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    Mitogenome-wise codon usage pattern from comparative analysis of the first mitogenome of Blepharipa sp. (Muga uzifly) with other Oestroid flies

    Outcome of DNA sequencing, assembly, and validationIn this study, initially total DNA was isolated from the finely chopped, full-grown pupa of Blepharipa sp. The NanoDrop spectrophotometer (1294 ng/μl) and the Qubit fluorometer (732.8 ng/μl) both found that the concentration of total DNA in the sample at an optimum level for mitochondrial DNA enrichment. The Tape Station profile showed that the size of the fragments of the mitogenomic library were in the range of 250 to 550 bp. The complete insert size distribution ranged from 130 to 430 bp, with the combined adapter size being ~ 120 bp with mitogenome fragments. The appropriate distribution of fragments and their concentrations (~ 27.1 ng/μl) were also found to be suitable for sequencing. Sequencing through Illumina NextSeq500 yielded 4,402,752 raw reads, of which around 3,663,404 high-quality reads were retained after post-quality filtering. The final scaffolding and assembly of contigs generated a 15,080 bp single scaffold MtDNA in Blepharipa sp. (N50 = 15,080).The sequencing outcome was validated by performing PCR amplification of one of the protein-coding genes, in this case, nad6. Where PCR amplification resulted in a single band of expected amplicon size (shown in Supplementary Method Online). Sanger sequencing and subsequent alignment of these amplicons showed almost 92% sequence similarity to our assembled Blepharipa sp. nad6 gene (see Supplementary Method Online). This provided strong evidence that our mitogenome assembly is reliable and can be used for general applications of mitochondrial genes, e.g., as a biomarker. The second mitogenomic region, the control region (CR) was suggested by the reviewer. We have discussed that CRs constitute repetitive A + T regions (“AT richness of Control Region and role of sequencing method” and “Impact of repeats on different sequencing technologies and assembly method” section). One or more repetitive regions within the CR identified in certain species (e.g. fish, human) have shown undesirable effects on PCR amplification and sequencing125,126. Many organisms have segmental duplications in CR induced by the appearance of pseudogenes that PCR can co-amplify127,128,129,130,131. Due to these associated problems, researchers generally rely on protein or ribosomal RNA genes for phylogenetics instead of CRs132,133,134. In this case, we also faced problems validating the CR. The PCR and gel electrophoresis using external PCR primers did not show a desirable single band as seen for nad6. As an alternative strategy, we used two pairs of primers, CR int_fwd and CR int_rev, internal primers, with CR15fwd and CR08rev primers, to perform a two-way sequencing of each amplicon, which generated multiple bands (see Supplementary Method Online, Figs. S1, S2). The most prominent bands were subjected to sequencing and yielded two mixed sequences, the best of which exhibited nearly 54% sequence resemblance with the Blepharipa sp. control region (see Method in Supplementary Note). Further mapping of the Illumina reads with the assembly revealed that the depth of coverage across the CR was not as deep as that of protein-coding genes such as cox2, and it was also not inflated only over a repeated section of the CR. The depth over 1–112 varied from 5 to 20×, and that for the 15,025–15,080 bp was around 30×. We did observe that our reads didn’t cover a 10 bp stretch of CR around 15,030–15,040 bp (see Method in Supplementary Note and Figs. S3–S6). We believe that our sequencing and assembly experiment was able to cover the majority of CR successfully with reasonable coverage barring that 10 bp stretch. Our results corroborate with the difficulties of CR sequencing seen with other species, and while this doesn’t reflect on the quality of our whole mitogenome assembly, researchers using mitogenomic CR regions for any kind of phylogenetic inference should proceed with caution.Size and organization of mitogenome
    Blepharipa sp. mitogenome organization and structureThe newly sequenced mitochondrial genome of Blepharipa sp. is closed circular and has a size of 15,080 bp, which falls within the typical insect mitogenome size (14 to 20 kb)135,136,137. Similar to other sequenced bilaterian mitogenomes, the Blepharipa sp. mitogenome has conventional gene content, a total of 37 genes (viz. 13 PCGs, 22 tRNAs, 2 rRNAs) and an AT-rich control region (CR) (Fig. 2A)138,139,140,141. Among these, 23 genes are present on the major strand (J strand or +ve strand), while the remaining 14 genes are present in the minor strand (N strand or –ve strand). The intron-less 13 PCGs are also separately encoded by these two strands, 9 PCGs (nad2, cox1, cox2, atp8, atp6, cox3, nad3, nad6, cytb) from the J strand and 4 PCGs (nad5, nad4, nad4l, nad1) from N strand covering 6899 bp and 4300 bp respectively constituting around 74.31% of the entire mitogenome (Fig. 2). The largest PCG present in this organism is nad5 (1716 bp), and the smallest one is the atp8 (165 bp). Excluding stop codons, the J strand has 2237 codons, and the N strand has 1430 codons. Apart from cox1 (TCG) and nad1 (TTG), 11 PCGs follow the canonical “ATN” start codon. Ten PCGs of this mitogenome have “TAA or TAG” as their stop codon except for cox1, cox2, and nad4, where they end with an incomplete stop codon, a single T (Fig. 2)142. A total of 22 tRNAs are interspersed all over the entire mitogenome, ranging from 63 bp (trnT) to 72 bp (trnV) in size. The J and N strands have 14 tRNAs and 8 tRNAs, respectively, with 928 bp and 528 bp of nucleotides. Typical clover-leaf shaped secondary structures of tRNAs have been observed with a few exceptions where trnC, trnF, trnP, and trnN lack a stable TΨC loop see Supplementary Fig. S7 online). Two N-strand rRNAs with nucleotides of 1360 bp and 783 bp are transcribed individually for rrnL and rrnS (Fig. 2B).Figure 2Complete mitochondrial genome structure of Blepharipa sp.; (A) Circular Map (B) Annotation and genome organization of mitogenome. tRNAs are represented as trn followed by the IUPAC-IUB single letter amino acid codes e.g., trnI denote tRNA-Ile.Full size imageThis mitogenome has 10 gene boundaries where genes overlap with adjacent genes, varying from 1 to 8 bp in length, for a total of 35 bp. The longest overlapping sequence of 8 bp is present over the trnW and trnC genes. Likewise, the total length of all intergenic spacer sequences (excluding the control region) is 139 bp, present at 15 gene boundaries. The length of each intergenic spacer varies between 1 and 40 bp, and the longest one is located between the trnE and trnF genes. In this organism, eleven pairs of genes are located discreetly but adjacent to each other and any PCG adjacent to tRNA, ending with an incomplete stop codon (cox1-trnL2, cox2-trnK). The control region’s length of this dipteran fly is 168 bp, and the nature of this region is highly biased towards A + T content (Fig. 2).Size comparison of Oestroidea mitogenome and their genesTo better understand the mitogenome of Blepharipa sp., it has been compared with the flies of the Oestroidea superfamily (blowflies, bot flies, flesh flies, uzi flies, and relatives). Various features have been taken into account for this comparison: mitogenome size, gene sizes, gene content, and how genes are placed in each mitogenome.The mitogenome of eukaryotic organisms shows that there are significant size differences across mammals, fungi, and plants. The typical size of an animal mitogenome is near about 16 kb, a fungal mitogenome is 19–176 kb, and a plant mitogenome is far larger, with a size range of 200 to 2500 kb143. We have shown that the Blepharipa sp. whole mitogenome size (15,080 bp) is 416 bp smaller than the average Oestroidea flies mitogenome. As for the Oestroidea superfamily, D. hominis (human bot fly), an Oestridae fly has the longest mitogenome of all (16,360 bp), and A. grahami, a Calliphoridae fly, has the shortest mitogenome of all (14,903 bp). Tachinid flies have a smaller average mitogenome size (~ 15,076 bp) than the other flies in this superfamily, and the Oestridae flies have a relatively larger mitogenome (~ 16,031 bp). We observed that the size of the total PCGs, tRNAs, and rRNAs are well-maintained across this superfamily, with an average length of 11,145 bp, 1482 bp, and 2113 bp, respectively (Fig. 1A, green, yellow, and blue line, Table 1).The difference in mitogenome size in insects can be attributed to variations in the length of non-coding regions, especially the control region that differs in length as well as the pattern of sequences (Fig. 1B)104,144. In addition, based on mtDNA sequence similarity among all the Oestroidea flies, Blepharipa sp. has high similitude with the Tachinid Fly E. flavipalpis (87.83%), followed by the two hairy maggot blowflies, Chrysomya albiceps (85.51%) and C. rufifacies (85.44%). Another well-studied uzi fly, E. sorbilans has an 84.82% sequence similarity with Blepharipa sp., while Gasterophilus horse botfly has the lowest sequence similarity (~ 77%) with Blepharipa sp. (Supplementary Data 3A).Gene content and arrangementWe found that the Oestroidea mitogenome represents the reserved gene arrangement of Ecdysozoan, for which it can be easily distinguishable from other bilaterians (Lophotrochozoa and Deuterostomia)140. The mitogenome of Blepharipa sp. and other Oestroidea have three core tRNA clusters, including (1) trnI-trnQ-trnM, (2) trnW-trnC-trnY and (3) trnA-trnR-trnN-trnS1-trnE-trnF, as depicted in Figs. 1C and 2. A comparative study revealed that the Oestroidea superfamily has 4 different kinds of mitogenome arrangements (Fig. 1C). The majority of the Oestroidea flies (25 out of 36) in this study have ancestral (A) dipteran type mitogenome sequences (Table 1)145. However, there are some minor inconsistencies exist in the Calliphoridae family (blowflies), such as the insertion of extra tRNAs (trnI in the genus Chrysomya and trnV in D. hominis) or the translocation of tRNA (trnS1 in C. chinghaiensis) (Fig. 1C)21,24. Barring this, all organisms, including Blepharipa sp., follow a standard dipteran gene arrangement and have 37 genes in their respective mitogenomes (insertion of tRNA into the genus Chrysomya and D. hominis raises gene count) (Fig. 1C (i)(ii), Table 1). In the case of dipterans other than the Oestroidea superfamily, species like gall midge (Cecidomyiidae), mosquitos (Culicidae), and crane flies (Tipulidae) exhibit various rearrangements in mitochondrial tRNAs, such as the absence, inversion, translocation, and extreme truncation of certain genes (Supplementary Data 1A)146,147.Non-coding regionsControl region (CR) of Blepharipa sp. and comparison with OestroideaThis region in the metazoan mitogenome is a single sizeable non-coding sequence containing essential regulatory elements for transcription and replication initiation; it is therefore named the control region148,149. Similar to other Diptera, the CR of Blepharipa sp. is also flanked by rrnS and the trnI-trnQ-trnM gene cluster (Fig. 2). Sequence similarity with other Oestroidea superfamily species indicates that this segment is variable due to the lack of coding constraints150. The CR sequence of Blepharipa sp. 75.49% similar to another tachinid fly Elodia flavipalpis, followed by Chrysomya bezziana (71.15%) (Supplementary Data 3B). Despite its overall high variation in nucleotides, this region harbors multiple different types of repeats (e.g., tandem repeats, inverted repeats)42,151 and conserved structures namely Poly-T stretch (15 bp), [TA(A)]n-like, G(A)nT-like stretches, and poly A tail (15 bp)152,153,154(Fig. 3A). Another conserved motif, “ATTGTAAATT” we found in the CR of Blepharipa sp. and E. flavipalpis (Fig. 3A). Such conserved structures are thought to play role in the regulatory process of transcription or replication. After binding with RNA polymerase,  they keep the initiating mode of transcription or replication by preventing the transition to elongation mode without affecting its open-complex structure155,156.Figure 3Conserved non-coding regions; (A) AT rich control region Alignment of Blepharipa sp. with other two Tachinidae species. (B) Three alignments of the common overlap region between trnW-trnC, atp8-atp6 and nad4-nad4l. (C) Three alignment of the consensus gap region between trnS2-nad1 (TACTAAAHHHHAWWMH), trnE-trnF (ACTAAHWWWAATTMHHWA), nad5-trnH (WGAYADATWYTTCAY) genes of all 36 Oestroidea mitogenome (where, W = A/T, H = A/T/C, Y = T/C, D = G/T/A, M = A/C).Full size imageThe CR is also known as the AT-rich region for having the maximum proportion of A/T nucleotides (91.4% for Blepharipa sp.) than other regions of the entire mitogenome. We observed that the Tachinidae family has higher A + T content than other groups, with the highest levels in the Mulberry uzi fly, E. sorbillans (98.10%), and AT poor CR regions identified in G. intestinalis (80.80%) and G. pecorum (80.82%) (Oestridae)42 (Supplementary Data 2A). In this study, the CR of thirteen species have above 90% A + T content, and the top 3 are the tachinid flies, led by A. grahami, D. hominis and Blepharipa sp. consecutively. The CR is prone to high mutation, yet the substitution rate is low due to high A + T content and directional mutation pressure144,154. This part of the mitogenome differs significantly in length among insects, ranging from 70 bp to 13 kb, and it accounts for most of the variation in mitogenome size153. We noted that the CR size of 36 Oestroidea flies ranges from 89 to 1750 bp, of which 16, 12, and 8 species can be categorized as large ( > 800 bp), medium (200–800 bp), and small ( 5 to  0.025 to  0.005 to  More

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    Effects of conservation tillage strategies on soil physicochemical indicators and N2O emission under spring wheat monocropping system conditions

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    Complexity–stability trade-off in empirical microbial ecosystems

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    Forest degradation drives widespread avian habitat and population declines

    The Acadian Forest of eastern Canada has shown a pervasive signal of forest degradation since 1985 (Fig. 1). Since 1985, >3 million ha have been clear-cut (Fig. 1d), with most of this area now occupied by either tree plantations and thinnings (Fig. 1c–e), which are dominated by single tree species20, or a mix of early successional tree species (Fig. 1a,d,e). Despite some ingrowth due to succession, old forest has declined by 39% during the period observed (Extended Data Fig. 1a,b; Supplementary Methods). The pattern of extensive harvest of old forest, followed by rapid regeneration of young forest appears to be common across many forest regions of North America (for example, central Canada, southeastern United States, western United States; Fig. 1b) (ref. 10) and can be considered ‘forest degradation’ in that these practices simplify forest structure, reduce tree species diversity and truncate old-forest age classes6. During the same 35-year time period, forest cover remained relatively stable, increasing by a net 6.5% (Fig. 3a, red line)21.Fig. 3: Forest degradation rather than loss drives habitat declines in old forest-associated bird species.a, Habitat trends (1985–2020) for the seven bird species exhibiting the greatest population declines according to SDMs; all of these species are old forest associated. During the same time interval, total forest cover did not decline (red line, right axis), indicating that habitat loss is a function of forest degradation rather than loss. b,c, Predicted habitat loss (pink) and gain (blue) between 1985 and 2020 for two example species: Blackburnian warbler (33% habitat loss; b) and golden-crowned kinglet (38% habitat loss; c). Habitat loss was quantified using SDMs with Landsat data as independent variables strongly predicted population trends for forest bird species.Full size imageOverall, SDMs using Landsat reflectance bands as predictors performed well for most forest bird species when tested on 50% spatially discrete hold-out data (Extended Data Fig. 2; (bar x) area under the curve (AUC) = 0.73 [range: 0.60–0.90]). SDMs therefore provided reliable estimates of habitat suitability and distribution for most of the 54 species. Species with lower model-prediction success tended to be associated with fine-scale forest structure (for example, individual tall trees, standing and fallen dead wood) which are poorly captured by satellite imagery.We back cast SDMs to quantify habitat change for all 54 forest bird species from 1985 to 2020. Habitat declines occurred for 66% of species during 1985–2020; 93% of species exhibited habitat reductions over the past decade (Fig. 3 and Extended Data Fig. 3). Species showing the greatest decreases in habitat were golden-crowned kinglet (Regulus satrapa; −38%) and Blackburnian warbler (Setophaga fusca; −33%; Supplementary Video 1) with seven species showing habitat declines >25% (Fig. 3). Most species with strongly declining habitat are associated with old forests22 (Fig. 4a,b), which is consistent with forest degradation due to harvesting of old forest. Indeed, clear-cut harvest alone was strongly associated with habitat declines for all old forest-associated species (Fig. 4c and Extended Data Figs. 4 and 5). Forest succession into old age classes was apparently insufficient to compensate for this rate of loss. Fifteen species exhibited habitat increases, but most (14 out of 15) of these tend to be associated with young or immature forests (Fig. 4a,b).Fig. 4: Evidence for the effect of forest degradation on mature-forest bird species.a, The relationship between habitat change, estimated from SDMs and independently derived population change estimates from the BBS for the Acadian forest. Bird species of mature (old) forests (M; dark green dots) exhibit the greatest habitat loss; this is generally reflected in strongly negative population trends. Bird species associated with regenerating forest (R; red dots) tend to have stable or increasing habitat but still show BBS population declines. b, The relationship between quantitatively derived estimates of mature-forest association and habitat change from 1985 to 2020. Mature forest-associated species tend to be losing the most habitat in relation to immature- (I; light-green dots) and regeneration-associated species. Successional stage categorizations (R, I, M) are from Birds of the World (BOW). The regression line was fit using a hierarchical Bayesian model (Supplementary Methods) and grey shading in b shows 95% credible intervals. Only a subset of species is shown in b (those with quantitative data for mature-forest associations; Supplementary Methods). c, The relationship between area clear-cut occurring from 1985 to 2020 in each species’ habitat within a 200 m-diameter buffer surrounding BBS routes (N = 90) and habitat loss (1985–2020) at the same scale for six mature forest-associated species. Black lines are regression lines and grey bands are 95% confidence intervals (regression estimates in Supplementary Table 3). As expected, clear-cutting is strongly associated with habitat loss, which indicates that ingrowth of new habitat is rarely compensated for by habitat loss (a signature of forest degradation via old age–class truncation).Full size imageSeveral lines of evidence support forest management as the primary driver of forest degradation rather than alternative mechanisms (for example, climate-mediated forest decline, natural disturbance, permanent deforestation). First, our SDMs did not include climate data so the reflectance changes from satellite imagery used in our SDMs were predominantly due to forest compositional changes. Although climate (for example, inter-annual differences in precipitation) can cause subtle differences in reflectance (leaf colour) over time, most changes in the magnitude of reflectance are due to changes in forest composition or cover rather than effects of climate23 (Supplementary Figs. 1 and 2). Indeed, if the observed habitat declines were due to climate effects or natural disturbance, we would expect to see parallel habitat declines in protected areas, which we did not (Extended Data Figs. 6 and 7). Second, species exhibiting the greatest declines in habitat are those most strongly associated with old forest (Fig. 4a,b), which is the primary target of timber harvest. Indeed, the amount of area clear-cut was strongly associated with habitat loss for old forest-associated bird species (Fig. 4c and Extended Data Figs. 4 and 5). Third, deforestation (defined as permanent conversion to another land-cover type)24 was not a primary driver of habitat loss in our region; deforestation contributed 0.95, and 20 species had posterior probabilities >0.8. Importantly, most of the species showing an effect of habitat loss along routes on changes in population decline have lost substantial habitat over the time period and are associated with old forest (for example, Blackburnian warbler, northern parula [Setophaga americana], red-breasted nuthatch [Sitta canadensis], boreal chickadee [Poecile hudsonicus], dark-eyed junco [Junco hyemalis]; Extended Data Fig. 8), which would be expected with the harvest of old forest—a component of forest degradation. It is important to note that this test is highly challenging because many factors can drive annual fluctuations in bird abundance (for example, weather, phenology, conditions during migration or on the wintering grounds). Also, in any given year, habitat change along BBS routes can be quite small for some species; this low inter-annual variation in a predictor variable can preclude high statistical power to detect effects.We estimated the net number of breeding individuals that have probably disappeared due to habitat loss from 1985 to 2020 using published accounts of territory sizes for each species22 (Supplementary Table 5). This calculation assumes that available habitat is consistently occupied, which is supported by strong associations between habitat amount along BBS routes and bird abundance over the long term. Across all species, back-cast SDMs indicate that a net 28,215,247 ha (282,153 km2) of habitat has been lost, equating to a loss of between 16,779,704 and 52,243,938 breeding pairs (33,559,408–104,487,876 individuals; Supplementary Methods and Supplementary Table 5). One might expect that forest degradation, rather than resulting in broad-scale declines across species, is simply causing species turnover from old forest-associated bird species to young-forest associates. However, it is important to note that we quantified net bird decline from an unbiased list of the 54 most common forest bird species in eastern Canada. This list included both early and late successional species. Such net bird declines could be due to the fact that (1) even some early seral species are losing habitat (probably due to conversion from diverse early successional forest to species-poor plantations and thinnings)26 and (2) in this region, more species occupy older forests than regenerating forests27.We also quantified overall population trends for 54 species of forest birds using data from the BBS (Fig. 6). These estimates give the total magnitude of population changes which include, but are not limited to, habitat loss or gain effects. Thirty-nine of the 54 species examined (72%) are in population decline (defined as having 95% credible intervals that do not bound zero). The magnitude of the declines for 15 forest bird species is severe ( >5% per year). It is notable that most species exhibiting both habitat loss and population declines are old-forest associates (Fig. 4a; bottom left quadrant, dark green dots), with old-forest species exhibiting the greatest habitat losses (Fig. 4b and Supplementary Methods; hierarchical regression, (hat beta) = −16.66 [6.32 SE]).Fig. 6: Population trends for forest-associated birds in eastern Canada.a, Population trend parameter estimates and posterior distributions for 54 species of forest birds derived from Bayesian models. Seventy-two percent of species that are sufficiently common to model experienced population declines from 1985 to 2019. Colour key is provided in Fig. 5. The vertical green line indicates a population trend of zero. Dashed vertical lines coincide with trends of −15% (−0.15), −10% (−0.10) and −5% (−0.05) annual population trends. b, Predicted linear population trends for 1985–2019 (regression lines are mean trends derived from Bayesian Poisson models, Supplementary Methods) including annual variation estimated from BBS data. Shaded purple areas reflect 95% credible intervals and reflect the magnitude of species population declines shown in a. Populations of these eight old forest-associated species have declined 60–90% over the period observed.Full size imageBBS declines are not restricted to old-forest species; several species in rapid population decline are early seral species (for example, Lincoln’s sparrow [Melospiza lincolnii], mourning warbler [Geothlypis philadelphia]; Fig. 4a, bottom right quadrant). Despite the fact that these species have gained habitat over 35 years, their populations continue to decline. Only three species (black-capped chickadee [Poecile atricapillus], hairy woodpecker [Leuconotopicus villosus] and ruby-throated hummingbird [Archilochus colubris]) are increasing in abundance. Populations of these species increased despite evidence of habitat decline (Fig. 4a, top left quadrant)—perhaps because each benefit from anthropogenic habitats and supplemental food. Importantly, habitat changes from 1985 to 2019 along BBS routes were representative of changes at the scale of the entire region for most species (Extended Data Fig. 9), so BBS population trends are highly likely to reflect population trends at the regional scale. This contrasts to the 1965–1985 period when mature-forest loss along routes was slower than in the broader region28.We also modelled BBS population trends over the past ten years, as this is the period of importance for informing listing decisions under the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). Nine species have exhibited population declines >30% over ten years (Supplementary Fig. 3), which meets the criterion for consideration as ‘threatened’ under COSEWIC Criterion A (ref. 29). More

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    Increasing incidence and spatial hotspots of hospitalized endometriosis in France from 2011 to 2017

    This first national descriptive study used an indicator, which comprehensively reflects incident all-type hospitalized cases coded endometriosis in the French territory up to the municipality scale. We observed an increase in the risk of being hospitalized from 2011 to 2017 and spatial heterogeneity with the identification of 20 scattered hotspots in Metropolitan France as well as in 2 overseas departments.Descriptive resultsThe annual incidence rate (12.9/10,000 PYs) of all-type hospitalized cases coded endometriosis in France in females aged 10–49 years was of the same order of magnitude as the rates observed in other countries (Italy, Iceland) using similar methods29,30. Moreover, a recent meta-analysis2 estimated the pooled incidence rate of endometriosis based on hospital data to be 13.6/10,000 PYs (95% CI: 10.9; 16.3), which situates the French estimation within the confidence interval and close to the pooled value.In our study, 68.3% of all-type cases and 83.2% of non-adenomyosis cases were aged 25–49 years, and only 3.6% (8.5% for non-adenomyosis cases) were under 24 years. In young females, this low percentage could reflect underdiagnosis or delayed diagnosis, because histologic evidence may occur after an interval of 5–10 years following the first signs of endometriosis31. Moreover, many cases are fortuitously diagnosed during fertility check-ups, which rarely take place before 25 years of age. This age distribution in France is close to the distribution observed in a recent Italian study (3.6%  50 years) carried out using similar methods in the population of the Friuli Venezia Giulia region from 2011 to 201330. The Italian authors remarked a noticeable percentage of incident cases over 50 years of age for non-adenomyosis cases (11.5%), close to our results (8.3%), even though endometriosis is expected to attenuate after menopause. They suggested that endometriosis deposits could still be potentially active in older patients and be reactivated in the presence of certain hormones30. This hypothesis seems quite relevant regarding the potential link with EDC exposure. Indeed, the developmental hypothesis supposes that reproductive disorders at adult age could result from early (i.e., prenatal, perinatal, or pubertal) exposure to EDCs in specific exposure windows. In males, this hypothesis has been especially developed according to the so-called “testicular dysgenesis syndrome (TDS)”32. The disruption of fetal androgen action with EDCs, specifically in the “masculinization programming window” (MPW), induces a shorter anogenital distance that is supposed to provide a life-long readout of the level of androgen exposure in the MPW33 and is consistently associated in animals and humans with TDS troubles (cryptorchidism, hypospadias, poor sperm quality)34.In females, the mirror concept of “ovarian dysgenesis syndrome” has been proposed, including a higher risk to develop endometriosis35. Interestingly, endometriosis has recently been associated with a shorter anogenital distance in women36, and this anthropological indicator, measurable using MRI, could be useful for a non-invasive diagnosis of the disease37.In addition, some authors suggest that endometriosis onset could occur in two steps: an early hormonal-developmental step and a second hormonal step at adult age38,39, or a first initiation step with a second promotion step based on experimental tumor production40. Overall, these hypotheses could contribute to the unexpected proportion of hospitalized endometriosis cases identified after menopause. Another explanation could be the large number of fortuitous diagnoses of endometriosis at the same time as hysterectomies performed for diverse indications in women at an older age.Temporal trendsStudies on the temporal trends of endometriosis incidence used diverse methods and delivered differing results according to the country as reviewed in a recent study1. Only three studies carried out with hospital data in the general population are available. A Finnish study showed a decrease in incidence from 1987 to 201241. An Icelandic study did not conclude to any trend from 1981 to 200029, and a recent Korean study only showed an incidence increase in young women aged 15–19 and 20–24 years, but not in other age groups42.In France, the increase in the risk of being hospitalized, observed for both adenomyosis and non-adenomyosis cases, could reflect a real increase in the incidence of endometriosis, consistent with the perception of numerous clinicians. We did not observe an upward trend in females under the age of 25 years, which could reflect the underdiagnosis of this population. The global increase could also relate to the increasing use of non-invasive examinations, like ultrasounds or pelvic MRI during the study period. Pelvic MRI was only recommended by the French Health Authority at the end of the study period43, although clinicians would have anticipated this recommendation, which is supported by the results of the additional analyses (Supplementary Material). In the study period, there was a 69% increase in cases who underwent this examination concurrently with hospitalization, which accounted for around a third of cases. The increasing use of MRI (or ultrasounds) would result in more and more cases treated without hospitalization and could explain the apparent increase of hospitalized incidence at later ages and less at younger ages.Regarding the secondary indicator, the incidence rate in the whole of France during the study period remained steady. However, the trends differed according to each type (Table 4). The risk did not increase for endometrioma, a type of endometriosis that is not expected to depend on the use of pelvic MRI, but it did increase for intestinal endometriosis, expected to be strongly influenced by pelvic MRI. Therefore, these results also support the role of pelvic MRI. As for the divergent evolution of specific types of endometriosis, experts believe that it could depend on shifting practice patterns such as the more frequent tendency to medically treat endometrioma.Table 4 Number of incident cases of hospitalized endometriosis and crude incident rate for specific types of endometriosis for the study period in the whole of France, in females aged 10 years and above.Full size tableAnother factor could also contribute to the global increase in hospitalized endometriosis. Several patient societies (EndoFrance, Endomind, Info-endometriose) have strongly advocated for better detection and care of this disease and provided targeted information, which may have resulted in increased awareness of patients and clinicians regarding the disease during the study period.These factors are likely interlinked with a possible real increase in endometriosis incidence, which could be confirmed by a longer monitoring period.Spatiotemporal and spatial trendsThe spatiotemporal and spatial heterogeneity of the risk of hospitalized endometriosis that we observed in France during the study period could be related to spatial disparities and different evolutions in terms of detection and hospital care. In half of the 20 hotspots in Metropolitan France, we identified a town where an expert clinic for endometriosis was operational during the study period (Fig. 4). In the overseas departments, we identified an expert clinic in the Reunion Island, where we also observed a high incidence. However, we identified expert clinics in areas with a low or moderate risk of hospitalized endometriosis, especially in Paris (four expert clinics), Lyon (two expert clinics), Rennes, Brest, and Angers. Adjusting the spatial model at the department scale with the density of gynecologists and obstetricians using the available data provided by the shared inventory of health professionals from 2011 to 2016 did not change the geographic distribution (data not shown). Adjusting for incident cases of non-endometriotic ovarian cysts only brought about some changes in several departments in the north where the risk attenuated, even though it stayed above 1 (data not shown).Taken together, these results indicate that the activity of local expert clinics could only partially explain the spatial and spatiotemporal heterogeneity of the risk of hospitalized endometriosis. The contribution of environmental factors remains possible and plausible, as we argued above.The results of the exploratory cluster detection performed in Metropolitan France showed a negative relation with the socioeconomic deprivation index. Indeed, a high socioeconomic status (SES) or education level has been associated with a higher frequency of endometriosis44,45, which probably reflects the better detection and patient care of women with high SES. However, this relation was inverted in a recent Swedish study, although the authors partly attribute this inconsistent finding to egalitarian health care in Sweden46.Among the 40 detected clusters (p  More