Philippa Kaur

Data collectionBased on the cropland extent, we first introduced a cropland distribution template, the Self-adapting Statistics Allocation Model of Global Cropland (SASAM-GC)16, as shown in Fig. S1. The global cropland extent map used herein was a global cropland synergy map with a 500-m spatial resolution representing approximately the year 2010, developed by the Smart Agriculture Innovation Team of the Key Laboratory of Agricultural Remote Sensing (AGRIRS) of the Chinese Academy of Agricultural Sciences in cooperation with the International Food Policy Research Institute (IFPRI) and the International Institute of Applied Systems Analysis (IIASA). The overall accuracy of the SASAM-GC products is 90.8%, which is higher than those of existing global farmland products such as the Climate Change Initiative Land Cover (CCI-LC), GlobeLand30, and Medium Resolution Imaging Spectrometer (MODIS) products.Vegetation indices are often used to depict crop growth, such as the ratio vegetation index (RVI)17, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI)18. Among them, the EVI is the most sensitive to high-biomass regions and less susceptible to atmospheric and soil interference19,20. MODIS vegetation index datasets are generated every 8 days or 16 days at spatial resolutions of 250 m, 500 m, and 1000 m. The 250-m spatial resolution is considered the best resolution for detecting crops21,22. Here, we used EVI time series with a spatial resolution of 250 m reported every 16 days in the MODIS product MOD13Q1 as the primary data to calculate the global cropping intensity; these data can be accessed at https://lpdaac.usgs.gov/products/mod13q1v006/.$${rm{EVI}}=2.5times frac{{{rm{rho }}}_{{rm{NIR}}}-{{rm{rho }}}_{{rm{Red}}}}{{{rm{rho }}}_{{rm{NIR}}}+6times {{rm{rho }}}_{{rm{Red}}}-7.5times {{rm{rho }}}_{{rm{Blue}}}+1}$$
(1)
In formula 1, ρNIR, ρRed, and ρBlue represent the reflectivity of the near-infrared band, the red band, and the blue band, respectively.The Food and Agricultural Organization of the United Nations statistical data (FAOSTAT) provides long-time-series cropland-related statistical data at the country level and can be accessed at http://www.fao.org/faostat/en/#data. FAOSTAT cropland data have been widely used in a variety of studies evaluating food security and hindcasting historical land-use changes23,24. Here, we defined the cropland area as the sum of areas characterizing arable land (land under temporary crops, temporary meadows used for mowing or pasture, market and kitchen gardens, and land that is temporarily fallow; abandoned land resulting from shifting cultivation was excluded) and permanent croplands (land cultivated with long-term crops that do not have to be replanted for several years). Additionally, the harvested area is used; this value refers to the area from which a crop is gathered. If the crop under consideration is harvested more than once during a year due to successive cropping (i.e., the same crop is sown or planted more than once in the same field during the same year), the area is counted as many times as the crop is harvested. Therefore, the sown area is recorded only for the crop reported. The statistical cropping intensity is defined as the harvested area divided by the cropland area and is used to validate the consistency between the cropping intensity obtained herein and that reported in the FAOSTAT product at the country level.Brief algorithmIn this study, a sixth-order polynomial function was used to reconstruct EVI time series for brief and rapid calculations at the global scale25. As the global cropping intensity ranges from 0 to 3 and the sixth-order polynomial function can have 3 maxima at most, we chose the sixth-order polynomial function (formula 2) in this study:$${rm{EVI}}({rm{t}})={{rm{a}}}_{0}+{{rm{a}}}_{1}{rm{t}}+{{rm{a}}}_{2}{{rm{t}}}^{2}+cdots +{{rm{a}}}_{6}{{rm{t}}}^{6}$$
(2)
where t is the time-series length referring to the beginning of the time series, EVI(t) is the fitted EVI time series, and a0, a1, … an are the fitted parameters of the sixth-order polynomial function. Then, the first derivative EVI′(t) and the second derivative EVI″(t) were calculated to find the real numerical solution of the equation’s maxima when EVI′(t) = 0and EVI″(t)  More

1.Caro, T. The adaptive significance of coloration in mammals. Bioscience 55, 125 (2005).Article 

Google Scholar 
2.Caro, T. The colours of extant mammals. Semin. Cell Dev. Biol. 24, 542–552 (2013).Article 

Google Scholar 
3.Schaller, G. B., Jinchu, H., Wenshi, P. & Jing, Z. The Giant Pandas of Wolong (University of Chicago Press, 1985). https://doi.org/10.1086/414647.Book 

Google Scholar 
4.Schaller, G. B. The Last Panda (University of Chicago Press, 1994).
Google Scholar 
5.Morris, R. & Morris, D. Men and Pandas (McGraw-Hill Book Company, 1966).
Google Scholar 
6.Morris, R. & Morris, D. The Giant Panda (Penguin Books, 1982).
Google Scholar 
7.Lazell, J. D. J. Color Patterns of the ‘Giant’ Bear (Ailuropoda melanoleuca) and the True Panda (Ailurus fulgens) (Mississippi Wildlife Federation, 1974).
Google Scholar 
8.Cott, H. B. Adaptive Coloration in Animals (Methuen & Co., Ltd., 1940).
Google Scholar 
9.Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. 41, 315–352 (1990).Article 

Google Scholar 
10.Stevens, M. & Merilaita, S. Animal camouflage: Current issues and new perspectives. Philos. Trans. R. Soc. B Biol. Sci. 364, 423–427 (2009).Article 

Google Scholar 
11.Caro, T., Walker, H., Rossman, Z., Hendrix, M. & Stankowich, T. Why is the giant panda black and white?. Behav. Ecol. 28, 657–667 (2017).Article 

Google Scholar 
12.Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).Article 

Google Scholar 
13.Merilaita, S. Crypsis through disruptive coloration in an isopod. Proc. R. Soc. B Biol. Sci. 265, 1059–1064 (1998).Article 

Google Scholar 
14.Cuthill, I. C. et al. Disruptive coloration and background pattern matching. Nature 434, 72–74 (2005).ADS 
CAS 
Article 

Google Scholar 
15.Stevens, M. & Merilaita, S. Defining disruptive coloration and distinguishing its functions. Philos. Trans. R. Soc. B Biol. Sci. 364, 481–488 (2009).Article 

Google Scholar 
16.Ruxton, G., Allen, W., Sherratt, T. & Speed, M. Avoiding Attack: The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry (Oxford University Press, 2019).
Google Scholar 
17.Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—A free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).Article 

Google Scholar 
18.van den Berg, C. P., Troscianko, J., Endler, J. A., Marshall, N. J. & Cheney, K. L. Quantitative colour pattern analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature. Methods Ecol. Evol. 11, 316–332 (2020).Article 

Google Scholar 
19.Troscianko, J., Skelhorn, J. & Stevens, M. Quantifying camouflage: How to predict detectability from appearance. BMC Evol. Biol. 17, 7 (2017).Article 

Google Scholar 
20.Caves, E. M. & Johnsen, S. AcuityView: An r package for portraying the effects of visual acuity on scenes observed by an animal. Methods Ecol. Evol. 9, 793–797 (2018).Article 

Google Scholar 
21.Marshall, N. J. Communication and camouflage with the same ‘bright’ colours in reef fishes. Philos. Trans. R. Soc. B Biol. Sci. 355, 1243–1248 (2000).CAS 
Article 

Google Scholar 
22.Barnett, J. B., Cuthill, I. C. & Scott-Samuel, N. E. Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (Tyria jacobaeae, erebidae). R. Soc. Open Sci. 5, 171396 (2018).ADS 
Article 

Google Scholar 
23.Barnett, J. B., Cuthill, I. C. & Scott-Samuel, N. E. Distance-dependent pattern blending can camouflage salient aposematic signals. Proc. R. Soc. B Biol. Sci. 284, 20170128 (2017).Article 

Google Scholar 
24.Stoner, C. J., Caro, T. M. & Graham, C. M. Ecological and behavioral correlates of coloration in artiodactyls: Systematic analyses of conventional hypotheses. Behav. Ecol. 14, 823–840 (2003).Article 

Google Scholar 
25.Caro, T., Walker, H., Santana, S. E. & Stankowich, T. The evolution of anterior coloration in carnivorans. Behav. Ecol. Sociobiol. 71, 177 (2017).Article 

Google Scholar 
26.Melin, A. D., Kline, D. W., Hiramatsu, C. & Caro, T. Zebra stripes through the eyes of their predators, zebras, and humans. PLoS ONE 11, e0145679 (2016).Article 

Google Scholar 
27.Land, M. F. & Nilsson, D.-E. Animal Eyes (Oxford University Press, 2012).Book 

Google Scholar 
28.Phillips, G. A. C., How, M. J., Lange, J. E., Marshall, N. J. & Cheney, K. L. Disruptive colouration in reef fish: Does matching the background reduce predation risk?. J. Exp. Biol. 220, 1962–1974 (2017).Article 

Google Scholar 
29.Li, Y. et al. Giant pandas can discriminate the emotions of human facial pictures. Sci. Rep. 7, 1–8 (2017).ADS 
Article 

Google Scholar 
30.Stevens, M., Párraga, C. A., Cuthill, I. C., Partridge, J. C. & Troscianko, T. S. Using digital photography to study animal coloration. Biol. J. Linn. Soc. 90, 211–237 (2007).Article 

Google Scholar 
31.Lind, O., Milton, I., Andersson, E., Jensen, P. & Roth, L. S. V. High visual acuity revealed in dogs. PLoS ONE 12, 1–12 (2017).
Google Scholar 
32.Pasternak, T. & Merigan, W. H. The luminance dependence of spatial vision in the cat. Vis. Res. 21, 1333–1339 (1981).CAS 
Article 

Google Scholar 
33.Clark, D. L. & Clark, R. A. Neutral point testing of color vision in the domestic cat. Exp. Eye Res. 153, 23–26 (2016).CAS 
Article 

Google Scholar 
34.Caves, E. M., Brandley, N. C. & Johnsen, S. Visual acuity and the evolution of signals. Trends Ecol. Evol. 33, 1–15 (2018).Article 

Google Scholar 
35.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. R. Soc. B Biol. Sci. 265, 351–358 (1998).CAS 
Article 

Google Scholar 
36.Nokelainen, O., Brito, J. C., Scott-Samuel, N. E., Valkonen, J. K. & Boratyński, Z. Camouflage accuracy in Sahara-Sahel desert rodents. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13225 (2020).Article 
PubMed 

Google Scholar 
37.Nokelainen, O., Stevens, M. & Caro, T. Colour polymorphism in the coconut crab (Birgus latro). Evol. Ecol. 32, 75–88 (2018).Article 

Google Scholar 
38.Nokelainen, O., Maynes, R., Mynott, S., Price, N. & Stevens, M. Improved camouflage through ontogenetic colour change confers reduced detection risk in shore crabs. Funct. Ecol. https://doi.org/10.1111/1365-2435.13280 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More

The arthritic degeneration visualized in the pathological Smilodon specimens could have arisen from one of three etiologies: traumatic, infective or degenerative arthritis. Findings on the specimens make infective or traumatic arthritis less likely. In the case of infective arthritis, the presupposition is that the animal developed typically before an insult that led to infection and subsequent obliteration of the hip joint. This assumption also holds true for the case of traumatic arthritis following an injury or fracture. However, the anatomical distortions of the right femoral head, in conjunction with the obliteration of the right acetabulum, suggest chronic changes that led to degeneration over time (Figs. 3, 4). The degeneration of the femoral head would not be expected if the degenerative change in the hip joint were due to infection or trauma, as the development of the pelvis and femur presumably would have been complete before the insult or injury occurred during the adult cat’s life.The condition of the right acetabulum and right femoral head demonstrates anatomy consistent with developmental distortion. Typically, the head of the femur develops in conjunction with the acetabulum of the pelvis16. The spherical femoral head fits into the concentric-shaped acetabulum to form a ball-and-socket joint that enables a four-legged mammal to ambulate, lie down, sit down, stand up, and generally function normally16. In developmental hip dysplasia, however, the acetabulum does not develop appropriately, and the articulation between the femoral head and acetabulum is lost. An elliptical (as opposed to concentric-shaped) acetabulum causes progressive subluxation (dislocation) of the femoral head17, which can result in coxa plana, or necrosis of the bony nucleus of the femoral head16. This subsequent coxa plana produces flattening and degeneration of the normally spherical femoral head18.Proper anatomical development and ossification of the hip joint rely on continuous and symmetrical pressure of the femoral head on the acetabulum, and dysplasia results from improper positioning of the femoral head within the acetabulum16. Dysplastic hips are characterized by pathological restructuring and accelerated remodeling of the joint in response to abnormal forces and tensions that create stress. This produces formation of new bone in some areas and resorption of bone in others, ultimately causing degenerative joint disease16. Dysplastic hips have varying degrees of deformity and malformation, but typically the acetabula are hypoplastic and deficient in various planes and dimensions (Supplementary Fig. S10).Further inspection of LACMHC 131 demonstrates anatomical changes consistent with chronic degeneration throughout the right hip joint and pelvis. The obliteration of the right innominate likely occurred over many years and progressively resulted in significant bony destruction and remodeling. These findings of a flattened femoral head in LACMHC 6963 in conjunction with a shallow acetabulum in LACMHC 131 are consistent with changes observed with mechanical instability of the hip joint and bony destruction secondary to dysplasia. Repeated subluxation events due to the dysplastic hip likely accelerated the destruction of the cartilage and joint, altering the biomechanical stresses through the joint. This increased stress along with cartilage loss likely led to a progressively hypertrophic and aberrant bone response with subchondral sclerosis and osteophyte formation in the acetabulum and pelvis. The external, anatomical deformities in these specimens are consistent with changes that have occurred over the animal’s lifespan and subsequently resulted in the gross morphology observed, with destruction of the hip joint on both the acetabular and femoral side.This type of pathology starts to impact movement at the time of first walking, although minimal pain tends to ensue at this time because of the animal’s flexibility at its early age19,20. As the joint cartilage wears out, however, bone begins to rub on bone. The resulting forces make the bone stiffer, producing osteophytes or bone spurs as well as sclerosis that manifests on CT imaging as increased bone density (Figs. 3 and 4; Supplementary Video S2; Supplementary Data S1–S2). At this point, loading the limb would cause pain, and range of motion would be limited. Therefore, the animal examined in this study would have spent as little time as possible on its right hindlimb, needing to compensate for the handicap by increasing the load on its left hindlimb. This compensation would explain the exostoses on the left ilium anterodorsal to the non-pathological acetabulum (Fig. 1; Supplementary File S1; Supplementary Video S1), indicating abnormal pulling of the quadriceps femoris muscles originating in this area.Hip dysplasia is a heritable, polygenic condition that affects a range of mammal species16, including humans17. Canine hip dysplasia (CHD) is one of the most prevalent orthopedic diseases in domestic dogs (Canis lupus familiaris)21 and is very well studied, in part because it is similar to developmental dysplasia of the human hip22. Feline hip dysplasia (FHD) has received less clinical attention than CHD, possibly because its functional impairment is less overt or because domestic cats (Felis catus) are able to compensate for the resulting lameness better than dogs20,23. The overall results of physiological changes from dysplasia are mechanical imbalance and instability in the hip joint causing displacement due to opposing forces from the acetabulum and femoral head, and osteophytes in the acetabulum to compensate for cartilage loss16.Embryologically, articular joints differentiate from skeletal mesenchyme in situ with the support of surrounding tissues that sustain mechanical and physiological forces that tend to pull on the joints16. In dogs, hip joints are normal at birth, as teratological factors and mechanical stresses that could displace the femoral head are rare at this time16. Epiphyseal ossification normally begins by 12 days of age; in dogs that eventually develop CHD, anatomical changes of the femoral head and pelvic socket begin before week three24. In dysplastic hips, the teres ligament, which is crucial for holding the femoral head in place, is too short; this produces luxation, or dislocation, of the top of the femoral head, beginning at around seven weeks16. This luxation increases throughout development, degrading the articular cartilage that surrounds the femoral head, delaying ossification of the femur and acetabulum16, and shortening the affected limb, as the femoral head becomes positioned higher in the acetabulum.In clinical reports of hip dysplasia in domestic cats, osteoarthritis (i.e., degenerative joint disease, DJD) of the hip secondary to FHD is well known19. For example, osteoarthritis was recorded in 43 of 45 (95.6%) of cats with FHD25. As well, in 5 of 13 (38.5%) cases of hip osteoarthritis with a radiographically or historically identifiable cause, hip dysplasia was pinpointed as the cause, with the remaining cases resulting from trauma or equivocal between trauma and dysplasia23. A recent study of FHD in Maine Coons—a large-bodied domestic cat breed in which hip dysplasia is known to be common—calculated a prevalence of 37.4%, finding severity to increase with age and body mass26. The same study further highlighted a genetic correlation between FHD and large body size within the Maine Coon26, inviting inquiry into how FHD impacts other breeds and non-domestic felid species across a range of body sizes.Reports of FHD in non-domestic large cats are rarer than in domestic cats. Captive snow leopards have exhibited hip dysplasia; across 14 zoos, seven cases were classified as moderate to severe, and at least two individual snow leopards needed total hip replacement before being able to breed27,28. Accounts of hip functional impairment in other captive large cats have tended to report osteoarthritis, which can be associated with FHD though may also stem from trauma and increased age29,30,31.For wild-caught large cats, the only comprehensive study of which we are aware is a survey of 386 individuals (283 wild-caught) across three felid genera mounted as exhibit skeletons in multiple North American natural history museums30. Though not focusing on hip dysplasia, the study tracked degenerative joint disease, which may be associated with dysplasia23,25. The sample recorded DJD in 9.7% of 31 tigers, 2.3% of 88 African lions, and 5.1% of 59 mountain lions (Puma concolor), and none in five other species of big cat. These frequencies are low compared to domestic cats, perhaps owing to differences in body size, diet, and lifestyle between large wild cats and domestic cats, as well as selective breeding constraining genetic variation in domestic animals. Furthermore, selection against hip dysplasia would be expected in the wild because hip dysplasia would compromise hunting19. Though this study identified instances of non-inflammatory osteoarthritis in the shoulder, elbow, and stifle joint, it found none in the hip. However, 4% of all joints afflicted by spondyloarthropathy—a form of inflammatory arthritis—included the hip30.What is the significance of Smilodon, an extinct Pleistocene predator, having the same congenital defect as living domestic cats and dogs? Previous workers have inferred social behavior from paleopathologies in fossil carnivorans ranging from the extinct Eurasian steppe lion7 to Pleistocene wolf-like canines from Italy8 and China9, interpreting signs of healing as evidence of survival after injury12. Given the severity of many injuries, authors have argued, the animal would have starved to death had it not operated within a social structure. The present hip dysplasia having manifested from a young age—hindering this animal’s ability to hunt prey and defend a home range over the course of its life—supports this assertion, although other inferences are possible.Sociality, the degree to which individuals live with conspecifics in groups32, is difficult to infer in Smilodon given that it has no living analogues or closely related taxa. Estimated to have weighed between 160 and 350 kg (3,14, this study), Smilodon was at least the size of the Amur tiger (Panthera tigris altaica), the largest living cat; some estimates reach 369 to 469 kg, placing Smilodon in the range of the largest extant ursids15,33. No living felid has Smilodon’s elongate, knife-like canines or stocky, powerful build. As well, Smilodon (of the extinct felid lineage Machairodontinae) is only distantly related to extant large felids (Felinae), introducing further uncertainty. Based on its robust morphology (e.g.,34,35) and on evidence from stable isotopes (e.g.,4), it likely stalked and ambushed prey; therefore, it may have been comparable to the African lion (Panthera leo), which has a similar hunting strategy and is the only truly social extant felid36. Yet sociality varies across felid species, including within a genus; for example, other extant pantherines like tigers (P. tigris) show incipient sociality37, while jaguars (P. onca) are solitary except for females with cubs. Social strategies also can vary within species, e.g., between sexes. For instance, African lion females are philopatric and social throughout their lives, while adult males are often nomadic and solitary until joining a gregarious pride, which itself usually lasts for only a few years38. This social variation complicates behavioral inferences based on ancestral reconstructions.Advocates of the solitary-cat hypothesis39,40 have cited Smilodon’s small relative brain size determined using endocranial casts as support for solitary behavior, because sociality exerts high cognitive demands. However, in 39 species across nine carnivoran families, larger relative brain size was found to correlate with problem-solving capabilities rather than social behavior41. Rather than analyses of overall encephalization across carnivoran families, studies of relative regional brain volume within families and species have been more informative regarding sociality42,43. In both African lions and cougars (Puma concolor, a solitary species), total relative endocranial volume was not sexually dimorphic; however, relative anterior cerebrum volume was significantly greater in female African lions than males, a difference absent in cougars38.Though regional endocranial studies have yet to be performed on Smilodon, the gregarious-cat hypothesis has drawn support from multiple lines of evidence. One is the abundance of Smilodon relative to prey at RLB10,11,34, although detractors have pointed out that some extant large cats aggregate at carcasses despite otherwise being solitary40. A full range of ages is present among RLB Smilodon; in contrast, animals interpreted to be solitary, such as the American lion Panthera atrox, are represented largely by adult individuals44. As well, the proportions of social and solitary species at RLB parallel those drawn to audio recordings of herbivore distress calls in the African savanna, suggesting that RLB Smilodon sample sizes are more consistent with it having been social rather than solitary45,46. The lack of size sexual dimorphism in Smilodon is more typical of modern solitary cats47 but could also be reflective of monogamy within a gregarious species, like modern wolves. Most relevant to the current study, the existence of healed injuries in Smilodon also has been interpreted as evidence for social behavior, with the assumption that surviving long after serious injury would be difficult if not impossible without cooperative sociality12. We now revisit this interpretation considering the novel diagnosis of hip dysplasia in this study.Smilodon’s large body size necessitated preying on megaherbivores for adequate sustenance3. To do so, like most large cats today, it would have used its hindlimbs for propulsion and acceleration48,49, a pounce behavior enabled by its morphology. Smilodon’s ratio of total forelimb to hindlimb length is greater while its ratio of tibia to femur length ranks lower than in living felids34. The shorter hindlimbs lacking the distal limb elongation in cursorial animals suggest that Smilodon was an ambush predator surpassing the ability of felids today50. Hunting large prey is dangerous51. After the initial hindlimb-powered leap, Smilodon would have grappled with its struggling prey, as evidenced by traumatic injuries in the rotator cuff and radiating from the ventral midline dorsolaterally to where the ribs articulate with the spine5. As it subdued prey with robust forelimbs35,48 under enough torque to injure the lumbar vertebrae5, Smilodon would have needed to leverage itself against the ground using its hindlimbs. Therefore, the pelvis and femur would have been critical to multiple phases of its hunting strategy.A dysplastic individual would have encountered much difficulty hunting in this manner. Yet, as evidenced by the complete fusion of its pelvic and femoral epiphyses (Figs. 1, 2) as well as its large body size (Figs. 6, 7), the individual in this study had reached adult age. (Studies of the detailed timing of epiphyseal fusion in large wild cats are lacking, but distal femoral epiphyses fuse at around the same time as or soon after proximal femoral epiphyses in domestic cats and dogs52,53. Given this, the broken distal femur likely had a fused epiphysis, as on its intact proximal end.) Limbs in African lions completely fuse between 4.5 and 5.5 years54,55,56, so it is reasonable to assume that adulthood in Smilodon likely started at around four years old. This estimate is reinforced by bone histological work quantifying at least four to seven lines of arrested growth (LAGs; one per growth year) in limb bones with fused epiphyses belonging to Smilodon fatalis from the Talara asphaltic deposits in Peru57. Some LAGs in the Talara histological specimens likely have been masked by secondary bone remodeling, which may be more extensive in larger-bodied taxa57, making these specimens possibly older than the number of visible LAGs suggest. Therefore, four years represents a likely minimum age for this individual, although it could have been much older.Ontogenetic growth patterns in teeth and bone further support inferences of sociality. In Smilodon, teeth appear to mature earlier than when sutures and long-bone epiphyses fuse, suggesting delayed weaning, prolonged juvenile dependence, and extended familial care until the adult hunting morphology—saber canines and robust limbs—was complete47. At RLB, most sampled Smilodon specimens show significant pulp cavity closure of the lower canine (14 of 19 specimens over approximately 80% closure), a sign of dental maturation58. This contrasts with RLB pantherine pulp cavities, which are more evenly distributed across the closure percentage range, suggesting that teeth mature earlier in Smilodon than in pantherines. (Other age assessments have ruled out the possibility that Smilodon juveniles were underrepresented relative to pantherines44). At Talara, age determination by dentition yields low estimates of juveniles (zero based on skulls; 8% based on dentaries), but age determination based on limb epiphyseal fusion yields higher estimates (41% juveniles)57. Histology of Talara Smilodon long bones reinforces this mismatch, as an apparent adult femur with fused epiphyses and seven LAGs was found to lack avascular and acellular subperiosteal lamellar bone57, suggesting that it had not yet finished growing. Further, prolonged parental care was interpreted in a recent description, from Pleistocene deposits in Corralito, Ecuador, of two subadult Smilodon fatalis individuals inferred to have been siblings and associated with an adult that was likely their mother59. This scenario of prolonged parental care, like that in the social African lion, would help explain how the individual in this current study survived to adulthood given its debilitating handicap.Novel application of CT visualization to an old question of paleopathology has enabled diagnosis of hip dysplasia, a lifelong condition, in an individual Smilodon fatalis saber-toothed cat. This individual was likely not the only Smilodon afflicted with hip dysplasia: multiple RLB Smilodon pelvic specimens, especially that described by Shermis11, exhibit gross morphology similar to the pathological pelvis examined in this study (Supplementary Figs. S6–S9). The individual examined in this study reached adulthood (at least four to seven years of age) but could never have hunted nor defended territory on its own, given its locomotor impairment that would have been present since infancy. As such, this individual likely survived to adulthood by association with a social group that assisted it with feeding and protection.Further conclusions are limited by the lack of a comprehensive and systematic comparative dataset comprising pathological post-crania from extant species, a persistent limitation of paleopathological studies5. Natural history museums may acquire cranial remains from zoos or similar institutions but often lack storage to accommodate postcranial skeletons, especially for large mammals. As well, while radiographic studies on domestic cats and dogs illustrate the nature of hip dysplasia, these studies tend to examine pathological bones in situ, still embedded in a muscular framework (e.g., Supplementary Fig. S10). This is opposed to the bones-only, flesh-free context of paleopathological specimens. Computed tomography and digital data may be key to building a comparative paleopathology dataset in the future.Within the scope of this study, we cannot rule out the hypothesis that the pathological animal was a scavenger and may have obtained food outside the context of a social structure. It is also possible that, regardless of its disability, its large size and fearsome canines made it a strong interference competitor. However, the pathological specimens examined here are consistent with the predominance of studies supporting a spectrum of social strategies in this extinct predator. In many extant carnivorans, sociality offers the benefits of cooperative hunting and rearing of young (e.g.,60): benefits that likely also applied to Smilodon in the late Pleistocene. As Smilodon coexisted with a rich megafaunal carnivore community including dire wolves (Aenocyon dirus), American lions (Panthera atrox), and short-faced bears (Arctodus simus), cooperative sociality may have aided its success as a predator in a crowded field. More

1.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).ADS 
Article 

Google Scholar 
2.Stillman, J. H. Heat waves, the new normal: Summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100 (2019).CAS 
Article 

Google Scholar 
3.Williams, C. M. et al. Biological impacts of thermal extremes: Mechanisms and costs of functional responses matter. Integr. Comp. Biol. 56, 73–84 (2016).Article 

Google Scholar 
4.Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Chang. 7, 718–722 (2017).ADS 
Article 

Google Scholar 
5.Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H. O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).ADS 
CAS 
Article 

Google Scholar 
6.Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B Biol. Sci. 278, 1823–1830 (2011).Article 

Google Scholar 
7.Rummer, J. L. et al. Life on the edge: Thermal optima for aerobic scope of equatorial reef fishes are close to current day temperatures. Glob. Chang. Biol. 20, 1055–1066 (2014).ADS 
Article 

Google Scholar 
8.Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).ADS 
CAS 
Article 

Google Scholar 
9.Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).ADS 
CAS 
Article 

Google Scholar 
10.Bennett, S., Duarte, C. M., Marbà, N. & Wernberg, T. Integrating within-species variation in thermal physiology into climate change ecology. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180550 (2019).Article 

Google Scholar 
11.Comte, L. & Olden, J. D. Evolutionary and environmental determinants of freshwater fish thermal tolerance and plasticity. Glob. Chang. Biol. 23, 728–736 (2017).ADS 
Article 

Google Scholar 
12.Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).Article 

Google Scholar 
13.Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Chang. 5, 215–225 (2015).ADS 
Article 

Google Scholar 
14.Moran, E. V., Hartig, F. & Bell, D. M. Intraspecific trait variation across scales: Implications for understanding global change responses. Glob. Change Biol. 22, 137–150 (2016).ADS 
Article 

Google Scholar 
15.McKenzie, D. J. et al. Intraspecific variation in tolerance of warming in fishes. J. Fish Biol. 98, 1–20 (2020).

Google Scholar 
16.Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).Article 

Google Scholar 
17.Doyle, C. M., Leberg, P. L. & Klerks, P. L. Heritability of heat tolerance in a small livebearing fish, Heterandria formosa. Ecotoxicology 20, 535–542 (2011).CAS 
Article 

Google Scholar 
18.Meffe, G. K., Weeks, S. C., Mulvey, M. & Kandl, K. L. Genetic differences in thermal tolerance of eastern mosquitofish (Gambusia holbrooki; Poeciliidae) from ambient and thermal ponds. Can. J. Fish. Aquat. Sci. 52, 2704–2711 (1995).Article 

Google Scholar 
19.Gradil, K. J., Garner, S. R., Wilson, C. C., Farrell, A. P. & Neff, B. D. Relationship between cardiac performance and environment across populations of Atlantic salmon (Salmo salar): A common garden experiment implicates local adaptation. Evol. Ecol. 30, 877–886 (2016).Article 

Google Scholar 
20.Fangue, N. A., Hofmeister, M. & Schulte, P. M. Intraspecific variation in thermal tolerance and heat shock protein gene expression in common killifish, Fundulus heteroclitus. J. Exp. Biol. 209, 2859–2872 (2006).CAS 
Article 

Google Scholar 
21.Chen, Z., Farrell, A. P., Matala, A. & Narum, S. R. Mechanisms of thermal adaptation and evolutionary potential of conspecific populations to changing environments. Mol. Ecol. 27, 659–674 (2018).Article 

Google Scholar 
22.Chen, Z., Farrell, A. P., Matala, A., Hoffman, N. & Narum, S. R. Physiological and genomic signatures of evolutionary thermal adaptation in redband trout from extreme climates. Evol. Appl. 11, 1686–1699 (2018).CAS 
Article 

Google Scholar 
23.Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).Article 

Google Scholar 
24.Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).Article 

Google Scholar 
25.Rodgers, G. G., Donelson, J. M., McCormick, M. I. & Munday, P. L. In hot water: Sustained ocean warming reduces survival of a low-latitude coral reef fish. Mar. Biol. 165, 1–10 (2018).Article 

Google Scholar 
26.Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Chang. 5, 61–66 (2015).ADS 
Article 

Google Scholar 
27.Meffe, G. K., Weeks, S. C., Mulvey, M. & Kandl, K. L. Genetic differences in thermal tolerance of eastern mosquitofish (Gambusia holbrooki; Poeciliidae) from ambient and thermal ponds. Can. J. Fish. Aquat. Sci. 52(12), 2704–2711 (1995).Article 

Google Scholar 
28.Baer, C. F. & Travis, J. Direct and correlated responses to artificial selection on acute thermal stress tolerance in a livebearing fish. Evolution 54(1), 238–244 (2000).CAS 
Article 

Google Scholar 
29.Klerks, P. L., Athrey, G. N. & Leberg, P. L. Response to selection for increased heat tolerance in a small fish species, with the response decreased by a population bottleneck. Front. Ecol. Evol. 7, 270 (2019).Article 

Google Scholar 
30.Morgan, R., Finnøen, M. H., Jensen, H., Pélabon, C. & Jutfelt, F. Low potential for evolutionary rescue from climate change in a tropical fish. Proc. R. Soc. B Biol. Sci. 117(52), 33365–33372 (2020).CAS 

Google Scholar 
31.Frölicher, T. L. Extreme climatic events in the ocean. In Predicting Future Oceans: Sustainability of Ocean and Human Systems Amidst Global Environmental Change (eds Cisneros-Montemayor A. M. et al.) 53–60 (2019).32.Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Chang. 3(1), 78–82 (2013).ADS 
Article 

Google Scholar 
33.Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507(7493), 492–495 (2014).ADS 
CAS 
Article 

Google Scholar 
34.Molinos, J. G. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Chang. 6(1), 83–88 (2016).ADS 
Article 

Google Scholar 
35.Cox, D. K. Effects of three heating rates on the critical thermal maximum of bluegill. In Thermal Ecology (eds Gibbons, J. W. & Sharitz, R. R.) (National Technical Information Service, 1974).
Google Scholar 
36.Currie, S. & Schulte, P. M. Thermal stress. In The Physiology of Fishes 4th edn (eds Evans, D. H. et al.) 257–279 (CRC Press, 2014).
Google Scholar 
37.Grafen, A. The phlyogenetic regression. Philos. Trans. R. Soc. Lond. 326, 119–157 (1989).ADS 
CAS 

Google Scholar 
38.Garland, T. Jr. & Ives, A. R. Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods. Am. Nat. 155, 346–364 (2000).Article 

Google Scholar 
39.Orme, D. The caper package: comparative analysis of phylogenetics and evolution in R http://cran.r-project.org/web/packages/caper/index.html (2013).40.Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl. Acad. Sci. 112(41), 12764–12769 (2015).ADS 
CAS 
Article 

Google Scholar 
41.Michonneau, F., Brown, J. W. & Winter, D. J. rotl: An R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).Article 

Google Scholar 
42.Freckleton, R. P. The seven deadly sins of comparative analysis. J. Evol. Biol. 22, 1367–1375 (2009).CAS 
Article 

Google Scholar  More

To capture the first-order characteristics of the distribution of eDNA shed by mesopelagic species, we used a mechanistic model with one spatial dimension (the vertical dimension). It treats migrating organisms as a continuous point source of eDNA and simulates temporal evolution of the eDNA concentration vertical profiles in a one-dimensional ocean with climatological conditions. Horizontal variation of the distribution of eDNA shed by mesopelagic species is not considered in this study. The point source in the model represents a number of individuals within a single species. The simulation includes relevant eDNA transport processes using a vertical Price-Weller-Pinkel (PWP) dynamical module42. We use this model to first explore the sensitivity of eDNA concentration profiles to a variety of biological and physical parameters and to assess vertical and temporal variability of the eDNA concentration. Table 1 outlines the parameters, separated by type, and ranges of values for each parameter used in the sensitivity analysis (see “Sensitivity analysis” section below) and in a case study of how the model can be used to determine the percent of individuals that migrate (see “Application to ecological questions” section below). We then discuss how the mechanistic model can be used in conjunction with field sampling to test ecological hypotheses, highlighting how analysis of eDNA concentration profiles can provide information regarding where and when organisms are located in the water column. Finally, we comment on implications of this work and how the model can be used to both design and interpret observations.Table 1 Parameters separated by category.Full size tableModel overviewGoverning equationsTo maintain simplicity and also provide a more realistic scenario where both settling and neutrally buoyant eDNA particles28 are considered, we assume that the eDNA material at any given time in the system consist of eDNA particles of two size classes. Here, large eDNA particles are on the order of 10 s of µm to 1 mm and represent materials such as fecal pellets, chunks of tissue, or gametes that would be subject to both settling and physical breakdown over time. Small eDNA particles are on the order of 0.1 µm to 10 s of µm and would be any eDNA shed from an organism that has a density such that they are near-neutrally buoyant including extracellular eDNA or forms such as sperm, urine, blood, or single cells. This size class of small particles is so small that we can neglect their settling. In practice, these two size fractions of eDNA would be captured using the common method of filtering water through a < 1 µm pore size filter43. Both small and large particles in the model are subject to the same eDNA decay rate constant and large particles break down over time into small particles.The equation describing the change of eDNA concentration in the form of large particles is:$$begin{aligned} frac{partial {C_{LP}}}{partial {t}} = - w_vfrac{partial {C_{LP}}}{partial {z}} + frac{partial {}}{partial {z}}left( kappa _zfrac{partial {C_{LP}}}{partial {z}}right) - kC_{LP} - delta C_{LP} + w_sfrac{partial {C_{LP}}}{partial {z}} + hat{S_{LP}} end{aligned}$$ (1) where (C_{LP}) is the concentration of large eDNA particles, (w_v) is the vertical velocity [L/T] with a positive value pointing upward, (kappa _z) is the vertical diffusivity [L(^{2})/T], k is the first order decay rate constant [1/T], (delta) is the breakdown rate of particles [1/T], (w_s) is the settling rate of eDNA [L/T], and (hat{S_{LP}}) is the shedding rate of new large eDNA particles.Similarly, the equation describing the temporal evolution of the concentration of small particles of eDNA, (C_{SP}), is:$$begin{aligned} frac{partial {C_{SP}}}{partial {t}} = - w_vfrac{partial {C_{SP}}}{partial {z}} + frac{partial {}}{partial {z}}left( kappa _zfrac{partial {C_{SP}}}{partial {z}}right) - kC_{SP} + delta C_{LP} + hat{S_{SP}} end{aligned}$$ (2) where the parameters are the same as Eq. (1). Note that there is no settling term in Eq. (2) as small eDNA particles are assumed to be neutrally buoyant and that the (delta) term in Eq. (2) has the opposite sign as that in Eq. (1), reflecting eDNA volume conservation during the breaking-down of the large particles into small particles.Physical modelThe 1-D vertical model is implemented in MATLAB. The physical module is based on a previously published 1-D model of physical processes in the mixed layer42 and is modified to include the relevant organism and eDNA parameters. The model spans from the surface down to 1500 m deep with a vertical resolution of 0.5 m and has a time step of 10 s. Each simulation runs for a total of 90 days, representing one of the four seasons: summer (July, August, September - JAS), fall (October, November, December - OND), winter (January, February, March - JFM), and spring (April, May, June - AMJ). The model has a zero-gradient open boundary condition at the bottom. The model is forced on the surface by climatological, 8-hourly meteorological conditions from the NCEP/NCAR Reanalysis at a location in the Northeast Atlantic Ocean (37.047(^circ) N, -71.25(^circ) W). Inputs include air temperature, short wave radiation, long wave radiation, precipitation, air pressure, air humidity, and winds. The wind stress and heat fluxes are calculated using the bulk formulae44.The initial vertical distribution of the temperature and salinity in the model are seasonal climatological profiles obtained from the NCEI World Ocean Atlas 2018 at a site in the Northwest Atlantic slope sea (39.125(^circ) N, −70.875(^circ) W; north of the Gulf Stream). The model also simulates the influences of vertical mixing and advection on the temperature, salinity and eDNA particles. The mixing influence is incorporated by prescribing synthetic vertical profiles of vertical diffusivity that combines observed seasonal mean mixed layer depth45 and simulated seasonal mean vertical diffusivity profile from an operational model46, both at a site in the Northwest Atlantic slope sea (see Supplemental Text S1 and Supplemental Fig. S1). Vertical advection profiles are set to increase linearly from 0 ms(^{-1}) at the surface to the maximum value of ((10^{-4}) ms(^{-1})) at 200 m and then decrease linearly back to 0 ms(^{-1}) at 400 m (Supplemental Fig. S2). This prescribed advection profile does not change seasonally and represents enhanced vertical motions associated with sub-mesoscale processes in the upper water column (e.g.,47). Note that the result of this study is not sensitive to the prescribed vertical profiles of diffusivity or vertical velocity (see below).The physical parameters that will be adjusted in this study include: the initial temperature and salinity profiles, the mixed layer depth, the vertical diffusivity profile, and the vertical velocity profile (see Ocean parameters in Table 1).Organism movementThis study focuses on eDNA shed by vertically migrating organisms living in the mesopelagic ocean. For simplicity, we assume the organisms reside at the constant depth of 500 m during the day and the constant depth of 50 m during the night. These depths fall within the range of where migrating organisms are found at these times41,48. As the objective of this study is to develop a qualitative understanding of the vertical distribution of the eDNA concentration, the exact depth a particular mesopelagic species resides at in the daytime is not crucial. Organisms begin migrating to the surface two hours before sunset, and the upward migration ends one hour after sunset. The time of sunset and sunrise are determined seasonally using NOAA’s ESL solar calculator for the year 2019 at 42.35(^circ) N, −71.05(^circ) W. Both upward and downward migrations are assumed to be in constant speed, and the migration times do not change within each season (Fig. 2).Figure 2Representative organism migration curves for each season. Migration times change with sunrise and sunset for each season. This illustration assumes that 50% of individuals migrate. The shading shows the percentage of individuals with black indicating 100%, i.e., all organisms, and grey indicating 50%. These numbers vary among the simulations.Full size imageThe migration parameters that can be adjusted in the model include: daytime residing depth, nighttime residing depth, the start and end times of migrations (and thus duration), the percent of individuals that migrate, and the layer thickness (i.e., “width” of school). (See Organism parameters in Table 1). In this study, we focus on the start and end times of migrations (and thus duration) and the percent of individuals that migrate.eDNA parametersWe assume organisms are continuously shedding eDNA, and the shedding rate is fixed in time in each simulation. The eDNA then remains in the water column, where it is subject to transport and decay as described by Eqs. (1) and (2). Although several studies have characterized eDNA shedding rates of different marine organisms29,49,50, shedding rates are highly variable across studies and have high error associated with them, likely due to high temporal variability29. Here, we use a constant shedding rate of 1 mass unit per time step (10 seconds). Note that this study focuses on the relative vertical distribution of the eDNA concentration and its temporal variability, rather than its absolute value. The particle size distribution of eDNA (and the breakdown rate of large to small particles) and the settling rate of eDNA are largely unknown and expected to be time-varying28. Here we use a breakdown rate of fecal pellets to apply to large eDNA particles breaking down into small eDNA particles51. The most well characterized parameter of eDNA is decay rate, which several studies have characterized as a function of water temperature29,49. The upper and lower limits of the decay rate have also been well established29,31. Finally, there are currently no estimates of eDNA settling rates. We use values of marine snow settling rates52 to apply to large eDNA particles that are subject to settling in the model.The eDNA-related parameters that are adjusted in this mechanistic model include: settling rate of large particles; ratio of large particles to small particles that are shed by an organism; eDNA breakdown rate (large particles to small particles); the eDNA decay rate. (See eDNA parameters in Table 1). Note that shedding rates in the model do not change over the course of each simulation.Sensitivity analysisA series of 90-day simulations were carried out with altered values of the aforementioned parameters to examine the sensitivity of the eDNA vertical distribution to the parameters. Table 1 provides a list of the parameters that were adjusted in the sensitivity analysis. Note that the diffusivity profile (both the shape of the profile and the mixed layer depth), the temperature profile (which impacts the eDNA decay rate), and the daytime length (which impacted migration times) all change with the season. Other sensitivity parameters include vertical advection, settling rate of large particles, the ratio of large to small eDNA particles shed by organisms, and the percent of individuals that migrate. A total of 972 simulations were conducted with the following sets of parameters: 4 mixing profiles (one for each season), 3 vertical advection profiles (upwelling, downwelling, none), 3 settling rates of large eDNA particles, 3 decay rate constant scenarios (two constant values representing high and low values in the literature31, one temperature-dependent rate29 for each season, Supplemental Fig. S3), 3 scenarios for the percent of individuals that migrate, and 3 ratios of large to small eDNA particles shed by the organisms.In order to assess the impact of the parameters on the eDNA profiles, several metrics were defined. First, the water column was divided into three sections: surface layer (0–100 m), mid-depth (100–450 m), and deep water (450–550 m), based on our representative daytime and nighttime depths (500 m and 50 m) used for the simulations. For each simulation, the mean and maximum eDNA concentration in each depth bin was recorded. Also, the cumulative eDNA concentration in each depth bin was normalized to calculate the proportion of eDNA in each depth bin at any given time during the simulation. For each simulation, the mean and standard deviation of the proportion of eDNA in each depth bin were calculated over the course of the 90 day simulation.A first-order comparison of the vertical length scales of eDNA transport by different processes were conducted. Here, we use a time scale of eDNA decay, T(_{90}), i.e., the time it takes for 90% of the released eDNA to decay, to determine the vertical length scale of each process. In particular, we would like to estimate, the vertical distances eDNA is transported by advection, mixing, and settling before 90% of the released eDNA has decayed, L(_{mix}), L(_{advect}) and L(_{settling}), respectively. These quantities are defined mathematically as,$$begin{aligned} T_{90} = frac{-ln(0.1)}{k_{avg}} end{aligned}$$ (3) $$begin{aligned} L_{mix} = sqrt{kappa _v T_{90}} end{aligned}$$ (4) $$begin{aligned} L_{advect} = w_{vm} T_{90} end{aligned}$$ (5) $$begin{aligned} L_{settle} = w_s T_{90} end{aligned}$$ (6) where (k_{avg}) is the average decay rate constant [T(^{-1})] for the whole water column over the 90 day simulation, (kappa _v) is the maximum vertical diffusivity coefficient [L(^{2})T(^{-1})] , (w_{vm}) is the maximum vertical velocity [LT(^{-1})] , and (w_s) is the settling rate [LT(^{-1})]. Note that because k is temperature dependent, the value of the decay rate constant will change both vertically (e.g., deeper water will be colder and have a lower decay rate constant) and temporally (e.g., at a given depth, the water temperature will be warmer in the middle of the day and thus will have a higher decay rate constant).Application to ecological questionsAfter obtaining a first-order understanding of the eDNA concentration profile and the impact of the parameters on the eDNA distribution, we ran another set of model simulations to examine if vertical and temporal eDNA concentration variability can shed light on an ecological question regarding what percentage of individuals within the same species migrate on a daily basis. To do this, for each season, all parameters were held constant except for the percent of individuals that migrate, P(_{m}). In each season, the value of P(_{m}) varies from 0 to 100% with an increment of 10%. A total of 44 such simulations (4 seasons x 11 percent migrate scenarios) were carried out. We then compared mean and maximum eDNA concentrations in the surface layer (0–100 m) to those in the deep water (450 to 550 m), and examined the relationship between the surface-to-deep ratios and P(_{m}). More

Modelling approachWe used the PISCES-v2 biogeochemical model, attached to the Nucleus for European Modelling of the Ocean version 4.0 (NEMO-v4) general ocean circulation model29. PISCES-v2 includes five nutrients pools (nitrate, ammonium, phosphate, silicic acid and dissolved iron), dissolved oxygen, the full carbon system and accounts for two phytoplankton (nanophytoplankton and diatoms) and two zooplankton types (microzooplankton and mesozooplankton). Bioavailable nitrogen in our simulations is considered to be the combination of nitrate and ammonium. Its nitrogen cycle includes nitrogen fixation, nitrification, burial, denitrification in both the water column and sediments, and coupled nitrification–denitrification. Nitrogen isotopes were integrated within PISCES-v2 for the purposes of this study, using nine new tracers (Supplementary Note 1). Horizontal model resolution varied between ~0.5° at the equator and poles, and 2° in the subtropics, whereas vertical resolution varied between 10 and 500 m thickness over 31 levels.We conducted simulations under both preindustrial control and climate change scenarios. The preindustrial control scenario from 1801 to 2100 maintained preindustrial greenhouse gas concentrations and only included internal modes of variability. The climate change simulation from 1851 to 2100 included natural variability, prescribed changes in land use, as well as historical changes in concentrations of greenhouse gases and aerosols until 2005, after which future concentrations associated with RCP8.5 were imposed30. The biogeochemical model (PISCES-v2) was run offline from the physical model (NEMO-v4) using monthly transports and other physical conditions generated by the low resolution version of the IPSL-CM5A ESM57.Experiments were initialized from biogeochemical fields created from an extensive spin-up of 5000 years under repeat physical forcing, followed by a 300-year simulation under the preindustrial control scenario. The preindustrial control simulation used in analysis was therefore the final 300 years of a 5600-year spin-up involving two repeat simulations of the preindustrial control scenario. We utilized a global compilation of δ15NNO320 supplemented with recent data to assess the isotopic routines in the model and conducted a thorough model-data skill assessment at replicating observed patterns in space (Supplementary Note 2 and Supplementary Figs. 1–3).Anthropogenic nitrogen depositionThe effect of increasing aeolian deposition of nitrogen was assessed in our simulations. Preindustrial nitrogen deposition was prescribed as the preindustrial estimate at 1850, whereas the historical to future deposition was created by linear interpolation between preindustrial (1850) and modern/future fields (2000, 2030, 2050 and 2100). These fields were provided by Hauglustaine et al.8. However, the rapid rise between 1950 and 2000 was maintained, such that 60% of the increase between the preindustrial and modern fields occurred after 1950 (Supplementary Fig. 4).The historical rise in anthropogenic nitrogen deposition was assessed by including it in additional simulations under both preindustrial control and climate change scenarios. Four initial experiments were therefore conducted: preindustrial control; preindustrial control plus anthropogenic nitrogen deposition; climate change; and climate change plus anthropogenic nitrogen deposition.Global model experimentsWe undertook four initial simulations to quantify the impacts of anthropogenic climate change and nitrogen deposition: a preindustrial control simulation from 1801 to 2100; a full anthropogenic scenario from 1851 to 2100; a climate change-only scenario without the increase in anthropogenic nitrogen deposition from 1851 to 2100; and a nitrogen deposition scenario without anthropogenic climate change from 1851 to 2100. Anthropogenic effects to nitrogen cycling were quantified by comparing mean conditions over the final 20 years of the twenty-first century (2081–2100) with mean conditions over the final 20 years of the preindustrial control simulation, whereas effects on nitrogen isotopes were quantified by comparing mean conditions over the final 20 years of the twenty-first century (2081–2100) with mean conditions over the historical period (1986–2005) from the same simulation.To understand the direct and indirect effects of climate change, we undertook two additional idealized simulations. First, we imposed temperature changes on biogeochemical rates, while maintaining ocean circulation associated with the preindustrial control scenario, to assess the direct effects of warming on biogeochemical processes. Second, we imposed the preindustrial control temperature field on biogeochemical processes, while altering the circulation in line with the climate change scenario, to assess the indirect effects of climate change (i.e., how changing circulation alters substrate supply to biogeochemical reactions). Each experiment was run from 1851 to 2100 and without the anthropogenic increase in atmospheric nitrogen deposition, parallel with the full climate change simulation.Agreement between the climate change simulation without anthropogenic nitrogen deposition was quantified using a pixel-by-pixel correlation analysis using Spearman’s rank correlation based on the non-parametric nature of the two-dimensional fields used for comparison. Fields were euphotic zone nitrate, twilight zone δ15NNO3, euphotic zone δ15NPOM, and vertically integrated NPP, zooplankton grazing, nitrogen fixation, water column denitrification and sedimentary denitrification.Depth zonesWe assessed changes in biogeochemical variables related to nitrogen cycling in two depth zones defined by light. The euphotic zone was defined by depths between the surface and 0.1% of incident irradiance as recommended by Buesseler et al.42. The twilight zone was also defined using light, as advocated by Kaartvedt et al.58. Depths between 0.1% and 0.0001% of incident irradiance defined the twilight zone. These definitions typically returned euphotic zone thicknesses of 137 ± 23 m (mean ± SD), and twilight zone thicknesses of 233 ± 37 m. The boundary between these depth zones were deepest in oligotrophic tropical and subtropical waters, and were shallowest in equatorial and temperate waters (Supplementary Fig. 7).Time of emergenceToE calculations determined when anthropogenic, anomalous trends emerged from the noise of background variability. ToE was calculated at each grid cell within both the euphotic and twilight zones (depth-averaged) and using annually averaged fields of ocean tracers. We therefore ignored temporal trends and variability at seasonal and sub-seasonal scales. Raw time series were first detrended and normalized using the linear slope and mean of the preindustrial control experiment, such that the preindustrial control time series varied about zero, while anomalous trends in experiments with climate change and/or nitrogen deposition deviated from zero. These detrended and normalized time series were smoothed using a boxcar (flat) moving average with a window of 11 years to filter decadal variability (Supplementary Fig. 12). Differences with the preindustrial control experiment were then computed.To determine whether the differences with the preindustrial control experiment were anomalous, we calculated a measure of noise from the raw, inter-annual time series of the preindustrial control experiment (1801–2100). A signal emerged from the noise if it exceeded 2 SDs, a threshold that represents with 95% confidence that a value was anomalous and is therefore a conservative envelope to distinguish normality from anomaly16.Furthermore, we required that anomalous values must consistently exceed the noise of the preindustrial control experiment until the end of the simulation (2100) to be registered as having emerged. Temporary emergences were therefore rejected, making our ToE estimates more conservative. A graphical representation of this process is shown in Supplementary Fig. 12.Isolating biogeochemical 15NO3 fluxesWe analysed the biogeochemical fluxes of 15NO3 and NO3 into and out of each model grid cell within the twilight zone, to determine whether the trends in δ15NNO3 were related to biogeochemical or physical changes. Fluxes of 15NO3 and NO3 included a net source from nitrification (NO3nitr) and net sinks due to new production (NO3new) and denitrification (NO3den). Although nitrification did not directly alter the 15N : 14N ratio in our simulations, the release of 15NO3 and NO3 by nitrification conveyed an isotopic signature determined by prior fractionation processes that produce ammonium (NH4). These processes include remineralization of particulate and dissolved organic matter, excretion by zooplankton and nitrogen fixation. The isotopic signatures of these processes were thus included implicitly in NO3nitr. For each grid cell, we calculated the biogeochemical tendency to alter δ15NNO3 based on the ratio of inputs minus outputs:$${Delta} {delta }^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{NO3}}}}}}}=left(frac{{,{!}^{15}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{nitr}}}}}}}-{,{!}^{15}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{new}}}}}}}-{,{!}^{15}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{den}}}}}}}}{{,{!}^{14}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{nitr}}}}}}}-{,{!}^{14}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{new}}}}}}}-{,{!}^{14}{{{{{rm{N}}}}}}{{{{{rm{O}}}}}}}_{3}^{{{{{{rm{den}}}}}}}}-1right)cdot 1000$$
(1)
This calculation excluded any upstream biological changes and circulation changes that might have altered δ15NNO3.0D water parcel modelWe simulated the nitrogen isotope dynamics in a recently upwelled water parcel during transit to the subtropics by building a 0D model. The model simulates state variables of dissolved inorganic nitrogen (DIN), particulate organic nitrogen (PON) and exported particulate nitrogen (ExpN), as well as their heavy isotopes (DI15N, PO15N and Exp15N) in units of mmol N m−3 over 100 days given initial conditions and constants listed in Supplementary Table 1.$$frac{Delta {{{{{rm{DIN}}}}}}}{Delta t}=-{{{{{mathrm{N}}}}}}_{{{{{{rm{uptake}}}}}}}+{{{{{mathrm{N}}}}}}_{{{{{{rm{recycled}}}}}}}$$
(2)
$$frac{Delta {{{{{rm{PON}}}}}}}{Delta t}={{{{{mathrm{N}}}}}}_{{{{{{rm{uptake}}}}}}}-{{{{{mathrm{N}}}}}}_{{{{{{rm{recycled}}}}}}}-{{{{{mathrm{N}}}}}}_{{{{{{rm{exported}}}}}}}$$
(3)
$$frac{Delta {{{{{rm{ExpN}}}}}}}{Delta t}={{{{{mathrm{N}}}}}}_{{{{{{rm{exported}}}}}}}$$
(4)
$$frac{Delta {{{{{rm{DI1}}}}}}{}^{15}{{{{{rm{N}}}}}}}{Delta t}=-{}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{uptake}}}}}}}+{}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{recycled}}}}}}}$$
(5)
$$frac{Delta {{{{{rm{PO}}}}}}{}^{15}{{{{{rm{N}}}}}}}{Delta t}={}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{uptake}}}}}}}-{}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{recycled}}}}}}}-{}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{exported}}}}}}}$$
(6)
$$frac{Delta {{{{mathrm{Exp}}}}}{}^{15}{{{{{rm{N}}}}}}}{Delta t}={}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{exported}}}}}}}$$
(7)
First, the model calculates maximum potential growth rate of phytoplankton (μmax) in units of day−1 (Eq. 8) using temperature and then finds nitrogen uptake (Nuptake, Eq. 10) using PON and limitation terms for nitrogen (Nlim, Eq. 9), light (Llim, Supplementary Table 1) and iron (Felim, Supplementary Table 1).$${mu }_{{{max }}}=0.6,{{{{{rm{da}}}}}}{y}^{-1}cdot {e}^{Tcdot {T}_{{{{{{rm{growth}}}}}}}}$$
(8)
$${{{{{mathrm{N}}}}}}_{{{{{mathrm{lim}}}}}}=frac{{{{{{rm{DIN}}}}}}}{{{{{{rm{DIN}}}}}}+{{{{{mathrm{K}}}}}}_{{{{{{rm{DIN}}}}}}}}$$
(9)
$${{{{{mathrm{N}}}}}}_{{{{{mathrm{uptake}}}}}}={mu }_{max }cdot {{{{{mathrm{L}}}}}}_{{{{{mathrm{lim}}}}}}cdot ,min ({{{{{mathrm{Fe}}}}}}_{{{{{mathrm{lim}}}}}},{{{{{mathrm{N}}}}}}_{{{{{mathrm{lim}}}}}})cdot {{{{{mathrm{PON}}}}}}$$
(10)
At a constant temperature of 18 °C, μmax is equal to ~1.9 day−1. Limitation terms for light and iron are set as constant and are used to prevent unrealistically high nitrogen uptake when nitrogen is high, such as occurs immediately following upwelling in the high-nutrient low-chlorophyll regions of the tropics. Fractionation by phytoplankton is calculated assuming an open system21, in this case where nitrogen can be lost through export of organic matter. To calculate the fractionation associated with uptake (15Nuptake, Eq. 11), we multiply the total nitrogen uptake (Nuptake, Eq. 10) by the heavy to light isotope ratio (({r}_{{{{{{rm{DIN}}}}}}}^{15}), Eq. 12) and the fractionation factor (εphy, Supplementary Table 1), which is converted from units of per mil (‰) to a fraction relative to one. This fractionation factor (εphy) is constant at 5‰ but is decreased towards 0‰ by the nitrogen limitation term (Nlim, Eq. 9), such that when nitrogen is limiting to growth, the fractionation during uptake decreases (last term on the right-hand side approaches 1).$${}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{uptake}}}}}}},=,{{{{{mathrm{N}}}}}}_{{{{{{rm{uptake}}}}}}}cdot {r}_{{{{{{rm{DIN}}}}}}}^{15}cdot left(1-frac{{{{{mathrm{N}}}}}_{{{{{mathrm{lim}}}}}}cdot {varepsilon }_{{{{{{rm{phy}}}}}}}}{1000}right)$$
(11)
$${r}_{{{{{{rm{DIN}}}}}}}^{15},=,frac{{{{mathrm{DI}}}}^{15}{{{{{{rm{N}}}}}}}}{{{{{{rm{DIN}}}}}}}$$
(12)
At each timestep, a fraction of the PON pool becomes detritus (Eq. 15) and this detritus is instantaneously recycled back to DIN or exported to ExpN and removed from the water parcel. The amount of detritus produced per timestep is calculated as the sum of linear respiration (Eq. 13) and quadratic mortality (Eq. 14) terms, where Presp (units of day−1), Kresp (units of mmol N m−3) and Pmort (units of (mmol N m−3)−1 day−1) are constants (Supplementary Table 1).$${{{{{rm{Respiration}}}}}},=,{{{{{mathrm{P}}}}}}_{{{{{{rm{resp}}}}}}}cdot {{{{{rm{PON}}}}}}cdot frac{{{{{{rm{PON}}}}}}}{{{{{{rm{PON}}}}}}+{{{{{mathrm{K}}}}}}_{{{{{{rm{resp}}}}}}}}$$
(13)
$${{{{{rm{Mortality}}}}}},=,{{{{{mathrm{P}}}}}}_{{{{{{rm{mort}}}}}}}cdot {{{{{rm{PON}}}}}}^{2}$$
(14)
$${{{{{rm{Detritus}}}}}},=,{{{{{rm{Respiration}}}}}},+,{{{{{rm{Mortality}}}}}}$$
(15)
Once we know the fraction of PON that becomes detritus at any given timestep, we must solve for the fraction of that detritus that becomes DIN through recycling (Eq. 17), and that which becomes ExpN through export (Eq. 18). The fraction of detritus that is recycled back into DIN is temperature dependent (Eq. 16), with higher temperatures increasing rates of recycling above a minimum fraction set by frecmin (Supplementary Table 1). The relationship with temperature is exponential, similar to phytoplankton maximum growth (μmax), but the degree of increase associated with warming is scaled down by a constant factor equal to Trec (Supplementary Table 1). The fraction that is exported to ExpN is the remainder (Eq. 18).$${f}_{{{{{{rm{recycled}}}}}}}={f}_{{{{{{rm{recmin}}}}}}}+{T}_{{{{{{rm{rec}}}}}}}cdot {e}^{Tcdot {T}_{{{{{{rm{growth}}}}}}}}$$
(16)
$${{{{{mathrm{N}}}}}}_{{{{{{rm{recycled}}}}}}}={{{{{rm{Detritus}}}}}}cdot {f}_{{{{{{rm{recycled}}}}}}}$$
(17)
$${{{{{mathrm{N}}}}}}_{{{{{{rm{exported}}}}}}}={{{{{rm{Detritus}}}}}}cdot (1-{f}_{{{{{{rm{recycled}}}}}}})$$
(18)
The major fluxes of Nuptake, Nrecycled and Nexported are now solved for. All that remains is to calculate the isotopic signatures of the recycling (Eq. 19) and export (Eq. 20) fluxes. These, similar to 15Nuptake (Eq. 11), are solved by multiplying against a standard ratio of heavy to light isotope (({r}_{{{{{{rm{PON}}}}}}}^{15}), Eq. 21).$${}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{recycled}}}}}}}={{{{{mathrm{N}}}}}}_{{{{{{rm{recycled}}}}}}}cdot {r}_{{{{{{rm{PON}}}}}}}^{15}$$
(19)
$${}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{exported}}}}}}}={{{{{mathrm{N}}}}}}_{{{{{{rm{exported}}}}}}}cdot {r}_{{{{{{rm{PON}}}}}}}^{15}$$
(20)
$${r}_{{{{{{rm{PON}}}}}}}^{15}=frac{{{{{{rm{PO}}}}}}{}^{15}{{{{{rm{N}}}}}}}{{{{{{rm{PON}}}}}}}$$
(21)
Finally, we calculate the δ15N values of the major pools in the model (DIN, PON and ExpN) as output (Eqs. 22–24). We assume in this model that the major pools of DIN, PON and ExpN represent the total amount of the light isotope (14N), whereas the DI15N, PO15N and Exp15N pools represent the relative enrichment in 15N compared to a standard ratio. For simplicity, we make the standard ratio equal to 1. Therefore, taking the ratio of the DI15N to DIN pools and subtracting one returns the isotopic signature. Multiplying this by 1000 converts this signature to per mil units (‰).$${delta }^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{DIN}}}}}}}=left(frac{{{{{{rm{DI}}}}}}{}^{15}{{{{{rm{N}}}}}}}{{{{{{rm{DIN}}}}}}}-1right)cdot 1000$$
(22)
$${}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{PON}}}}}}}=left(frac{{{{{{{rm{PO}}}}}}}^{15}N}{{{{{{rm{PON}}}}}}}-1right)cdot 1000$$
(23)
$${}^{15}{{{{{rm{N}}}}}}_{{{{{{rm{ExpN}}}}}}}=left(frac{{{{{mathrm{Exp}}}}}{}^{15}{{{{{rm{N}}}}}}}{{{{{{rm{ExpN}}}}}}}-1right)cdot 1000$$
(24) More

Study setupSix adults and six elderly, who were included in a previously conducted in vivo GOS intervention study [11], donated their faecal material for the current study (Fig. S1) at their first visit or at least 4 weeks after the intervention period. Each participant defecated into a stool collector (Excretas Medical BV, Enschede, the Netherlands). Directly after defecation, faecal material was divided into two portions. A small portion (~0.5 g) was frozen immediately. The remaining faeces was anoxically cryo-conserved and used as inoculum for the in vitro incubations. The viability of different microbial groups in the anoxically cryo-conserved faecal material was determined with propidium monoazide (PMA) dye. The in vitro incubations lasted for 24 h with samples collected in duplicate to compare microbiota composition, carbohydrate degradation and metabolite production between age groups (adults vs elderly). The degrading capacity for two typical bifidogenic carbohydrates, i.e., GOS and 2′-FL, was determined for the microbiota of all six adults and six elderly and compared to a non-carbohydrate control. To further extend these experiments, we also studied the degradation of other typical bifidogenic carbohydrates, i.e. FOS, inulin, and IMMP, using the faecal inocula of three adults and three elderly for which sufficient material was still available.ParticipantsThe six adults (20–30 yrs) and six elderly participants (70–85 yrs) of the intervention study [11] were randomly contacted and participated in the current study, who differed significantly in age, but not in sex, BMI, alcohol consumption, smoking, medication use or dietary fibre intake (Table 1). None of the participants took acid inhibitors (e.g., proton pump inhibitors), nor antibiotics 90 days prior to the study, nor did any of the participants have a chronic disorder or major surgery, as these factors potentially could have limited participation, completion of the study, or interfered with the study outcomes. Detailed description of the inclusion and exclusion criteria has been provided previously [11]. Subject codes as shown in the results were randomly assigned in the data analysis phase and cannot be traced back to individual subjects without the specific randomization key. The study was approved by the medical Ethics Committee of the Maastricht University Medical Center+ and registered in the US National Library of Medicine (http://www.clinicaltrials.gov) with the registration number NCT03077529 [11].Table 1 Characteristics of adults (n = 6) and elderly (n = 6) included in this study.Full size tableDietary intakeParticipants in the current study completed the dietary records on 3 consecutive days, after instructed to record their food, beverage and dietary supplement intake based on standard household units. Their nutrient intake was analyzed using the online dietary assessment tool of The Netherlands Nutrition Centre (www.voedingcentrum.nl).CarbohydratesFive different carbohydrates, i.e., GOS, 2′-FL, FOS, inulin and IMMP were used as sole carbon sources in this study. GOS and the human milk oligosaccharide 2′-FL (Fucα1-2Galβ1-4Glc) were kindly provided by Friesland Campina (Amersfoort, The Netherlands). In order to mimic the actual portion of GOS utilized by intestinal microbiota, purified GOS with  0.05) to explain the observed difference, using the prc function in the vegan package [30]. As for the metabolite data, redundancy analysis (RDA) in combination with Monte Carlo permutation was performed to assess to what extent explanatory variables, i.e., incubation time, subject- and carbohydrate-specificity, could explain the overall variation in metabolite data, using the rda function in the vegan package [30]. To assess the effect of age group (adult vs elderly) on the degradation of carbohydrates/concentration of metabolites during incubation, we analyzed the data using two-way mixed ANOVA, with one between-subjects factor (age group) and one within-subjects factor (incubation time), using the anova_test function in the rstatix package [31]. False discovery rate (FDR) correction according to the Benjamini–Hochberg procedure was applied for multiple testing when applicable. A corrected P value < 0.05 was considered to indicate significant difference. More

1.Ma, Z. J. et al. Rethinking China’s new great wall. Science 346, 912–914 (2014).CAS 
Article 

Google Scholar 
2.Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225 (2019).Article 
CAS 

Google Scholar 
3.Wang, X. et al. Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine. Remote Sens. Environ. 238, 110987 (2020).Article 

Google Scholar 
4.Blum, M. D. & Roberts, H. H. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nat. Geosci. 2, 488–491 (2009).CAS 
Article 

Google Scholar 
5.Murray, N. J., Clemens, R. S., Phinn, S. R., Possingham, H. P. & Fuller, R. A. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Front. Ecol. Environ. 12, 267–272 (2014).Article 

Google Scholar 
6.Gedan, K. B., Silliman, B. R. & Bertness, M. D. Centuries of human-driven change in salt marsh ecosystems. Ann. Rev. Mar. Sci. 1, 117–141 (2009).Article 

Google Scholar 
7.Syvitski, J. P. M., Vörösmarty, C. J., Kettner, A. J. & Green, P. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308, 376–380 (2005).CAS 
Article 

Google Scholar 
8.Cui, B., He, Q., Gu, B., Bai, J. & Liu, X. China’s coastal wetlands: understanding environmental changes and human impacts for management and conservation. Wetlands 36, 1–9 (2016).Article 

Google Scholar 
9.Gong, P. et al. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 64, 370–373 (2019).Article 

Google Scholar 
10.Han, Q., Niu, Z., Wu, M. & Wang, J. Remote-sensing monitoring and analysis of China intertidal zone changes based on tidal correction. Sci. Bull. 64, 456–473 (2019).
Google Scholar 
11.Mao, D. et al. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS J. Photogramm. Remote Sens. 164, 11–25 (2020).Article 

Google Scholar 
12.Wang, X. et al. Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 163, 312–326 (2020).Article 

Google Scholar 
13.Mcowen, C. J. et al. A global map of saltmarshes. Biodivers. Data J. 5, e11764 (2017).Article 

Google Scholar 
14.Giri, C. et al. Status and distribution of mangrove forests of the world using Earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159 (2011).Article 

Google Scholar 
15.Chen, Y. et al. Effects of reclamation and natural changes on coastal wetlands bordering China’s Yellow Sea from 1984 to 2015. Land Degrad. Dev. 30, 1533–1544 (2019).Article 

Google Scholar 
16.Hu, Y. et al. Mapping coastal salt marshes in China using time series of Sentinel-1 SAR. ISPRS J. Photogramm. Remote Sens. 173, 122–134 (2021).Article 

Google Scholar 
17.Zhang, X. et al. Quantifying expansion and removal of Spartina alterniflora on Chongming Island, China, using time series Landsat images during 1995–2018. Remote Sens. Environ. 247, 111916 (2020).18.Chen, B. Q. et al. A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens. 131, 104–120 (2017).Article 

Google Scholar 
19.Hu, L., Li, W. & Xu, B. Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics. Int. J. Appl. Earth Obs. Geoinf. 73, 88–98 (2018).Article 

Google Scholar 
20.Jia, M., Wang, Z., Zhang, Y., Mao, D. & Wang, C. Monitoring loss and recovery of mangrove forests during 42 years: the achievements of mangrove conservation in China. Int. J. Appl. Earth Obs. Geoinf. 73, 535–545 (2018).Article 

Google Scholar 
21.Jia, M. et al. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sens. Environ. 255, 112285 (2021).Article 

Google Scholar 
22.Ma, T., Li, X., Bai, J. & Cui, B. Tracking three decades of land use and land cover transformation trajectories in China’s large river deltas. Land Degrad. Dev. 30, 799–810 (2019).Article 

Google Scholar 
23.Wang, K. Evolution of Yellow River delta coastline based on remote sensing from 1976 to 2014, China. Chin. Geogr. Sci. 29, 181–191 (2019).Article 

Google Scholar 
24.Zhao, Y. F. et al. Assessing natural and anthropogenic influences on water discharge and sediment load in the Yangtze River, China. Sci. Total Environ. 607, 920–932 (2017).Article 
CAS 

Google Scholar 
25.Yim, J. et al. Analysis of forty years long changes in coastal land use and land cover of the Yellow Sea: the gains or losses in ecosystem services. Environ. Pollut. 241, 74–84 (2018).CAS 
Article 

Google Scholar 
26.Wang, S. et al. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nat. Geosci. 9, 38–41 (2016).
Google Scholar 
27.Chen, Y. et al. Land claim and loss of tidal flats in the Yangtze Estuary. Sci. Rep. 6, 24018 (2016).CAS 
Article 

Google Scholar 
28.Yang, M. et al. Spatio-temporal characterization of a reclamation settlement in the Shanghai coastal area with time series analyses of X-, C-, and L-band SAR datasets. Remote Sens. 10, 329 (2018).29.Han, X., Pan, J. & Devlin, A. T. Remote sensing study of wetlands in the Pearl River Delta during 1995–2015 with the support vector machine method. Front. Earth Sci. 12, 521–531 (2018).Article 

Google Scholar 
30.Liu, L., Xu, W., Yue, Q., Teng, X. & Hu, H. Problems and countermeasures of coastline protection and utilization in China. Ocean Coast. Manag. 153, 124–130 (2018).Article 

Google Scholar 
31.Yunxuan, Z. et al. Degradation of coastal wetland ecosystem in China: drivers, impacts, and strategies. Bull. Chin. Acad. Sci. 31, 1157–1166 (2016).
Google Scholar 
32.Jiang, T. T., Pan, J. F., Pu, X. M., Wang, B. & Pan, J. J. Current status of coastal wetlands in China: degradation, restoration, and future management. Estuar. Coast. Shelf Sci. 164, 265–275 (2015).Article 

Google Scholar 
33.Sun, Z. et al. China’s coastal wetlands: conservation history, implementation efforts, existing issues and strategies for future improvement. Environ. Int. 79, 25–41 (2015).Article 

Google Scholar 
34.Ren, C. et al. Rapid expansion of coastal aquaculture ponds in China from Landsat observations during 1984–2016. Int. J. Appl. Earth Obs. Geoinf. 82, 101902 (2019).35.Gu, J. et al. Losses of salt marsh in China: trends, threats and management. Estuar. Coast. Shelf Sci. 214, 98–109 (2018).Article 

Google Scholar 
36.Wang, W., Liu, H., Li, Y. & Su, J. Development and management of land reclamation in China. Ocean Coast. Manag. 102, 415–425 (2014).Article 

Google Scholar 
37.Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
38.Barbier, E. B. A global strategy for protecting vulnerable coastal populations. Science 345, 1250–1251 (2014).CAS 
Article 

Google Scholar 
39.He, Q. et al. Economic development and coastal ecosystem change in China. Sci. Rep. 4, 5995 (2014).40.Zhou, C. et al. Preliminary analysis of C sequestration potential of blue carbon ecosystems on Chinese coastal zone. Sci. China Life Sci. 46, 475–486 (2016).
Google Scholar 
41.Zhang, Q. et al. Propagule types and environmental stresses matter in saltmarsh plant restoration. Ecol. Eng. 143, 105693 (2020).Article 

Google Scholar 
42.Cui, B., Yang, Q., Yang, Z. & Zhang, K. Evaluating the ecological performance of wetland restoration in the Yellow River Delta, China. Ecol. Eng. 35, 1090–1103 (2009).Article 

Google Scholar 
43.Pan, X. Research on Xi Jinping’s thought of ecological civilization and environment sustainable development. IOP Conf. Ser. Earth Environ. Sci. 153, 062067 (2018).44.Hansen, M. H., Li, H. & Svarverud, R. Ecological civilization: interpreting the Chinese past, projecting the global future. Glob. Environ. Change. 53, 195–203 (2018).Article 

Google Scholar 
45.Moreno-Mateos, D., Power, M. E., Comín, F. A. & Yockteng, R. Structural and functional loss in restored wetland ecosystems. PLoS Biol. 10, e1001247 (2012).CAS 
Article 

Google Scholar 
46.He, Q. Conservation: ‘No net loss’ of wetland quantity and quality. Curr. Biol. 29, R1070–R1072 (2019).CAS 
Article 

Google Scholar 
47.Gong, P., Li, X. & Zhang, W. 40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Sci. Bull. 64, 756–763 (2019).Article 

Google Scholar 
48.Wang, X. et al. Gainers and losers of surface and terrestrial water resources in China during 1989–2016. Nat. Commun. 11, 3471 (2020).49.Zou, Z. H. et al. Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016. Proc. Natl Acad. Sci. USA 115, 3810–3815 (2018).CAS 
Article 

Google Scholar  More