Prionace glauca, by sampling the least mobile component of the populations, i.e., young‐of‐year and small juveniles , at three reported nursery areas, namely, western Iberia, Azores, and South Africa. Samples were collected in two different time periods (2002–2008 and 2012–2015) and were screened at 12 nuclear microsatellites and at a 899‐bp fragment of the mitochondrial control region. Our results show temporally stable genetic homogeneity among the three Atlantic nurseries at both nuclear and mitochondrial markers, suggesting basin‐wide panmixia. In addition, comparison of mtDNA CR sequences from Atlantic and Indo‐Pacific locations also indicated genetic homogeneity and unrestricted female‐mediated gene flow between ocean basins. These results are discussed in light of the species' life history and ecology, but suggest that blue shark populations may be connected by gene flow at the global scale. The implications of the present findings to the management of this important fisheries resource are also discussed.Highly migratory, cosmopolitan oceanic sharks often exhibit complex movement patterns influenced by ontogeny, reproduction, and feeding. These elusive species are particularly challenging to population genetic studies, as representative samples suitable for inferring genetic structure are difficult to obtain. Our study provides insights into the genetic population structure one of the most abundant and wide‐ranging oceanic shark species, the blue shark  Studies on the population structure of highly mobile pelagic marine fishes are often limited by the difficulty of obtaining representative samples across the range of often‐widespread species, and by the expected low level of genetic differentiation among populations due to the potential for high gene flow between distant locations. Highly mobile elasmobranchs are no exception, and the above limitations in studying these charismatic but elusive species are of particular concern given their high vulnerability to population depletion when faced with even low rates of fishing mortality , which is likely the most exploited shark species globally  test whether there is genetic differentiation among blue shark nurseries in the Atlantic Ocean off western Iberian, Azores, and South Africa; and (2) test the temporal stability of the observed patterns of genetic diversity. To this purpose, 12 nuclear microsatellite loci and a 899‐bp fragment of the mitochondrial DNA control region were used to estimate the levels of genetic diversity and differentiation among sample collections of young‐of‐the‐year (YOY) and small juvenile blue sharks (≤2 year old) within each nursery area, that is, during a stage of limited movement , off western Iberia (IB) and off western South Africa (SA), as well as from Brazil  were obtained from observers on board commercial fishing vessels operating in Brazilian waters prior to 2012, although no other biological information  is available. They are thus not considered as samples from juvenile blue sharks, but given the lack of information from specimens from that region, they were analyzed and compared to the remaining sample collections. Tissue samples were obtained from dorsal fins or muscle tissue (~1 cm3) preserved in 96% ethanol and stored at room temperature. Total genomic DNA (gDNA) was extracted from each tissue sample using the EasySpin® Genomic DNA Tissue Kit , according to the manufacturers' instructions.The sampling locations correspond to three reported nursery areas of l Figure . A totall Figure  accordinl Figure . Samplesl Figure . AdditioCarcharodon carcharias by Pardini et al.  was amplified using primers developed for the white shark i et al. , that isP. glauca were obtained from Fitzpatrick, Shivji, Chapman, and Prodöhl , 3 μl of ultra‐pure water, 1 μl of primer mix (details available from the authors upon request), and 1 μl of gDNA (5–20 ng). All multiplex PCR reactions began with a hot start at 95°C for 15 min followed by: (1) 17 cycles of a denaturation step of 95°C for 30 s, a touchdown annealing step of 90 s and between 62 and 54°C with a 0.5°C decrease per cycle, and an extension step at 72°C for 30 s; (2) 15 cycles of a denaturation step as above, annealing at 54°C for 45 s, and extension as above, and by (3) 8 cycles of a denaturation step as above, annealing at 53°C for 30 s, and extension as above. A final extension step was conducted at 60°C for 30 min. Two loci , 3.2 μl of ultra‐pure water, 0.04 μmol/l of T3‐tailed forward primer, 0.4 μmol/l of reverse primer, 0.4 μmol/l of fluorescent dye, and 1 μl of gDNA. Single locus PCR conditions were similar to those for multiplex reactions except that the annealing steps were decreased by 15 sec at the 54 and 53°C cycles of the PCR program. The presence, length, and quality of each amplification product were verified by electrophoresis on a 2% (w/v) agarose gel and diluted 0.8× if needed. One microliter of each diluted PCR product was added to 10 μl of deionized formamide and to 0.2 μl of the internal size standard Genescan‐500 LIZ (Applied Biosystems) and run on an ABI 3130xl Genetic Analyzer (Applied Biosystems). Genemapper software 4.1 (Applied Biosystems) was used to manually score individual genotypes.Microsatellite loci developed specifically for  Prodöhl  and Mend Prodöhl , but onl Prodöhl . Polymer2.2h), and nucleotide diversity (π) per sample collection, and the average number of nucleotide differences among sequences within each sample collection (k) and over all sequences. The relationships among haplotypes and their spatial distribution were inferred by constructing a 95% parsimony inference network  and for C. limbatus (Accession no.s GU245557–GU245566) to serve as outgroups.Mitochondrial DNA diversity indices were calculated with DnaSP version 5  was chosen. Genetic relatedness among all pairs of small juvenile blue sharks were then estimated and visually inspected for the presence of outlier pairs. The distribution of observed pairwise relatedness values across all juvenile blue sharks was also compared to the values expected between parent‐offspring (PO), full‐siblings (FS), half‐siblings (HS), and unrelated pairs (UN). This was meant to check the presence of possibly related individuals as they may bias estimates of genetic diversity and differentiation , and observed and expected heterozygosities , were estimated in FSTAT version 2.9.3.2  was performed to test different null hypotheses of genetic structure: (1) genetic homogeneity among nursery collections, at each time period; (2) genetic homogeneity across Atlantic nursery collections between time periods ; and (3) genetic homogeneity between the Brazilian collection and the three Atlantic nurseries. A locus‐by‐locus AMOVA was also performed on the nuclear microsatellite data using Arlequin, to infer the contribution of the different loci to the overall genetic structure. In all cases, statistical significance was assessed by 10 000 permutations of the dataset. As the locus‐by‐locus AMOVA did reveal contrasting signals of genetic differentiation among loci, we performed outlier tests aiming at detecting loci not conforming to neutrality expectations using LosiTan  and the corresponding alpha level  were estimated in POWSIM version 4.1 , and an Ne estimate obtained as described below. Power analyses were conducted using 50 individuals per deme and allelic frequencies at microsatellite loci.The statistical power of the microsatellite‐based K‐means method was run for K = 1–12 , and the best K value was chosen using the Bayesian Information Criterion  on the multilocus microsatellite genotype data, following the method of Jombart, Devillard, and Balloux  implemenK‐value tested, using 500,000 burn‐in steps followed by 1 million steps. Criteria for choosing the best K‐values followed those indicated in the software manual. Given the reduced number of microsatellite loci genotyped for the Brazilian collections, the DAPC and STRUCTURE were run with two datasets: One dataset included only the nursery sample collections making use of the full set of microsatellite markers, and a second dataset included all nursery and the Brazilian sample collections with the reduced set of loci.Genetic admixture and differentiation among blue shark nursery collections was also evaluated with the clustering method implemented in STRUCTURE , Japan (n = 20), west Australia (n = 4), and east Australia (n = 17). Briefly, the nucleotide sequence for haplotype PG01  and equally differentiated haplotypes (k: 3.00–3.57).A total of 237 individuals from the three Atlantic blue shark nurseries and from off Brazil were successfully sequenced for a 899‐bp fragment of mtDNA CR. The final alignment included two indels and 29 polymorphic sites, of which 19 were transitions and 11 were transversions. Fifty‐two haplotypes were recovered from the final alignment  showing an overall mean P. glauca haplotypes confirmed the existence of the two haplogroups (with moderate support) and showed haplogroup B nested within haplogroup A. The two haplogroups have equal relative frequencies in all nursery sampling collections, although haplogroup A is more frequent in the Brazilian collection (Table The haplotype network showed two groups of haplotypes (or haplogroups) separated by two‐step mutations, with the most frequent haplotypes in each group being connected by a minimum of four mutation steps and by two low‐frequency haplotypes Figure . Each haon Table .HO ranging from 0.20 to 0.93 , demonstrating that small juvenile blue sharks across all nursery collections followed closely the distribution of values expected from unrelated pairs of individuals . Thus, all individuals collected at nursery areas were considered unrelated and included in further genetic analyses.Twelve microsatellite loci were genotyped for 302 juvenile blue sharks from the three Atlantic nursery areas , showing a total number of alleles per locus ranging from 2 to 35 (mean: 10.6) and i et al.  showed ts Figure . Also, oAR between 7.15 and 7.96 and mean HO of 0.61 and 0.65  despite the two North Atlantic nurseries showing lower values in 2,010. When considering the reduced set of microsatellite loci and the Brazilian collection, similar levels of genetic diversity were also found among all collections (Table Pgla03 for SA2000s and Pgla05 for IB2000s deviated from HWE expectations (after FDR correction), both due to heterozygote deficits. Locus Pgla07 did not conform to HWE expectations across all nursery samples or in the Brazil sample (after FDR correction), also due to heterozygote deficit, and was removed from further analyses.The levels of genetic diversity at the nuclear microsatellite loci were very similar among all nursery sample collections, with mean ns Table . Linkage3.2ST tests based on mtDNA haplotype frequency data showed no significant genetic differentiation among sample collections, with comparisons including Brazil showing larger ΦST values  being significant , for an α value of 0.04, regardless of the number of loci used  or sampling scheme tested . For the lower FST value and 11 loci, increasing the sample size per deme from 50 to 500 individuals still showed <20% power to reject the null hypothesis.Power simulations indicated our microsatellite panel had high power (>95%) to detect pairwise Pgla03 showed FST and FCT values about 1–2 orders of magnitude higher than the remainder loci. Removal of this locus from the analyses yielded nonsignificant FCT values (data not shown). Despite these results, all microsatellite loci were found to conform to neutrality expectations, both when considering only the samples from nursery areas genotyped at 11 microsatellite loci (n = 6 “populations”), and when considering all Atlantic collections genotyped at only six loci (n = 7 “populations”).Results of the AMOVA confirmed the lack of significant genetic heterogeneity among nursery collections at the mtDNA CR or at the microsatellite loci, regardless of the time period considered. Likewise, no significant temporal differences were detected when considering the three nursery collections together per time period . However, the AMOVA showed that samples from Atlantic nursery areas were weakly but significantly differentiated from the Brazilian collection both at the mtDNA CR and at the nuclear microsatellite loci Table . HoweverK‐means clustering method (data not shown) or location information .The DAPC had little discriminating power among Atlantic nursery collections, or among nursery collections and Brazil, as indicated by the very low alpha values  and IP collections indicated low and nonsignificant differences in all comparisons .The final alignment of homologous mtDNA CR sequences spanning 373 bp in length produced 41 haplotypes, of which 11 were shared between Atlantic and IP samples Table . The res3.4FS test detected significant deviations from selective neutrality and population equilibrium in Atlantic nurseries (considered together per time period) and in the Brazilian collection. In all cases, significantly large negative Fu's Fs values were found , suggesting past population expansions. When considering the two mtDNA CR haplogroups detected above, both haplogroups also had significant negative Fu's FS values , consistent with past population size expansion. On the other hand, analysis of historical population size changes using nuclear microsatellite loci found no signal of a recent population bottleneck, regardless of the parameter combination used (P > 0.05).The estimated contemporary effective size of the Atlantic blue shark population was 4,513 . Neutrality tests based on mtDNA CR sequences and Fu's 44.1P. glauca.Genetic homogeneity at the Atlantic‐wide level was found for the blue shark in the present study. Juvenile sharks born at three distinct Atlantic nursery areas, located on either side of the Equator, share the same genetic composition at 11 nuclear microsatellites and at the mtDNA CR Tables . The lacThe genetic homogeneity among Atlantic nursery areas and across hemispheres is in agreement with previously published genetic studies on the population genetics of blue sharks. Specifically, genetic homogeneity at mitochondrial and nuclear markers was found in blue sharks sampled at various locations within the North Atlantic Queiroz, , and witPgla03), which also showed evidence of null alleles at two sample collections (SA2000s and AZ2000s) and significant deviations from HWE expectations (in SA2000s). Future confirmation of this signal is thus needed, and we caution against definitive conclusions regarding the existence of a discrete population of blue sharks in the western South Atlantic.The Brazilian blue shark samples show some discordance with the above pattern of unrestricted gene flow at large spatial scales. Low but significant genetic differentiation was found between the Atlantic nurseries and Brazil at both nuclear and mtDNA loci, albeit with a stronger signal at the microsatellite loci. This result is surprising given the absence of genetic differences among Atlantic collections on either side of the Equator, and the potential for long distance dispersal and apparent lack of barriers for the blue sharks throughout the Atlantic and Pacific oceans  within as well as between ocean basins       Benavides et al., Carcharhinus plumbeus , Portnoy, McDowell, Heist, Musick, & Graves, Carcharhinus brachyurus , Benavides et al., Galeocerdo cuvier  Bernard et al., Sphyrna lewini , Duncan, Martin, Bowen, & De Couet, Current distribution patterns of genetic diversity are also influenced by a variety of factors including historical demographic events , vicariant/dispersal events, selection, etc., which may be shaped by both biological and environmental processes. Environmental variability associated with the glacial‐interglacial cycles of the Late Quaternary predating the appearance of modern climatic conditions (~11.5 ky before present) have been recurrently invoked to explain present‐day phylogeographic patterns in a variety of living organisms , and the distinct estimation methods used  of blue sharks may be in the order of 10‐5. The Ne/Nc ratio of blue sharks appears to be much smaller than the one observed in another pelagic carcharhinid, the sandbar shark  and longer AL (11 vs. 5 year).Surprisingly,  Clarke, . Assumin4.3FST values are very small, as the relationship between FST and mNe  has a long flat right hand tail as FST approaches zero and Ne > 1,000s  and Pacific oceans . IS, AV, and JRM collected, analyzed, and interpreted the data. AV led the writing of the text, with contributions and critical revision by JRM, IS, CdS, CSJ, LRN, GM. Click here for additional data file. Click here for additional data file.


Hypoxia has been widely known to stabilise the transcription factor hypoxia inducible factor 1-alpha (HIF1α) and mediate the induction of hypoxic transcriptional programs after ischemia. However, we find that the transcription factors Jun Proto-Oncogene (c-JUN), Nuclear Factor Of Kappa Light Polypeptide Gene Enhancer In B-Cells (NFκB) and signal transducer and activator of transcription 3 (STAT3) bind genes regulated after 2hours (hs) of omitted glucose and oxygen before HIF1α. Potent HIF1α responses require 6hs of hypoxia to substantiate transcriptional regulation comparable to either c-JUN or STAT3. Phosphorylated STAT3 protein is at its highest after 5 min of oxygen and glucose (OGD) deprivation, whereas maximum HIF1α stabilisation requires 120 min. We show that STAT3 regulates angiogenic and metabolic pathways before HIF1α, suggesting that HIF1α is not the initiating trans-acting factor in the response of pericytes to ischemia.Brain pericytes are important to maintain vascular integrity of the neurovascular unit under both physiological and ischemic conditions. Ischemic stroke is known to induce an inflammatory and hypoxic response due to the lack of oxygen and glucose in the brain tissue. How this early response to ischemia is molecularly regulated in pericytes is largely unknown and may be of importance for future therapeutic targets. Here we evaluate the transcriptional responses in  In the brain, pericytes surround the entire microvasculature and take an important role in the neurovascular unit ,2 by maiIn the literature, hypoxia-inducible transcription factor (HIF-1) has long been considered the most important transcriptional regulator of cellular responses to hypoxia . While tIn this study, we present a bioinformatic analysis of the transcriptome of human brain pericytes after exposure to a) glucose deprivation, b) hypoxia (0.5–0.7% oxygen) and c) combined oxygen and glucose deprivation (OGD). Our analysis provides evidence of different gene expression profiles of human brain pericytes subjected to hypoxia with or without glucose in the favour of genes involved in cell metabolism, apoptosis and angiogenesis. Using a set of ChIP-seq analyses, we show that STAT3 occupied target genes are regulated before the HIF1α target genes. Lack of glucose in the absence of oxygen emphasises STAT3 activity, but does not have the same relative impact on HIF1α. We show that hypoxic or OGD treated pericytes respond to the treatments by transcriptional programs initiated primarily by STAT3, c-JUN and NF-κB, and not HIF1α and we confirm this temporal regulation at protein level. We further analyse STAT3 regulated genes and show that they involve angiogenic and metabolic pathways. These findings have implications for modulating pericyte activity early in ischemic stroke, and identify a set of regulated genes that are targets of STAT3, prior to the induction of HIF1 regulated genes.2 conditions . The cells grew exponentially with a doubling time of approximately 48 hs, reaching approximately 85% confluence after 48-72hs. Commercially available pericytes (ScienCell) were expanded in pericyte medium (ScienCell) according to the manufacturers instructions.We previously established and characterized an adult human brain pericytes line, obtained from brain tissue harvested from individuals undergoing ventriculostomy or surgery for intractable temporal lobe epilepsy as described . All pro2 and then washed with phosphate buffered saline (PBS). First, cells were exposed to oxygen deprivation (0.5–0.7% oxygen) conditions generated in a humidified, gas-tight hypoxia chamber incubator (Electrotek) with a gas composition of 85% N2, 5% CO2 and 10% H2 . Si. Si-/- m volumes . In contWe conclude that STAT3 phosphorylation activity, that mediates transcriptional activation of STAT3, was present at its maximum already after 5min of OGD treatment. The transcription mediated significant overexpression of the STAT3 BRGs c-MYC and GDF15 after 2h of OGD treatment suggesting that the protein modifications of STAT3 results in early activation of STAT3 target genes that directs the early hypoxic responses. GO:term analysis suggests that the major STAT3 BRG responses regulate metabolic and angiogenic processes. This implies that STAT3 initiates a transcriptional program necessary for pericytes to adapt to hypoxic and OGD conditions prior to the time point when HIF1α’s activity is induced.Our findings suggest that STAT3 is a potential target for intervention in pathologies where there is hypoxia or OGD, e.g. asphyxiation, tumor pathologies or in ischemic stroke.S1 FigWestern blot of (A) nuclear extracts (B) or whole cell extracts from Sciencell pericytes treated with normoxia, hypoxia or OGD. (A) 5,10 or 15 min treatments followed by extraction of nuclear proteins. In (A) anti-pS727STAT3 was used to visualise nuclear phosphorylation of STAT3 required for transcriptional activity. PY705 was used to measure the nuclear phosphorylation required for dimerisation and nuclear translocation of STAT3. Nuclear anti-HIF1α was measured at 5, 10 or 15 minutes of treatments. Anti-STAT3 indicated the steady state nonphosphorylated STAT3 in the nuclear extracts and was compared to the pS727- and pY705-STAT3 levels. (B) Pericytes treated with normoxia, hypoxia or OGD for 15, 45, 75 or 120min prior to whole cell lysis. Western blot of pY705-STAT3, HIF1α or total STAT3.(TIF)Click here for additional data file.S1 File(XLSX)Click here for additional data file.


Gymnogeophagus setequedas is a rare and rheophilic species of tribe Geophagini, considered endangered in Brazilian red lists. Its previously known geographical distribution range was the Paraná River basin, in Paraguay, and a tributary of the Itaipu Reservoir in Brazil. Since its description no specimens have been collected in the original known distribution area. However, recent records of G. setequedas in the lower Iguaçu River, in a region considered highly endemic for the ichthyofauna, extended the known geographical distribution and may represent one of the last remnants of the species. The aim of this study was to estimate the genetic diversity and population structure of G. setequedas, using microsatellite markers and mitochondrial haplotypes, in order to test the hypothesis of low genetic diversity in this restricted population. Muscular tissue samples of 86 specimens were obtained from nine locations in the Lower Iguaçu River basin, between upstream of the Iguaçu Falls and downstream of the Salto Caxias Reservoir. Seven microsatellites loci were examined and a total of 120 different alleles were obtained. The number of alleles per locus (NA) was 17.429, effective alleles (NE) 6.644, expected heterozygosity (HE) 0.675, observed (HO) heterozygosity 0.592, and inbreeding coefficient (FIS) 0.128. Twelve haplotypes in the D-Loop region were revealed, with values of h (0.7642) and π (0.00729), suggesting a large and stable population with a long evolutionary history. Thus, both molecular markers revealed high levels of genetic diversity and indicated the occurrence of a single G. setequedas population distributed along a stretch of approximately 200 km. The pattern of mismatch distribution was multimodal, which is usually ascribed to populations in demographic equilibrium. Nevertheless, the construction of a new hydroelectric power plant, already underway between the Salto Caxias Reservoir and Iguaçu Falls, could fragment this population, causing loss of genetic diversity and population decline, and for this reason it is necessary to maintain the Iguaçu River tributaries and downstream area from the Lower Iguaçu Reservoir free of additional dams, to guarantee the survival of this species. Gymnogeophagus setequedasThe largest biodiversity asset in the world is located in Brazil . HoweverGymnogeophagus setequedas was described based on specimens collected in tributaries of the Paraná River in Paraguay and Brazil, near the Sete Quedas region, an area currently submerged due to construction of the Itaipu Hydroelectric Power Plant , Middle Iguaçu , and Lower Iguaçu   . The por region) . The regandscape .G. setequedas preferred lentic environments, as in the other species of the genus , thus presenting the first data of a population study of this species threatened with extinction.The genetic diversity status of species is the starting point for systematic planning of actions that should be taken to ensure the survival of species and reduce their risk of extinction. No works are known which focus on the biological  or genetresented . Thus, tFigure 1).Our study area comprises a stretch of the Lower Iguaçu River basin, between upstream Iguaçu Falls and downstream Salto Caxias Reservoir : two points in the main channel of the Iguaçu River (IGU 1 and IGU 2 – PNI), near the Iguaçu falls, and seven tributaries of the Iguaçu River  . The samples were collected in 2012 (November), 2013 (November and December), and 2014 . The specimens were captured using nets of different mesh sizes and electric fishing. Samples of muscle and rayed fins were taken from the fish, stored in microtubes containing 100% ethanol and kept at -20°C. Specimens were fixed in 10% formalin and preserved in 70% ethanol and deposited in the fish collection of the Zoology Museum at the Universidade Estadual de Londrina under catalog number: MZUEL 16332, 16353, 16354, 17094–17096.Samples of 86 TM 1000 was used for determination of DNA concentrations and samples were diluted in ultrapure water, 10 ng/μL for microsatellite markers and 5 ng/μL for mtDNA D-loop markers.Total DNA was extracted from muscle or rayed fins preserved in 95% EtOH following the phenol/chloroform protocol of Geophagus brasiliensis  (Cross-amplification tests were conducted from seven loci described for  Gbra96) . ReagentG. setequedas mitochondrial DNA was amplified using PCR. The primers used were L 5′-AGAGCGTCGGTCTTGTAAACC-3′ (http://lowelab.ucsc.edu/tRNAscan-SE. Sequences of the 12 different haplotypes were deposited in GenBank (MG581478 to MG581489).Part of the control region (D-loop) of the AAACC-3′  and H 5′AAACC-3′ . PCR reaAAACC-3′  in BioEdAAACC-3′ . NCBI’s AAACC-3′  was usedAAACC-3′ , availabThe first step for genetic analyses was to define the number of existing populations. For this we used population analyzes based on Bayesian approaches that mainly include “attribution methods.” These methods calculate the probability of the different genotypes being observed in each population and assign the individuals to the populations according to the possibilities of the genotypes belonging to them, without any a priori inference. Thus, such analyzes allow to infer which population an individual belongs to, regardless of their collection site .K) was estimated by using the admixture model and correlated allele frequencies among populations, with K ranging from 1 to 10 (K = 1–10)   . A totalPr (X/K)  and ΔK aatistics  by Strucatistics . Graphs atistics . Genetic.3.5.1.3 . Signifile tests .NA), effective number of alleles (NE), expected and observed heterozygosity  were obtained with POPGEN v. 1.31 (FIS) was obtained with Fstat v2.9.3 program , later adjusted by the Bonferroni sequential correction  and nucleotide diversity (π) were obtained from DnaSP v.5 program (1) was used to construct haplotype networks from mtDNA data based on the median-joining algorithm  was K = 1, from ln Pr(X/K). The graphic representation of ΔK showed that there were no well-defined groups, the ancestral values were distributed homogeneously among individuals and samples, indicating the occurrence of a single G. setequedas population . Molecular Variance Analysis (AMOVA), conducted for all samples showed very little variation among them . Following this result, all other analyzes were based on a single population.Bayesian clustering analysis (Structure) applied to microsatellite data indicated that the most probable NA) was 17.429, effective alleles (NE) 6.644, expected heterozygosity (HE) 0.675, observed (HO) heterozygosity 0.592, and inbreeding coefficient (FIS) 0.128.In the entire sample, a total of 120 different alleles were obtained from seven microsatellite loci. The number of alleles per locus (P < 0.05) in the Hardy–Weinberg equilibrium (HWE) at the majority of microsatellite loci, only the locus Gbra96 showed significant deviation. This correction was also applied to the linkage disequilibrium (LD) tests, and a significant value was found only in locus Gbra96. The Micro-Checker program found no null alleles among the samples.After applying the Bonferroni sequential correction, there were no significant deviations  and four indels sites were found. Twelve different haplotypes were revealed, of which four haplotypes  were singletons . The H2 haplotype was the most frequent, observed in 37 samples from four different locations . Although SIL, FLO, GON, COT, and CAP present only one haplotype each, these haplotypes are shared with other locations, except for H5 found only in CAP. The IGU2 has the highest number of haplotypes (N = 8), followed by STO (N = 6), AND (N = 3), and IGU1 (N = 2). Haplotype (h) and nucleotide (π) diversity values were 0.7642 and 0.00729, respectively (Table 2).From the amplification and sequencing of mtDNA of 82 Table 3). The mismatch distribution graphic demonstrated a multimodal distribution for haplotypes , which is usually ascribed to populations in demographic equilibrium. In the neutrality tests, Tajima test values (D) and Fu test values (Fs) were negative and not significant (Table 2).The signed-rank test did not produce significant values in any of the mutational models . In the mode-shift test, the samples showed typical L-shaped distribution (non-bottleneck) in the frequency of the alleles in the mode-shift test (Table 4).Bayesian gene flow analysis of the microsatellite data revealed contemporary migration values among the samples within the confidence interval (95%). Values for non-immigrants in each sample, ranging from 79 to 82.6%. However, migration estimates were also obtained and showed similar values among the majority of samples. The lowest migration estimates were from IGU 1 to COT and IGU 2 to AND, both with 1.9%. On the other hand, the highest migration values were from FLO to IGU2, and to GON, and COT to CAP, with 2.7%. Among all the samples, FLO was the one that obtained the highest percentage of migrants, for the largest number of sites  and the expected heterozygosity (0.67) are as expected for freshwater fish. In addition to the high diversity in the nuclear markers, the G. setequedas analyzed showed significant variations in mitochondrial DNA, exhibiting high levels of genetic diversity. According to h is considered the haploid equivalent of HE in data on diploids. The similarity among these estimates suggests that the current variations in nuclear and mitochondrial DNA are evenly distributed throughout G. setequedas population.According to as G. setequedas . In addition, analysis of the microsatellite data using Bottleneck program showed no significant recent bottlenecks. The absence of recent bottlenecks is corroborated by the high haplotype (h > 0.5) and nucleotide diversity (π > 0.5%) values in the mtDNA. According to G. setequedas, as the Iguaçu waterfalls have exerted effective geographic isolation on ictiofauna of the Iguaçu river  presented the highest rates of migration, and the highest levels of admixture in samples were found in the Iguaçu 2 and Gonçalves Dias rivers. The specimens from rivers further upstream in the drainage  appear to have more levels of admixture between them. This factor might suggest that entry into the upper tributaries is more likely than the lower. However, the highest rates of migrants to Iguaçu 1 are from most upstream tributaries, suggesting that populations of G. setequedas  without additional dams. Long-term monitoring of genetic diversity and inbreeding could also help conserve this population and provide a basis for future decisions.The results presented here demonstrate that the population of This study was carried out in strict accordance with the recommendations provided in the Guide for the Care and Use of Laboratory Animals. Collection was authorized by the System of Authorization and Information on Biodiversity – SISBIO (SISBIO n°. 25648-3 and 25648-4), by the Chico Mendes Institute for Biodiversity Conservation ICMBio 003/2014 and Official SEI n°. 63/2016-DIBIO/ICMBio), and by the Environmental Institute of Paraná – IAP (n°. 37788 and 43394). The sampling protocol was approved by the Ethics Committee on the Use of Animals – CEUA of the Universidade Estadual do Oeste do Paraná (n°. 62/09).LS-S, DF, MM, and OS designed the research. LA, SP, SM, and MM collected data. LS-S, TK-D, and DF performed the molecular genetic studies. All authors contributed to the writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Anseriformes and Charadriformes. We highlight the importance of epidemiological surveillance systems and the possible role of different migratory birds in the transmission of AIVs within the Americas. Our findings demonstrate the limited information on AIVs in Latin America and highlight the need of more studies on AIVs at the regional level, particularly those focused on identifying the endemic subtypes in regional wild birds.Avian influenza or bird flu is a highly contagious acute viral disease that can occur in epidemics and cross-border forms in poultry and wild birds. The characteristics of avian influenza viruses (AIVs) allow the emergence of new viral variants, some with zoonotic and pandemic potential. AIVs have been identified in Latin America; however, there is a lack of understanding of these viruses at the regional level. We performed a systematic literature review on serological or molecular evidence of AIVs circulation in Latin America. Methods were designed based on the PRISMA and STROME guidelines. Only peer-reviewed studies published between 2000 to 2015 and data was analysed based on country, viral subtype, avian species, and phylogenetic origins. From 271 studies initially found only twenty-six met our inclusion criteria. Evidence of AIVs infection was found in most Latin American countries, with Mexico as the country with the largest number of conducted studies and reported cases during the period analysed, followed by Chile and Argentina. Most of the AIVs were early reported through surveillance systems and at least 14 different subtypes of influenza viruses were reported in birds, and the presence of both low (92.9%) and high (7.1%) pathogenic AIVs was shown in Latin America. Of the reported AIVs in Latin America, 43.7% belong to migratory birds, 28.1% to local wild birds, and 28.1% to poultry. The migratory bird population mainly comprises families belonging to the orders  Orthomyxoviridae family. Influenza viruses also includes types B, C  OR (“influenza”[All Fields] AND “birds”[All Fields]) OR “influenza in birds”[All Fields] OR (“avian”[All Fields] AND “influenza”[All Fields]) OR “avian influenza”[All Fields]) AND (“Latin America”[MeSH Terms] OR (“Latin”[All Fields] AND “America”[All Fields]) OR “Latin America”[All Fields]).The articles were selected using a two-stage approach. During the first stage, the publications were selected based on their titles and abstracts, excluding the publications that were not considered relevant for this study. During the second stage, the full text of the articles that were previously selected in the first phase was reviewed. At this point, the articles that did not meet the previously established criteria were excluded. As previously reported , two resDue to the big amount of genomic data of AIVs and the use of the STROME ID guide, the quality of the studies and risk of bias was evaluated according to the criteria of the QUADOMICS—an adaptation of QUADAS, a quality assessment tool used in systematic reviews of diagnostic accuracy studies—was developed to assess quality issues specific to ‘-omics’ research, including the quality assessment of studies included in systematic reviews . The met® 7 for Windows® .Descriptive statistics were used in all cases to evaluate the results (frequencies and percentages). To examine the frequency of AIVs studies in Latin America over time, the analysed period was divided into quartiles, and presented as absolute percentage over the total studies. Statistical analyses were performed using GraphPad PrismTwo hundred and seventy one peer reviewed articles were initially found from the two selected databases; 49.8% (n = 135) of these were duplicates and were excluded during the first selection phase. Of the remaining 136 references, 61.8% (n = 84) did not meet the inclusion criteria of the study and were also excluded. After reviewing the full text of the remaining articles (n = 52), only 50% (n = 26) met all the inclusion criteria for this study . Of thesWe conducted quality assessment according to QUADOMICS, The results showed that almost 77% of the studies (n = 20) were classified as “high quality.” The remaining 23% (n = 6) were classified as “low quality.” . None ofOf the articles analysed, 23.07% (n = 6) were exclusively published by Latin American authors –45, wherThere were publications on AIVs originating in most Latin American countries. Mexico was the country with the largest number of conducted studies and reported cases during the period analysed, followed by Chile and Argentina . After rAnseriformes  and Charadriformes  (Anseriformes (mainly ducks) and Charadriformes (mainly represented by seagulls), but there are also reports of avian influenza in pelicans, toucans, owls, and doves, among others [Of the reported cases of AIVs in Latin America, 43.7% corresponded to migratory birds, 28.1% to local wild birds, and 28.1% to poultry. The migratory bird population mainly comprises families belonging to the orders atchers) . Birds og others , 49–51.Studies analysing samples from clinically ill animals that participated in diagnostic programmes in Brazil (27 to 87%) , 51, ChiIn some specific cases, such as in Colombia  and GuatEach country reported showed a different pattern of circulating subtypes over time. For example in Mexico, the LPAI H5N2 virus circulated during the first quartile of the study (2002 to 2004). Then, no reports were published for 4 years. However, between 2009 and 2012, a LPAI H7N3 viruses were initially isolated and then became a HPAI viruses over time as a result of mutations in its genome . FurtherAnas cyanoptera) and were phylogenetically linked to North American viruses [Most published research studies from Latin American countries located in Central America and the Caribbean were conducted in Mexico, Guatemala, El Salvador, and Honduras. The LPAI H5N2 viruses were identified on multiple occasions from 2000 to 2005 , 60 and  viruses . Simulta viruses , demonst viruses .et al were seeking to understand the phylogenetic and geographic origins of these HPAI H7N3 viruses. Authors found that the virus originated in the North American lineage through genetic reassortment events, whereby five of the eight segments  were introduced by wild birds migrating along the central North American migratory flyway and the PB2, PB1, and PA gene segments were introduced through the Western North American flyway [The genomic characterization of a HPAI H7N3 viruses isolated from laying hens in 2012 in Jalisco, Mexico determined that these viruses had high percentage of sequence identity (> 97%) in its eight genomic segments with AIVs from North America specially those isolated from ducks in Mississippi, Missouri, Arkansas, and Illinois . Furthern flyway .Anas discors—Blue-winged teal), indicating the importance of this avian species in the maintenance and transmission of AIVs in the continent [In El Salvador, Guatemala and Honduras , 60, 63,ontinent . In addiontinent , 58, 64.Arenaria interpres). Through phylogenetic analysis, de Araujo et al, 2014, concluded that these H11N9 viruses were genetically related to AIVs isolated in the same year from the same species in New Jersey, USA [Although two serological studies reported the presence of only H3 AIVs since the late 90s in Brazil, a diverssey, USA .Anas cyanoptera), in Bolivia in 2001 [One of the first isolations of AIVs in South America were LPAI H7N3 viruses recovered from wild aquatic birds, Cinnamon teal  have not been reported in other Latin American countries , 49, 57.Chile is one of the countries with the greatest diversity of AIVs reported. AIVs were first reported in Chile in 2002 , 47 and Larus dominicanus). In this study, samples were collected on the Atlantic coast of southern Argentina, and resulted in the molecular characterization of an H13N9 AIV. Phylogenetic analysis of the 8 gene segments of these viruses showed that the 6 internal gene segments were phylogenetically related to viruses isolated in Chile and Bolivia in previous years, indicating a pattern of regional genetic evolution with limited reassortment events [Rhynchotus rufescens) at the coastal region of the Rio de la Plata, which were found dead [In Argentina, all AIVs publications include low pathogenic subtypes (n = 4) and none HPAI. Between 2006 and 2007, a sentinel surveillance program identified AIVs in wild seagulls  has only been reported in breeding turkeys from two farms in Valparaiso (Chile) in July 2009, where it caused mild infections, reproductive symptoms and a gradual process of natural recovery , 68. Altst century. We found an increased number of studies on AIVs over time due to better regional surveillance systems that aimed for early diagnosis to prevent the spread of HPAI strains [Studies on AIVs in Latin America are limited, although several AIVs subtypes have been identified in Latin American countries since the 90s –71, howe strains , 68. How strains . TherefoOne of the most important results of this systematic review is the consolidation of information on AIVs subtypes that have circulated in Latin America. Furthermore, the scientific studies on these viruses in Latin America and their phylogenetic relationships with North American viruses, demonstrate the transboundary movement of AIVs within the Americas possibly associated to natural migration of avian species , 58. HowWorldwide, wild aquatic birds are the natural reservoirs of all AIVs , 60, 64.It is possible to assume that the wide genetic variety described in countries like Guatemala is linked to the spatial location of this country, as it is situated in a “geographical bottleneck”  that funnels millions of birds migrating along the North American Mississippi, Pacific, and Atlantic flyways . NeverthAnas cyanoptera in Bolivia [Larus dominicanus in Argentina [In addition, analyses of AIVs isolated from  Bolivia  and fromrgentina , 54 suggrgentina .It is important to highlight that phylogenetic analyses of AIVs in Latin America has demonstrated their relationships with viruses previously isolated in North America. These findings suggest that there is viral transmission between the Americas , 64, 65 Although none of the studies reported the presence of avian influenza viruses or genes from the Eurasian lineage during the period evaluated (2000–2015), the recent incursion of an Eurasian H5 subtype HPAI virus in North America  highlighTimely identification of disease and mortality events in wild birds is extremely important for early detection of AIVs , 61. HenBiosecurity measures to confront outbreaks of HPAI viruses have proven to be the most effective strategy to reduce the spread of the virus and the disease in countries in Europe and Asia , 79, thaIt has been established that HPAI viruses have originated from LPAI genetic precursor viruses of wild origin that have managed to infect poultry and mutate , 82. In Although virulence is a polygenic trait in AIVs, one direct virulence factor is linked to the haemagglutinin cleavage site . In all Systematic reviews allow the revision of all the scientific evidence on a given topic avoiding selection bias. Using this process, the summarized information can be presented to propose hypotheses that explain the behaviour of the data and to identify areas of knowledge where further research is needed . In thisAs required by PRISMA and the STROME-ID statement, we also recognize the limitations of this study . Our metIt is a fact that the number of peer-reviewed published studies of AIVs in birds in Latin America represents only a small part of the true panorama of the infection (publication bias). The economic resources available for surveillance and research, geopolitical situation and other factors of each country could add different bias that authors could not control. This bias has been critically analysed and conclusions presented has been showed in the light of this important topic as has been determined by others .The identification of multiple AIVs subtypes in Latin America is a reality that includes both HPAI and LPAI viruses. However, information about these viruses and their associated epidemiology in the region is scarce. It is undeniable that more AIVs studies are needed at the regional level to identify and differentiate endemic subtypes in regional wild birds. Efficient epidemiological surveillance systems are needed also to allow the timely identification of new AIVs introduced in the region. It is necessary to understand the roles of different migratory birds in the spread of AIVs between North and South America. Additionally, it would be important to identify the main risk factors involved in the transmission of AIVs between wild birds and poultry. Furthermore, identify and implement better biosecurity practices to minimize such risk. It is also important to strengthen inter-institutional collaboration at the regional and transnational level to better understand the dynamics of transmission and distribution of the virus, as well as to optimize the use of resources in these countries, where research funds are very limited compared to the funding of developed countries. It is necessary to maximize regional efforts for the early detection of AIVs in both local and migratory wildlife and to maintain biosafety and biocontainment barriers to prevent infection in poultry.Due to the emergence of new strains and multiple viral subtypes, it is necessary to make adjustments to the circulation analysis and prediction models that have been previously used elsewhere, focusing on the specific conditions of Latin America, both from the point of view of local and migratory wildlife and from that of poultry systems . FinallyS1 Table(XLSX)Click here for additional data file.S2 TableMethodological quality assessment using the QUADOMICS Tool.(XLSX)Click here for additional data file.S3 Table(DOC)Click here for additional data file.


Passer domesticus) from Romania and Bulgaria, regions characterized by high landscape heterogeneity compared to Western Europe. We asked whether morphological divergence, complemented with genetic data in this human commensal species, was best explained by environmental variation, geographic distance, or landscape resistance—the effort it takes for an individual to disperse from one location to the other—caused by either natural or anthropogenic barriers. Using generalized dissimilarity modeling, a matrix regression technique that fits biotic beta diversity to both environmental predictors and geographic distance, we found that a small set of climate and vegetation variables explained up to ~30% of the observed divergence, whereas geographic and resistance distances played much lesser roles. Our results are consistent with signals of selection on morphological traits and of isolation by adaptation in genetic markers, suggesting that selection by natural environmental conditions shapes population divergence in house sparrows. Our study thus contributes to a growing body of evidence that adaptive evolution may be a major driver of diversification.Both neutral and adaptive evolutionary processes can cause population divergence, but their relative contributions remain unclear. We investigated the roles of these processes in population divergence in house sparrows ( Morphological data were analyzed for adult individuals only, and because of sexual dimorphism in this species , for males and females independently.|x¯−y¯|/σx+σy, where x¯ and y¯ are the averages for populations x and y and σx¯ and σy¯ are their standard deviations. Because we only had partial datasets—one with three morphological variables  for all locations, and one with all morphological variables  for only nine locations, we evaluated which one was the most appropriate to use. Our assessment suggested that the three‐variables‐all‐locations dataset gave the most robust results (Appendix S1).Raw morphological measures are unlikely to be independent from one another due to allometric relationships, and as a result, we used the raw morphological data to create a size and several independent shape components using the “PCA ratio spectrum” method developed by Baur and Leuenberger  (Appendi2.5P. hispaniolensis) as house sparrows, we genetically identified individuals to species from southern sampling sites using the cytochrome c oxidase subunit I (COI) mitochondrial gene (Appendix S1).DNA was extracted using the QIAGEN DNeasy Blood & Tissue Kit  following the manufacturer's protocol. Because of potential misidentification of Spanish sparrows ; hence, all individuals were kept for subsequent analyses. To assess the level of genetic structure, we conducted two Bayesian clustering analyses: STRUCTURE . Loci were checked for the presence of null alleles using MICRO‐CHECKER  method implemented in FreeNA with 10,000 bootstrap replicates . We also tested whether morphological divergence and genetic population divergence were concordant using a Mantel test with 999 permutations.For subsequent landscape genetic analyses, we calculated site pairwise 2.7http://www.worldclim.org/) . We also used a measure of surface moisture based on the QuikSCAT microwave instrument  at 30 arcsec resolution. Although the home‐range sizes of individual birds are likely much smaller, spatial heterogeneity in climate variables within each grid cell is small compared to that between distant grid cells, and dispersal has been reported to be up to 1.7 km , processed in ArcGIS 10.0  using the “line density tool.” The output cell size was set to 0.0083333 degrees  to match the other environmental variables, and because of the short natal dispersal and small home‐range sizes of sparrows .Because sparrows are commensal with anthropogenic activity, we also included two measures of human habitation as predictors: road density and human population density. The road density layer was created out of a shape file of roads , one of them was excluded from further analyses. Of those pairs, we retained the one that is more easily interpretable .To reduce this set of environmental variables to a smaller suite that each provided unique information, we extracted their values at the sampling sites using ArcMap 10.2.2  and computed Pearson correlation coefficients (logistic regression in the case of the Danube River barrier) in R 3.1.2 (Table S4). When two variables had a Pearson correlation coefficient ≥0.7 (or 2.8FST) or morphological differences as response variables. The predictor variables consisted of environmental variables, geographic distance, resistance distance, and the Danube barrier, and were selected to determine the biotic variation that is explained by IBA, isolation by distance  outside the sampling locations and the calculated relationship between the environment and biological variation. We visualized this variation in the response variables in three‐dimensional RGB color space. To do so, we followed the computationally tractable approach from Fitzpatrick and Keller . BrieflyFinally, to inform conservation practices in Romania and Bulgaria, we visually assessed whether current protected areas capture genetic and morphological variation in house sparrows sufficiently well (Appendix S1).33.1We used the “PCA ratio spectrum” method to distinguish between the size and shape components of the morphological measurements. PCA results of the shape component are shown in Table S5. For both males and females, the first two extracted principal shape components explained all of the observed variation. PCA ratio spectra for wing, tail, and tarsus length are nearly identical for males and females  and suggest that most variation along the first axis is explained by the ratio of tail and tarsus, and along the second axis by the ratio of tail and wing. These results for the dataset with just wing, tail, and tarsus length but for all locations are supported by those for the all‐variables‐nine‐locations dataset for the first axis. This was, however, not the case for the second axis, where tail and wing are close to one another on the axis (explaining very little of the variation), bill depth is positioned on one end of the spectrum, contrasted on the other end by tarsus and culmen in males and by tarsus and head measures in females. Along the third axis, most variation is explained by the ratio between culmen and tail in both males and females, but bill depth is also important in males, whereas it is not in females. We conducted subsequent landscape genetic analyses using the PC scores of the size component and the first two shape components in males and females separately.3.2NE) ranged from 3.184 to 6.887; HO from 0.583 to 0.846; and HE from 0.590 to 0.794 (Table S6). Two microsatellite loci were found to be out of HWE in many sampling locations: Pdo31 significantly deviated from HWE in 15 locations and Pdo7 in 25 locations. These loci were, therefore, omitted from further analyses. After Bonferroni correction, no loci were in significant LD. We found a signal for the presence of null alleles and therefore calculated ENA‐corrected . Inclusion of the spatial component using GENELAND supported this finding. When we did not use a model for null alleles, all ten independent runs inferred six clusters (K = 6). However, assignments of cluster membership were highly inconsistent between runs (not shown), and we therefore concluded that there was little evidence for significant population structure based on these analyses. A lack of clear population genetic structure, however, does not necessarily mean a lack of IBD or IBA; merely that selection pressures may be relatively low, or there is a much relatively recent or ongoing gene flow. In fact, correlation analyses between genetic divergence and environmental heterogeneity may be better suited to identify potential patterns of IBD or IBA than those purely based on genetic data. We, therefore, proceeded with landscape genetic analyses using the ENA‐corrected FST values .Mantel tests between 3.3Among the morphological variables, models for the first shape component shape PC1) in males and for the size and second shape (shape PC2) components in females performed better than random models . The variables most important in explaining the observed genetic variation were annual precipitation (Bio 12), mean leaf area index , mean temperature of the driest quarter (Bio 9), and precipitation of the driest month (Bio 14) . Road density was also retained as an explanatory variable, but did not contribute as much as the above‐mentioned climate and vegetation variables.The best fit generalized dissimilarity model for microsatellites, where all variables were entered in the model, explained 24.95% of the observed variation Table  and only44.1FST to estimates of morphological divergence (QST or PST). Perhaps more importantly, the study in France found fine‐scale spatial autocorrelation, suggesting IBDL at short distances, but the potential effect of distance was not included in subsequent correlative analyses with environmental factors, making it difficult to assess the relative importance of IBDL versus IBA.We examined whether neutral  or selective evolutionary processes are the most important drivers of house sparrow population divergence in Romania and Bulgaria and whether measures of human habitation play a role in the divergence in this human commensal species. We found that IBDL could not explain either morphological or genetic divergence, whereas environmental variables explained a large proportion (up to 30%) of the observed variation. Our results for morphological measurements were thus consistent with a signal of selection. Although the number of polymorphic microsatellite markers was relatively low, and a large set of SNP markers will be more suited to get insight into population divergence and selection at the genetic level, results for microsatellites were nevertheless consistent with a pattern of IBA, and thus support the morphological data in the notion that adaptive processes are more important than neutral ones in driving population divergence. Our results suggesting that divergent natural selection is a main driver of intraspecific variation in this species are in agreement with findings for populations in Norway ; larger differences, however, result in exponentially increasing divergence. These results thus suggest that divergence in morphological traits is not shaped by the same environmental variables as in microsatellites. Further insight into this issue comes from the correlations between FST and morphological divergence, as well as from a comparison of the spatial patterns of variation shown in the GDM maps . Of all variables entered into the models, only a small set was selected that explained most of the observed variation , notably mean temperature of the driest quarter (Bio 9), annual precipitation Bio 12), and mean and minimum leaf area index . However, visual examination of the shape of the response curves suggests that the effects of those predictor variables vary between response variables. For instance, for the shape PC1 in males  and the size component in females , there is a very steep response to small changes in the mean temperature of the driest quarter (Bio 9), which then quickly levels off. In contrast, for genetic variation, small differences in the mean temperature of the driest quarter (Bio 9) do not result in larger 2, and meAlthough in our study selective processes appeared to be the most important factors underlying population divergence, most of the variation  could not be explained, despite the fact that many predictor variables were considered. We can only speculate about additional factors that may cause population divergence. One explanation may be that habitat conditions other than the ones included may cause strong divergent selection or limit dispersal between populations. Such conditions should be measured at much smaller scales than those used in our study and may include microhabitat characteristics such as the grain size of crops grown, types of cattle feed used, and available to this granivorous species, or food availability, which was found to be related to population divergence in a valley in France  on population divergence of house sparrows in Romania and Bulgaria. First, although road density was selected as a predictor in microsatellite variation and in the shape component PC2 in females, it did not contribute much to explaining the observed variation. Similarly, human population density was among the predictors in the model for microsatellite variation, but this variable retained a comparatively low importance score. Even though we have not sampled house sparrows in cities and thus lack information on this extreme end of the range of niches available along gradients in human‐dominated landscapes, our results are broadly concordant with those of Vangestel et al. , who fou4.3Although house sparrows are listed by the IUCN as of least concern IUCN , their pIn summary, we found that selection by environmental variables, but not IBDL, is the main driver of population divergence in Romanian and Bulgarian house sparrow populations. Variables related to climate and vegetation best explained intraspecific variation, whereas those related to human habitation contributed comparatively little. Our study thus contributes to a growing body of literature suggesting that divergent selection may be a key driver of population divergence in many species and populations, and it improves our understanding of the spatial patterns and drivers of biodiversity in an understudied region.None declared.Environmental variables , morphological data, and microsatellite genotypes: Dryad doi: 10.5061/dryad.9qk7d. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Flank instability and lateral collapse are recurrent processes during the structural evolution of volcanic edifices, and they affect and are affected by magmatic activity. It is known that dyke intrusions have the potential to destabilise the flanks of a volcano, and that lateral collapses may change the style of volcanism and the arrangement of shallow dykes. However, the effect of a large lateral collapse on the location of a new eruptive centre remains unclear. Here, we use a numerical approach to simulate the pathways of magmatic intrusions underneath the volcanic edifice, after the stress redistribution resulting from a large lateral collapse. Our simulations are quantitatively validated against the observations at Fogo volcano, Cabo Verde. The results reveal that a lateral collapse can trigger a significant deflection of deep magma pathways in the crust, favouring the formation of a new eruptive centre within the collapse embayment. Our results have implications for the long-term evolution of intraplate volcanic ocean islands. Flank instability and lateral collapse are a potential hazard at volcanic edifices. Here, the authors use numerical simulations to show that at Fogo volcano, lateral collapse can trigger a significant deflection of magma pathways in the crust, demonstrating how volcanic edifices may evolve. Sector collapses are rather common during the evolution of a volcanic edifice, and may happen at any volcano4. Two prominent historical examples are Mount St. Helens, 1980, and Kobandai volcano, 1888, which removed 2.8 and 1.5 km3 of rocks, respectively5. However, giant lateral collapses with volumes that may exceed 100 km3 have been identified mainly at hot-spot ocean islands6. Lateral collapses might be preceded by stages of slow spreading or rifting processes7.Especially tall and active volcanoes are prone to flank instability which may lead to failure and sector collapse9. For instance, submarine mapping surveys reveal deposits of about 70 giant landslides that occurred at the Hawaiian Islands, and about 20 events at the Canary Islands11. Furthermore, previous studies showed that flank collapses are recurrent at similar locations15 and often decapitate a volcano’s plumbing system16. Lateral collapses seem to occur more frequently when volcanoes are active11, and their interaction with magmatic activity has been addressed by several studies .Flank failures represent one of the most prominent sources of a large-scale volcanic hazard at ocean island volcanoes and have therefore been the subject of numerous studies18. It was shown that dyke intrusions may interact with decollement faults through the stress changes induced by the dyke inflation19. These interactions are sometimes associated with episodic and catastrophic events20. Previous studies have also shown that stress changes due to flank failure may enhance future volcanic activity22, alter the eruptive style and the chemistry of the erupted lavas26 and promote fault reactivation27. As a consequence of a flank collapse, some volcanoes display the reorganisation of crustal magma reservoirs28, while other volcanoes show a migration, or the development of new volcanic rift zones29.Also, the idea of a mechanical effect of magmatic intrusions (dykes) on the stability of the volcanic edifice has been proposed and was subject to previous research30, analogue experiments32 and numerical models33 have been used to demonstrate that flank collapses may affect the orientation of dykes and the distribution of volcanic vents. Because dykes tend to be oriented perpendicular to the minimum compressive stress, they can be used to identify paleo stress fields. For instance, Tibaldi30 inferred from dyke orientations and dip angles within the volcanic edifice of Stromboli volcano, Italy, that the stress field due to the topography of the collapse embayment is responsible for the orientation of dykes in the vicinity of the collapse scarp. McGuire and Pullen31 used analogue models to explain the orientation of fissures at Mt. Etna considering regional and gravitational stresses due to the volcanic cone and the Valle del Bove topographies. Acocella et al.32 showed with similar analogue experiments that dykes injected within a volcanic cone and in the vicinity of the collapse topography, would tend to reorient parallel to the collapse scarp. Furthermore, Tibaldi et al.33 used numerical, finite-element models, to show that the stress field within the volcanic edifice associated with the topography of the collapse embayment at Stromboli volcano is able to explain the orientation of dyke intrusions parallel to the collapse scarp.Field data35, including but not limited to Fogo (Cabo Verde), El Hierro, La Palma and Tenerife (Canary Islands), St. Vincent and Martinique (Lesser Antilles), La Réunion (France) and Stromboli  , and the purely elastic stress change associated with the flank collapse.Recent studies demonstrated that unloading forces introduced by large surface mass redistributions, as they occur, e.g., during caldera collapse or continental rift formation, have the potential to significantly change the orientation of magmatic intrusions, even at a depth of several kilometres within the crust, and therefore dictate the location of post-event volcanic activity39 in order to compute the most favourable pathways for magma ascent from the base of the crust to the base of a volcanic edifice that has been affected by a flank collapse. We chose to base our numerical model on the example of Fogo volcano, Cabo Verde, because the post-collapse growth of the Pico do Fogo stratocone represents a striking example of vent relocation that occurred as a consequence of the collapse of the volcano’s eastern flank36. The simulated dyke paths are compared to the location of post-collapse volcanism at Fogo and discussed in the light of observations from several examples worldwide, particularly from ocean island volcanoes, where a large number of lateral collapses has been recognised41, and a shift and clustering of post-collapse volcanic activity within the collapse embayment has been highlighted by several previous studies43.We use a 2D boundary element (BE) model which simulates magmatic dyke propagation44. The island is built up from the remnants of a single, steep sided Monte Amarelo volcano that featured a central summit crater and multiple-flank vents distributed over three radial rift zones  that were centrally fed by laterally propagating dykes35. A catastrophic lateral collapse (with a volume of ~130–160 km3) occurred ~73 kyr ago and caused the whole eastern side of the volcano to slide into the ocean45. The remaining prominent lateral collapse amphitheatre has been gradually filled by the new Chã das Caldeiras volcano with two prominent NNE and SE rift zones, a diffusely defined WSW rift zone and a centre, the Pico do Fogo cone, that is shifted ~4 km to the east with respect to the inferred centre of the pre-collapse edifice35. Most of the post-collapse activity and all historic eruptions took place within the collapse scar46 that is enclosed by the almost vertical Bordeira cliffs. However, during the early growth of the Chã das Caldeiras volcano, eruptive activity has persisted along the older Monte Amarelo rift zones at levels of or below the Chã das Caldeiras plain35. A thin layer of post-collapse lavas may even exist on the lower levels of the WSW rift zone (which is referred to as Ribeira do Pico member35); nevertheless, this rift zone (within and outside the Bordeira escarpment) has been the least productive since the lateral collapse of the Monte Amarelo volcano35.Fogo is located in the Atlantic Ocean off the coast of West Africa. It is the only historically active volcanic island of the Cabo Verde archipelago35, the presence of two approximately N- (NNE) and S- (SSE) oriented rift zones and the geometry of the flank collapse (W–E oriented). We consider dykes starting at the depth of the Moho, as both petrological and geodetic studies show no evidence of shallow magmatic reservoirs within the crust beneath Fogo volcano48.We apply our model to a vertical W-E cross section of the crust, beneath Fogo Island, intercepting the centre of the former summit of the Monte Amarelo volcano. We base this decision on the existence of preferential N-S-trending post-collapse intrusions in the Chã das Caldeiras35. We vary the geometry and the intensity of the pre-collapse volcanic loading by introducing the concept of “effective volcanic loading”  magnitude is the parameter that most significantly affects the position of dyke arrivals at the base of the volcano  Fig. –3. We fiFurthermore, we find that the smaller the EVL magnitude, the deeper the dykes deviate from the initial vertical propagation direction, and the deeper this deviation, the further away dykes propagate from the centre of the Monte Amarelo volcano Fig. . The amo1) seems to focus the dyke trajectories more at the centre of the load. This effect is much less pronounced when using a trapezoidal load (EVL2) and disappears for a planar shape of the load (EVL3). The results obtained for EVL2 and EVL3 are indeed rather similar. This also implies that our model is only sensitive to the exact location of the pre-collapse topographic summit when EVL1 is applied .The modelled unloading area associated with the flank collapse has an extension of 13 km and a maximum elevation difference of Δsee Fig. . Also, f21, who found that edifice destruction processes may allow more dense magma to be erupted. According to our results, the minimum volume of buoyant magma (with Δρ = 300 km m−3) that is needed for the dykes to reach the eastern flank of the volcano is ~2.5 × 10−3 km3 . Using this volume, those dykes propagating towards the western flank stall between 1.1 and 2.5 km below the base of the volcanic edifice . The corresponding volume needed for the dykes to reach the western flank is about one order of magnitude higher: ~3.5×10−2 km3 . This result was obtained for the scenario shown in Fig. Furthermore, we find that less buoyant dykes get arrested at a deeper level underneath the western flank (in the vicinity of the collapse scarp), as compared to dykes propagating towards the eastern flank of the volcano (underneath the collapse embayment) Fig. . We explIn the last step, we tested the effect of progressive refilling of the collapse embayment. We find that the dyke trajectories focus at the location of the initial dyke arrivals as an effect of the additional load. Iterating this procedure, and therefore increasing the load on the eastern flank, has the effect that progressively more dykes are attracted towards the eastern flank. This in turn results in a small but gradual westward migration of vents at the eastern flank see Fig. –6. For oFrom our simulated dyke trajectories, we infer that the percentage of loading stress related to the volcano’s formation that was preserved in the crust at the time of the flank collapse  was between 40 and 60% 48 , and it is one order of magnitude smaller than the critical volume needed to reach the western flank. However, our estimate depends on a variety of parameters: we found that when increasing the buoyancy by 33% (from 300 to 400 kg m−3), the critical volume decreases by ~60% (from ~2.5×10−3 to ~0.95×10−3 km3). When increasing Kr by 85% (from 100 to 185 MPa m1/2), the critical volume increases by 120% (from ~2.5×10−3 to ~5.5×10−3 km3). Variations of the critical volumes as a function of other rock and magma parameters are shown in Supplementary Table Our results for low-buoyancy dyke intrusions show that unloading-induced stress gradients favour the propagation along dyke pathways pointing towards the collapsed flank. This indicates that even though modelled dyke trajectories predict magmatism on both sides of the volcano upon a flank collapse event, magmatism on the collapsed flank is more likely. The most recent dyke intrusion at Fogo volcano (during the 2014–2015 eruption) carried a volume of magma that was estimated to be between 2.7×1036, based on morphological evidence. Our findings have important general implications for many other intraplate ocean island volcanoes. A number of examples with striking similarities in the migration of volcanic activity following a volcanic flank collapse are shown in Figs. The results obtained for the Fogo case study explain the shift of active vents within the collapse embayment after a sector collapse, as proposed by Day et al.34. At Tenerife , where the Tope de Coroa volcanic peak dominates the topography of the southwestern flank of the island. Similarly to the Pico do Fogo stratocone, it lies within a horseshoe-shaped escarpment that has been interpreted as a landslide scar29.Moreover, the relocation of volcanic activity in response to flank movements can affect a whole volcanic rift zone. One very famous example can be found on the Island of Hawaii. Here, a link exists between the westward migration of the south-west rift zone of Mauna Loa and the gravitational seaward slumping of the western flank of the volcano51, large-scale flank movements currently occur at Kīlauea volcano, Hawaii52, Piton de la Fournaise53, Mt. Etna27 and possibly elsewhere in the world. Therefore, large-scale flank failure may occur in the future.Even though none of such giant flank collapse events have been identified anywhere on Earth since the QuaternaryWe observe that the smaller collapses in Table 33.Some of the presented examples feature shallow magma reservoirs within or right below the volcanic edifice . Even though the shifts in pre- and post-collapse vent locations  generally agree with our model results , a direct comparison is rather difficult given the fact that the volume uncertainties for these two cases are very large Table . At Stro15. A self-sustaining mechanism has been proposed, whereby scarp-parallel dyke emplacement is promoted by the collapse topography and flank movements, and recurrent phases of flank instability are triggered by dykes striking in that direction15. In addition, a feedback effect has been proposed15, whereby the decompression resulting from a sector collapse causes the drainage of magma until a critical mass of the cone is again reached, followed by another collapse of the same flank.Many of the above examples have in common that flank collapses reoccur at the same locations or slightly further downslope . This has been noticed by several previous studies15, and provides an efficient way of reloading the collapse embayment mainly.The fact that in our model, dyke trajectories below the volcanic edifice are deflected towards the collapse embayment, supports the concept of a feedback effect between repeated collapses and edifice reloading52.In the chain of consequences, the location of future collapse scarps will also be affected by a shift in the pathways of dykes Fig. , as the 54. At Mount St. Helens, for instance, the 1980 flank collapse has not been followed by a shift of the location of volcanic activity. The growth of a new lava dome occurred over the course of several episodes approximately at the same location as the pre-collapse summit of the volcano, within the collapse embayment. The volume of the Mount St. Helens collapse was estimated to ~1.5 km3; this is rather small when compared to the examples listed in Table 3 which is one of the largest known in the Quaternary5. After the collapse, a new cone formed that was shifted ~2.5 km towards the collapse direction, within the inferred collapse embayment55. However, before that activity, a first cone formed only ~0.5 km from the location of the pre-collapse summit, then the vent shifted as described above and finally, the latest volcanic activity restarted at the approximate location of the previous summit56. Petrological studies suggested that several crustal reservoirs exist at different depths between ~4 and ~25 km beneath Mt. Shasta56, which could imply that cones at different locations might be linked to reservoirs at different depths.All volcanoes considered in this study Table  are situ57. Therefore, our concept contributes to an improved understanding of the mutual interactions between the structural evolution of a volcano and its eruptive activity.Our model, applied to Fogo Island, can explain the location of the Pico do Fogo stratocone, provided that the elastic loading due to the growth of Fogo volcano, which was actually preserved in the crust at the time of the collapse ranges between 40 and 60%. Our results have general implications for volcanic islands worldwide. We found that large lateral collapses, with an unloading of more than 7% of the EVL on the crust, may promote a significant deflection of magmatic intrusions beneath the base of the volcanic edifice towards the flanks of the volcano, favouring the collapsed embayment . The dykes are nucleated at the base of the crust . The trajectories within the crust are determined by testing the incremental elongation of dykes in different directions. Our algorithm chooses the direction where the elastic and gravitational energy releases are maximal60. In the current application, we varied the dyke buoyancy (which is done by either changing the magma-rock density contrast or the dyke volume). This allows us to estimate the depth and location where intrusions may get stuck, particularly when the volume of the magma is close to the critical volume for propagation61. As the initial model input is the cross section of the intrusion , we estimate the 3D volume of the intrusion assuming that the width of the intrusion (out-of-plane dimension) is equal to the dyke length59. Our estimate of a critical volume for propagation depends on various parameters such as rock fracture toughness (Kr), magma buoyancy (Δρ), bulk modulus of magma (K) and rock rigidity (G). Among these parameters, the most important ones are Kr and Δρ to which the critical volume is quite sensitive  and a bulk modulus for magma K =  GPa (according to ref. 62). We set the shear modulus of the rock to G = 20 GPa, Poisson’s ratio to v = 0.25, the rock fracture toughness to Kr = 100 MPa m1/2 (according to ref. 54) and the density of the crust to ρc = 2700 kg m−3 (according to ref. 62).Dykes in our model are represented as boundary element mixed-mode cracks in plane strain approximation. The model describes a vertical cross section of the dyke intrusion, perpendicular to the dyke plane. The dykes open under assigned normal and shear stresses that are the magma overpressure and the shear component of the topographic stress, respectively. The overpressure along the dyke is given by the difference between the magma pressure and the confining stress. The latter is defined as the superposition of the lithostatic pressure (isotropic and depth dependent) and the normal component of the topographic stress. Topographic stresses are computed using analytical formulas for loading and unloading forces at the surface of an elastic half-space35. A fundamental parameter of our model is the amount of elastic loading which is effectively acting on the crust. We introduced the concept of the EVL in order to account for the fact that during the growth of Fogo volcano , the elastic loading stress induced in the crust may have been partially released by different processes, such as fluid intrusions, earthquakes and viscoelastic effects49. The magnitude of the EVL indicates the percentage of the elastic loading stress that is preserved in the crust, while the remainder  has been released by the above-mentioned processes. Since it is difficult to estimate the amount of loading stress that was acting within the crust at the time of the flank collapse, we tested different effective loading geometries (EVL types) and intensities (EVL magnitudes). For instance, if we assume that 40% of the elastic loading stress was released during the development of the volcanic edifice, we scale the elastic loading by a factor of 0.6, and say that we use an EVL = 0.6. The different EVL geometries that were tested are a triangular load (EVL1), a trapezoidal load (EVL2) and a planar load (EVL3), as shown in Fig. 1, represents a volcano that grows in height while at the same time maintaining a constant base area. The second was obtained by subtracting a triangular load from a larger one with identical slopes. This would be the 2D representation of a volcanic edifice that maintains a constant slope while growing, so that the effective load is shaped like a layer covering the flanks of the volcano. Finally, the third, simple planar-loading scenario was introduced in order to evaluate the effect of the edifice’s slope on the dyke trajectories beneath the volcano.The pre-collapse volcanic edifice is an ~40-km-wide volcano of an ~5000-m elevation (above the ocean floor), which is centred at the location of the summit of the Monte Amarelo volcanoh) between the W-E-trending topography profiles before and after the collapse  and the acceleration due to gravity (g = 9.81 m s−2). The maximum amount of unloading, the corresponding location and the total extension of the unloading area are fairly well constrained by the results of previous studies51. The amount of maximum unloading is constrained by the maximum height difference between the Monte Amarelo volcano and the Chã das Caldeiras plain (Δhmax ~ 1600 m). It is centred at the former Monte Amarelo summit and extends over an area of ~2 km . The region of unloading extends to ~13 km eastward from the Bordeira escarpment. The eastern boundary of the unloading area is constrained by the submarine sudden change in slope where the landslide debris starts being exposed . Since we lack detailed information on the W-E topography profile immediately after the flank collapse (before the formation of the Pico do Fogo stratocone), we consider three different unloading profiles , namely linear, quadratic and cubic decays .We model the stress changes within the crust that were induced by the Monte Amarelo flank collapse by means of unloading forces acting at the base of the volcanic edifice . The unloading force distribution represents the mass removed by the flank collapse, which is the difference  code for dyke propagation relies. The 2D nature of our model restricts our results to a cross section of the 3D natural case. For the Fogo case study, this means that our results apply to N-S-trending intrusions . However, we expect our calculations to be stable for a relatively wide range of orientations (W-E ± ~20°), given the similarity of all radial topography profiles that are oriented about ±20° with respect to the W-E direction. This holds under the negligence of the Pico do Fogo stratocone, which does not affect our stress calculation as it developed after the giant flank collapse event. Also, our plane strain approximation requires the dyke width  to be smaller than the extension of the topographic features in the same direction. We also argue that this condition is met, since the two most recent dyke intrusions (which occurred in 2014–2015 and 1995) were about 2- to 4-km long33. Here, we aim to investigate the effect of the stress change induced by the flank collapse within the crust, below the volcanic edifice. We neglect the shear tractions associated with the growth of the volcanic edifice , which might be transmitted at the interface between the volcanic edifice and the ocean floor17. As shown by analytical and numerical solutions, the principal stress orientations are generally affected by this shear stress66. It is noteworthy that the stress field below the volcanic edifice is less affected by the approximation of vertical forces than the stress field within the edifice. In fact, numerical simulations17 show that the stress field within the volcanic edifice is affected by the slope of the edifice, while at the base of the edifice, where our simulations stop, the directions of compressive stresses17 are in general agreement with the orientation of the principal stresses obtained by vertical forces associated with a triangular load , the maximum compression vectors are more vertical  than the ones obtained by Dieterich17. In addition, the study of Van Wyk de Vries and Matela65 shows that stress vectors at the base of an edifice are vertical only when the substratum has a low viscosity. However, in the spatial region below the volcanic cone (where we compute the dyke paths), the directions of maximum compression are mostly vertical65, which are in agreement with our simulations for the EVL2 and EVL3 scenarios . McTigue and Mei64 conclude that the main differences between the vertical load approximation and a stress field which considers both vertical forces and tractions are mainly confined to shallow depths (within the volcanic edifice), and at the sides of the volcanic edifice, i.e., in regions where we do not compute dyke propagation paths.The formulation that we use to compute crustal stresses implies that we do not address the stress field due to the collapse within the volcanic edifice, which has been modelled by previous studies67, as well as the thermal effect due to the presence of the reservoir68, might result in a different stress pattern with respect to the one that we considered in our study. Our results would only be relevant for those cases where the shallow reservoir developed at a later stage, after the collapse. In such cases, we would speculate that the formation of a post-collapse shallow reservoir beneath the collapsed flank might be a consequence of the stress redistribution due to the collapse: magmatic dykes that are deflected towards the collapse embayment, at the base of the volcanic edifice, may intrude at this location, and progress towards a shallower depth  also favoured by unbutteressing stress. Within the volcanic edifice, magmatic dykes may stall and accumulate because of low-density layers and horizontal discontinuities69, or because of the stress induced by possible flank movements which would foster increased intruded vs. erupted magma volumes57.The analytical approach that we used for the calculation of the stress beneath the volcano does not account for the mechanical interaction of the collapse with a magmatic reservoir. If the reservoir is deep enough , such an interaction may be negligible. However, the mechanical interaction of a shallow reservoir (located within or right below the volcanic edifice) with a flank collapse70. The magnitude of bending stresses depends on the location at which the effect of loading is considered. When we compute dyke trajectories, in a region centred beneath the volcano, the effect of bending should be less significant than at the edges of the load70. In addition, lateral variations of bending stresses have characteristic wavelengths of ~100–200 km and therefore should not introduce significant lateral stress gradients within the region that we considered for dyke propagation (~15–20 km).In our stress model, surface loading and unloading forces act on an elastic half-space. Therefore, our model does not account for the bending of the lithosphere. The effect of lithosphere bending beneath the loading and close to its edges causes further horizontal compression near the surface and extension at the base of the lithosphere. The opposite  is expected on the sides of the loaded region, where the bending is convexIn conclusion, our simplified method to compute the loading stresses, and in particular the plane strain approximation imposes a number of limitations to the applicability of our models. The advantage of such simplifications, however, is that we are able to introduce the concept of EVL and that we can explore the effect of the elastic stress release which may occur during the growth of the volcanic edifice. In addition, by varying our simplified loading geometries (EVL types), we are able to explore the effect of different orientations of the principal stresses close to the loaded area. Most importantly, the plane strain approximation allows us to simulate the dyke propagation paths for the different stress scenarios. As of the time of this writing, no 3D models exist that have the capability to simulate the propagation of magmatic dykes and at the same time allow for complex trajectories interacting with a heterogeneous external stress field (as our 2D model for dyke propagation does). Our dyke propagation model accounts for many physical parameters characterising the crust and the intrusions, such as rock rigidity, Poisson’s ratio, fracture toughness, magma buoyancy and dyke overpressure. This facilitates a novel quantitative comparison between the dyke deflection and the magnitude of the stress change due to the collapse.An extension of the applicability of our model to more complex scenarios, which may include, for instance, the mechanical and thermal effect due to a shallow reservoir, possible strong heterogeneities in the crust or interactions with tectonic forces and 3D topography, may be achieved in the future by coupling our dyke propagation model with the stress field provided by dedicated, e.g., finite-element models. This would allow us to reduce some of the current assumptions and simplifications.In our model, we varied the geometry and the intensity of the EVL, the geometry of the collapse segment and the buoyancy of the magma. In this way, we were able to discriminate the most critical parameters that affect the trajectories of magmatic dykes in our simulations. The results show that the location of the post-collapse eruptive activity highly depends on the interplay between the amount of the EVL and the unloading due to the collapse, and therefore on the difference between the pre- and post-collapse topographies. Especially, the pre-collapse topography may only be vaguely reconstructed. Moreover, it is difficult to quantify the amount of stress which has been released within the crust over long time scales (Myr), by brittle rock failure, fluid intrusions and other non-elastic processes. We introduced the flexible parameter EVL in order to compensate for this lack of knowledge.48 results in the volume accommodated by the dyke close to the surface, approximately between 0- and 2-km depth. Similarly, our estimate for the critical volume is given for a compressible magma at vanishing confining stresses; therefore, the two can be directly compared. However, we estimate the (3D) critical volume from a plane strain dyke model (2D cross section), based on the assumption of a constant dyke width (out-of-plane length) that is equal to the dyke length. This might systematically overestimate the 3D volume, as in reality, we expect the tip of a 3D hydrofracture to be rounded in the direction of propagation59. On the other hand, we are also neglecting the magma loss in the dyke tale due to cooling and viscous effects, and therefore, the critical cross section for propagation computed by our model may be underestimated. In addition, several other parameters  would also affect the critical volume for propagation (cf., Supplementary Table The volume of the 2014–2015 dyke intrusion at Fogo volcano, as estimated by means of InSAR measurementsmate the D criticaAll the relevant data that have been used in the present study are available from the authors.Supplementary InformationPeer Review File