. ed. ac. uk). This linked trees  (Heled and Drummond 2010)  for the mitochondrial genes COI and 16S and for the nuclear genes PEPCK (phosphoenolpyruvate carboxykinase) and opsin (long-wavelength rhodopsin copy 1). Methods for the extraction, amplification, sequencing, and alignment for the gene sequences are described in  Williams et al. (2016). This study examined 4416 specimens and extracted DNA from 171 specimens. We reduced the dataset to a single example sequence for each gene for each of the species, using the species recognised from an integrative analysis of COI-coalescents and morphology  (Williams et al.
2016). Details of primers are in Table  A1  and sequence accession numbers are in Table  A2. As outgroups, we include several close relatives of Mendacibombus, using a selection of species from other bumblebee subgenera  (Williams et al. 2008 ) for which relationships are strongly supported  (Cameron et al. 2007  The phylogeny had to be calibrated with a date from a molecular study because there are no known fossils of Mendacibombus species. Hines (2008)  estimated the age of divergence between the subgenus Mendacibombus and all of the remaining extant bumblebees to be at 40-25 Ma, although this estimate was then fixed at 34 Ma for most of her subsequent analyses (consistent with earlier suggestions by  Skorikov 1923; P. H. Williams 1985). We used 34 Ma for the mean and initial age estimate for the most recent common ancestor of Mendacibombus plus all other bumblebees (tMRCA), assuming a normal distribution with a standard deviation of 4 Myr, which places the 5% tails of the tMRCA distribution at 41-27 Ma (late Eocene to mid Oligocene).
Ancestral distributions To assess the roles of SDD, vicariance, and extinction in explaining current distributions from the phylogeny, we used Bayesian methods implemented in the RASP package (version 3. 0, accessed 2015:  Yu et al. 2014 ) with a corridor model. Statistical divergence-vicariance analysis (S-DIVA:  Yu et al. 2010 ) assumes that all distribution changes happen only at nodes (associated with speciation). This contrasts with a more gradualist model of distribution changes happening along branches in parametric methods  (Ree and Sanmartin 2009) , such as the analogous statistical dispersal-extinction-cladogenesis analysis (S-DEC, also known as Bayes-Lagrange analysis:  Yu et al. 2014). Both procedures take account of the uncertainty in the estimate of the species' phylogeny by using a sample of 10,000 trees from the phylogenetic analysis of the combined gene data (after a 1% burn-in).
(2002). Maps for these nine variables were compiled both for the present and for past epochs. Climate data for current conditions  were extracted from the WorldClim database (www. worldclim. org; Hijmans et al. 2005 ) and managed at a resolution of 2. 5 × 2. 5 minute grid cells (ca 5 × 5 km). Hadley Centre coupled ocean-atmosphere general circulation models (HadCM3L) were used for the Miocene and Pliocene. These models incorporate the effect of changes in atmospheric CO 2 concentration  (Beerling and Royer 2011). The simulations were generated at a 2. 5° × 3. 75° grid resolution, corresponding to: (1) a 400 ppm CO 2 Late Miocene simulation representing the early warm period of the Miocene and the Mid Miocene Climate Optimum around 15 Ma; (2) a 280 ppm CO 2 Miocene simulation representing the cold and dry conditions prevailing around 8 Ma; and (3) a 560 ppm CO 2 Pliocene simulation of the conditions around the Mid Pliocene warming event (3.
Results We screened genome assemblies of 142 plant species (Table  S1 ) for sequences related to ophiovirus proteins. We identified only one statistically significant match, in the recently published genome assembly of eelgrass (Zostera marina)  [6]. This sequence -hereafter referred to as Zostera marina endogenous ophioviral element (OphVe-ZosMar) spanned 567 nucleotides. When virtually translated, this sequence shared ~34-38% amino acid (aa) identity with the coat protein (CP) of known ophioviuses (Figure  1 ). The putative protein coding sequence of OphVe-ZosMar produced numerous, highly significant hits to ophioviruses when used to search GenBank, and no hits to sequences derived from other species. The OphVe-ZosMar element was identified in a large scaffold (accession # LFYR01000112. 1: positions 103766-104332), and was flanked on either side by DNA sequences that exhibited no statistically significant similarity to any other viral or plant sequences in GenBank (Figure  S1 ).
A sequence derived from a distantly related element (or a fragment of an related extant RNA virus) was identified in an RNA library of Zostera noltei (GenBank: HACV01019525. 1). This fragment could potentially indicate the presence of an orthologous insert in a second seagrass species, but amino acid identity with OphVe-ZosMar was relatively low (Figure  S2 ), suggesting that they are derived from a distinct virus or germline incorporation event. We used maximum likelihood to infer the phylogenetic relationships between known ophioviruses and the OphVe-ZosMar. As shown in Figure  2 , the phylogeny discloses two well-supported subgroups within the Ophioviridae, one that contains the ophioviruses infecting citrus and blueberry (group 1), and another containing all other ophioviruses (group 2).
Genome screening and sequence analysis Plant genome assemblies (Table  S1 ) were downloaded from NCBI (www. ncbi. nlm. nih. gov/genome/). Screening was performed using the databaseintegrated genome-screening tool (available from http://giffordlabcvr. github. io/DIGStool/). ORFs were inferred by manual comparison of putative peptide sequences to those of closely related exogenous parvoviruses in the alignment editing software Se-Al  [11]. The putative peptide sequences of Ophiovirus-related EVEs were aligned those of representative ophioviruses using MUSCLE  [12]  and PAL2NAL  [13]. Phylogenies were reconstructed from this alignment, using maximum likelihood as implemented in RaxML  [14] , and the VT protein substitution model as selected using ProTest  [15].
Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr -1 (1981-2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data.
Biome models and simulations In ISIMIP2a, to examine model responsiveness and limitations in forcing data, we used four meteorological forcing datasets for the historical period  (Sheffield et al 2006 , Weedon et al 2014 ): Global Soil Wetness Project 3 (GSWP3, 1901 -2010 ), Water and Global Change (WATCH, 1901 -2001) , WATCH Forcing Data with ERA-Interim  (WFDEI, 1901 -2010 ), and Princeton's Global Meteorological Forcing Dataset (Princeton, 1901 -2012). These datasets provide daily solar radiation, temperature, precipitation, humidity, and wind conditions at a spatial resolution of 0. 5°× 0. 5°in latitude and longitude. They were produced from different reanalysis data and by different downscaling methods to cover the range of uncertainty in forcing data. De-trended 30-yr data were also provided for each dataset to conduct spin-up runs under a stationary condition.
Historical changes in atmospheric CO 2 concentration were prescribed by combining data from Meinshausen et al (2011) through 2005 and Dlugokencky and Tans (2014) from 2006 to 2013. See the ISI-MIP web page (www. isimip. org/) for the detailed simulation protocol and model description. In the biome sector, eight models provided simulation output (table  1 ). These models differ in structure of the biogeochemical scheme and parameterization of vegetation dynamics, and thus in their responsiveness to environmental change. As revealed in the first phase of ISIMIP, the biome models produce substantially different projections of productivity, biomass, and soil carbon pool  (Friend et al 2014 , Nishina et al 2015). In terms of GPP calculation, the models differ in parameterizations of canopy radiation transfer, leaf phenology, and physiological limitation on photosynthetic capacity.
Benchmarking datasets We used two global GPP data-driven products, which are independent of the ISIMIP simulations, for benchmarking of annual and monthly GPP. First, we adopted the satellite-derived GPP product based on Moderate Resolution Imaging Spectroradiometer (MODIS; Zhao et al 2005) fAPAR and a light-use efficiency model from 2000 to 2012. The original data, which were in GeoTIFF format with a 5 0 -mesh grid, were averaged and converted into 0. 5°-mesh. Second, we adopted up-scaled flux measurement data  (Beer et al 2010)  from 1982 to 2011; the original spatial resolution of the data was 0. 5°. The data were produced using site-based flux measurement data, climate data, SeaWiFS vegetation index, and a regression-model ensemble algorithm  (Jung et al 2009). These data were used for benchmarking of GPP by gridbased linear regression.
Note that the CARBONES GPP dataset (Kuppel et al 2014), although adopted by other studies (e. g. Anav et al 2015) , was not used in this study, because the dataset is a hybrid of observational data and the ORCHIDEE simulation. Simulated GPP values were also correlated with solar-induced fluorescence (SIF; mW m -2 nm -1 sr -1 ) data at the far-red peak (wavelength 737 nm) measured by the Global Ozone Monitoring Instrument 2 (GOME-2; Joiner et al 2013). SIF is expected to be closely correlated with biophysical and biochemical properties and processes of photosynthesis (e. g. quantum yield; Genty et al 1989). In particular, SIF from satellites tends to capture vegetation activity under clear sky conditions, in which photosynthesis and fluorescence respond in a similar manner  (Porcar-Castell et al 2014).
Therefore, observed SIF data have been used for model benchmarking as a proxy of GPP  (Guanter et al 2014 , Zhang et al 2016b). However, SIF is not completely linked with photosynthetic biophysical processes, and the data contain intrinsic noises and biases due to low signal levels. The GOME-2 SIF data are, even at a monthly basis, a bit noisy for grid-based comparison at the original 1°-mesh resolution, so they were used after aggregation into a 5°-mesh during the period from 2007 to 2010.