dataesr/scientific-paragraphs-categorization huggingface dataset:
2001 ): the very large Saharo-Sindian -Horn of Africa region overlaps with no less than three different biogeographic realms, supposed to include units from lower hierarchical levels: Palearctic, Afrotropic, and Indo-Malay. We acknowledge that, at a finer scale, our delineation of bioregions might be debatable. This is mainly caused by reduced local data availability or the occurrence of non-native species records difficult to spot in large datasets. For example, the Cape Verde archipelago belongs to both the Mediterranean-Macaronesian and the Saharo-Sindian -Horn of Africa regions whereas it constitutes a single ecoregion in  Olson et al. 's map (2001) , the "Cape Verde Islands dry forests". Nonetheless, the impact of such issues on downstream analysis (e. g. , ancestral states reconstruction in phylogenies, comparison of various biological variables across bioregions etc.
) highly depends on the research question and spatial scale. Furthermore, our new bioregionalization scheme fairly reflects the autochthonous distribution areas of the studied species (see Table  S1 ). As biogeographic processes impact differently across spatial scales and groups of organisms, delineating bioregions is an important first step for the study of biodiversity spatial patterns and species assemblages responses to global changes. A wide range of aridity conditions is observed within and between bioregions, providing the opportunity to anticipate different responses of tree assemblages to future climate change. Our approach makes use of available geographical data, i. e. , occurrence records and climate spatial grids from online databases, and a user-friendly bioregionalization web application, to delimit and identify tree biodiversity patterns associated with warm drylands at the global scale.
For the MTE analyses sediment samples were digested in teflon bombs using a mixed solution (v/v) 20 mL HClO4 and 10 mL HF, method following the protocol of  [29]. Then, MTE (Fe, Mn, Pb, Zn, Cu, Ni, and Co) were analyzed by flame atomic absorption spectrometry (SOLAAR). Phosphate fractionation was carried out according to the chemical method of  [20]. The inorganic fraction phosphate: iron boundphosphate, Fe(OOH)-P and calcium-bound-phosphate, CaCO3-P, have been extracted with chelating agents (NTA and EDTA). Acid Soluble Organic Phosphate (ASOP) has required a single extraction with H2SO4 0. 25 M. Then followed a 2 M NaOH (90°C) with heating step to extract the Residual Organic Phosphate (ROP). The GIS spatial representation has been performed with Surfer software  [2]. Statistical analyses, with a Principal Component Analysis (PCA) and inter-elemental correlation have been performer by PCA R-software.
| Phylogenetic analyses We built the COI database by combining our single cell sequences with publicly available sequences obtained from  Heger et al. (2011)  (Table  S1 ), ending up with a total of 52 sequences from Cyphoderia   S1 ). To obtain the most complete picture of the global diversity of Cyphoderiidae, we included environmental sequences retrieved from the EukBank public repository, which contains eukaryotic metabarcodes based on the V4 region of the SSU rRNA gene, generated from about 13,000 samples distributed worldwide, involving 122 projects from over 90 research teams  (Berney et al. , 2017). Based on this database, we perform a blastn analyses  (Altschul et al. , 1990)  to extract the amplicon sequence variants (ASVs) belonging to Cyphoderiidae, obtaining 64 sequences that were added to our initial data set.
Next, marginal likelihood values were calculated for each model, using path sampling (PS) and stepping-stone (SS) sampling  (Baele et al. , 2013). We then compared the model with the lowest marginal likelihood estimated, as by ps and ss (Table  S2 ) against the remaining models in pairwise Bayes Factors comparison  (Kass & Raftery, 1995)  (Table  S3 ). Finally, we also extracted the time of the most recent common ancestor (tMRCA) for the nodes of interest (freshwater clades 1 and 2, see Section 3) from the beast posterior distribution of each run using treestat  (Vargas et al. , 2014 (Vargas et al. , , 2020)) , in order to capture the uncertainty in divergence time estimates for those nodes. Our sensitivity analysis indicated that the most adequate priors for our database was birth-death  (Gernhard, 2008)  as tree prior and the uncorrelated lognormal distribution, for the relaxed molecular clock.
| Ancestral state reconstructions (ASR) To reconstruct the ancestral habitat state, we used the combined MCC tree based on the SSU data set and the three beast runs, as described above. This tree was pruned to remove the outgroups, using the function drop. tip in the R package ape version 5. 5  (Paradis et al. , 2004). We used the phytools R package  (Revell, 2012)  to infer the habitat history of Cyphoderiidae. We estimated the posterior probability of the discrete characters marine and freshwater for each ancestral node, using an empirical Bayesian inference method, that is, stochastic character mapping  (Huelsenbeck et al. , 2003). We used the function make. simmap (Bollback, 2006)  to generate 500 stochastic character maps from our data set, under two different models: all rates different (ARD) model, which allows different rates of transition between all states, and equal rates (ER), which enforces the same rate of transition between states.
We then summarized these sets of stochastic maps with the function densityMap, plotting the posterior probability of being in each state across all the edges and nodes of the tree. For the COI data set, we used the Bayesian tree, as ML and BI recovered identical topologies. We first transformed this nonultrametric consensus tree into an ultrametric one with the function chronos in ape R package  (Paradis et al. , 2004)  and then resolved the basal polytomy in the mrbayes tree by outcome assigning a branch length of 0. 00001. To reconstruct ancestral states on this tree, we followed the same procedure described above.
However, given the size of the database explored, we can reasonably assume that we have a good representation of the groups diversity. We thus performed our diversification analyses assuming that we have retrieved all the diversity in Cyphoderiidae, that is. , assuming a sampling fraction of 1. We used the MCC tree described above, without outgroups, and further removed the intraspecific variability by pruning the tree to leave only one single terminal per species (74 tips). We then used the function ltt. plot in the package ape version 5. 5  (Paradis et al. , 2004)  to plot the number of lineage diversification events through time. These lineage-through-time (LTT) plots were drawn for each of the individual beast runs, the combined MCC tree, and two subtrees representing the trajectories of the freshwater lineages, and the marine lineages, separately.
For each tree, we also estimated the gamma statistics  (Pybus & Harvey, 2000) , and assessed its significance by comparing the observed value against a distribution of values from 1000 random trees with 74 tips, generated under a Yule model with the empirical speciation rate estimated in ape. We used a reversible jump Metropolis-coupled Markov Chain Monte Carlo (MCMCMC) approach implemented in the Bayesian analysis of macroevolutionary mixtures (bamm version 2. 5);  Rabosky, 2014; Rabosky, Donnellan, et al. , 2014) , following the bamm tutorials  (Mitchell & Rabosky, 2017) , to model the dynamics of speciation and extinction over time and across branches in the combined MCC tree above (with every tip representing a single species). The prior block parameter values were chosen using bam- mtools package  (Rabosky, Grundler, et al.
, 2014) , with a Poisson rate prior of 1. 0. We ran four chains with 10 × 10 6 generations, sampling every 1000th, the final posterior distribution was estimated from 1000 trees. MCMCMC operators were used under default settings. bamm has been criticized recently because of its biased modelling of the probability of rate shifts occurring in extinct lineages (i. e. , a lineage undergoes a rate shift and then goes extinct), leading in some cases to incorrect likelihood estimation  (Laudanno et al. , 2021; Moore et al. , 2016). We used here an alternative lineage-specific birth-death shift model (lsbds), which uses the incremental-time, state-dependent differential equations developed by  Maddison et al. (2015)  to model shifts in diversification rates dynamically  (Höhna et al. , 2019) , and which is implemented in the Bayesian software revbayes version 1.
1. 1 (Höhna et al. , 2014; Höhna, Landis, et al. , 2016). We followed the tutorial (https:// revba yes. github. io/tutor ials/divra te/branch_speci fic. html), using four rate categories (i. e. , allowing for three rate shifts in the speciation rate), ρ = 1 (global sampling fraction), and a chain length of 10 × 10 6 generations. We then obtained the branch-specific diversification rate estimates for each branch of the tree with a rejection-free stochastic rate mapping algorithm and dividing the phylogeny into 500 time slices  (Freyman & Höhna, 2019). We also performed two additional analyses to explore the effect of assuming varying levels of missing species in the phylogeny: using a global sampling fraction of ρ =. 5 and ρ =. 25. Both bamm and lsbds model changes in diversification rates over time that are lineage-dependent.
To explore the possibility of a rate shift that affected all lineages in the phylogeny simultaneously (e. g. , a mass extinction event), we ran an episodic birth-death model to estimate the timing and magnitude of discrete shifts in diversification and relative extinction rates. We used the compound Poisson process of mass extinction (comet) model  (May et al. , 2016)  implemented in the R package tess  (Höhna, May, et al. , 2016) , with a lognormal empiricalHyperPriorForm and sampling probability = 1, a chain length of 20 × 10 6 iterations, sampling every 100th generation, and a minimum number of effective sample size of 1000 to stop the run. comet uses independent compound Poisson process priors to estimate the posterior probability of the timing and magnitude of rate shifts in speciation and extinction; mass extinction events that deplete the standing diversity by a certain fraction (ρ) can also be estimated by integrating out speciation and extinction rate shifts via Bayes factor comparisons  (May et al.
, 2016). Additionally, we ran an episodic birth death model that is implemented in revbayes and uses auto correlated Brownian rates across time slices, and a horseshoe estimator for sparse signals  (Magee et al. , 2020). We followed the tutorial (https://revba yes. github. io/tutor ials/divra te/ebd. html#Hoehn a2015a) with ρ = 1 and a chain length of 10 × 10 6 generations. We used the R package revgadgets version 1. 0  (Tribble et al. , 2021)  to visualize the rates. For both comet and revbayes, we performed two additional analyses changing the global sampling fraction to ρ =. 5 and ρ =. 25, considering that we only sampled the 50% and 25% of the total diversity of Cyphoderiidae, respectively. To explore if diversification rate shifts across branches detected by bamm and lsbds (results) could be explained by transitions between the marine and fresh-water habitat, we performed statedependent extinction and speciation diversification rate analyses.
We first ran binary state speciation and extinction (BiSSE)  (Maddison et al. , 2007)  analysis using the R package diversitree (FitzJohn, 2012) and two states: (1) marine and (  2 ) freshwater. We performed empirical Bayesian estimation of the parameters using the MCMC option. As our tree only included 74 tips, we used an exponential prior with a rate of 1/(2r) (where r is the character independent diversification rate) as recommended in the diversitree tutorial; the step size (argument w) was set as in  Revell and Betancur (2017). We performed two runs of 30,000 steps, with a final burnin of 20% for all runs. We also repeated the BiSSE analysis in revbayes  (Höhna, Landis, et al. , 2016) , which implements a fully Bayesian approach, following the procedures in  (López-Estrada et al. , 2021)  and (https://revba yes.
github. io/tutor ials/ morph/ morph_more. html) (Figure  3c ). We ran the analysis with a log-standard deviation of = 0. 587405 and a chain length of 40,000 generations; we used 500 time slices to estimate the timing and number of state changes along branches using stochastic character mapping. The BiSSE model has been criticized because it assumes a null model for statistical testing, the constant-rate birth death process (i. e. , heterogeneity in diversification rates occurs virtually in any phylogeny;  Rabosky & Goldberg, 2015 , 2017). To test for the existence of diversification rate shifts that are unrelated to state transitions in the focal trait (marine-freshwater habitats), we compared four different SSE models (explained in Appendix 1) in the R package HiSSE  (Beaulieu & O'Meara, 2016)  using the Akaike information criterion (AIC).
We reconstructed the ancestral states along morphospace axes (hypervolume centroid), using the function phylomorphospace in the phytools package  (Revell, 2012; Sidlauskas, 2008). The hyperphylomorphospace resulting from this combination of approaches allows the evaluation of both lineage morphological similarity (distance between each lineage) and the trajectory of the phenotype (directionality of tree branches). The procedure followed is described in Appendix 1.
(b) bamm: Estimated branch-specific speciation rates in the pruned SSU rRNA tree; the net-diversification graph shows results from the episodic birth-death model in comet (compound poissonPoisson prior model). (c) Estimates of speciation and extinction rates from the episodic birth-death model implemented in revbayes. (d) Estimates of speciation and extinction rates, and relative extinction rates, from the episodic birth-death model implemented in comet; the extinction plot depicts also the probability over time of mass extinction events; significance was assessed using Bayes Factor comparisons (BF >2, positive support  Kass & Raftery, 1995). environmental SSU rRNA V4 sequence data set revealed that the majority of Cyphoderiidae amplicon sequence variants (ASVs) stored there come from marine coastal sediments (Figure  1  and Figure  S2 ).
We used the broadscale eukaryotic primers EK555F (5′-AGTCTGGTGCCAGCAGCCGC-3′) and EK1498R (5′-CACCTACGGAAACCTTGTTA-3′). The PCR profile was the following: initial denaturation at 96°C for 5 min, followed by 40 cycles at 96°C for 30 s, 58°C for 30 s and 72°C for 90 s, and a final extension step at 72°C for 10 min. PCR products were directly sequenced without any additional purification step. PCR products were sequenced using Sanger dideoxy-technology in both directions by the company Macrogen Inc. (Macrogen Europe). Raw sequence quality control and the assembling were performed by using the geneious prime software (version 2019. 0. 4). Finally, best match the identity of the sequences was performed by blastn analysis  (Altschul et al. , 1990). NCBI accession for COI OK120268-OK120292 and for SSU OK058389 (Table  S1 ).
Microscopical observation Sampled cells were isolated and documented using an inverted light microscope (Leica DMI8) mounted with DIC lenses, and a Leica MC170 HD camera controlled by the Leica application suite (version 4. 12. 0). For electron microscopy, shells were placed on stubs and coated in 8 nm gold with a Balzers SCD 004 sputter coater with a tension of 15 kV, and then observed with a Hitachi S-3000N scanning electron microscope (SEM). We used the software imagej (version 1. 52)  (Schneider et al. , 2012)  to obtain the shell measurements from the images.
Tree analyses COI and SSU rRNA sequences databases were aligned using mafft  (Katoh et al. , 2002)  and, resulted in a total alignment of 837 bp for COI and 1889 bp for SSU, respectively. Tree topologies and node supports were evaluated with both Bayesian inferences (BI) and maximum likelihood (ML) for each marker/gene individually. ML analyses were conducted using iq-tree  (Nguyen et al. , 2015). Best substitution models were selected with modelfinder  (Kalyaanamoorthy et al. , 2017)  under the Bayesian information criterion (BIC). Node support was assessed with 1000 nonparametric bootstrap replicates. Bayesian inference (BI) analyses were conducted using mrbayes 3. 2. 7a (Ronquist et al. , 2012)  implemented in the CIPRES Science Gateway version 3  (Miller et al. , 2010). Posterior probabilities were calculated with the Metropolis-coupled Markov Chain Monte Carlo (MCMCMC) method by sampling trees  (Huelsenbeck & Ronquist, 2001; Larget & Simon, 1999).
and three from Pseudocorythion acutum, which were used to root the tree. The SSU rRNA gene database included sequences from Heger F I G U R E 1 Maps showing the localities from where Cyphoderiidae COI and SSU rRNA sequences were retrieved. Triangles and diamonds represent freshwater and marine environments, respectively. The colours of the symbols represent the different molecular clades et al. (2010) and Wylezich et al. (2002), as well as environmental clones from Bass and Cavalier-Smith (2004) (Table.
Bayesian phylogenetic tree based on COI sequences of family Cyphoderiidae. Posterior probability values (Bayesian analysis) and bootstrap values (maximum likelihood analysis) are represented at each node. Pie charts at nodes represent marginal probabilities for ancestral state reconstructions obtained in phytools (red colour for marine environment, blue for freshwater). Branch colours represent the molecular clades. GenBank accession codes are given on the tips. Species names correspond to the taxonomic assignation of the organisms based on morphology for each mitochondrial lineage; indices correspond to illustrations. Symbols represent the original habitat of the organism. The images on the right are scanning electron microscopy photographs showing the shell morphology at the same size scale, together with details of the scaling patterns of each phylogroup.
Cyphoderia (freshwater 1, 2, marine 1, 2, C. compressa, C. littoralis and C. major clades) with a PP = 1/ML = 100 and PP = 1/ ML = 100, respectively. The freshwater clade 1, 2 and Cyphoderia major clade were recovered with a PP = 1/ML = 100, PP = 1/ ML = 100 and PP = 1/ML = 100, respectively. Sequences from the EukBank database grouped into a new freshwater clade (3) with a PP = 1/ML = 100, coming from a single geographic location (Lake Sanabria, Spain). The freshwater clade 1 is composed of four nuclear lineages, Cyphoderia major clade by one, and freshwater clade 2 by two all supported by PP = 1 and ML <85. Cyphoderia compressa is the sister group to freshwater clade 1 with a PP =0. 99 and ML =98; recovering the same topology as in our COI tree. EukBank sequences also highlighted the existence of new clades, only present in marine environments (marine clades 1, 2, 3; Figure 3a and Figure S2).
ACK N OWLED G EM ENTS We express our gratitude to  Dr I. García-Cunchillos ,  M. Blázquez ,  M. Rincón ,  M. Villar-DePablo  for helpful discussions on the methodology used in the manuscript. We also acknowledge the help of  Y. Ruiz-León  (electron microscopy),  E. Cano  and  M. García-Gallo  (molecular biology laboratory),  M. Mazuecos  and  S. Martín-Herranz  (data analysis), as well as  C. Zamora-Muñoz  and  J. J. Soler  (fieldwork). This study was funded by the  Spanish Government   PGC2018-094660-B-I00  ( MCIU/AEI/FEDER,UE)  to E. L. and an " Atracción de Talento Investigador" grant  awarded by the  Consejería de Educación, Juventud y Deporte, Comunidad de Madrid (Spain)  ( 2017-T1/AMB-5210 ) to R. G. M; a  National Geographic  Grant  EC-86022R-21  to R. G. M. and E. L. ; and a  FPU  fellowship  FPU19/06077  from the  Spanish Ministry of Science  awarded to F.
DATA AVA I L A B I L I T Y S TAT E M E N T The genetic data generated in this article are available in GenBank Nucleotide Database at https://www. ncbi. nlm. nih. gov/genba nk/ and can be accessed with the accession number OK120268-OK120292 and OK058389. The morphological data is deposited in Morphobank and can be accessed at http://morph obank. org/perma link/?P4082. The nexus alignment and sensitive phylogenetic analyses for SSU without eDNA sequences, SSU and COI are deposited.
and Cyphoderia (freshwater 1, 2, marine 1, 2, C. compressa, C. littoralis and C. major clades) with a PP = 1/ML = 100 and PP = 1/ ML = 100, respectively. The freshwater clade 1, 2 and Cyphoderia major clade were recovered with a PP = 1/ML = 100, PP = 1/ ML = 100 and PP = 1/ML = 100, respectively. Sequences from the EukBank database grouped into a new freshwater clade (3) with a PP = 1/ML = 100, coming from a single geographic location (Lake Sanabria, Spain). The freshwater clade 1 is composed of four nuclear lineages, Cyphoderia major clade by one, and freshwater clade 2 by two all supported by PP = 1 and ML <85. Cyphoderia compressa is the sister group to freshwater clade 1 with a PP =0. 99 and ML =98; recovering the same topology as in our COI tree. EukBank sequences also highlighted the existence of new clades, only present in marine environments (marine clades 1, 2, 3; Figure 3a and Figure S2).
Farm survey Between mid-September and mid-October 2020, the head of the same farming households who had been interviewed in 2007 by  Baudron et al. (2011)  was re-interviewed using a similar questionnaire. These households had been sampled systematically on three transects along the agricultural intensification gradient described above in 2007 by the same team of enumerators. Each household which had at least one cultivated field on one of the transects had been interviewed in 2007, although some of these households were located several kilometres away from the corresponding fields. From the sample of 176 farms surveyed in 2007, 141 farms were re-surveyed in 2020 (23 households had emigrated from the area, the head of 8 households had died between 2007 and 2020 and these farms had been abandoned, the head of one household in the 2007 database had married the head of another household already included in the 2020 survey, the head of one household was in jail, and the heads of four households could not be found in the area for several weeks).
Secondary data: human and wildlife populations Secondary data related to population were extracted from reports issued by the Zimbabwe National Statistics Agency (ZimStats). In addition, to explore possible effects of land cover changes on wildlife populations in the study area around the period of investigation, we used data of the aerial census conducted by the African Wildlife Foundation (AWF) for the year 2003  (Dunham, 2004) , and data of the aerial census conducted by the Great Elephant Census in 2014  (Dunham et al. , 2015). Both aerial censuses used similar strata, as well as the same general survey methodology, with sampling done around August-September in parallel transects with a fixed-wing aircraft at a speed of about 160 km h -1 and 300 ft. above ground  (Dunham et al. , 2015).
Study Design The mCRC cohort (n = 237) collected in the CIRT study was used in this analysis. CIRT is a prospective, single device, multicentre observational study of patients with primary and metastatic hepatic malignancies treated with TARE with Y90 resin microspheres as the standard of care. The CIRT methodology was published by Helmberger et al. 21 Sites were invited to participate if they had reported to have at least 40 TARE cases overall and ten cases in the twelve months prior to invitation. The 27 participating sites were identified and enrolled from April 2014 until April 2017, of which 24 sites enrolled mCRC patients. 22 ata was collected using a customized electronic data capturing system and electronic case report form that was developed by ConexSys Inc (Lincoln, RI) and hosted on a local secure server in Vienna, Austria maintained by ITEA (Vienna, Austria).