This was followed by significant increases in functional diversity of the bacterial community, including metabolic capacity, lignin and cellulose metabolism, and salicylic acid synthesis. These findings indicate that the most striking compositional and functional bacterial community changes took place during the active phase of the process. They also pinpoint functional attributes that may be related to the potential beneficial effects of distilled grape marc vermicompost when applied on soil and plants.Vermicomposting has been found as a profitable approach to dispose of and treat large quantities of raw grape marc. However, less information is available with regard to its efficiency for treating distillery winery byproducts, even though distillation has been widely used as a way to economically valorize grape marc. As such, we sought to characterize the compositional and functional changes in bacterial communities during vermicomposting of distilled grape marc by using 16S rRNA high-throughput sequencing. Samples were collected at the initiation of vermicomposting and at days 14, 21, 28, 35 and 42. There were significant changes ( Grape marc, the major solid byproduct of the winemaking industry, has been used as an additive in animal feeding , and as Nevertheless, the process of distillation also involves the generation of liquid and solid distillery effluents such as vinasses and distilled grape marc that must be treated, disposed of or reused properly to avoid negative environmental impacts . As in rNonetheless, there is scarce information about the feasibility of vermicomposting for treating distillery residues . Both raVitis vinifera L. cv. Albariño in a pilot-scale vermireactor by means of 16S rRNA high-throughput sequencing.However, it is not known yet if the bacterial communities involved in the various stages of vermicomposting of marcs derived from the distillation process will undergo similar compositional and functional changes as those reported for raw grape marc over the course of the process. Therefore, the aim of the present study was to characterize the taxonomic and functional diversity of the bacterial communities involved in the vermicomposting of distilled grape marc of Vitis vinifera cv. Albariño grapes since this grape variety represents 95% of the annual white grape harvest in northwestern Spain. The distilled grape marc was kindly supplied by a winery located in Pontevedra . It was stored at 4 °C until needed and turned and moistened with water during two days prior to the vermicomposting trial.We chose marc derived from distillation of Eisenia andrei) was close to its maximum capacity, having a value of 10,923 ± 1783 individuals m−2 that corresponds to 289 ± 72 mature earthworms m−2 and 10,634 ± 1767 immatures m−2, with a mean biomass of 1704 ± 251 g m−2. The earthworm population density in the present study was 36 times higher when compared to that in Kolbe et al. [The distilled grape marc was processed in a rectangular metal pilot-scale vermireactor (4 m long × 1.5 m wide × 1 m high) housed in a greenhouse with no temperature control over a period of 42 days. The vermireactor set-up has been previously described by Kolbe et al. . The popFor the characterization of the molecular and the microbial properties, the distilled grape marc layer (12 cm height) was divided into five sections; five samples (10 g) were taken at random from each section at day 0 (fresh distilled grape marc) and after 14, 21, 28, 35 and 42 days of vermicomposting. Samples were bulked and stored in plastic bags at −80 °C until needed.2 after 6 h of incubation. The evolved CO2 was trapped in NaOH and then measured by titration with HCl to a phenolphthalein endpoint after adding excess BaCl2 [Microbial activity was assessed as basal respiration by measuring the rate of evolution of COss BaCl2 .® kit  according to manufacturer’s protocols. DNA quality and quantity were determined using BioTek’s Take3™ Multi-Volume Plate , as previously described by Kolbe et al. [DNA was extracted from 0.25 g  of distilled grape marc using the MO-BIO PowerSoile et al. . In totaassignTaxonomy function in DADA2, which implements the RDP naive Bayesian classifier [The DADA2 pipeline (version 1.12) was used to infer the amplicon sequence variants (ASVs) present in each sample . Standarassifier ,31. A miThe functional composition of the metagenomes was predicted using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software package (PICRUSt) . Predictp-values adjusted by false discovery rate (FDR < 0.05). Because multiple pairwise Wald tests were conducted for each pairwise time comparison , we further adjusted these p-values using the Benjamini–Hochberg method to correct for multiple testing. After correction, nonsignificant contrasts were considered to have an effect size (log2-fold change) of zero.ASVs with less than three sequences and not present in at least 5% of the samples were removed, thus eliminating 59.88% of the ASVs but only 1.85% of the sequences. Rarefaction curves indicated that the sampling depth was optimal for all samples in the full data set . We decided not to consider the fresh distilled grape marc because it is not an “earthworm-processed material”. An approximate maximum-likelihood phylogenetic tree was inferred using FastTree 2.1 . TaxonomMicrobial activity assessed as basal respiration significantly decreased from the beginning of the experiment (day 0) until day 28, followed by no more noticeable changes until the end of the trial on day 42 . This poGamma-, Alpha-, and Deltaproteobacteria) followed distinct trends over the course of the process , earthworms led to an increase in its differential abundance after 14 days of vermicomposting . This inBacterial alpha-diversity assessed as both ASV richness A and ChaPrincipal coordinate analysis using unweighted UniFrac distances showed a clear separation between day 0 (fresh distilled grape marc) and days 14–42 (vermicompost samples) along the first dimension (PCoA1), which accounted for 51.65% of the total variance A. The seRhodobacteraceae, Rhizobiaceae, Sphingomonadaceae and Burkholderiaceae, and from the genera Devosia, Acinetobacter, Yersinia, Pseudoxanthomonas, Stenotrophomonas and Pseudomonas, among others , and Patescibacteria (one ASV from the family Saccharimonadaceae)  .20,5 = 7.95, p = 0.0003; F20,5 = 3.86, p = 0.013 respectively; 20,5 = 7.77, p = 0.0003; Our PICRUSt analysis showed a significant increase in genes classified only as “metabolism” in KEGG functional hierarchies after 14 days of vermicomposting, followed by no more noticeable changes until the end of the trial . The samThe bacterial richness of distilled grape marc, assessed as ASV and Chao1 richness, was about three times higher than that in the raw grape marc at the beginning of their respective vermicomposting trials . The samWhen we compared with the raw grape marc bacterial community composition , the relBased on Venn diagrams twenty ASVs  were shared by both winery byproducts on day 0 , while 1Vermicomposting of distilled grape marc of the white grapevine cultivar Albariño was characterized by rapid bacterial community compositional changes. After 14 days of vermicomposting, the most striking shift at the phylum level involved a decrease in the abundance of Firmicutes , as prevE. andrei reduced the abundance of Gram-positive (G+) bacteria to a greater extent than that of G− bacteria [E. andrei increased the Actinobacteria abundance independently of the initial feedstock.The phylum Bacteroidetes is associated with copiotrophic environments , but unlbacteria ,49. Thisbacteria  observedThe rapid bacterial compositional changes were accompanied by an increase in richness and both taxonomic and phylogenetic diversity of distilled grape marc within the first 14 days of vermicomposting , as earlMycobacterium, Thermomicrobia, Rhodobacteraceae, Flavobacteriaceae, Pseudomonas, Chryseobacterium, and Acinetobacter) were also previously found in casts of E. andrei [Burkholderiaceae and genus Devosia that belongs to the order Rhizobiales) and to plant disease suppression (Pseudomonas) [Chitinophagaceae and other representatives of the phylum Bacteroidetes, which are known to produce a large number of plant cell degrading enzymes with the ability of degrading macromolecules such as chitin and cellulose [The presence of a core of common bacteria was evidenced in the vermicomposted samples (days 14 to 42) obtained from distilled grape marc . The cor. andrei . Furtherdomonas) . Chitinoellulose , were alAnalysis of functional diversity with PICRUSt revealed that vermicomposting of distilled grape marc resulted in a rapid increase in the metabolic capacity of the bacterial community after 14 days followed by no more significant changes for the duration of the trial . DespiteIn addition, processing of distilled grape marc through vermicomposting resulted in increases in the abundance of specific genes involved in the breakdown of cellulose and lignin  inset, aProteobacteria and Bacteroidetes dominated the bacterial microbiome over the course of the process as previously shown for raw grape marc, and they contributed with a higher number of ASVs to the core microbiome of distilled grape marc. The compositional changes were accompanied by a rapid increase in richness and taxonomic diversity of bacterial communities on day 14, with values similar to those reported for raw grape marc at the end of the process. Although there was an increase in the metabolic capacity of the bacterial communities after 14 days, the functional diversity of distilled grape marc was lower when compared to raw grape marc. In conclusion, vermicomposting appears as a promising sustainable option to process the valorized distilled grape marc from Albariño grapes, yielding an end product with richer and higher bacterial diversity, and with functional attributes that may enhance the role of distilled grape marc vermicompost for its further use as plant growth promoter and/or soil organic amendment.


Ephydatia muelleri is a freshwater sponge found across the northern hemisphere. Here, we present its 326 Mb genome, assembled to high contiguity (N50: 9.88 Mb) with 23 chromosomes on 24 scaffolds. Our analyses reveal a metazoan-typical genome architecture, with highly shared synteny across Metazoa, and suggest that adaptation to the extreme temperatures and conditions found in freshwater often involves gene duplication. The pancontinental distribution and ready laboratory culture of E. muelleri make this a highly practical model system which, with RNAseq, DNA methylation and bacterial amplicon data spanning its development and range, allows exploration of genomic changes both within sponges and in early animal evolution.The genomes of non-bilaterian metazoans are key to understanding the molecular basis of early animal evolution. However, a full comprehension of how animal-specific traits, such as nervous systems, arose is hindered by the scarcity and fragmented nature of genomes from key taxa, such as Porifera.  Reconstructing the early molecular evolution of animals requires genomic resources for non-bilaterian animals. Here, the authors present the chromosome-level genome of a freshwater sponge together with analyses of its genome architecture, methylation, developmental gene expression, and microbiome. In animals, the events that enabled this transformation can often be inferred by comparing the genomes of living representatives of non-bilaterian animals to those of bilaterians and their sister taxa, and determining shared characters and key differences between them2. However, the origins of several fundamental metazoan traits, such as tissues and nervous systems, are still unknown. Determining the origin of these characteristics requires more robust and contiguous genomic resources than are currently available for non-bilaterian animal taxa.One of the key events in the history of life was the evolutionary transition from unicellular organisms to multicellular individuals in which differentiated cell types work cooperatively3 and are an essential reference group for comparative studies. The benchmark genome for sponges, Amphimedon queenslandica4, has provided a wealth of insight into the genomic biology of sponges6, yet studies of other sponge species have suggested that traits in A. queenslandica may not be representative of the phylum as a whole8. For example, its genome is one of the smallest measured in sponges6, it is highly methylated in comparison to other animals9, may have undergone some gene loss even in well-conserved families10 and has been described as possessing an ‘intermediate’ genomic state, between those of choanoflagellates and metazoans5. There are over 9200 species of sponge , and understanding whether the unusual characteristics of A. queenslandica are typical of this large and diverse phylum can only be tested with additional, and more contiguous, genome assemblies.Porifera, commonly known as sponges, are one of the first lineages to have evolved during the rise of multicellular animals12 and therefore are central to understanding the processes and mechanisms involved in the initial metazoan radiation. Sponges possess the fundamental characteristics shared by all animals, including development through embryogenesis to form tissues and signalling to coordinate whole body behaviour14. Most also have a highly conserved body plan, consisting of canals and pumping cells that filter water effectively15. However, within the four classes of sponges , several groups differ from this Bauplan. Glass sponges (Hexactinellida) have syncytial tissues and are the only sponges shown to propagate electrical signals16 while Cladorhizida (Demospongiae) are carnivorous and capture crustaceans with hook-like spicules17. Despite the diversity of sponges in the most species-rich class, the Demospongiae, only one group, the Spongillida, made the transition to freshwater some 250–300 million years ago (Mya)  Fig. , later d19, they can withstand extremely cold temperatures and even freezing, as they inhabit some lakes that see temperatures below −40 °C20, and can also tolerate extreme heat and desiccation in desert sand dunes and high up on tree trunks22. Freshwater sponges are both unfamiliar and yet so common worldwide that under the right conditions they can foul drinking water reservoirs, waste treatment plants, intake pipes, and cooling systems for power plants23. The main adaptation which permits colonisation of such extreme habitats is the production of sophisticated structures called gemmules, a distinctive stage in the life of these sponges21. The events that allowed colonisation of freshwater which are required for adaptation to extreme habitats by sponges are not yet fully understood24. Whether genomes of freshwater species are remarkably changed from those seen in marine sponges and other animals is also yet to be investigated.The transition to freshwater is one of the most remarkable evolutionary trajectories marine animals can undergo, as it requires a complete spectrum of physiological adaptations to novel habitats. Not only can freshwater sponges, which consist of a single layer of cells over a scant extracellular matrix, control their osmolarity in freshwaterEphydatia muelleri   Ephydatia muelleri genome using PacBio, Chicago, and Dovetail Hi-C libraries sequenced to approximately 1490 times total coverage (Supplementary Note Ephydatia muelleri chromosomes (2n = 46)29 as single sequences, with one chromosome represented by two sequences likely split at the centromere , a possible symbiont species, was removed from the final assembly and analyzed separately (Supplementary Note One scaffold (the 25th largest), containing the partial genome of a member of a E. muelleri assembly size corresponds well to the predicted genome size based on Feulgen image analysis densitometry (0.34 pg) and flow cytometry (0.33 pg)30, and is twice the size of the well-studied A. queenslandica genome 31. The genome is approximately 43% G+C . Nearly 47% of the genome is repetitive sequences, compared to A. queenslandica 43% and S. ciliatum 28% 5, S. ciliatum 31, and Tethya wilhelma 8. The gene annotations contain 90.10% of the 303 eukaryotic BUSCO genes  have similarity to other organisms as determined by DIAMOND BLASTx , and nearly half of these hits  were best-matches to other sponge sequences  model33. The DCJ-DS model predicts that dosage sensitive genes would tend to stay on the same chromosome, although the local order may change. We find this is clearly observable in shorter chromosomes  and yet we found evidence for conserved syntenic regions and even local gene order within scaffolds between mes Fig. . For exadae Fig. . Only tees4 Fig. .E. muelleri and two choanoflagellates, Monosiga brevicollis and Salpingoeca rosetta , or in the lineage leading to the last common ancestor of sponge and other animals, or, alternatively, in both of these lineages.In contrast to the conserved synteny between animal lineages, we detected no conserved syntenic regions between tta Fig. . While E34. To understand the processes underlying gene regulation in non-bilaterian metazoans, data on the three-dimensional architecture of chromatin is needed from these clades. We used HOMER and Bowtie2 to analyse Hi-C data from E. muelleri and found that as in other animals the genome is organised into topologically associating domains (TADs) as well as loops, although we did not find mammalian-like corner peaks at the edges of the predicted TADs  but does possess a suite of non-CTCF zinc finger proteins which form a sister group to the bilaterian CTCF proteins36. Besides these CTCF-like sequences, the E. muelleri genome contains both cohesin and structural maintenance of chromosome (SMC) sequences, which are highly conserved throughout eukaryotes  and a single calcareous sponge (S. ciliatum), which suggested sponges may have highly variable levels of genomic methylation. To assess whether the high levels of methylation seen in A. queenslandica are common to other demosponges, we carried out whole genome bisulfite sequencing on tissue from a fully developed (Stage 5) E. muelleri genomic DNA sample. The global genomic level of methylation in E. muelleri is 37% (mCG/CG), which is higher than most invertebrates profiled to date9, but much lower than A. queenslandica (81%) and S. ciliatum (51%)  Fig. . The sliA. queenslandica genomes are highly depleted for CpG dinucleotides9. Congruent with the intermediate methylation levels, we found that the genome of E. muelleri is also depleted for CpG dinucleotides, more than most invertebrates but less than in A. queenslandica  than in A. queenslandica, but marginally lower in absolute levels than those seen in S. ciliatum  numbers of shared orthogroup losses in three of four freshwater lineages, compared with the direct marine counterexamples of such a pattern . We found that the largest clusters included genes involved in chemokine binding, and one cluster included over 50 metabotropic GABA receptors , 48% of which are on scaffolds 4, 13 and 22. Most of these are expressed, alongside other enlarged gene complements such as cortactin (52 clusters), NBAS (35) and integrin beta (36) , as well as aquaglyceroporins , which control movement of glycerol, arsenite, and silicic acid among other compounds. However, in freshwater sponges only aquaglyceroporins are present, while many aquaporins are lost compared to outgroups   >1/<−1) across the gemmule-hatching process  of the ess Fig. . The patess Fig. . At Stagess Fig. . Many oflia Fig. . Similar0/39,245 45. Exactly how contractions are coordinated is still unknown, and the potential position of ctenophores rather than sponges as sister to the rest of animals on the tree of life48 provocatively implies that sponges could have lost neurons.Sponges have no nervous system and yet they contract in response to a range of stimuli49, but the synapse and in particular the proteins that compose its scaffolding and chemical neurotransmitter complement, are agreed to be an important component50. One difficulty is that genes in the neuronal synapse also have other tissue-specific functions. One family of genes with tissue-specific functions are the SyNaptosomal-associated proteins (SNAPs) of vertebrates. These proteins are members of the Soluble N-ethylmaleimide sensitive factor attachment protein REceptor (SNARE) protein superfamily51 which function in membrane fusion at the cell surface. In mammalian cells SNAP-25 mediates the fusion of vesicles with the presynaptic membrane of neurons, while its paralogue SNAP-23 mediates the fusion of vesicles in other regions of the cell surface52. The presence of genes encoding SNAP-23/25-like SNARE proteins has sometimes been inferred to indicate presence of neuron-specific protein machinery53. However, homologues of SNAP SNAREs are widely conserved among eukaryotes without nervous systems, such as plants54. This raises the question of whether particular SNAP SNARE genes found in early-branching metazoan lineages indicate an early origin of neuron-specific protein machinery.It has been difficult to identify a single character of neurons shared by all animalsE. muelleri arose from an independent duplication that occurred in Porifera  channels in sensing changes in water moving through the sponge55. We found a large diversification of TRP channels in the E. muelleri genome, and these grouped with the TRPA and TRPML families , but no evidence for more rapid electro-chemical signalling. While we find no signature for any aspect of conventional nervous tissues in the E. muelleri genome, we cannot rule out the possibility that the phylum Porifera as a whole, or individual lineages within it (including E. muelleri), have lost these neuron-related components.In 58. The release of the genome of A. queenslandica4 opened a window into the study of the mechanisms of sponge–microorganism interactions57. To unravel the recognition mechanisms developed by host and microbes to facilitate symbioses, high quality genomes  are fundamental.Most animals possess diverse symbiotic microbial consortia, which provide their hosts with metabolic advantages and new functions, and sponges are no differentE. muelleri offers a model that allows exploration of eukaryotic patterns of microbial recognition in unique environments. We studied the microbiome of 11 different specimens of E. muelleri collected from six locations across 6500 km in the Northern hemisphere, and found that this species contained between 865 and 4172 unique amplicon sequence variants (ASVs)  and Ferruginibacter , and one was an unclassified bacterium  from the Sooke Reservoir, at the head tank of the city of Victoria, British Columbia drinking water system. A voucher specimen is deposited with the Royal British Columbia Museum (RBCM019-00140-001)  with previously published RNAseq datasets used for training. As the basis for gene prediction, the non-masked genome was used, to avoid artefacts, missed exons or missing gene portions caused by masked areas of the genome. The BUSCO v2/3 set61 was used in genome mode to determine gene recovery metrics. RepeatModeler 2.0 and RepeatMasker 4.1.0 were used sequentially to predict repetitive content within the genome as described in Supplementary Note http://bitbucket.org/EphydatiaGenome/ or available in Supplementary Data 62 and other software, as detailed in Supplementary Note Gene models were predicted using AUGUSTUS 3.3.2 annotation software  and Kenny et al.24 and detailed in Supplementary Note 7.3.Automated annotation of gene sequences was performed using DIAMOND 0.9.31 BLASTx67 was used to map RNAseq reads to the reference E. muelleri genome. edgeR v3.14.068 was used to estimate the expression levels of all transcripts across all replicate samples. Full details are provided in Supplementary Note RNAseq was performed by LC Sciences . HISAT 2.069 used when necessary to test absence. Reciprocal BLAST was used to ensure assignment of identity, with the identity of key gene families assessed using phylogenetic inference as shown in Supplementary Note E. muelleri samples was assessed with Mothur v.1.41.3 and an adaptation of MiSeq SOP protocol70 as fully described in Supplementary Note Gene family content was assessed using targeted manual BLAST, with HMMER v3.3Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9


Desmodus rotundus) captured in México and housed for a rabies vaccine efficacy study in Madison, Wisconsin. Bat mortalities occurred in México and Wisconsin, but rabies cases reported herein are only those that occurred after arrival in Madison (n = 15). Bats were confirmed positive for rabies virus (RABV) by the direct fluorescent antibody test. In accordance with previous reports, we observed long incubation periods (more than 100 days), variability in clinical signs prior to death, excretion of virus in saliva, and changes in rabies neutralizing antibody (rVNA) titers post-infection. We observed that the furious form of rabies  manifested in three bats, which has not been reported in vampire bat studies since 1936. RABV was detected in saliva of 5/9 bats, 2–5 days prior to death, but was not detected in four of those bats that had been vaccinated shortly after exposure. Bats from different capture sites were involved in two separate outbreaks, and phylogenetic analysis revealed differences in the glycoprotein gene sequences of RABV isolated from each event, indicating that two different lineages were circulating separately during capture at each site.We report mortality events in a group of 123 common vampire bats ( Desmodus rotundus) has become the primary rabies virus (RABV) reservoir. Vampire bat-associated rabies is a major burden to the livestock industry and a significant public health concern across the tropical and subtropical Western Hemisphere [Eptesicus fuscus) when delivered orally and topically [D. rotundus. We captured wild vampire bats in the state of San Luis Potosí, located in central México, where rabies is endemic in the southern part of the state [Rabies is an ancient infectious disease with one of the highest case fatality rates, causing tens of thousands of human fatalities and millions of cattle deaths annually, worldwide . Since tmisphere ,2,3,4. Omisphere ,6. The vhe state , and traThe main route of RABV transmission to naive animals is by direct inoculation  inflicted by a rabid host ,2. VampiA total of 123 common vampire bats were captured from seven sites throughout San Luis Potosí  during aad lib.We captured bats during the night using mist nets placed at cave entrances, natural or man-made roosting areas , around livestock corrals, or known flight pathways. We focused on capturing males and did not collect visibly pregnant or lactating females. Sex and age category  were judged by development of external reproductive organs and body size. Following field health inspection, bats were housed temporarily in a room within an animal facility of the Universidad de Matehuala, School of Veterinary Medicine, in Matehuala, San Luis Potosí, until transport to Madison, Wisconsin. The animal room had natural light and ventilation, and humidity was manipulated daily with humidifiers. Bats were grouped by sex and location of origin in mesh cages and were kept on a diet of citrated beef blood and water Most bats were held in captivity in México for 4–6 weeks, but the last group captured was held for only 1-week prior to departure. On September 4, 2018, a total of 93 bats  were transported to Madison, Wisconsin, in custom-made cages built in compliance with biosafety and transport guidelines. During the 2-day travel time, bats were fed once with citrated bovine blood at night.Upon arrival at the NWHC, bats were placed in a biosafety level 3 animal room with controlled temperature (28–34 °C) and humidity (>40%). The bats were combined into new cages of up to 20 animals per cage, but separated by sex and by site of capture, as availability of cages permitted. Husbandry practices remained similar to those in México, and all animal procedures for transport and captivity were approved by the NWHC animal care and use committee under appropriate permits as listed below. After arriving at the NWHC, bats were given an acclimation/quarantine period time of 104 days prior to the experimental vaccination.We obtained an initial (baseline) plasma sample from 88 available bats within days 34–57 after arrival at the NWHC, and periodically thereafter, to determine rabies virus neutralizing antibodies (rVNA). Baseline samples were sent to the Centers for Disease Control and Prevention  and were tested using the micro-neutralization test, a modified version of the Rapid Fluorescent Focus Inhibition Test (RFFIT). Subsequent samples were tested at the NWHC following the same micro-neutralization protocol . When pohttps://www.geneious.com). Sequence alignments were trimmed to include only the coding region of the nucleoprotein and glycoprotein genes. Maximum likelihood phylogenetic analysis implemented in MEGA software 7.0.26 [In México, a subset of dead bats was sent to the rabies reference laboratory in the city of San Luis Potosí (SPHL) for rabies diagnosis by direct antigen detection (DFA) following the official Mexican standard for rabies diagnosis . In Wisce 7.0.26  was condWe collected oral swabs in RNAlater  from bats from Loma after the death of 2 bats from that site occurring within a week of arrival at the NWHC. We sent this set of samples to a commercial laboratory for RABV diagnosis using RT-PCR . More oral swabs were serially collected from a group of bats involved in a natural outbreak of rabies that peaked in late December. We started swabbing these bats regularly (every 3–5 days) after the outbreak began and for 2 months thereafter. After collection, saliva samples were placed in vials containing 0.5 mL of viral transport media and stored at −80 °C until further testing. These swabs were tested at the NWHC following the LN34 pan-lyssavirus real-time RT-PCR ,18, withIn México, bat mortalities were first recorded during capture at the Loma site on Aug 4–5, 2018 . A totalJust prior to departure, all surviving animals (n = 93) were given a health check and administered subcutaneous fluids to avoid dehydration while in transit. No signs of disease were observed, and all bats were placed in their correspondent cage for transportation.During transport, one male bat from Loma  was lethargic and did not survive the 48 h drive to Wisconsin; it tested positive for rabies by DFA at WSLH. On Sept 24, 18 days after arrival at NWHC, one of the 11 bats (#565) died and was also positive for RABV . The othOn Dec 6, 2018, increased vocalization, uneasy behavior, and apparent fighting were recorded in a cage that housed 17 males from Catedral, Milagro, and Guadalcázar. Four days later , Bat #576  was found dead without prior manifestation of clinical signs and tested positive for RABV . On day During Days 121–146, 8 more bats died in Cages 4 and 5 and all tested positive for RABV by DFA. Vocalization was often noticed 3–4 days prior to a new mortality in both México and the United States . OverallRABV nucleic acid was detected in oral swabs from 5  of the 9 rabies-positive bats sampled 2–5 days prior to their death . Bat #58All bats that died at NWHC on Days 95–146 (n = 10) were negative for rVNA at baseline; half of these had rVNA at the time of death . None ofVaccination with RCN-MoG had no impact on survival of seronegative bats in Cage 5 as 4/4 vaccinates  succumbed to rabies, compared to unvaccinated bats in Cage 4 (4/5). However, RABV nucleic acid was not detected in the saliva of 4 vaccinated bats that succumbed to rabies, unlike the saliva samples from unvaccinated individuals  that were consistently RABV-positive. Seven bats  survived up to 231 days, despite being co-housed with bats shedding RABV in saliva, as confirmed by detection of RABV nucleic acid in oral swabs by real-time RT-PCR . For theNucleoprotein (N) and glycoprotein (G) sequences were determined for RABV isolated from brain samples sent to CDC after rabies testing at the WSLH. Phylogenetic analysis revealed that these sequences were consistent with RABV circulating in vampire bats in México. It also revealed two clusters of vampire bat sequences , here caIn this report, we describe two separate rabies mortality events that occurred in a group of common vampire bats captured in Central México and transported to NWHC as part of a study to test the efficacy of a recombinant rabies vaccine candidate. We identified mortality events as independent introductions of RABV lineages from bats collected at Loma and Catedral. This was possible by integrating information obtained from a phylogenetic analysis and a detailed mortality timeline, which traced contacts among bats (co-housing history) and potential incubation periods between cases.Lengthy incubation periods are known to occur after RABV exposure in vampire bats and other species of bats that usually undergo torpor in temperate climates ,10,19,20Catedral and Loma sites are located in the southern (rabies endemic) region of San Luis Potosí, whereas Milagro and Guadalcázar are located farther north in areas without or with scarce reports of rabies cases. We observed differences in both survival and clinical presentation after RABV infection between bats from sites considered “rabies-free” and endemic sites. None of the known exposed bats from Guadalcázar and Milagro survived infection, whereas 18/22 Loma bats  and 7/9 from Catedral bats survived co-housing with rabid bats. All bats that died from Guadalcázar and Milagro (n = 8) showed clinical signs (including three furious), whereas bats from endemic sites died without any visible clinical signs. One of the three deaths from Loma  was not suspected of RABV infection and was considered an incidental death. It was somewhat surprising to us that it was positive for RABV by DFA.The presence of clinical signs in experimentally infected vampire bats is not always observed ,22,23,24Antibody response to RABV and other immune mechanisms  that drive survival after rabies infection are poorly understood in bats ,25. NotaAs part of our scheduled vaccine study, and in an attempt to halt the ongoing rabies epizootic, we vaccinated bats (n = 10) from Cage 5 on Jan 4, 2019. These bats were collected from the Catedral, Guadalcázar, and Milagro locations, and some had detectable rVNA prior in their initial sample . Other iThe genetic differentiation of two RABV lineages circulating within a single State in México was surprising; particularly, considering the close geographic distance between Loma and Catedral (~7 miles). It is likely that the RABV lineage identified in the index case from Catedral (#576) had been recently introduced from a more distant site, since a spillover of such lineage was not observed in bats captured in Loma. Our results indicate a complex dissemination dynamic of RABV associated with vampire bats in San Luis Potosí. More robust sampling is encouraged to further understand these dynamics in wild populations of bats.The natural transmission dynamics of rabies in vampire bats and their serologic response after exposure to RABV is not entirely understood  but cert


All of these properties were studied with classical investigation techniques, like impedance or the new Ionomer nc Analysis (INCA) method.The need to reduce pollution and the continuous increase in petrol cost have reinforced the interest in fuel cells (FCs), efficient and clean systems for the conversion of fuel into energy. Polymer electrolyte membrane fuel cells (PEMFCs) exhibit excellent characteristics in their weight, volume, and current density for automotive applications and cogeneration systems. Unfortunately, the high cost of perfluorinated membranes and the low stability of anionic membranes in an alkaline environment still limit their use. During the past years, this kind of membranes has been widely explored in terms of synthesis of new materials, grafting of strong and stable functional groups, mechanical properties, conductivity, FC performance, and so forth. This special issue of Membranes is dedicated to this exciting research field, with some excursions in related fields, focusing on commercial polymers, like Nafion® membranes with an annealing procedure in the presence of the plasticizing solvent DMSO. It was applied for the first time through the Ionomer nc Analysis (INCA) method in order to evaluate ionomer thermomechanical properties and to probe the increase of crystallinity during the annealing treatment. Complementary differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) measurements confirmed the increase of polymer stiffness over the whole range of temperature. To reflect the broad scope of this topic, contributions from leading scientists across the world, whose research addresses ionomeric membranes from different perspectives, sharing a common vision of pollution reduction and the search for sustainable energy sources, have been gathered. Giancola et al.  investig2 nanoparticles, to form composite membranes with Aquivion® ionomer. The composite with 4 wt% of CeO2@HNT-NH2 showed unchanged tensile properties but presented high proton conductivity and increased stability to radical attack compared with nonmodified Aquivion®. Akrout et al.  focused 2 solutions in order to measure the gross power density extracted under high salinity gradient and to evaluate the effect of Mg2+  on the efficiency in energy conversion. In all tests, Nafion 117 exhibited superior performance when 0.5/4.0 M NaCl was fed through 500 µm thick compartments at a linear velocity of 1.5 cm·s−1. However, the gross power density of 1.38 W·m−2 detected in the case of pure NaCl solutions decreased to 1.08 W·m−2 in the presence of magnesium chloride. Although Nafion membranes exhibited better performance than CMX and Fuji-CEM-80050, their use is limited by high cost, and a significant reduction of membrane price is required for affordable RED applications. Avci et al.  studied 2), which forms as a byproduct during heat treatment. The membranes obtained using this activation method had high thermal, mechanical, and chemical stabilities. In I-ViR free studies for fuel cell evaluation, high performances similar to those using Nafion were obtained, and by using a constant current method, a stability of 4000 h was attained. Kim et al.  focused Schiavone et al.  describe4I salt. The protruded appearance on the samples’ surface was evidently shown at high salt concentrations (~50 wt %) in the field-emission scanning electron microscopy (FE-SEM) images. The system incorporated with 40 wt % of NH4I salt exhibited a high ion transference number. A potential cutoff of 1.33 V was recorded for the electrolyte system as decomposition voltage. Marf et al.  focused A review by Sazali et al.  examinedA review by Dickinson and Smith  focused A review by Umar et al.  reportedI am confident that the articles contained in the Special Issue will serve to further stimulate advances in this research area. I thank all our friends and colleagues who contributed papers to the themed issue.


GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs  in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing  Esophageal cancer is a common malignant tumor in the world. It is estimated that there were 572,000 new cases of esophageal cancer and more than 50,000 cases of esophageal cancer deaths in 2018 . At presThe occurrence of esophageal cancer is the result of a combination of genetic, behavioral, and dietary factors, including family history of esophageal cancer, smoking, drinking, hot drinks, and pickled foods . DespiteAt present, the commonly used screening method for esophageal cancer is endoscopy, but it is only implemented in some economically developed areas. Due to the high cost of endoscopy, invasive injury and low acceptability of residents, it is not widely used in many areas, so it has a broad prospect to find sensitive and specific biomarkers for early screening of esophageal cancer . It is oWith the rapid development of bimolecular technology, the genetic research of esophageal cancer is increasing. A series of susceptible polymorphic sites and tumor related somatic mutations have been found gradually, and the understanding of the genomic characteristics of esophageal cancer is gradually in-depth . In addiMore importantly, epigenetic changes are often early events in the process of disease occurrence, which provides an opportunity to find tumor specific biomarkers. In this study, we used the miRNA, mRNA, DNA methylation and clinical data in database to search for potential biomarkers of ESCC by using the knowledge and methods of bioinformatics from the mechanism of ceRNAs. This study will definitely provide theoretical guidance on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.https://www.ncbi.nlm.nih.gov/geo/) and humans Cancer Genome Database TCGA (https://www.cancer.gov/)  . Table 1er.gov/) .GSE59973 and GSE114110  was used for background expression value correction and data normalization of the original data in each dataset . The proSE114110 . DiffereGSE52826  or less than −1 (down-regulated gene) were as the cut-off value of the expression chip data.In order to confirm the results, we also downloaded the methylation (platform: Illumina HumanMethylation450 BeadChip) and expression (IlluminaHiSeq) microarray data from TCGA database for validation. Through DNA research and analysis, the DNA methylation dataset in this study was determined to be GSE52826 , and theGSE52826 . The adjThe 12 miRNAs selected in the above steps were used for target prediction, and the target prediction software RNA22 was used , which ip value less than 0.05 is the potential ceRNA pair. The specific formula is as follows: Hypergeometric testing is the most commonly used prediction method in the ceRNA mechanism . After tGSE120356 was used to calculate the Pearson correlation coefficient , and the target genes were predicted by miRDB  (Gene Ontology (GO) is a comprehensive information tool providing gene function of individual genomic products, including three aspects: molecular function (MF), biological process (BP) and cellular component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for advanced biological functions and utilities. Both analyses and annotations are based on the DAVID database (rf.gov/)  and are P ≤ 0.05.With the survival time of all esophageal cancers in the TCGA database, the Survival package in the R language was used to perform survival analysis on the differential lncRNA . The difIn this study, we collected 10 pairs of ESCC specimens from patients in the First Affiliated Hospital of China Medical University from 2015 to 2018. All patients were confirmed histologically. All patients participating in the study signed the informed consent form and the study was approved by the Institutional Ethics Committee of China Medical University.Total RNA of tissues was extracted, and the sample was fully ground with liquid nitrogen, added 1ml Trizol solution, mixed, and placed at room temperature for 5 min to fully crack. Then, 14,000 g centrifuged at 4 °C for 15 min, and the RNA was divided into three layers, in the upper water phase, and transferred to another new RNase free EP tube. Later, isopropanol was added, gently and thoroughly mixed, and allowed to stand at room temperature for 10 min. At 4 °C, the samples were centrifuged at 14,000 g for 10 min, RNA precipitation was collected and washed twice with 75% ethanol. According to the amount of precipitation, an appropriate amount of DEPC water was added to dissolve the precipitation.RNA samples were reverse transcribed into cDNA using TransScript One Step gDNA Removal and cDNA Synthesis SuperMix. RT qPCR was performed using TB Green Premix Ex Taq. The PCR conditions included an initial step at 95 °C for 10 min, followed by 40 cycles of amplification and quantification . GAPDH was used as an endogenous control for normalization. The sequences of the primers used for RT qPCR were as follows:HCG22 forward, 5′ CCTGGGGAGAGGTGTCATTT 3′  and reverse, 5′ TGGTCTCTGGGTGCTTAGTG 3′; RNF217-AS1 forward, 5′  TGGGAATGACAGCAGAAAG 3′  and reverse, 5′  TCCGCAGAGTGAACAAGAA 3′; HCP5 forward, 5′  GATGACTATGGGGTGAGGGG 3′  and reverse, 5′  TATGGAGATGAGGTGTGCCG 3′; BBOX1-AS1 forward, 5′  GGCACATTTGGAAGTT 3′  and reverse, 5′ TCAGGGTAACCGTAGC 3′.GSE59973 and GSE114110, respectively. The threshold of selecting differential miRNAs was p.value < 0.05 and log2—FC—>1. In the volcano maps of differential miRNAs of GSE59973 and GSE114110, the black regions in the graph represented the distribution of non-differentiated miRNAs, the red fractions showed significantly differentially up-regulated miRNAs, and the green fractions represented significantly differentially down-regulated miRNAs , methylation sequencing (methel SEQ) and transcriptome sequencing (RNA SEQ). By analyzing the microRNA SEQ data of esophageal cancer from TCGA database, we obtained 12 miRNAs differentially expressed in esophageal cancer and adjacent tissues.Since AFAP1-AS1 has been verified to be differentially expressed in esophageal adenocarcinoma, more and more differentially expressed lncRNAs have been found in tumors. AFAP1-AS1 is a long non-coding RNA amplified from bladder cancer cells by cDNA terminal rapid amplification. It has been confirmed that AFAP1-AS1 was highly expressed in embryonic tissues but not in normal tissues. AFAP1-AS1 was highly expressed in Barrett’s esophagus, non-small cell lung cancer and esophageal cancer . GutschnThe forked head box (Fox) protein family consists of 19 subfamilies of transcription factors, which share a highly conserved DNA binding domain of about 110 amino acids, i.e., the forked head box domain  . ForkheaSmtn gene, also known as smoothelin gene, encodes the structural protein that constitutes the cytoskeleton. It is specifically expressed in contractile smooth muscle cells, which is related to the contraction of smooth muscle cells and may also be related to the differentiation of smooth muscle cells (smtn gene is still in the exploratory stage, and little is known about its structure and function. Therefore, the methylation of these genes in esophageal squamous cell carcinoma is of great value.DNA methylation can also be used as a biomarker for the diagnosis of esophageal squamous cell carcinoma . In ordele cells . At presTo sum up, we used a variety of data sets and bioinformatics comprehensive analysis to identify 11,696 pairs of ceRNA relationships in the screening stage. These candidate genes are significantly enriched in multiple pathways, mainly related to FoxO signaling pathway and calcineurin cascade reaction. Through the analysis of TCGA clinical data and the verification of molecular biological methods, four key lncRNAs related to the prognosis of ESCC patients were found, including RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22. These findings can significantly improve our understanding of the etiology and potential molecular events of ESCC, and these candidate lncRNAs and signaling pathways may be used as therapeutic targets of ESCC.10.7717/peerj.8831/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.8831/supp-2Supplemental Information 2Click here for additional data file.


Cryptopygus  and Friesea , were included. This experimental design allowed us to quantify the relative importance of ecological factors (different regions of occurrence) and/or phylogenetic divergence in the host (different Orders) in shaping the Collembola microbiome. The diversity and richness of springtail microbiomes was lower in the Antarctic taxa compared to published information from species from temperate regions. The microbiome composition was predominantly species-specific, with a limited core microbiome shared across the four species examined. While both geographic origin and host species influenced the associated microbiomes, the former was the prevalent driver, with closer similarity between springtails from the same bioregion than between those belonging to the same genus.Collembola are a key component of the soil biota globally, playing an important role in community and ecosystem dynamics. Equally significant are their associated microbiomes, that can contribute to key metabolic functions. In the present study, we investigated the bacterial community composition of four Antarctic springtail species to assess if and how the extreme Antarctic environment has shaped the collembolans’ microbiomes. Springtails were collected from two biogeographical regions, the maritime and the continental Antarctic. From each region, two endemic species, belonging to the genera  Despite the importance of springtails for soil dynamics, few studies to date have investigated their associated microbiomes, thus limiting our knowledge of the various metabolic, physiological and immunological functions to which the microbiota may contribute. In this respect, the study of the microbiomes associated with Antarctic springtails, adapted to extreme and almost pristine environments, may provide an important advance in understanding of bacterial contribution to springtail biology and, in turn, to Antarctic terrestrial ecosystem functioning.The Collembola (springtails) is one of the richest taxa of basal Hexapoda and, due to their important role in soil ecosystem functioning, one of the most studied components of the soil fauna. Since springtails can feed on fungal hyphae, bacteria, algae, mosses, spores and decaying organic matter, their effects on soil dynamics can be direct  or indirect 10. Three main biogeographic regions are generally recognised in Antarctica: the sub-, maritime and continental Antarctic7. The latter two experience the harshest climatic conditions, with chronically low to very low temperatures, often low annual precipitation and limited availability of liquid water9.Antarctic terrestrial ecosystems experience some of the harshest conditions on our planet, with habitats  limited to less than 0.3% of the continent’s area11, as well as allowing the identification of currently unknown biotic associations. Existing studies of springtail microbiomes have suggested that they are characterised by the dominance of Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria15. It has also been suggested that some of these taxa may play a role in antibiotic production, the provision of metabolic pathways for nitrogen and terpene metabolism, and the production of chitinases necessary for the digestion of food components14. Nevertheless, the application of Next Generation Sequencing (NGS) techniques to study springtail-associated microbiomes has to date mostly focused on model species from temperate environments, such as Folsomia candida13, Orchesella cincta14, Folsomia quadrioculata15 and other Entomobryomorpha16. However, Collembola display substantial evolutionary diversity and, during their long evolutionary history, have adapted to occupy almost every terrestrial ecosystem on Earth, including Antarctica’s frigid deserts5. There is therefore a need to expand knowledge of their microbiomes to include additional taxa adapted to different ecological conditions.Examination of Antarctic springtail microbiomes may shed new light on the evolutionary adaptation to the abiotic stresses that typify Antarctica’s extreme and isolated ecosystems18). Two species, Cryptopygus antarcticus antarcticus  and Friesea antarctica  (until recently known as Friesea grisea19) were collected in ACBR3 North-West Antarctic Peninsula, specifically from the South Shetland Islands and Adelaide Island (both in the maritime Antarctic). Cryptopygus terranovus  and F. propria  , were sampled from ACBR8 North Victoria Land .In this study, we characterized the microbiomes associated with different Antarctic springtail species using the V3 region of the bacterial 16S ribosomal RNA (rRNA) encoding gene as a molecular marker. Four springtail species, belonging to two different genera from different Orders, were selected from two different Antarctic Conservation Biogeographic Regions , versus that of two distinct biogeographic regions .All four species are endemic to different regions within Antarctica and, given their poor dispersal capabilities and the occurrence of insurmountable geographical barriers, have been isolated in these regions for many millions of years during their evolutionSequencing produced 259,086–327,432 reads per replicate , Actinobacteria (27%), Firmicutes (14%) and Bacteroidetes (11%)  and the latter on phylogenetic distance only . In the Principal Coordinates Analysis (PCoA) based on weighted UniFrac distances, the first axis (PCoA1) explained 34% of variation and the second (PCoA2) explained 23.3% . The Antarctic springtail microbiomes were dominated by Proteobacteria, Bacteroidetes, Firmicutes and Actinobacteria , although some uncertainty may result from unequal sequencing depth. These data would suggest that, as reported for macro-organisms, the richness of bacterial species associated with Antarctic springtails decreases at higher latitudes. A similar pattern was observed in a comparison of the bacterial community composition of Antarctic and Australian soils26, although not in a comparison of the diversity of Arctic and other Northern Hemisphere soils27. Antarctic soils are characterized by lower levels of organic carbon, as a result of lower temperatures a more limited vegetation, than those of the Arctic28. Multiple factors therefore appear to underlie reduced diversity of microorganisms in Antarctic soils. We suggest that the same environmental factors may have acted over evolutionary time to limit the diversity of Antarctic springtail microbiomes compared with those of Collembola from lower latitudes.Interestingly, the overall OTU richness observed in this study, seems to be considerably lower than that described for the temperate springtails 25, and may also be the case in the Antarctic springtails studied here, in which the numbers of OTUs unique to each sample outnumbered those shared , as shown in a comparison of whole-body microbiomes between natural and laboratory-reared populations of the Antarctic tardigrade Acutuncus antarcticus25.The data obtained in the present study also support a major influence of environmental factors in shaping microbiome composition. While this result appears plausible, as it is based on the comparison of two pairs of species, some caution is needed in the interpretation of data from the C. a. antarcticus and F. antarctica (63) (maritime Antarctic), and between C. terranovus and F. propria (89)  were higher than the numbers shared between species belonging to the same genus . Therefore, it appears that the phylogenetic relatedness of hosts seems not to be the primary driver affecting their associated bacterial community composition, and this latter should be rather connected with their high degree of endemism and the long-term survival of hosts in a given biogeographical region of Antarctica. This evidence contrasts with the ubiquity hypothesis of microbial distribution and, as other studies have suggested32, refutes the idea that for microbes “everything is everywhere”33.Analyses of beta diversity identified a higher similarity between the microbiomes associated with different springtail genera originating in the same Antarctic bioregion than with species belonging to the same genus but originating from different bioregions Fig. . The num34), may have further fostered the development of highly specific microbial interactions. This may be a direct consequence of time, with divergence in the associated microbial community paralleling divergence in the hosts. Such differential selection, arising from diverse environmental conditions, may have led to the development of novel and varied ecological interactions .It is also possible that the extreme conditions experienced in Antarctica mean that environmental control is so pronounced that it has become the primary factor shaping not only the evolution and adaptation of both Antarctic terrestrial prokaryotes and invertebrate fauna, but also their physiological and ecological interactions. Furthermore, the long separation time between biota of the continental Antarctic and maritime Antarctic, from few millions to many tens of millions of years . These regions experience very different environmental conditions that strongly impact on the composition and functioning of their terrestrial ecosystems. Moreover, their microbiota and invertebrate fauna have long evolutionary histories in isolationF. antarctica were sampled during the austral summer of 2002–2003 in collaboration between the British Antarctic Survey (BAS) and the Italian National Antarctic Program (PNRA). Specimens of C. terranovus and F. propria from Victoria Land were collected during PNRA-supported expeditions in the austral summers of 2017–2018 and 2018–2019, respectively. Samples of C. a. antarcticus, originating from the Antarctic Peninsula, were collected in January 2018 and reared in a substrate of Antarctic mosses in a climate chamber at + 2 °C from July 2018, in the Department of Systems Ecology of the Vrije Universiteit Amsterdam.The samples analysed in the present study were collected from the two main Antarctic biogeographical regions, the maritime and continental Antarctic Table . In the F. antarctica). In the analyses described below two types of comparisons are made, (1) partitioning samples across “region”  and (2) across “genera” .Throughout the text we use the term “replicate” to refer to individual DNA isolates, while the terms “sample” or “species” are used to designate groups of replicates from the same springtail species collected in the same sampling location  following the manufacturer’s protocol. Purified PCR products were re-amplified with primers linked to Illumina adapters with the same reaction mixture following a 12 cycle program: denaturation at 98 °C for 10 s, annealing and extension at 72 °C for 1 min, as well as an initial denaturation at 98 °C for 2 min and a final elongation at 72 °C for 8 min. PCR products were visualized on a 1.5% agarose gel and purified using Beckman Coulter Agencourt AMPure XP protocols, with a ratio DNA:beads of 1:1. Purified products were quantified using the Qubit dsDNA HS Assay Kit . Samples were then indexed using Herculase II Fusion DNA Polymerase Nextera XT Index Kit V2 and sequenced on a MiSeq Illumina platform at the Macrogen Inc. facility .Between 15 and 20 animals were pooled, rinsed in 70% ethanol and their DNA extracted using the QIAamp UCP DNA Micro kit  following the manufacturer’s instructions. A minimum of three replicates for each species and location were included in the study (see Table https://www.ncbi.nlm.nih.gov/sra/PRJNA681366).All amplicon data are available from the NCBI Sequence Read Archive (SRA) database under the project number PRJNA681366 . Sequences were filtered and merged using the DADA2 plugin, truncating the forward and the reverse reads after 230 and 188 bp, respectively, based on the read quality38. Sequences were clustered using a 97% identity threshold with VSEARCH39, including chimera removal. Taxonomic identification of the OTUs was performed with the feature-classifier plugin trained on the SILVA v132 database40. OTUs classified as mitochondrial or chloroplast were removed, as well as sequences corresponding to the springtail ribosomal 18S gene. A phylogenetic analysis was carried out using the maximum likelihood optimization criterion, as implemented in the phylogeny plugin, using the fasttree method41. Alpha and beta diversity were computed in R v3.6.1 (2019-07-05), using the R packages microbiome42, microbiomeSeq43, phyloseq v1.2844, vegan v2.5-645 and limma v3.40.646, after data normalization to a sampling depth of 1318 (the minimum number of reads observed in the smallest library; see Table 47 on the original and not normalized OTU table. Finally, the data were visualized using the R packages ggplot2 v3.2.148 and gplots 3.0.1.149. These two latter tools were applied to generate the figures shown in the present paper.Demultiplexed raw sequences were trimmed to remove 16S primers using Trimmomatic v0.39Supplementary Tables.


Southeast Asian forests are dominated by the tree family Dipterocarpaceae, whose abundance and diversity are key to maintaining the structure and function of tropical forests. Like most biodiversity, dipterocarps are threatened by deforestation and climate change, so it is crucial to understand the potential impacts of these threats on current and future dipterocarp distributions. We developed species distribution models (SDMs) for 19 species of dipterocarps in the Philippines, which were projected onto current and two 2070 representative concentration pathway (RCP) climate scenarios, RCP 4.5 and 8.5. Current land cover was incorporated as a post-hoc correction to restrict projections onto intact habitats. Land cover correction alone reduced current species distributions by a median 67%, and within protected areas by 37%. After land cover correction, climate change reduced distributions by a median 16% (RCP 4.5) and 27% (RCP 8.5) at the national level, with similar losses in protected areas. There was a detectable upward elevation shift of species distributions, consisting of suitable habitat losses below 300 m and gains above 600 m. Species-rich stable areas of continued habitat suitability  fell largely outside current delineations of protected areas, indicating a need to improve protected area planning. This study highlights how SDMs can provide projections that can inform protected area planning in the tropics. Among the major drivers of biodiversity loss, anthropogenic land use  and climate change are recognised as the most urgent threats4. Land cover change immediately reduces the extent of suitable habitat for resident species5 and has second-order implications for species persistence in fragmented landscapes such as reduced habitat connectivity7. Similarly, climate change has long-term implications for species, with predicted alterations to temperature, precipitation and seasonality leading to concomitant shifts in species distribution8. Indeed, both field-based historical studies10 and climate change informed projections of plant species distributions indicate shifts and changes in distribution12. Distribution assessments through the use of species distribution models (SDM) could estimate the risk these drivers pose to species, better-asserting species vulnerabilities15.Biodiversity loss worldwide is proceeding at unprecedented rates17, and is expected to be heavily impacted by climate change18. The Philippine archipelago is particularly emblematic of the threats to Southeast Asia (SEA), being a biodiversity hotspot with a large number of endemics but which is also biologically undersurveyed19, resulting in a lack of contemporary occurrence records21. The Philippines has also experienced intensive logging and agricultural expansion, leaving only 3% of its primary forests intact23, and is exceptionally vulnerable to climate change18. In this regard, as the dominant family of trees in SEA forests, species from the family Dipterocarpaceae are of exceptional interest26. These non-pioneering, often fast-growing and highly productive trees are amongst the tallest in the tropics, and comprise the majority of above-ground carbon stores and emergent individuals28. With high functional variation and effective exploitation of different environmental niches, their abundance and diversity are key to maintaining the structure and function of tropical forests29. Considering the significance of dipterocarps to forests in the Philippines26, shifts in their distribution are likely to affect the integrity of ecosystems and its constituent species33. It is of great importance, therefore, to understand the potential effects of anthropogenic land use and climate change on dipterocarp distributions in the Philippines to better conserve remaining forests36.Southeast Asia is a hotspot of both biodiversity and deforestation38. Protected areas provide safe havens for species, potentially preventing habitat loss from anthropogenic land use or as buffers against the changing climate39. Thus, the coverage afforded by protected areas and the distribution changes within them are important for distribution assessments and conservation efforts.A potential mitigation measure against these threats is protected areas39. Part of meeting the challenges of climate change would be to ensure that these areas are protected or prioritised for protection40.Areas of continued habitat suitability under climate change are similarly important, whose availability is especially crucial in safeguarding the persistence of species with limited dispersal abilities such as dipterocarps42, and is crucial for distribution assessments43. Incorporating land cover as a variable into a SDM—the most common practice—generates new, restrictive, requirements for the use of species occurrence data44. Specifically, the land cover data being integrated into SDMs is often contemporary, implying that occurrence data should be correspondingly recent to allow for accurate occurrence-land cover associations. Temporal mismatches between historical species occurrences and contemporary land cover may pose constraints for SDMs by inaccurately assigning species-habitat associations, in cases where the land cover has changed after the date of occurrence data collection . This is an important consideration for tropical developing countries such as in Southeast Asia, which have experienced rapid rates of deforestation17, but which also tend to have high biodiversity and be poorly biologically surveyed20. Nevertheless, occurrence points located on changing landscapes remain important representations of species’ environmental niches and should be included where possible. Simply excluding occurrences mismatched against contemporary land cover would likely introduce sampling biases and niche truncations, biasing SDM estimates47. In this study, we investigated the effects of anthropogenic land use and climate change on the current and future distributions of dipterocarps in the Philippines. Our study applied the well-established maximum entropy modelling approach  to model species distributions for the current and two 2070 climate scenarios. Our specific objectives were to assess the effects of land cover and climate change on the distribution of dipterocarps within and outside of protected areas, and subsequently to infer the adequacy of protection currently afforded to areas of continued habitat suitability.It is widely recognised that land cover should be included as a spatially-explicit variable in SDMs2. The two largest islands of Luzon and Mindanao constitute two-thirds of the total land area. The topography of the country varies greatly and ranges from sea level to 2954 m above sea level (asl) . The tropical climate is influenced by the southwest and northeast monsoons, characterised by a wet season from June to November and a dry season from December to May. The mean annual rainfall varies from 965 to 4064 mm across the country48. The mean monthly temperature ranges from 26 to 28 °C for warmer months (March to October) and from 25 to 26 °C for the cooler months (November to February). The coldest month is January (mean = 25.5 °C) while the warmest month is May (mean = 28.3 °C)48.The Philippines is the world’s second-largest archipelago, consisting of more than 7600 islands covering about 300,000 km49, and a cleaned, filtered, and verified georeferenced database of Philippine tree species published by Ramos et al.21 , although several stretched as far back as 1902. While this could lead to temporal mismatches with the contemporary land cover map, we avoided this by applying land cover post-hoc (see below). The data represented 26 of 54 species, and 5 of 6 genera of dipterocarps in the Philippines, of which, 19 species and 4 genera were eventually assessed 2 at the equator)58, which served as the base data for current projections  Global Circulation Models (GCMs)61 for Southeast Asia62: Canadian Earth System Model, second generation (CanESM2); Community Earth System Model, version 1 of Biogeochemistry (CESM1-BGC); Community Earth System Model, version 5.0 of the Community Atmosphere Model (CESM1-CAM5); Centre National de Recherches Météorologiques, Climate Model version 5 (CNRM-CM5); and Model for Interdisciplinary Research On Climate, version 5 (MIROC5), were also sourced from CHELSA and averaged for two representative concentration pathways (RCPs): RCP 4.5 and 8.5 for 207058. The future climate scenarios assumed that global annual greenhouse gas (GHG) emissions would peak by 2040 and subsequently decline substantially for RCP 4.5, or that GHG emissions would continue to rise until 2070 for RCP 8.5, respectively a “best-case” and “worst-case” scenario 63. Climate change for the Philippines is projected to increase mean temperatures by 0.9–1.9 °C (RCP 4.5) and 1.2–2.3 °C (RCP 8.5) with greater extremes and fluctuations, and increased mean precipitation with greater frequency and intensity of extreme rainfall events64.Bioclimatic variables were sourced from CHELSA at 30 arc seconds resolution  was performed on the eight soil variables60. The first five principal components (PCs) were selected as they explained more than 90% of the total variance (93.74%) , clay content mass fraction, coarse fragments volumetric, sand content mass fraction, cation exchange capacity, soil pH in H68. MaxEnt was selected among other algorithms as it has been shown to be consistently among the top-performing SDMs for presence-only datasets70, and model transferability72. While an ensemble of modelling algorithms approach has been suggested to achieve better predictions73, the converse has been suggested as well74. Nevertheless, we chose to use a single well-tuned model that has been calibrated for model transferability while adopting recommended approaches to prevent over-parameterisation and overfitting75.MaxEnt is a widely used machine learning software that estimates a species probability distribution through species-climate correspondence using maximum entropy, while bound by the variables and occurrence data provided76, a ‘block’ partitioning of equal presence points approach  was used for species with occurrence points greater than 25 (see77). The more spatially independent training data for the models would reveal potential overfitting78, which is important when modelling climate change projections of species’ suitable habitats. A jackknife approach  was adopted for species with occurrence points less than or equal to 25, an approach that can identify better performing and less overfitted models built with small sample sizes80.All 26 species were individually modelled using MaxEnt with species-specific background extent, feature class, and regularisation multiplier . A fixed 10 km exclusion buffer was also used to prevent points containing both presence and pseudo-absence83. Due to the computational intensity of this tuning process, the ‘block’ approach was used for all species regardless of occurrence number and only 1000 pseudo-absences were selected84. A model performance criterion of Area Under the Curve (AUC) was used to assess model performance.The optimal background extent was selected by modelling species with different sets of pseudo-absences that were limited by a distance buffer from occurrence points , using a Michaelis–Menten model to fit distance buffer and model performance, and selecting the lowest distance buffer for which the model’s performance exceeds the theoretical asymptotic performance value86, and 19 regularisation multipliers determined using a geometric progression with a common ratio of 0.75 for eight sequences with a scale factor of 1 based on the default, and a sequential increase of 0.5 from the default 1 to 6.0, was considered. The wider than normal range of regularisation multipliers allowed the evaluations of candidate models with larger values while still accounting for relatively small changes that could also affect model performance88. The five combinations of feature classes and 19 regularisation multipliers generated 95 unique combinations of parameters that were used to build candidate MaxEnt models for each species; 10,000 randomly sampled pseudo-absences limited by the exclusion buffer and background extent determined in the previous tuning was used89.For the selection of MaxEnt parameters, an extensive round of species-specific tuning was performed for the model’s feature class and regularisation multiplier. A total of five combinations of the available feature classes 90, assuring against the selection of models overfitted to the calibration data. In cases where more than one candidate model had the lowest OR, the model with the highest AUC was selected as the most optimal91. Selected models were then and used to project the species’ geographical suitability in the Philippines for the current and future climate scenarios. While Akaike Information Criterion corrected for small sample size (AICc) has been recommended for the selection of MaxEnt models to prevent overfitting88, it has been shown to lead to oversimplified models with low predictive performance92, which was also observed for this study. Thus, OR and AUC with an appropriate cross-validation approach to prevent overfitting and improve model transferability was selected as the criterion for model selection87. The tuning of parameters was conducted in R using the ‘ENMeval’ and ‘SDMtune’ packages93.Candidate models with the lowest Omission Rates (OR) based on the lowest presence threshold  were selected94, i.e., true positive rate and true negative rate. Models of species with AUC < 0.7591, TSS < 0.4596, or OR > 0.2590, were excluded due to their poor predictive accuracy. Though largely arbitrary97, it allowed for an assessment of model performance and the exclusion of underperforming models. The final models for each species were then used to project their potential distributions across the Philippines under different climate scenarios. The continuous outputs of habitat suitability were converted into binary maps using the maxSSS threshold94.Selected models were evaluated with AUC, OR, and True Skill Statistics (TSS), based on the maximising the sum of sensitivity and specificity (maxSSS) threshold for TSS and ORConsidering potential temporal mismatches from using land cover as a predictor variable and the required integration of habitat loss from anthropogenic land use, the land cover was incorporated post-hoc through land cover correction (LCC). This was done by masking areas of unsuitable land cover types; in essence, using land cover to correct the distribution of suitable habitat. To create the masking layer, a static land cover map of the Philippines was sourced from PhilGIS.org and converted to a binary map, with each land cover type reclassified as either ‘suitable’ or ‘unsuitable’ for dipterocarps  represented higher conservation priority landscapes, as opposed to a general species distribution that includes areas of potentially less definitive suitability101.Of the 22 land cover types present in the land cover map, 17 were considered unsuitable for dipterocarps in this study  and two LCC scenarios each (with and without LCC). Changes in suitable habitat for each species were classified as loss, gain, and stable. Loss referred to areas that were originally suitable for the species but were unsuitable after climate change. Gain referred to areas that were initially unsuitable for the species but became suitable after climate change. Stable referred to areas that persisted as suitable habitat after climate change, which have also been referred to as species-specific 102. This allowed assessing the extent of protection afforded to species—both individually and in aggregate—for current and future climate scenarios. This was particularly important for evaluating stable areas, which should be given a high conservation priority owing to their continued suitability for dipterocarps under climate change.Changes in suitable habitat for each species were calculated for areas within demarcated protected areas of the Philippines, obtained from the World Database of Protected Areas103. We calculated the frequency values for each species across elevation bins at 50 m intervals. Raw frequency values were then scaled against the maximum value obtained for the current climate scenario, where the scaled value of 1 would represent the maximum frequency of currently suitable habitats observed across elevation bins . The scaled values were then aggregated and used to analyse the potential shifts in elevation distribution for dipterocarps. All analyses were conducted in R with the ‘raster’ package67.To further examine the impact of climate change on the distribution of dipterocarps, elevation values for each species projection of suitable habitat—including loss, gain, and stable—was extracted from the GTOPO30 Digital Elevation Model86.Of the 26 species modelled, only 19 were accepted with an average AUC of 0.91 (s.d. 0.05), TSS of 0.69 (s.d. 0.13) and OR of 0.13 (s.d. 0.06)  and 27.0% RCP 8.5 (IQR 8.4%–57.0%), with gains of 15.5% RCP 4.5 (IQR 8.3%–34.8%) and 20.8% RCP 8.5 (IQR 11.5%–42.5%)  to 87.5% (S. astylosa), and gains of 0.4%  to 193.9%  for RCP 8.5 , with a mean difference of 10.1% for loss and 10.4% for gain.At the national level, climate-induced losses were projected at a median of 16.3% RCP 4.5 (IQR 3.1%–39.5%) and 20.0% RCP 8.5 (IQR 3.8%–58.6%), and gains of 22.8% RCP 4.5 (IQR 7.5%–53.2%), and 30.8% RCP 8.5 (IQR 11.2%–76.6%) , with a mean difference of 8.6% for loss and 17.5% for gain. While losses within protected areas were not significantly different from those at the national level , gains within protected areas were significantly greater .Changes in suitable habitat within protected areas showed similar trends: median losses of 13.3% Under projections of climate change, species distributions of suitable habitat across the elevation gradient experienced shifts. The scaled elevation distributions averaged across species decreased at elevations below 400 m asl and increased at 600–900 m asl from current to both future climate scenarios Fig. a. These 2)  Fig. a. In conned Fig. a. Under Protected areas, which comprised a small proportion of total area, mirrored the trends observed at the national level for current and both future climate scenarios Fig. b. The pr104 or included it as a variable in the model itself106. Here, we incorporated land cover post-hoc, which allowed us to leverage the maximum amount of occurrence-environment data without incorporating errors generated from those temporal mismatches 47. As such, our approach directly accounted for the spatial constraints imposed by anthropogenic land use when assessing the impacts of climate change41. Accounting for anthropogenic land use is particularly important for the Philippines as dipterocarps are typically lowland species26, many of which are threatened with habitat loss as a major driver for their decline52, and the tropical lowlands have been the hardest hit by deforestation23. Indeed, the Philippines has only 3% of its primary forest remaining as a result of forest conversions to anthropogenic land use23.Incorporating land cover into species distribution models in dynamic geographic regions such as Southeast Asia presents a considerable challenge for model accuracy, because of temporal mismatches between  occurrence records and (contemporary) land cover data. Indeed, previous research investigating changes in suitable habitat under climate change typically did not include land cover107. For example, although D. validus and H. plagata were projected under either climate change scenario to experience almost no loss of suitable habitat, both had their potential distributions substantially reduced with LCC, which allowed for a more thorough assessment of future threats to species distributions. Constraining species distributions based on land cover suitability has important implications for conservation interventions, as improved spatial assessments will facilitate geographically targeted allocation of resources towards mitigating biodiversity loss108.Incorporating LCC, therefore, imposed significant and necessary spatial constraints on the extent of both current and future species distributions; this was a conservative approach that prevented the overestimation of species future distribution, which would have resulted in an underestimation of a species conservation priority20. Importantly, the corrections applied in this study were based on a static contemporary land cover map. For the Philippines, this is unlikely to be a problem because the vast majority of quality forest has already been lost, while forest loss in the majority of provinces has been less than 6% from 2000–2014 . However, in regions where deforestation rates are high or expected to increase going forward, such as Cambodia109 or Indonesia16, it may be more appropriate to incorporate projections of future land cover42.The post-hoc LCC approach has broad applicability when projecting the effects of climate change on species in regions that have experienced rapid recent land cover change, and for regions that are poorly surveyed—conditions that describe much of the global tropics111. Consequently, this could trigger bottom-up effects on plant-animal mutualistic networks, such as extinction cascades, which have been predicted to arise from the extinction of other plant species33. The differential response to climate change across closely associated dipterocarp species further suggests the potential for future disaggregation of tree communities, which will affect the structure and function of the forests they form31. This is especially true for dipterocarps, considering their dominance and structural importance across forests in SEA29, which further reinforces the importance of dipterocarp persistence, and conservation efforts to ensure it35.Our results revealed a high degree of variability in dipterocarp responses to climate change. While some species were projected to remain relatively unaffected by climate change, others were projected to experience a substantial loss of suitable habitat, especially along lower elevations. Suitable habitat loss for a species indicates, at a minimum, an increase of environmental pressure favouring mortality over recruitment in those locales, which may ultimately manifest in local extirpations or functional extinctions40. The availability of climatic macrorefugia is especially vital for species with dispersal limitations and long maturation time, which would hinder a species’ ability to rapidly migrate following projected shifts in suitable habitat39. This applies to dipterocarps, as they generally exhibit both traits113. The loss of these areas would mean the loss of source populations, potentially limiting the recovery and survival of species following climate change39. With the projected low availability of such refugia for several of the species studied, climatic macrorefugia represent areas of exceptional conservation importance.An important outcome of this study was the projections of suitable habitat that remained so under climate change—identified as stable in this study. These areas represent species-specific in-situ climatic macrorefugia that can potentially secure the persistence of species under climate change114. Our findings highlight a critical gap in the Philippines protected area system in that it covers a relatively small proportion of suitable habitat for dipterocarps currently, and a similarly low proportion of species-rich climate macrorefugia under both RCP scenarios. Given the importance of dipterocarps in maintaining the structure and function of forest in SEA, a persuasive case can be made to integrate dipterocarp distributions into protected area network designs101.The goal of protected areas is to safeguard species both now and for future generations, the latter of which is gaining relevance given the large projected shifts in climatic conditions for protected areas globally115, and progressive planning will need to incorporate species range shifts under climate change116. Our study affirms the importance of integrating SDMs and species refugia into long-term planning of protected areas118. Here we highlight three applications of this research for incorporating SDMs into protected area planning. First, individual or stacked maps of suitable habitat loss would reveal protected areas most heavily affected by climate change, and consequently, most at risk of underperformance114. Second, mapping areas of continued habitat suitability for multiple species will allow the identification of species-rich climatic macrorefugia inside and outside protected areas, therefore prioritizing conservation effort for dipterocarps40. For example, the lowlands of northern and southern Sierra Madre, and Cordillera mountain ranges were identified as unprotected species-rich areas for Greater Luzon. When applied across the entire country, a larger set of priority landscapes would emerge to support site-specific, long-term conservation planning for the protected area system, ensuring the increased coverage of critically important species-rich climatic macrorefugia. Third, distribution maps of suitable habitat gain highlight potentially biodiverse regions in the future, which could be integrated into protected areas over the long-term114, notwithstanding the underlying assumption of full dispersal, which is largely unrealistic for dipterocarps owing to dispersal limitations119.Most existing protected areas have been designed based on the current distribution of species and richness120. Alongside suitable habitat projections of other key taxa and the inclusion of various cost metrics, our results could help indicate cost-effective areas for protected area expansion in the Philippines120. Moreover, with increasing commitments to restoring forest cover in SEA, suitable habitat projections before LCC would provide crucial information on locations for which dipterocarp reforestation could be done successfully35. Thus, the stacked maps of loss, stable, and gain can directly aid in informing more effective planning of conservation priorities and reforestation projections, better preparing protected areas for the challenges of protecting species under climate change118.Finally, these SDM outputs could contribute to long-term spatially-explicit conservation planning through platforms such as Marxan, which demarcates protected areas based on set conservation targets at a minimal total costSupplementary Information.