dataesr/scientific-paragraphs-categorization huggingface dataset:
g. seamounts, canyons, and plains, 21 in total). Provincial bioregions were defined by the Integrated Marine and Coastal Regionalisation of Australia (IMCRA)  (Commonwealth of Australia, 2006). All 50 features relevant to goals three and four are contained in a spatial dataset termed 'key ecological features'. The wording of the third goal is significant. It appears to relate to broad types of benthic and demersal features, not necessarily to all recognised features within each type. This interpretation is supported by our quantitative results (Section 3. 2, below). 2 https://parksaustralia. gov. au/marine/management/resources/scientificpublications/goals-and-principles-establishment-national-representative-syste m-marine-protected-areas/   1 ). In Australia, marine jurisdiction is divided among the states, the Northern Territory, and the Commonwealth.
Pelagic longlining and trawling were selected because they are major Commonwealth fishing methods and the only fishing effort datasets with extensive and recent spatial data. By using petroleum prospectivity we assumed that prospective areas are likely to be drilled. To strengthen the validity of this assumption we focused on 'mediumhigh' and 'high' prospectivity areas. When quantifying the overlap between broad zones and Commonwealth fishing effort for each iteration of the MPA system, we used the entire span of effort data (2011 to 2014). We supplemented our assessment of iterations and threats to biodiversity by analysing the biomass of fish catch per unit area (kg/km 2 ) in each planning region and by each broad zone in the ten years preceding each iteration. Further, using data from  Larcombe and Marton (2018)  and  Mobsby (2018) , we compared estimations of annual forgone fisheries catch and gross value of production for each iteration, with the reported Commonwealth fisheries catch and gross value of production of the year preceding each MPA system iteration.
A small amount of area promising for offshore petroleum extraction in 2008 was protected by each iteration of the MPA system. Most of this area was in the Townsville Basin within the Coral Sea Marine Park. However, petroleum operations have not been permitted there by any MPA system iteration  (Bioregional Advisory Panel, 2015; Director of National Parks, 2013a , 2018). We did not find a clear reason behind this limitation on petroleum operations, although it is possibly an unofficial extension of the petroleum drilling moratorium established in the Great Barrier Reef region in 1970  (Kriwoken, 1991). Off north-western Australia, where most offshore petroleum extraction occurs nationally, prospective areas have been largely avoided by the MPA system iterations. The prospectivity data were collected between four and ten years prior to iterations.
MPA planners, in Australia and globally, must frame their goals in a manner that explicitly and quantifiably provide for effective mitigation of the range of threats to biodiversity. Commonwealth fishing effort hours were summaries of AFMA log book data on effort distribution from 2011 to 2014. Resolution was 0. 1 degree where five or more vessels operated. Where less than five vessels operated, effort information is not disclosed, and resolution was 1 degree. Because of this, the effort distribution data used in the analysis was only where five or more vessels operated between 2011 and 2014.
Threat Relative prospectivity of the East, North, North-west, and Southwest planning regions Geoscience  Australia (2008a Australia ( , 2008b Australia ( , 2008c Australia ( , 2008d) )  Vector Relative petroleum prospectivity is a measure of how promising an area is for petroleum extraction. Spatial data was not available for the South-east planning region. Note that the East planning region dataset includes the Coral Sea and Temperate East planning regions.
Data We test our method on the two datasets proposed in  Vervier et al. (2016)  and  Menegaux and Vert (2019) , called small and large. Both were extracted from the NCBI bacterial database (reference), and both have a training set and a validation set. In small (resp. large) the training database is composed of 356 (resp. 2961) bacterial genomes, coming from 51 (resp. 774) species. The validation database is 52 (resp. 193) different genomes, coming from the same 51 (resp. 774) species. For both training and validation, reads of length L = 200 are randomly extracted from the reference genomes. We call a training epoch a set of reads such that each base-pair appears once on average (coverage of 1). Epochs are generated on the fly directly during training. We perform data augmentation by adding the possibility to inject random mutations to training reads at a fixed rate r, as this was shown to improve performance by  Menegaux and Vert (2019)  For validation, we create 4 separate sets from the validation genomes.
Embedding canonical k-mers Learning an embedding for each canonical k-mer, rather than for all of them, cuts memory usage by roughly a factor of 2. This enables training a fastDNA model with k = 14 and d = 50, rather than the state of the art k = 13, d = 100 presented in  Menegaux and Vert (2019). As shown in Figure  3 , this new model is better in both sensitivity and specificity than the previous best, for all levels of sequencing noise, on the large dataset. For reads with little noise, it is also better than the alignment method BWA, the latter's performance being however more robust to sequencing noise. This confirms the findings of  Vervier et al. (2016) ;  Menegaux and Vert (2019)  that increasing k in machine learning-based compositional models can be beneficial, and lead to model competitive with alignment-based approaches.
Embedding contigs For the rest of this section, we focus on the proposed algorithm Brume, which learns an embedding per contig of the cdBG of the training set in order to reduce the memory footprint of the embedding layer. This allows us to explore machine learning-based compositional models for larger values of k. Classifying performance for the large dataset ). The embedding dimension is = 50, and the models were trained for 50 epochs. We compare to the results of the BWA aligner (orange dotted line), and to the fastDNA method (yellow), with best parameters k = 14 and training mutation rate r = 4%).
Performance on the small dataset Figure  4  shows the performance of Brume on the small dataset as a function of k, with k ranging from 11 to 31. Notice that due to memory constraints, previous work has only investigated values of k up to 12 or 13. Notice also that we stopped at k = 31 not for memory reasons, but because the performance of the models do not seem to improve by further increasing k. Finally, note that for k less than 13, there is almost a one to one mapping between k-mers and contigs, so results between fastDNA and Brume are expected to be identical. We see in Figure  4  that recall is maximum at k = 15, which coincides with the maximum number of contigs suggesting that perhaps the shattering dimension is what counts ultimately. Larger k lead systematically to a decrease in recall, and to an increase in precision from k = 17 upwards.
Performance on the large dataset We now turn to the more challenging and realistic large datasets. Figure  5  shows the performance on the large dataset as a function of k. As reference we show the performance of BWA, and the best performance fastDNA achieved by embedding canonical k-mers (achieved for k = 14, d = 50, r = 4%). We do not have values for k = 17 and k = 19 because the dBGs were too large to be built or to fit in memory. Overall, we see that, like on the small dataset, there is an important benefit in F1 score when k increases. In particular, for k between 23 and 31, the F1 score of Brume is above both fastDNA and BWA on all four datasets. An interesting observation is that models with larger k's look generally more robust to sequencing errors. One can see in Figure  5  that their performance, in terms of F1, recall and precision, decreases more slowly than that of fastDNA with higher levels of noise.
A possible explanation is that one mutation in a read leads all k neighboring k-mers to be corrupted. The larger k is, the more specific they are and most of them will not find a match in the cdBG, and therefore will not impact the embedding or the classification. On the contrary, fastDNA treats these erroneous k-mers as any other. Similarly to the small dataset, the increase in F1 score for larger k's hides the fact that recall (resp. precision) consistently decreases (resp. increases) with larger k's. The increase in precision for larger k can be at least partly attributed to an increased proportion of reads that are not classified. As seen in figure  6 , this proportion can go over 10% for the large dataset, and increases with both k and the level of noise. Unclassified reads are reads with no matching k-mers found in the dBG of the training set, and the predictions are left blank for them: they count as errors for recall but do not count for precision.
Such analyses of static spatial patterns of metacommunities h produced numerous ecological insights on the processes structur metacommunities and their variation across biomes, taxa and along environmen gradients  (Cottenie 2005 , Henriques-Silva et al. 2013 , Heino et al. 2015). Howev results on simulated datasets challenge these findings and suggest that partition alone does not allow unambiguously grasping metacommunity dynamics (Gilber Bennett 2010, Peres-Neto & Legendre 2010). Here we address whether and h analysing temporal patterns of diversity in metacommunities allows better inferr their underlying processes. Ecosystems and their constituent communities are highly dynamic (e. g. ,  Brok 1985 , Tscharntke et al. 2005 , Malard et al. 2006 , Acuña et al. 2014 , Bertrand et 2016)  influences of the processes driving metacommunity dynamics on diversity patter The processes at play are labelled on each arrow, and the end of arrow represe expected increase while the origin represent expected decrease (the origin of arrow) in patterns of corresponding boxes.
We ran a path analy on such datasets based on the heuristic causal model (Fig. 2 ) with the function "se of the R package "lavaan"  (Rosseel 2012 ) and reporting standardized p coefficients. Since path analyses were based on distance matrices, we used permutation-based approach developed by  Fourtune et al. (2018)  that takes i account the non-independence of the data points and that allows to confidently t for the significance of each path. We followed a Benjamini-Hochberg procedure adjust the significance criterion (of 1%) for multiple testing. We assessed mode with the Standardized Root Mean Square Residual (SRMR) that is a standard meas of model fit for path analyses.
Empirical datasets A-Freshwater fishes We tested the applicability of our conceptual framework on four case studies. T first case study is based on the AFB ("Agence Française pour la Biodiversité", i. e. French Agency for Biodiversity) database synthesizing freshwater fish communit from yearly samples in more than 1500 sites in France  (Poulet et al. 2011 ). Here, restrain our analysis to a subset synthesizing temporal data from the Garon Dordogne river drainage in South-Western France (see  Fourtune et al. 2016 details). This sub-dataset included 32 sites that were thoroughly monitored each y between 1995 and 2011 and for which precise environmental variables w available. This dataset included 51 fish species, for a total of 257,393 sampled fish Six environmental variables were recorded for each site: elevation, slope, avera temperature in January 2011, average temperature in July 2011, width of the mi bed, and width of the water slide.
B-Aquatic invertebrates The second dataset compiles aquatic invertebrate communities across the Rhô river drainage in France. Benthic invertebrates were sampled on 6 sites of different watersheds for a total of 66 sites. They were sampled for six mon consecutively from the end of autumn to the beginning of summer for two yea 2014 and 2015, for a total of 12 sampling dates. The rivers considered intermittent and as such, subject to temporary cessation of flow and/or absence surface water; when some sites were dry, they were not sampled at this da Invertebrates were identified to the genus level but information was kept at family level when no taxa were identified at the genus level for this family, result in a total of 231 taxa. Five environmental variables were measured for each site each sampling date: temperature, pH, conductivity, concentration in dioxygen a number of days since the last rewetting event of the watershed.
C-Freshwater snails The third dataset concerns the malacological fauna -27 species -of a freshwa ponds network in the Guadeloupe Island (Lesser Antilles). 250 sites are yea sampled since 2001 (17 years), where species densities are recorded. Spec densities were multiplied by pond area to obtain estimated species abundances each pond that were subsequently log-transformed. Each site is characterized by temporally constant environmental variables (size, depth, vegetation cover, wa quality, litter and a synthetic index of hydrological and vegetation stability, see La et al. (  2013 ) for additional details), and one temporally varying but spatially const variable (annual rainfall). Geographical distances among sites were computed Euclidean distances and were log-transformed. Missing data and empty sites w removed prior to analyses leading to a total of ca.
D-Aquatic plants The fourth dataset compiles aquatic plant communities in shallow lakes used for f farming. These lakes are in general dried out during one year every 3 years. total of 81 sampling events and 84 sampled plant species  (Arthaud et al. 201  Average species cover was multiplied by lake areas to obtain estimated spec abundances in each lake. Two environmental variables were used: chlorophy concentration that corresponds to water turbidity and light transmission, and number of years since the last drying event.
Analysis of empirical datasets Applied to the four datasets, our statistical framework revealed very consist patterns across case studies (Fig. 4 ). The influence of demographic stochasticity w evidenced in all case studies (see the dashed lines from <J> to βsor). Geograp distances Δx were found to affect community dissimilarity (βsor) in all case stud both directly (putatively through dispersal limitation) and indirectly throu environmental distances ΔE. Environmental distances ΔE were found to influen community dissimilarity (βsor) in all case studies. Temporal distances Δt were fou to impinge on environmental distances in three of the four case studies and direc affect community dissimilarity in half of the case studies. Finally, differences in lo species richness ΔS were found to affect community dissimilarity in all case stud with differences in local community sizes ΔJ influencing ΔS in three of the four c studies.
cific nity y in eam gradient in species richness with more species near the outlet of the river netwo  (Muneepeerakul et al. 2008 , Blanchet et al. 2014 ) and the introduction of non-nat species that may not be homogeneous across the river network. Our analysis reve that such potential drivers may have a dominant impact on the overall f metacommunity structure at the regional scale. Applied to the invertebrate metacommunity data, the main driver of commun turnover was also the heterogeneity in richness among local communities (Fig. 4  This may result from the fact that this dataset comprises perennial and intermitt sites, and the latter ones generally harbour species-poor, original communities w taxa especially adapted to recover from disturbances  (Datry et al. 2014). The ot main drivers were demographic stochasticity and dispersal which may be explain by the intensity of local disturbances and regional disconnections caused by dry events.
Temporal and spatial distances also have a strong effect on environmen distances as expected for intermittent rivers, as the stochasticity of drying eve leads to a high spatiotemporal variability of the environment. For the last two datasets, environmental variation was found to be the m driver of community dissimilarity. This environmental variation was found to be b spatially and temporally structured. This highlights the fact that environmen filtering is both varying across space and time. This further supports our call for integrated spatiotemporal approach to analyse metacommunity patterns and better decipher the ecological drivers that shape metacommunity dynamics. More generally, we found very consistent results among the four case stud despite the variety of sampled taxonomic groups (plants, aquatic invertebrat molluscs and fishes) and habitats (lakes, ponds, perennial streams and intermitt rivers).
Applying the proposed framework to metacommunity data The proposed framework requires temporal data of metacommunity composit and temporal environmental variables that are thought to be influential for system studied. Since the approach is exploratory, it does not require a minim amount of sampled dates nor of sampled locations (beyond 2) to be operational the studied datasets, the number of sampled dates varied from 2 to 17, while In the number of sampled locations varied from 24 to 250. Our approach relies on analysis of community dissimilarity indices, so that it can be applied to species-r communities that contain a substantial amount of rare species with low occurren frequencies. The proposed approach is easy to conduct, since it does not require a advanced statistical training. It allows performing a first exploratory analysis empirical data to assess the respective influences of complementary drivers metacommunity dynamics (see  Kingsolver & Schemske 1991 and Shipley 2000 related discussions).
g. , a polyphagous moth defoliator for tree communit  Tenow et al. 2013). In this case again, absolute time may not be a pertinent varia and may be fruitfully replaced by the state of outbreak (x-vt) where v is the speed the travelling wave and x is the position of the site considered. Our proposition simple and versatile approach to analyse standardized path coefficients, althou this may not always be the choice to be favoured  (Grace & Bollen 2005) , so t researchers should evaluate the pros and cons of this choice for their particular c study. Although the proposed framework appears powerful and robust, it is important keep in mind that only simple linear relationships are modelled in the path analy Our analysis of simulated datasets supports this simple assumption (Fig. S5 -10) a variable transformation procedures can be used to correct obvious non-linearities done here for some empirical case studies using log-transformation of geograph distances.
Supplementary material The C++ code to simulate the spatiotemporal dynamics of metacommunities available at https://github. com/franckjabot/metacommunity_simulator; the R scri to perform the path analysis of metacommunity spatio-temporal data and a tuto  Note that there is no directional trend that would indicate that the transi dynamics from the initial conditions are not terminated. Note also that in the seco scenario (b), the temporal dynamics is slower, but without trend. ent ond.
Standardized estimates and p-values for the path analysis of th mollusc dataset p-values equal to 0 actually mean <0. 001. Significant effects at the 5% level wit Benjamini-Hochberg correction are depicted in bold. Standardized estimates and p-values for the path analysis of th aquatic plant dataset p-values equal to 0 actually mean <0. 001. Significant effects at the 5% level wit Benjamini-Hochberg correction are depicted in bold.
Acknowledgements This preprint has been peer-reviewed and recommended by  Peer Community Ecology  (https://doi. org/10. 24072/pci. ecology. 100027). We thank the reviewers their constructive comments. This work has been funded by the  Irstea-Carnot  proj "MetaRISC". This research was financed by the  French government IDEX-IS  initiative  16-IDEX-0001  ( CAP 20-25 ). It has been partly undertaken at SETE laboratory which is part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TU  ( ANR-10-LABX-41 ). FM is funded by the  CNRS  and the  ANR -funded ARSENIC and N projects (grants no. ANR-14-CE02-0012  and  ANR-17-CE32-0011 ).
Data accessibility Data are available online: http://doi. org/10. 5281/zenodo. 3381338 (freshwater fishes dataset). http://doi. org/10. 5281/zenodo. 3377490 (aquatic invertebrates dataset) http://doi. org/10. 5281/zenodo. 3379615 (molluscs dataset) http://doi. org/10. 5281/zenodo. 3383940 (aquatic plants dataset) http://doi. org/10. 5281/zenodo. 3381340 (simulated datasets).
Conflict of interest disclosure The authors of this preprint declare that they have no financial conflict of inter with the content of this article. FJ, FL, FM, SB, FM and TD are recommenders for Ecology Appendices Appendix S2: Additional information on the simulated scenarios. We here provide some details on the simulated scenarios: the parameter sets us (Table  S2 ), the numerical results of the path analyses (Table  S3 ) and some descript patterns of the various scenarios (Fig. S5-S10 ). These supplementary figures dep for each of the six scenarios the response of community dissimilarity to the vario simulated drivers: temporal distance, spatial distance and environmental distance. Appendix S3: Additional information on empirical analyses. We here provide the numerical results of the path analyses conducted for the fo datasets (Tables  S5-8 ).
To date, few studies have examined temporal dynamics of metacommunities(Datry et al. 2016). We here argue that t limited emphasis on the temporal dynamics of metacommunities reflects (i) a lack a general quantitative framework to analyse temporal changes (but see eNuvoloni et al. 2016) and (ii) the scarcity of proper empirical datasets involving ti series. spa decomposed into two components representing turnover between sites in same different environment, respectively. The dashed boxes represent two components environmental variation affecting metacommunity dynamics, namely, hab variation and varying community size. The solid arrows represent expec.
i-m At day 7, colonic lamina propria leukocytes were analyzed by flow cytometry. i-k After gating on Lin -CD45 + cells, the different subsets of NKp46 + (ILC: NK1. 1 + ; ILC1: T-bet + , cNK: Eomes + ; ILC3: RORγt + ) and NKp46 -ILC (ILC3: RORγt + ; ILC2: GATA3 + ) were analyzed in eight mice per group. l, m Colon samples from two mice were pooled and Ly6C +/-Ly6G -monocyctes and Ly6C + Ly6G + neutrophils (PMN) were analyzed by flow cytometry (6-8 samples per group). i, l Representative contour plots are shown. Numbers indicate percentages. a-f, i-m Data were pooled from two independent experiments with a total of 15-16 mice per group. Data show means + SEM; *p ≤ 0. 05; **p ≤ 0. 005; ***p ≤ 0. 001; ****p ≤ 0. 0001 (two-tailed Mann-Whitney U test). Source data are provided as source data file.
b, c, e, f Data show mean +/± SEM for the respective experimental groups. b-f 5-7 mice were analyzed. g At experimental days 3 (upper row) and 10 (lower row), colon samples were isolated and incubated for 24 h. Cytokine levels were determined in culture supernatants and normalized to tissue mass (n = 11-13/group). h At day 5, frequencies of GM-CSF-producing cells isolated from the colonic lamina propria were determined by flow cytometry. After gating on Lin -CD45 + cells, GM-CSF production was analyzed for NKp46 + CD90 -cNK as well as NKp46 +/-CD90 + helper-like ILC. Pooled results (mean + SEM) from four independent experiments with a total of seven colon samples (pooled from 2 to 3 mice) per group are shown. Statistical significances were determined using a, e, f, g, h two-tailed Mann-Whitney U and b, c two-tailed unpaired Student's t test (*p ≤ 0.