Philippa Kaur

The structure and microstructure of the fibresThe surfaces of the natural fibres are presented from Figs. 6, 7, 8, 9, 10 and of the synthetic fibres are presented in Figs. 11 and 12.Figure 6SEM of jute fibre [Fot.M.Kurpińska].Full size imageFigure 7SEM of bamboo fibre [Fot.M.Kurpińska].Full size imageFigure 8SEM of sisal fibre [Fot.M.Kurpińska].Full size imageFigure 9SEM of cotton fibre [Fot.M.Kurpińska].Full size imageFigure 10SEM of ramie fibre [Fot.M.Kurpińska].Full size imageFigure 11SEM of polymer fibre [Fot.M.Kurpińska].Full size imageFigure 12SEM of polypropylene (PP) fibre [Fot.M.Kurpińska].Full size imageThe basic components of natural fibres influencing their properties are cellulose, hemicellulose, lignin, waxes, oils, and pectin. Cellulose is mainly composed of three elements such as carbon, hydrogen, and oxygen, and it is the material basis that forms the cell wall natural fibre. Typically, cellulose remains in the form of micro-fibrils within the cell wall of a plant. Cellulose is the main factor affecting the tensile strength along natural fibre and the cellulose content is closely related to the plant’s age and content decreases with the increasing age of the plant6.Hemicellulose is an amorphous substance offering a low degree of polymerization and it exists between fibres. Hemicellulose is a complex polysaccharide with xylan as the predominant chain, and the branches mainly include 4-O-methyl-D-glucuronic acid, L-arabinose, and D-xylose. Lignin is a kind of polymer with complex structures and of many types. The basic units of lignin include: guaiacyl, syringyl monomers, and p-hydroxyphenyl monomers. The structural units in lignin are mainly connected by ether bonds and carbon–carbon single bonds. Usually, lignin is not evenly distributed in the plant fibre wall9.In addition to three main components, lignin often contains various sugars, fats, protein substances, and a small amount of ash elements. These chemical compositions affect not only the properties of natural fibres, but also the possibility of a specific application of fibre. The composition of individual natural fibres and their properties are presented in Table 1. Figure 6a–c shows longitudinal and cross-sectional views of the untreated jute fibre. Externally, the fibre is smooth and shiny. The presence of hemicellulose influences the high hygroscopicity of jute fibres. The structure of the jute fibre shows that the fibre swells when it absorbs water. Possible swelling of the fibre in the cross-section by approx. 30%. The microscope scans of indicate the succinylated regions. This is due to the chemical bonding of the succinic anhydride molecule with the hydroxyl group of the cellulose present in the fibre. The encircled region in the top side shows an unsuccinylated region with naturally waxy impurities16.Figure 7a shows the scanning electron micrograph (SEM) of the bamboo fibre. According to the SEM analysis, the microstructure of bamboo is anisotropic. At the Fig. 7b–c it can be recognized that the orientation of cellulose fibrils was placed almost along the fibre axis which may affect to maximize the modulus of elasticity. Factors affect the mechanical properties of bamboo fibres are the chemical composition and structure of bamboo fibres, moisture content, age of bamboo, etc. In addition, the age of the plant affects the chemical composition and structure of fibre. These factors and the natural humidity influence their change of mechanical properties. The hemicellulose content directly influences the tensile strength. This parameter increases with the decrease in the hemicellulose content in the bamboo fibre18.The cell structure of bamboo fibres is complex, and the middle layer of the cell wall has a multi-layer structure. The lignification of the thin and thick layers in the multilayer structure varies. The multi-layered cell wall structure leads to better fracture resistance and promotes internal sliding between the cell wall layers during tension. The angle of the microfiber alignment is also an important factor influencing the mechanical properties of the fibre. Typically, the tensile strength and modulus of elasticity of a fibre increase as the angle between the interposition of the microfibers decreases. Hence, the smaller microfibril angle is an important factor that contributes to the good mechanical properties of bamboo fibre. Large voids between bamboo fibre molecules can be seen, which impact good hygroscopicity19. The moisture content is an important factor affecting the mechanical properties of bamboo fibres. Figure 8a–c shows the morphology of the sisal fibre. The surface of the sisal fibre has higher roughness, and it increases the bonding area between the fibre and cement paste. This leads to increase the mechanical properties of the composites38.Figure 9a–c shows images of the cotton fibres. At the microscope image, a cotton fibre looks like a twisted ribbon or a collapsed and twisted tube. These twists are called convolutions: there are about 60 convolutions per centimetre. The weaves give the cotton an uneven surface of the fibres, which increases the friction between the fibres, but at the same time they can prevent fibres from evenly dispersing in the cement matrix. The outer layer, the cuticle is a thin film of mostly fats and waxes. Figure 9b shows the waxy layer surface with some smooth grooves. The waxy layer forms a thin sheet over the primary wall that forms grooves on the cotton surface19. The cotton fibre surface comprises non-cellulosic materials and amorphous cellulose in which the fibrils are arranged in a criss-cross pattern. Owing to the non-structured orientation of cellulose and non-cellulosic materials, the wall surface is unorganized and open. This gives flexibility to the fibre. The basic ingredients, responsible for the complicated interconnections in the primary wall, are cellulose, hemicelluloses, pectin, proteins, and ions. In the core of fibre, only the crystalline cellulose is present, what is highly ordered and has a compact structure with the cellulose fibrils lying parallel to one another18.SEM micrograph of the surface and cross section of the ramie fibre are shown at Fig. 10a–c. The surface of the ramie fibres is dense but porous. There are many micropores and continuous bubbles in the porous structure of a single bundle of a ramie fibre Fig. 10c. This structure has some effect for low absorption of water, moreover, it is also related to the fibre distribution in the cement composites. In case of the short ramie fibre, due to its random distribution in composites, the strength of the composite may be affected. Cellulose, lignin, and hemicellulose weight materials can form a dense layer on the surface of the ramie fibres, so the water absorptivity is low. This special structure of the fibre with a dense matrix, and at the same time, with a characteristic pore arrangement has an influence on the adhesion of the cement matrix and the strength of the cement composite18.The surface and cross section of multifilament macrofibre is demonstrated at Fig. 11a–c. From the chemical point of view, this type of fibres belongs to the polymers from the group of polyolefins, composed of units of the formula: –[CH2CH (CH3)]–. They are obtained by low-pressure polymerization of propylene. They are made of 100% pure co-polymer twisted bundles of multifilament fibres Fig. 11c. Polypropylene is one of two most commonly used plastics, in addition to polyethylene. Polypropylene is a hydrocarbon thermoplastic polymer2.Figure 12a–c shows the structure of a bundle of polypropylene (PP) fibres in the form of a 3D mesh. They are made of isotactic polypropylene, called propylene, CH2=CHCH3 obtained from crude oil. They are one of the finest polypropylene fibres. The surface of the fibres is smooth Fig. 12b 2.The consistency—fluidityThe results of fluidity are shown at Fig. 13. The fluidity of the composite not modified with fibres is 145 mm and is a reference to other test results. The use of bamboo fibres increased the composite fluidity and composite flow by 8.6% (157.5 mm). The use of polymer fibers and jute increased the consistency by about 7%, while the use of sisal fibres by 3%. The use of PP fibres (122.5 mm) had the greatest impact on the loss of consistency by 15.5%. The use of cotton and frame fibres resulted in a reduction of workability and consistency by 13.8% and 3.5%, respectively.Figure 13Results of fluidity test.Full size imageBased on the research results, it was found that in the case of using bamboo fibres characterizing a high absorption of 120–145%, the consistency of composite increased by 8.2% compared to the consistency of composite without fibres. In the case of a change in consistency, the chemical composition of natural fibres, their surface, and the total length in the volume of composite are significant, too. There is a noticeable regularity related to the cellulose content in natural fibres. If the higher cellulose content, it reduces the consistency of the composite. For example, the cellulose content in bamboo fibres is the lowest and amounts to 40–45%, while the cellulose content in cotton fibres is the highest, ranging from 80 to 94%. It can also be recognized that consistency and workability will be influenced by the hemicellulose content.The higher the hemicellulose content, it impacts the higher consistency of the composite. It is similar referring to the content of lignin. It was noticed that the higher the lignin content, the higher the composite consistency was found. Regarding the total length of the fibres, a regularity is apparent that the greater the total length of fibres, e.g., in the case of cotton fibres, the greater decrease in consistency is visible. In the case of polymer and polypropylene (PP) fibres, the consistency is influenced by the surface of the fibre, the number of fibres, and their total length in the volume of the composite. Increasing the total length of PP fibres by approx. 15% resulted in a reduction of the consistency of approx. 20%.Flexural and compressive strengthAssigning mechanical properties of fibre reinforced composite, particular emphasis was placed on the determination of the flexural strength of the composite. This parameter was appointed by the 3-point test. Figure 14. shows the flexural strength of plain composite and 7 groups of different fibre reinforced composites on the 2nd, 7th, 28th, and 56th days.Figure 14Flexural strength test results.Full size imageIt can be seen that the bending strength of composites with the addition of natural fibres, ramie, bamboo, jute, and sisal are similar. The bending strength of composites with PP and polymer fibres is lower. It should be noted that the strength of the cotton fibre-reinforced composite is much lower than that of all the others tested. The reason may be the low tensile strength of the cotton fibres used. When mixing the composites, a tendency to create conglomerates of cotton fibres was also noticed, which may affect the strength of the composites.The test results clearly show that the effectiveness of the added natural fibres depends on the chemical composition and mechanical properties, and above all, on their adhesion to the cement matrix. The adhesion of the natural fibre to the cement matrix has a significant influence on the mechanical properties of the cement composite, in particular on compression and bending strength. The highest bending strength was achieved by cement composites modified with ramie fibres. Ramie fibres are characterized by the highest tensile strength among the tested synthetic and natural fibres, ranging from 400 to 1000 MPa. The results of the compressive strength are shown in Fig. 15.Figure 15Compressive strength test results.Full size imageThe analysis of the test results shows that the use of dispersed fibres reduced the early compressive strength after 2 days from 8.5 to 33%. The exception is the ramie fibres, the use of which increased the early strength by 6.6%. Within 28 days, as in the case of early strength, the use of all types of synthetic and natural fibres resulted in a decrease in strength from 4.6 to 26.5%. The exception is the use of ramie fibres, which increased the compressive strength by 7.2% after 28 days. After 56 days, a decrease in strength was noticed in the case of using PP and polymer synthetic fibres as well as natural cotton and bamboo from 5.5 to 11.9%.On the other hand, the increase in compressive strength after 56 days from 5.8 to 16.4% was visible in the case of using fibres such as sisal, jute and ramie. The highest compressive strength was achieved by the composite with a ramie fibre. The fibre of the ramie is characterized by the highest modulus of elasticity ranging from 24.5 to 128 GPa and is over 100% higher than the Young’s modulus of the other fibres.Shrinkage testFigure 16A shows that the samples after demolding showed expansion for about 2 days, and from the third day after demolding, the length of the samples was shortened. The lowest degree of expansion in the first days was shown by samples without fibres and samples containing cotton fibres. In this case, the expansion did not exceed 0.02 mm/m. However, the same samples finally showed the highest shrinkage after 180 days, which was 0.06 mm/m.Figure 16Testing the change in length of samples.Full size imageThe highest expansion within 48 h after deformation was shown by samples containing sisal fibres, while these samples finally after 180 days showed the lowest deformation of the length of the samples, which was 0.001 mm/m. The samples containing the synthetic fibres showed an expansion of about 0.02–0.03 mm/m in 48 h and the final shrinkage after 180 days was 0.03 mm/m for both the polymer and PP fibre samples. The bamboo and ramie fibres initially showed an expansion of 0.04–0.06 mm/m while their final shrinkage was 0.02 mm/m. The samples with jute fibres showed an expansion of 0.04 mm/m and the final shrinkage of the samples was 0.04 mm/m. Figure 16a,b shows the results of testing the change in length of samples over time.After 180 days, the total deformation of the samples was determined. Samples containing sisal fibers showed a slight expansion of about 0.001 mm/m, while the highest deformation (shrinkage) was shown for composite samples without fibers and with cotton fibres, which was 0.06 mm/m. Samples with bamboo, jute, PP, polymer and ramie fibres showed a shrinkage from 0.02 to 0.04 mm/m. Only the samples containing the sisal fibre showed a slight expansion of 0.001 mm/m.Ultimately, the samples containing sisal fibres were characterized by the lowest deformability. This phenomenon is related to the fibre structure and the total length of the fibres in a sample with dimensions of 40 × 40 × 160mm. For example, in a sample containing sisal fibres, their total length is 5856.7 m. Otherwise, a sample containing jute fibres, their total length in the sample is only 7.4 m. Therefore it was found that the fibre structure, its diameter, the cellulose content and the total length of the fibres in the element are important factors of deformation as a result of shrinkage or expansion of the fibre reinforced composite.Water absorption of composite testHigher water absorption (8.5%) compared to the composite without fibres was noticed in the case of using both synthetic fibres and with the exception of the use of ramie fibres, which caused a slight reduction in water absorption to 8.2%. It can be recognized that the water absorption rate of the 8 groups of samples is slightly different, the highest is the polymer fibre-reinforced composite (9.2%); the lowest water absorption rate refers to ramie fibre-reinforced composite (8.2%). The difference in water absorption rates is presented at Fig. 17.Figure 17Water absorption of composite (%).Full size imageExcept for cotton fibre-reinforced composite, the water absorption rate of another plant fibre-reinforced composite is lower than that of synthetic fibre-reinforced composite. Probably because of the fact that ramie, sisal, and jute fibres all have good moisture absorption and release properties. It is commonly known that plant fibre-reinforced cement-based materials have reduced strength and initial properties due to their performance degradation in a humid environment, so their long-term durability could become problematic. Sisal fibres (with noticed absorption of 95–100%) have absorbed more cement slurry on their surface than jute fibres (absorption of fibre 7–12%). This phenomenon could be explained by the fact that the slurry became the impregnation of the fibre. The absorbability of the composite was tested after the composite had completely hardened. Probably a fibre that is characterized by high absorption—sisal is very well “embedded” in the matrix, therefore the bending strength results for composites with sisal fibre were higher by 8–10%. More

Millien, V. et al. Ecotypic variation in the context of global climate change: Revisiting the rules. Ecol. Lett. 9, 853–869 (2006).Article 
PubMed 

Google Scholar 
Calder, W. A. Size, Function and Life History (Harvard University Press, 1984).
Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Göttinger Stud. 3, 595–708 (1847).
Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. PNAS 116, 21609–21615 (2019).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235 (1964).Article 
ADS 

Google Scholar 
Lomolino, M. V. Body size evolution in insular vertebrates: Generality of the island rule. J. Biogeogr. 32, 1683–1699 (2005).Article 

Google Scholar 
Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).Article 
PubMed 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Meiri, S., Cooper, N. & Purvis, A. The island rule: Made to be broken?. Proc. R. Soc. B. 275, 141–148 (2008).Article 
PubMed 

Google Scholar 
Millien, V. Relative effects of climate change, isolation and competition on body-size evolution in the Japanese field mouse, Apodemus argenteus. J. Biogeogr. 31, 1267–1276 (2004).Article 

Google Scholar 
Millien, V. & Damuth, J. Climate change and size evolution in an island rodent species: New perspectives on the island rule. Evolution 58, 1353–1360 (2004).Article 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Riddle, B. R. & Brown, J. H. The island rule and a research agenda for studying ecogeographical patterns. J. Biogeogr. 33, 1503–1510 (2006).Article 

Google Scholar 
Sargis, E. J., Millien, V., Woodman, N. & Olson, L. E. Rule reversal: Ecogeographical patterns of body size variation in the common treeshrew (Mammalia, Scandentia). Ecol. Evol. 8, 1634–1645 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnosky, A. D., Hadly, E. A. & Bell, C. J. Mammalian response to global warming on varied temporal scales. J. Mammal. 84, 354–368 (2003).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 
ADS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 
PubMed 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Bergmann’s rule and climate change revisited: Disentangling environmental and genetic responses in a wild bird population. PNAS 105, 13492–13496 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: Is there any evidence?. Evol. Appl. 7, 156–168 (2014).Article 
PubMed 

Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51, 385–390 (1970).Article 

Google Scholar 
Wigginton, J. D. & Dobson, F. S. Environmental influences on geographic variation in body size of western bobcats. Can. J. Zool. 77, 802–813 (1999).Article 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148, 213–218 (2006).Article 
PubMed 
ADS 

Google Scholar 
Wagner, J. A. Schreber’s saugthiere, supplementband, 2. Abtheilung 1841(37–44), 553 (1841).
Google Scholar 
Hawkins, M. T. Family Tupaiidae (treeshrews). In Handbook of the Mammals of the World, Volume 8 Insectivores, Sloths and Colugos (eds Wilson, D. E. & Mittermeier, R. A.) (Lynx Edicions, 2018).
Google Scholar 
Roberts, T. E., Lanier, H. C., Sargis, E. J. & Olson, L. E. Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia. Mol. Phylogenet. Evol. 60, 358–372 (2011).Article 
PubMed 

Google Scholar 
Zhang, L., Yang, F., Wang, Z. K. & Zhu, W. L. Role of thermal physiology and bioenergetics on adaptation in tree shrew (Tupaia belangeri): The experiment test. Sci. Rep. 7, 41352 (2017).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Zhu, W., Zhang, H. & Wang, Z. Seasonal changes in body mass and thermogenesis in tree shrews (Tupaia belangeri): The roles of photoperiod and cold. J. Therm. Biol. 37, 479–484 (2012).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0 (2017).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.4 (2020).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Helgen, K. M. Order Scandentia. In Mammal Species of the World: A Taxonomic and Geographic Reference 3rd edn (eds Wilson, D. E. & Reeder, D. M.) (Johns Hopkins University Press, 2005).
Google Scholar 
Collins, P. M. & Tsang, W. N. Growth and reproductive development in the male tree shrew (Tupaia belangeri) from birth to sexual maturity. Biol. Reprod. 37, 261–267 (1987).Article 
CAS 
PubMed 

Google Scholar 
Heaney, L. R. Island area and body size of insular mammals: Evidence from the tri-colored squirrel (Callosciurus prevosti) of Southeast Asia. Evolution 32, 29–44 (1978).PubMed 

Google Scholar 
Husson, L., Boucher, F. C., Sarr, A. C., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47, 843–853 (2020).Article 

Google Scholar 
Juman, M. M., Woodman, N., Olson, L. E. & Sargis, E. J. Ecogeographic variation and taxonomic boundaries in Large Treeshrews (Scandentia, Tupaiidae: Tupaia tana Raffles, 1821) from Southeast Asia. J. Mammal. 102, 1054–1066 (2021).Article 

Google Scholar 
Hinckley, A. et al. Challenging ecogeographical rules: Phenotypic variation in the Mountain Treeshrew (Tupaia montana) along tropical elevational gradients. PLoS ONE 17, e0268213 (2022).Article 
CAS 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Palombo, M. R. & van der Geer, A. A. Of mice and mammoths: evaluations of causal explanations for body size evolution in insular mammals. J. Biogeogr. 39, 842–854 (2011).Article 

Google Scholar 
Teta, P., de la Sancha, N. U., D’Elía, G. & Patterson, B. D. Andean rain shadow effect drives phenotypic variation in a widely distributed Austral rodent. J. Biogeogr. 49, 1767–1778 (2022).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, S. Climatic change and body size in two species of Japanese rodents. Biol. J. Linn. Soc. 82, 263–267 (2004).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, J. Global warming, Bergmann’s rule and body size in the masked shrew Sorex cinereus in Alaska. J. Anim. Ecol. 74, 803–808 (2005).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 
PubMed 

Google Scholar 
Cronk, Q. C. B. Islands: stability, diversity, conservation. Biodivers. Conserv. 6, 477–493 (1997).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. PNAS 106, 9322–9327 (2009).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Recent spatial and temporal changes in body size of terrestrial vertebrates: Probable causes and pitfalls. Biol. Rev. 86, 531–541 (2011).Article 
PubMed 

Google Scholar 
Theriot, M. K., Lanier, H. C. & Olson, L. E. Harnessing natural history collections to detect trends in body-size change as a response to warming: A critique and review of best practices. Methods Ecol. Evol. (2022).Rohwer, V. G., Rohwer, Y. & Dillman, C. B. Declining growth of natural history collections fails future generations. PLoS Biol. 20, e3001613 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Morphological distinctiveness of Javan Tupaia hypochrysa (Scandentia, Tupaiidae). J. Mammal. 94, 938–947 (2013).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Island history affects faunal composition: The treeshrews (Mammalia: Scandentia: Tupaiidae) from the Mentawai and Batu Islands, Indonesia. Biol. J. Linn. Soc. 111, 290–304 (2014).Article 

Google Scholar 
Sargis, E. J., Campbell, K. K. & Olson, L. E. Taxonomic boundaries and craniometric variation in the treeshrews (Scandentia, Tupaiidae) from the Palawan faunal region. J. Mamm. Evol. 21, 111–123 (2014).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Bell, T. N. & Olson, L. E. Skeletal variation and taxonomic boundaries among mainland and island populations of the common treeshrew (Mammalia: Scandentia: Tupaiidae). Biol. J. Linn. Soc. 120, 286–312 (2017).
Google Scholar 
Juman, M. M., Olson, L. E. & Sargis, E. J. Skeletal variation and taxonomic boundaries in the Pen-tailed Treeshrew (Scandentia, Ptilocercidae: Ptilocercus lowii Gray, 1848). J. Mamm. Evol. 28, 1193–1203 (2021).Article 

Google Scholar 
Juman, M. M., Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Taxonomic boundaries in Lesser Treeshrews (Scandentia, Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. https://doi.org/10.1093/jmammal/gyac080 (2022).Article 

Google Scholar 
Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Coming of age: Morphometric variation in the hand skeletons of juvenile and adult Lesser Treeshrews (Scandentia: Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. 101, 1151–1164 (2020).Article 

Google Scholar 
Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L. & Ram, K. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.2, https://CRAN.R-project.org/package=rgbif.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. 7, 109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Meiyappan, P. & Jain, A. K. Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front. Earth Sci. 6, 122–139 (2012).Article 
ADS 

Google Scholar 
Ryan, W. B. F. et al. Global multi-resolution topography synthesis. Geochem. Geophys. 10, Q03014 (2009).
Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Clavel, J., Merceron, G. & Escarguel, G. Missing data estimation in morphometrics: How much is too much? Syst. Biol. 63, 203–218 (2014).Article 
PubMed 

Google Scholar 
Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659–660 (2004).Article 

Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).Book 
MATH 

Google Scholar  More

Al-Mutawa, A. M., Al Murbati, W. M., Al Ruwaili, N. A., Al Orafi, A. S., Al Orafi, A., Al Arafati, A., Nasrullah, A., Al Bahow, M. R., Al Anzi, S. M., Rashisi, M. & Al Moosa, S. Z. Desalination in the gcc. the history, the present & the future. Available from: https://www.gcc-sg.org/en-us/CognitiveSources/DigitalLibrary/Lists/DigitalLibrary/WaterandElectricity/1414489603.pdf Second edition, The Cooperation Council for the Arab States of the Gulf (GCC) General Secretariat (2014).Global Water Intelligence. DesalData. https://www.desaldata.com/. Accessed 2022-05-01 (2022).Sharifinia, M., Afshari Bahmanbeigloo, Z., Smith Jr, W. O., Yap, C. K. & Keshavarzifard, M. Prevention is better than cure: Persian gulf biodiversity vulnerability to the impacts of desalination plants. Glob. Change Biol. 25(12), 4022–4033 (2019).Article 

Google Scholar 
Connor, R. The United Nations World Water Development Report 2015: Water for a Sustainable World. Number 79. UNESCO, (2015).Al-Senafy, M., Al-Fahad, K. & Hadi, K. Water management strategies in the Arabian gulf countries. In Developments in Water Science, volume 50, pages 221–224. Elsevier, (2003).Ulrichsen, K.C.. Internal and external security in the arab gulf states. Middle East Policy16(2), 39 (2009).Verner, D. Adaptation to a changing climate in the Arab countries: a case for adaptation governance and leadership in building climate resilience. Number 79. World Bank Publications, (2012).Einav, R., Harussi, K. & Perry, D. The footprint of the desalination processes on the environment. Desalination 152(1–3), 141–154 (2003).Article 

Google Scholar 
Dawoud, M. A. Environmental impacts of seawater desalination: Arabian Gulf case study. Int. J. Environ. Sustain.1(3) (2012).Chow, A. C. et al. Numerical prediction of background buildup of salinity due to desalination brine discharges into the Northern Arabian Gulf. Water 11(11), 2284 (2019).Article 

Google Scholar 
Lee, K. & Jepson, W. Environmental impact of desalination: A systematic review of life cycle assessment. Desalination 509, 115066 (2021).Article 

Google Scholar 
Hosseini, H. et al. Marine health of the Arabian gulf: Drivers of pollution and assessment approaches focusing on desalination activities. Mar. Pollut. Bull. 164, 112085 (2021).Article 
PubMed 

Google Scholar 
Le Quesne, W. J. F. et al. Is the development of desalination compatible with sustainable development of the Arabian Gulf?. Mar. Pollut. Bull. 173, 112940 (2021).Article 
PubMed 

Google Scholar 
Kress, N., & Galil, B. Impact of seawater desalination by reverse osmosis on the marine environment. Efficient Desalination by Reverse Osmosis: A guide to RO practice. IWA, London, UK, pp. 177–202 (2015).Reynolds, R. M. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman: Results from the Mt Mitchell expedition. Mar. Pollut. Bull. 27, 35–59 (1993).Article 

Google Scholar 
Swift, S. A. & Bower, A. S. Formation and circulation of dense water in the Persian/Arabian Gulf. J. Geophys. Res. Oceans 108(C1), 1–4 (2003).Article 

Google Scholar 
Pous, S. P., Carton, X., & Lazure, P. Hydrology and circulation in the strait of hormuz and the Gulf of Oman: Results from the gogp99 experiment: 1. strait of hormuz. J. Geophys. Res. Oceans109(C12), (2004).Pous, S., Lazure, P. & Carton, X. A model of the general circulation in the persian gulf and in the strait of hormuz: Intraseasonal to interannual variability. Cont. Shelf Res. 94, 55–70 (2015).Article 

Google Scholar 
Johns, W. E., Yao, F., Olson, D. B., Josey, S. A., Grist, J. P. & Smeed, D. A. Observations of seasonal exchange through the Straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. J. Geophys. Res. Oceans108(C12) (2003).Hassanzadeh, S., Hosseinibalam, F. & Rezaei-Latifi, A. Numerical modelling of salinity variations due to wind and thermohaline forcing in the Persian gulf. Appl. Math. Model. 35(3), 1512–1537 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Price, A. R. G. Western Arabian gulf echinoderms in high salinity waters and the occurrence of dwarfism. J. Nat. Hist. 16(4), 519–527 (1982).Article 

Google Scholar 
Sheppard, C. R. C. Similar trends, different causes: Responses of corals to stressed environments in Arabian seas. In Proceedings of the 6th International Coral Reef Symposium Townsville, Australia, volume 3, pp. 297–302 (1988).Coles, S. L. & Jokiel, P. L. Effects of salinity on coral reefs. In Connell, D. W., & Hawker, D. W. editors, Pollution in tropical aquatic systems, pp. 147–166. CRC Press, Florida (1992).Coles, S. L. Coral species diversity and environmental factors in the Arabian gulf and the Gulf of Oman: A comparison to the Indo-Pacific region. Atoll Res. Bull. (2003).D’Agostino, D. et al. Growth impacts in a changing ocean: Insights from two coral reef fishes in an extreme environment. Coral Reefs 40(2), 433–446 (2021).Article 

Google Scholar 
Bœuf, G. & Payan, P. How should salinity influence fish growth?. Compar. Biochem. Physiol. Part C Toxicol. Pharmacol. 130(4), 411–423 (2001).Article 

Google Scholar 
Baudron, A. R., Needle, C. L., Rijnsdorp, A. D. & Marshall, C. T. Warming temperatures and smaller body sizes: Synchronous changes in growth of north sea fishes. Glob. Change Biol. 20(4), 1023–1031 (2014).Article 

Google Scholar 
Dore, M. H. I. Forecasting the economic costs of desalination technology. Desalination 172(3), 207–214 (2005).Article 

Google Scholar 
Karagiannis, I. C. & Soldatos, P. G. Water desalination cost literature: Review and assessment. Desalination 223(1–3), 448–456 (2008).Article 

Google Scholar 
Al Barwani, H. H. & Purnama, A. Evaluating the effect of producing desalinated seawater on hypersaline Arabian Gulf. Eur. J. Sci. Res. 22(2), 279–285 (2008).
Google Scholar 
Lee, W. & Kaihatu, J. M. Effects of desalination on hydrodynamic process in Persian Gulf. Coast. Eng. Proc. 36, 3–3 (2018).Article 

Google Scholar 
Ibrahim, H. D. & Eltahir, E. A. B. Impact of brine discharge from seawater desalination plants on Persian/Arabian gulf salinity. J. Environ. Eng. 145(12), 04019084 (2019).Article 

Google Scholar 
Campos, E. J. D. et al. Impacts of brine disposal from water desalination plants on the physical environment in the Persian/Arabian Gulf. Environ. Res. Commun. 2(12), 125003 (2020).Article 

Google Scholar 
Ibrahim, H. D., Xue, P. & Eltahir, E. A. B. Multiple salinity equilibria and resilience of Persian/Arabian Gulf basin salinity to brine discharge. Front. Mar. Sci. 7, 573 (2020).Article 

Google Scholar 
Ibrahim, H. D. Simulated effects of seawater desalination on Persian/Arabian Gulf exchange flow. J. Environ. Eng. 148(4), 04022012 (2022).Article 

Google Scholar 
Purnama, A. Assessing the environmental impacts of seawater desalination on the hypersalinity of arabian/persian gulf. In The Arabian Seas: Biodiversity, Environmental Challenges and Conservation Measures, pp. 1229–1245. Springer, (2021).GEBCO Compilation Group. The GEBCO_2021 grid: A continuous terrain model of the global oceans and land, (2021).Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13(2), 224–230 (1961).Article 

Google Scholar 
Nakamura, M., Stone, P. H. & Marotzke, J. Destabilization of the thermohaline circulation by atmospheric eddy transports. J. Clim. 7(12), 1870–1882 (1994).Article 

Google Scholar 
Pasquero, C. & Tziperman, E. Effects of a wind-driven gyre on thermohaline circulation variability. J. Phys. Oceanogr. 34(4), 805–816 (2004).Article 

Google Scholar 
Lucarini, V. & Stone, P. H. Thermohaline circulation stability: A box model study. part ii: coupled atmosphere-ocean model. J. Clim. 18(4), 514–529 (2005).Article 

Google Scholar 
Wunsch, C. Thermohaline loops, stommel box models, and the sandström theorem. Tellus A Dyn. Meteorol. Oceanogr. 57(1), 84–99 (2005).
Google Scholar 
Privett, D. W. Monthly charts of evaporation from the N. Indian Ocean (including the Red Sea and the Persian Gulf). Q. J. R. Meteorol. Soc. 85(366), 424–428 (1959).Article 

Google Scholar 
Chao, S.-Y., Kao, T. W. & Al-Hajri, K. R. A numerical investigation of circulation in the Arabian Gulf. J. Geophys. Res. Oceans 97(C7), 11219–11236 (1992).Article 

Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730), 1999–2049 (2020).Article 

Google Scholar 
Thoppil, P. G. & Hogan, P. J. Persian Gulf response to a wintertime shamal wind event. Deep Sea Res. Part I 57(8), 946–955 (2010).Article 

Google Scholar 
Paparella, F., Chenhao, X., Vaughan, G. O. & Burt, J. A. Coral bleaching in the Persian/Arabian Gulf is modulated by summer winds. Front. Mar. Sci. 6, 205 (2019).Article 

Google Scholar 
Gutiérrez, J.M., Jones, R. G., Narisma, G.T., Alves, L.M., Amjad, M., Gorodetskaya, I.V., Grose, M., Klutse, N.A.B., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L.O., Mernild, S.H., Ngo-Duc, T., van den Hurk, B. & Yoon, J.-H. Atlas. In V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, editors, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, (2021). Available from http://interactive-atlas.ipcc.ch/.Alosairi, Y., Imberger, J., & Falconer, R. A. Mixing and flushing in the Persian Gulf (Arabian Gulf). J. Geophys. Res. Oceans116(C3) (2011).Whitehead, J. A. Internal hydraulic control in rotating fluids – applications to oceans. Geophys. Astrophys. Fluid Dyn. 48(1–3), 169–192 (1989).Article 
MATH 

Google Scholar 
Dougherty, W. W., Yates, D. N., Pereira, J. E., Monaghan, A., Steinhoff, D., Ferrero, B., Wainer, I., Flores-Lopez, F., Galaitsi, S., & Kucera, P., et al. The energy–water–health nexus under climate change in the united arab emirates: Impacts and implications. In Climate Change and Energy Dynamics in the Middle East, pp. 131–180. Springer, (2019).Al-Shehhi, M. R., Song, H., Scott, J. & Marshall, J. Water mass transformation and overturning circulation in the Arabian gulf. J. Phys. Oceanogr. 51(11), 3513–3527 (2021).
Google Scholar 
Hausfather, Z. & Peters, G. P. Emissions-the “business as usual’’ story is misleading. Nature 577, 618–620 (2020).Article 
PubMed 

Google Scholar 
Al-Ghouti, M. A., Al-Kaabi, M. A., Ashfaq, M. Y. & Da’na, D. A. Produced water characteristics, treatment and reuse: A review. J. Water Process Eng. 28, 222–239 (2019).Article 

Google Scholar 
Riegl, B. M. & Purkis, S. J. Coral reefs of the gulf: adaptation to climatic extremes in the world’s hottest sea. In Coral reefs of the Gulf, pp. 1–4. Springer, (2012).Burt, J. A. et al. Insights from extreme coral reefs in a changing world. Coral Reefs 39(3), 495–507 (2020).Article 

Google Scholar 
D’Agostino, D. et al. The influence of thermal extremes on coral reef fish behaviour in the Arabian/Persian gulf. Coral Reefs 39(3), 733–744 (2020).Article 

Google Scholar 
Lachkar, Z., Mehari, M., Lévy, M., Paparella, F., & Burt, J.A. Recent expansion and intensification of hypoxia in the Arabian gulf and its drivers. Front. Mar. Sci. 1616 (2022).De Verneil, A., Burt, J. A., Mitchell, M., & Paparella, F. Summer oxygen dynamics on a southern Arabian Gulf coral reef. Front. Mar. Sci. 1676 (2021).Petersen, K. L. et al. Impact of brine and antiscalants on reef-building corals in the gulf of aqaba-potential effects from desalination plants. Water Res. 144, 183–191 (2018).Article 
PubMed 

Google Scholar 
Sanchez-Lizaso, J. L. et al. Salinity tolerance of the mediterranean seagrass posidonia oceanica: recommendations to minimize the impact of brine discharges from desalination plants. Desalination 221(1–3), 602–607 (2008).Article 

Google Scholar 
Cambridge, M. L., Zavala-Perez, A., Cawthray, G. R., Mondon, J. & Kendrick, G. A. Effects of high salinity from desalination brine on growth, photosynthesis, water relations and osmolyte concentrations of seagrass posidonia australis. Mar. Pollut. Bull. 115(1–2), 252–260 (2017).Article 
PubMed 

Google Scholar 
Cambridge, M. L. et al. Effects of desalination brine and seawater with the same elevated salinity on growth, physiology and seedling development of the seagrass posidonia australis. Mar. Pollut. Bull. 140, 462–471 (2019).Article 
PubMed 

Google Scholar 
Kelaher, B. P., Clark, G. F., Johnston, E. L. & Coleman, M. A. Effect of desalination discharge on the abundance and diversity of reef fishes. Environ. Sci. Technol. 54(2), 735–744 (2019).Article 
PubMed 

Google Scholar 
Gegner, H. M. et al. High salinity conveys thermotolerance in the coral model aiptasia. Biol. Open 6(12), 1943–1948 (2017).PubMed 
PubMed Central 

Google Scholar 
Ochsenkühn, M. A., Röthig, T., D’Angelo, C., Wiedenmann, J. & Voolstra, C. R. The role of floridoside in osmoadaptation of coral-associated algal endosymbionts to high-salinity conditions. Sci. Adv. 3(8), e1602047 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Gegner, H. M. et al. High levels of floridoside at high salinity link osmoadaptation with bleaching susceptibility in the cnidarian-algal endosymbiosis. Biol. Open 8(12), bio045591 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Thoppil, P. G. & Hogan, P. J. A modeling study of circulation and eddies in the Persian Gulf. J. Phys. Oceanogr. 40(9), 2122–2134 (2010).Article 

Google Scholar 
Pous, S., Carton, X. & Lazure, P. A process study of the tidal circulation in the Persian gulf. Open J. Mar. Sci. 2(04), 131–140 (2012).Article 

Google Scholar 
Haney, R. L. Surface thermal boundary condition for ocean circulation models. J. Phys. Oceanogr. 1(4), 241–248 (1971).Article 

Google Scholar  More

MacPherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Bio. Ecol. 220, 127–150 (1998).Article 

Google Scholar 
Freitas, C., Olsen, E. M., Knutsen, H., Albretsen, J. & Moland, E. Temperature-associated habitat selection in a cold-water marine fish. J. Anim. Ecol. 85, 628–637 (2016).Article 
PubMed 

Google Scholar 
Michelot, C. et al. Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet. Deep. Res. Part II Top. Stud. Oceanogr. 141, 224–236 (2017).Article 

Google Scholar 
Davoren, G. K., Montevecchi, W. A. & Anderson, J. T. Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour. Mar. Ecol. Prog. Ser. 256, 229–242 (2003).Article 

Google Scholar 
Chiarello, A. G. et al. A translocation experiment for the conservation of maned sloths, Bradypus torquatus (Xenarthra, Bradypodidae). Biol. Conserv. 118, 421–430 (2004).Article 

Google Scholar 
Fukuda, Y. et al. Environmental resistance and habitat quality influence dispersal of the saltwater crocodile. Mol. Ecol. 31, 1076–1092 (2022).Article 
PubMed 

Google Scholar 
O’Leary, S. J., Dunton, K. J., King, T. L., Frisk, M. G. & Chapman, D. D. Genetic diversity and effective size of Atlantic sturgeon, Acipenser oxyrhinchus oxyrhinchus river spawning populations estimated from the microsatellite genotypes of marine-captured juveniles. Conserv. Genet. 15, 1173–1181 (2014).Article 

Google Scholar 
Brüniche-Olsen, A. et al. Genetic data reveal mixed-stock aggregations of gray whales in the North Pacific Ocean. Biol. Lett. 14, 1–4 (2018).Article 

Google Scholar 
Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias Del Sur) feeding ground. J. Hered. 111, 263–276 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bowen, A. B. W. et al. Origin of hawksbill turtles in a Caribbean feeding area as indicated by genetic markers. Ecol. Appl. 6, 566–572 (1996).Article 

Google Scholar 
Paxton, K. L., Yau, M., Moore, F. R. & Irwin, D. E. Differential migratory timing of western populations of Wilson’s Warbler (Cardellina pusilla) revealed by mitochondrial DNA and stable isotopes. Auk 130, 689–698 (2013).Article 

Google Scholar 
Anderson, E. C., Waples, R. S. & Kalinowski, S. T. An improved method for predicting the accuracy of genetic stock identification. Can. J. Fish. Aquat. Sci. 65, 1475–1486 (2008).Article 

Google Scholar 
Debevec, E. M. SPAM (version 3.2): Statistics program for analyzing mixtures. J. Hered. 91, 509–511 (2000).Article 
PubMed 

Google Scholar 
Bolker, B. M., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Incorporating multiple mixed stocks in mixed stock analysis: ‘Many-to-many’ analyses. Mol. Ecol. 16, 685–695 (2007).Article 
PubMed 

Google Scholar 
Neaves, P. I., Wallace, C. G., Candy, J. R. & Beacham, T. D. CBayes: Computer Program for Mixed Stock Analysis of Allelic Data. Free Program Distributed by the Authors Over the Internet. at (2005).Pella, J. & Masuda, M. Bayesian methods for analysis of stock mixtures from genetic characters. Fish. Bull. 99, 151–167 (2001).
Google Scholar 
Bolker, B., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Sea turtle stock estimation using genetic markers: Accounting for sampling error of rare genotypes. Ecol. Appl. 13, 763–775 (2003).Article 

Google Scholar 
Okuyama, T. & Bolker, B. M. Combining genetic and ecological data to estimate sea turtle origins. Ecol. Appl. 15, 315–325 (2005).Article 

Google Scholar 
Nishizawa, H. et al. Composition of green turtle feeding aggregations along the Japanese archipelago: Implications for changes in composition with current flow. Mar. Biol. 160, 2671–2685 (2013).Article 

Google Scholar 
Naro-Maciel, E. et al. Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: A focus on mtDNA and dispersal modelling. J. R. Soc. Interface 11, 20130888 (2014).Proietti, M. C. et al. Green turtle Chelonia mydas mixed stocks in the western South Atlantic, as revealed by mtDNA haplotypes and drifter trajectories. Mar. Ecol. Prog. Ser. 447, 195–209 (2012).Article 

Google Scholar 
van der Zee, J. P. et al. Population recovery changes population composition at a major southern Caribbean juvenile developmental habitat for the green turtle, Chelonia mydas. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
Shamblin, B. M. et al. Mexican origins for the Texas green turtle foraging aggregation: A cautionary tale of incomplete baselines and poor marker resolution. J. Exp. Mar. Bio. Ecol. 488, 111–120 (2017).Article 

Google Scholar 
Seminoff, J. A. et al. Status Review of the Green Turtle (Chelonia mydas) Under the Endangered Species Act. (NOAA Technical Memorandum, NOAA-NMFS-SWFSC, 2015).Chaloupka, M. et al. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

Google Scholar 
Bjorndal, K. A. & Bolten, A. B. Annual variation in source contributions to a mixed stock: Implications for quantifying connectivity. Mol. Ecol. 17, 2185–2193 (2008).Article 
PubMed 

Google Scholar 
Roland, J., Keyghobadi, N. & Fownes, S. Alpine Parnassius butterfly dispersal: Effects of landscape and population size. Ecology 81, 1642–1653 (2000).Article 

Google Scholar 
Vanschoenwinkel, B., De Vries, C., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).Article 

Google Scholar 
Shamblin, B. M. et al. Mitogenomic sequences better resolve stock structure of southern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2340 (2012).Article 
PubMed 

Google Scholar 
Witherington, B., Hirama, S. & Hardy, R. Young sea turtles of the pelagic Sargassum-dominated drift community: Habitat use, population density, and threats. Mar. Ecol. Prog. Ser. 463, 1–22 (2012).Article 

Google Scholar 
Putman, N. F. & Mansfield, K. L. Direct evidence of swimming demonstrates active dispersal in the sea turtle ‘lost years’. Curr. Biol. 25, 1221–1227 (2015).Article 
PubMed 

Google Scholar 
Mansfield, K. L., Wyneken, J. & Luo, J. First Atlantic satellite tracks of ‘lost years’ green turtles support the importance of the Sargasso Sea as a sea turtle nursery. Proc. R. Soc. B Biol. Sci. 288, 20210057 (2021).Putman, N. F. et al. Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean. Ecography (Cop.) 43, 506–517 (2020).Article 

Google Scholar 
Putman, N. F. & Naro-Maciel, E. Finding the ‘lost years’ in green turtles: Insights from ocean circulation models and genetic analysis. Proc. R. Soc. B Biol. Sci. 280, 20131468 (2013).Naro-Maciel, E., Hart, K. M., Cruciata, R. & Putman, N. F. DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate. Ecography (Cop.) 40, 586–597 (2017).Article 

Google Scholar 
Ehrhart, L. M., Redfoot, W. E. & Bagley, D. A. Marine turtles of the central region of the Indian River Lagoon system, Florida. Florida Sci. 70, 415–434 (2007).
Google Scholar 
Redfoot, W. & Ehrhart, L. Trends in size class distribution, recaptures, and abundance of juvenile green turtles (Chelonia mydas) utilizing a rock riprap lined embayment at Port Canaveral, Florida, USA, as developmental habitat. Chelonian Conserv. Biol. 12, 252–261 (2013).Article 

Google Scholar 
Ehrhart, L., Redfoot, W., Bagley, D. & Mansfield, K. Long-term trends in loggerhead (Caretta caretta) nesting and reproductive success at an important western Atlantic rookery. Chelonian Conserv. Biol. 13, 173–181 (2014).Article 

Google Scholar 
Bolten, A. B. Techniques for measuring sea turtles. in Research and Management Techniques for the Conservation of Sea Turtles. (eds. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A. & Donnelly, M.). 1–5 (1999).Bagley, D. A. Characterizing Juvenile Green Turtles, (Chelonia mydas), from Three East Central Florida Developmental Habitats. (University of Central Florida, 2003).Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Faircloth, B. & Glenn, T. Preparation of an AMPure XP Substitute. AKA Serapure https://doi.org/10.6079/J9MW2F26 (2016).Article 

Google Scholar 
Abreu-Grobois, F. A. et al. New mtDNA Dloop primers which work for a variety of marine turtle species may increase the resolution of mixed stock analyses. in Proceedings of the 26th Annual Symposium on Sea Turtle Biology. 179 (International Sea Turtle Society, 2006).Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).Article 
PubMed 

Google Scholar 
Wright, S. Evolution and the Genetics of Populations. Vol. 4. Variability Within and Among Natural Populations. (University of Chicago Press, 1978).Hays, G. C. Ocean currents and marine life. Curr. Biol. 27, R470–R473 (2017).Article 
PubMed 

Google Scholar 
Engstrom, T. N., Meylan, P. A. & Meylan, A. B. Origin of juvenile loggerhead turtles (Caretta caretta) in a tropical developmental habitat in Caribbean Panamá. Anim. Conserv. 5, 125–133 (2002).Article 

Google Scholar 
Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute, F. W. C. F. W. R. I. Index Nesting Beach Survey (INBS). (2021).Cuevas Flores, E. A., Guzmán Hernández, V., Guerra Santos, J. J. & Rivas Hernández, G. A. El uso del Conocimiento de las Tortugas Marinas Como Herramienta para la Restauración de sus Poblaciones y Hábitats Asociados. (Universidad Autónoma del Carmen, 2019).Pineda, O. G. & Rocha, A. R. B. Las Tortugas Marinas en México: Logros y Perspectivas para su Conservación. (CONANP, 2016).Varela, R. G., Quílez, G. Z. & Harrison, E. Report on the 2014 Green Turtle Program at Tortuguero, Costa Rica. (2015).Azanza Ricardo, J. et al. Nesting ecology of Chelonia mydas (Testudines: Cheloniidae) on the Guanahacabibes Peninsula. Cuba. Rev. Biol. Trop. 61, 1935–1945 (2013).PubMed 

Google Scholar 
Nalovic, M. A. et al. Sea Turtles in the North Atlantic & Wider Caribbean Region. (2020).Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

Google Scholar 
Kruschke, J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. https://doi.org/10.1016/B978-0-12-405888-0.09999-2 (Academic Press, 2015).Ruiz-Urquiola, A. et al. Population genetic structure of greater Caribbean green turtles (Chelonia mydas) based on mitochondrial DNA sequences, with an emphasis on rookeries from southwestern Cuba. Rev. Investig. Mar. 31, 33–52 (2010).
Google Scholar 
Long, C. A. et al. Incongruent long-term trends of a marine consumer and primary producers in a habitat affected by nutrient pollution. Ecosphere 12, e03553 (2021).Article 

Google Scholar 
Phillips, K. F., Stahelin, G. D., Chabot, R. M. & Mansfield, K. L. Long-term trends in marine turtle size at maturity at an important Atlantic rookery. Ecosphere 12, 7 (2021).Article 

Google Scholar 
Bjorndal, K. A., Bolten, A. B. & Chaloupka, M. Y. Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean. Ecol. Appl. 15, 304–314 (2005).Article 

Google Scholar 
Naro-Maciel, E. et al. The interplay of homing and dispersal in green turtles: A focus on the southwestern atlantic. J. Hered. 103, 792–805 (2012).Article 
PubMed 

Google Scholar 
Monzón-Argüello, C. et al. Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles. J. Biogeogr. 37, 1752–1766 (2010).Article 

Google Scholar 
Luke, K., Horrocks, J. A., LeRoux, R. A. & Dutton, P. H. Origins of green turtle (Chelonia mydas) feeding aggregations around Barbados, West Indies. Mar. Biol. 144, 799–805 (2004).Article 

Google Scholar 
Bass, A. L., Epperly, S. P. & Braun-McNeill, J. Green turtle (Chelonia mydas) foraging and nesting aggregations in the Caribbean and Atlantic: Impact of currents and behavior on dispersal. J. Hered. 97, 346–354 (2006).Article 
PubMed 

Google Scholar 
Lahanas, P. N. et al. Genetic composition of a green turtle (Chelonia mydas) feeding ground population: Evidence for multiple origins. Mar. Biol. 130, 345–352 (1998).Article 

Google Scholar 
Foley, A. M. et al. Characteristics of a green turtle (Chelonia mydas) assemblage in northwestern Florida determined during a hypothermic stunning event. Gulf Mex. Sci. 25, 131–143 (2007).
Google Scholar 
Bass, A. L., Lagueux, C. J. & Bowen, B. W. Origin of green turtles, Chelonia mydas, at ‘Sleeping Rocks’ off the Northeast coast of Nicaragua. Copeia 1998, 1064 (1998).Article 

Google Scholar 
Bass, A. L. & Witzell, W. N. Demographic composition of immature green turtles (Chelonia mydas) from the East Central Florida Coast: Evidence from mtDNA markers. Herpetologica 56, 357–367 (2000).
Google Scholar 
Bjorndal, K. A., Parsons, J., Mustin, W. & Bolten, A. B. Threshold to maturity in a long-lived reptile: Interactions of age, size, and growth. Mar. Biol. 160, 607–616 (2013).Article 

Google Scholar 
Perrault, J. R. et al. Maternal health status correlates with nest success of leatherback sea turtles (Dermochelys coriacea) from Florida. PLoS ONE 7, e31841 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Montero, N. et al. Warmer and wetter conditions will reduce offspring production of hawksbill turtles in Brazil under climate change. PLoS ONE 13, 1–16 (2018).Article 

Google Scholar 
Shamblin, B. M. et al. Geographic patterns of genetic variation in a broadly distributed marine vertebrate: New insights into loggerhead turtle stock structure from expanded mitochondrial DNA sequences. PLoS ONE 9, 85956 (2014).Article 

Google Scholar 
Anderson, J. D., Shaver, D. J. & Karel, W. J. Genetic Diversity and Natal Origins of Green Turtles (Chelonia mydas) in the Western Gulf of Mexico. J. Herpetol. 47, 251–257 (2013).Article 

Google Scholar  More

Time using the rock as refugeTemperature had an effect in the refuge usage of both species when analysed together (lme.zig: F3,192 = 7.97, p = 0.0001; Fig. 1A). However, species behaved differently (lme.zig: F1,192 = 14.79, p = 0.0004; Fig. 1A). As hypothesised, there was an interaction between temperature and species (lme.zig: F3,192 = 11.90, p  0.14, Fig. 1B).Size had an effect in the time exploring the rock (lme: F1,192 = 6.91, p = 0.012, Fig. 3) when species were analysed together, but there was no interaction with temperatures (lme: F3,192 = 0.42, p = 0.74, Fig. 3). We found that the interaction between species and size was close to be significant (lme: F1,192 = 3.62, p = 0.064, Fig. 3), implying that possibly smaller fish spent more time exploring the rock than bigger fish. However, when analysed separately, we did not find an effect of size in the exploring behaviour neither for twoline skiffias (lme: F1,96 = 2.99, p = 0.099, Fig. 3) nor for guppies (lme: F1,96 = 0.33, p = 0.569, Fig. 3).Figure 3Proportion of the total time observed (600 s) fish of different sizes spent exploring the rock. Lines represent the areas where the density of data is higher.Full size imageTime spent swimmingTemperature had an effect in the time spent swimming for both species when analysed together (lme: F3,192 = 23.48, p  More

Variation in environmental parameters across space and time in Bohai BayThe environmental parameters of samples collected near the Tianjin coastal area from different stations and seasons exhibited high temporal and spatial heterogeneity. The seawater temperature was 28.09 ± 0.53 °C in Aug, 17.48 ± 2.36 °C in May, and 19.55 ± 1.26 °C in Oct (Table 1). The seasonal variation in seawater temperature corresponded to the meteorological characteristics in Bohai Bay, with warm seawater in summer and relatively cool seawater in spring. The salinity was 29.69 ± 2.71‰ in Aug, 33.19 ± 0.33‰ in May, and 30.15 ± 1.63‰ in Oct. Seasonal variations in salinity may be mainly related to freshwater loading. According to the precipitation observed data of Bohai Bay in previous years, the rainfall amount and days in summer are the most19, which may lead to the increase in runoff and the relatively low salinity in summer. Chlorophyll a (Chl a) was highest in May, with lower levels in Aug and Oct. The dissolved inorganic nitrogen (DIN) was significantly higher in May and Aug than in Oct. The higher level of DIN in May and Aug may be related to terrestrial input and supply for the demand of phytoplankton growth. In October, the temperature and DIN content were both not suitable for phytoplankton growth, and Chl_a showed the lowest value. Spatially, the DIN distribution across the three seasons was rather similar, with high values observed in nearshore waters and low values in offshore waters (Dataset S1 & Fig. S1), which suggested that terrestrial input was an important source of DIN. The pH, soluble reactive phosphate (SRP) and chemical oxygen demand (COD) showed relatively higher values in October than in August and May, which may be caused by the dead phytoplankton release and terrestrial loadings through coasts and rivers. The dissolved oxygen (DO), conductivity and depth did not show significant variation among sampling times (Table 1), while the conductivity and depth had relatively higher values at offshore stations (Dataset S1) since the more remote the sampling water was, the greater the depth was in Bohai Bay and the closer it was to the open sea with higher salinity and conductivity. The ordination plot showed distinct partitioning of samples from nearshore and offshore sites along principal component axis 1 (PC1) (Fig. 1). The ordination plot could explain 73.49% of the total variation in the geo-physical–chemical parameters and revealed a linear positive correlation between different parameters (Fig. 1). AN, DIN, nitrate and Chl_a were most crucial in the partitioning of samples from May and the other 2 months; salinity, longitude, depth and conductivity were crucial for the partitioning of samples from offshore and nearshore stations; pH, COD, SRP, nitrite and temperature were crucial for the partitioning of samples from nearshore stations in August and October and samples from offshore stations. Overall, the principal component analysis (PCA) plot clearly showed both the temporal and spatial variation of the measured environmental parameters, indicating that complex biogeochemical processes and hydrodynamic conditions lead to the variation among sites and seasons.Table 1 The independent-samples t test of environmental variables and α-diversity among different months.Full size tableFigure 1Biplot of the principal component analysis (PCA) for environmental parameters in the seawater samples of the Bohai Bay coastal area across different seasons and sites. The two principal components (PC1 and PC2) explained 73.49% of the total variation in the environmental data and showed clear partitioning of offshore samples (in blue font) from other nearshore samples along PC1 and partitioning of May samples from August and October along PC2. The variables transparency and latitude were strongly correlated with PC1, and the variables ammonia nitrogen (AN), COD, pH, soluble reactive phosphate (SRP), and nitrite were strongly correlated with PC2. Chlorophyll a (Chl_a), dissolved inorganic nitrogen (DIN), nitrate and DO were mainly positively correlated with samples from May, while salinity, longitude, depth and conductivity were mainly positively correlated with offshore samples. Blue arrows represent environmental parameters, and circles in color represent sampling points.Full size imageProkaryotic α/β-diversity variationMeasures of α-diversity showed significant differences in shannon, evenness, faith_pd and OTU richness between samples from May/Aug and Oct (Fig. 2, Table 1). Principal coordinates analysis (PCoAs) based on weighted UniFrac (WUF) distance and unweighted UniFrac (UUF) distance showed that the PCC from different sampling months separated across the first and second principal coordinates (Fig. 3A-B). Both the analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA/ADONIS) results indicated that the prokaryotic communities varied significantly across different sampling months when using a WUF distance metric (ANOSIM, r = 0.709, P = 0.001; ADONIS, R2 = 40.0%, P = 0.001) and UUF distance metric (ANOSIM, r = 0.934, P = 0.001; ADONIS, R2 = 38.7%, P = 0.001). At the same time, the prokaryotic α– and β-diversity both showed high within-month variability in Aug (Figs. 2, 3C–D), which indicated that the community varied greatly among different sites in Aug.Figure 2Alpha diversity of shannon, eveness, faith_pd (phylogenetic diversity) and OTU richness value of the prokaryotic community of all the samples from different stations at different sampling times.Full size imageFigure 3Principal coordinate analysis (PCoA) based on unweighted (A) and weighted (B) UniFrac distances for prokaryotic communities in the surface waters; box plots showing the unweighted (C) and weighted (D) UniFrac distances among each station at different sampling times.Full size imageCorrelation between prokaryotic α/β-diversity and physical, chemical and geographic factorsThe α-diversity measurements exhibited significant positive correlations with temperature, pH, SRP, AN and un_ionN (Dataset S2). The correlation between α-diversity indexes and geo factors (longitude and latitude) was not strong or significant both in samples across the three sampling times or from each sampling time (Dataset S2).The environmental variation significantly correlated with β-diversity among the three seasons (r_weighted = 0.4558, r_unweighted = 0.4631, P = 0.001, Table 2), with pH, AN, temperature, un_ionN, COD, nitrite, SRP, salinity, DO and DIN as the main individual determinants. However, it did not show significant correlations with β-diversity at any sampling time except in Oct (Table S1).Table 2 Spearman’s rank correlation between environmental/spatial variability (Euclidean distance) and prokaryotic β-diversity (weighted/unweighted UniFrac distance) among all samples from different season.Full size tableThe geographic distance was not correlated with prokaryotic β-diversity (variation in community composition; r  0.05; Table 2) among the three sampling times. However, samples from Aug and Oct exhibited a significant correlation between β-diversity and geographic distance (Table S1).Factors driving the PCC variationPERMANOVA using the UUF/WUF distance indicated that temperature variation explained the largest part of community variation among the investigated factors (34.90%/19.83%, P = 0.001, Dataset S3), with AN (31.84%/13.56%, P = 0.001) and salinity (12.91%/6.21%, P = 0.001) as the second and third most significant sources of variation.The variance partitioning analysis (VPA) conducted on both UUF/WUF distances showed that almost 100% percent of the variation in PCC among all three sampling times was explained by the detected environmental factors. In May, no environmental or spatial factors could be selected as significantly explain the PCC variation; in Aug, the joint effects of environmental and spatial factors could explain 49.5% of the variation; in Oct, based on WUF distance, the spatial factors could purely explain 10.5%, environmental factors could purely explain 38.8%, their joint effects could explain 28.2%, and based on UUF distance, the joint effects of environmental factors and trend could explain 13.7% of the PCC variation. These results indicated dramatic shifts in the spatial or environmental factor effects on the PCC variation at different sampling times in Bohai Bay (Table 3).Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).Full size tableDistinct seasonal features at the phylum and OTU levelsThere were notable differences in the proportions of various phyla among different seasons (sampling month). In May, there was a greater proportion of Alphaproteobacteria (41.41%), Planctomycetes (6.42%), Actinobacteria (3.86%), Firmicutes (1.48%), Acidobacteria (0.45%), TM7 (0.16%), Tenericutes (0.16%), OD1 (0.13%), and WPS-2 (0.09%) than in Aug and Oct, whereas Gammaproteobacteria (44.23%), GN02 (0.08%) and SAR406 (0.04%) were depleted in May and Aug but enriched in Oct. In Aug, Bacteroidetes (13.98%), Deltaproteobacteria (6.93%), Verrucomicrobia (4.5%), Chloroflexi (0.36%), Lentisphaerae (0.97%), TM6 (0.25%), Nitrospirae (0.08%), Chlamydiae (0.07%), Chlorobi (0.07%), Spirochaetes (0.04%) and OP8 (0.03%) were significantly enriched than in the other two sampling times (Duncan test; Table S2).At the OTU level, OTUs with relative abundance  > 0.01% (1040 OTUs) were used to perform the difference analysis, and 175 OTUs in May, 281 OTUs in Aug, and 210 OTUs in Oct were identified as seasonal specific OTUs (ssOTUs). The cooccurrence network showed that the ssOTUs were clustered separately (Fig. 4A). Furthermore, the separation of the three modules contained most of the ssOTUs specific to different seasons (Fig. 4A-B). All the ssOTUs of different seasons comprised a taxonomically broad set of prokaryotes at the phylum (phylum Proteobacteria is grouped at the class level) level (Fig. 4C) belonging to various phyla with different proportions. Betaproteobacteria, Verrucomicrobia, Gemmatimonadetes, Epsilonproteobacteria, PAUC34f., and Euryarchaeota did not show significant differences among the three sampling times at the phylum level, but features belonging to these phyla showed differences at the OTU level (Fig. 4C, Dataset S4). In addition, the phylum ssOTUs belonging to, such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Actinobacteria, and Deltaproteobacteria, were not only enriched at one sampling time (Dataset S4) but also enriched at the other two sampling times (Fig. 4C, Dataset S4). These results revealed that different seasons do not strictly select specific microbial lineages at the phylum level, but a finer level analysis could more strictly reflect the seasonal variation.Figure 4Co-occurrence patterns of seasonal sensitive OTUs (A). Co-occurrence network visualizing significant correlations (ρ  > 0.7, P  0.01%. Different colors represent ssOTUs in May (green), Aug (red) and Oct (blue). Cumulative relative abundance (as counts per million, CPM; y-axis in × 1000) of all the sensitive modules in the networks (B). The phylum attribution of ssOTUs in each season (C). The y-axis is the percentage of the number of OTUs that belong to a particular phylum that accounts for the total number of all the OTUs.Full size imageRegression analysis between the relative abundance of modules to which the ssOTUs belonged and the environmental factors was also conducted, and module 1 abundance, to which the Aug-ssOTUs belonged, showed a significant positive correlation with temperature (R2 = 0.77, P = 6.609e−62), AN (R2 = 0.43, P = 7.416e−25), and un_ionN (R2 = 0.75, P = 1.366e−58) and a negative correlation with SRP (R2 = 0.81, P = 6.762e-17). This may be caused by the functional role of the microbes in Aug. In the Aug-ssOTUs, Deltaproteobacteria showed a higher ratio than in the other 2 months (Fig. 4c), and in the following functional analysis, Deltaproteobacteria contributed to the genes related to nitrogen fixation, which may help to explain why there was a positive correlation of Aug-ssOTUs to AN and un_ionN. The module 2 abundance to which the May-ssOTUs belonged showed a significant negative correlation with pH (R2 = 0.65, P = 4.026e−44), temperature (R2 = 0.19, P = 2.325e−10), un_ionN (R2 = 0.025, P = 0.01779), and SRP (R2 = 0.12, P = 4.104e−07) and a positive correlation with AN (R2 = 0.26, P = 5.174e−14). In the May-ssOTUs, the ratio of Alphaproteobacteria was the highest, and Alphaproteobacteria were reported to be pH-sensitive groups in marine environments20, which prefer neutral pH environments21. In this study, the pH in May was 8.04 ± 0.07, in Aug was 8.39 ± 0.09, in Oct was 8.38 ± 0.07, and the pH in May was the closest to neutral, and the ratio decreased with increasing pH in Oct and Aug. The abundance of module 3, to which the Oct-ssOTUs belonged, showed a significant positive correlation with SRP (R2 = 0.81, P = 0.16e-10) and pH (R2 = 0.054, P = 0.00075) and a negative correlation with temperature (R2 = 0.44, P = 2.276e−25), AN (R2 = 0.75, P = 4.51e−58), and un_ionN (R2 = 0.6, P = 3.995e-39) (Fig. S2). Phosphate has been identified to limit primary productivity22, which is of great importance in the structure of dominant bacterial taxa in marine environments23. In the Oct-ssOTUs, the ratio of Gammaproteobacteria was the highest, as reported. Gammaproteobacteria was significantly explained by SRP during the seasonal variation in the Western English Channel, with Rho equal to 0.7523, which suggested the sensitivity of it to SRP, and in that study, it also showed a negative correlation between temperature and Gammaproteobacteria and a positive correlation between SRP and Gammaproteobacteria. Although the correlation was not significant, the variation trend was consistent, which indicates that the phenomenon observed in this study was not unexpected. In addition, most ammonia-oxidizing bacteria belong to the Betaproteobacteria and Gammaproteobacteria classes are chemolithoautotrophs that oxidize ammonia to nitrite24. Gammaproteobacteria and Betaproteobacteria both had higher ratios in Oct-ssOTUs, and the functional prediction results also showed that pmoA/amoA and pmoB/amoB, which encode ammonia monooxygenase, were mainly contributed by OTUs from Gammaproteobacteria and Betaproteobacteria (Dataset S10). The utilization of ammonia may explain the negative correlation between the Oct-ssOTUs and AN.Community assembly processes across different sampling months and sitesBased on the analysis of phylogenetic turnover, unweighted βNTI mostly ranged from -2 to 2 across different sites at a single sampling time in May, Aug and Oct, revealing that PCC variations across different sampling sites at a single time were mostly affected by stochastic processes. The unweighted βNTI was greater than 2 across May–Aug, May–Oct and Aug-Oct (Fig. 5A), which revealed that the variations in PCC across different sampling times were mostly affected by deterministic processes. The RCbray values across any two sampling times were equal to 1, and in each sampling time, the RCbray values ranged from − 1 to 1 (Fig. 5B). Combining the βNTI and RCbray values, the community assembly processes were quantified at each sampling time and at any two sampling times. As shown in Fig. 5C, turning over of the community during different sampling times was mainly governed by selection; among the different sites in May and Oct, it was mainly governed by “undominated” processes; community turn over in Aug was mainly governed by the influence of “Dispersal Limitation”. These results indicated that the shifts in the assembly of prokaryotic communities during different sampling times were caused by strong “heterogeneous selection” (βNTI  > 2), and the community variation at each sampling time was mainly caused by stochastic processes.Figure 5Patterns of distribution of unweighted βNTI (A) and RCbray (B) values across different sampling times. Quantification of the features that impose community assembly processes in and among different sampling times. (C) Pie charts give the percent of turnover in community composition governed primarily by Selection acting alone (white fill), Dispersal Limitation (green line fill), Homogenizing Dispersal (blue line fill) and undominated process (cyan fill).Full size imagePrediction of the metabolic potential at different sampling timesThe NSTI scores of each sample ranged from 0.033 to 0.096, with a mean of 0.058 (Dataset S5). Microbial functions were detected in all the samples from the three sampling times, and it was found that the relative abundances of 242 pathways were significantly changed between samples from May and samples from Aug (Dataset S6). The relative abundances of 321 pathways were significantly changed between samples from May and Oct (Dataset S7), and the relative abundances of 370 pathways were significantly changed between samples from Aug and Oct (Dataset S8).Genes related to energy metabolism were given more attention. For nitrogen metabolism genes relevant with nitrogen fixation (nifD, nifK) were detected only enriched in Aug, while genes relevant with nitrate reduction and denitrification (narG, narZ, nxrA, narH, narY, nxrB, narI, narV, nirD, nasA, nasB) were detected enriched in May, genes related with ammonia oxidation were both detected enriched in Oct and Aug. For sulfur metabolism, genes relevant with thiosulfate oxidation (soxA, soxB, soxC, soxX, soxY and soxZ) were only enriched in Aug, while genes relevant with sulfate and sulfite reduction (cysNC, aprA, aprB, cysJ, cysI, cysK, dsrA) were detected enriched in May and Oct (Fig. 6).Figure 6The LEfSe analysis indicated significantly differential abundances of PICRUSt predicted genes relevant to energy metabolism in different months of samples.Full size imageProkaryotic taxa contributed to the significantly varied functional genes related to nitrogen and sulfur metabolism at different sampling times. At the species level, the taxa contributing to nifK and nifD mainly belonged to Deltaproteobacteria and Firmicutes, and the taxa contributing to the sox-series genes mainly belonged to Alphaproteobacteria and Gammaproteobacteria (Fig. S3). The denitrification-related functional genes that were enriched in May were mainly contributed by members from Alphaproteobacteria and Gammaproteobacteria. The taxa contributing to dsrA, aprA and aprB were mainly from Deltaproteobacteria, including members of Desulfarculaceae, Desulfobacteraceae, Desulfobulbaceae, Desulfovibrionaceae and Syntrophobacteraceae (Fig. S4). More

Simulation experimental set upLaboratory monitoring of leakage migration process can provide an important basis for field tests. The designed and improved ERT device can better describe the migration range of leakage in soil41. In this experiment, a parallel potential monitoring device was used to improve the monitoring of leakage fluid migration. The simulation experiment in the laboratory is carried out in a (100 cm*100 cm*50 cm) plexiglass tank. Sand and clay shall be screened with a 2.36 mm square sieve, watered and compacted with a board to ensure that the soil layer is in close contact with the measuring electrode.Electrode arrangementThe ground wire of high-density electrical method instrument is connected to the electrodes arranged around the bottom of the tank as the power electrode C2, as shown in Fig. 2a. The host is connected to the electrode system. The electrode system consists of 47 electrode grids with a spacing of 0.08 m. The measuring electrode P1 is connected to the mainframe through a wire 0.05 m below the grid center. The geomembrane is located 0.03 m above the measuring electrode P1. The collection device is used as a monitoring system for various leachate. The arrangement of electrodes is shown in Fig. 2b. The power supply electrode C1 is placed at a certain depth in the middle of the saturated sand to provide a constant current. The location of electrode C1 and leakage point is shown in Fig. 2c. The layers from the bottom of the tank are silty clay, geomembrane, silty clay and saturated sand, as shown in Fig. 2d.Figure 2Set-up of leachate migration simulation experiment: (a) Schematic diagram of electrode C2 layout; (b) Schematic diagram of electrical system laying; (c) Position of electrode C1 and leakage point; (d) Schematic diagram of simulated experimental soil layer.Full size imageComposition of monitoring systemThe electrode system is used to monitor the background electric field and artificial electric field of the landfill site. In the experiment, the electrode system is laid in the clay layer under the geomembrane. It is composed of detection electrodes distributed in a grid at a certain distance.The electrical signal conversion system adjusts the measurement mode, sampling accuracy, acquisition frequency and other parameters of the electrode in the field according to the instructions of the mainframe, and transmits the collected electrical signal to the mainframe.The mainframe can control the operation of the monitoring system. The possible leachate points and their pollution range are determined by collecting data. The system mainly includes mainframe and its software system, power supply, etc., as shown in Fig. 3.Figure 3Se2432 parallel electric method instrument.Full size imageLeachate devicePlace 4 leakage bottles above the tank. No.1 and No.4 bottled water are used to simulate the leakage liquid formed by the direct infiltration of rainwater in slag through geomembrane and as a reference. Because Cl-1 is a typical pollutant in the landfill. No. 2 bottle containing 20 g/L NaCl solution is used to simulate inorganic salt leakage in urban life. No. 3 bottle containing 20 ml/L ethanol solution is used to simulate the leakage liquid containing a large amount of organic matter in municipal solid waste. The characteristics of leachate have been summarized in Table1.Table 1 The characteristics of leachate.Full size tableBefore the experiment, configure four solutions, close the injection, use an electric meter to check the conductivity of each measuring point. After each measuring point has no open circuit, supply power to the soil layer through the mainframe to measure the background electric field of the soil. Then open the injection, adjust the flow rate, release the solution at a fixed flow rate, record the soil electric field in the process of leakage every half an hour, collect the potential values of each measuring point, process the data through the potentiometry and potential difference method, and form the relevant potential horizontal profile and longitudinal section of the soil.Principle of potentiometric detection technologyWhen there are leakage points in the landfill, power is supplied to the landfill, and the current forms a current loop through the geomembrane. If there are n (n = 1,2,3…) leakage points in the geomembrane, the power supply current is I, and the artificial electric field will form a leakage electric field at the leakage point, which can be used as a point power supply.$$I = int dI = int j cdot dS$$
(1)
where I is the current intensity, j is the current density vector, and S is the area passing through the current.When there are n leakage points, I will be shunted. If a leakage point is regarded as a finite surface, the current intensity I as:$$I = {I_1} + {I_2} + cdot cdot cdot + {I_{text{n}}} = sumlimits_{i = 1}^n {int_{S_i} {jdS} }$$
(2)
Generally, the power supply current field of landfill site will be affected by the formation medium structure. It is assumed that the formation medium structure is composed of three layers, each layer has uniform properties and stable conductivity, and the layers from top to bottom are: landfill layer, with resistivity of ρ1. The saturated leakage liquid layer above the geomembrane has a resistivity of ρ2. The clay layer under the geomembrane has a resistivity of ρ3. The electrode C1 is arranged in the garbage layer for power supply, and the electrode C2 is arranged at the lower part of the geomembrane away from the electrode system area. The electrode C2 can be regarded as a far pole.Because of the ρ1  > ρ2, the conductivity of the saturated leakage liquid layer at the upper part of the geomembrane is better than that of the landfill layer, so that there is almost no reflected current between the ρ1 layer and the ρ2 layer, that is, the current generated by the power supply electrode C1 is all transmitted to the ρ2 layer. Because of the ρ3  > ρ2, it can be considered that the interface between ρ2 layer and ρ3 layer has both a reflection current, and a transmission current through the leakage point. The potential generated at the detection electrode P1 under the geomembrane is formed by the action of transmission current. The total potential of point P1 is obtained by the superposition of the potential of point power supply passing through n leakage points at P1.$${U_{P1}} = sumlimits_{i = 1}^n {frac{{{I_i}{rho_3}}}{{2pi {{text{r}}_{iP1}}}}}$$
(3)
Parallel potential difference methodThe test adopts pole–pole arrangement, and the calculation formula of apparent resistivity is as follows:$$rho = 2pi {text{aR}}$$
(4)
where ρ is apparent resistivity; a is the distance between electrodes C1 and P1; R is measuring resistivity.When there are loopholes in the geomembrane of the landfill, the leakage liquid will gradually penetrate into the soil layer under the geomembrane through the loopholes, resulting in the change of the conductivity of the soil layer under the geomembrane. The pole-pole acquisition mode of Se2432 parallel electrical instrument is used to obtain the original data (potential difference) of each measuring point on the grid. After current normalization, the apparent resistivity of the soil layer is obtained. The electrical properties of different depths of the soil layer can be obtained by inversion of the apparent resistivity data of the soil layer, so as to determine the occurrence point and distribution range of leakage.The monitoring grid is 5 × 5. The spacing between measuring points is 0.08 m. The measurement method adopted by Se2432 parallel electric method instrument is cross diagonal measurement method. Figure 4 shows that it only needs to measure the potential values on the measuring points on the horizontal, vertical and 45° diagonal lines.Figure 4Schematic diagram of cross-diagonal measurement method.Full size imageTheoretical calculation of test modelTheoretical results of 10 × 10 grid monitoringAccording to the experimental model and statistical data, the resistivity of the clay layer under the geomembrane is assumed ρ = 10 Ω· m, the resistivity ratio of tap water, NaCl solution and ethanol solution after penetrating into the soil layer ρNo.1:ρNo.2:ρNo.3 = 5:3:10. If the four leakage points set by the model are regarded as four conductive resistors, the ratio of the current passing through the four leakage points is INo. 1:INo. 2:INo. 3:INo. 4 = 6:10:3:6.The calculation model is 10 × 10 grid, and the spacing of measuring points is 0.05 m. The potential value on each measuring point is calculated according to Eq. 3, and the obtained data is processed with surfer software to obtain the potential contour map, as shown in Fig. 5. Among them, points 1, 2, 3 and 4 are the leakage positions of water, NaCl solution, ethanol solution and water respectively, and the spacing between leakage points is 0.15 m.Figure 510 × 10 Grid theory detection potential contour map.Full size imageFigure 5 shows that the leakage fields formed by the four kinds of leaking liquids interfere with each other from the theoretical calculation results. The leachate current at point 2 is larger, the high potential closed loop is obvious, and its center corresponds to the leakage center. The reason for this is that the NaCl solution contains conductive particles that increase the conductivity of the leak point. Point 1 and 4 are the same as water, and the leakage electric field is almost the same. Its closed loop is obvious, and the high potential center also corresponds to their leakage position. There is almost no closed loop effect at point 3 under the influence of 1, 2 and 4. The results show that the leakage field formed by high resistance leakage liquid is not easy to be detected by potentiometric detection, and low resistance leakage is suitable to be detected by potentiometric detection.Theoretical results of 12 × 12 grid monitoringThe resistivity of the clay layer under the geomembrane is assumed ρ = 10Ω·m. In consideration of the mutual influence between the leachate and appropriately reduce its influence effect, the resistivity ratio of water, NaCl solution, and ethanol solution after penetrating into the soil layer is set as ρNo.1:ρNo.2:ρNo.3 = 20:15:24, the ratio of the current passing through the four leakage points is INo.1:INo.2:INo.3:INo.4 = 6:8:5:6. And adjust the distance between the two points to 0.28 m. 12 × 12 grid was used for detection, and the spacing of detection points is 0.04 m. Calculate the potential value of each detection point according to Eq. 3, and use Surfer to obtain the detection contour map of four kinds of leakage, as shown in Fig. 6.Figure 612 × 12 Grid theory detection potential contour map.Full size imageTheoretical calculation results show that when the distance between the leakage points is large and the distance between the detection points is small, the potentiometric method can detect the leakage position of various leachates well. At the same time, the diffusion range of different leachates in the same plane is roughly the same, and they all gradually diffuse outward from the center of the leakage point, and the potential value gradually decrease. Point 2 has the largest potential closed loop range, which also has a certain impact on the leakage points of adjacent points 1 and 3. Point 1 and point 4 are water leakage. Affected by different leakage liquids, the leakage electric field of the two same leakage liquids is obviously different. The potential closed loop range of point 1 is larger than that of point 4. Point 3 is the leakage of ethanol solution. Because its resistance is the largest, the range of potential closed loop is the smallest.Figure 7 shows that the leakage fields around the leachates are funnel-shaped, and its size is related to the type of leachate. Therefore, different network density should be designed for different types of leakage liquid, so as to use the most economical scheme to detect the leakage point.Figure 712 × 12 Grid theory detects potential 3d view.Full size imageInterpretation and discussion of resultsLaboratory simulation experiment researchFigure 8a shows the background electric field potential of soil layer. The four injection pipes are opened at the same time and adjusted to the same flow rate. Under the condition of continuous leakage, monitor the leakage field potential at an interval of 1 h. Figure 8b shows the leakage electric field potential value for 1 h. Reduce the injection pipes flow rate to 1/2 of the initial value. Figure 8c shows the monitoring results of 2 h soil layer leakage field potential. Figure 8d shows the soil leakage field potential monitored after 30 min of sealing the injection pipes.Figure 8Leakage field potential diagram of soil layer: (a) Background electric field of soil layer; (b) Potential distribution of soil layer after 1 h of leakage; (c) Potential distribution of soil layer after 2 h of leakage; (d) Potential distribution of soil layer after closing the injection tube for 30 min.Full size imageFigure 8a shows that the background potential contour of the experimental soil layer is at a lower value. Few current lines pass through the monitoring area. A dense closed potential circle of high potential value is formed at point 2. The current flow at point 2 is greater than the other points 1, 3 and 4. The analysis result may be that in the process of watering and compaction, the clay layer under the geomembrane is not uniform, and the compaction degree of the soil layer is different, resulting in different potential values ​​obtained by monitoring. The permeability at point 2 is better than other points, so when the flow rate of the leakage liquid is large, the leakage liquid under the geomembrane gathers near point 2 and spreads out around. After the clay is watered and compacted, the soil compaction is small and the pore water content is large, resulting in a high potential abnormal area in the lower left corner of point 3.Point 2 forms a closed loop of anomaly potential contour much higher than the background electric field, while the value of potential contour coil at leakage point 3 is lower than the surrounding value. It can be analyzed that positions 2 and 3 are leakage points. The leachate at point 2 is a high concentration NaCl solution containing more conductive particles, which will reduce the resistivity of the soil layer under the geomembrane at point 2. Thus, the passing current is increased to form a high potential closed loop. The leachate at point 3 is ethanol solution, which will increase the resistivity of the soil layer under the geomembrane at point 3. So as to reduce the passing current and form a low potential closed loop. Figure 8b shows that the potential contour is consistent with the influence of NaCl solution and ethanol solution on the soil layer under the geomembrane. It can be concluded that point 2 and point 3 are leakage points. The electric field formed after water leakage at point 1 and point 4 cannot clearly distinguish the leakage points.During the monitoring process, the leachate was continuously released from the injection pipe, and the results reflected the dynamic characteristics. Figure 8b shows the phenomenon that the leachate from point 1 and point 4 aggregates around point 2, which is consistent with the inference of better permeability at point 2. Figure 8b,c show that when the flow rate of the leachate is changed and the flow rate of the injection pipe is reduced, the high-potential region of the entire electric field is reduced. Under the influence of gravity, the leachate will migrate longitudinally, and the closed-loop abnormally high-potential regions and abnormally low-potential regions at points 2 and 3 also decrease.Compared with the surrounding potential contours, the difference is more obvious. Figure 8d shows that when the injection pipe stops leaking for a period of time, the leachate migrates longitudinally along the leakage point. At this time, the electric field of the soil layer is similar to the original background electric field, but the potential value is higher than the background electric field, indicating that the leachate is stagnant in the pores of the soil layer, it is the result of changing the electrical properties of the soil layer. The parallel potential method can collect the potential value of each point in the field at one time, which provides a basis for real-time monitoring of landfill leachate.Figure 9 shows the inversion results of the horizontal section of the experimental model. The blue area corresponds to the distribution range of the low resistance anomaly. There are no jump or distortion points in the profile. The resistivity in the longitudinal direction basically shows a change from low to high. The upper layer seepage liquid migrates, and the bottom soil layer is characterized by low humidity and high resistivity. The low-resistance areas formed by the leakage of NaCl solution are widely distributed in the horizontal section. The distribution range is 0–0.28 m, and the migration scale of the leakage liquid can be clearly seen. The morphological characteristics of water leakage in different parts are basically the same. The distribution range is 0–0.18 m. The leakage of ethanol solution is only reflected at 0–0.06 m, and the distribution range is the smallest at the same depth. The ethanol solution also had the slowest migration rate.Figure 9Inversion map of plane section at different depths.Full size imageFigure 10 shows the inversion results of the X–Z longitudinal section of the test model. The two apparent resistivity profiles at Y = 0.24 m and Y = 0.32 m show that there is no low-resistance area in the shallow layer on the soil layer, indicating that the geomembrane in this area is not damaged. The low resistance zone in the middle is caused by the lateral migration of leakage fluid. The low-resistance anomaly area at the top of the profile can be judged as a leak point or formed by the migration of nearby leachate. Combined with the horizontal section, the leakage depth is similar, and the lateral migration speed of leachate is faster than the longitudinal migration speed. Four leak points can be distinguished, delineating the general location of the leak.Figure 10X–Z longitudinal section on different Y axes.Full size imagePhysical model experimentThe potential value of each electrode was monitored after 2 h of leakage, and the resistivity profiles at different positions were obtained by the potential difference method.It can be seen from Fig. 11 that the potential difference method can monitor the leakage of leachate in different directions. The morphological features of the plume formed by the downward migration of the leak point are approximately funnel-shaped in longitudinal section. The affected area of ​​the soil layer can be obtained in time. Figure 11b shows that the potential difference at the monitoring point is very different on both sides. After 2 h of leakage, a large amount of leakage liquid exists in the soil layer. When the water content in the soil layer increases, the diffusion rate of the ethanol solution increases, showing high resistance characteristics. At the same time, due to the action of gravity, there is a lot of vertical migration, and the potential value changes greatly. The profile clearly shows that the distribution area of ​​high potential difference is large, and the distribution of low potential is small. Figure 11c shows that since the migration rate of leachate in the horizontal direction is greater than that in the vertical direction, the potential difference of the monitoring point in the middle region is smaller, and a closed region of a high-potential circle is formed in the middle. The difference between the two results in a smaller potential difference area. Figure 11d shows that almost all the low-potential areas on the monitoring point are on the left side, because the leakage rate of NaCl solution in the horizontal direction is similar to that in the vertical direction under the condition of good soil compaction. At this time, a large number of conductive particles are contained, resulting in a large high-potential region. The difference between the two forms a large area of ​​low potential difference on the left. This is in good agreement with the lower resistance characteristics of the NaCl solution. Figure 11e shows that the two low-resistance regions correspond to the two leakage centers. The low potential difference region is formed by migration around the leak point. The migration speed in the horizontal direction is similar to that in the vertical direction, and the water migration speed on the left is slower than that of the sodium chloride solution on the right. Figure 11e,f show that the monitoring results are the same, but the resulting potential difference is also increased. This is affected by the distance between the monitoring point and the leak point. When the monitored point and the leakage point are located on the same section, the soil layer is the most severely affected area by leakage. Through the change of the potential difference, the leakage range and the location of the leakage point can be better judged.Figure 11Electrical resistivity tomograms of profile: (a) Resistivity of the slitting profile Y = 0; (b) Resistivity of the slitting profile Y = 0.08; (c) Resistivity of the slitting profile Y = 0.16; (d) Resistivity of the slitting profile Y = 0.24; (e) Resistivity of the slitting profile Y = 0.32; (f) Resistivity of the slitting profile Y = 0.4.Full size image More

United Nations. [World population prospects 2019]. United Nations. Department of Economic and Social Affairs. World Population Prospects 2019. (2019).Consortium, I. & Commission, E. The circular Bio-society in 2050. (2018).Ramaswami, A., Russell, A. G., Culligan, P. J., Rahul Sharma, K. & Kumar, E. Meta-principles for developing smart, sustainable, and healthy cities. Science (1979) 352, 940–943 (2016).CAS 

Google Scholar 
Cooper, C. M., Troutman, J. P., Awal, R., Habibi, H. & Fares, A. Climate change-induced variations in blue and green water usage in U.S. urban agriculture. J. Clean. Prod. 348, 567–579 (2022).Article 

Google Scholar 
Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).Article 
CAS 

Google Scholar 
Paul, S., Dutta, A., Defersha, F. & Dubey, B. Municipal food waste to biomethane and biofertilizer: A circular economy concept. Waste Biomass Valorizat. 9, 601–611 (2018).Article 
CAS 

Google Scholar 
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).Article 
CAS 
PubMed 

Google Scholar 
Bergstrand, K. J. Organic fertilizers in greenhouse production systems—A review. Sci. Hortic. 295, 1–8 (2022).Article 

Google Scholar 
Chiaregato, C. G., França, D., Messa, L. L., dos Santos Pereira, T. & Faez, R. A review of advances over 20 years on polysaccharide-based polymers applied as enhanced efficiency fertilizers. Carbohydr. Polym. 279, 1–10 (2022).Article 

Google Scholar 
Timilsena, Y. P. et al. Enhanced efficiency fertilisers: A review of formulation and nutrient release patterns. J. Sci. Food Agric. 95, 1131–1142 (2015).Article 
CAS 
PubMed 

Google Scholar 
Chen, J. et al. Environmentally friendly fertilizers: A review of materials used and their effects on the environment. Sci. Total Environ. 613–614, 829–839 (2018).Article 
PubMed 

Google Scholar 
Aguilera, E., Lassaletta, L., Sanz-Cobena, A., Garnier, J. & Vallejo, A. The potential of organic fertilizers and water management to reduce N2O emissions in Mediterranean climate cropping systems. A review. Agric. Ecosyst. Environ. 164, 32–52 (2013).Article 
CAS 

Google Scholar 
Lv, G. et al. Biochar-based fertilizer enhanced Cd immobilization and soil quality in soil-rice system. Ecol. Eng. 171, 1–12 (2021).Article 

Google Scholar 
Clark, M. J. & Zheng, Y. Fertilizer rate influences production scheduling of sedum-vegetated green roof mats. Ecol. Eng. 71, 644–650 (2014).Article 

Google Scholar 
Samoraj, M. et al. Biochar in environmental friendly fertilizers—Prospects of development products and technologies. Chemosphere 296, 1–7 (2022).Article 

Google Scholar 
Dimkpa, C. O., Fugice, J., Singh, U. & Lewis, T. D. Development of fertilizers for enhanced nitrogen use efficiency—Trends and perspectives. Sci. Total Environ. 731, 1–9 (2020).Article 

Google Scholar 
Fertahi, S., Ilsouk, M., Zeroual, Y., Oukarroum, A. & Barakat, A. Recent trends in organic coating based on biopolymers and biomass for controlled and slow release fertilizers. J. Control. Release 330, 341–361 (2021).Article 
CAS 
PubMed 

Google Scholar 
García-Garizábal, I., Causapé, J. & Abrahao, R. Nitrate contamination and its relationship with flood irrigation management. J. Hydrol. (AMST) 442–443, 15–22 (2012).Article 

Google Scholar 
Adu-Poku, D., Ackerson, N. O. B., Devine, R. N. O. A. & Addo, A. G. Climate mitigation efficiency of nitrification and urease inhibitors: Impact on N2O emission—A review. Sci. Afr. 16, 1–7 (2022).
Google Scholar 
Ding, W., Qin, H., Yu, S. & Yu, S. L. The overall and phased nitrogen leaching from a field bioretention during rainfall runoff events. Ecol. Eng. 179, 1–9 (2022).Article 

Google Scholar 
Li, X. et al. Loss of nitrogen and phosphorus from farmland runoff and the interception effect of an ecological drainage ditch in the North China Plain—A field study in a modern agricultural park. Ecol. Eng. 169, 1–10 (2021).Article 

Google Scholar 
Michalsky, R. & Pfromm, P. H. Thermodynamics of metal reactants for ammonia synthesis from steam, nitrogen and biomass at atmospheric pressure. AIChE J. 58, 3203–3213 (2012).Article 
CAS 

Google Scholar 
Pleissner, D. Decentralized utilization of wasted organic material in urban areas: A case study in Hong Kong. Ecol. Eng. 86, 120–125 (2016).Article 

Google Scholar 
Masullo, A. Organic wastes management in a circular economy approach: Rebuilding the link between urban and rural areas. Ecol. Eng. 101, 84–90 (2017).Article 

Google Scholar 
Zeng, Y., de Guardia, A., Ziebal, C., de Macedo, F. J. & Dabert, P. Nitrogen dynamic and microbiological evolution during aerobic treatment of digested sludge. Waste Biomass Valorizat. 5, 441–450 (2014).CAS 

Google Scholar 
Nagarajan, S., Eswaran, P., Masilamani, R. P. & Natarajan, H. Chicken feather compost to promote the plant growth activity by using Keratinolytic Bacteria. Waste Biomass Valorizat. 9, 531–538 (2018).Article 
CAS 

Google Scholar 
Bhat, S. A., Singh, J. & Vig, A. P. Earthworms as organic waste managers and biofertilizer producers. Waste Biomass Valorizat. 9, 1073–1086 (2018).Article 
CAS 

Google Scholar 
Mekki, A., Arous, F., Aloui, F. & Sayadi, S. Treatment and valorization of agro-wastes as biofertilizers. Waste Biomass Valorizat. 8, 611–619 (2017).Article 
CAS 

Google Scholar 
Liu, T. et al. Black soldier fly larvae for organic manure recycling and its potential for a circular bioeconomy: A review. Sci. Total Environ. 833, 1–10 (2022).Article 

Google Scholar 
Siddiqui, S. A. et al. Black soldier fly larvae (BSFL) and their affinity for organic waste processing. Waste Manag. 140, 1–13 (2022).Article 
PubMed 

Google Scholar 
Bortolini, S. et al. Hermetia illucens (L.) larvae as chicken manure management tool for circular economy. J. Clean. Prod. 262, 1–10 (2020).Article 

Google Scholar 
Diener, S., Studt Solano, N. M., Roa Gutiérrez, F., Zurbrügg, C. & Tockner, K. Biological treatment of municipal organic waste using black soldier fly larvae. Waste Biomass Valorizat. 2, 357–363 (2011).Article 
CAS 

Google Scholar 
Cai, M. et al. Rapidly mitigating antibiotic resistant risks in chicken manure by Hermetia illucens bioconversion with intestinal microflora. Environ. Microbiol. 20, 4051–4062 (2018).Article 
CAS 
PubMed 

Google Scholar 
Yang, C. et al. Characteristics and mechanisms of ciprofloxacin degradation by black soldier fly larvae combined with associated intestinal microorganisms. Sci. Total Environ. 811, 1–8 (2022).Article 

Google Scholar 
Pang, W. et al. The influence on carbon, nitrogen recycling, and greenhouse gas emissions under different C/N ratios by black soldier fly. Environ. Sci. Pollut. Res. 27, 42767–42777 (2020).Article 
CAS 

Google Scholar 
Beskin, K. v. et al. Larval digestion of different manure types by the black soldier fly (Diptera: Stratiomyidae) impacts associated volatile emissions. Waste Manag. 74, 213–220 (2018).Gligorescu, A. et al. Pilot scale production of Hermetia illucens (L.) larvae and frass using former foodstuffs. Clean Eng. Technol. 10, 1–10 (2022).Rosa, R. et al. Life cycle assessment of chemical vs enzymatic-assisted extraction of proteins from black soldier fly prepupae for the preparation of biomaterials for potential agricultural use. ACS Sustain. Chem. Eng. 8, 14752–14764 (2020).Article 
CAS 

Google Scholar 
Surendra, K. C. et al. Rethinking organic wastes bioconversion: Evaluating the potential of the black soldier fly (Hermetia illucens (L.)) (Diptera: Stratiomyidae) (BSF). Waste Manag. 117, 58–80 (2020).Hasnol, S. et al. A review on insights for green production of unconventional protein and energy sources derived from the larval biomass of black soldier fly. Processes 8, 1–13 (2020).Article 

Google Scholar 
Wong, C. Y. et al. Rhizopus oligosporus-assisted valorization of coconut endosperm waste by black soldier fly larvae for simultaneous protein and lipid to biodiesel production. Processes 9, 1–14 (2021).Article 

Google Scholar 
Raksasat, R. et al. Blended sewage sludge–palm kernel expeller to enhance the palatability of black soldier fly larvae for biodiesel production. Processes 9, 1–13 (2021).Article 

Google Scholar 
Dortmans B.M.A., Diener S. & Verstappen B.M. Black Soldier Fly Biowaste Processing A Step-by-Step Guide. (2017).European Parliament. Regulation (EC) No 767/2009 of the European Parliament and of the council. (2009).Italian Government. Norme in materia ambientale. (Dlgs, 2006).European Parliament. Regulation (EC) No 178/2002 of the European Parliament and of the Council. Official Journal of the European Communities (2002).Palma, L., Fernandez-Bayo, J., Niemeier, D., Pitesky, M. & VanderGheynst, J. S. Managing high fiber food waste for the cultivation of black soldier fly larvae. NPJ Sci. Food 3, 1–7 (2019).Article 

Google Scholar 
Righi, C. et al. Suitability of porous inorganic materials from industrial residues and bioproducts for use in horticulture: A multidisciplinary approach. Appl. Sci. 12, 5437 (2022).Article 
CAS 

Google Scholar 
Barbi, S. et al. Preliminary study on sustainable NPK slow-release fertilizers based on byproducts and leftovers: A design-of-experiment approach. ACS Omega 5, 27154–27163 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macavei, L. I., Benassi, G., Stoian, V. & Maistrello, L. Optimization of Hermetia illucens (L.) egg laying under different nutrition and light conditions. PLoS ONE 15, 1–12 (2020).Article 

Google Scholar 
Leni, G., Maistrello, L., Pinotti, G., Sforza, S. & Caligiani, A. Production of carotenoid-rich Hermetia illucens larvae using specific agri-food by-products. J. Insects Food Feed 1, 1–12 (2022).
Google Scholar 
Caligiani, A. et al. Composition of black soldier fly prepupae and systematic approaches for extraction and fractionation of proteins, lipids and chitin. Food Res. Int. 105, 812–820 (2018).Article 
CAS 
PubMed 

Google Scholar 
Montgomery, D. C. Design and Analysis of Experiments Eighth Edition. Design vol. 2 (2012).Barbi, S., Messori, M., Manfredini, T., Pini, M. & Montorsi, M. Rational design and characterization of bioplastics from Hermetia illucens prepupae proteins. Biopolymers 110–118, (2019).Eriksson, L., Johansson, E., Kettaneh-Wold, N., WikstrÄom, C. & Wold, S. Design of Experiments: Principles and Applications. (2008).Morris, P. & John, P. W. M. Statistical Design and Analysis of Experiments. Math. Gaz. 83, 189–200 (1999).Article 

Google Scholar 
Kros, J. F. & Mastrangelo, C. M. Comparing multi-response design methods with mixed responses. Qual Reliab Eng Int 20, 527–539 (2004).Article 

Google Scholar 
Fernandez Pulido, C. R., Caballero, J., Bruns, M. A. & Brennan, R. A. Recovery of waste nutrients by duckweed for reuse in sustainable agriculture: Second-year results of a field pilot study with sorghum. Ecol Eng 168, 1–8 (2021).Kaya, M. et al. Biological, mechanical, optical and physicochemical properties of natural chitin films obtained from the dorsal pronotum and the wing of cockroach. Carbohydr. Polym. 163, 162–169 (2017).Article 
CAS 
PubMed 

Google Scholar 
Kaya, M. et al. On chemistry of γ-chitin. Carbohydr. Polym. 176, 177–186 (2017).Article 
CAS 
PubMed 

Google Scholar 
Poerio, A. et al. Extraction and physicochemical characterization of chitin from cicada orni sloughs of the south-eastern French mediterranean basin. Molecules 25, 1–12 (2020).Article 

Google Scholar 
Sagheer, F. A. A., Al-Sughayer, M. A., Muslim, S. & Elsabee, M. Z. Extraction and characterization of chitin and chitosan from marine sources in Arabian Gulf. Carbohydr. Polym. 77, 410–419 (2009).Article 

Google Scholar 
Waśko, A. et al. The first report of the physicochemical structure of chitin isolated from Hermetia illucens. Int. J. Biol. Macromol. 92, 316–320 (2016).Article 
PubMed 

Google Scholar 
Wang, K. et al. Preparation of bacterial cellulose/silk fibroin double-network hydrogel with high mechanical strength and biocompatibility for artificial cartilage. Cellulose 27, 1845–1852 (2020).Article 
CAS 

Google Scholar 
Morin, A. & Dufresne, A. Nanocomposites of Chitin Whiskers from Riftia Tubes and Poly(caprolactone). Macromolecules 35, 2190–2199 (2002).Article 
CAS 

Google Scholar 
George Socrates. Infrared and Raman Characteristic Group Frequencies: Tables and Charts. (John Wiley & Sons, 2004).Chen, P. & Zhang, L. New evidences of glass transitions and microstructures of soy protein plasticized with glycerol. Macromol. Biosci. 5, 237–245 (2005).Article 
CAS 
PubMed 

Google Scholar 
Robertson, N.-L.M., Nychka, J. A., Alemaskin, K. & Wolodko, J. D. Mechanical performance and moisture absorption of various natural fiber reinforced thermoplastic composites. J. Appl. Polym. Sci. 130, 969–980 (2013).Article 
CAS 

Google Scholar 
Chavez, M. The sustainability of industrial insect mass rearing for food and feed production: Zero waste goals through by-product utilization. Curr. Opin. Insect. Sci. 48, 44–49 (2021).Article 
PubMed 

Google Scholar 
Fisher, H. J. et al. Black soldier fly larvae meal as a protein source in low fish meal diets for Atlantic salmon (Salmo salar). Aquaculture 521, 1–12 (2020).Article 

Google Scholar 
Figueiredo, L. R. F., Nepomuceno, N. C., Melo, J. D. D. & Medeiros, E. S. Glycerol-based polymer adhesives reinforced with cellulose nanocrystals. Int. J. Adhes. Adhes. 110, (2021). More