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    Mechanisms of light harvesting complex proteins in photoprotection of the brown tide alga

    AbstractThe propensity of Aureococcus anophagefferens to form harmful brown tide blooms has been linked to rapid light responses, but the underlying molecular mechanisms remain elusive. Here, we find that two glutamic residues in plastid luminal and C terminal domains in light harvesting complex (LHC) proteins are crucial to the alga’s unique photoadaptation capacity. Specifically, we demonstrate that glutamate residues contribute to the induction of non-photochemical quenching (NPQ). Protein structure analysis further indicates that these acidic residues can form stable hydrogen bonds under protonation, causing changes in the secondary structure of LHC. Our data suggest that this is the initial action of amino acids under light-induced lumen acidification, which then drives the function of NPQ through a complex process. This photoprotection mechanism, along with low light adaptation, enables this alga to thrive throughout water columns with spatially contrasting and temporally fluctuating irradiance, with implications of bloom formation.

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    IntroductionThe ability to efficiently harvest light energy and protect against photodamage is crucial for the ecological success of phytoplankton. Aureococcus anophagefferens is widely distributed across the global oceans, causing large-scale brown tide blooms frequently observed in the coastal waters of the United States, South Africa, and China1,2,3,4. These outbreaks pose serious threats to shellfish farming, fishing, and tourism industries, adversely affecting local economies and ecosystems3. Brown tides typically erupt in relatively enclosed, shallow and turbid estuaries characterized by low light intensity, high organic matter concentrations, and abundant trace elements5.The competitive advantage of A. anophagefferens in low-light environments is critical for its ability to form brown tide blooms and derives from its unique cellular responses and gene regulation. This species achieves maximum growth rates under low light conditions6, enabled by its large repertoire of 62 light-harvesting protein genes, which are several times more abundant than in competing species, thereby enhancing light capture efficiency5,7. These adaptations demonstrate its exceptional light-harvesting abilities.Low-light-adaptive algae are typically more vulnerable to high-light stress8,9. A. anophagefferens, living in shallow waters, must cope with occasional oversaturated light intensities through diel cycles or vertical mixing. In algae, photoprotection is achieved by dissipating excess light energy, mostly through non-photochemical quenching (NPQ), a mechanism shared by both algae and vascular plants10. Energy-dependent fluorescence quenching (qE), triggered by the proton gradient (ΔpH) across thylakoid membranes, represents the fastest NPQ component11 and typically associates with the xanthophyll cycle12,13,14. In addition, qE is related to the protein function of photosystem II subunit S (PsbS) in vascular plants, which belongs to the light-harvesting complex (LHC) family15. Photosynthetic algae exhibit similar photoprotection mechanisms, such as the LhcSR protein in green algae and the Lhcx protein in diatoms16,17. However, the specific process by which the ΔpH ultimately triggers qE remains unclear. Diatoms utilize the xanthophyll cycle to respond to ΔpH but cannot directly trigger qE12,18,19. The decreased pH in the thylakoid lumen triggers qE through the protonation of proteins, a process sensed by acidic amino acids15,19.In vascular plants, the PsbS protein with four transmembrane helices senses the ΔpH through two glutamate residues symmetrically positioned on the two loops on the lumenal side20. Although the PsbS gene is also present in green algae, its function remains unknown21. The LhcSR or Lhcx proteins in algae contain acidic amino acid residues on the lumenal side, but unlike the PsbS protein, they both have a three-transmembrane helix structure, leaving their mechanism for sensing the proton gradient unclear. In C. reinhardtii, the D117, E221 and E224 of LhcSR3 are essential for NPQ induction19. However, the equivalent residues (D95 and E205) in Lhcx1 of the diatom Phaeodactylum tricornutum are not required for inducing NPQ22. Therefore, while acidic amino acids potentially play an important role, there is no consensus on their significance.Here, we isolate and culture A. anophagefferens from a brown tide bloom, investigating how its light-harvesting system provides competitive advantages that enable the alga to form blooms. Brown tide is one of the harmful algal blooms creating devastating impacts on the aquatic ecosystem, the coastal environment, and economy, as well as public health concerns. Focusing on the numerous LHC proteins of A. anophagefferens, we examine the roles of these proteins, particularly their association with brown tide outbreaks in estuaries, and the underlying molecular mechanisms. We discover the critical roles of two glutamic residues in the LHC proteins of A. anophagefferens. Data from multiple experiments verify their connection to thylakoid acidification. In this study, we provide insight into the proton gradient response sites and common patterns of algal LHC proteins, which suggest significant implications for understanding the effects of light on brown tide blooms.Results and discussionThe strong adaptability of A. anophagefferens to light changesSimilar to populations in the US and Africa,A. anophagefferens frequently blooms in shallow estuaries of China, particularly in the Bohai Sea. We conducted year-round sampling in Bohai Sea from 2013 to 2014 (Fig. 1a). Surface light intensity varied considerably across sampling sites, with approximately one-third receiving less than 200 μmol photons m−2 s−1 (Fig. 1b). Light attenuation increased with depth: about two-thirds of sites recorded below 200 μmol photons m−2 s−1 at one meter depth, while 91.43% of sites fell below 100 μmol photons m−2 s−1 at two meters. Field surveys revealed that A. anophagefferens maintained a uniform vertical distribution, whereas competing phytoplankton species exhibited varying distribution patterns. Dinoflagellates tended to prefer the surface with stronger light intensity. In contrast, the light intensity preferences of diatoms varied among species, resulting in non-uniform vertical distributions (Fig. 1c). These findings demonstrate A. anophagefferens’ exceptional adaptation to light variability. Its ability to adapt to low light helps them survive in the light-deficient coastal waters5, while its uniform abundance from the sea surface to depths suggests strong photoprotection. This strong photo-plasticity is exceptional. The only comparable case, best to our knowledge, is the cyanobacterium Prochlorococcus, which differentiates into different ecotypes: low-light-adapted strains that thrive in the deep euphotic zone and high-light-adapted strains that thrive at the surface23. They adjust the light capture efficiency through special pigments (Chl b and divinyl Chl a) and proteins (Pcb proteins), enabling them to survive widely in the ocean24. However, studies have found that this niche differentiation involves genetic divergence. In coral symbiotic dinoflagellates, those adapted to high-light environments can avoid photodamage through photoprotective mechanisms, while those adapted to low-light environments are more susceptible to photoinhibition25. Among vascular plants, Hedera helix is sensitive to changes in light intensity and can rapidly induce non-photochemical quenching (NPQ) shortly after a slight increase in light intensity26. From this perspective, A. anophagefferens and H. helix share similar low-light response characteristics. Both species are well adapted to thrive in low-light environments, are sensitive to minor fluctuations in light intensity, and possess strong photoprotective mechanisms.Fig. 1: Distribution patterns of irradiance and dominant algal species in the brown tide outbreak area of the Bohai Sea, China.a Map of sampling stations. b Vertical profile of photosynthetically active radiation (PAR) in the study area. 35 independent replicates were performed. c Vertical distribution of abundance for dominant species at each site throughout the year. The black bars and white bars represent 1 m above the bottom and 1 m below the surface, respectively. In the boxplot, the interquartile range (IQR) is represented by the box, and the median is indicated by the center line. The whiskers extend up to 1.5 × IQR from the upper and lower quartiles. Source data are provided as a Source Data file.Full size imageHigh LHC gene abundance in A. anophagefferens with unique glutamic acid residues and evidence of their role in photoprotectionThe strong photoadaptation of A. anophagefferens observed from the uniform vertical abundance distribution is consistent with the high number of LHC protein genes (AaLHC) in its genome5. To examine the role of AaLHC proteins in the photoprotection, we conducted a response analysis for the 62 AaLHC found in A. anophagefferens to different irradiance environments. Two expression patterns emerged (Fig. 2a). The 11 AaLHC in Group I showed increased expression levels when transferred from low light to high light, and from darkness to light of white and blue spectra. In contrast, the 51 AaLHC in Group II exhibited the opposite trend, with expression levels decreasing under high light stress and only increasing after a longer period of transfer from darkness to a lighted environment. Among them, the four genes AaLhc27, AaLhc37, AaLhc42, and AaLhc52 in Group I were more sensitive to short-term exposure. Additionally, weighted gene co-expression network analysis (WGCNA) identified the 11 AaLHC almost all in the same module (blue module, Supplementary Tables 1, 2), which were associated with short-term high light stress and short-term exposure to white and blue light, suggesting that these genes respond rapidly to changes in the light environment (Fig. 2b). We further analyzed these four significantly responsive genes using qPCR. Results showed that their expression was upregulated immediately after cells were exposed to high light, and dropped quickly when the cultures were returned to a low light environment (Fig. 2c). These genes were also upregulated when the algal cultures were transferred from darkness to low light or from white light to blue light (light stress in comparison, which has a shorter wavelength and higher energy). This result suggests that AaLhc27, AaLhc37, AaLhc42, and AaLhc52, and potentially others in Group I AaLHC as well, likely play the same role in light response.Fig. 2: Apparent relationship between LHC gene expression, glutamic acid residues in LHC, and NPQ in A. anophagefferens.a Two gene transcription response patterns of AaLHC genes after the transfer from low light to high light, from darkness to white light, and from darkness to blue light. b The gene expression matrix of AaLHC after exposure to high light and low light for 1, 3, and 6 h, as well as the transfer from darkness to blue light and white light for 1 h and longer periods. Same modules have similar gene expression patterns. c The expression response of AaLhc27, AaLhc37, AaLhc42, and AaLhc52 genes in Group I to light changes, including the transfer from a low-light adapted environment to high light, from darkness to low light, and from white light to blue light, and transitioning between two different light environments. Values are shown as the mean ± s.d. of three biological replicates. The red, pink, gray, blue, and white bars represent high light, low light, darkness, blue light, and white light, respectively. d Simulated structures of AaLHCs, showing short distance between the glutamic acid residues (red) and details of the lumenal side of AaLhc27. n = 1 simulation e NPQ changes during 10 min of exposure to actinic high light (white bar) followed by 10 min of recovery in darkness (black bar) for the control and NH4Cl-added cultures. Arrows indicate the timing of NH4Cl addition. Values are shown as the mean ± s.d. of three biological replicates. Source data are provided as a Source Data file.Full size imageWe further analyzed their amino acid sequences to identify the reasons behind the differences in photoprotective capabilities between the two groups of AaLHC. We noted that the AaLHC proteins in Group I all had two conserved glutamic acid (E) residues (henceforth termed AaLhc2E), while AaLHC proteins in Group II had none (in most cases) or one (Supplementary Fig. 1). Further structural simulations revealed that these two E residues were located on the lumenal loop and the C-terminal domain within the thylakoid membrane (Fig. 2d). Moreover, the two glutamic residues were located very closely to each other (<3.0 Å apart) in all the 11 AaLhc2Es. The proximity potentially facilitates structural adjustments of the protein when they are protonated, as documented for LhcSR in the chlorophyte C. reinhardtii19. Based on the key role of the proton gradient (ΔpH) across the thylakoid membrane in NPQ, we manipulated ΔpH in A. anophagefferens by adding NH4Cl to the algal culture to disrupt ΔpH, following a previously reported protocol12. The results showed that without NH4Cl treatment, NPQ could be induced by light and relaxed in the dark. NH4Cl addition rapidly led to a NPQ decrease (Fig. 2e). This result indicates that potentially, the cross-membrane proton gradient may be modulated or sensed involving the two E residues in the luminal loop, thereby regulating NPQ.As the xanthophyll cycle is coupled with and crucial for NPQ11, we investigated the potential concerted action of them. We detected all xanthophyll pigments in A. anophagefferens, including trace amounts of violaxanthin (Vx), antheraxanthin (Ax), and zeaxanthin (Zx), and observed diadinoxanthin (Dd) de-epoxidation to diatoxanthin (Dt) under high light irradiation (Supplementary Fig. 2a). Furthermore, treatment with NH4Cl inhibited the de-epoxidation state (DPS), indicating that the Dd-Dt cycle, rather than the Vx-Ax-Zx cycle, responds to pH changes. In addition, the conversion of fucoxanthin (Fx) to 19′-butanoyloxyfucoxanthin (19′-BFx)7 also appeared to occur under high light but was unaffected by ΔpH (Supplementary Fig. 2b), indicating that the role of Fx-to-19′-BFx conversion in photoprotection is independent of the ΔpH-modulated NPQ pathway.Similarity of AaLHC with glutamic acid residues to the photoprotective Lhcx/LhcSR in other algaeAs Lhcx/LhcSR is the best known photoprotective protein complex and shared by Archaeplastids, Stramenopiles, Alveolates and Rhizaria27, we compared AaLHCs with it and other light-harvesting proteins. Our phylogenetic analysis results showed that the 62 AaLHC proteins were divided into three categories, which clustered with Lhcf, Lhcr, and Lhcx/LhcSR, respectively (Fig. 3a). Among them, all the 11 AaLhc2E proteins clustered with Lhcx/LhcSR. This indicates that the two E residues are ancient and play a crucial functional role.Fig. 3: The phylogeny, evolution and sequence characteristics of LHCs in A. anophagefferens and other algae.a Phylogenetic tree of LHC-related proteins. Blue font depicts Lhcf, orange font Lhcr, and red font Lhcx/LhcSR. Glutamic residues are represented by red semicircles. Bootstrap values greater than 70% and 90% are indicated by black and gray circles, respectively. Abbreviation explanation: Aa, Aureococcus anophagefferens; Bn, Bigelowiella natans; Ce, Chlamydomonas eugametos; Cr, Chlamydomonas reinhardtii; Cc, Cyclotella cryptica; Ig, Isochrysis galbana; Km, Karlodinium micrum; Mv, Mesostigma viride; Msp, Micromonas sp.; Ol, Ostreococcus lucimarinus; Ot, Ostreococcus tauri; Pt, Phaeodactylum tricornutum; Pp, Physcomitrella patens; So, Scenedesmus obliquus; Tp, Thalassiosira pseudonana. b Protein sequence alignment of Lhcx/LhcSR from 15 Plantae species, with the positions of two glutamic residues highlighted in red and pink.Full size imageAlignment analysis of the 11 AaLhc2E proteins and the Lhcx/LhcSR proteins (Fig. 3a) revealed that transmembrane helices 1 and 3 were relatively conserved (Fig. 3b). The two conserved E residues of the three-transmembrane Lhcx/LhcSR were located at the ends of the helices extending into the lumen. Differently, the two E residues (E122 and E226) in the four-transmembrane PsbS of vascular plants are both located on the loop in the thylakoid lumen28. So far, it has been unclear whether algal LHC proteins all possess conserved acidic residues that respond to pH and are crucial to NPQ. In C. reinhardtii, residues D117, E221, and E224 in LhcSR3 are considered essential for inducing NPQ19. In the model diatom P. tricornutum, residues with potential protonation functions (D95 and E205 in Lhcx1) appear to be unnecessary for NPQ, while tryptophan residues located in the transmembrane helices may be essential22,29. Therefore, it is necessary to experimentally demonstrate that the two conserved glutamate residues in AaLhc2E are directly involved in inducing NPQ in A. anophagefferens. Such experimental evidence is presented in the next section.Crucial role of E residues in the function of AaLHC for NPQAs a functional genetic system is not yet available for A. anophagefferens, we chose a diatom model for the functional demonstration of AaLhc2Es. Based on the conservation of transmembrane helices 1 and 3 of the Lhcx/LhcSR among photosynthetic species, we compared the LHC protein sequences of A. anophagefferens and the model diatom Thalassiosira pseudonana. Results showed that the transmembrane regions of LHC proteins between these two species are also highly similar (Fig. 4a). We used T. pseudonana as the model to determine the function of a AaLhc2E for photoprotection. We overexpressed AaLhc27 in T. pseudonana (Tp_AaLhc27E). To verify the function of the E residues, we mutated E102 and E210 in AaLhc27E to Q102 and Q210, respectively, and included them in the heterologous expression experiment (Tp_AaLhc27Q) (Fig. 4b, Supplementary Fig. 3). We chose to replace the glutamic acid (E) residues with glutamine (Q) to minimize biochemical alterations except for the charge change. As a result, the AaLhc27E-expressing T. pseudonana strain (Tp_AaLhc27E) exhibited a greater NPQ under high light, while the AaLhc27Q-expressing strain (Tp_AaLhc27Q) displayed a smaller change in NPQ induction compared to the wild-type (Fig. 4c), indicating that the E residues play a crucial role in the function of AaLHC27 for NPQ. The capacity of NPQ is closely related to the expression of photoprotective LHCs and xanthophyll pigments bound to them30,31. Compared to the wild-type and Tp_AaLhc27Q, the DPS of Tp_AaLhc27E increased more rapidly upon light induction (Fig. 4d), indicating that the xanthophyll cycle is involved in the AaLHC27E-based NPQ regulation. Notably, the centric diatom T. pseudonana exhibits lower DPS values than the pennate diatom P. tricornutum32. Apart from the influence of growth conditions and light history33, this difference may result from the distinct topologies of NADPH-dehydrogenases (type-1 and type-2)32. The suppression of DPS elevation upon DCMU addition (Fig. 4d) suggests that T. pseudonana likely possesses a non-membrane-spanning type-2 NADPH dehydrogenase, which is probably not involved in generating the lumenal proton gradient that facilitates DDE-mediated de-epoxidation. Whether this enzyme releases protons on the stromal side and, together with abundant NADPH, promotes the epoxidation of Dt also warrants further investigation. Furthermore, our growth measurements indicated that the promoted high light adaptation by AaLHC27E enabled the AaLhc27E-expressing T. pseudonana to grow better under higher light conditions (200 and 800 µmol photons m−2 s−1) compared to the AaLhc27Q-expressing strain and the wild type strain (Fig. 4e). Given that T. pseudonana was transformed with only one AaLhc2E, whereas A. anophagefferens possesses eleven AaLhc2Es, this suggests that the multiple AaLhc2Es in A. anophagefferens may provide greater advantages in dissipating excess energy and growing well under intense and fluctuating light conditions.Fig. 4: Cross-species expression of AaLhc27 and analysis of NPQ-inducing function and growth rate based on the conservation of LHC protein transmembrane sequences.a Comparison of transmembrane sequences of LHC proteins from A. anophagefferens and T. pseudonana. b Schematic diagram of the expression of AaLhc2E gene, AaLhc27E, and its mutant AaLhc27Q in T. pseudonana. c NPQ induction differences in mutants and wild type. Values are shown as the mean ± s.d. of three biological replicates. d De-epoxidation state (DPS) of T. pseudonana and its two mutants, together with their responses after DCMU addition. Values are shown as the mean ± s.d. of three biological replicates. e Growth rates in T. pseudonana mutants and wild type under different light intensities. Values are shown as the mean ± s.d. of three biological replicates. Source data are provided as a Source Data file.Full size imageTo shed light on how proton gradient dynamics may alter AaLhc2E structures and modulate NPQ, we conducted fourier transform infrared (FTIR) spectroscopy analysis of AaLHC27 under varying pH conditions. D2O (D = deuterium) detergent buffer was used to avoid interference from O–H bending absorption, which overlaps with the Amide I region corresponding to the C = O vibrations in the protein backbone. Therefore, the FTIR results will be discussed in terms of pD instead of pH (pD = pH + 0.4). At pD 7.5, the FTIR spectrum of AaLHC27E exhibited a prominent band at 1570 cm−1, which originated from the stretching vibrations of the deprotonated carboxylic acid in the deprotonated E residues (Fig. 5a)34. Notable alteration in the FTIR spectrum was detected at pD 5.0, where a broad positive band in the region of 1700 to 1750 cm−1 was observed, indicative of the presence of deuterated carboxyls (COOD). The characteristic of protonated (deuterated) carboxyls suggests that nearly all E residues were protonated at pD 5.0. In addition, comparing the FTIR spectra at pD 5.0 and pD 7.5 revealed changes in the Amide I region from 1600 to 1700 cm−1, where a negative band at 1640 cm−1 and a broad positive band at 1690 cm−1 indicated a change in the Amide I band, and hence a conformational change in the secondary structure of the AaLHC27E. In contrast, only a minimal change was detected in the FTIR spectra of the AaLHC27Q mutant under different pD conditions. Clearly, mutating the two E residues to Q abolished proton response sensitivity while the general secondary structure of the protein was not altered.Fig. 5: FTIR spectroscopy and pH responses of AaLhc2E.a FTIR spectra of AaLHC27E and AaLHC27Q at pD 7.5 (blue), pD 5.0 (red), and the difference spectra of pD 5.0 minus 7.5 (black). b Structural presentation of stable interactions between lumenal E residues (yellow) and other residues (blue) in an acidic environment. The distance between amino acid residues was indicated by red dashed lines, and hydrogen bonds were represented by purple dashed lines. c Interaction between E residues and other amino acid residues in 11 AaLhc2E proteins at acidic pH. d Schematic diagram of the response of E residues under the influence of the proton gradient. The left and right figures represent the neutral and acidic environments, respectively. Source data are provided as a Source Data file.Full size imageAt acidic pH, the E102 and E210 of AaLHC27 were in close proximity to I217 and I116 (2.4 Å), respectively, which likely contributes to the formation of stable interactions (Fig. 5b). Similarly, the two E residues of AaLhc2E proteins could form stable hydrogen bonds with amino acid residues on the lumenal side, and these residues are located in the C-terminal domain and the lumenal loop, respectively (Fig. 5c). Therefore, in an acidic lumenal environment, the glutamic residues on helix1 and helix3 become protonated and can easily approach the amino acid residues of the other within a very short range to establish stable interactions, thereby leading to changes in the protein’s secondary structure (Fig. 5d). Through 1000 ns of constant pH molecular dynamics (CpHMD) simulations, the dynamic stability changed little from a neutral environment to an acidic environment (Supplementary Fig. 4, Supplementary Tables 3–6). To further observe the major motion states of the four systems (AaLHC27E_pH_4.5, AaLHC27E_pH_7, AaLHC27Q_pH_4.5, and AaLHC27Q_pH_7), the slowest motions were captured using time-lagged independent component analysis (tICA), and Markov state models (MSMs) were built to examine the differences in state transitions across the systems. The MSM models showed that each state transition varied in time and conformation across the four systems. Specifically, in the AaLHC27E system, both neutral and acidic conditions led to the formation of three metastable states, while the AaLHC27Q system formed four metastable states under both conditions. The most significant structural difference among the four systems was in the lumenal side (e.g., residues 118-130). Under both acidic and neutral conditions, the AaLHC27Q system exhibited a clear helix secondary structure. In contrast, the AaLHC27E system exhibited a partial loop structure along with the helix structure under both conditions (Supplementary Figs. 5, 6).Our results clearly indicate the crucial role of the two luminal E residues in AaLHC27 for NPQ in A. anophagefferens. Given the highly similarity in sequence and simulated structure, including the spatial proximity of the two glutamate residues, the other 10 AaLhc2E proteins likely serve the same function. However, experimental proof remains to be obtained in future research. Further research is also required to understand if the structurally conserved E residues in LHCs in other algae play the same role. It is worth noting that the total of 11 AaLhc2E proteins (and 62 total LHC proteins) in A. anophagefferens far exceeds that of many other algae but is comparable to some lineages living in highly light-variable environments. The intertidal brown alga Ectocarpus siliculosus and the polar ocean-adapted diatom Fragilariopsis cylindrus both contain 11 Lhcx-like/Lhcx proteins35,36. The intertidal environment undergoes extreme light changes daily. The polar environment experiences complete light to complete dark annually, but F. cylindrus flourishes and forms blooms there37. Emiliania huxleyi, which thrives and forms large-scale blooms in habitats ranging from the equator to the subarctic, possesses even more (17) LHCs with photoprotective functions38, although the apparent plasticity within species can potentially be attributed to genetic differentiation into distinct geographic populations. Our findings provide insights into a molecular mechanism by which LHCs mediate photoprotection in A. anophagefferens, with implications in other variable light-adapted lineages.Our results on AaLhc2Es also have significant ecological implications for the brown tide alga A. anophagefferens, which thrives and forms massive blooms in turbid coastal waters with dynamic light radiation, high organic matter and metals concentrations5. Harmful algal blooms occur when the causative alga outgrows co-existing species due to competitive advantages in photoenergy harvesting (E), nutrient acquisition (N), defense against environmental stress, grazing, and microbial attacks (D), and sometimes sexual reproduction (S)- known as ENDS drivers- with photoprotection being one significant D component39. While the high number of LHC genes in A. anophagefferens have been linked to the species’ ability to grow in a dim light environment5, we demonstrate here that at least some are crucial for protection against photodamages. Combined with the substantial genetic capacity for utilizing organic nutrients, trace metal, and vitamins5,40,41, the large LHC protein repertoire means A. anophagefferens is strongly equipped in “END” and poised to thrive in its coastal habitat and form brown tides.MethodsSampling design and algal cultivationA total of 20 phytoplankton sampling sites were established in the brown tide outbreak area (the northwestern part of the Bohai Sea, China) (Fig. 1a, Supplementary Table 7). Samples were collected monthly from June 2013 to May 2014. At each site, phytoplankton samples were collected from the surface layer (1 m) and the bottom layer (1 m above seafloor), and preserved in 1.5% Lugol’s solution. Phytoplankton species identification and counting were conducted under a microscope (Olympus CX31, Japan). Dominant species was classified based on the dominance index (Y ≥ 0.02). Light intensity was measured for 35 sampling sites at the water surface, 1 meter below the water surface, and at the bottom. Light intensities were measured with a PAR sensor (Onset HOBO MX2202, USA).A. anophagefferens used in this study was isolated from the coastal waters of Qinhuangdao in the Bohai Sea in July 2012 during a brown tide event. It was preserved in the algal collection of the Research Center of Harmful Algae and Marine Biology at Jinan University, China. Cultures were grown in artificial seawater supplemented with f/2 nutrients42 at 20 °C, under a light intensity of 50 μmol photons m−2 s−1 with a 12:12 light:dark cycle, and experiments were initiated during the logarithmic growth phase. T. pseudonana (CCMP 1335) was cultivated under the same conditions. Wild-type and two genotypes of T. pseudonana were maintained in semi-continuous cultures under 50, 200, and 800 μmol photons m−2 s−1. Cultures were diluted daily according to their respective growth rates. Steady state was reached at final dilution rates of 0.37, 0.60 and 0.50 d−1 under 50, 200 and 800 μmol photons m−2 s−1, respectively. To accurately calculate growth rate, one-mL samples were collected daily before and after dilution from each culture and fixed using Lugol’s solution. Cells were counted using a hemocytometer under an inverted microscope (BX53, Olympus, Tokyo, Japan). Growth rates (μ, in d−1) were calculated as: μ = (lnN2 – lnN1) / (t2 – t1), where N2 and N1 represent the cellular concentrations at t2 (before dilution on day 2) and t1 (after dilution on day 1), respectively.Experiment with different light conditions and fluorescence analysesDifferent light environments were used for the study of algal responses to light, including low light (LL, 50 μmol photons m−2 s−1), high light (HL, 800 μmol photons m−2 s−1), white light (WL, 100 μmol photons m−2 s−1), blue light (BL, 100 μmol photons m−2 s−1 at 450 nm), and darkness (D). The growth of T. pseudonana and its AaLhc-overexpressing strains were conducted under an irradiance of 50, 200, and 800 μmol photons m−2 s−1. The determination of NPQ capacity was conducted using a Phyto-PAM Phytoplankton Analyzer (Walz, Germany). After different algal strains were calibrated to the same chlorophyll a concentration, kinetic NPQ measurements were initiated. In the experiment on A. anophagefferens, NPQ was induced by 10 min of 764 μmol photons m−2 s−1, followed by a 10-min recovery phase in darkness to provide information on the relaxation kinetics. After 5 min high light exposure, 1 mM and 20 mM NH4Cl were added, respectively. In the experiment on T. pseudonana and its AaLhc-overexpressing mutants, NPQ was induced by 30-min exposure to a photon flux of 764 μmol photons m−2 s−1, followed by a 30-min recovery phase in the dark. The experiment was conducted with 3–5 parallel samples. NPQ was calculated as Fm/Fm’ −1.Pigment analysisA. anophagefferens in the exponential growth phase was collected for pigment detection at 0 h, 0.5 h, and 1 h under HL and LL irradiation. NH4Cl was added 25 min after HL exposure. Samples of T. pseudonana and its mutants in the exponential growth phase were taken at 0 h and 0.5 h under HL irradiation and after an additional 0.5 h in darkness. Under identical experimental conditions, 40 μM DCMU was added to inhibit linear electron transport. The samples obtained by filtration (25 mm GF/F membrane, 0.7 µm pore size) were immediately flash-frozen in liquid nitrogen and stored at −80 °C. Pure methanol was added for pigment extraction. Pigment analysis was performed using a 1200 Series HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with an automatic sampler (Agilent 1100 series G1329A). The system was fitted with a reversed-phase Inertsil C8 column (150 × 4.6 mm, 5 μm particle size; GL Science) and eluted with mobile phase A (methanol: acetonitrile: 0.25 M pyridine solvent, 50: 25: 25, v: v: v) and mobile phase B (methanol: acetonitrile: acetone, 20: 60: 20, v: v: v)43. Elution was performed following the method described in ref. 44. with a flow rate of 0.9 mL min-1.RNA-seqIn the high-light induction experiment, algae cells cultured under LL were transferred to a HL environment for 1 h, 3 h, and 6 h. Additionally, cells that had been kept in darkness for 12 h were transferred to WL (1 h and long-term) and BL (1 h and long-term). Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, US). After the total RNA was qualified, RNA-seq libraries was sequenced in BGI-tech (Shenzhen, China) using an Illumina NovaSeq 6000 (San Diego, CA, USA) at Sangon Biotech (Shanghai, China). Sequencing quality was assessed using FastQC and MultiQC43,45. For the significance of gene expression difference, a cut off of: qValue < 0.05 and |log2FC | ≥ 1 was used. RNA-seq data were uploaded to the NCBI Sequence Read Archive (SRA) database (PRJNA680884 and PRJNA787041).WGCNAThe gene expression matrix, which contained HL and LL (1 h, 3 h, and 6 h), was employed to perform the gene co-expression network analysis using WGCNA46, followed by the identification of the modules that clustered with LHC-encoding genes. Similarly, the gene co-expression network analysis was also performed within BL (1 h and long-term), WL (1 h and long-term) and D (darkness).Real-time quantitative PCR (RT-qPCR)In the high-light induction experiment, the process involves transferring from LL to HL for 0.5 h, 1 h, 2 h, and 3 h, then exposing to LL again (0.5 h, 1 h, 2 h, 3 h), followed by HL exposure (0.5 h, 1 h, 2 h, 3 h). For low light experiments, the transfer is from D to LL for 1 h, 2 h, and 3 h, with the LL + D group receiving LL exposure for 1 h followed by D for 1 h and 2 h. In the blue light induction experiment, the transfer is from WL to BL for 1 h of exposure, followed by 1 h of WL exposure. For the validation of the exogenous genes AaLhc27E and AaLhc27Q in T. pseudonana, HL exposure was applied to the algae cells cultured under LL conditions for 0.5 h, 1 h, and 3 h. Algal cells were collected by centrifugation and performed RNA extraction. RNA was reverse transcribed using the HiScript II 1st Strand cDNA Synthesis Kit (Vazyme-Biotect, China). Reverse transcription PCR was performed using 50 ng cDNA with the AceQ qPCR SYBR Green Master Mix (Vazyme-Biotect, China). RT-qPCR was performed with the CFX Connect Real-Time PCR Detection System (Bio-Rad, CA, USA). The 18S rRNA and ACT4 were used as reference genes for experiments with A. anophagefferens and T. pseudonana, respectively. The gene-specific primers used for this study were listed in Supplementary Table 8. Experiments were repeated three times independently.Prediction of the LHC protein structureAlphaFold3 [https://alphafoldserver.com/] was used to predict the three-dimensional structures. The 50 AaLHC prediction models are selected based on predicted template modeling (pTM) score (range from 0.62 to 0.92) for the best ranking. A pTM score above 0.5 indicates that the predicted fold might be the true structure47. The structures were visualized using PyMOL [http://www.pymol.org].Phylogenetic analysisA total of 151 amino acid sequences of LHC proteins were obtained by the NCBI database (https://www.ncbi.nlm.nih.gov/) and JGI genome portal (https://genome.jgi.doe.gov/) from A. anophagefferens, Bigelowiella natans, Chlamydomonas eugametos, C. reinhardtii, Cyclotella cryptica, I. galbana, Karlodinium micrum, Mesostigma viride, Micromonas sp., Ostreococcus lucimarinus, Ostreococcus tauri, P. tricornutum, Physcomitrella patens, Scenedesmus obliquus and T. pseudonana (Supplementary Table 9)48. These sequences were aligned using MUSCLE (v5.1)49 and Clustal Omega (v1.2.4)50 with default parameters. The maximum likelihood (ML) phylogenetic tree was estimated using IQ-TREE (v1.6.5)51. The recommended best-fitting model LG + F + R5 was selected by using ModelFinder in IQ-TREE (v1.6.5). ML analysis was run with 1000 replicates via a bootstrap test. WebLogo was used to build sequence logos from LHCs52.Molecular dynamics (MDs) simulationsMDs simulations were performed using the Gromacs constant pH program53, with the CHARMM36m force field selected, and the TIP3P water model was employed. The pHbuilder tool was used to generate topology files for CphHMD, with pH values set to 4.5 and 7, respectively, to determine all titratable protein residues in the protein. A cubic box with a distance of 1.5 nm from the protein was established, and water molecules were added. The pHbuilder tool was used to add an appropriate number of positive/negative ions (sodium ions/chloride ions) and buffer to ensure the system is a net-neutral system, and to generate the corresponding structural and topology files for the system. Four systems were constructed, namely AaLHC27E_pH_4.5, AaLHC27E_pH_7, AaLHC27Q_pH_4.5, and AaLHC27Q_pH_7, respectively. Each protein was solvated in a cubic box (1.5 nm buffer) with water molecules, followed by ion addition (Na⁺/Cl⁻) and buffer components to ensure charge neutrality. Energy minimization utilized the steepest descent algorithm (5000 steps) with Verlet cutoffs, PME electrostatics (rcoulomb = 1.2 nm, fourierspacing = 0.14), and force-switch vdW treatment (rvdw = 1.2 nm). Bond constraints were applied via LINCS. Equilibration comprised NVT (100 ps, leap-frog integrator, V-rescale thermostat at 300 K, tau_t = 0.5 ps) and NPT phases (100 ps, C-rescale barostat at 1 bar, isotropic coupling, tau_p = 5.0 ps, refcoord_scaling = com). Production runs employed CpHMD with identical parameters to NPT, excluding refcoord_scaling. During the 1000 ns production simulations, conformational snapshots were recorded at 1 ns intervals for all systems. System stability was assessed using built-in modules for root-mean-square deviation (RMSD), residue fluctuations (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA). Markov state models (MSMs) and time-lagged independent component analysis (tICA) were computed via PyEMMA54. To encode secondary structural integrity, protein backbone root-mean-square deviation (RMSD) relative to the energy-minimized reference structure was employed as the primary feature for tICA dimensionality reduction. This RMSD-based metric explicitly captures global conformational variations while preserving secondary structure signatures.Construction of vectors for Lhc and overexpressionpTHa-K1 vector, containing the fcp8 promoter from T. pseudonana, was used for overexpression55. The encoding gene sequence of AaLhc27 was cloned into the pTHa-K1 vector to generate a pTHa-AaLhc27E plasmid. The Mut Express II Fast Mutagenesis Kit V2 (Vazyme-Biotect, China) was used for site-directed mutagenesis of E102 and E210 to Q102 and Q210, respectively, to generate another pTHa-AaLhc27Q plasmid. Plasmids were transformed into wild-type T. pseudonana cells by microprojectile bombardment using the Bio-Rad Biolistic PDS-1000/He Particle delivery system (Bio-Rad, CA, USA)56. With the 100 μg mL−1 nourseothricin resistance screening, colonies appeared after 14 days. The colonies were verified for successful transformation through PCR sequencing.Plasmid construction, expression, purification and refolding of AaLHC27E and AaLHC27QThe pET-28a-AaLHC27E and pET-28a-AaLHC27Q vectors containing a C-terminal His6-tag were transformed into Escherichia coli BL21 (DE3) cells, respectively. All transformants were incubated in LB medium with 100 mg/L kanamycin at 37 °C. To induce protein expression, isopropyl-beta-D-thiogalactopyranoside (IPTG) was added to a final concentration of 1 mM when the bacterial solution was cultured to OD 600 = 0.6. Cells were incubated at 37 °C for 5 h, collected by centrifugation at 4 °C and resuspended in lysis buffer (50 mM Tris-base, 500 mM NaCl, 8 M Urea, pH 7.4, Sangon-Biotect, China). After sonication and centrifugation (10,000 × g, 4 °C, 30 min), the supernatant was collected. His-tag Protein Purification Kit (Beyotime, China) was used to purify the unfolded AaLHC27E and AaLHC27Q. Then exchange buffer to 50 mM Hepes, 0.1% LDS, pH 5.0 (Dialysis Membranes, 14 kDa, Beyotime, China) for removing the high concentration of urea and adjusting the pH. Recombinant proteins of AaLHC27E and AaLHC27Q were refolded following the method described in refs. 57,58. SDS-page gel electrophoresis analysis (Sangon-Biotect, China) was carried out to check the yield of recombinant protein at each step (Supplementary Fig. 7).FTIR spectroscopySamples of AaLHC27E and AaLHC27Q were loaded in a Specac Omni Cell demountable cell with calcium fluoride (CaF2) windows with a 6 μm path length Mylar spacer. Infrared spectra were recorded using an FTIR spectrometer (IRTracer-100, Shimadzu, Japan) equipped with a Deuterated L-Alanine Triglycine Sulfate detector. Samples were solubilized in D2O detergent buffer. Measurements for the pD 5.0 and 7.5 samples were conducted sequentially, and differential FTIR spectra were generated by normalizing to the integrated absorbance of the amide I band (1550–1750 cm−1).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    Data availability

    All relevant data supporting the conclusions of this study are included within the main text and/or the Supplementary Information. The raw sequencing data have been deposited in the NCBI BioProject database under accession numbers PRJNA680884 and PRJNA787041. The transcription response patterns in this study are available in Supplementary Dataset S1. The Gene expression modules based on WGCNA in this study are available in Supplementary Dataset S2. AlphaFold3 models and confidence metrics are provided in Supplementary Dataset S3. Source data are provided with this paper.
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    Download referencesAcknowledgmentsDr. Tao Cheng from Qilu University of Technology is greatly acknowledged for assistance with the FTIR spectroscopy. This work was supported by the National Natural Science Foundation of China (No. 42176142 [L.C.], 41906111 [Y.D.] and 41806127 [L.C.]), the Guangdong Provincial Key R&D Program (No. 2023B1111050011 [SH.L.]), and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. 311022009 [L.C.]), and the Basic and applied basic research project of Guangzhou (2023A04J1548 [Y.D.] and 2023A04J1549 [L.C.]).Author informationAuthor notesThese authors contributed equally: Lei Cui, Lei Xie.Authors and AffiliationsKey Laboratory of Eutrophication and Red Tide Prevention of Guangdong Higher Education Institute, College of Life Science and Technology, Jinan University, Guangzhou, ChinaLei Cui, Lei Xie, Lei Zhang, Baoling Yang, Juanchan Xu, Liying Tan, Bingqing Xiao, Yuelei Dong & Songhui LuCollege of Food Science and Engineering, Foshan University, Foshan, ChinaJianwei ZhengDepartment of Marine Sciences, University of Connecticut, Groton, CT, USASenjie LinGuangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, College of Life Science, South China Normal University, Guangzhou, ChinaYuelei DongAuthorsLei CuiView author publicationsSearch author on:PubMed Google ScholarLei XieView author publicationsSearch author on:PubMed Google ScholarJianwei ZhengView author publicationsSearch author on:PubMed Google ScholarLei ZhangView author publicationsSearch author on:PubMed Google ScholarBaoling YangView author publicationsSearch author on:PubMed Google ScholarJuanchan XuView author publicationsSearch author on:PubMed Google ScholarLiying TanView author publicationsSearch author on:PubMed Google ScholarBingqing XiaoView author publicationsSearch author on:PubMed Google ScholarSenjie LinView author publicationsSearch author on:PubMed Google ScholarYuelei DongView author publicationsSearch author on:PubMed Google ScholarSonghui LuView author publicationsSearch author on:PubMed Google ScholarContributionsL.C., Y.D., and SH.L. designed research; L.C., L.X., Y.D., L.T., J.X., and B.X. performed research; L.C., Y.D., L.X., J.Z., L.Z., B.Y., and SJ.L. analyzed data; and L.C., Y.D., SJ.L., and SH.L. wrote the paper.Corresponding authorsCorrespondence to
    Lei Cui, Senjie Lin, Yuelei Dong or Songhui Lu.Ethics declarations

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    Reprints and permissionsAbout this articleCite this articleCui, L., Xie, L., Zheng, J. et al. Mechanisms of light harvesting complex proteins in photoprotection of the brown tide alga.
    Nat Commun 16, 11089 (2025). https://doi.org/10.1038/s41467-025-66000-7Download citationReceived: 24 March 2025Accepted: 24 October 2025Published: 12 December 2025Version of record: 12 December 2025DOI: https://doi.org/10.1038/s41467-025-66000-7Share 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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    Data assimilation reveals behavioral dynamics of sea cucumbers as a model for slow-moving benthic animals

    AbstractUnderstanding the movement behavior of Japanese sea cucumbers (Apostichopus japonicus) is essential for ecological research and fisheries management. However, tracking their locomotion is challenging due to their slow movement and environmental variability. In this study, we employed acoustic telemetry combined with a data assimilation approach using the Kalman filter to estimate movement trajectories with high accuracy, overcoming the limitations of traditional visual tracking methods. To characterize movement complexity, we applied fractal dimension analysis, quantifying the randomness and variability of individual locomotion across different environmental conditions. Additionally, we examined the influence of key environmental factors, including water temperature, diel cycles, and boulder presence, using Generalized Linear Models (GLM). The results indicate that during the growing stage, higher water temperatures significantly increased movement activity, while boulder zones influenced movement differently depending on the season. This study also provides long-term tracking data on released sea cucumbers, offering new insights into their settlement and dispersal patterns. By combining acoustic telemetry, data assimilation, fractal analysis, and statistical modeling, we established a framework to investigate the behavioral dynamics of slow-moving benthic organisms. These findings enhance our understanding of sea cucumber ecology and provide a quantitative framework for future studies on marine invertebrate movement.

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    Download referencesAcknowledgementsWe would like to express our sincere gratitude to Mr. Manabu Shimono, Mr. Toru Miyagawa, and Itaru Araki from the Fisheries Technology Extension Office for their invaluable support during the diving surveys. We also appreciate the assistance of Ms. Yuki Nitta in the preparation of this manuscript. This study was made possible through the use of research equipment purchased under the Demonstration Project for the Enhancement of Resources of Important Export Species, funded by the Fisheries Agency of Japan. We extend our deepest thanks to all those involved. This study highlights that even species with limited mobility, such as sea cucumbers, can yield valuable insights into animal behavior.FundingThis research was partially supported by a research grant from The South Hokkaido Science Promotion Foundation for the project “Development of Behavioral Information Acquisition Technology for Sea Cucumbers Using State Estimation Techniques and Understanding Individual Movement Characteristics.” Additionally, funding was provided by the Hokkaido Research Organization under two separate projects: (1) a project on marine ranching of Japanese common sea cucumber, and (2) a commissioned project aimed at demonstrating the enhancement of fisheries resources important for export. We sincerely appreciate the support of all those involved.Author informationAuthor notesErica SasanoPresent address: Kansai Electric Power Co., Inc., Osaka, 530-8270, JapanKouki KandaPresent address: Organo Co., Inc., Tokyo, 136-8631, JapanAuthors and AffiliationsFaculty of Fisheries Sciences, Hokkaido University, Sapporo, 060-0810, JapanTsutomu Takagi, Erica Sasano & Kouki KandaGraduate School of Environmental Science, Hokkaido University, Sapporo, 060-0810, JapanYuto TanakaHakodate Fisheries Research Institute, Hakodate, 040-0051, JapanYuichi SakaiAuthorsTsutomu TakagiView author publicationsSearch author on:PubMed Google ScholarYuto TanakaView author publicationsSearch author on:PubMed Google ScholarErica SasanoView author publicationsSearch author on:PubMed Google ScholarKouki KandaView author publicationsSearch author on:PubMed Google ScholarYuichi SakaiView author publicationsSearch author on:PubMed Google ScholarContributionsThe manuscript was written by T.T. The experimental design was conceived by Y.S. and T.T. The diving surveys and measurements were conducted by Y.S., Y.T., E.S., and K.K. The analyses, including statistical analysis, were performed by T.T. and Y.T. Figures in the manuscript were created by T.T. and Y.T. Y.S. and T.T. secured the funding for this study. All the authors reviewed the manuscript.Corresponding authorCorrespondence to
    Tsutomu Takagi.Ethics declarations

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    The authors declare no competing interests.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
    Reprints and permissionsAbout this articleCite this articleTakagi, T., Tanaka, Y., Sasano, E. et al. Data assimilation reveals behavioral dynamics of sea cucumbers as a model for slow-moving benthic animals.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-29171-3Download citationReceived: 03 April 2025Accepted: 14 November 2025Published: 12 December 2025DOI: https://doi.org/10.1038/s41598-025-29171-3Share 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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    KeywordsData assimilationSea cucumberFractal dimensionMovement ecologyAcoustic telemetryBenthic organism tracking More

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    Satisfying multidimensional human well-being efficiently and equitably through dynamic urban planning

    AbstractAchieving multidimensional human well-being in cities has become a key issue for sustainable development. Land-use planning can help to advance human well-being by equitably and efficiently locating industries in areas with higher productivity and lower environmental impact. To this end, we analyzed how cities deliver ecological and economic benefits at both city and grid scales by incorporating three key characteristics: distance decay, dynamic accumulation, and interactive effects. We integrated the “source-flow-sink” theory with spatial mapping models to link benefit supply with the populations they serve. Based on this, we proposed a dynamic urban planning framework that develops an industry suitability index—combining multidimensionality, efficiency, and equity—to support land-use decision-making. Application in Ordos City, China, indicates that high-suitability lands for industry are scarce. In our scenarios, a comprehensive strategy yielded multiple benefits with relatively less land conversion, while other strategies trade ecological for economic benefits.

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    Download referencesAcknowledgementsThis paper was supported by the National Natural Science Foundation of China (No. 72404267), the Postdoctoral Fellowship Program of CPSF (No. GZB20240816), the China Postdoctoral Science Foundation (No. 2024M753474), the National Natural Science Foundation of China (No. 32471735), Yunnan Science and Technology Department (No. 202501AS070088), Yunnan Revitalization Talent Support Program Innovation Team Project (No. 202405AS350019), The 14th Five-Year Plan of the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences (No. E3ZKFF7B).Author informationAuthors and AffiliationsDepartment of Natural Resources and Environmental Sciences, The University of Illinois at Urbana-Champaign, Champaign, IL, USAShi Xue, North Joffe-Nelson & Devin J. GoodsonCenter for Integrative Conservation, Xishuangbanna Tropical Botanical Garden & Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences, Mengla, ChinaZhou Fang, Zhongde Huang & Yang BaiYunnan International Joint Laboratory of Southeast Asia Biodiversity Conservation, Menglun, ChinaZhou Fang, Zhongde Huang & Yang BaiUniversity of Chinese Academy of Sciences, Beijing, ChinaZhongde Huang & Yang BaiSchool of Economics and Finance, Hohai University, Changzhou, ChinaChanggao ChengSchool of Sociology and Population Studies, Nanjing University of Posts and Telecommunications, Nanjing, ChinaQin ZhouThe School of Operations Research and Information Engineering, Cornell University, Ithaca, NY, USASirui ZhangDepartment of Electronic Engineering, Shanghai Jiaotong University, Shanghai, ChinaZhengjun ZhangInstitute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing, ChinaYuanjun ZhuBusiness School, Hohai University, Nanjing, ChinaTing Wang & Yue ZhangAuthorsShi XueView author publicationsSearch author on:PubMed Google ScholarZhou FangView author publicationsSearch author on:PubMed Google ScholarZhongde HuangView author publicationsSearch author on:PubMed Google ScholarChanggao ChengView author publicationsSearch author on:PubMed Google ScholarQin ZhouView author publicationsSearch author on:PubMed Google ScholarSirui ZhangView author publicationsSearch author on:PubMed Google ScholarNorth Joffe-NelsonView author publicationsSearch author on:PubMed Google ScholarDevin J. GoodsonView author publicationsSearch author on:PubMed Google ScholarZhengjun ZhangView author publicationsSearch author on:PubMed Google ScholarYuanjun ZhuView author publicationsSearch author on:PubMed Google ScholarTing WangView author publicationsSearch author on:PubMed Google ScholarYue ZhangView author publicationsSearch author on:PubMed Google ScholarYang BaiView author publicationsSearch author on:PubMed Google ScholarContributionsS.X.: writing—original draft, review & editing, conceptualization. Z.F.: conceptualization, methodology, writing—original draft. Z.H.: methodology, supervision, writing—original draft. C.C.: writing—original draft, visualization. Q.Z.: writing—original draft, literature review. S.Z.: data curation, coding. Z.Z.: coding. N.J.-N.: writing—review & editing. D.J.G.: writing—review & editing. T.W.: formal analysis. Y.Z.: writing—review & editing. Y.Z.: writing—review & editing. Y.B.: writing—review & editing.Corresponding authorsCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleXue, S., Fang, Z., Huang, Z. et al. Satisfying multidimensional human well-being efficiently and equitably through dynamic urban planning.
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    Segmentation of plateau zokor mounds in alpine meadows from UAV images using an improved UNet network

    AbstractPlateau zokor mounds, created by the burrowing activity of Plateau zokor, cause significant damage to crops, grasslands, and infrastructure, particularly in the alpine meadows of the Tibetan Plateau. Traditional field surveys are inefficient and labor-intensive, limiting the ability to conduct large-scale monitoring. Accurate detection of zokor mounds is essential for effective rodent control and sustainable grassland management. This study introduces VGG–Dice–PSA UNet(VDP_UNet), an enhanced deep learning model designed to segment zokor mounds from UAV imagery captured at 30 m. Based on the UNet architecture, VGG16 is used to replace the original UNet backbone, enabling the model to capture global contextual information and enhance feature extraction in complex backgrounds. Additionally, a Polarized Self-Attention (PSA) module is integrated into the feature fusion stage following the encoder–decoder skip connections to better capture fine-grained semantic features related to zokor mounds. To reduce overfitting and address class imbalance, Dice Loss is introduced during training. VDP_UNet was trained and evaluated on a custom high-resolution zokor mound dataset. It achieved an IoU of 51.99%, MIoU of 75.63%, mean Pixel Accuracy of 82.66%, Precision of 71.44%, FPS of 42.13 f/s, Accuracy of 99.27%, and an F1-score of 68.41%, outperforming recent deep learning models. Experimental results indicate that the proposed VDP_UNet model efficiently segments zokor mounds in alpine meadows, markedly improving the extraction of mound features from UAV images. Furthermore, this study establishes a practical foundation for estimating mound areas in real sample plots and provides solid technical support for rodent control and the sustainable development of alpine ecosystems.

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    IntroductionThe plateau zokor (Eospalax baileyi), a member of the order Rodentia and family Spalacidae1, is a subterranean rodent species endemic to the grasslands of the Tibetan Plateau. Its burrowing and mound-building activities strongly influence primary productivity and herbivore interactions, while accelerating soil nutrient turnover, enhancing microbial activity2, and promoting decomposition processes3. The presence of zokor mounds not only disrupts plant community succession and affects carbon sequestration4, but also reduces available grazing area5, accelerates soil erosion6, and contributes to a decline in biodiversity7, ultimately diminishing ground cover and productivity in alpine meadows8. These impacts have made zokor activity one of the key drivers of grassland degradation9. Furthermore, the spread of zokor mounds poses serious threats to forestry operations and grassland ecological security10,11,12. Therefore, the precise identification and extraction of zokor mounds from UAV images is essential for enhancing the effectiveness of rodent damage control efforts in alpine grasslands and supporting long-term ecological sustainability.Currently, due to limited monitoring capabilities, rodent control in alpine meadows largely relies on indiscriminate large-scale extermination efforts. Although this approach can suppress rodent outbreaks in the short term, it overlooks the multiple ecological roles of the plateau zokor within grassland ecosystems and compromises both ecological stability and functional integrity. Therefore, scientifically monitoring rodent activity is a fundamental prerequisite for implementing tiered management strategies that balance biodiversity conservation with effective control measures. At present, rodent damage monitoring in China’s grasslands primarily relies on manual field surveys13, which focus on counting burrows, bare patches, and mounds14,15,16, while often neglecting the importance of affected area. Traditional monitoring methods are inefficient and costly, making it difficult to simultaneously extract the number and area of zokor mounds over large regions17 or meet the demands of high-precision monitoring18. The emergence of remote sensing technologies offers new opportunities for rodent damage assessment. For example, Wang et al.19 utilized UAV imagery combined with supervised classification methods to investigate zokor mound distribution across plots of varying densities in Ruoergai County, Sichuan Province, enabling the classification of rodent damage severity and risk prediction. In Maqu County, Gansu Province, Hua et al.20 employed UAV remote sensing and hierarchical sampling to establish a three-level sample system and estimate the area affected by plateau pika damage. Although these studies represent progress in remote sensing-assisted monitoring of rodent mounds, the process still heavily depends on manual interpretation, with algorithmic models playing only a supplementary role21. Moreover, supervised classification performs poorly in complex scenarios, limiting its applicability. Therefore, there is an urgent need to develop rodent mound monitoring approaches that integrate remote sensing imagery with deep learning techniques to improve overall monitoring efficiency and application scope.UAV imaging enables rapid acquisition of large-scale, high-resolution remote sensing data, providing a strong foundation for fine-grained target identification. When combined with machine learning, it allows efficient detection of zokor mound locations and contours, greatly reducing the need for manual image analysis22. As a result, the integration of UAV remote sensing imagery and machine learning represents a vital approach and emerging research direction for efficient zokor mound monitoring. In existing studies, Qi et al.23 proposed a detection framework that combines UAV images, object-based image analysis, and the correlation-based feature selection algorithm to efficiently extract rodent burrow patches in desert grasslands. However, the study was limited by a small number of samples. Li et al.24 applied “3S” technologies in conjunction with the maximum likelihood method and decision tree classification to estimate the affected area of rodent damage in the Altun Mountains. Yet, the image resolution was insufficient for small-scale or topographically complex regions. Sandino et al.25 developed an automated method integrating UAV-based hyperspectral imaging, machine learning, and image processing to detect termite mounds, though the accuracy of the model still requires improvement. In recent years, the advancement of deep learning has greatly enhanced the efficiency of image processing, especially in feature extraction, and has driven the intelligent analysis of remote sensing data26. In the context of rodent monitoring, deep learning has been widely used for tasks such as burrow detection27,28, population estimation29, and predictive modeling. However, studies focusing on extracting zokor mound areas using deep learning methods remain relatively scarce. Given that deep learning–based semantic segmentation algorithms can automatically learn complex image features and demonstrate superior accuracy and generalization performance30, developing a precise deep learning model for the automated detection of zokor mounds across large-scale alpine meadow regions offers a more efficient and intelligent solution for rodent damage monitoring.To address the aforementioned challenges, this study proposes a deep learning-based segmentation model named VGG–Dice–PSA UNet(VDP_UNet). The model enhances the traditional UNet architecture by replacing its encoder with VGG16, enabling improved capture of global contextual information from input images. To further strengthen feature representation under complex background conditions, a Polarized Self-Attention (PSA) module is integrated into the feature fusion stage following the encoder–decoder skip connections. Additionally, Dice Loss is adopted to alleviate class imbalance between zokor mound and background samples. We conducted a comprehensive performance comparison between VDP_UNet and both traditional and state-of-the-art segmentation methods. The results demonstrate that VDP_UNet consistently outperforms other approaches, accurately extracting zokor mounds in challenging environments and exhibiting strong generalization capabilities. This method offers robust technical support for the efficient monitoring of zokor mounds in alpine meadows.Materials and methodsOverview of the study areaThe study area is located in Zhuaxixiulong Town, Tianzhu Tibetan Autonomous County, Gansu Province (37°12′13″N, 102°46′11″E; elevation: 2,890.38 m), adjacent to the eastern edge of the Tibetan Plateau (As shown in Fig. 1). It represents a typical habitat for the plateau zokor. The region is characterized by a plateau continental climate, marked by significant annual temperature variation and pronounced diurnal temperature differences. The soil type is subalpine meadow soil, and the vegetation consists of typical alpine meadow. The climate features two primary seasons: a warm season from May to October and a cold season from November to April of the following year. The area experiences no absolute frost-free period, with an average annual temperature of approximately − 0.1 °C. The mean annual precipitation is 416.0 mm, most of which occurs between July and September31. The plateau zokor is the only subterranean rodent species in this region32. Its burrowing activities result in the formation of mounds on the soil surface33, producing a characteristic “mound–vegetation” mosaic pattern across the grassland landscape.Fig. 1Overview map of the study area. (a) Administrative divisions of Tianzhu county. (b) Digital elevation model of Tianzhu county. (c) Representative UAV image of plateau zokor mounds during the peak grass period. (d) Representative UAV image of plateau zokor mounds during the returning green period.Full size imageData collection and preprocessingData acquisitionIn this study, a DJI Mavic 2 Pro quadcopter UAV (https://www.dji.com/mavic-2) equipped with a Hasselblad L1D-20c camera was used to capture RGB images of plateau zokor mounds. The camera features a 1-inch, 20-megapixel CMOS image sensor, enabling the acquisition of high-resolution images. Data collection was conducted on April 12, 13, and 15, 2021, as well as on June 5, 13, 15, 17, and 19, 2023, covering both the returning green period and peak grass period of the alpine meadow vegetation cycle. The UAV followed pre-programmed flight paths at an altitude of 30 m, capturing multi-directional images with both forward and side overlap rates maintained at 75% to ensure full coverage. The resolution of the collected zokor mound images was 5280 × 3956 pixels. To ensure image quality and minimize interference from environmental factors such as lighting and wind, all flights were conducted under clear skies with calm wind conditions and ample sunlight. In total, 876 high-quality zokor mound images were acquired for further analysis.Data preprocessingInitial annotations were generated using the automatic labeling tool on the Roboflow platform (Roboflow, Des Moines, Iowa, US). Targets were classified into two categories: zokor mounds (labeled “ZM”) and non-mound areas. To support the subsequent segmentation task, category and location data were saved in JSON format. Annotation was carried out using a tagging system powered by Grounding DINO, which identifies and labels surface mounds of varying sizes formed by zokor digging activity. Developed by IDEA Research, Grounding DINO is an open-vocabulary object detection model that integrates object recognition with multimodal understanding34. By leveraging natural language prompts, it enables fast and accurate identification of multiple relevant objects within an image. Unlike traditional detection models, Grounding DINO incorporates a language understanding module, giving it the ability to recognize unfamiliar categories in open-world scenarios35. In this study, it proved especially effective in handling densely distributed and complex zokor mound scenes, significantly improving labeling efficiency and accuracy—thereby offering strong support for dataset development.Fig. 2Flowchart of data collection and processing: (a) flip, add noise, and adjust brightness; (b) translate and add noise; (c) add noise; (d) flip; (e) translate and adjust brightness.Full size imageFollowing the preliminary annotation using DINO, experts conducted a meticulous image-by-image review with Labelme to ensure the accurate labeling of plateau zokor mounds. Images exhibiting excessive overexposure or underexposure that compromised visual details were excluded. As a result, an initial dataset of 869 images was finalized. This dataset was then randomly split into a training set (90%) and a validation set (10%), with no separate test set used in this experiment. Because deep learning models are typically sensitive to sample size, five data augmentation techniques—translation, flipping, mirroring, noise addition, and brightness adjustment—were applied to the training images to prevent overfitting and improve model robustness. After augmentation, a total of 5,214 images were generated, forming the alpine meadow plateau zokor mound dataset, as shown in Fig. 2. Among these, 4,692 images were used for training, and 522 images were used for performance validation during training. For image annotation, OpenCV was used to create pixel-level labels, assigning a value of 1 to annotated regions (zokor mounds) and 0 to unannotated regions (non-mound areas), facilitating subsequent semantic segmentation training.Network architectureTo mitigate the issues of gradient vanishing and exploding during deep neural network training, and to enhance the ability to extract zokor mound features from remote sensing imagery, we optimized the traditional UNet architecture and proposed an improved model specifically designed for zokor mound extraction in UAV images. Specifically, the original UNet encoder was replaced with the VGG16 backbone, which offers a favorable balance between depth and computational efficiency. This replacement enhances the model’s capability to represent complex spatial structures and RGB features in remote sensing images, enabling more accurate identification of subtle differences in zokor mounds. In addition, to further strengthen the model’s focus on key regions, we introduced the PSA module after the skip connections to enhance feature representation. This module improves the model’s sensitivity to local spatial structure and semantic information, especially in scenes with complex backgrounds or densely distributed targets. Given that zokor mounds typically occupy a small portion of remote sensing images—resulting in a pronounced class imbalance—we adopted Dice Loss as the loss function. This choice improves the model’s training performance under imbalanced positive and negative samples and enhances detection precision. Based on the above improvements, we propose the VDP_UNet segmentation model, architecture is shown in Fig. 3.Fig. 3Architecture of the VDP_UNet model.Full size imageUNet networkUNet is a classic semantic segmentation network that adopts a U-shaped encoder–decoder architecture36. The encoder is composed of multiple layers of convolution and max pooling, progressively extracting spatial and texture features from the image. Through successive 3 × 3 convolutions followed by ReLU activation, UNet enhances deep feature representation while preserving local information. Each downsampling operation halves the spatial resolution and increases the number of feature channels to capture more abstract high-level semantic information.The decoder gradually restores the image resolution using transposed convolutions, and employs skip connections to pass shallow features from corresponding encoder layers37, enabling effective feature fusion. This fusion aids in recovering fine details and improves segmentation accuracy. The fused features are further refined through 3 × 3 convolutions and finally processed by a 1 × 1 convolution to produce the semantic segmentation output.VGG16This study employs the structurally stable and widely used convolutional neural network VGG16 as the backbone for feature extraction38. VGG16 consists of multiple consecutive 3 × 3 convolutional layers, max pooling layers, several fully connected layers, and a Softmax classification layer, offering strong hierarchical feature representation capabilities. By stacking small-sized convolutional kernels, the network effectively expands the receptive field and enhances feature abstraction while controlling computational complexity, thereby improving its ability to capture fine image details. This architecture enables precise extraction of critical information—such as texture, edges, and shape—of plateau zokor mounds from high-resolution remote sensing images of alpine meadows. As a result, it significantly improves the model’s ability to detect and segment small targets under complex background conditions.Polarized self-attention modulePolarized Self-Attention (PSA) is a lightweight attention mechanism specifically designed for pixel-level regression tasks39. It splits the attention process into Channel-only Self-Attention and Spatial-only Self-Attention, modeling high-resolution attention separately along the channel and spatial dimensions. This design enables more precise capture of key structures and semantic information in images, significantly enhancing the expressiveness and discriminative power of feature representations.In this study, PSA is implemented using a parallel structure that computes channel-only and spatial-only self-attention simultaneously. In the channel branch, the input feature map X is first passed through two 1 × 1 convolution layers to produce features q and v. The feature q is compressed to a single channel to extract a compact global representation, while v retains richer information with C/2 channels. The compressed q is then normalized using the Softmax function to highlight relative importance across channels. This normalized q is multiplied with the reshaped v to generate a channel-wise aggregated representation, which is then processed by another 1 × 1 convolution and LayerNorm to restore the original C dimensions. Finally, the attention weights are activated with a Sigmoid function, scaled to the [0,1] range, and multiplied channel-wise with the input feature map to enhance the output. In the spatial branch, two 1 × 1 convolutions are applied to extract a spatial feature map v and a globally averaged 1 × 1 feature q, which are used to compute spatial correlations. The resulting attention map is reshaped and passed through a Sigmoid activation, then multiplied pixel-wise with the input feature map to emphasize critical spatial regions. The overall architecture of the PSA module is shown in Fig. 4.Fig. 4Parallel architecture of polarized self-attention.Full size imageThe computations for the Channel-only branch, Spatial-only branch, and the parallel arrangement of both branches are as follows:$${A^{ch}}(X)={F_{SG}}[{W_{z {theta _1}}}(({sigma _1}({W_v}(X)) times {F_{SM}}({sigma _2}({W_q}(X))))]$$
    (1)
    $${A^{sp}}(X)={F_{SG}}[{sigma _3}({F_{SM}}({sigma _1}({F_{GP}}({W_q}(X)))) times {sigma _2}({W_v}(X)))]$$
    (2)
    $$PS{A_p}(X)={Z^{ch}}+{Z^{sp}}={A^{ch}}(X){ odot ^{ch}}X+{A^{sp}}(X){ odot ^{sp}}X$$
    (3)
    where X is an input tensor, Wq, Wv and Wz are 1 × 1 convolution layers respectively, σ1, σ2 and σ3 are tensor reshape operators, FSG() represents the attention-weighted operation on the value feature matrix, FSM(·) is a softmax operator, “×” is the matrix dotproduct operation, FGP(·) is a global pooling operator, where ⊙ch is a channel-wise multiplication operator, where ⊙sp is a spatial-wise multiplication operator and “+” is the element-wise addition operator.Dice loss functionDice Loss offers significant advantages in segmentation tasks involving small foreground objects and imbalanced class distributions40. Unlike cross-entropy loss, which calculates errors independently at each pixel and often overlooks small targets, Dice Loss directly optimizes the overlap between the predicted region and the ground truth, placing greater emphasis on overall structural consistency. This makes it particularly effective for accurately identifying small yet important plateau zokor mounds in alpine meadows. Additionally, Dice Loss helps mitigate the negative effects of class imbalance and enhances the model’s ability to segment edge regions, significantly reducing contour blurring. Its formula is shown in Eq. (4):$$Dice:Loss=1 – frac{{2mathop sum nolimits_{{i=1}}^{N} {p_i}{y_i}+gamma }}{{mathop sum nolimits_{{i=1}}^{N} {p_i}+mathop sum nolimits_{{i=1}}^{N} {y_i}+gamma }}$$
    (4)
    Where pi represents the predicted probability of the i-th pixel, yi represents the ground truth label of the i-th pixel, N denotes the N-th pixel, and γ is a smoothing term used to prevent division by zero.Parameter setting detailsThis study was conducted on a Windows 11 operating system using the PyTorch 2.0.0 deep learning framework. The server is equipped with an Intel(R) Core(TM) i9-14900 K processor and an NVIDIA GeForce RTX 4090 GPU, utilizing the CUDA v11.8 parallel computing platform and the cuDNN 8.9.7 deep neural network acceleration library. Python 3.8.20 was used as the programming language. Optimization was performed using the Adam optimizer with a momentum of 0.9 and a batch size of 16. Images were processed at a resolution of 512 × 512 pixels. The initial learning rate was set to 0.0001, with a minimum learning rate of 0.000001. The learning rate decay followed a cosine schedule, and training lasted for 80 epochs.Evaluation metricThe model’s performance was evaluated using key metrics derived from the confusion matrix: True Positives (TP) represent the number of pixels correctly identified as zokor mounds; False Positives (FP) are non-mound pixels mistakenly predicted as mounds; True Negatives (TN) refer to pixels correctly identified as non-mounds; and False Negatives (FN) are actual mound pixels incorrectly classified as non-mounds. Choosing appropriate evaluation metrics is critical for comprehensively assessing the effectiveness of the proposed mound extraction model. In this study, several essential metrics were used to evaluate semantic segmentation performance, including Intersection over Union (IoU), Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA), Precision, Recall, Accuracy, F1-score, and FPS.IoU measures the ratio of the overlap between the predicted and ground truth regions of a specific class to their union:$$IoU=frac{{TP}}{{TP + FP + FN}}$$
    (5)
    MIoU is used to measure the overlap between the predicted and actual zokor mound areas:$$MIoU=frac{1}{{k+1}}cdotmathop sum nolimits_{{i=0}}^{k} frac{{{P_{ii}}}}{{mathop sum nolimits_{{j=0}}^{k} {P_{ij}}+mathop sum nolimits_{{j=0}}^{k} {P_{ji}} – {P_{ii}}}}$$
    (6)
    In Eq. (6), (k + 1) represents the number of categories. Pii is the count of True Positive, Pij represents False Negative, and Pji indicates False Positive. In this context, ‘i’ signifies the true category, while ‘j’ refers to the other categories.MPA is calculated by averaging the pixel accuracy for each class, where pixel accuracy refers to the ratio of correctly classified pixels of a given class to the total number of pixels in that class:$$MPA=frac{1}{C}sumlimits_{{i=1}}^{C} {frac{{{n_{ii}}}}{{sumlimits_{j} {{n_{ij}}} }}}$$
    (7)
    C represents the number of classes (for a binary classification task, C = 2), nii denotes the number of pixels correctly classified as class i, and nij is the total number of pixels belonging to class i.Precision refers to the proportion of correctly classified zokor mound pixels among all pixels that were predicted to be zokor mounds:$$Precision=frac{{TP}}{{TP+FP}}$$
    (8)
    Recall evaluates the ratio of correctly classified zokor mound pixels to the total number of pixels labeled as zokor mounds:$$Recall=frac{{TP}}{{TP+FN}}$$
    (9)
    Accuracy is used to measure the proportion of correctly classified pixels by the model at the pixel level:$$Accuracy=frac{{TP+TN}}{{TP+FP+TN+FN}}$$
    (10)
    The F1-score, a key metric, is the harmonic mean of precision and recall; a higher F1-score indicates better model performance:$$F1 – score=2 times frac{{Precision times Recall}}{{Precision+Recall}}$$
    (11)
    FPS (Frames Per Second): Under the same hardware conditions, a higher FPS indicates stronger real-time processing capability of the model. FPS is calculated as: FPS = 1/latency, where latency refers to the time required for the network to process a single image.ResultsAblation studyTo validate the effectiveness of the proposed VDP_UNet method in segmenting zokor activity areas (zokor mounds) in alpine meadow regions, detailed ablation experiments were conducted on the constructed dataset. Various experimental configurations were designed to assess the impact of each improved module on the overall model performance and to quantify their contributions. Model performance was evaluated using IoU, MIoU, MPA, F1-score, and FPS. The corresponding experimental results are summarized in Table 1.Table 1 Ablation experiment results. Bold values indicate the best performance. A “√” denotes that the module is included, while a “-” indicates it is not.Full size tableThe experimental results demonstrate that in Case 2, integrating VGG16 alone led to improvements of 1.26% in IoU, 8.2% in MIoU, 5.24% in MPA, and 14.92% in F1-score compared to the baseline model, highlighting VGG16’s advantage in extracting zokor mound features. The deeper architecture of VGG16 allows it to capture global contextual information from images, enabling the model to better focus on relevant mound features and improving its ability to accurately identify mound regions.In Case 3, the Dice Loss function was added on top of the VGG16 backbone. By directly measuring the overlap between predicted results and ground truth labels, Dice Loss helps the model converge more effectively. In contrast, the baseline model uses cross-entropy with a predefined weight map, which struggles to emphasize hard examples and often overlooks contextual structural information. Compared to Case 2, Case 3 showed further increases of 1.97% in IoU, 0.97% in MIoU, 3.41% in MPA, 1.77% in F1-score, and 0.65 f/s in FPS.
    Case 4
    introduced the PSA module. The parallel structure of Channel-only and Spatial-only Self-Attention in PSA effectively enhances fine-grained features of zokor mounds in complex backgrounds and improves localization accuracy. Compared to Case 2, IoU, MIoU and F1-score increased by 0.46%, 0.24% and 0.42%, respectively, though MPA slightly decreased by 0.42%.
    In Case 5, the combination of Dice Loss and the PSA module on the VGG16 backbone achieved the best overall performance, with IoU, MIoU, MPA, F1-score, and FPS reaching 51.99%, 75.63%, 82.66%, 68.41%, and 42.13f/s, respectively—improvements of 4.87%, 10.01%, 9.47%, 18.12%,and 8.57 f/s, over the baseline. These results validate the effectiveness of the proposed VDP_UNet model for zokor mound segmentation in the complex environment of alpine meadows.The training and validation loss curves of the UNet and VDP_UNet models are shown in Fig. 5. As training progressed, the loss curves began to stabilize at epoch 70 and reached a steady state by epoch 80.Fig. 5Comparison of loss curves between the UNet (a) and VDP_UNet (b) models.Full size imageComparative experimentsComparison with classical algorithmsTo ensure recognition accuracy, the proposed method was compared against several classic models on the alpine meadow zokor mound dataset, including SegFormer41, DeepLabV3+42, BiSeNetV243, Fast-SCNN44, and DANet45, All models were trained for 80 epochs.As shown in Table 2, VDP_UNet outperforms several mainstream semantic segmentation models across key metrics such as Accuracy, MIoU, and F1-score. While SegFormer achieved the highest Precision (72.32%), its relatively low Recall and F1-score indicate an issue with under-detection, and therefore it was not selected as the baseline model. DeepLabV3 + achieved a slightly higher Recall than VDP_UNet, but fell short in other performance indicators. BiSeNetV2 and DANet demonstrated relatively balanced results across metrics and maintained stable overall performance, yet still underperformed compared to the proposed method. Fast-SCNN performed the worst on this dataset, struggling to effectively capture the small-scale features of zokor mounds. Overall, VDP_UNet strikes a strong balance between accuracy and robustness, confirming its effectiveness in extracting zokor mound regions under complex background conditions and offering valuable support and methodological insight for future research.Table 2 Comparative analysis with classic semantic segmentation models. Bold font indicates the best values.Full size tableFigure 6 illustrates the loss curves of different semantic segmentation models. Although the SgFormer model achieved the highest accuracy, its loss decreased too rapidly and became stable around the 30th epoch, indicating that the model might suffer from an improper learning rate setting or premature convergence. The Fast-SCNN model showed relatively poor training performance, with large fluctuations in its loss curve throughout the training process and no clear convergence. Overall, the VDP_UNet model exhibited faster convergence and the lowest final loss value, demonstrating superior convergence and stability.Fig. 6Comparison of loss curves for different semantic segmentation models. (a) SegFormer model, (b) DeepLabV3 + model, (c) BiSeNetV2 model, (d) Fast-SCNN model, (e) DANet model, and (f) VDP_UNet model.Full size imageBackbone network comparisonThis experiment compared the performance of models using different backbone networks—UNet’s original backbone, VGG16, and ResNet50—on the zokor mound dataset. The experimental results are shown in Table 3. The results indicate that the VGG16 backbone demonstrates a stronger ability to capture contextual information from zokor mound images. It achieved Precision, IoU, and MIoU values of 76.04%, 48.38%, and 73.82%, respectively, representing improvements of 25.01%, 1.26%, and 8.20% over the original UNet backbone, and achieving the best overall performance.Table 3 Results from choosing the backbone, bold font indicates the best values.Full size tablePolarized self-attentionTo verify the effectiveness of the Polarized Self-Attention (PSA) module in semantic feature fusion, a comparative experiment was conducted based on the VGG16 backbone using CBAM, Triplet, ECA, and PSA modules, as shown in Table 4. The PSA module integrates features through a parallel combination of channel and spatial attention mechanisms, achieving Precision, IoU, and MIoU values of 78.85%, 48.84%, and 74.06%, respectively—improvements of 2.81%, 0.46%, and 0.24% over the other modules. These results clearly demonstrate the superiority of the PSA module compared to the baseline modules, indicating that incorporating Polarized Self-Attention enables more effective integration of semantic information and enhances segmentation accuracy.Table 4 The network performance is compared by using different network blocks, bold font indicates the best values.Full size tableLoss functionA series of comparative experiments were conducted to verify the effectiveness of the proposed loss function. The experiments were carried out based on the VGG16 backbone and PSA module, as shown in Table 5. Three loss functions—Dice Loss, Focal Loss, and Dice + Focal—were introduced for comparison, where “Origin” represents the baseline model without additional loss function. The results show that the baseline model achieved IoU, MIoU, F1-score, and FPS values of 48.84%、74.06%、65.63%、28.78f/s, respectively. After introducing Dice Loss, these metrics improved to 51.99%, 75.63%, 68.41%, and 42.13 f/s, representing the best overall performance. These findings confirm the effectiveness of the proposed loss function in the zokor mound segmentation task and demonstrate its ability to significantly enhance feature extraction accuracy.Table 5 The network performance is compared by adding different loss function, bold font indicates the best values.Full size tableResult visualizationTo visually demonstrate the performance of the VDP_UNet model in detecting zokor mound regions, two UAV images captured at an altitude of 30 m were randomly selected and enlarged for qualitative analysis. The comparison group included UNet, other comparative models, and manual annotations, resulting in a total of eight sets of experimental results, as shown in Fig. 7. In the figure, “Original” denotes the raw image, “Label” represents the manually annotated ground truth, and the black-and-white images indicate the model predictions, where white areas correspond to detected zokor mounds and black areas represent non-mound regions. The first sample was collected during the flourishing grass stage, while the second corresponds to the regreening stage, demonstrating the model’s adaptability and detection performance across different vegetation growth phases.Fig. 7Visualization results.Full size imageAs shown in Fig. 7, zokor mounds exhibit more distinctive visual features during the flourishing grass stage, especially in terms of color, making them easier to identify. In contrast, during the regreening stage, the mounds often overlap with ground objects such as livestock footprints and surface patches, resulting in less distinguishable features and a higher likelihood of confusion. Compared with the manually annotated results, DeepLabV3+, BiSeNetV2, Fast-SCNN, and DANet performed unsatisfactorily on the zokor mound test set—showing significant false detections and omissions during the flourishing grass stage and failing to accurately delineate mound boundaries in the complex backgrounds of the regreening stage. Although SegFormer achieved a slight improvement in detection accuracy, its boundary perception capability remained limited.In contrast, the proposed VDP_UNet exhibits superior detection performance across all four representative scenarios. This improvement is largely attributed to the VGG16 backbone, which enhances the model’s capacity to capture contextual and semantic features of zokor mounds. Furthermore, the integration of a parallel PSA module—combining channel-only and spatial-only self-attention—significantly boosts the extraction of fine-grained details and edge information. The use of Dice Loss to address class imbalance further enhances label prediction accuracy. Collectively, these enhancements demonstrate the robustness and reliability of VDP_UNet in accurately identifying zokor mound features under complex background conditions.Application of the VDP_UNet model in field sites and area EstimationTo validate the effectiveness of the proposed method and explore its potential applications in zokor mound monitoring and sustainable ecosystem management, four zokor mound UAV images—randomly selected and excluded from training—were analyzed. A comparative study was conducted using traditional area calculation methods in ENVI (https://envi.geoscene.cn/). This study primarily calculated the total zokor mound area by counting the number of target pixels labeled as 1 (zokor mound) in the segmentation result maps, with non-zokor mound areas labeled as 0, then converting these pixel counts to actual ground area based on flight altitude and camera sensor parameters. The related area estimation principle is illustrated in Fig. 8.Fig. 8Workflow diagram for zokor mound area calculation in real field sites.Full size imageThe formula for calculating the actual area of zokor mounds is as follows:$$Are{a_{zokor,mound}}={C_{pixel}} times GS{D^2}={C_{pixel}} times {(frac{{H times SW}}{{f times IW}})^2}$$
    (12)
    Where Cpixel is the number of pixels, GSD is the ground sampling distance, H is the flight altitude (meters), SW is the sensor width (meters), f is the focal length (meters), and IW represents the image width (pixels).Fig. 9Comparison of UAV images and segmentation results of zokor mounds in the real field using ENVI and VDP_UNet. “Label” refers to the precise manual annotations. Notes: ENVI’s classification performance is poor with large errors; therefore, only images (a)–(d) segmented by VDP_UNet are shown, with the corresponding zokor mound areas in the field being 17.14 m², 20.47 m², 22.90 m², and 41.76 m², respectively.Full size imageAs shown in Fig. 9, images (a)–(c) were captured during the returning green period, while image (d) was taken during the peak grass period. The ENVI software was used to perform supervised classification based on the maximum likelihood method, with regions of interest (ROIs) manually defined. Post-processing steps included clustering analysis and principal and minor component analysis. During the peak grass period, when zokor mounds exhibit distinct texture and color differences from the surrounding vegetation, both ENVI and the proposed VDP_UNet produce satisfactory segmentation results. However, in the returning green period, zokor mounds often blend with livestock manure, bare soil, and other ground features, resulting in a complex background. This complexity limits ENVI’s ability to capture discriminative statistical features, leading to numerous false positives. In contrast, VDP_UNet maintains strong performance in this challenging setting, accurately identifying zokor mound regions with segmentation results that closely align with the original images, despite occasional misclassifications of non-mound areas.Overall, ENVI’s reliance on traditional statistical classification methods results in a complex processing workflow and issues like blurred boundaries and misclassification, revealing clear limitations in zokor mound area extraction. By comparison, VDP_UNet enables large-scale, efficient UAV image processing and demonstrates superior overall performance in comparative experiments. Its segmentation results closely align with actual zokor mound distribution, providing more accurate area estimates, thus validating VDP_UNet’s applicability and effectiveness for zokor damage area extraction in alpine meadow environments.Subsequently, 100 zokor mound images that were not used for training were analyzed. For each image, the ground truth mound area and the predicted value were calculated to generate a scatter plot, as shown in Fig. 10. The closer the points are to the reference line y = x, the more consistent the predicted values are with the ground truth.Fig. 10Comparison between the ground truth and predicted values of zokor mounds in sample plots.Full size imageDiscussionPerformance comparison of the VDP_UNet model with similar methods on the Zokor mound datasetSemantic segmentation models have been widely applied across numerous object detection tasks, spanning fields such as agriculture, water resource management, forest fire early warning46, and forestry monitoring47, For example, Zhang et al.48 used the DD-DA model for precise segmentation of gully erosion in the Northeast Black Soil region; Chen et al.49 proposed Res_AUNet, which effectively enhanced the extraction of sunlight reflection areas on water surfaces; to address the efficiency bottlenecks and lack of explicit structural information fusion in traditional ViT models when processing high-resolution images, Chu et al.50 designed a structurally improved Twins network architecture. Additionally, this study compared several mainstream semantic segmentation models, including M-DeepLabV3 + with a MobileNet backbone, V2-HRNet based on HRNetV2, and the lightweight M-PSPNet, a MobileNet-based variant of PSPNet. To comprehensively evaluate the performance of the proposed VDP_UNet model in zokor mound area extraction, all models were trained and tuned under identical conditions, including the same data splits, augmentation strategies, number of epochs, early stopping policy, learning rate schedules, and a consistent hyperparameter tuning budget, to ensure fairness and reliability of the comparison results.At a flight altitude of 30 m, using UAVs to capture zokor mound images significantly improves data acquisition efficiency. However, it also presents challenges such as smaller target sizes and less distinguishable features, making mound extraction more difficult. To validate the effectiveness of the proposed method, we conducted comparative experiments between VDP_UNet and several mainstream semantic segmentation models tailored for small-object detection in UAV imagery, as shown in Table 6. The results demonstrate that VDP_UNet outperforms other models in terms of Precision (71.44%), MIoU (75.63%), Accuracy (99.27%), and F1-score (68.41%), achieving the best overall performance. The DD-DA model achieved the highest Recall (66.75%), indicating strong capability in detecting zokor mounds. However, compared to DD-DA, VDP_UNet maintains high precision while also achieving competitive recall, highlighting its superior ability to capture fine-grained mound features.Table 6 Performance comparison with existing semantic segmentation models. The bold font indicates the best values.Full size tableAmong the other models, Res_AUNet and M-PSPNet performed relatively poorly in terms of both precision and intersection over union, struggling to extract discriminative features of zokor mounds. Twins and V2-HRNet showed strengths in precision but suffered from lower recall, suggesting a higher risk of missed detections. Notably, introducing the PSA module between the encoder and decoder in Res_AUNet enhanced the model’s representational power in complex backgrounds and improved its sensitivity to small-object semantic features. Nevertheless, there is still room for improvement in both its precision and recall metrics.Limitations and future considerationsAlthough the proposed VDP_UNet model exhibited strong overall performance in this study, several limitations remain. Some missed detections were observed, and the model’s precision still requires improvement. One contributing factor may be the limited availability of original zokor mound data, which could have hindered effective feature extraction and, consequently, model training. Additionally, the dataset used was confined to alpine meadows, which restricts the model’s applicability to a relatively narrow ecological context. This study also focused exclusively on newly formed mounds, whereas a more comprehensive evaluation of rodent damage in grasslands should include semi-new and old mounds as well. Moreover, no comparison was made between the actual measured areas of zokor mounds and the area estimates generated by the proposed method, leaving a gap in assessing the model’s practical utility.Future research will focus on the following three directions. First, beyond visible light imagery, multimodal approaches that incorporate thermal infrared data, textual descriptions, and other complementary sources should be explored to enhance the extraction of zokor mound features across diverse modalities. Integrating various data types may improve the model’s generalization capability in different environmental contexts. Second, while this study primarily demonstrated the feasibility of applying the segmentation model in alpine meadows, practical deployment remains limited. Future research could explore model lightweighting with respect to parameter count and model size, and incorporate boundary-aware loss and class-balance calibration to facilitate deployment on edge devices and support new application scenarios. Third, the scope of zokor mound research should be broadened to include different successional stages of mound development and to incorporate field-based area measurements. This includes, but is not limited to, identifying new, semi-new, and old mounds across diverse habitats such as alpine meadows and alpine shrub-meadows in regions like Qinghai, Tibet, Gansu, and Sichuan. These efforts will improve the applicability of mound area extraction methods, ensure greater consistency with real-world field conditions, and enhance both the model’s generalizability and the reliability of experimental outcomes.ConclusionThe widespread presence of zokor mounds poses a significant threat to the sustainable development of alpine meadow ecosystems. Accurately identifying their distribution not only facilitates the assessment of rodent damage but also serves as an indirect indicator of zokor activity intensity. To enhance the precision and efficiency of zokor mound extraction, this study proposes a deep semantic segmentation model—VDP_UNet—integrating a polarized self-attention mechanism. In VDP_UNet, the encoder is replaced with VGG16 to better capture global contextual information of mound regions. Additionally, a Polarized Self-Attention (PSA) block is introduced in the feature fusion stage following encoder-decoder skip connections to strengthen the representation of fine-grained features in complex backgrounds. The Dice loss function is employed to address sample imbalance and further improve overall model performance. Compared with classical semantic segmentation networks and several state-of-the-art methods, VDP_UNet achieves superior results across multiple evaluation metrics, demonstrating clear advantages in zokor mound extraction tasks. This approach provides a practical and effective solution for accurately detecting zokor mounds, offering strong potential for applications in rodent damage monitoring and ecological management. To support the real-world application of deep learning in this domain, a dedicated alpine meadow zokor mound dataset was constructed using UAV imagery collected at a 30-meter flight altitude. This dataset fills a critical gap in high-quality remote sensing data for zokor mounds and lays a solid foundation for future research and model development.

    Data availability

    1. The data that support the findings of this study are available in “Scicense Data Bank” at: [https://github.com/Yangyang875/Plateau-Zokor-Mounds] 2. The source code employed in the current research can be accessed on the GitHub page: [https://github.com/Yangyang875/VDP_UNet].
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    Lianguo Wang or Limin Hua.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleYang, Y., Wang, L. & Hua, L. Segmentation of plateau zokor mounds in alpine meadows from UAV images using an improved UNet network.
    Sci Rep 15, 43680 (2025). https://doi.org/10.1038/s41598-025-27413-yDownload citationReceived: 05 September 2025Accepted: 04 November 2025Published: 12 December 2025Version of record: 12 December 2025DOI: https://doi.org/10.1038/s41598-025-27413-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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    KeywordsSemantic segmentationVDP_UNet networkAlpine meadowZokor mound datasetRodent management More

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    Chromosome-level genome assembly and annotation of the kuruma shrimp Marsupenaeus japonicus

    AbstractThe kuruma shrimp Marsupenaeus japonicus is one of the most economically important shrimp species in the world. Here, we constructed a chromosome-level genome assembly of M. japonicus by combining PacBio long reads, Illumina short reads and Hi-C scaffolding. The genome size was 1.64 Gb with a scaffold N50 length of 40.61 Mb, and 97.83% (1.60 Gb) of the assembled sequences were anchored to 43 chromosomes. The genome contained 62.79% repeat sequences and 21,172 protein-coding genes, of which 83.20% were functionally annotated. The completeness of M. japonicus genome assembly is highlighted by a BUSCO score of 91.0%. Evolutionary analysis indicated that M. japonicus was closely related to Litopenaeus vannamei and Penaeus monodon, with an estimated divergence time from their common ancestor of 88.33 million years ago. In sum, the chromosome-level genome assembly and annotation provide fundamental resources for genetic breeding and molecular mechanism related studies of M. japonicus.

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    Data availability

    All the raw sequencing data used for genome assembly were deposited in the NCBI Sequence Read Archive (SRA) database under the accession number SRP58162351. The chromosome-level assembly of the M. japonicus genome was deposited in the European Nucleotide Archive (ENA) under the accession number PRJEB10264252. The genome annotation files were deposited at the figshare (https://doi.org/10.6084/m9.figshare.28874273.v1)53.
    Code availability

    All commands and pipelines used in data processing were executed according to the manual and protocols of the corresponding bioinformatic software. No specific code has been developed for this study.
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    Download referencesAcknowledgementsThis work was supported by the National Key R&D Program of China (2024YFD2401703), Fujian Special Fund for the Development of Marine and Fishery (FJHYF-L-2025-08), China Agriculture Research System (CARS-48), Fujian Special Fund for the Development of Marine and Fishery (FJHYF-ZH-2023-04), and Fujian Provincial Science and Technology Planning Projects (2022L3001).Author informationAuthor notesThese authors contributed equally: Yiming Wei, Hao Xu.Authors and AffiliationsState Key Laboratory of Marine Environmental Science; State Key Laboratory of Mariculture Breeding; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms; College of Ocean and Earth Sciences, Xiamen University, Xiamen, 361102, ChinaYiming Wei, Hao Xu, Huiyang Huang, Shaoxiong Ding & Yong MaoFisheries College, Tianjin Agricultural University, Tianjin, 300384, ChinaZhixiong ZhouJiangsu Key Laboratory of Marine Bioresources and Environment, Jiangsu Ocean University, Lianyungang, 222005, ChinaPanpan WangMarine Science Research Institute of Shandong Province (National Oceanographic Center, Qingdao), Qingdao, 266104, ChinaWenzhi ChengAuthorsYiming WeiView author publicationsSearch author on:PubMed Google ScholarHao XuView author publicationsSearch author on:PubMed Google ScholarZhixiong ZhouView author publicationsSearch author on:PubMed Google ScholarPanpan WangView author publicationsSearch author on:PubMed Google ScholarWenzhi ChengView author publicationsSearch author on:PubMed Google ScholarHuiyang HuangView author publicationsSearch author on:PubMed Google ScholarShaoxiong DingView author publicationsSearch author on:PubMed Google ScholarYong MaoView author publicationsSearch author on:PubMed Google ScholarContributionsY.W., H.X., H.H., S.D. and Y.M. conceived this project; Y.W., H.X., Z.Z., P.W. and W.C. collected the samples and performed the experiments; Y.W., H.X., Z.Z., P.W., W.C., H.H., S.D. and Y.M. performed the research and analyzed the data; Y.W., H.X., S.D. and Y.M. drafted the manuscript. All authors have read and approved the final manuscript for publication.Corresponding authorsCorrespondence to
    Shaoxiong Ding or Yong Mao.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleWei, Y., Xu, H., Zhou, Z. et al. Chromosome-level genome assembly and annotation of the kuruma shrimp Marsupenaeus japonicus.
    Sci Data (2025). https://doi.org/10.1038/s41597-025-06317-6Download citationReceived: 21 May 2025Accepted: 13 November 2025Published: 12 December 2025DOI: https://doi.org/10.1038/s41597-025-06317-6Share 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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    Aeolian sand migration induced land degradation and desertification hotspots identification in the semi-arid rain shadow regions of Anantapur, India

    AbstractThis study presents a multi-temporal geospatial modeling approach to identify and map land degradation and desertification hotspots in the aeolian-dominated semi-arid regions of Bommanahal, Beluguppa, and Kanekal Mandals of Anantapur District, Andhra Pradesh, India. Landsat 4–5 TM (1990), Landsat 7 ETM+ (2000, 2010), and Landsat 8 OLI/TIRS (2020) datasets were processed through a standardized workflow comprising radiometric calibration, atmospheric correction, LST retrieval, and spectral index computation. Three diagnostic indices: Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI), and Normalized Difference Salinity Index (NDSI), were integrated with Land Surface Temperature (LST) to quantify vegetation stress, soil texture variability, and salinity conditions. Correlation and regression analyses were employed to evaluate the relationships between LST and index-derived DN values, after which stratified sample extraction and buffer-based zonal statistics were used to delineate surface degradation intensity. A composite hotspot map was generated using mask extraction and cell statistics to merge the most degraded pixel clusters, identifying approximately 192 km2 as severe degradation zones. Model performance was validated using ROC-AUC analysis, yielding an accuracy of 0.851. The study demonstrates a reproducible GIS workflow for semi-arid degradation assessment and provides a robust spatial framework for targeted land restoration, sustainable resource planning, and long-term environmental management in vulnerable dryland ecosystems.

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    Data availability

    The raw data is obtained from NRSC Bhuvan and USGS website (https://bhuvan.nrsc.gov.in/ and https://earthexplorer.usgs.gov/) which is available free of cost and the findings of this study are available from the corresponding author, upon reasonable request.
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    Download referencesAcknowledgementsThe authors are grateful to the Director of the CSIR-National Geophysical Research Institute for providing the necessary facilities to conduct this work and for granting us permission to publish this paper. The first author expresses sincere gratitude to the Science and Engineering Research Board – National Post-Doctoral Fellowship (SERB-NPDF) for their valuable support (Fellowship Ref. No. PDF/2023/000774) during the tenure at CSIR-NGRI, Hyderabad. The authors also thank Editor-in-Chief/Handling Editor and anonymous reviewers for their constructive comments and valuable suggestions, which greatly improved the quality of the manuscript. This work is funded by the Main Lab Project MLP-7014 of the CSIR–National Geophysical Research Institute [Grant No. MLP-7014-28 (AKP)]. The CSIR-NGRI Library reference number is NGRI/Lib/2025/Pub-128.
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    Author informationAuthors and AffiliationsCSIR-National Geophysical Research Institute, Uppal Road, Hyderabad, Telangana, 500007, IndiaPradeep Kumar Badapalli & Sakram GugulothuDepartment of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, 516005, IndiaAnusha Boya Nakkala & Raghu Babu KottalaDepartment of Geology, Government College (Autonomous), Anantapur, Andhra Pradesh, 515001, IndiaPadma Sree PujariAuthorsPradeep Kumar BadapalliView author publicationsSearch author on:PubMed Google ScholarAnusha Boya NakkalaView author publicationsSearch author on:PubMed Google ScholarRaghu Babu KottalaView author publicationsSearch author on:PubMed Google ScholarPadma Sree PujariView author publicationsSearch author on:PubMed Google ScholarSakram GugulothuView author publicationsSearch author on:PubMed Google ScholarContributionsDr. Pradeep Kumar Badapalli: Manuscript preparation, Methodology creation and Validation. Remote Sensing and GIS mapping work. Mrs. Anusha Boya Nakkala: Manuscript statistics Generation, and correction. Dr. Raghu Babu Kottala: Manuscript corrections, English expert. Dr. Padma Sree Pujari: Manuscript corrections, data collection. Dr. Sakram Gugulothu: Manuscript corrections, corresponding author and validation.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleBadapalli, P.K., Nakkala, A.B., Kottala, R.B. et al. Aeolian sand migration induced land degradation and desertification hotspots identification in the semi-arid rain shadow regions of Anantapur, India.
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    KeywordsLand degradation hotspotsSpectral indicesCorrelationBuffer zonesGIS and remote sensing analysis More

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    Egg mass gelatinous envelopes of yellow goosefish Lophius litulon protect the internal eggs and larvae from salinity stress and desiccation

    AbstractSome teleosts, including yellow goosefish Lophius litulon, spawn gelatinous egg masses, but the biological function of the gelatinous matrix remains poorly understood due to limited experimental access. This study tested whether the gelatinous membrane of L. litulon egg masses, which naturally float just below the sea surface in coastal areas, protects eggs and larvae from abiotic stress. Salinity stress experiments using 782 and 605 eggs were conducted. Embryos retained within gelatinous membranes maintained high survival and normal growth after 45 and 90 h of exposure to 50% diluted salinity, whereas those removed showed significant reductions (two-way ANOVA; p < 0.001 and = 0.015, respectively). Additionally, experiments using 449 and 599 larvae examined responses to 24-hour exposure under desiccation and elevated temperature conditions (2–3 °C above the appropriate temperature). The gelatinous matrix retained moisture, showing stable survival and growth (two-way ANOVA; p = 0.838 and 0.804, respectively), although heat stress reduced survival (two-way ANOVA; p < 0.001). These results indicate that the gelatinous matrix offers physical protection from some types of environmental stress, likely an adaptive trait for survival in variable coastal habitats. This study provides the first experimental evidence of such protective functions in gelatinous egg masses of marine fishes.

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    Reprints and permissionsAbout this articleCite this articleIshikawa, T., Nakaya, M., Eno, Y. et al. Egg mass gelatinous envelopes of yellow goosefish Lophius litulon protect the internal eggs and larvae from salinity stress and desiccation.
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    Global species delimitation of smooth-shelled blue mussels in the Mytilus edulis complex

    AbstractThe species is often described as the basic unit of biology, yet defining what constitutes a species has been a long-standing challenge. The advent of modern molecular techniques in conjunction with advanced analytical approaches now provide new opportunities for the robust and repeatable delineation of taxa from previously difficult to assess species complexes. Using marine mussels of the genus Mytilus (smooth-shelled blue mussels of the globally distributed Mytilus edulis species complex) we tested regionally differentiated putative taxa from the Northern and Southern hemispheres using a Bayesian species delimitation model that infers species trees. Using a multilocus panel of 54 single nucleotide polymorphic loci (SNPs) we tested four alternative hypotheses against the hypothesis of the currently recognised taxonomy to better understand the evolutionary history and the contemporary species of this complex. Only one model provided a better fit than the contemporary taxonomy model: this best fit model included the three reference Northern hemisphere taxa (M. edulis, M. galloprovincialis, M. trossulus) plus M. chilensis (Chile), M. platensis (Argentina), M. planulatus (Australia) and the newly recognised M. aoteanus (New Zealand). Phylogenetic reconstruction based on our nuclear DNA-based SNP data suggests that M. trossulus is the oldest of the modern smooth-shelled blue mussels, that a first migration event from the north to the south occurred that gave rise to M. platensis and M. chilensis in South America, subsequently that M. edulis and M. galloprovincialis diverged in the Northern hemisphere, and that subsequently again there was a second migration event from the north that gave rise to M. planulatus in Australia and M. aoteanus in New Zealand. Our findings provide very strong support for earlier mitochondrial DNA-based phylogenetic findings for globally distributed blue mussels and also help to clarify uncertainty about the number of north-to-south migration events that gave rise to important Mytilus speciation events in South America and in Australasia.

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    Data availability

    All data analysed during this study are included in this published article. Additionally, the DNA sequences are stored in GenBank (KT713378–82; HQ257471; KJ871039–57; KT713368–74).
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    Download referencesAcknowledgementsWe thank the CIPRES Science Gateway for providing access to its platform, which facilitated several of our analyses.FundingThis work was funded by the projects FONDECYT 1230212 (JT) and FONDECYT 1251077 (POC).Author informationAuthors and AffiliationsCentro de Investigación Marina Quintay (CIMARQ), Universidad Andres Bello, Quintay, 2340000, ChilePablo A. OyarzúnDepartamento de Acuicultura y Recursos Agroalimentarios, Universidad de Los Lagos, Av. Fuchslocher 1305, Osorno, ChileJorge E. ToroInstitute of Oceanology, Polish Academy of Sciences, Powstańców Warszawy 55, Sopot, 81-712, PolandMałgorzata Zbawicka & Roman WenneSchool of Biological Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New ZealandJonathan P. A. GardnerAuthorsPablo A. OyarzúnView author publicationsSearch author on:PubMed Google ScholarJorge E. ToroView author publicationsSearch author on:PubMed Google ScholarMałgorzata ZbawickaView author publicationsSearch author on:PubMed Google ScholarRoman WenneView author publicationsSearch author on:PubMed Google ScholarJonathan P. A. GardnerView author publicationsSearch author on:PubMed Google ScholarContributionsP.A.O.: Conceptualisation, methodology, and writing—review and editing. J.E.T.: Investigation and validation. M.Z.: Investigation and validation. R.W.: Investigation and validation. J.P.A.G.: Conceptualisation, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
    Pablo A. Oyarzún.Ethics declarations

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    The authors declare no competing interests.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleOyarzún, P.A., Toro, J.E., Zbawicka, M. et al. Global species delimitation of smooth-shelled blue mussels in the Mytilus edulis complex.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-29759-9Download citationReceived: 05 September 2025Accepted: 19 November 2025Published: 12 December 2025DOI: https://doi.org/10.1038/s41598-025-29759-9Share 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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    KeywordsCryptic speciesMolluscsSNPsSpeciationTaxonomy More