dataesr/scientific-paragraphs-categorization huggingface dataset:
(Schmitt et al. 2021). Three sympatric gene pools were identified in a lowland forest in French Guiana, likely corresponding to three biological species, described as Symphonia sp. 1, Symphonia sp. 2 and Symphonia sp. 3  (Schmitt et al. 2021). To avoid the influence of admixture on the estimation of N e , we first divided the dataset in three subsets based on the analysis of genetic structure performed in  (Schmitt et al. 2021) , selecting only the individuals with a Q-value (cluster membership coefficient) ≥ 95% to each of the three genetic clusters (Species 1, Species 2 and Species 3; Supplementary File 1). We then selected the 125 genomic scaffolds with the largest number of SNPs (see Table  1 ). For the annual mercury, M. annua, an annual plant with great variety of mating systems (monoecious, dioecious, androdioecious) and ploidy levels (2x, 4x-12x)  (Obbard et al.
For the apricot, P. armeniaca, we estimated N e using whole genome resequencing data (21× depth of coverage by ILLUMINA technology) for wild Central Asian, self-incompatible populations of the species  (Groppi et al. 2021). Variant sites were mapped to the eight chromosomes of the species and ranged between 2. 3 and 6. 2 million per chromosome (total number of variant sites: 24 M). As these exceeded the total number allowed in GONE, we downsampled the number of SNPs prior to the analyses. We also analysed the datasets by considering the different gene pools recovered in  Groppi et al. (2021)  (Supp. Fig. S20 ), namely the Southern (red cluster) and Northern (yellow cluster) gene pools, as obtained in fastStructure  (Raj et al. 2014 ) (see next subsection).
Data analyses in GONE Analyses for all species. We performed N e estimation in the software GONE  (Santiago et al. 2020). GONE generates contemporary or recent historical estimates of Ne (i. e. in the 100-200 most recent generations) using the LD method. GONE requires linkage information, ideally represented by SNPs mapped to chromosomes. Chromosome mapping is rarely available for non-model species, and in our case was only fully available for the apricot (P. armeniaca) dataset. In the absence of chromosome mapping information for the other species, we used the linkage information associated with genomic scaffolds. In terms of requirements, GONE accepts a maximum number of chromosomes of 200 and a maximum number of SNPs of 10 million, with a maximum number of SNPs per chromosome of 1 million, although the software uses up to 50,000 random SNPs per chromosome for the computations when the total number of SNP is larger.
A complete workflow of the analyses carried out in GONE is available at https://github. com/Ralpina/Ne-plant-genomic-datasets  (Gargiulo, 2023). Influence of missing data on N e estimation. The influence of missing data on N e estimation in GONE was evaluated using the dataset from F. sylvatica. After keeping 67 individuals with less than 95% missing data, we permuted individuals (without replacement) to generate 150 datasets of 35 individuals, and estimated N e in GONE for each dataset. Proportion of missing data per individual for each permuted dataset was calculated in vcftools v0. 1. 16  (Danecek et al. 2011 ) from an average of ~25% to 95%; results were plotted in R v4. 2. 2 (R Core Team 2019). In addition, we used the dataset of P. armeniaca to evaluate how N e changed when manually introducing missing data.
We selected all individuals from the Northern gene pool with a Q-value (cluster membership coefficient) ≥ 99% (77 individuals) to rule out the influence of admixture, and replaced some of the individual genotypes with missing values using a custom script (available at: https://github. com/Ralpina/Ne-plant-genomicdatasets). We generated four datasets, each with a proportion of missing data per individual of 20%, 40%, 60% and 80%, respectively, and then computed N e in GONE for each dataset obtained. Influence of number of SNPs on N e estimation. The influence of the number of SNPs on N e estimation in GONE was evaluated using the dataset of P. armeniaca. From the Northern gene pool, we first selected the individuals with a Q-value ≥ 99% to rule out the influence of admixture. We drew random subsets of variant sites (without replacement) including 40K, 80K, 150K, 300K, 500K, 3.
We then estimated N e in GONE and obtained 95% confidence intervals across the 50 resampled datasets of the same Q-value class within a gene pool (using the R function stat_summary mentioned above). We also combined all individuals from the two gene pools (255 individuals), resampled 77 individuals 50 times without replacement, and estimated N e in GONE and the related confidence intervals as explained above. Effect of using genomic scaffolds rather than chromosomes. We evaluated the effect of using genomic scaffolds to estimate linkage groups when chromosome information is not available. Using the downsampled dataset of 3. 5M SNPs from P. armeniaca, we selected from the Northern gene pool 45 random individuals with a Q-value ≥ 99%, to rule out the influence of admixture. For this dataset, five different chromosome maps were then created, progressively assigning SNPs to 8 (true value), 16, 32, 64 and 128 chromosomes (as if they were genomic scaffolds).
Data analyses in NeEstimator We also used the LD method as implemented in the software NeEstimator v2  (Do et al. 2014)  to estimate N e in our datasets. NeEstimator assumes that SNPs are independently segregating (typically, SNPs at short physical distances, for example those in the same short genomic scaffolds or loci, are filtered previous to analysis, see below), and therefore it provides an N e estimate based on the LD generated by random genetic drift, which reflects N e in very recent generations  (Waples et al. 2016 ). However, accuracy and precision will be both affected by (1) the assumption of independent segregation in genomic data sets, as SNPs are necessarily packed on a limited number of chromosomes and thus they provide non-independent information, and especially (2) the occurrence of overlapping pairs of loci, each locus appearing in multiple pairwise comparisons (i.
e. two aspects of the issue known as pseudoreplication;  (Purcell et al. 2007; Waples et al. 2016; 2022). Although the influence of this issue on bias and precision is difficult to address completely, some bias corrections have been proposed, for example applying a correction based on the genome size of the species being analysed (formula in  Waples et al. 2016 ), using only one SNP per scaffold or thinning scaffolds based on discrete window sizes  (Purcell et al. 2007 ). To explore the changes in precision, we also estimated N e on datasets thinned by subsampling SNPs using windows of 10,000 positions or 5,000 positions, such that no two SNPs found at a distance smaller than 10,000 or 5,000, respectively, were included in the analyses (-thin option in vcftools). As low-frequency alleles upwardly bias N e , we followed the recommendations in  (Waples and Do 2010 ) and used a threshold minimum allele frequency, P crit , to filter rare alleles occurring at a frequency lower than 0.
Results and Discussion Our study explores the limitations associated with genomic datasets when estimating N e using the LD method as implemented in the software GONE. The estimation of N e in GONE failed for the three biological species of S. globulifera, as the software returned the error "too few SNPs" for each of the three species datasets. This was caused by the relatively small number of SNPs per scaffold (averaging ~250 SNPs) and, in turn, by the relatively short length of the scaffolds (length ranging from 5,421 to 931 positions). We then estimated N e for the three species datasets using NeEstimator. N e ranged from 93 (CI: 39-Infinite) in Species 3, to 246 (CI: 199-320) in Species 2 and to 371 (CI: 318-441) in Species 1 (Table  2 ). These estimates may not reflect the population-wide true effective population size, but rather a quantity close to the neighbourhood size (Ns), i.
e. the inverse of the probability of identity by descent of two uniting gametes, which is typical of sparsely sampled continuous populations of forest trees (see  Neel et al. 2013; Nunney 2016; Santos-del-Blanco et al. 2022). Moreover, we observed that thinning datasets increased N e estimates and generated broader CIs, ranging from 213 (CI: 38-Infinite) for Species 3, 773 (CI: 316-Infinite) in Species 2, and 1,382 (CI: 677-56,918) in Species 1 (Table  2 ). An increase in N e estimates and their CIs is expected when reducing the number of closely linked SNPs, as thousands of physically linked loci with limited recombination are predicted to bias N e downward while also narrowing CIs because of pseudoreplication  (Waples et al. 2016; 2022). Removing the MAF filter on the full dataset produced the expected inflating effect of rare alleles on N e (Waples and Do 2010); however, when considering the thinned dataset, including rare alleles reduced N e estimates rather than increasing them.
Influence of missing data on N e estimation The effect of missing data on N e estimation is evident from the results obtained when analysing the dataset of F. sylvatica, in both GONE and NeEstimator, and from the results obtained when analysing the dataset of P. armeniaca in which genotype data were manually excluded. For F. sylvatica, 35 individuals had a proportion of missing data < 50% (Fig. 1B ). Increasing the proportion of missing data in the permuted datasets of 35 individuals produced acute increases in N e estimates in GONE (see Fig. 1A ); for instance, increasing the median proportion of missing data per individual from 25% to 35% produced N e estimates increasing from 200 to 3 millions. Likewise, when missing data proportion per individual of P. armeniaca increased above 20%, we obtained N e estimates that were > 350 times larger than those obtained from the original dataset (average missing data proportion per individual ~ 8%) (Fig.
2 ). N e estimation in GONE failed when the datasets included a proportion of missing data exceeding 60% in each individual. This relationship between missing data and N e estimates is consistent with what was previously found (e. g. ,  Marandel et al. 2020) , although the loss of accuracy in the N e estimation is extreme and suggests that individuals with > 20% missing data should be removed from the dataset before estimating N e in GONE. However, in the specific case of F. sylvatica and in other species with large effective population sizes, reducing the sample size (S) to a number << true N e introduces a further bias in the N e estimation using the LD method, in addition to the sampling error already expected because of the finite sample size  (Peel et al. 2013). In NeEstimator, even selecting the 35 individuals with < 50% missing data failed to produce N e estimates in F.
sylvatica, in contrast with a previous study showing that the method implemented in the software was relatively robust with a similar percentage of missing data  (Nunziata and Weisrock 2018). As NeEstimator handles missing data using a weighted harmonic mean that assumes random and independent occurrence of missing data  (Peel et al. 2013; Do et al. 2014 ), a higher and therefore non-random occurrence of missing data in the longest scaffolds of the beech dataset might have prevented the N e calculation in NeEstimator.
Influence of number of SNPs on N e estimation The influence of the number of SNPs per chromosome was explored using the dataset from P. armeniaca (Northern gene pool), which was the only dataset with SNPs fully mapped to chromosomes. Increasing the number of SNPs per chromosome affected the point N e estimates only slightly, and influenced the precision of the estimates more obviously, especially for a total number of above 300,000 SNPs, corresponding to an average of 10,000 SNPs per chromosome of P. armeniaca used by GONE (Fig. 3 ). Accuracy and precision of N e estimates based on LD are expected to be affected by two types of pseudoreplication: (1) the non-independent information content provided by thousands of linked SNPs, and especially (2) the occurrence of overlapping pairs of loci, each locus appearing multiple times in pairwise comparisons  (Waples et al.
However, as the information content of a dataset depends on the amount of recombination and on the pedigree of the individuals included in the analyses, an estimation based on a small number of samples will not necessarily be representative of the entire population, especially if N e is large  (King et al. 2018; Santiago et al. 2020). Furthermore, the marginal benefit of increasing the number of SNPs beyond tens of thousands is counterbalanced by poor precision if CIs are generated using incorrect degrees of freedom, which is often the case with thousands of nonindependent SNPs  (Do et al. 2014; Jones et al. 2016; Moran et al. 2019; Luikart et al. 2021; Waples et al. 2022). The estimates produced by NeEstimator from the datasets of P. armeniaca subsampled with two thinning windows (Table  2 ) revealed high consistency in point estimates and jackknife confidence intervals obtained, regardless of the thinning windows and the P crit used, and despite the number of SNPs being halved in one of the two thinned datasets compared to the other.
However, the estimates were lower than those obtained in GONE and based on at least ten times more SNPs. Considering that a downward rather than an upward bias is expected due to pseudoreplication when closely linked and overlapping pairs of loci are used, the higher N e estimates in GONE can be explained with the higher power associated with many SNPs, and therefore an improved accuracy and precision when thinning datasets is offset by the reduced power associated with reducing the number of SNPs in the analysis  (Waples et al. 2022).
For M. annua, we attempted N e estimation despite the small sample sizes using both GONE and NeEstimator. All point N e estimates were generally small (< 50) and confidence intervals were narrow, with larger N e values for the Core gene pool (Table  2 ). These estimates are much smaller than those provided by  González-Martínez et al. (2017)  for present-day, coalescent N e using extensive demographic modelling in fastsimcoal2, which produced values of about ~500,000 for the Core gene pool and ~40,000-50,000 for the other gene pools. In M. annua, we did not observe substantial differences in the N e estimates obtained from the thinned datasets (for example an increase in N e estimates due to partially removing the influence of pseudoreplication, as observed for S. globulifera) and this suggests that the downward bias caused by a small sample size is potentially much stronger than the downward bias associated with pseudoreplication (Table  2 ).
Influence of admixture on N e estimation The impact of admixture on N e estimation was explored using the dataset of P. armeniaca. Estimates of N e in the most recent generation generally decreased when the Q-value of the individuals included in the analysis increased (Fig. 5A ). The larger N e estimates in the most recent generations (1-4) when including more admixed individuals are consistent with the upward bias predicted by  Waples and England (Waples and England 2011)  for a sampled subpopulation that does not include all potential parents ("drift LD"); with higher admixture proportions (Fig. 5A ), the N e estimated for each gene pool (subpopulation) using the LD method tends to approach the N e of the metapopulation instead  (Waples and England 2011). However, the N e estimate we obtained when combining the two gene pools ("all" in Fig.
Effect of using genomic scaffolds rather than chromosomes To evaluate the effect of using genomic scaffolds as a proxy for linkage groups when chromosome information is not available, we sorted SNPs from the P. armeniaca dataset into a progressively larger number of scaffolds or chromosomes assumed. This produced inconsistent N e estimates across the datasets with increasing number of chromosomes assumed, with N e values progressively rising from around 3×10 3 for 8 chromosomes (true value) to > 8×10 5 when the number of chromosomes assumed was equal to 128 (Fig. 6 ). The algorithm implemented in GONE is based on the assumption that LD among pairs of SNPs at different genetic distances provides differential information about N e at different times in the past  (Santiago et al. 2020). Loosely linked loci give information about N e in recent generations, as their recombination rate is higher and rate of LD-decay slower than that of closely linked loci  (Sved and Feldman 1973).
Therefore, the behaviour of the N e estimates observed in Fig. 6  can be explained by considering that assigning potentially linked SNPs to different chromosomes free to recombine causes their LD to decay more slowly, overestimating N e. This finding suggests that the N e estimates found in GONE for M. annua and F. sylvatica may be further biased since, for both datasets, scaffolds were used as a proxy for chromosomes (Table  1 ).
Practical recommendations when estimating contemporary N e in GONE In this study, we have considered some of the technical limitations when estimating N e for plant genomic datasets, including: (i) the occurrence of missing data, (ii) the limited number of SNPs/individuals sampled, (iii) the lack of genetic/linkage maps and of information about how SNPs map to chromosomes when estimating N e using the software GONE. In addition, we have explored some biological limitations that may affect N e estimation using the LD method, such as the occurrence of population structure. Our empirical results corroborate some previous findings, for example about the importance of having large samples sizes (ideally > 30 per subpopulation), especially when populations are large, and highlight the following requirements that genomic datasets should satisfy: • non-random missing data should not exceed 20% per individual.
g. ,  Neel et al. 2013 ). This bias and potential other biases associated with (i) further sources population structure (i. e. overlapping generations, demographic fluctuations including bottlenecks, reproductive strategies causing variance in reproductive success, etc. ) and (ii) further technical issues associated with sampling strategies and genomic datasets can add up and generate results that are misleading for conservation. Therefore, a careful consideration of the issues above is essential when designing and interpreting studies focused on the estimation of N e and other related indicators for conservation.
Acknowledgements and Funding information This study was carried out within the short-term scientific mission "Estimating  effective population size in genomic datasets: test of methods and assumptions ", organised by  Working Group 2 of the European Cost Action   CA18134  " Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BiKE )". The work on F. sylvatica was supported by the  Genoscope , the  Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA)  and  France Génomique  ( ANR-10-INBS-09-08 ). We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12), to the  Bordeaux Bioinformatics Center (CBiB) , and to the  Royal Botanic Gardens, Kew HPC (KewHPC)  for providing computing and storage resources.
Data and code availability The SNP matrices used in this study can be accessed at the following links: https://doi. org/10. 5281/zenodo. 4727831 (Symphonia globulifera), https://datadryad. org/stash/dataset/doi:10. 5061/dryad. 74631 (Mercurialis annua), https://doi. org/10. 57745/FJRYI1 (Fagus sylvatica), https://doi. org/10. 5281/zenodo. 8124822 (Prunus armeniaca). The analyses carried out in this study and the related scripts are available at: https://github. com/Ralpina/Ne-plant-genomic-datasets  (Gargiulo, 2023).
systems, Core (16) (exome) Martínez et al. sample size analyses based Mediterranean capture 2017 on dioecious (12) populations; obligate outcrosser (González- Martínez et al. 2017) Fagus Perenn Monoecious, Mt. Ventoux, Whole genome 0. 81 12-150 ~470K ~13 M See data Influence of sylvatica L. ial predominant France (167) sequencing (with 27 (scaffolds) (with 27 (with 27 availability missing data (tree) outcrossing scaffolds) scaffolds) scaffolds) section (Merzeau et al. 1994) Prunus Perenn Monoecious, Southern (56) Whole genome 0. 07 8 ~3 M ~24 M (3. 5 Groppi et al. Influence of armeniaca ial self- Northern (199) sequencing (chromoso (440K) M in the 2021 number of L. (tree) incompatible (see mes) subsample SNPs, of (Groppi et al. Supplementary d dataset) sample size, of 2021) Table 1) population structure, of using scaffolds.
We focused on germline mutations across different species that were available in public datasets. We tested whether yearly germline mutation rates across 37 vertebrate species (including 23 mammalian species, 10 bird species, 3 reptilian species, and 1 species of Actinopterygii) (9) could explain the variability in cancer risk and death, using cancer mortality data from the Zoological Information Management System (ZIMS) software (https://species360. org/).
Results & Discussion We used previously published germline mutation rate data (68 species)  (9)  and tested for cancer mortality (37 matching species) using the ZIMS software. For a given species, cancer mortality is measured as the number of animals that died of cancer divided by the number of animals that died (including dying of cancer, but not of neonatal issues and parental neglect: see Methods). Merging the animals in both datasets, the common garter snake (Thamnophis sirtalis) had the highest germline mutation rate (1. 27 x 10 -8 ), and the house mouse (Mus musculus) had the highest cancer mortality (0. 36). The snowy owl (Bubo scandiacus) had the lowest germline mutation rate (1. 02 x 10 -10 ), and the southern screamer (Chauna torquata), the White-faced saki monkey (Pithecia pithecia), and Darwin's rhea (Rhea pennata) had the lowest cancer mortality (zero).
Still, the causal relationship between germline mutation rate and cancer prevalence is unknown across vertebrates, i. e. , whether an increased germline mutation rate is associated with an increased somatic mutation rate. Reproducing our results on an even larger number of species, with additional comparative oncology databases, and understanding the interactions between life history variables selecting for changes in germline mutation rate and cancer mortality, would help to answer our central question: what explains variation in cancer susceptibility across species? For now, the species we identified with an association between germline mutation rate and increased cancer mortality may harbor species-wide hereditary risk for cancer and thus may be under the risk of extinction. These species, in particular, may benefit from early cancer screening to diagnose tumors at an earlier and more treatable clinical stage of development.
Acknowledgements: We thank  Profs. Erwin Neher  and  Takafumi Miki  for helpful comments on the manuscript. This work was supported by  CNRS  ( UMR 8003 ), by  Fondation pour la Recherche Médicale  (grant  SPF201809007190  to V. T. ) and by the  European Community  ( ERC Advanced Grant  'Single Site' nb. 294509  to A. M. ). We acknowledge the use of the animal rearing service of the  BioMedTech Facilities  ( INSERM US36 , CNRS UMS2009 ,  Université Paris Cité).