Philippa Kaur

Abstract

Heat stress represents a major limitation for livestock production systems, negatively affecting feed efficiency, animal health and welfare, and overall performance. In this context, the objective of this study was to identify genomic regions, candidate genes, biological pathways, and functional networks associated with dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle exposed to varying levels of thermal stress. The dataset comprised records from 22,838 animals, with genotypes available for 18,567 individuals. The data were collected during 296 feed efficiency trials between 2011 and 2023 across 21 Brazilian farms. Genome-wide association studies (GWAS) were performed using the single-step GBLUP (ssGBLUP) approach to account for genotype-by-environment (G×E) interactions in Nellore cattle. Environmental variation was modeled using the temperature-humidity index (THI) as the environmental gradient, with analyses stratified across three environmental gradients (EG): low (THI = 66), medium (THI = 74), and high (THI = 81). Fifty-one SNPs were significantly associated with RFI, including 27 shared across all three EGs, 10 exclusive to the low EG, one to the high EG, and 13 shared between the moderate and high EGs. These associations were mapped to 44 candidate genes, with 19 genes commonly identified across all EGs, including key candidates such as PIPOX, GTF2F2, KCTD4, MYO18A, and NFIA. For DMI, 136 significant SNPs were identified: 12 and 39 exclusive to the low and moderate EGs, respectively; 28 shared across all EGs; and 57 shared between the moderate and high EGs. These variants were linked to 58 candidate genes, of which 19 were common to all EGs, including NCAPG, LCORL, FAM13A, HERC3, CCND1, and FGF19. Gene network analyses revealed a clear reconfiguration of interaction structures across thermal gradients, particularly for RFI, where gene connectivity declined with increasing THI levels. For DMI, gene networks remained highly integrated, especially in the lowest THI level. Functional annotation highlighted both conserved and environment-specific regulatory architectures, involving key biological processes such as growth regulation, lipid and protein metabolism, intracellular signaling, stress response, and neuroendocrine control. These findings uncover the environmental sensitivity of RFI and DMI, highlight the complex and dynamic genomic basis of these traits under varying climatic conditions, and support the identification of candidate genes for genomic selection programs aiming to enhance climatic resilience in tropical beef cattle.

Data availability

The data analyzed in this study were obtained from the National Association of Breeders and Researchers (ANCP). The phenotypic and genotypic information was provided to the authors for academic research purposes only. The following restrictions apply: the dataset is not publicly available and its use requires formal authorization. Requests to access these datasets should be directed to Dr. João Carlos G. Giffoni Filho, President of ANCP (email: [email protected]).
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Download referencesAcknowledgementsThe authors thank the National Association of Breeders and Researchers (ANCP, Ribeirão Preto, SP, Brazil) for providing the datasets for the research.FundingThe authors thank the São Paulo Research Foundation (FAPESP, Brazil) for financial support through a PhD scholarship in Brazil and an international research internship (BEPE), both granted to the first author (Grant Numbers #2022/15385-4 and #2023/13417-9). This study was also partially financed by the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil)—Finance Code 001.Author informationAuthors and AffiliationsDepartment of Animal Science, School of Agricultural and Veterinarian Sciences (FCAV), São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, BrazilJoão B. Silva Neto, Lucio F. M. Mota & Gustavo R. D. RodriguesDepartment of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USAJoão B. Silva Neto, Luiz F. Brito & Gustavo R. D. RodriguesDepartment of Animal Science, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, SP, 13635-900, BrazilFernando BaldiAuthorsJoão B. Silva NetoView author publicationsSearch author on:PubMed Google ScholarLuiz F. BritoView author publicationsSearch author on:PubMed Google ScholarLucio F. M. MotaView author publicationsSearch author on:PubMed Google ScholarGustavo R. D. RodriguesView author publicationsSearch author on:PubMed Google ScholarFernando BaldiView author publicationsSearch author on:PubMed Google ScholarContributionsJ.B. Silva Neto: Conceptualization, formal analysis, investigation, methodology, validation, writing—original draft and editing; L.F. Brito: Conceptualization, formal analysis, supervision, validation, writing—original draft and review; L.F.M. Mota: Conceptualization, Formal analysis, writing—review; G.R.D. Rodrigues: Visualization, writing—original draft and review; F. Baldi: Conceptualization, Data curation, formal analysis, project administration, supervision, validation, writing—original draft and review.Corresponding authorCorrespondence to
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The collection of the phenotypes was restricted to routine on-farm procedures that did not cause any inconvenience or stress to the animals. Therefore, no specific ethical approval was required. In accordance with national legislation and institutional guidelines, ethical review was not necessary for this study, as all data were obtained from an existing database and no additional animal procedures were conducted.

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Reprints and permissionsAbout this articleCite this articleNeto, J.B.S., Brito, L.F., Mota, L.F.M. et al. Modeling genotype-by-environment interactions across climatic conditions reveals environment-specific genomic regions and candidate genes underlying feed efficiency traits in tropical beef cattle.
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KeywordsClimate resilienceDry matter intakeFunctional enrichmentNellore cattleResidual feed intakeTemperature-humidity indexTropical environments More

AbstractThe Greenland shark (Somniosus microcephalus) is the longest-living vertebrate and inhabits the exceptionally dim and cold waters of the Arctic deep sea. Due to its extreme lifespan, harsh environmental conditions, and prevalent corneal parasitisation, the Greenland shark has previously been thought to have impaired or degenerated vision. Here, we present genomic, transcriptomic, histological and functional evidence that the Greenland shark retains an intact visual system well-adapted for life in dim light. Histology and in vitro opsin expression revealed visual adaptations typical of deep-sea species, including densely packed, elongated rods and a short-wavelength shift in rod visual pigment sensitivity compared to shallow-water sharks. In situ hybridisation confirmed the presence of essential visual cell types: rods, Müller glia, and bipolar, amacrine, and ganglion cells. Moreover, despite being over a century old, the examined specimens showed no obvious signs of retinal degeneration. Using whole genome and retinal RNA-sequencing, we further show that dim-light (rod-based) vision genes are intact and robustly expressed, while many bright-light (cone-based) vision genes have become pseudogenized and/or are no longer expressed. Finally, we identify robust expression of DNA repair-associated genes in the retina, which may help support long-term maintenance of retinal integrity over the Greenland shark’s extreme lifespan.

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IntroductionThe Greenland shark (Somniosus microcephalus [Bloch & Schneider 1801]1; Fig. 1a; Fig. S1) is the longest-living vertebrate on Earth, with an estimated lifespan of up to 400 years2. This deep-sea shark inhabits regions from the temperate North Atlantic to the frigid waters of the Arctic Ocean, enduring temperatures as low as −1.1 °C and depths approaching 3000 meters3,4,5. In the waters surrounding Greenland, their eyes are frequently parasitized by copepods (Ommatokoita elongata), which are thought to obscure vision by attaching to the cornea6,7,8,9. Despite their slow-moving nature, Greenland sharks are opportunistic feeders, consuming a range of prey, from fish and squid to seals and large carrion, such as whale carcasses3,5,10. The unique combination of extreme longevity, persistent low temperatures, high-pressure conditions, and parasitised eyes presents unparalleled challenges for its ability to see – raising fundamental questions about the nature and function of the visual sensory system in this enigmatic species.Fig. 1: Visual system of the Greenland shark.a Representative photograph of the head and eye of a Greenland shark, Somniosus microcephalus. b Transverse section through the retina of the Greenland shark (n = 1 individual), alongside schematic illustration of the retinal layers. Scale bar: 50 µm. c Tile plot summarising phototransduction gene mining. Note that most sharks (including the Greenland shark) lack a copy of cngb1. d Species tree showing phylogenetic placement of the Greenland shark (blue), and five comparison species (green; Scyliorhinus canicula, S. torazame, Isurus oxyrinchus, Rhincodon typus, and Chiloscyllium plagiosum) used in this study, within the class, Chondrichthyes. Scale is in million years. e Schematic of the components of the rod-based phototransduction cascade found in the genome of the Greenland shark, with gene names coloured by evidence for relaxed (orange) or diversifying (purple) selection. Abbreviations: PRL, photoreceptor layer; ONL, outer nuclear layer; INL, inner nuclear layer; GCL, ganglion cell layer. Photograph in panel a was provided with permission for use by Ghislain Bardout from the Under the Pole expedition. Panel e was created using BioRender [Fogg, L. (2025) https://BioRender.com/ybje926]. Source data are provided as a Source Data file.Full size imageVertebrate visual systems have evolved across diverse photic environments, from bright terrestrial habitats to the perpetual darkness of caves and the deep sea. In most vertebrate species, vision relies on two types of retinal photoreceptors: rods, optimized for low-light (scotopic) vision, and cones, specialized for bright-light (photopic) vision11. Rods are highly sensitive, with abundant chromatophore-bound visual pigments (opsins) and an efficient phototransduction cascade, while cones provide saturation-resistant, broad-spectrum photic sensitivity12. The relative proportion of rods and cones and the spectral tuning of their opsins are primarily shaped by ecological pressures13,14,15,16,17,18,19,20,21. While rod visual pigments (based on the RH1 opsin) are typically maximally sensitive to blue-green wavelengths (460–530 nm), cone visual pigments (SWS1, SWS2, RH2, and LWS) span a broader range of peak sensitivities, from ultraviolet to red light (350–600 nm)22. Deep-sea and nocturnal species often exhibit rod-dominated retinas, sometimes to the complete exclusion of cones, as in certain nocturnal reptiles23, deep-sea teleost fishes24 and deep-sea sharks25,26,27,28,29.In the most extreme conditions, vision can be entirely lost. Cavefishes, for instance, have evolved in complete darkness, leading to retinal degeneration, loss of phototransduction gene expression, and widespread pseudogenization of vision-related genes30,31,32. Given the Greenland shark’s exceptionally dim and potentially obstructed visual environment in combination with its extreme longevity, it has been speculated that it, too, may have lost its ability to see5,8,33. However, behavioural observations suggest that these sharks may still rely on sight34, and their optic tectum—a brain region that processes visual information—is comparable in size to that of other visually capable elasmobranchs35. Furthermore, the Greenland shark has a tapetum lucidum, a specialized reflective layer behind the retina that enhances photon capture in low-light conditions36.In this study, to determine whether the Greenland shark possesses a lifelong functional visual system, we conducted a comprehensive integrative analysis incorporating genomics, transcriptomics, in situ hybridisation (RNAscope), ultramicrotomy, chromatin staining, mass spectrometry, in vitro opsin regeneration, and spectrophotometry. Our findings support the presence of a preserved and functional visual system in the adult Greenland shark, which seems well-adapted to extreme low-light conditions. Additionally, transcriptomic data suggest a role for DNA repair mechanisms in maintaining retinal integrity over centuries, potentially contributing to the longevity of vision in the longest-living vertebrate.Results and DiscussionStructural and genomic basis of vision in the Greenland sharkVertebrates typically possess a duplex retina containing both rods and cones. Many deep-sea fish species feature rod-dominated or even pure-rod retinas to enhance scotopic vision13,28. Using histology, we show that the Greenland shark retina also exhibits a pure-rod structure. In addition, the retina displays several morphological features associated with dim-light adaptation, including densely packed and elongated rods, and comparatively thin inner retinal layers (Fig. 1b), resembling those of other deep-dwelling or nocturnal sharks28,37,38. All retinal layers are intact in the Greenland shark, including the photoreceptor layer, the outer nuclear layer, the inner nuclear layer, and the ganglion cell layer (Fig. 1b, Fig. S45–S46). That is, even though the retinas inspected were from adult Greenland sharks estimated to be over a century old, there were no obvious signs of retinal degeneration.To further assess whether the Greenland shark retina shows signs of age-related degeneration, we performed a TUNEL (Terminal deoxynucleotidyl transferase dUTP Nick End Labeling) assay on cryosections of the retina. This assay detects DNA fragmentation, a hallmark of apoptosis and other forms of cell death often associated with degenerative processes in vertebrates39,40,41,42. As a positive control, sections were treated with DNase I to induce DNA breaks, which produced a robust TUNEL signal as expected, confirming the assay’s sensitivity and functionality. In contrast, no TUNEL-positive cells were observed in the untreated retinal sections from the Greenland shark (Fig. 2e). This absence of detectable DNA fragmentation suggests there is no ongoing DNA damage and cell death within the retina.Fig. 2: Intact visual circuit and subcellular visual adaptations in the Greenland shark.a Immunofluorescent staining of retinal cross-sections from the Greenland shark labelling active (H3K27Ac) and repressed (H4K20me3) chromatin, counter-stained with Hoechst. b Higher magnification image of H4K20me3 and H3K27Ac localization in photoreceptor nuclei (n = 1 individual). Dashed white line demarcates the nuclear membrane. c Distribution of H4K20me3 and H3K27Ac immunofluorescent signal intensity in the nucleus. Dashed line indicates the center of the nucleus. d In situ hybridisation of key retinal cell markers in retinal cross-sections (n = 1). Note that the expression of elovl2, a key gene in synthesis of VLC-PUFAs, is confined to the retinal pigment epithelium cells in the Greenland shark, in contrast to previous work showing zebrafish expression in Müller glia71 and human expression in cones67. e In situ DNA fragmentation detected by TUNEL staining in retinal cross-sections from the Greenland shark (n = 1). DNase I treatment was used as a positive control. f PUFA composition of bovine (n = 5) and Greenland shark retinas (n = 2) showing the relative abundance of each PUFA, normalized to the total signal intensity of all detected PUFAs by liquid chromatography-mass spectrometry (LC-MS). Since LC-MS cannot reliably distinguish between omega-3 and omega-6 species, we have reported only the total number of carbon atoms and double bonds for each lipid species in the panels. Brackets highlight the most abundant sets of PUFAs in each animal’s retina, allowing for a straightforward visual comparison of the relative abundance of specific fatty acids. Scale bars: 25 µm (a, d), 5 µm (b), 10 µm (e). Abbreviations: RPE, retinal pigment epithelium; ONL, outer nuclear layer; INL, inner nuclear layer; GCL, ganglion cell layer. Source data are provided as a Source Data file.Full size imageThe absence of obvious signs of retinal degeneration is remarkable given that even in healthy aging, vertebrate retinas (including those of humans) undergo progressive photoreceptor loss and DNA damage over time40. For example, at published rates of age-related rod loss (approximately 0.2–0.6% per year), a human living to 400 years of age would be expected to lose over 50–90% of their rod photoreceptors43,44,45. Although it is worth noting that metabolic temperature effects may attenuate rates of cellular decline in cold-water species, such as the Greenland shark. Systematic studies of retinal aging in other long-lived species remain scarce. However, existing data suggest that elephants retain stable photoreceptor populations with age46, and turtles and tortoises can exhibit slow or negligible senescence (though the retina itself was not examined in this group)47. The oldest Greenland shark specimen analysed here was estimated to exceed 130 years of age – making it two- to four-fold older than the eldest individuals examined in elephants, turtles and tortoises. Thus, the Greenland shark provides a striking example of long-term preservation of retinal integrity, supported at both molecular and histological levels, and highlights the extraordinary potential for neuronal maintenance in extreme longevity.Vertebrate photoreception relies on a cascade of biochemical reactions mediated by numerous phototransduction genes, many of which are specialised for either rods, cones, or non-visual photoreceptive cells, such as intrinsically photosensitive retinal ganglion cells (ipRGCs)12,48. Deep-dwelling fish species typically rely more on scotopic vision and, hence, the rod-specific phototransduction genes16,49,50. To examine the phototransduction gene repertoire of the Greenland shark, we generated a draft genome for this species and screened it for phototransduction genes. We retrieved functional copies for the full complement of genes required for rod-based phototransduction (Fig. 1c), including rh1, sag, gnat1, gucy2f, pde6a, pde6b, pde6d, pde6g, grk1, cnga1, gnb5, rcvrn and gngt1. The same set of rod-specific phototransduction genes was found in the genomes of five other shark species (Scyliorhinus canicula, S. torazame, Isurus oxyrinchus, Rhincodon typus, and Chiloscyllium plagiosum; Fig. 1c; Fig. S2), which typically inhabit the upper pelagic zone of tropical to warm temperate seas51,52,53,54,55. Unlike in the other shark genomes inspected, only one gene typically associated with cone phototransduction (grk7) was retrieved from the Greenland shark genome, and recent evidence suggests grk7 may also function in rods in some elasmobranchs56. The remaining cone-associated phototransduction genes present in the other sharks were either not found in our draft genome (arrc) or were pseudogenized (rh2, gnat2, pde6c, cnga3, and cngb3) (Fig. 1c). This suggests that Greenland sharks rely on rod-based vision, just like another deep-dwelling shark, the lanternshark Etmopterus spinax25 and many deep-sea teleosts13.We further found that the Greenland shark has a single functional copy of the visual opsin gene rh1, and the non-visual opsins opn3, opn5, rrh and va—a pattern similar to that of other sharks29,57. Like several other shark species58, the Greenland shark lacks a functional copy of the green-sensitive cone opsin gene (rh2), and this gene also shows signs of pseudogenization in the Greenland shark’s genome. Unique among sharks—but similar to what has happened in some cavefish species30—is the pseudogenization of the non-visual opsin gene opn4 in the Greenland shark. In mammals, opn4 encodes melanopsin, a photopigment expressed in intrinsically photosensitive retinal ganglion cells (ipRGCs) that contribute to non-image-forming light responses, including circadian photoentrainment59,60. However, in dim-light environments, such as the deep sea, rods and cones are primarily responsible for circadian entrainment61, potentially reducing the functional importance of melanopsin-based pathways. The loss of opn4 in the Greenland shark may thus reflect an adaptation to its deep-sea habitat. Supporting this idea, Greenland sharks do not exhibit a clear circadian rhythm in their diel vertical movement patterns33,62.Finally, by computing the ω ratio (i.e., dN/dS) and conducting selection tests, we found that some of the rod-specific phototransduction genes, including rh1, showed evidence of relaxed selection relative to other sharks, suggesting that they are under less pressure to be maintained in the Greenland shark genome compared to other species (Fig. 1e; Fig. S13–S43). However, no loss-of-function mutations were detected in this pathway, which would be expected if they were in the process of being pseudogenized30. Furthermore, some rod genes (e.g., gnat1 and sag) showed evidence of diversifying positive selection, which may suggest adaptive changes related to the optimisation of rod-based vision in the Greenland shark.Subcellular adaptations and circuitry of the Greenland shark retinaTo investigate whether the retinal tissue of the Greenland shark actively maintains nuclear organization, which is indicative of active transcription and hence cellular metabolism, we assessed the presence of histone modifications associated with active (H3K27Ac) and repressive (H4K20me3) chromatin states63. We found clear signals for both histone markers in all nuclear layers of the retina (Fig. 2a), indicating active maintenance of chromatin structure across all cell types. Notably, in rod cells, the active H3K27Ac marker was primarily localized to the centre of the nucleus, while the repressive H4K20me3 marker was predominantly found near the nuclear lamina (Figs. 2b, c). This nuclear organization pattern has previously been associated with diurnality (and therefore, being active in bright light) in mammals64. That we now also found this pattern in an elasmobranch species that is predominantly active in dim light suggests that associations between nuclear architecture and diel activity may differ across lineages and ecological contexts. Overall, the presence of both active and repressive chromatin states suggests that the retina of the Greenland shark is indeed metabolically and transcriptionally active.To assess the integrity of the visual circuit, we employed cell-type specific probes in fluorescent RNA in situ hybridization (RNAscope) to target specific retinal cell types, including rods (using a probe for rh1), GABAergic amacrine cells (gad1), rod bipolar cells (pkcα), Müller glia (glul), retinal ganglion cells (rbpms) and glycinergic amacrine cells (slc6a9)65,66. Using this approach, we confirmed the presence of all key cell populations necessary for rod-based vision, confirming the integrity of the retinal circuitry in the Greenland shark (Fig. 2d).Next, we examined the polyunsaturated fatty acid (PUFA) composition of the Greenland shark retina, with a particular focus on omega-3 docosahexaenoic acid (DHA, 22:6n-3) and very-long-chain polyunsaturated fatty acids (VLC-PUFAs) containing 24 or more carbon atoms. These lipids support rhodopsin function via membrane fluidity and pigment packing67,68,69 and have been associated with counteracting cold-induced membrane rigidity70. First, we noted that elovl2, a key gene in synthesis of VLC-PUFAs in zebrafish71 and humans67, is expressed in the Greenland shark retina, specifically in the retinal pigment epithelium (RPE) cells (Fig. 2d). Liquid chromatography-mass spectrometry (LC-MS) analysis revealed that the retina of the Greenland shark contains an exceptionally high proportion of DHA (41%) compared to the bovine retina (26%) (Fig. 2e), and that VLC-PUFAs constituted 45% of total retinal lipids in the Greenland shark, compared to 35% in mammals. Moreover, the dominant VLC-PUFAs of the Greenland shark featured longer carbon chains (36 carbons) compared to the bovine ones (32 carbons; Fig. 2e) or to mouse72. The higher proportion of PUFAs and the longer carbon chains in the Greenland shark compared to other vertebrates might represent yet another adaptation to the deep-sea environment, as recently proposed73. Taken together, our data show that the lipid composition of disc membranes in Greenland shark appears to be optimal to support rhodopsin function.Rhodopsin spectral sensitivity and corneal light transmission in the Greenland sharkIn vertebrates, the spectral sensitivity of the visual pigment formed by the rod opsin is typically tuned to the prevailing wavelengths of light in the environment22. This is also true for the Greenland shark. Spectroscopic analysis of purified, in vitro-expressed Greenland shark rhodopsin bound to 11-cis-retinal revealed a maximum absorbance wavelength (λmax) of 458 nm (Fig. 3b), which is shorter than that of most shallow-dwelling sharks58, and even particularly blue-shifted compared to other deep-sea species49. Short-wavelength shifting of the rhodopsin λmax is a typical adaptation found in deep-sea fishes49, suggesting adaptive evolution of the Greenland shark rhodopsin for life in the deep sea. Furthermore, the extreme blue-shift observed in the Greenland shark rhodopsin may reflect adaptation to the highly transparent, blue-dominated light environments of deep, high-latitude Arctic waters74.Fig. 3: Visual pigment spectral sensitivity and corneal transmission in the Greenland shark.a Workflow for expression and immunoaffinity purification of S. microcephalus rhodopsin reconstituted with 11-cis-retinal in HEK293S cells, followed by UV-vis spectroscopy analysis. b UV-vis spectroscopic measurements of S. microcephalus rhodopsin. Inset depicts the difference (between bleached and unbleached) spectra. c Schematic optical setup used for measuring corneal transmittance. d Corneal transmittance of human and S. microcephalus samples. Top: broadband radiance measurements (425–600 nm); bottom: focused blue light range (450–500 nm). Dashed lines indicate the average percentage (%) transmittance between right and left human corneas. Source data are provided as a Source Data file.Full size imageFinally, we investigated whether the parasitized cornea of the Greenland shark allows penetration of light to the retina. Using a SpectralLED tunable light source coupled to an integrating sphere and a spectroradiometer (Fig. 3c), we measured light transmission through fixed Greenland shark corneas compared to fixed human donor corneas (Fig. 3d). All shark corneas analysed had parasites bound to the corneal edges (Fig. S47). We found that human corneas had an average transmittance of 95%, while the six shark corneas ranged from 70 to 100% transmittance. In the blue light range specifically (450–500 nm), human corneas had an average transmittance of 94%, while the shark corneas ranged from 66 to 100% transmittance. These findings suggest that Greenland shark corneas allow light to reach the retina, despite the presence of parasites.Retinal gene expression in the Greenland sharkGiven that we found a fully intact phototransduction gene repertoire and cell circuitry for rod-based vision in the Greenland shark, we sought to determine whether the expression levels of the phototransduction genes were comparable to those in other shark species and thus, biologically relevant. To address this, we sequenced bulk retinal transcriptomes from the Greenland shark and compared them to publicly available transcriptomic data from retinas of five representative shark species: S. canicula, S. torazame, I. oxyrinchus, R. typus, and C. plagiosum. Our analysis revealed that key rod phototransduction genes were expressed at biologically relevant levels in the retina of the Greenland shark, including rhodopsin (rh1), rhodopsin kinase (grk1), arrestin (sag), transducins (gnat1 and gngt1), and phosphodiesterases (pde6a, pde6b, and pde6g) (Fig. 4b). Expression levels of these genes were found to be comparable to those observed in adult specimens of other shark species (S. canicula, S. torazame, and C. plagiosum) and higher than those in species sampled as juveniles (I. oxyrinchus and R. typus) (Fig. 4b). In contrast, we observed either no expression or low expression of cone phototransduction genes in the Greenland shark (Fig. 4c), consistent with a visual system specialized for scotopic vision. Interestingly, individuals from our comparison group that were juveniles (e.g., I. oxyrinchus and R. typus) exhibited relatively lower levels of most phototransduction genes compared to adults from other species. While these patterns may reflect developmental differences, we acknowledge that species and life stage are not fully independent in our dataset, and this may contribute to the observed variation. Overall, our findings suggest that the Greenland shark preferentially relies on rod-based phototransduction for vision. The transcriptomic basis for scotopic vision in this species is fully intact, comparable to other adult elasmobranchs, and well-suited to its dim light environment.Fig. 4: Transcriptomic basis of vision in the Greenland shark.a Phylogeny of species used for gene expression analyses. b–d Retinal expression of genes involved in rod-based phototransduction (b), cone-based or non-visual photoreception (c) and DNA repair (d) in Somniosus microcephalus (blue; n = 3 individuals) and five comparison species (green; Scyliorhinus canicula [n = 5], S. torazame [n = 2], Isurus oxyrinchus [n = 3], Rhincodon typus [n = 1], and Chiloscyllium plagiosum [n = 5]). Species names have been abbreviated. Data are transcripts per million (TPM) plotted on a log-scaled axis. Data are presented as box plots (centre line at the median, upper bound at 75th percentile, lower bound at 25th percentile) with whiskers at minimum and maximum values. Dotted line demarcates data derived from transcriptomes from adult (left) and juvenile (right) specimens. Genes which were not retrieved or were pseudogenized are marked with a cross. Note that some genes given in (b) are involved in all types of phototransduction (i.e., rvcrn, grk1, gnb5 and gucy2f). Source data are provided as a Source Data file.Full size imageLastly, we investigated whether efficient DNA repair mechanisms may contribute to the preservation of retinal structure and maintenance of photoreceptor integrity in the retina of the Greenland shark, as has been suggested for other organisms and recently shown in the bowhead whale75. Specifically, we focused on the ERCC1-XPF DNA repair complex which is known to have an important role in supporting retinal health and has been linked to retinal aging across a range of eukaryotes40,76,77,78,79,80,81. Notably, UV sensitivity and early-onset vision loss are hallmarks of the human progeroid syndrome, Xeroderma Pigmentosum, caused by mutations in the ERCC1-XPF complex82. Similarly, ercc1 knockout mice exhibit accelerated retinal aging and degeneration40, highlighting the complex’s role in protecting ocular tissues. We found that shark species with the longest lifespans, including the Greenland shark, retain the ercc1 gene, while shorter-lived sharks lacked this gene83,84,85,86,87,88, and that the Greenland shark exhibits elevated expression of ercc4 (xpf) compared to other sharks (Fig. 3d). This suggests that a robust DNA repair system may help preserve retinal integrity and function over the extremely long lifespan of the Greenland shark. Future studies could help clarify the role of the ERCC1–XPF complex in retinal maintenance in sharks and may reveal additional DNA repair pathways that contribute to visual longevity in this species.Preservation of vision over centuriesOur findings provide compelling evidence that the Greenland shark (S. microcephalus) retains functional vision, despite extreme longevity, corneal parasitisation, and an environment characterized by minimal light. The rod phototransduction pathway remains intact, and the loss or pseudogenization of most cone pathway genes strongly suggests a reliance on scotopic vision. Active transcription of rod phototransduction pathway genes, supported by RNA-seq, RNAscope and chromatin staining, indicates that the retina is functionally preserved. Furthermore, key visual adaptations revealed by histological analyses and in vitro opsin expression are well-aligned with the deep-sea ecology of this species, including elongated, densely packed rods and a short-wavelength shift in rhodopsin sensitivity. In addition, corneal transmission remains intact despite chronic parasitism. The absence of obvious retinal degeneration in exceptionally old individuals, alongside the preferential retention and elevated expression of DNA repair genes linked to retinal degeneration (ercc1, ercc4), suggests a potential mechanism underpinning their long retinal health span. Together, these findings highlight the extraordinary adaptability of vertebrate sensory systems in extreme environments and the remarkable preservation of organ function over hundreds of years.MethodsAnimal tissue collection and preservationThe Greenland sharks used in this study were caught between 2020 and 2024 using scientific long lines off the coast of the University of Copenhagen’s Arctic Station on Disko Island, Greenland (69°15’N, 53°34’W). All sampling was carried out in accordance with laws and regulations under a permit to collect Greenland sharks for scientific purposes. The work was carried out with authorisation from the Government of Greenland under permits (2020-26794, 2022-24744, 2023-6108, 2024-119) from the Ministry of Fisheries, Hunting & Agriculture and a non-exclusive licence (G24-051) for the utilization of Greenland genetic resources issued by the Ministry of Foreign Affairs, Business and Trade. Individuals were euthanized immediately after capture by direct spinal cord transection. Total body length (TL) was measured, and eyecups were dissected and either fixed whole in 4% paraformaldehyde [PFA; 4% (w/v) PFA in 0.01 M phosphate-buffered saline] or the retina was removed and fixed in RNAlater or 100% ethanol. Ages were estimated from TL based on the values provided in Nielsen et al. (2016), which were calculated using the von Bertalanffy growth model2. Details of all individuals can be found in Table S1.The bovine eyes used in this study (n = 5) were obtained from a commercial slaughterhouse. Each eye was dissected to remove the anterior segment, lens, and vitreous. The retina was then carefully separated from the underlying eyecup and stored at –80 °C until further processing.Human donor tissue collection and preservationThe human donor eyes used in this study were obtained and managed in compliance with the Declaration of Helsinki. De-identified eyeballs from a 50-year-old female donor were obtained from the Willed Body Program at the University of California, Irvine (Irvine, CA) within 12 h postmortem, in compliance with ethical requirements of the program. The program is registered with the respective state boards to facilitate the distribution of deceased human remains for research purposes. Prior to tissue processing, serological testing was performed on donor blood to exclude samples positive for blood-borne pathogens. Whole eyes were dissected, the anterior segments were further isolated and 5 mm bio punches were taken for further analysis. All samples are preserved in the laboratory of Dorota Skowronska-Krawczyk.Retinal histologyRetinal morphology was assessed for one PFA-fixed eye each from three adult specimens of S. microcephalus. Whole, enucleated eyes were post-fixed in 2.5% glutaraldehyde and 2% PFA in 0.1 M PBS, progressively dehydrated in increasing concentrations of ethanol, infiltrated with EMbed-812 resin and polymerized at 60˚ C for 48 h. For light micrographs, 1 μm-thick radial sections were cut on a Reichert-Jung Ultracut E ultramicrotome, deplastified, stained with epoxy tissue stain (Electron Microscopy Sciences; toluidine blue and basic fuchsin), and imaged under brightfield illumination on a Leica DM4 microscope.Additionally, hematoxylin and eosin (H&E) staining was performed on 4 μm-thick paraffin sections from a separate eye. Sections were processed following a standard protocol89: deparaffinized in xylene, rehydrated through a graded ethanol series to distilled water, stained with hematoxylin for 5 min, rinsed in running tap water, differentiated in 1% acid alcohol, and blued in an alkaline solution. Counterstaining with eosin was carried out for 1–2 min, followed by dehydration through graded ethanol, clearing in xylene, and mounting.Whole-genome sequencingA draft genome was sequenced for the Greenland shark, S. microcephalus. Briefly, DNA was extracted from frozen tissue from two adult retinas using Zymo Quick-DNA Miniprep Plus kit. DNA quality was assessed on an Agilent TapeStation and library preparation and sequencing was performed by the Department of Biosystems Science and Engineering (ETH Zurich). Individual libraries were prepared using NEBNext Ultra II FS DNA Library Prep kit (Illumina) with 150 bp insert size, pooled, and sequenced as 150 bp paired-end reads on an Illumina NovaSeq6000 with an SP flow cell (300 cycles). Quality control and adapter removal were performed with fastp (v.0.23.4)90, filtered reads were assembled using ABySS (v.2.3.1) using a k-mer size of 80 and k-mer coverage threshold of two91 and the completeness of the assembly was assessed using BUSCO (v.5.4.5) using the vertebrata odb10 database92 (see Table S3 for assembly statistics).Chondrichthyes phylogenyA species tree was constructed for 17 species in the class Chondrichthyes (see Table S2 for accession numbers). All genome assemblies available for Chondrichthyes were downloaded from the NCBI Genome database. Completeness of each genome was assessed using BUSCO (v.5.4.5) using the vertebrata odb10 database and assemblies with completeness scores over 80% were retained (14 species). An additional three species were included in the phylogeny: S. microcephalus and another two species which were used for phototransduction gene mining and expression analyses (S. torazame, 76.0% complete; R. typus, 75.6% complete). All single-copy orthologs (obtained from BUSCO analysis) present in at least half of the species were aligned using MUSCLE (v.5.1)93, alignments were trimmed using trimAl (v.1.4.1)94, and maximum likelihood trees were made for each ortholog using IQ-TREE (v.2.0)95. An unrooted species tree was estimated by inputting concatenated gene trees into ASTRAL (v.5.7.8)96, and the tree was dated and rooted using the least-squared method in IQ-TREE and visualised in iTOL (v.6.9.1)97.Phototransduction and DNA repair gene miningPhototransduction and DNA repair genes were mined from the newly generated draft genome assembly for S. microcephalus and from published genome assemblies for five other shark species (S. canicula, GCF_902713615.1_sScyCan1.198; S. torazame, GCA_003427355.1_Storazame_v1.099, R. typus, GCF_021869965.1_sRhiTyp1.1100; I. oxyrinchus, GCA_026770705.1101; and C. plagiosum, GCF_004010195.1102), which were selected because they also had publicly available RNAseq data. Genome annotations were obtained for all NCBI genomes, except for I. oxyrinchus. The I. oxyrinchus genome assembly was annotated by inputting publicly available RNAseq data for this species (see Table S2 for NCBI accession numbers), protein annotations for the four other shark species, and Vertebrata orthologs from OrthoDB (v.12.0)103 into BRAKER (v.3)104.For S. microcephalus, phototransduction and DNA repair gene coding sequences (CDS) were mined using a combination of exon prediction and mapping guided by publicly available reference sequences from NCBI. For the opsin genes, we specifically searched for all known genes (including rh1, sws1, sws2, lws, and rh2) using multiple reference species. For the exon prediction approach, scaffolds of interest in the genome assembly were identified using TBLASTN (v.2.11.0)105 and EXONERATE (v.2.4.0)106 was used to predict exons on those scaffolds similar to reference sequences. If the exon prediction approach did not yield a CDS, or if the CDS was fragmented, the CDS was retrieved or completed using a mapping approach. Briefly, raw genomic reads were mapped to reference sequences using HISAT2 (v.2.2.1)107 and the consensus sequence was extracted in Geneious Prime (v.2022.2.2; Biomatters Ltd).Phototransduction and DNA repair genes were also mined from another five shark species with publicly available genome assemblies. For S. canicula, R. typus and C. plagiosum, functional genome annotations were available and thus, genes were mined by filtering the annotation (GFF) file to keep only matched gene names and AGAT (v.1.4.1)108 was used to extract the CDS for the longest isoform per gene. For the two species without functional genome annotations (S. torazame and I. oxyrinchus), all genes were mined using the exon prediction approach described above and annotation files were manually edited to incorporate mined genes.The identity of every gene mined was confirmed phylogenetically. To that end, gene trees for each clade of interest were generated by downloading reference sequences for outgroup species (Homo sapiens, Mus musculus and Danio rerio) and other chondrichthyan species from NCBI, aligning these with mined genes using MUSCLE, trimming alignments using trimAl, generating maximum likelihood phylogenies using IQ-TREE and rooting in iTOL (Fig. S2-S12).Selection tests on phototransduction and DNA repair genesThe selective forces acting on the newly mined genes were assessed for S. microcephalus. For each gene clade, the protein sequences of the Greenland shark were aligned with those from other species in Chondrichthyes using MUSCLE (after removing stop codons and frameshifts present in pseudogene sequences). This protein alignment was then converted to a codon alignment using trimAl and used to construct a gene tree for each clade using IQ-TREE. The codon alignment and the corresponding gene tree were then used to compute maximum likelihood estimates of ω with PAML. Three branch models were used: (i) a free-ratio model, which allows a different ω value per branch; (ii) a two-ratio model assuming one ω for the Greenland shark branches and one ω for all other branches; and (iii) a null one-ratio model assuming that every branch has the same ω ratio. For all genes, the best model was always the free-ratio model (Model 1). The choice between Model 2 and Model 3 was always made using the mean of a likelihood ratio test, using the χ2 distribution with one degree of freedom. Finally, for each gene clade, RELAX109 and aBSREL110 was implemented via the HyPhy framework (v.2.5.63)111 to look for signs of relaxed selection or positive selection in the Greenland shark genes. Greenland shark branches were assigned as “test” branches, while all other branches were assigned as background branches, in sequential runs of RELAX and aBSREL.Rhodopsin absorption spectrum measurementsThe peak spectral sensitivity of the S. microcephalus rhodopsin (RHO) was assessed using in vitro protein regeneration and spectrophotometry (Fig. 3a). In brief, HEK293S cells were transfected to express Greenland shark RHO modified to contain the 1D4 epitope derived from the C-terminus of bovine rhodopsin (TETSQVAPA). The plasmid, pRP[Exp]-CBA-[SharkRH1], was constructed and packaged by VectorBuilder (vector ID: VB241011-1418sns). After 48 h, cells were pelleted down for 1D4-affinity chromatography. Immunoaffinity 1D4 resin was prepared by conjugating purified, anti-Rho antibody (1D4) to CNBr-activated Sepharose 4B beads (Cytiva). Pelleted cells were homogenized, and RHO pigments were reconstituted with 40 µM 11-cis-retinal for 1 h at room temperature to regenerate RHO from apo-opsin. Then, 10% DDM was added to solubilize the membrane-enriched pellet, followed by 1 h incubation at 4 °C and centrifugation (21,300 x g) for 5 min at 4 °C. The supernatant was then filtered through a 0.22 µm polyethersulfone membrane and incubated with 250 µL of 1D4-resin for 1 h at 4 °C. Rho-1D4-resin mixture was loaded onto a centrifuge column, washed in a buffer containing 50 mM HEPES (pH 7.5), 0.25 M NaCl, and 10% DDM, and eluted with C-terminal nonapeptide (synthesized by GenScript, NJ, USA) overnight at 4 °C. Absorption spectra were recorded using a Varian Cary 50 Scan UV-Vis spectrophotometer (Varian Australia Pty Ltd). The unbleached sample was used as blank, after which the sample was bleached for 10 min with a white-light, 875-Lumens bulb. The difference in absorption spectrum was then recorded.Nuclear chromatin staining and quantificationEyecups were fixed in 4% PFA overnight, cryoprotected by immersion in a sucrose gradient (10% and 20% sucrose for 1 h at room temperature, and 30% sucrose overnight at 4 °C), embedded in Tissue-Tek OCT (Sakura, Torrance, CA) and frozen on a conductive metal block placed on dry ice. After cryosectioning, sections were blocked in 5% bovine serum albumin (BSA), 0.3% TritonX-100 for 1.5 h at room temperature to minimize nonspecific binding. Sections were then incubated with primary antibodies (mouse anti-trimethyl histone H4 (sc-134216), 1:200 and rabbit anti-H3K27ac (ab-4729), 1:200) diluted in 5% BSA and 0.1% TritonX-100 overnight at 4 °C. Following three washes with PBS, sections were incubated with fluorescently-labeled secondary antibodies diluted in 5% BSA and 0.1% TritonX-100 for 1 h at room temperature. Following three washes with PBS, nuclei were counterstained with Hoescht 33342 (Thermo), and sections were mounted using ProLong Gold Antifade (Thermo). Immunostained sections were imaged on a Zeiss LSM900 confocal microscope with Airyscan 2 at 40X magnification. Quantification of nuclear chromatin distribution was performed using ImageJ. For each cell, two straight lines were drawn across the nucleus, and intensity profiles for the H4K20me3, H3K27ac, and Hoechst channels were measured. The intensity profiles were normalized within each channel and plotted against the normalized distance along each line.Fluorescence in situ hybridizationIn situ hybridization was performed using the RNAscope® Multiplex Fluorescent Assay v2 (ACD Diagnostics) following modifications112. Briefly, frozen histologic sections of fixed shark eyes were pretreated as per the manual using hydrogen peroxide and target retrieval reagents, including protease IV. Probes were then hybridized according to the protocol and then detected with TSA Plus® Fluorophores fluorescein, cyanine 3, and cyanine 5. Sections were mounted with Prolong Gold Antifade (Thermo Fisher) and imaged (Keyence BZ-X700). Probes specific for mouse and human transcripts were designed by the manufacturer (see Table S5).TUNEL assayDNA fragmentation was detected using the Click-iT™ Plus TUNEL Assay Kit, Alexa Fluor™ 488 (Thermo Fisher Scientific, C10617), following the manufacturer’s protocol. As a positive control for DNA fragmentation, selected sections were treated with 1U DNase I for 30 min at 37 °C. Slides were counterstained with Hoechst 33342 and images were acquired using a Zeiss LSM900 confocal microscope with Airyscan 2 at 20X magnification.Fatty acid analysisLipid purification and fatty acid quantification were performed following the method of Gao and colleagues113. Briefly, lipids from B. taurus (n = 5) and S. microcephalus (n = 2) retinas were extracted according to the methodology of Bligh and Dyer114. Specifically, the tissue was homogenized in 200 μL water, transferred to a glass vial, and 750 μL 1:2 (v/v) CHCl3: MeOH was added and vortexed. Then, 250 μL CHCl3 was added and vortexed. Finally, 250 μL ddH2O was added and vortexed. The samples were centrifuged at 3000 RPM for 5 min at 4 °C. The lower phase was transferred to a new glass vial and dried under nitrogen stored at -20 °C until subsequent lipid analysis. The total fatty acids were released through acid hydrolysis and extracted with hexane, then the sample was evaporated under nitrogen and stored at -20 °C until subsequent lipid analysis. LC-MS/MS separation of VLC-PUFAs was achieved on an Acquity UPLC® BEH C18 column (1.7 μm, 2.1 × 100 mm, Waters Corporation). The Q Exactive mass spectrometer (Thermo Fisher Scientific) was operated in a full MS scan mode (resolution 70,000 at m/z 200) in negative mode. For the compounds of interest, a scan range of m/z 250–800 was chosen. The identification of fatty acids was based on retention time and formula.Corneal transmittanceWe measured light transmission through 5 mm diameter bio-punches of six corneas from five Greenland sharks and a pair of corneas from a human donor for reference. Using a SpectralLED tunable light source coupled to an integrating sphere and a spectroradiometer (PhotoResearch-655 SpectraScan®), we measured spectral radiance of samples between wavelengths 425–600 nm as shown in the optical setup in Fig. 3c. Transmittance was calculated as a % representing the ratio between the radiance measurements of corneal samples to reference radiance (no corneas).Retinal transcriptome sequencingRetinal transcriptomes were sequenced for three individuals for S. microcephalus. Briefly, total RNA was extracted from one RNAlater-fixed retina from each of three adult sharks using the QuickRNA Miniprep kit (Zymo). RNA quality was assessed on an Agilent TapeStation and library preparation and sequencing was performed by the Department of Biosystems Science and Engineering (ETH Zurich). Individual libraries were prepared using TruSeq stranded total RNA ribo-zero gold kit (Illumina), pooled, and sequenced as 100 bp paired-end reads using BRAVO Sequencing on an Illumina NovaSeq6000 with an S4 flow cell (200 cycles). Quality control and adapter removal was performed with fastp (v.0.23.4).Phototransduction and DNA repair gene expressionPhototransduction and DNA repair gene expression was quantified for the Greenland shark and five other shark species for which retinal transcriptome data were available on NCBI (see Table S2 for accession numbers). For the five other species, transcriptomes were pre-processed using fastp and mapped to annotated genomes using STAR (v.2.7.10b)115 with –outFilterMultimapNmax 1 –outFilterMatchNminOverLread 0.4 –outFilterScoreMinOverLread 0.4 options, and mapped singletons were filtered out for paired-end data. Read counts were performed using the HTSeq-count script from the HTSeq framework (v.2.0.2)116 and were used to calculate TPM values for all genes. For the genes of interest, TPM values were plotted against the proportion of reads mapped (calculated as the number of reads mapped divided by the total number of reads in the transcriptome) in R (v.4.4.0)117 and a linear regression was performed to generate an equation that describes the relationship between the two variables for each gene (Fig. S44).For the Greenland shark, an annotation file was manually generated for all genes that were mined, and this was used to guide mapping against those sequences using STAR. Similar to the other species, mapped singletons were filtered out and read counts were performed using HTSeq-count. The equations generated using the other five shark species were used to extrapolate TPM values from the proportion of mapped reads for each gene expressed in the Greenland shark.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Newly identified coding sequences (PV442159-PV442194; https://www.ncbi.nlm.nih.gov/nuccore/?term=PV442159:PV442194[accn]) as well as raw genomic and transcriptomic reads and genome assembly are available through GenBank (PRJNA1246101; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1246101) and the NCBI Sequencing Read Archive (see Table S2 for accession numbers). All other data are available via Figshare (https://figshare.com/s/9d86a7c9eccced4c6505) or are provided in the main manuscript or Supplementary Information. Source data are provided with this paper.
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R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2022).Download referencesAcknowledgementsWe dedicate this article to the memory of our co-author, Prof. John Fleng Steffensen, who sadly passed away before its publication. John was not only an exceptional scientist but also a kind and inspiring collaborator. His pioneering research on Greenland sharks, including the landmark study that first revealed their extraordinary longevity, laid the foundation for our current work. We are deeply grateful for his insight, enthusiasm, and enduring inspiration, and we recognise his lasting contribution to the field. We would like to thank the crew of the RV Porsild as well as the staff at the Arctic Station in Qeqertarsuaq, Greenland for invaluable help fishing for Greenland sharks. We thank David Salom for his technical expertise with the rhodopsin spectral sensitivity experiment. We thank Gulab Zode for generously providing the human corneal samples used in this study. We thank Ghislain Bardout and the Under the Pole team for kindly providing the photographs in Figure 1a and Figure S1. We acknowledge the staff of the Department of Biosystems Science and Engineering, ETH Zurich for genome and transcriptome sequencing. Figure 1e was created in BioRender [Fogg, L. (2025) https://BioRender.com/ybje926]. All genomic and transcriptomic computations were performed at sciCORE (http://scicore.unibas.ch/), the center of scientific computing at the University of Basel (with support from the Swiss Institute of Bioinformatics). We acknowledge the following funding sources: JFS was supported by the Independent Research Fund Denmark (9040-00303B), the Danish Center for Marine Research (2022-01) and the Carlsberg Foundation (CF20-0519; CF23-1455); WS was supported by the Swiss National Science Foundation and the University of Basel; DSK laboratory was supported by an NIH grant (U01EY034594) and in part by the support to the Gavin Herbert Eye Institute at the University of California, Irvine from an unrestricted grant from Research to Prevent Blindness and from an NIH core grant (P30 EY034070).Author informationAuthor notesDeceased: John Fleng Steffensen.These authors contributed equally: Walter Salzburger, Dorota Skowronska-Krawczyk.Authors and AffiliationsZoological Institute, Department of Environment Sciences, University of Basel, Basel, SwitzerlandLily G. Fogg, Maxime Policarpo, Nicolas Boileau & Walter SalzburgerGavin Herbert Eye Institute, Brunson Center for Translational Vision Research, Department of Ophthalmology and Visual Sciences, University of California, Irvine, CA, USAEmily Tom, Fangyuan Gao & Dorota Skowronska-KrawczykDepartment of Physiology and Biophysics, University of California, Irvine, CA, USAEmily Tom, William Cho & Dorota Skowronska-KrawczykEvolution of Sensory and Physiological Systems, Max Planck Institute for Biological Intelligence, Martinsried, GermanyMaxime PolicarpoDepartment of Cognitive Sciences, School of Social Sciences, University of California Irvine. 3151 Social Sciences Plaza, Irvine, CA, USADoreen Hii, Aaron E. Fawcett & Cherlyn J. NgUniversity of Copenhagen, Marine Biological Section, Department of Biology, Helsingør, DenmarkAmalie Bech-Poulsen, Kirstine F. Steffensen & John Fleng SteffensenDepartment of Biological Sciences, Indiana University South Bend, South Bend, IN, USAPeter G. BushnellVirginia Institute of Marine Science, Gloucester Point, VA, USARichard BrillAuthorsLily G. FoggView author publicationsSearch author on:PubMed Google ScholarEmily TomView author publicationsSearch author on:PubMed Google ScholarMaxime PolicarpoView author publicationsSearch author on:PubMed Google ScholarWilliam ChoView author publicationsSearch author on:PubMed Google ScholarFangyuan GaoView author publicationsSearch author on:PubMed Google ScholarDoreen HiiView author publicationsSearch author on:PubMed Google ScholarAaron E. FawcettView author publicationsSearch author on:PubMed Google ScholarNicolas BoileauView author publicationsSearch author on:PubMed Google ScholarAmalie Bech-PoulsenView author publicationsSearch author on:PubMed Google ScholarKirstine F. SteffensenView author publicationsSearch author on:PubMed Google ScholarCherlyn J. NgView author publicationsSearch author on:PubMed Google ScholarPeter G. BushnellView author publicationsSearch author on:PubMed Google ScholarJohn Fleng SteffensenView author publicationsSearch author on:PubMed Google ScholarRichard BrillView author publicationsSearch author on:PubMed Google ScholarWalter SalzburgerView author publicationsSearch author on:PubMed Google ScholarDorota Skowronska-KrawczykView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: L.F., D.S.K., W.S., J.F.S., R.B., and P.G.B. Methodology: L.F., M.P., E.T., D.S.K., N.B., D.H., A.E.F., C.J.N., and W.S. Investigation: L.F., E.T., W.C., F.G., D.H., A.E.F., C.J.N., D.S.K., A.B., K.F.S., P.G.B., J.F.S., and R.B. Formal analysis: L.F., E.T., W.C., F.G., D.H., A.E.F., C.J.N., and D.S.K. Visualization: L.F., E.T., W.C., F.G., D.H., A.E.F., and D.S.K. Funding acquisition: W.S., D.S.K., and J.F.S. Supervision: L.F., C.J.N., W.S., and D.S.K. Writing – original draft: L.F., E.T., D.S.K., and W.S. Writing – review & editing: L.F., E.T., M.P., W.C., F.G., D.H., A.E.F., N.B., A.B., K.F.S., C.J.N., P.G.B., J.F.S,. R.B., W.S., and D.S.K.Corresponding authorsCorrespondence to
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AbstractSugarcane (Saccharum officinarum L.), as a long-duration and nutrient-intensive crop, is particularly susceptible to micronutrient depletion, especially under intensive cultivation. Despite the essential roles of zinc (Zn) and boron (B) in plant growth and metabolism, their management is often neglected, and their residual effects on ratoon crops remain underexplored. The present study aims to optimize Zn and B supplementation to enhance yield and quality in plant cane while assessing their carry-over effects on ratoon productivity for improved and sustainable micronutrient management in tropical agroecosystems. Field experiments were conducted at three sites on the College Farm, NAU, Navsari during the winter seasons from 2017 to 18 to 2019–20 for plant cane and from 2018 to 19 to 2020–21 for ratoon cane, to evaluate the direct effects of B and Zn application on plant sugarcane and their residual effects on ratoon sugarcane. The treatments included four levels of boron (0, 1.0, 2.0, and 3.0 kg ha⁻¹) and four levels of zinc (0, 5.0, 7.5, and 10.0 kg ha⁻¹), applied along with the recommended dose of fertilizers. The experiment was laid out in a factorial randomized block design with three replications, and the data were subjected to pooled analysis of variance over the years. Significant individual effects of boron and zinc on sugarcane growth and yield was observed. Millable cane height, weight, and the yield of cane and green trash were significantly higher with a B application of 3 kg ha-1 and a Zn application of 10 kg ha-1. Nutrient application influenced the chemical composition of sugarcane, increasing brix (%), sucrose (%), and commercial cane yield (%), particularly at the same application rates. Nutrient content and uptake in sugarcane, specifically nitrogen (N), phosphorus (P₂O₅), potassium (K₂O), boron (B), and zinc (Zn) increased significantly with the application of boron at 3 kg ha-1 and zinc at 10 kg ha-1. No noticeable interaction effect was observed between B and Zn on the yield and quality parameters of both the sugarcane and its ratoon.

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Download referencesAcknowledgementsAuthors would like to thank the Central Instrumentation Laboratory and Department of Soil Science, Navsari Agricultural University for the structural support to conduct this study.FundingThe research was funded by the Department of Soil Science, N. M. College of Agriculture, Navsari Agricultural University, Navsari 396450.Author informationAuthors and AffiliationsDepartment of Soil Science, N. M. College of Agriculture, Navsari Agricultural University, Navsari, 396450, IndiaVallabh Jerambhai Zinzala, Sonal Tripathi, Kamlesh Ganeshbhai Patel, Jaimin Ranjitrai Naik, Narendra Singh & Deepasree AmmamkuzhiyilAssistant Research Scientist, College Farm, N. M. College of Agriculture, Navsari Agricultural University, Navsari, 396450, IndiaJagadish Vitthalbhai PatelDepartment of Agricultural Statistics, N. M. College of Agriculture, Navsari Agricultural University, Navsari, 396450, IndiaNitin VarshneyAuthorsVallabh Jerambhai ZinzalaView author publicationsSearch author on:PubMed Google ScholarJagadish Vitthalbhai PatelView author publicationsSearch author on:PubMed Google ScholarSonal TripathiView author publicationsSearch author on:PubMed Google ScholarKamlesh Ganeshbhai PatelView author publicationsSearch author on:PubMed Google ScholarJaimin Ranjitrai NaikView author publicationsSearch author on:PubMed Google ScholarNarendra SinghView author publicationsSearch author on:PubMed Google ScholarNitin VarshneyView author publicationsSearch author on:PubMed Google ScholarDeepasree AmmamkuzhiyilView author publicationsSearch author on:PubMed Google ScholarContributionsAll authors contributed equally to the research design, development, and the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
Deepasree Ammamkuzhiyil.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleZinzala, V.J., Patel, J.V., Tripathi, S. et al. Optimizing boron and zinc supplementation for cane growth and its residual effect on the ratoon crop.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-29338-yDownload citationReceived: 05 May 2025Accepted: 17 November 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-29338-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsBoron fertilizationMicronutrient managementNutrient use efficiencyRatoon cropResidual effectSoil fertilitySugarcane productivityZinc application More

AbstractUnderstanding the mechanisms that shape ecosystem resistance to increasing livestock grazing pressure, a major driver of land degradation, is essential for predicting its impacts and informing sustainable land management strategies. This issue is particularly relevant in drylands, which host half of the world’s livestock production and are highly vulnerable to desertification caused by overgrazing. Here we conduct a standardized field survey across 73 dryland sites in 25 countries to simultaneously evaluate how climatic, edaphic, vegetation and grazing-related factors influence ecosystem resistance—defined here as the capacity to maintain vegetation cover under increasing grazing pressure. We found that increasing grazing pressure reduced vegetation cover in 80% of sites, with an average decline of 35%. Plant species richness emerged as the strongest predictor of ecosystem resistance, with higher richness associated with lower vegetation cover loss. Functional trait data indicated that this positive effect was mainly explained by complementarity in trait values among plants, rather than by functional redundancy. Our results indicate that conserving plant diversity is key to strengthening ecosystem resistance and sustaining dryland functioning under intensifying grazing pressure.

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Fig. 1: Response of vegetation cover to increasing grazing pressure (resistance) across global drylands and latitudinal zones.Fig. 2: Drivers of changes in vegetation cover to increasing grazing pressure (resistance).Fig. 3: Relationships between vegetation cover responses to increasing grazing pressure (resistance) and functional dispersion and redundancy.

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Data availability

The dataset needed to reproduce our results are available via Figshare at https://doi.org/10.6084/m9.figshare.29132654 (ref. 90).
Code availability

The R script used is available via Figshare at https://doi.org/10.6084/m9.figshare.29132654 (ref. 90).
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Biancari, L. et al. Plant diversity enhances ecosystem resistance to increasing grazing pressure in global drylands. Figshare https://doi.org/10.6084/m9.figshare.29132654 (2025).Download referencesAcknowledgementsWe thank all participants of the BIODESERT global field survey. This survey was funded by the European Research Council (ERC grant agreement 647038), awarded to F.T.M. F.T.M., L.B. and E.G. acknowledge support by the King Abdullah University of Science and Technology (KAUST). M.R.A., G.R.O. and L.Y. acknowledge support by the University of Buenos Aires and CONICET. E.V. was supported by the Spanish Ministry of Science, Innovation and Universities (grant nos. PID2022-140398NA-I00 and CNS2024-154579).Author informationAuthors and AffiliationsEnvironmental Science and Engineering, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi ArabiaLucio Biancari, Emilio Guirado & Fernando T. MaestreIFEVA, Facultad de Agronomía, Universidad de Buenos Aires, CONICET, Buenos Aires, ArgentinaGastón R. Oñatibia, Laura Yahdjian & Martín R. AguiarCátedra de Ecología, Departamento de Recursos Naturales y Ambiente, Facultad de Agronomía, UBA, Buenos Aires, ArgentinaGastón R. Oñatibia, Laura Yahdjian & Martín R. AguiarAix Marseille Univ, CNRS, Avignon Université, IRD, IMBE, Aix-en-Provence, FranceYoann Le Bagousse-PinguetUniversité Clermont Auvergne, INRAE, VetAgro Sup, Unité Mixte de Recherche Ecosystème Prairial, Clermont-Ferrand, FranceNicolas GrossDepartamento de Ciencias Agrarias y Medio Natural, Escuela Politécnica Superior, Instituto Universitario de Investigación en Ciencias Ambientales de Aragón, Universidad de Zaragoza, Huesca, SpainHugo SaizInstitute of Plant Sciences, University of Bern, Bern, SwitzerlandHugo SaizCentre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, AustraliaDavid J. EldridgeDepartamento de Biodiversidad, Ecología y Evolución, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, Madrid, SpainEnrique ValenciaDepartamento de Suelos, Biosistemas y Ecología Agroforestal, Misión Biológica de Galicia, Pontevedra, SpainXoaquín MoreiraInstituto Universitario de Investigación en el Olivar y el Aceite de Oliva-INUO, Universidad de Jaén, Jaén, SpainVictoria OchoaInstituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, SpainBeatriz Gozalo & Jaime Martínez-ValderramaGrupo de Ecoloxía Animal, Universidade de Vigo, Vigo, SpainSergio AsensioInstituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, SpainCésar PlazaDepartment of Agricultural and Food Chemistry, Faculty of Sciences, Universidad Autónoma de Madrid, Madrid, SpainCésar PlazaDepartamento de Ingeniería y Morfología del Terreno, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid, SpainMiguel García-GómezUniversidad Nacional de Luján-CONICET, Luján, ArgentinaJuan J. GaitánEstación Experimental de Zonas Áridas, CSIC, Almería, SpainJaime Martínez-ValderramaDepartamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Móstoles, SpainBetty J. MendozaAuthorsLucio BiancariView author publicationsSearch author on:PubMed Google ScholarGastón R. OñatibiaView author publicationsSearch author on:PubMed Google ScholarYoann Le Bagousse-PinguetView author publicationsSearch author on:PubMed Google ScholarNicolas GrossView author publicationsSearch author on:PubMed Google ScholarLaura YahdjianView author publicationsSearch author on:PubMed Google ScholarMartín R. AguiarView author publicationsSearch author on:PubMed Google ScholarHugo SaizView author publicationsSearch author on:PubMed Google ScholarDavid J. EldridgeView author publicationsSearch author on:PubMed Google ScholarEnrique ValenciaView author publicationsSearch author on:PubMed Google ScholarXoaquín MoreiraView author publicationsSearch author on:PubMed Google ScholarVictoria OchoaView author publicationsSearch author on:PubMed Google ScholarBeatriz GozaloView author publicationsSearch author on:PubMed Google ScholarSergio AsensioView author publicationsSearch author on:PubMed Google ScholarCésar PlazaView author publicationsSearch author on:PubMed Google ScholarEmilio GuiradoView author publicationsSearch author on:PubMed Google ScholarMiguel García-GómezView author publicationsSearch author on:PubMed Google ScholarJuan J. GaitánView author publicationsSearch author on:PubMed Google ScholarJaime Martínez-ValderramaView author publicationsSearch author on:PubMed Google ScholarBetty J. MendozaView author publicationsSearch author on:PubMed Google ScholarFernando T. MaestreView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: L.B., F.T.M., Y.L.B.P., N.G., G.R.O., L.Y. and M.R.A. Methodology: F.T.M., N.G., Y.L.B.P., D.J.E. and H.S. Investigation: F.T.M., Y.L.B.P., N.G., H.S., D.J.E., E.V., X.M., V.O., B.G., S.A., C.P., E.G., M.G.G., J.J.G., J.M.V., B.J.M., G.R.O. and L.Y. Formal analysis: L.B., G.R.O., H.S., Y.L.B.P. and N.G. Writing—original draft: L.B., F.T.M., G.R.O., L.Y. and M.R.A. Writing—review and editing: L.B., F.T.M., G.R.O., M.R.A., L.Y., X.M., E.V., H.S., D.J.E., C.P., N.G., Y.L.B.P., J.M.V., V.O., B.G., S.A., E.G., M.G.G., J.J.G. and B.J.M. Supervision: F.T.M.Corresponding authorCorrespondence to
Lucio Biancari.Ethics declarations

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Nature Ecology & Evolution thanks Yongfei Bai, Timm Döbert and Dafeng Hui for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Location of the 73 experimental sites surveyed.Background colors represent aridity index for drylands (areas with an aridity index [mean annual precipitation/potential evapotranspiration] lower than 0.65). Pictures illustrate examples of low grazing plots (left) and high grazing plots (right) across four sites. Photo credits: Matthew Bowker (USA), Juan J. Gaitan (Argentina), Alice Nunes (Portugal), David J. Eldridge (Australia).Extended Data Fig. 2 Effects of grazing pressure on species richness (A), Shannon diversity index (B), and Pielou evenness index (C).Mean and 95% confidence intervals are shown (n = 73 sites, each representing a pair of low- and high-grazing plots). Differences between grazing pressures were evaluated using paired t tests (two-sided). For panel A (richness): t = 1.19, P = 0.238, mean difference = −0.99 [95 % CI = −2.64 to 0.67]. For panel B (diversity): t = 1.34, P = 0.183, mean difference = −0.096 [95 % CI = −0.237 to 0.046]. For panel C (evenness): t = 0.90, P = 0.373, mean difference = −0.022 [95 % CI = −0.070 to 0.027]. None of the tests were statistically significant (P > 0.05).Extended Data Fig. 3 Importance of predictor variables to explain the response of vegetation cover to increasing grazing pressure (resistance) including (A) Shannon’s diversity index, and (B) Pielou’s evenness index.Importance is based on the sum of Akaike weights of all models where each predictor is present using a multimodel inference approach. MAP = mean annual precipitation, MAT = mean annual temperature, SF = soil fertility, SAC = soil sand content, DIV = Shannon’s diversity index, EVE = Pielou’s evenness index, RWC = relative woody cover, FQ = forage quality, HR = herbivore richness, and LS = dominant livestock species.Extended Data Fig. 4 Effects of key predictors on the response to increasing grazing pressure (resistance).Structural equation model showing the relationships among aridity (estimated as 1-Aridity Index), herbivore richness, soil sand content, relative woody cover, latitude, plant species richness, and resistance (estimated as a log response ratio: ln[vegetation cover high grazing pressure/vegetation cover low grazing pressure]). Numbers on arrows are fully standardized path coefficients. Blue and red arrows indicate positive and negative relationships, respectively. Asterisks indicate significance level: ** p<0.01 and *** p<0.001.Supplementary informationSupplementary InformationSupplementary Figs. 1–8 and Tables 1 and 2.Reporting SummaryPeer Review FileRights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleBiancari, L., Oñatibia, G.R., Le Bagousse-Pinguet, Y. et al. Plant diversity enhances ecosystem resistance to increasing grazing pressure in global drylands.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-025-02952-9Download citationReceived: 22 May 2025Accepted: 25 November 2025Published: 05 January 2026Version of record: 05 January 2026DOI: https://doi.org/10.1038/s41559-025-02952-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractThe photovoltaic (PV) fishery breeding model integrates the generation of solar energy with aquaculture, yet its impacts on aquatic organisms remain poorly understood. This study investigated how PV panel shading affects the intestinal microbial ecosystem of Litopenaeus vannamei. We conducted a controlled 80-day experiment comparing shrimp reared under PV panels (ZG group) versus those reared in traditional open ponds (CK group), with quadruplicate 800 m² ponds per group under standardized conditions (80 shrimp/m², salinity 15–18‰). High-throughput 16 S rRNA sequencing was employed to analyze microbial composition, diversity, and predicted functional profiles. The growth data were collected daily during the initial 20-day period and subsequently at five-day intervals thereafter. The results demonstrate that the ZG group exhibited significantly reduced body length compared to the CK group after 20 days of culture (P < 0.05), while body weight was significantly lower after 16 days (P < 0.05).‌ The results of the intestinal microbiota analysis showed that Proteobacteria and Firmicutes were the main components of the intestinal microbiota in the CK and ZG groups, while Oceanobacillus and Candidatus_Electronema were present as indicator species in the CK and ZG groups, respectively. Analysis of the Chao1 index and Shannon index revealed no significant differences in either the diversity or evenness of the intestinal microbiota of L. vannamei among the experimental groups. In addition, significant differences between the groups were detected by the β-diversity analysis. A predicted bacterial function analysis also revealed significant differences in functional abundance between the two groups. This study provides critical insight into how PV shading alters shrimp microbiota and growth performance, offering practical guidance for optimizing sustainable PV-aquaculture integrated systems.

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Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (CRA024106) that are publicly accessible at [https://ngdc.cncb.ac.cn/gsa](https:/ngdc.cncb.ac.cn/gsa) .
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Download referencesFundingThis research was supported by Innovation of High Quality Fish Breeding Materials and Methods and Selection of New Varieties (Breeding Research Project) (2021YFYZ0015) and Sichuan Freshwater Fish Innovation Team of the National Modern Agricultural Industrial Technology System (SCCXTD-2025-15). In addition, We would like to thank Tongwei New Energy Co., Ltd. For their financial support in this study.Author informationAuthors and AffiliationsFisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute), Chengdu, Sichuan, ChinaZhongmeng Zhao, Han Zhao, Huadong Li, Yuanliang Duan, Zhipeng Huang, Jian Zhou & Qiang LiTongwei New Energy Co., Ltd, Chengdu, Sichuan, ChinaXingyu Chen & Yongshuang WangAuthorsZhongmeng ZhaoView author publicationsSearch author on:PubMed Google ScholarXingyu ChenView author publicationsSearch author on:PubMed Google ScholarYongshuang WangView author publicationsSearch author on:PubMed Google ScholarHan ZhaoView author publicationsSearch author on:PubMed Google ScholarHuadong LiView author publicationsSearch author on:PubMed Google ScholarYuanliang DuanView author publicationsSearch author on:PubMed Google ScholarZhipeng HuangView author publicationsSearch author on:PubMed Google ScholarJian ZhouView author publicationsSearch author on:PubMed Google ScholarQiang LiView author publicationsSearch author on:PubMed Google ScholarContributionsZ.Z.M., and L.Q. conceived and designed research. Z.Z.M., Z.H., W.Y.S, and C.X.Y. conducted experiments. Z.Z.M., Z.H., L.H.D., D.Y.L., H.Z.P., Z.L., and Z.J. analyzed data. Z.Z.M., C.X.Y., and L.Q. wrote the manuscript. All authors read and approved the manuscript.Corresponding authorsCorrespondence to
Xingyu Chen or Qiang Li.Ethics declarations

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All animal handling procedures were approved by the Animal Care and Use Committee of the Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (20220323002 A), following the recommendations in the U.K. Animals (Scientific Procedures) Act, 1986. At the same time, all methods were carried out by relevant guidelines and regulations.

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Reprints and permissionsAbout this articleCite this articleZhao, Z., Chen, X., Wang, Y. et al. Effects of the photovoltaic fishery breeding model on intestinal microbiota structure and diversity in Litopenaeus vannamei.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34429-xDownload citationReceived: 17 October 2025Accepted: 29 December 2025Published: 04 January 2026DOI: https://doi.org/10.1038/s41598-025-34429-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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Keywords
Litopenaeus vannamei
The photovoltaic fishery breeding modelIntestinal microbiotaStructural compositionDiversity More

AbstractThe predatory mite, Neoseiulus baraki Athias-Henriot has been reported from the Asia, Africa and Americas, frequently in association with eriophyid and tetranychid mites, these are the most important pests of fig trees in different parts of the world. The objective of our study was to examine the influence of different diets on biological characteristics and life table parameters of the predatory mite Neoseiulus baraki under laboratory conditions. All trials were conducted on fig leaf discs in an incubator at 33 ± 2 °C, 55 ± 5% RH, and a photoperiod of 12:12 (L: D) h. As food sources for the predatory mite, nymphal stages of fig bud mite Aceria ficus (Cotte) (Acari: Eriophyidae), different life stages of two spotted spider mite Tetranychus urticae Koch (Acari: Tetranychidae), corn pollen Zea mays L. and citrus pollen Citrus aurantium L. were selected. The results show that food type did not significantly effect on N. baraki survival; it varied between 95 and 98%. Development time was significantly shorter for N. baraki females fed on A. ficus (5.17 ± 0.16 days) than T. urticae (6.49 ± 0.31 days) or corn pollen (6.72 ± 0.20 days) or citrus pollen (6.91 ± 0.30 days). Female longevity varied from 21.31 ± 2.09 to 27.43 ± 1.78 days; the maximum value was noted on a diet of A. ficus. The longest oviposition period and greatest value of fecundity was observed on A. ficus, followed by T. urticae, corn pollen and citrus pollen. The net reproduction rate (Ro), finite rate of increase (λ) and intrinsic rate of increase (rm) reached the highest value on A. ficus. Considering these results, in the absence or scarcity of the primary prey in the fig orchards, corn pollen or citrus pollen can be recommended as supplementary or an alternative food for N. baraki. Furthermore, N. baraki has promising qualities to suppress A. ficus and T. urticae populations and is suitable as biocontrol agents against these pests.

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Download referencesAcknowledgementsThe researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2026).FundingThis research was funded by Qassim University (QU-APC-2026).Author informationAuthors and AffiliationsDepartment of Plant Protection, College of Agriculture and Food, Qassim University, P.O. Box 6622, Buraydah, 51452, Saudi ArabiaMahmoud M. Al-AzzazyDepartment of Plant Production, College of Agriculture and Food, Qassim University, Buraydah, 51452, Saudi ArabiaSaleh S. AlhewairiniAuthorsMahmoud M. Al-AzzazyView author publicationsSearch author on:PubMed Google ScholarSaleh S. AlhewairiniView author publicationsSearch author on:PubMed Google ScholarContributionsThis manuscript was drafted by Mahmoud M. Al-Azzazy and Saleh S. Alhewairini. Laboratory work and statistical analysis were performed by Mahmoud M. Al-Azzazy and Saleh S. Alhewairini. All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to
Mahmoud M. Al-Azzazy or Saleh S. Alhewairini.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleAl-Azzazy, M.M., Alhewairini, S.S. Influence of different diets on biological characteristics and life table parameters of the predatory mite, Neoseiulus baraki Hughes (Acari: Phytoseiidae).
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34143-8Download citationReceived: 15 November 2025Accepted: 24 December 2025Published: 04 January 2026DOI: https://doi.org/10.1038/s41598-025-34143-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsBiological control
Aceria ficus

Tetranychus urticae
Alternative food More

Abstract

The complete mitochondrial genome of Clypeaster virescens was sequenced and analyzed to clarify its genomic features and evolutionary placement within Echinoidea. The 15,781 bp circular mitogenome encoded 37 mitochondrial genes, including 13 protein-coding genes, 22 tRNA genes, and 2 rRNAs, along with one control region. The nucleotide composition of the mitochondrial genome exhibits a high A + T content, with negative A-T skew and G-C skew. Using a 35-taxon dataset (34 echinoids and one holothuroid outgroup), phylogenetic analyses based on the complete mitochondrial genome robustly placed C. virescens within a well-supported Clypeasteroida clade alongside S. mai and A. mannii. The recovered topology also resolved major echinoid orders with strong support, including the early divergence of Echinothurioida and Diadematoida and the close relationship between Clypeasteroida and Spatangoida. These findings provide the first complete mitogenome for C. virescens, expand available molecular resources for Clypeasteroida, and establish a stable phylogenetic framework for future evolutionary and comparative studies on irregular echinoids.

Data availability

The data that support the findings of this study are freely available in GenBank of NCBI (https://www.ncbi.nlm.nih.gov/), with accession number PQ838327.
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Download referencesFundingThe study was supported by the Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes (2024J002); National Natural Science Foundation of China (NSFC) (NO.42576115); Zhejiang Provincial Natural Science Foundation of China (LY22D060001&LY20C190008); Key research and development projects in Xizang (XZ202301ZY0012N).Author informationAuthors and AffiliationsMarine Science and Technology College, Zhejiang Ocean University, Zhoushan, 316022, ChinaJinghua Wu, Mingzhe Han, Luxiu Gao, Shuaishuo Kang, Xinyi Niu, Bingjian Liu & Tianming WangNational Engineering Laboratory of Marine Germplasm Resources Exploration and Utilization, Zhejiang Ocean University, Zhoushan, 316022, ChinaLuxiu GaoAuthorsJinghua WuView author publicationsSearch author on:PubMed Google ScholarMingzhe HanView author publicationsSearch author on:PubMed Google ScholarLuxiu GaoView author publicationsSearch author on:PubMed Google ScholarShuaishuo KangView author publicationsSearch author on:PubMed Google ScholarXinyi NiuView author publicationsSearch author on:PubMed Google ScholarBingjian LiuView author publicationsSearch author on:PubMed Google ScholarTianming WangView author publicationsSearch author on:PubMed Google ScholarContributionsJHW, BJL and TMW conceived and designed the research. JHW, MZH, LXG, SSK, XYN, BJL and TMW conducted experiments, analyzed data, and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to
Bingjian Liu or Tianming Wang.Ethics declarations

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All international, national, and institutional guidelines for the care and use of animals were followed.

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Reprints and permissionsAbout this articleCite this articleWu, J., Han, M., Gao, L. et al. The first complete mitochondrial genome and phylogenetic analysis of Clypeaster virescens (Clypeasteroida, Clypeasteridae).
Sci Rep (2026). https://doi.org/10.1038/s41598-025-33261-7Download citationReceived: 27 September 2025Accepted: 17 December 2025Published: 04 January 2026DOI: https://doi.org/10.1038/s41598-025-33261-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsClypeasteroida
Clypeaster virescens
MitogenomePhylogenetic More

AbstractUrbanization and low-carbon development are critical issues of global concern. As urbanization has reached its middle to late stages, cities face the dual pressures of development and environmental challenges. This study constructed a theoretical framework for urban vitality in six dimensions: social, economic, cultural, environmental, spatial, and perceptual. Using methods such as spatial syntax, entropy-weighted TOPSIS, deep learning models, and geographic detectors, we analysed the distribution characteristics of urban vitality in Yantai’s central area, explored how vitality-contributing factors influenced carbon emissions, and elucidated the association of urban vitality with carbon emissions. The results indicated that (1) urban vitality exhibited a multicentred distribution pattern of “low in the hinterland—high along the coast”; (2) significant differences existed in the impacts of various vitality dimensions on urban carbon emissions; (3) different urban vitality factors have varying levels of explanatory power regarding the spatial distribution of carbon emissions, with maximum building height exhibiting the strongest explanatory power, while the selection degree shows the weakest; and (4) the interactions between these factors typically demonstrate a two-factor enhancement, with the interaction between maximum building height and integration having the most significant effect on urban carbon emissions. This study innovatively integrates three-dimensional spatial and cultural perception perspectives, addressing the biases found in previous research that represented urban vitality from a singular viewpoint. It provides a more comprehensive framework and methodology for evaluating urban vitality, and the findings can offer recommendations for building low-carbon, high-vitality, and sustainable urban environments.

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Download referencesAcknowledgementsWe would like to extend our heartfelt gratitude to Dr. Jiang Hongqiang from Ludong University for his invaluable technical guidance. Additionally, we express our deepest appreciation to the anonymous reviewers and editors for their meticulous work and thoughtful suggestions, which have greatly enhanced this paper.FundingThis research was funded by the Youth Innovation Team Project in Universities of Shandong Province, grant number (2022RW026); the National Natural Science Foundation of China, grant number (42377207); the national natural science foundation of China, grant number (42207553); the Shandong Taishan Scholar Young Expert Program (tsqn202306240); the Shandong Provincial Humanities and Social Sciences Project, grant number (2022-YYGL-31); the general project of Undergraduate Teaching Reform in Shandong Province, grant number (Z2021177); the Key project of Research and Development Program in Shandong Province, grant number (2022RKY07006); the open foundation of State Key Laboratory of Lake Science and Environment , grant number (2022SKL005); the open foundation of State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, CAS, grant number (SKLLQG2024); and Innovation Project for graduate students of Ludong University (Grant Number: IPGS2025-060).Author informationAuthors and AffiliationsSchool of Resources and Environmental Engineering, Ludong University, Yantai, 264025, ChinaYige Zhang, Xiaohui Wang, Longsheng Wang, Yanfeng Zhang & Junxi SongCollege of Architecture and Urban Planning, Tongji University, Shanghai, 200000, ChinaYu YeSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, 264025, ChinaGuodong LiuNanjing Institute of Geography and Lake Research, Limnology of Chinese Academy of Sciences, Nanjing, 210044, ChinaShimou YaoAuthorsYige ZhangView author publicationsSearch author on:PubMed Google ScholarXiaohui WangView author publicationsSearch author on:PubMed Google ScholarYu YeView author publicationsSearch author on:PubMed Google ScholarLongsheng WangView author publicationsSearch author on:PubMed Google ScholarYanfeng ZhangView author publicationsSearch author on:PubMed Google ScholarJunxi SongView author publicationsSearch author on:PubMed Google ScholarGuodong LiuView author publicationsSearch author on:PubMed Google ScholarShimou YaoView author publicationsSearch author on:PubMed Google ScholarContributionsYige Zhang: Conceptualization, methodology, software, writing—original draft preparation and formal analysis. Xiaohui Wang: Conceptualization, validation, writing—original draft preparation and funding acquisition. Yu Ye: Software, validation and methodology. Longsheng Wang: Investigation, funding acquisition, writing—review and editing. Yanfeng Zhang: Investigation and data curation. Junxi Song: Investigation. Guodong Liu: Visualization. Shimou Yao: Conceptualization and supervision.Corresponding authorsCorrespondence to
Xiaohui Wang or Longsheng Wang.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleZhang, Y., Wang, X., Ye, Y. et al. Exploring the impact of urban vitality on carbon emission mechanisms using multi-source data.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-29624-9Download citationReceived: 09 February 2025Accepted: 18 November 2025Published: 03 January 2026DOI: https://doi.org/10.1038/s41598-025-29624-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsUrban vitalityUrban carbon emissionsHuman perceptionEntropy-weighted TOPSISOptimal parameter geographic detector More