Philippa Kaur

Mangrove forests thrive along tropical and subtropical shorelines and their distribution extends to warm temperate regions1. They are globally recognized for the valuable ecosystem services they provide2 but are expected to be substantially influenced by climate change-related physical processes in the future3,4. Under warming winter temperatures, poleward expansion is predicted for mangroves5,6, with potential implications for ecosystem structure and functioning, as well as human livelihoods and well-being7,8. The global distribution, abundance and species richness of mangroves is governed by a broad range of biotic and environmental factors, including temperature and precipitation9 and diverse geomorphological and hydrological gradients10. Climate and aspects related to coastal geography (for example, floodplain area) determine the availability of suitable habitat for establishment11,12. However, the potential for mangroves to track changing environmental conditions and expand their distributions ultimately depends on dispersal11,13. The importance of dispersal in controlling mangrove distributions has been demonstrated by mangrove distributional responses to historical climate variability14, past mangrove (re)colonization of oceanic islands15 and from the long-term survival of mangrove seedlings planted beyond natural range limits16. As such, quantifying changes in the factors that influence dispersal is important for understanding climate-driven distributional responses of mangroves under future climate conditions.In mangroves, dispersal is accomplished by buoyant seeds and fruits (hereafter referred to as ‘propagules’). In combination with prevailing currents, the spatial scale of this process, ranging from local retention to transoceanic dispersal over thousands of kilometres13, is determined by propagule buoyancy17, that is, the density difference between that of propagules and the surrounding water. Hence, the course of dispersal trajectories for propagules from these species depends on the interaction between spatiotemporal changes in both propagule density and that of the surrounding water, rendering this process sensitive to climate-driven changes in coastal and open-ocean water properties. The biogeographic implications of such density differences were recognized more than a century ago by Henry Brougham Guppy, who discussed18 ‘the far-reaching influence on plant-distribution and on plant-development that the relation between the specific weight of seeds and fruits and the density of sea-water must possess’.Since the time of Guppy’s early observations, climate change from human activities has driven pronounced changes in ocean temperature and salinity, with further changes predicted throughout the twenty-first century19. Ocean density is a nonlinear function of temperature, salinity and pressure20; therefore, these changes may influence dispersal patterns of mangrove propagules by altering their buoyancy and floating orientation. As Guppy noted18, ‘[for] plants whose seeds or fruits are not much lighter than seawater […] the effect of increased density of the water is to extend the flotation period’ or ‘to increase the number that floated for a given period’. Guppy also reported that the seedlings of the widespread mangrove genera Rhizophora and Bruguiera present exceptional examples of propagules with densities somewhere between seawater and freshwater18. Previous studies of the impacts of climate change on mangroves have focused on factors such as sea level rise, altered precipitation regimes and increasing temperature and storm frequency4,21,22,23 but the potential impact of climate-driven changes in seawater properties on mangroves has not yet been examined. This is somewhat surprising, as the ocean is the primary dispersal medium of this ‘sea-faring’ coastal vegetation and dispersal is a key process that governs a species’ response to climate change by changing its geographical range. This knowledge gap contrasts with recent efforts to expose links between climate change and dispersal in other ecologically important marine taxa such as zooplankton and fish species24,25,26,27.In this study, we investigate predicted changes in sea surface temperature (SST), sea surface salinity (SSS) and sea surface density (SSD) for coastal waters bordering mangrove forests (hereafter referred to as ‘coastal mangrove waters’), over the next century. Using a biogeographic classification system for coastal and shelf areas28, we examine spatiotemporal changes in these surface ocean properties, with a particular focus on the world’s two major mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) region, including all of the Americas, West and Central Africa and (2) the Indo West Pacific (IWP) region, extending from East Africa eastwards to the islands of the central Pacific1. Finally, we synthesize available data on the density of mangrove propagules for different mangrove species and explore the potential impact of climate-driven changes in SSD on propagule dispersal.To assess changes in SST and SSS throughout the global range of mangrove forests, we used present (2000–2014) and future (2090–2100) surface ocean properties from the Bio-ORACLE database29,30. SSD estimates were derived from these variables using the UNESCO EOS-80 equation of state polynomial for seawater31. Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from the 2015 Global Mangrove Watch (GMW) database32. After removing duplicates, our dataset contained 10,108 unique mangrove occurrence locations, with corresponding present conditions and predicted future changes in mean SST, SSS and SSD. Under the low-warming scenario RCP 2.6, mean SST of coastal mangrove waters is predicted to change by +0.64 (±0.11) °C and mean SSS by −0.06 (±0.25) practical salinity units (PSU). Combined, this results in an average change in mean SSD of −0.25 (±0.20) kg m−3 in coastal mangrove waters by the late twenty-first century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in mean SSS and −0.71 (±0.32) kg m−3 in mean SSD is predicted (Supplementary Table 3). Under RCP 8.5, our study predicts a change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a corresponding change in SSD of −1.17 (±0.56) kg m−3 (−2.53–0.03 kg m−3) (Supplementary Table 4).Fig. 1: Global map showing the change in sea surface variables across mangrove bioregions under RCP 8.5.a–c, Change in SST (a), SSS (b) and SSD (c). Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP.Full size imageSpatial variability in predicted surface ocean property changes was examined by considering the two major mangrove bioregions (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the World (MEOW) biogeographic classification28 (Fig. 3). Both the range and changes in mean SST were comparable for the AEP and IWP mangrove bioregions, for all respective RCP scenarios (Fig. 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in both mangrove bioregions is predicted to warm ~2.8 °C by 2100, which is roughly 4.5 times the predicted increase in mean SST under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for the RCP 8.5 scenario are generally consistent with reported global ocean temperature trends33 and show that the greatest warming occurs in coastal waters near the Galapagos Islands (change in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respectively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4).Fig. 2: Change in surface ocean properties for coastal waters bordering mangrove forests and in the two major mangrove bioregions, the AEP and IWP, for different RCPs.a–c, Variation in SST (a), SSS (b) and SSD (c) under various RCP scenarios. Grey indicates global distribution (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The cat-eye plots50 show the distribution of the data. Median and mean values are indicated with black and white circles, respectively, and the vertical lines represent the interquartile range.Full size imageFig. 3: Global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under RCP 8.5.a, Global map showing the provinces (colour code and numbers) from the MEOW database28 used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located.Full size imagePredicted SSS changes exhibit an opposite trend in the AEP and IWP bioregions, with increased salinity in the AEP and reduced salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. 2b); this is reflected in contrasting SSD changes in both mangrove bioregions (Fig. 2c) and associated with predicted global changes in precipitation, with extensions of the rainy season over most of the monsoon domains, except for the American monsoon34. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. The maximum decrease in mean SSS (−2.01 PSU) is predicted for the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary Table 4). Within the IWP, the Western Indian Ocean region shows little or no changes in SSS, which contrasts with the pronounced freshening trends predicted in the eastern part of this ocean basin and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. 3c and Supplementary Table 4). Within the AEP, salinity increases exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in the Warm Temperate Northwest Atlantic and +0.68 in the West African Transition (Fig. 3c and Supplementary Table 4). The spatial heterogeneity in SSS across the global range of mangrove forests corresponds with observed changes in SSS35. Trends in SSD (Fig. 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP scenarios predict an overall decrease in SSD for both mangrove bioregions; however, the predicted decrease in SSD in the IWP region was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) stronger than in the AEP (Figs. 2 and 3d and Supplementary Tables 1–4).Propagule density values from our literature survey range from 1,080 kg m−3 for different mangrove species (Fig. 4 and Supplementary Table 5). The low densities reported for Heritiera littoralis propagules provide a strong contrast with the near-seawater propagule densities reported for Avicennia and members of the Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating characteristics of the latter may be particularly sensitive to changes in SSD. To illustrate the potential influence of changing ocean conditions on mangrove propagule dispersal, we considered threshold water density values (1,020 and 1,022 kg m−3) that are within the range where elongated propagules of important mangrove genera tend to change floating orientation (Fig. 4a). More specifically, we determined the ocean surface area with an SSD below or equal to these thresholds under different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean surface covered by mangrove coastal waters (coastal waters bordering present mangrove forests) with a density ≤1,020 kg m−3 increases ~27% by 2100, notably more so in the IWP (~37%) than in the AEP (~6%) (Supplementary Table 6). A threshold of 1,022 kg m−3 results in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) (Supplementary Table 7). Similar spatial patterns are observed for open-ocean waters within the global latitudinal range of mangroves (Fig. 5 and Supplementary Figs. 1 and 2).Fig. 4: Potential effect of future declines in SSD on mangrove propagule dispersal.a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. 51. b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear.Full size imageFig. 5: Future changes in SSD.a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m−3 (a,b) and 1,022 kg m−3 (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global mangrove extent from the 2015 GMW dataset32. Magenta-coloured circles represent SSD values More

Key delimitation trapping survey performance factorsTrap attractivenessThe performance of the current Medfly design was unexpectedly inferior to that of the leek moth even with a more vagile target insect, 2.8 times greater trap density in the core, and a grid size over three times larger. Despite all those factors, p(capture) for the leek moth grid with 1/λ = 20 m was 15 percentage points greater than that for Medfly at 30 days duration. Thus, trap attractiveness was the key determinant for delimiting survey performance, as it was for detection13.One straightforward way to improve p(capture) and the accuracy of boundary setting, while also cutting costs, would be to develop more attractive traps. Poorly attractive traps include food-based attractants48 and traps based solely on visual stimuli36. But developing better traps is difficult. Pheromone-based attractants generally perform best49, but these are unavailable for many insects. For instance, scientists have searched for decades for effective pheromones for Anastrepha suspensa (Loew) and A. ludens (Loew) without success50. Common issues include the complexity of components, costs of synthesis, and chemical stability.Trap densitiesAll else being equal, increasing the trap density will generally improve p(capture) for any survey grid, and intuitively this can help compensate for using less attractive traps. However, the impact of increasing density is limited when attractiveness is low13,47, and large surveys or grids with many traps can become prohibitively expensive51. The Medfly grid designers likely understood that the available trap and lure was not highly attractive, and used higher densities in inner bands to try to reach some desired (non-quantitative) survey performance level. By contrast, the designers of the leek moth grid used a (constant) density three times smaller, likely because the trap and lure were known to be relatively strong. Here, for both species, marginal ROI decreased as densities increased (Tables 2, 3). Hence, increasing densities has limited benefit, but may be useful when better lures are unavailable13.In that context, the use of variable densities in the Medfly grid is understandable. At its standard size, the survey grid would require 8,100 traps if the core trap density were constant (Table 1). The designers likely intuited that lower densities could be used in outer bands because captures there were less likely. However, doing so reduces the likelihood of detection in outer bands and could increase the possibility of undetected egress, especially with longer survey durations. As far as we know, natural egress has not been raised as a concern following the numerous Medfly quarantines that have used this survey grid over the years, in Southern California in particular52.Generally, however, we think the variable Medfly grid densities run counter to delimitation goals. Greater core and Band 2 densities have proportionally more impact on p(capture), but only a few detections in the core are necessary to confirm the presence of the population (Goal 1), and inner area detections probably contribute little to boundary setting (see below). Therefore, lower or intermediate densities (at most) may be optimal for the core when considering ROI. For the outer bands, increasing densities might improve boundary setting (Goal 2) and help mitigate potential egress, but the sizes of those bands already limit cost efficiency (Table 2), making greater densities less advisable. Our simulation results can help elucidate how to balance these interests to achieve delimitation goals while minimizing costs47.Grid size considerationsThe simulation results indicated that the standard survey sizes for these two pests were excessive. We have verified that empirically for Medfly using trapping detections data53. A 14.5-km grid has been widely used for many other insects in the CDFA (2013) guidelines10, such as Mexfly and OFF, and the same analysis indicated that those are also oversized for use in short-term delimitation surveys53. From the same analysis, the predicted survey radius for leek moth, with D = 500 m2 per day, would be 2,382 m, or a diameter of nearly 4.8 km, which matches the results here. Similarly, Dominiak and Fanson45 analyzed trapping data for Qfly and found that the recommended quarantine area distance of 15 km could be reduced to 3 to 4 km.Grids with radii larger than 4.8-km only seem necessary for highly vagile insects, those with D ≥ 50,000 m2 per day47. This should not be surprising. Small insect populations are unlikely to move very far31,54, especially if hosts are available20,39,55. The (proposed) short duration of a delimitation survey would also limit dispersal potential (see below). Many delimiting survey plans may be oversized, because they were developed before much dispersal research had been done37, thus uncertainty was high. Our dispersal distance analysis included species with a wide range of dispersal abilities, so it can be used generally to choose smaller survey grid radii53.Reducing grid sizes down to about 4.8-km diameters may have little impact on p(capture), since detections in bands outside that distance contributed little to overall performance. The cores of both the leek moth and Medfly grids accounted for 86 percent or more of overall p(capture). While core area detections will confirm the presence of the population, they are less useful for defining spatial extent. The furthest detections from the presumed source are usually used to delimit the incursion46,56 (although in our experience formal boundary setting exercises seem rare). Delimiting surveys may often yield few captures anyway, because adventive populations can be very small and subject to high mortality31. Because size reductions eliminate traps in proportionally larger outer areas, the impact on survey costs is substantial. Removing just the outermost bands of each grid would directly reduce costs by $11,200 for leek moth (400 traps) and by $7,488 for Medfly (288 traps; Table 1).Another reason for the large size of the standard Medfly grid may be that it was designed for monitoring and management in addition to delimitation57. Medfly quarantines end after at least three generations without a detection, so the surveys may last for months. The grid size was reportedly originally determined by multiplying the estimated dispersal distance by three (PPQ, personal communication), to account for uncertainty. This implies that the estimated distance was about 2,400 m per 30 days. Thus, the design may not have been built for the 30-d duration used here, but our recommended design is valid if a shorter delimitation activity without further monitoring is appropriate.Although it seemed too large for leek moth, an 8-km grid for delimitation could be appropriate for some other moths. For example, the delimiting survey plans for Spodoptera littoralis (Boisduval) and S. exempta Walker use this size9. S. littoralis is described as dispersing “many miles”, and S. exempta can travel hundreds of miles9, which clearly exceeds the described dispersal ability of leek moth. On the other hand, the survey plan for summer fruit tortrix moth (Adoxophyes orana Fischer von Röeslerstamm) also specifies an 8-km grid for delimitation but contains little information on dispersal, suggesting only that most movement is local8. Like leek moth, a 4.8-km grid for that species seems likely to be more appropriate.Limiting egress potential is probably the main consideration when setting survey size, but uncertainty about the source population location may also be a factor. Survey grids placed over the earliest insect detection may sometimes be off center from the location of the source population54. However, so far as we know for our agency, most adventive populations have been localized, based on post-discovery detections (PPQ, personal communication). Likewise, we have found53 and other researchers have found that dispersal distances for different species in outbreaks and mark-recapture studies are often less than 1 km58,59,60. That may often be the case for detection networks of traps (e.g., for high risk fruit flies), which increase the likelihood of capture before the population has had much time to grow and disperse. Here, we focused explicitly on localized populations, but allowed for uncertainty in the simulations by varying outbreak locations over one mile in the central part of the grid. If the outbreak population is very large and has extensively spread out (e.g., spotted lanternfly, Lycorma delicatula (White) in 201461), delimitation will not be localized, but “area-wide”2. The results here do not apply to area-wide outbreaks, and we are currently studying how to effectively delimit them.Optimizing delimitation surveysMany trapping survey designs in use were based not on “hard” science but on local experience62. Scientists have recognized the need for more cost-effective surveillance strategies63,64. Quantitatively assessing p(capture) in different designs for the same target pest allows us to determine grid sizes and densities that lower costs while maintaining performance. Results here demonstrated that the sizes and densities of these two survey grids could be optimized to save up to $20,244 per survey for the leek moth and $38,168 per survey for the Medfly. In practical terms, that means more than five leek moth surveys could be run for the cost of one standard design survey. Additionally, over seven Medfly delimitation surveys could be funded by the budget of one standard plan. The magnitudes of reduction seen here may be typical, since about 90 percent of the costs in trapping surveys are for transportation and maintenance related to traps65.Quantifying survey performance was not possible until very recently, so it has been little discussed in the literature5,66, and no standard thresholds exist. We think 0.5 may be a reasonable minimum threshold for the choice of p(capture), to try to ensure that population detection is “more likely than not”. Designs that aim to maximize p(capture) could be realistic with high attractiveness traps, but those designs seem very likely to have lower ROIs (e.g., Table 2). Even for the most serious insect pests, we think targeting near-perfect population detection during delimitation is likely not justified. Designs achieving p(capture) from 0.6 to 0.75 could be highly effective in terms of both costs and performance.Another potential area of improvement is grid shape. Circular grids perform as well as square grids but use fewer traps and less service area to achieve equivalent p(capture)47. Moreover, detections in the corners of a square grid are evidence that insects could have traveled beyond the square along the axes, resulting in uncertain boundary setting. Most published survey grids are square10,46, but many field managers tend to use approximately circular trapping grids in the field (PPQ, personal communication). The conversion to a circular grid with a radius of half the square side length reduces the area and number of traps by around 21 percent47. Our findings were consistent with that value.This new quantification ability also indicates that some delimiting survey designs in the U.S.A. may not be performing as well as expected47. For instance, the delimiting survey design for Mexfly uses approximately 31 traps per km2 in the core of a 14.5 km square grid11, but the traps are only weakly attractive (1/λ ≈ 5 m). In this scenario, p(capture) was only around 0.23 with a 30-d survey duration47. A much greater density ( > 80 traps per km2) could be used in the core to achieve p(capture) ≥ 0.5, but this may not be feasible depending on the survey budget.Technical and modeling considerationsExamining diffusion-based movement for these two insects in TrapGrid can give insight into why simulations indicated that smaller grids may be adequate47. The value of σ for Medfly after 30 days is only about 1,550 m. In a normal distribution, σ = 1,550 m gives a 95th percentile distance of 2,550 m, which is similar to the estimated distance above of 2,400 m. Over 90 days, σ = 2,700 m for Medfly, which gives a 95th percentile distance of 4,441 m, still much shorter than the grid radius of 7,250 m. A 95th percentile of 7,250 m requires σ ≈ 4,408 m, which equals t = 253 days. In addition, the maximum total distance (up to 39 days after detection) we observed in trapping detections data for Medfly in Florida was about 4,800 m53.The same calculations for leek moth give σ ≈ 490 m for 30 days, with a 95th percentile distance of only 806 m. That is half the length of the recommended shortened radius above of 2.4 km, and nearly five times shorter than the radius of the standard 8-km grid. A 95th percentile of 4,000 m requires σ = 2,432 m, which implies t = 740 days, which is about two years. Therefore, the leek moth grid is arguably even more oversized than the Medfly grid.The default capture probability calculation in the current version (Ver. 2019-12-11) of TrapGrid is not sensitive to population size32 and does not consider the effects of ambient factors (e.g., wind speed and direction, rainfall, temperature). Many other factors can also impact trapping survey outcomes, such as topography of the environment, availability of host plants, seasonality of pest, and population dynamics. These factors are not considered in the current version of TrapGrid. More

Siderius, C., Biemans, H., Kashaigili, J. & Conway, D. Water conservation can reduce future water-energy-food-environment trade-offs in a medium-sized African river basin. Agric. Water Manag. 266, 107548 (2022).
Google Scholar 
Zhao, G., Liang, R., Li, K., Wang, Y. & Pu, X. Study on the coupling model of urbanization and water environment with basin as a unit: A study on the Hanjiang Basin in China. Ecol. Ind. 131, 108130 (2021).
Google Scholar 
Zhu, Q. et al. Relationship between ecological quality and ecosystem services in a red soil hilly watershed in southern China. Ecol. Ind. 121, 107119 (2021).
Google Scholar 
Fu, A. et al. The effects of ecological rehabilitation projects on the resilience of an extremely drought-prone desert riparian forest ecosystem in the Tarim River Basin, Xinjiang, China. Sci. Rep. 11, 18485 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai, D. et al. Comprehensive assessment of the water environment carrying capacity based on the spatial system dynamics model, a case study of Yongding River Basin in North China. J. Clean. Prod. 344, 131137 (2022).
Google Scholar 
Basu, H., Dandele, P. S. & Srivastava, S. K. Sedimentary facies of the Mesoproterozoic Srisailam Formation, Cuddapah basin, India: Implications for depositional environment and basin evolution. Mar. Pet. Geol. 133, 105242 (2021).
Google Scholar 
Capella, W. et al. Sandy contourite drift in the late Miocene Rifian Corridor (Morocco): Reconstruction of depositional environments in a foreland-basin seaway. Sed. Geol. 355, 31–57 (2017).
Google Scholar 
Ilevbare, M. & Omodolor, H. E. Ancient deposition environment, mechanism of deposition and textural attributes of Ajali Formation, western flank of the Anambra Basin, Nigeria. Case Stud. Chem. Environ. Eng. 2, 100022 (2020).
Google Scholar 
Qiao, J. B., Zhu, Y. J., Jia, X. X. & Shao, M. A. Multifractal characteristics of particle size distributions (50–200 m) in soils in the vadose zone on the Loess Plateau, China. Soil Tillage Res. 205, 104786 (2021).
Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).CAS 

Google Scholar 
Rodríguez-Lado, L. & Lado, M. Relation between soil forming factors and scaling properties of particle size distributions derived from multifractal analysis in topsoils from Galicia (NW Spain). Geoderma 287, 147–156 (2017).ADS 

Google Scholar 
Mozaffari, H., Moosavi, A. A. & Dematte, J. A. M. Estimating particle-size distribution from limited soil texture data: Introducing two new methods. Biosys. Eng. 216, 198–217 (2022).
Google Scholar 
Sudarsan, B., Ji, W., Adamchuk, V. & Biswas, A. Characterizing soil particle sizes using wavelet analysis of microscope images. Comput. Electron. Agric. 148, 217–225 (2018).
Google Scholar 
Pollacco, J. A. P., Fernández-Gálvez, J. & Carrick, S. Improved prediction of water retention curves for fine texture soils using an intergranular mixing particle size distribution model. J. Hydrol. 584, 124597 (2020).
Google Scholar 
Richer-de-Forges, A. C. et al. Hand-feel soil texture and particle-size distribution in central France. Relationships and implications. CATENA 213, 106155 (2022).CAS 

Google Scholar 
Du, W. et al. Insights into vertical differences of particle number size distributions in winter in Beijing, China. Sci. Total Environ. 802, 149695 (2022).ADS 
CAS 
PubMed 

Google Scholar 
Darder, M. L., Paz-González, A., García-Tomillo, A., Lado, M. & Wilson, M. G. Comparing multifractal characteristics of soil particle size distributions calculated by Mie and Fraunhofer models from laser diffraction measurements. Appl. Math. Model. 94, 36–48 (2021).
Google Scholar 
Ke, Z. M. et al. Multifractal parameters of soil particle size as key indicators of the soil moisture distribution. J. Hydrol. 595, 125988 (2021).
Google Scholar 
Qi, F. et al. Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil Tillage Res. 184, 45–51 (2018).
Google Scholar 
Tyler, S. W. & Wheatcraft, S. W. Fractal scaling of soil particle-size distribution: Analysis and imitations. Soil Sci. Soc. Am. J. 56, 362–369 (1992).ADS 

Google Scholar 
Zhang, Y. et al. Effects of fractal dimension and water content on the shear strength of red soil in the hilly granitic region of southern China. Geomorphology 351, 106956 (2020).
Google Scholar 
Ahmadi, A., Neyshabouri, M.-R., Rouhipour, H. & Asadi, H. Fractal dimension of soil aggregates as an index of soil erodibility. J. Hydrol. 400, 305–311 (2011).ADS 

Google Scholar 
Gao, Z., Niu, F., Lin, Z. & Luo, J. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China. CATENA 203, 105373 (2021).
Google Scholar 
Xu, G. et al. New method for the reconstruction of sedimentary systems including lithofacies, environments, and flow paths: A case study of the Xisha Trough Basin, South China Sea. Mar. Pet. Geol. 133, 105268 (2021).
Google Scholar 
Li, Z., Yu, X., Dong, S., Chen, Q. & Zhang, C. Microtextural features on quartz grains from eolian sands in a subaqueous sedimentary environment: A case study in the hinterland of the Badain Jaran Desert, Northwest China. Aeolian Res. 43, 100573 (2020).
Google Scholar 
Chen, T. et al. Modeling the effects of topography and slope gradient of an artificially formed slope on runoff, sediment yield, water and soil loss of sandy soil. CATENA 212, 106060 (2022).
Google Scholar 
George, C. F., Macdonald, D. I. M. & Spagnolo, M. Deltaic sedimentary environments in the Niger Delta, Nigeria. J. Afr. Earth Sci. 160, 103592 (2019).
Google Scholar 
Tian, Y. et al. Petrology, lithofacies, and sedimentary environment of Upper Cretaceous Abu Roash “G” in the AESW Block, Abu Gharadig Basin, Western Desert, Egypt. J. Afr. Earth Sci. 145, 178–189 (2018).ADS 

Google Scholar 
Cheng, Z., Jalon-Rójas, I., Wang, X. H. & Liu, Y. Impacts of land reclamation on sediment transport and sedimentary environment in a macro-tidal estuary. Estuar. Coast. Shelf Sci. 242, 106861 (2020).
Google Scholar 
Wei, X., Li, X. G. & Wei, N. Fractal features of soil particle size distribution in layered sediments behind two check dams: Implications for the Loess Plateau, China. Geomorphology 266, 133–145 (2016).ADS 

Google Scholar 
Wang, S. et al. Grain size characteristics of surface sediment and its response to the dynamic sedimentary environment in Qiantang Estuary, China. Int. J. Sediment Res. 37, 457–467 (2022).
Google Scholar 
Wided, S., Jalila, S. & Kamel, R. Grain size analysis and characterization of sedimentary environment along the Bizerte Coast, N-E of Tunisia. J. Afr. Earth Sc. 184, 104353 (2021).
Google Scholar 
Cai, X., Yang, Y. E., Ringler, C., Zhao, J. & You, L. Agricultural water productivity assessment for the Yellow River Basin. Agric. Water Manag. 98, 1297 (2011).
Google Scholar 
Fu, J., Zang, C. & Zhang, J. Economic and resource and environmental carrying capacity trade-off analysis in the Haihe river basin in China. J. Clean. Prod. 270, 122271 (2020).
Google Scholar 
Zhang, K. et al. Confronting challenges of managing degraded lake ecosystems in the anthropocene, exemplified from the Yangtze River Basin in China. Anthropocene 24, 30–39 (2018).
Google Scholar 
Huybrechts, N., Zhang, Y. F. & Verbanck, M. A. A new closure methodology for 1D fully coupled models of mobile-bed alluvial hydraulics: Application to silt transport in the Lower Yellow River. Int. J. Sedim. Res. 26(1), 36–49 (2011).
Google Scholar 
Cheng, D. Z. Strengthen the financial foundation of ecological protection and development of the Yellow River Basin. People Tribune 27, 76–78 (2021).
Google Scholar 
Yang, W. N., Zhou, L. & Sun, D. Q. Ecological vulnerability assessment of the Yellow River basin based on partition: Integration concept. Remote Sens. Nat. Resourc. 33(03), 211–218 (2021).
Google Scholar 
Sun, H. et al. Exposure of population to droughts in the Haihe river basin under global warming of 1.5 and 2.0 °C Scenarios. Q. Int. 453, 74–84 (2017).ADS 

Google Scholar 
Mandelbrott, B. B. The Fractal Geometry of Nature (W.H. Freeman and Company, 1983).
Google Scholar 
Samiei-Fard, R., Heidari, A., Konyushkova, M. & Mahmoodi, S. Application of particle size distribution throughout the soil profile as a criterion for recognition of newly developed geoforms in the Southeastern Caspian coast. CATANA 203, 105362 (2021).CAS 

Google Scholar 
Guo, J. Y. et al. Grain size characteristics and source analysis of aeolian sediment feed into river in Ulanbuh Desert along bank of Yellow River. J. China Inst. Water Resour. Hydropower Res. 19(01), 15–24 (2021).
Google Scholar 
Ge, T. T., Xue, Y. J., Jiang, X. Y., Zou, L. & Wang, X. C. Sources and radiocarbon ages of organic carbon in different grain size fractions of Yellow River-transported particles and coastal sediments. Chem. Geol. 534, 119452 (2020).ADS 

Google Scholar 
Hou, C. Y., Yi, Y. J., Song, J. & Zhou, Y. Effect of water-sediment regulation operation on sediment grain size and nutrient content in the lower Yellow River. J. Clean. Prod. 279, 123533 (2021).CAS 

Google Scholar 
Ni, S. M., Feng, S. Y., Zhang, D. Q., Wang, J. G. & Cai, C. F. Sediment transport capacity in erodible beds with reconstituted soils of different textures. CATANA 183, 104197 (2019).
Google Scholar 
Li, J. L. et al. Multifractal features of the particle-size distribution of suspended sediment in the Three Gorges Reservoir, China. Int. J. Sedim. Res. 36(4), 489–500 (2021).
Google Scholar 
Wang, W. F., Liu, R. T., Guo, Z. X., Feng, Y. H. & Jiang, J. Y. Physical and chemical properties and fractal dimension distribution of soil under shrubs in the southern area of Tengger Desert. J. Desert Res. 41(01), 209–218 (2021).
Google Scholar 
Wang, K., Pei, Z. Y., Wang, W. M., Hao, S. R. & Pang, G. H. Influence of the flat cycle on the fractal characteristics of soil pore structure in Salix psammophila. Sci. Technol. Eng. 21(07), 2647–2654 (2021).
Google Scholar 
Gao, G. L. et al. Fractal approach to estimating changes in soil properties following the establishment of Caragana korshinskii shelterbelts in Ningxia, NW China. Ecol. Indic. 43, 236–243 (2014).CAS 

Google Scholar 
Liu, X., Zhang, G. C., Heathman, G. C., Wang, Y. Q. & Huang, C. H. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154(1), 123–130 (2009).ADS 

Google Scholar 
Xu, G. C., Li, Z. B. & Li, P. Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. CATENA 101, 17–23 (2013).CAS 

Google Scholar 
Zhao, S. Q., Chi, D. Q., Jia, F. C., Deng, Y. P. & Sun, C. T. Fractal characteristics of saline soil particles in different regions. Jiangsu Agric. Sci. 49(06), 203–207 (2021).
Google Scholar  More

Anderson, P. K. et al. Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19, 535–544 (2004).PubMed 

Google Scholar 
Brasier, C. M. The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).
Google Scholar 
Waage, J. K. & Mumford, J. D. Agricultural biosecurity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 863–876 (2008).CAS 
PubMed 

Google Scholar 
IPPC. Surveillance guide—A guide to understand the principal requirements of surveillance programmes for national plant protection organizations. Second edition. http://www.fao.org/documents/card/en/c/cb7139en (2021) https://doi.org/10.4060/cb7139en.Parnell, S., van den Bosch, F., Gottwald, T. & Gilligan, C. A. Surveillance to inform control of emerging plant diseases: An epidemiological perspective. Annu. Rev. Phytopathol. 55, 591–610 (2017).CAS 
PubMed 

Google Scholar 
Cunniffe, N. J., Cobb, R. C., Meentemeyer, R. K., Rizzo, D. M. & Gilligan, C. A. Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California. Proc. Natl. Acad. Sci. 113, 5640–5645 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gottwald, T. R., Dixon, W., Parnell, S. & Riley, T. Huanglongbing: The dragon arrives in the USA. In Huanglongbing-Greening International Workshop, July 14–21 13–14 (2006).Herms, D. A., Stone, A. K. & Chatfield, J. A. Emerald ash borer: The beginning of the end of ash in North America?. Ornam. Plants Annu. Rep. Res. Rev. 2003, 62–71 (2004).
Google Scholar 
Sansford, C. E. Pest Risk Analysis for Hymenoscyphus pseudoalbidus (anamorph Chalara fraxinea) for the UK and the Republic of Ireland. https://webarchive.nationalarchives.gov.uk/ukgwa/20140904094312mp_/http://www.fera.defra.gov.uk/plants/plantHealth/pestsDiseases/documents/hymenoscyphusPseudoalbidusPRA.pdf (2013).Alonso Chavez, V., Parnell, S. & van den Bosch, F. Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape. J. Theor. Biol. 407, 290–302 (2016).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Bourhis, Y., Gottwald, T. R., Lopez-Ruiz, F. J., Patarapuwadol, S. & van den Bosch, F. Sampling for disease absence-deriving informed monitoring from epidemic traits. J. Theor. Biol. 461, 8–16 (2019).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Mastin, A. J., van den Bosch, F., van den Berg, F. & Parnell, S. Quantifying the hidden costs of imperfect detection for early detection surveillance. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180261 (2019).
Google Scholar 
Mastin, A. J., van den Bosch, F., Gottwald, T. R., Alonso Chavez, V. & Parnell, S. R. A method of determining where to target surveillance efforts in heterogeneous epidemiological systems. PLoS Comput. Biol. 13, e1005712 (2017).PubMed 
PubMed Central 

Google Scholar 
Parnell, S., Gottwald, T. R., Gilks, W. R. & van den Bosch, F. Estimating the incidence of an epidemic when it is first discovered and the design of early detection monitoring. J. Theor. Biol. 305, 30–36 (2012).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Parnell, S., Gottwald, T. R., Cunniffe, N. J., Alonso Chavez, V. & van den Bosch, F. Early detection surveillance for an emerging plant pathogen: A rule of thumb to predict prevalence at first discovery. Proc. R. Soc. B Biol. Sci. 282, 20151478 (2015).
Google Scholar 
Silva, G. et al. Plant pest surveillance: From satellites to molecules. Emerg. Top. Life Sci. 5, 275–287 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mastin, A. J., Gottwald, T. R., van den Bosch, F., Cunniffe, N. J. & Parnell, S. Optimising risk-based surveillance for early detection of invasive plant pathogens. PLoS Biol. 18, e3000863 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martelli, G. P., Boscia, D., Porcelli, F. & Saponari, M. The olive quick decline syndrome in south-east Italy: A threatening phytosanitary emergency. Eur. J. Plant Pathol. 144, 235–243 (2015).
Google Scholar 
Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of DNA sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95, 668 (2013).
Google Scholar 
Ben Moussa, I. E. et al. Seasonal fluctuations of sap-feeding insect species infected by Xylella fastidiosa in Apulian olive groves of southern Italy. J. Econ. Entomol. 109, 1512–1518 (2016).PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa to grapevine by the meadow spittlebug. Phytopathology 106, 1285–1290 (2016).CAS 
PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa by naturally infected Philaenus spumarius (Hemiptera, Aphrophoridae) to different host plants. J. Appl. Entomol. 141, 80–87 (2017).
Google Scholar 
Saponari, M. et al. Infectivity and transmission of Xylella fastidiosa by Philaenus spumarius (Hemiptera: Aphrophoridae) in Apulia, Italy. J. Econ. Entomol. 107, 1316–1319 (2014).PubMed 

Google Scholar 
European Commission. Commission Implementing Regulation (EU) 2020/1201 of 14 August 2020 as regards measures to prevent the introduction into and the spread within the Union of Xylella fastidiosa (Wells et al.). (2021).EFSA et al. Guidelines for statistically sound and risk-based surveys of Xylella fastidiosa. EFSA. Support. Publ. 17, 1873 (2020).EFSA et al. General guidelines for statistically sound and risk-based surveys of plant pests. EFSA Support. Publ. 17, 1919E (2020).Bourhis, Y., Gottwald, T. & van den Bosch, F. Translating surveillance data into incidence estimates. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180262 (2019).CAS 

Google Scholar 
Cornara, D. et al. Spittlebugs as vectors of Xylella fastidiosa in olive orchards in Italy. J. Pest Sci. 90, 521–530 (2017).
Google Scholar 
Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: when an old acquaintance becomes a new threat to European agriculture. J. Pest Sci. 91, 957–972 (2018).
Google Scholar 
Almeida, R. P. P., Blua, M. J., Lopes, J. R. S. & Purcell, A. H. Vector transmission of Xylella fastidiosa: Applying fundamental knowledge to generate disease management strategies. Ann. Entomol. Soc. Am. 98, 775–786 (2005).
Google Scholar 
Purcell, A. H. & Finlay, A. H. Evidence for noncirculative transmission of Pierce’s disease bacterium by sharpshooter leafhoppers. Phytopathology 69, 393–395 (1979).
Google Scholar 
Hill, B. & Purcell, A. H. Acquisition and retention of Xylella fastidiosa by an efficient vector, Graphocephala atropunctata. Phytopathology 85, 209 (1995).
Google Scholar 
Hill, B. L. & Purcell, A. H. Multiplication and movement of Xylella fastidiosa within grapevine and four other plants. Phytopathology 85, 1368 (1995).
Google Scholar 
Huang, Q., Bentz, J. & Sherald, J. L. Fast, easy and efficient DNA extraction and one-step polymerase chain reaction for the detection of Xylella fastidiosa in potential insect vectors. J. Plant Pathol. 88, 77–81 (2006).CAS 

Google Scholar 
Harper, S. J., Ward, L. I. & Clover, G. R. G. Development of LAMP and real-time PCR methods for the rapid detection of Xylella fastidiosa for quarantine and field applications. Phytopathology 100, 1282–1288 (2010).CAS 
PubMed 

Google Scholar 
EFSA et al. Pest survey card on Xylella fastidiosa. EFSA Support. Publ. 16, (2019).Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 8723 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
EPPO. PM 7/24 (4) Xylella fastidiosa. EPPO Bull. 49, 175–227 (2019).Landa, B. B. et al. Emergence of a plant pathogen in Europe associated with multiple intercontinental introductions. Appl. Environ. Microbiol. 86, 1–15 (2019).
Google Scholar 
Castro, C., DiSalvo, B. & Roper, M. C. Xylella fastidiosa: A reemerging plant pathogen that threatens crops globally. PLoS Pathog. 17, e1009813 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in Apulia: Where we stand. Phytopathology 109, 175–186 (2019).CAS 
PubMed 

Google Scholar 
Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4, 432–439 (2018).CAS 
PubMed 

Google Scholar 
Gottwald, T. et al. Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control. Proc. Natl. Acad. Sci. 117, 3492–3501 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mendel, J., Furton, K. G. & Mills, D. An Evaluation of scent-discriminating canines for rapid response to agricultural diseases. HortTechnology 28, 102–108 (2018).
Google Scholar 
ECDC. Guidelines for the Surveillance of Invasive Mosquitoes in Europe. (2012).Kading, R. C., Golnar, A. J., Hamer, S. A. & Hamer, G. L. Advanced surveillance and preparedness to meet a new era of invasive vectors and emerging vector-borne diseases. PLoS Negl. Trop. Dis. 12, e0006761 (2018).PubMed 
PubMed Central 

Google Scholar 
Kumagai, L. B. et al. First report of Candidatus Liberibacter asiaticus associated with citrus huanglongbing in California. Plant Dis. 97, 283 (2013).CAS 
PubMed 

Google Scholar 
Ben Moussa, I. E. et al. Evaluation of “Spy Insect” approach for monitoring Xylella fastidiosa in symptomless olive orchards in the Salento peninsula (Southern Italy). IOBC WPRS Bull. 121, 77–84 (2017).
Google Scholar 
Cruaud, A. et al. Using insects to detect, monitor and predict the distribution of Xylella fastidiosa: A case study in Corsica. Sci. Rep. 8, 15628 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yaseen, T. et al. On-site detection of Xylella fastidiosa in host plants and in “spy insects” using the real-time loop-mediated isothermal amplification method. Phytopathol. Mediterr. https://doi.org/10.14601/Phytopathol_Mediterr-15250 (2015).Article 

Google Scholar 
López-Mercadal, J. et al. Collection of data and information in Balearic Islands on biology of vectors and potential vectors of Xylella fastidiosa (GP/EFSA/ALPHA/017/01). EFSA Support. Publ. 18, 6925E (2021).
Google Scholar 
Cunty, A. Detection, identification and surveillance of Xylella fastidiosa on vectors in France https://zenodo.org/record/3551122#.XjGqBs77SUl. (2019) https://doi.org/10.5281/zenodo.3551122.Kottelenberg, D., Hemerik, L., Saponari, M. & van der Werf, W. Shape and rate of movement of the invasion front of Xylella fastidiosa spp. pauca in Puglia. Sci. Rep. 11, 1061 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Korup, O. Landslides in the Fluvial System. Treatise on Geomorphology Vol. 9 (Elsevier Ltd., 2013).
Google Scholar 
Kuo, C. W. & Brierley, G. The influence of landscape connectivity and landslide dynamics upon channel adjustments and sediment flux in the Liwu Basin, Taiwan. Earth Surf. Process. Landf. 39, 2038–2055 (2014).ADS 
Article 

Google Scholar 
Tunnicliffe, J. F., Leenman, A. & Reeve, M. The influence of large, chronic landslides on the fluvial system AGU Fall Meeting Abstracts, EP33A-3620 (2014).
Fox, G. A., Purvis, R. A. & Penn, C. J. Streambanks: A net source of sediment and phosphorus to streams and rivers. J. Environ. Manag. 181, 602–614 (2016).CAS 
Article 

Google Scholar 
Biswas, S. P. Restoration of riverine health. Handb. Ecol. Ecosyst. Eng. https://doi.org/10.1002/9781119678595.ch14 (2021).Article 

Google Scholar 
Lutgen, A. et al. Nutrients and heavy metals in legacy sediments: Concentrations, comparisons with upland soils, and implications for water quality. J. Am. Water Resour. Assoc. 56, 669–691 (2020).ADS 
CAS 
Article 

Google Scholar 
Emenike, P. C. et al. An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria. Environ. Pollut. 265, 114795 (2020).CAS 
PubMed 
Article 

Google Scholar 
Fox, G. A. & Wilson, G. V. The role of subsurface flow in hillslope and stream bank erosion: A review. Soil Sci. Soc. Am. J. 74, 717–733 (2010).ADS 
CAS 
Article 

Google Scholar 
Duró, G., Crosato, A., Kleinhans, M. G., Roelvink, D. & Uijttewaal, W. S. J. Bank erosion processes in regulated navigable rivers. J. Geophys. Res. Earth Surf. 125, 1–26 (2020).Article 

Google Scholar 
Keesstra, S. D. et al. Evolution of the morphology of the river Dragonja (SW Slovenia) due to land-use changes. Geomorphology 69, 191–207 (2005).ADS 
Article 

Google Scholar 
Pizzuto, J. & O’Neal, M. Increased mid-twentieth century riverbank erosion rates related to the demise of mill dams, South River, Virginia. Geology 37, 19–22 (2009).ADS 
Article 

Google Scholar 
Abam, T. K. S. Factors affecting distribution of instability of river banks in the Niger delta. Eng. Geol. 35, 123–133 (1993).Article 

Google Scholar 
Jordan, C. et al. Sand mining in the Mekong Delta revisited—current scales of local sediment deficits. Sci. Rep. 9, 1–14 (2019).Article 
CAS 

Google Scholar 
Hackney, C. R. et al. River bank instability from unsustainable sand mining in the lower Mekong River. Nat. Sustain. 3, 217–225 (2020).Article 

Google Scholar 
Yang, S. L., Milliman, J. D., Li, P. & Xu, K. 50,000 dams later: Erosion of the Yangtze River and its delta. Glob. Planet. Change 75, 14–20 (2011).ADS 
Article 

Google Scholar 
Royall, D. Land-use impacts on the hydrogeomorphology of small watersheds. Ref. Modul. Earth Syst. Environ. Sci. https://doi.org/10.1016/B978-0-12-818234-5.00010-9 (2021).Article 

Google Scholar 
Johnson, P. & Royall, D. Evaluating the effects of urbanization age on the morphology of low-order urban streams in the U.S. southern Piedmont. Phys. Geogr. 40, 1–27 (2019).Article 

Google Scholar 
Zaimes, G., Tamparopoulos, A. E., Tufekcioglu, M. & Schultz, R. C. Understanding stream bank erosion and deposition in Iowa, USA: A seven year study along streams in different regions with different riparian land-uses. J. Environ. Manag. 287, 112352 (2021).Article 

Google Scholar 
Zaimes, G. N. & Schultz, R. C. Riparian land-use impacts on bank erosion and deposition of an incised stream in north-central Iowa, USA. CATENA 125, 61–73 (2015).Article 

Google Scholar 
Simon, A., Curini, A., Darby, S. E. & Langendoen, E. J. Bank and near-bank processes in an incised channel. Geomorphology 35, 193–217 (2000).ADS 
Article 

Google Scholar 
Rinaldi, M. & Casagli, N. Stability of streambanks formed in partially saturated soils and effects of negative pore water pressures: The Sieve River (Italy). Geomorphology 26, 253–277 (1999).ADS 
Article 

Google Scholar 
Wynn, T. & Mostaghimi, S. The effects of vegetation and soil type on streambank erosion, Southwestern Virginia, USA. J. Am. Water Resour. Assoc. 42, 69–82 (2006).ADS 
Article 

Google Scholar 
Hecker, G. A., Meehan, M. A. & Norland, J. E. Plant community influences on intermittent stream stability in the great plains. Rangel. Ecol. Manag. 72, 112–119 (2019).Article 

Google Scholar 
Konsoer, K. M. et al. Spatial variability in bank resistance to erosion on a large meandering, mixed bedrock-alluvial river. Geomorphology 252, 80–97 (2016).ADS 
Article 

Google Scholar 
Abernethy, B. & Rutherfurd, I. D. Does the weight of riparian trees destabilize riverbanks?. River Res. Appl. 16, 565–576 (2000).
Google Scholar 
Collison, A. J. C. The distribution and strength of riparian tree roots in relation to riverbank reinforcement. Hydrol. Process. 15, 63–79 (2001).Article 

Google Scholar 
Simon, A. & Collison, A. J. C. Quantifying the mechanical and hydrologic effects of riparian vegetation on streambank stability. Earth Surf. Process. Landf. 27, 527–546 (2002).ADS 
Article 

Google Scholar 
Krzeminska, D., Kerkhof, T., Skaalsveen, K. & Stolte, J. Effect of riparian vegetation on stream bank stability in small agricultural catchments. CATENA 172, 87–96 (2019).Article 

Google Scholar 
Yu, G. A. et al. Effects of riparian plant roots on the unconsolidated bank stability of meandering channels in the Tarim River, China. Geomorphology 351, 106958 (2020).Article 

Google Scholar 
Halder, A. & Mowla Chowdhury, R. Evaluation of the river Padma morphological transition in the central Bangladesh using GIS and remote sensing techniques. Int. J. River Basin Manag. 1–15 (2021).
Bernier, J. F., Chassiot, L. & Lajeunesse, P. Assessing bank erosion hazards along large rivers in the Anthropocene: A geospatial framework from the St. Lawrence fluvial system. Geomat. Nat. Hazards Risk 12, 1584–1615 (2021).Article 

Google Scholar 
Lawler, D. M., Grove, J. R., Couperthwaite, J. S. & Leeks, G. J. L. Downstream change in river bank erosion rates in the Swale-Ouse system, northern England. Hydrol. Process. 13, 977–992 (1999).ADS 
Article 

Google Scholar 
Gholami, V., Sahour, H. & Hadian Amri, M. A. Soil erosion modeling using erosion pins and artificial neural networks. CATENA 196, 104902 (2021).Article 

Google Scholar 
Simon, A., Pollen-Bankhead, N. & Thomas, R. E. Development and application of a deterministic bank stability and toe erosion model for stream restoration. Geophys. Monogr. Ser. 194, 453–474 (2011).ADS 

Google Scholar 
Klavon, K. et al. Evaluating a process-based model for use in streambank stabilization: Insights on the Bank Stability and Toe Erosion Model (BSTEM). Earth Surf. Process. Landf. 42, 191–213 (2017).ADS 
Article 

Google Scholar 
Partheniades, E. Erosion and deposition of cohesive soils. J. Hydraul. Div. 91, 105–139 (1965).Article 

Google Scholar 
Fredlund, D. G., Morgenstern, N. R. & Widger, R. A. Shear strength of unsaturated soils. Can. Geotech. J. 15, 313–321 (1978).Article 

Google Scholar 
Myers, D. T., Rediske, R. R. & McNair, J. N. Measuring streambank erosion: A comparison of erosion pins, total station, and terrestrial laser scanner. Water (Switzerland) 11, 1846 (2019).
Google Scholar 
Casagli, N., Rinaldi, M., Gargini, A. & Curini, A. Pore water pressure and streambank stability: Results from a monitoring site on the Sieve River, Italy. Earth Surf. Process. Landf. 24, 1095–1114 (1999).ADS 
Article 

Google Scholar 
Tufekcioglu, M. et al. Stream bank erosion as a source of sediment and phosphorus in grazed pastures of the Rathbun Lake Watershed in southern Iowa, United States. J. Soil Water Conserv. 67, 545–555 (2012).Article 

Google Scholar 
Palmer, J. A., Schilling, K. E., Isenhart, T. M., Schultz, R. C. & Tomer, M. D. Streambank erosion rates and loads within a single watershed: Bridging the gap between temporal and spatial scales. Geomorphology 209, 66–78 (2014).ADS 
Article 

Google Scholar 
Pollen, N. & Simon, A. Estimating the mechanical effects of riparian vegetation on stream bank stability using a fiber bundle model. Water Resour. Res. 41, 1–11 (2005).Article 

Google Scholar 
Pollen-Bankhead, N. & Simon, A. Sensitivity of post-hurricane beach. Earth Surf. Process. Landf. 34, 471–480 (2009).ADS 
Article 

Google Scholar 
Wasige, J. E. et al. A land use and land cover classification system for use with remote sensor data. Prof. Pap. 100, 753–764 (1976).
Google Scholar 
Al-Doski, J., Mansor, S. B., Ng, H., San, P. & Khuzaimah, Z. Land cover mapping using remote sensing data. Am. J. Geogr. Inf. Syst. 2020, 33–45 (2020).
Google Scholar 
Okeke, C. A. U., Ede, A. N. & Kogure, T. Monitoring of riverbank stability and seepage undercutting mechanisms on the Iju (Atuwara) River, Southwest Nigeria. IOP Conf. Ser. Mater. Sci. Eng. 640, 012105 (2019).Article 

Google Scholar 
Abam, T. K. S. Aspects of alluvial river bank recession: Some examples from the Niger delta. Environ. Geol. 31, 211–220 (1997).Article 

Google Scholar 
Okeke, C. A. U., Azuh, D., Ogbuagu, F. U. & Kogure, T. Assessment of land use impact and seepage erosion contributions to seasonal variations in riverbank stability: The Iju River, SW Nigeria. Groundw. Sustain. Dev. 11, 100448 (2020).Article 

Google Scholar 
Voltz, T. et al. Riparian hydraulic gradient and stream-groundwater exchange dynamics in steep headwater valleys. J. Geophys. Res. Earth Surf. 118, 953–969 (2013).ADS 
Article 

Google Scholar 
Thomas, J., Kumar, S. & Sudheer, K. P. Channel stability assessment in the lower reaches of the Krishna River (India) using multi-temporal satellite data during 1973–2015. Remote Sens. Appl. Soc. Environ. 17, 100274 (2020).
Google Scholar 
Ran, Y. et al. A higher river sinuosity increased riparian soil structural stability on the downstream of a dammed river. Sci. Total Environ. 802, 149886 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Midgley, T. L., Fox, G. A. & Heeren, D. M. Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks. Geomorphology 145–146, 107–114 (2012).ADS 
Article 

Google Scholar 
Daly, E. R., Miller, R. B. & Fox, G. A. Modeling streambank erosion and failure along protected and unprotected composite streambanks. Adv. Water Resour. 81, 114–127 (2015).ADS 
Article 

Google Scholar 
Saleem, A. et al. Spatial and temporal variations of erosion and accretion: A case of a large tropical river. Earth Syst. Environ. 4, 167–181 (2020).ADS 
Article 

Google Scholar 
Biswas, R. N., Islam, M. N., Islam, M. N. & Shawon, S. S. Modeling on approximation of fluvial landform change impact on morphodynamics at Madhumati River Basin in Bangladesh. Model. Earth Syst. Environ. 7, 71–93 (2021).Article 

Google Scholar 
Li, J., Tooth, S., Zhang, K. & Zhao, Y. Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change. J. Environ. Manag. 278, 111559 (2021).Article 

Google Scholar 
Graziano, M. P., Deguire, A. K. & Surasinghe, T. D. Riparian buffers as a critical landscape feature : Insights for riverscape conservation and policy renovations. Diversity 14, 172 (2022).Article 

Google Scholar 
Rauch, H. P., von der Thannen, M., Raymond, P., Mira, E. & Evette, A. Ecological challenges* for the use of soil and water bioengineering techniques in river and coastal engineering projects. Ecol. Eng. 176, 106539 (2022).Article 

Google Scholar 
East, A. E. et al. Channel-planform evolution in four rivers of Olympic National Park, Washington, USA: The roles of physical drivers and trophic cascades. Earth Surf. Process. Landf. 42, 1011–1032 (2017).ADS 
Article 

Google Scholar 
Kumar, P. et al. Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations. Sci. Total Environ. 784, 147058 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laubel, A., Kronvang, B., Hald, A. B. & Jensen, C. Hydromorphological and biological factors influencing sediment and phosphorus loss via bank erosion in small lowland rural streams in Denmark. Hydrol. Process. 17, 3443–3463 (2003).ADS 
Article 

Google Scholar 
Veihe, A., Jensen, N. H., Schiøtz, I. G. & Nielsen, S. L. Magnitude and processes of bank erosion at a small stream in Denmark. Hydrol. Process. 25, 1597–1613 (2011).ADS 
Article 

Google Scholar 
Kronvang, B., Andersen, H. E., Larsen, S. E. & Audet, J. Importance of bank erosion for sediment input, storage and export at the catchment scale. J. Soils Sediments 13, 230–241 (2013).Article 

Google Scholar 
Rajakumari, S., Meenambikai, M., Divya, V., Sarunjith, K. J. & Ramesh, R. Morphological changes in alluvial and coastal plains of Kandaleru river, Andhra Pradesh using RS and GIS, Egypt. J. Remote Sens. Space Sci. 24, 1071–1081 (2021).
Google Scholar 
Zegeye, A. D., Langendoen, E. J., Steenhuis, T. S., Mekuria, W. & Tilahun, S. A. Bank stability and toe erosion model as a decision tool for gully bank stabilization in sub humid Ethiopian highlands. Ecohydrol. Hydrobiol. 20, 301–311 (2020).Article 

Google Scholar 
Shields, F. D. J., Morin, N. & Cooper, C. M. Design of large woody debris structures for channel rehabilitation. In Seventh Federal Interagency Sedimentation Conference, Vol. 8 (2001).C A U, Okeke A N, Ede (2019) Mechanisms of riverbank failure and channel instability on the Nkisi River Southeast Nigeria. IOP Conference Series: Materials Science and Engineering 640(1), 012104. https://doi.org/10.1088/1757-899X/640/1/012104Article 

Google Scholar  More

Chen T, Nomura K, Wang X, Sohrabi R, Xu J, Yao L, et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature.2020;580:653–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaparro JM, Badri DV, Vivanco JM. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 2014;8:790–803.CAS 
PubMed 
Article 

Google Scholar 
Manching HC, Carlson K, Kosowsky S, Smitherman CT, Stapleton AE. Maize phyllosphere microbial community niche development across stages of host leaf growth. F1000Research. 2017;6:1698.PubMed 
Article 

Google Scholar 
Wagner MR, Lundberg DS, Del Rio TG, Tringe SG, Dangl JL, Mitchell-Olds T. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun. 2016;7:12151.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 2016;14:e1002352.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Durán P, Thiergart T, Garrido-Oter R, Agler M, Kemen E, Schulze-Lefert P, et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell. 2018;175:973–83. e14PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Carrión VJ, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science. 2019;366:606–12.PubMed 
Article 
CAS 

Google Scholar 
Karasov TL, Almario J, Friedemann C, Ding W, Giolai M, Heavens D, et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe. 2018;24:168–79.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Coleman-Derr D, Desgarennes D, Fonseca-Garcia C, Gross S, Clingenpeel S, Woyke T, et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. N. Phytol. 2016;209:798–811.CAS 
Article 

Google Scholar 
Xiong C, Zhu YG, Wang JT, Singh B, Han LL, Shen JP, et al. Host selection shapes crop microbiome assembly and network complexity. N. Phytol. 2021;229:1091–104.CAS 
Article 

Google Scholar 
Lemonnier P, Gaillard C, Veillet F, Verbeke J, Lemoine R, Coutos-Thévenot P, et al. Expression of Arabidopsis sugar transport protein STP13 differentially affects glucose transport activity and basal resistance to Botrytis cinerea. Plant Mol Biol. 2014;85:473–84.CAS 
PubMed 
Article 

Google Scholar 
Nobori T, Cao Y, Entila F, Dahms E, Tsuda Y, Garrido-Oter R, et al. Dissecting the co-transcriptome landscape of plants and microbiota members. bioRxiv; 2022. p. 2021.04.25.440543.Yamada K, Saijo Y, Nakagami H, Takano Y. Regulation of sugar transporter activity for antibacterial defense in Arabidopsis. Science. 2016;354:1427–30.CAS 
PubMed 
Article 

Google Scholar 
Baker RF, Leach KA, Braun DM. SWEET as sugar: new sucrose effluxers in plants. Mol Plant. 2012;5:766–8.PubMed 
Article 

Google Scholar 
Tegeder M, Hammes UZ. The way out and in: phloem loading and unloading of amino acids. Curr Opin Plant Biol. 2018;43:16–21.CAS 
PubMed 
Article 

Google Scholar 
O’Leary BM, Neale HC, Geilfus CM, Jackson RW, Arnold DL, Preston GM. Early changes in apoplast composition associated with defence and disease in interactions between Phaseolus vulgaris and the halo blight pathogen Pseudomonas syringae Pv. phaseolicola. Plant Cell Environ. 2016;39:2172–84.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Rico A, Preston GM. Pseudomonas syringae pv. tomato DC3000 uses constitutive and apoplast-induced nutrient assimilation pathways to catabolize nutrients that are abundant in the tomato apoplast. Mol Plant-Microbe Interact. MPMI. 2008;21:269–82.CAS 
PubMed 
Article 

Google Scholar 
Yu X, Lund SP, Scott RA, Greenwald JW, Records AH, Nettleton D, et al. Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites. Proc Natl Acad Sci USA. 2013;110:E425.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Winter H, Riens B, Heldt HW. Further studies of the phloem loading process in leaves of barley and spinach. The comparison of metabolite concentrations in the apoplastic compartment with those in the cytosolic compartment and in the sieve tubes. Bot Acta. 1995;108:270–5.CAS 
Article 

Google Scholar 
Chen LQ, Hou BH, Lalonde S, Takanaga H, Hartung ML, Qu XQ, et al. Sugar transporters for intercellular exchange and nutrition of pathogens. Nature. 2010;468:527–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xin XF, Nomura K, Aung K, Velásquez AC, Yao J, Boutrot F, et al. Bacteria establish an aqueous living space in plants crucial for virulence. Nature. 2016;539:524–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paulsen IT, Press CM, Ravel J, Kobayashi DY, Myers GSA, Mavrodi DV, et al. Complete genome sequence of the plant commensal Pseudomonas fluorescens Pf-5. Nat Biotechnol. 2005;23:873–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 
Article 

Google Scholar 
Hoek TA, Axelrod K, Biancalani T, Yurtsev EA, Liu J, Gore J. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLOS Biol. 2016;14:e1002540.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zimmermann J, Obeng N, Yang W, Pees B, Petersen C, Waschina S, et al. The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans. ISME J. 2020;14:26–38.CAS 
PubMed 
Article 

Google Scholar 
Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol. 2021;5:195–203.PubMed 
PubMed Central 
Article 

Google Scholar 
Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:1–17.Article 

Google Scholar 
Gerlich SC, Walker BJ, Krueger S, Kopriva S. Sulfate metabolism in C4 Flaveria species is controlled by the root and connected to serine biosynthesis. Plant Physiol. 2018;178:565–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gowik U, Bräutigam A, Weber KL, Weber APM, Westhoff P. Evolution of C4 photosynthesis in the genus Flaveria: How many and which genes does it take to make C4? Plant Cell. 2011;23:2087–105.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKown AD, Dengler NG. Vein patterning and evolution in C4 plants. Botany. 2010;88:775–86.CAS 
Article 

Google Scholar 
Gentzel I, Giese L, Zhao W, Alonso AP, Mackey D. A simple method for measuring apoplast hydration and collecting apoplast contents. Plant Physiol. 2019;179:1265–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mayer T, Mari A, Almario J, Murillo-Roos M, Syed M, Abdullah H, et al. Obtaining deeper insights into microbiome diversity using a simple method to block host and nontargets in amplicon sequencing. Mol Ecol Resour. 2021;21:1952–65.PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.R-project.org/.Callahan B, McMurdie PJ, Rosen M, Han A, Johnson A, Holmes S. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:61217.Article 
CAS 

Google Scholar 
Oksanen J, Blanchet GF, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package [Internet]. 2020. Available from: https://CRAN.R-project.org/package=vegan.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schlechter RO, Jun H, Bernach M, Oso S, Boyd E, Muñoz-Lintz DA, et al. Chromatic bacteria – A broad host-range plasmid and chromosomal insertion toolbox for fluorescent protein expression in bacteria. Front Microbiol. 2018;9:3052.PubMed 
PubMed Central 
Article 

Google Scholar 
Lohaus G, Pennewiss K, Sattelmacher B, Hussmann M, Hermann Muehling K. Is the infiltration-centrifugation technique appropriate for the isolation of apoplastic fluid? A critical evaluation with different plant species. Physiol Plant. 2001;111:457–65.CAS 
PubMed 
Article 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 

Google Scholar 
Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dal Bello M, Lee H, Goyal A, Gore J. Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat Ecol Evol. 2021;5:1424–34.PubMed 
Article 

Google Scholar 
Sattelmacher B. The apoplast and its significance for plant mineral nutrition. N. Phytol. 2001;149:167–92.CAS 
Article 

Google Scholar 
Regalado J, Lundberg DS, Deusch O, Kersten S, Karasov T, Poersch K, et al. Combining whole-genome shotgun sequencing and rRNA gene amplicon analyses to improve detection of microbe–microbe interaction networks in plant leaves. ISME J. 2020;14:2116–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Weng FCH, Joubert PM, Metcalf CJE, Lindow S, Koskella B. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc Natl Acad Sci USA. 2020;117:1148–59.CAS 
PubMed 
Article 

Google Scholar 
Remus-Emsermann MNP, Lücker S, Müller DB, Potthoff E, Daims H, Vorholt JA. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ Microbiol. 2014;16:2329–40.CAS 
PubMed 
Article 

Google Scholar 
Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
Herren CM. Disruption of cross-feeding interactions by invading taxa can cause invasional meltdown in microbial communities. Proc R Soc B Biol Sci. 2020;287:20192945.Article 

Google Scholar 
Rahme LG, Mindrinos MN, Panopoulos NJ. Plant and environmental sensory signals control the expression of hrp genes in Pseudomonas syringae pv. phaseolicola. J Bacteriol. 1992;174:3499–507.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morella NM, Zhang X, Koskella B. Tomato seed-associated bacteria confer protection of seedlings against foliar disease caused by Pseudomonas syringae. Phytobiomes J. 2019;3:177–90.Article 

Google Scholar 
Cha JY, Han S, Hong HJ, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29.CAS 
PubMed 
Article 

Google Scholar 
Lundberg DS, Jové R de P, Ayutthaya PPN, Karasov TL, Shalev O, Poersch K, et al. Contrasting patterns of microbial dominance in the Arabidopsis thaliana phyllosphere. bioRxiv. 2021;2021.04.06.438366.Ikawa Y, Tsuge S. The quantitative regulation of the hrp regulator HrpX is involved in sugar-source-dependent hrp gene expression in Xanthomonas oryzae pv. oryzae. FEMS Microbiol Lett. 2016;363:fnw071.Wei ZM, Sneath BJ, Beer SV. Expression of Erwinia amylovora hrp genes in response to environmental stimuli. J Bacteriol. 1992;174:1875–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Akashi H, Gojobori T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci USA. 2002;99:3695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oña L, Kost C. Cooperation increases robustness to ecological disturbance in microbial cross-feeding networks. Ecol Lett. 2022;25:1410–20.Cadot S, Guan H, Bigalke M, Walser JC, Jander G, Erb M, et al. Specific and conserved patterns of microbiota-structuring by maize benzoxazinoids in the field. Microbiome. 2021;9:103.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Voges MJEEE, Bai Y, Schulze-Lefert P, Sattely ES. Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome. Proc Natl Acad Sci USA. 2019;116:12558–65.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Aulakh MS, Wassmann R, Bueno C, Kreuzwieser J, Rennenberg H. Characterization of root exudates at different growth stages of ten rice (Oryza sativa L.) cultivars. Plant Biol. 2001;3:139–48.CAS 
Article 

Google Scholar 
Dietz S, Herz K, Gorzolka K, Jandt U, Bruelheide H, Scheel D. Root exudate composition of grass and forb species in natural grasslands. Sci Rep. 2020;10:10691.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Mack, R. M. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710. https://doi.org/10.1890/1051-0761(2000)010[0689:BICEGC]2.0.CO;2 (2000).Article 

Google Scholar 
Pyšek, P. et al. A global assessment of invasive plant impacts on resident species, communitiesand ecosystems: The interaction of impact measures, invading species’ traits and environment. Glob. Change Biol. 18, 1725–1737. https://doi.org/10.1111/j.1365-2486.2011.02636.x (2012).ADS 
Article 

Google Scholar 
Wittenberg, R. & Cock, M. J. W. Invasive Alien Species: A Toolkit of Best Prevention and Management Practices (CAB International, 2001).Book 

Google Scholar 
DAISIE. Delivering Alien Invasive Species Inventories for Europe. http://www.europe-aliens.org/speciesFactsheet.do?speciesId=23539# (2018).Hejda, M., Pyšek, P. & Jarošík, V. Impact of invasive plants on the species richness, diversity and composition of invaded communities. J. Ecol. 97, 393–403. https://doi.org/10.1111/j.1365-2745.2009.01480.x (2009).Article 

Google Scholar 
Chmura, D. et al. The influence of invasive Fallopia taxa on resident plant species in two river valleys (southern Poland). Acta Soc. Bot. Pol. 84(1), 23–33. https://doi.org/10.5586/asbp.2015.008 (2015).Article 

Google Scholar 
Stefanowicz, A. M., Stanek, M., Nobis, M. & Zubek, S. Few effects of invasive plants Reynoutria japonica, Rudbeckia laciniata and Solidago gigantea on soil physical and chemical properties. Sci. Total Environ. 574, 938–946. https://doi.org/10.1016/j.scitotenv.2016.09.120 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stefanowicz, A. M., Stanek, M., Nobis, M. & Zubek, S. Species-specific effects of plant invasions on activity, biomass and composition of soil microbial communities. Biol. Fertil. Soils 52, 841–852. https://doi.org/10.1007/s00374-016-1122-8 (2016).CAS 
Article 

Google Scholar 
Zubek, S. et al. Invasive plants affect arbuscular mycorrhizal fungi abundance and species richness as well as the performance of native plants grown in invaded soils. Biol. Fertil. Soils 52, 879–893. https://doi.org/10.1007/s00374-016-1127-3 (2016).Article 

Google Scholar 
Krinke, L. et al. Seed bank of an invasive alien, Heracleum mantegazzianum, and its seasonal dynamics. Seed Sci. Res. 15, 239–248. https://doi.org/10.1079/SSR2005214 (2005).Article 

Google Scholar 
Gioria, M. & Osbourne, B. Similarities in the impact of three large invasive plant species on soil seed bank communities. Biol. Invasions 12, 1671–1683. https://doi.org/10.1007/s10530-009-9580-7 (2010).Article 

Google Scholar 
Kundel, D., van Kleunen, M. & Dawson, W. Invasion by Solidago species has limited impacts on soil seed bank communities. Basic Appl. Ecol. 15, 573–580. https://doi.org/10.1016/j.baae.2014.08.009 (2014).Article 

Google Scholar 
Dong, H., Liu, T., Liu, Z. & Song, Z. Fate of the soil seed bank of giant ragweed and its significance in preventing and controlling its invasion in grasslands. Ecol. Evol. https://doi.org/10.1002/ece3.6238 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Harper, J. L. Population Biology of Plants (Academic Press, 1977).
Google Scholar 
Gioria, M. & Pyšek, P. The legacy of plant invasions: Changes in the soil seed bank of invaded plant communities. Bioscience 66(1), 40–53. https://doi.org/10.1093/biosci/biv165 (2015).Article 

Google Scholar 
Gioria, M. & Osborne, B. Resource competition in plant invasions: Emerging patterns and research needs. Front. Plant Sci. 5, 501. https://doi.org/10.3389/fpls.2014.00501 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Holmes, P. M. & Cowling, R. M. Diversity, composition and guild structure relationships between soil-stored seed banks and mature vegetation in alien plant-invaded South African fynbos shrublands. Plant Ecol. 133, 107–122. https://doi.org/10.1023/A:1009734026612 (1997).Article 

Google Scholar 
Gioria, M., Pyšek, P. & Moravcová, L. Soil seed banks in plant invasions: Promoting species invasiveness and long-term impact on plant community dynamics. Preslia 84, 327–350 (2012).
Google Scholar 
Tokarska-Guzik, B. et al. Rośliny Obcego Pochodzenia w Polsce ze Szczególnym Uwzględnieniem Gatunków Inwazyjnych (Generalna Dyrekcja Ochrony Środowiska, 2012).
Google Scholar 
Thompson, K., Bakker, J. P. & Bekker, R. M. The Soil Seed Banks of North West Europe: Methodology, Density and Longevity (Cambridge University Press, 1997).
Google Scholar 
Gioria, M., Le Roux, J. J., Hirsch, H., Moravcová, L. & Pyšek, P. Characteristics of the soil seed bank of invasive and non-invasive plants in their native and alien distribution range. Biol. Invasions 21, 2313–2332 (2019).Article 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: Species traits, habitats, propagule pressure, residence time. Ecology 96(3), 762–774. https://doi.org/10.1890/14-1005.1 (2015).Article 
PubMed 

Google Scholar 
Hager, H. A., Rupert, R., Quinn, L. D. & Newman, J. A. Escaped Miscanthus sacchariflorus reduces the richness and diversity of vegetation and the soil seed bank. Biol. Invasions 17, 1833–1847. https://doi.org/10.1007/s10530-014-0839-2 (2015).Article 

Google Scholar 
Robertson, S. G. & Hickman, K. Aboveground plant community and seed bank composition along an invasion gradient. Plant Ecol. 213(9), 1461–1475. https://doi.org/10.1007/s11258-012-0104-7 (2012).Article 

Google Scholar 
Fumanal, B., Gaudot, I. & Bretagnolle, F. Seed-bank dynamics in the invasive plant, Ambrosia artemisiifolia L.. Seed Sci. Res. 18(2), 101–114 (2008).Article 

Google Scholar 
Funk, J. L. et al. Keys to enhancing the value of invasion ecology research for management. Biol. Invasions 22, 2431–2445. https://doi.org/10.1007/s10530-020-02267-9 (2020).Article 

Google Scholar 
Jalas, J. Problems concerning Rudbeckia laciniata (Asteraceae) in Europe Fragmenta Floristica et Geobotanica. Supplementum 2(1), 289–297 (1993).
Google Scholar 
Tokarska-Guzik, B. The Establishment and Spread of Alien Plant Species (Kenophytes) in the Flora of Poland (Prace Naukowe Uniwersytetu Śląskiego w Katowicach, 2005).
Google Scholar 
EPPO. Rudbeckia laciniata (Asteraceae). EPPO Reporting Service—Invsive Plants. European and Mediterranean Plant Protection Organization. https://www.eppo.int/INVASIVE_PLANTS/ias_lists.htm (2009).Zelnik, I. The presence of invasive alien plant species in different habitats: Case study from Slovenia. Acta Biol. Sloven. 55(2), 25–38 (2012).
Google Scholar 
Vojniković, S. Tall cone flower (Rudbeckia laciniata L.)—new invasive species in the flora of Bosnia and Herzegovina. Herbologia 15(1), 39–47. https://doi.org/10.5644/Herb.15.1.05 (2015).Article 

Google Scholar 
Auld, B., Morita, H., Nishida, T., Ito, M. & Michael, P. Shared exotica: Plant invasions of Japan and south eastern Australia. Cunninghamia 8, 147–152 (2003).
Google Scholar 
Akasaka, M., Osawa, T. & Ikegami, M. The role of roads and urban area in occurrence of an ornamental invasive weed: A case of Rudbeckia laciniata L.. Urban Ecosyst. 18, 1021–1030 (2015).Article 

Google Scholar 
GBIF. Global Biodiversity Information Facility. Checklist dataset. https://www.gbif.org/species/3114229 (2021).Francírková, T. Contribution of the invasive ecology of Rudbeckia laciniata in the Czech Republic. In Plant Invasions: Species Ecology and Ecosystem Management (eds Brundu, G. et al.) 89–98 (Backhuys Publishers, 2001).
Google Scholar 
Moravcová, L., Pyšek, P., Jarošík, V., Havlíčková, V. & Zákravský, P. Reproductive characteristics of neophytes in the Czech Republic: Traits of invasive and non-invasive species. Preslia 82, 365–390. https://doi.org/10.1371/journal.pone.0123634 (2010).CAS 
Article 

Google Scholar 
Kościńska-Pająk, M., Musiał, K. & Janiszewska, K. Embryological processes in ovules of Rudbeckia laciniata L. (Asteraceae) from Poland. Mod. Phytomorphol. 5, 19–23 (2014).
Google Scholar 
Urbatsch, L. E. & Cox, P. B. Rudbeckia laciniata in Flora of North America Editorial Committee. http://floranorthamerica.org/Rudbeckia_laciniata (2021).Jankowska-Błaszczuk, M. Zróżnicowanie banków nasion w naturalnych i antropogenicznie przekształconych zbiorowiskach leśnych. Monograph. Bot. 88, 25 (2000).
Google Scholar 
Osawa, T. & Akasaka, M. Management of the invasive perennial herb Rudbeckia laciniata L. (Compositae) using rhizome removal. Jpn. J. Conserv. Ecol. 14(1), 37–43. https://doi.org/10.18960/hozen.14.1_37 (2009).Article 

Google Scholar 
Gleason, H. A. & Cronquist, A. Manual of Vascular Plants of Northeastern United States and Adjacent Canada (The New York Botanical Garden, 1991).Book 

Google Scholar 
Gioria, M. & Osborne, B. The impact of Gunnera tinctoria (Molina) Mirbel invasions on soil seed bank communities. J. Plant Ecol. 2(3), 153–167. https://doi.org/10.1093/jpe/rtp013 (2009).Article 

Google Scholar 
Kleyer, et al. The LEDA Traitbase: A database of life-history traits of Northwest European flora. J. Ecol. 96, 1266–1274. https://doi.org/10.1111/j.1365-2745.2008.01430.x (2008).Article 

Google Scholar 
Ruprecht, E., Fenesi, A. & Nijs, I. Are plasticity in functional traits and constancy in performance traits linked with invasiveness? An experimental test comparing invasive and naturalized plant species. Biol. Invasions 16, 1359–1372. https://doi.org/10.1007/s10530-013-0574-0 (2014).Article 

Google Scholar 
Wróbel, M. Origin and spatial distribution of roadside vegetation within the forest and agricultural areas in Szczecin Lowland (West Poland). Pol. J. Ecol. 54(1), 137–143 (2001).
Google Scholar 
Dajdok, Z. & Pawlaczyk, P. Inwazyjne Gatunki Roślin Mokradłowych Polski (Wydawnictwo Klubu Przyrodnikow, 2009).
Google Scholar 
de Waal, L. C., Child, L. E., Wade, M. & Brock, J. H. Ecology and Management of Invasive Riverside Plants (Wiley, 1994).
Google Scholar 
Pyśek, P. & Prach, K. Plant invasions and the role of riparian habitats: A comparison of four species alien to central Europe. J. Biogeogr. 20, 413–420 (1993).Article 

Google Scholar 
Kucharczyk, M. & Krawczyk, R. Kenophytes as river corridor plants in the vistula and the san river valleys. Teka Komisji Ochrony Kształtowania Środowiska Przyrodniczego 1, 110–115 (2004).
Google Scholar 
Walck, J. L. et al. Defining transient and persistent seed banks in species with pronounced seasonal dormancy and germination patterns. Seed Sci. Res. 15(3), 189–196. https://doi.org/10.1079/SSR2005209 (2005).ADS 
Article 

Google Scholar 
Gioria, M. & Pyšek, P. Early bird catches the worm: Germination as a critical step in plant invasion. Biol. Invasions 19, 1055–1080. https://doi.org/10.1007/s10530-016-1349-1 (2017).Article 

Google Scholar 
Gioria, M., Pyšek, P. & Osborne, B. Timing is everything: Does early and late germination favor invasions by herbaceous alien plants?. J. Plant Ecol. 11(1), 4–16. https://doi.org/10.1093/jpe/rtw105 (2018).Article 

Google Scholar 
Perglová, I. et al. Differences in germination and seedling establishment of alien and native Impatiens species. Preslia 81, 357–375 (2009).
Google Scholar 
Haines, D. F., Larson, D. L. & Larson, J. L. Leafy spurge (Euphorbia esula) affects vegetation more than seed banks in mixed-grass prairies of the Northern Great Plains. Invas. Plant Sci. Manage. 6, 416–432. https://doi.org/10.1614/IPSM-D-12-00076.1 (2013).Article 

Google Scholar 
Gioria, M., Jarosík, V. & Pyšek, P. Impact of invasions by alien plants on soil seed bank communities: Emerging patterns. Perspect. Plant Ecol. Evol. Syst. 16, 132–142. https://doi.org/10.1016/j.ppees.2014.03.003 (2014).Article 

Google Scholar 
Gioria, M. & Osbourne, B. Assessing the impact of plant invasions on soli seed bank communities: Use of univariate and multivariate statistical approaches. J. Veg. Sci. 20, 547–556. https://doi.org/10.1111/j.1654-1103.2009.01054.x (2009).Article 

Google Scholar 
Tokarska-Guzik, B., Bzdega, K., Knapik, D. & Jenczała, G. Changes in plant species richeness in some riparian plant communities as a result of their colonisation by taxa of Reynoutria (Fallopia). Biodivers. Res. Conserv. 1–2, 122–130 (2006).
Google Scholar 
Dölle, M. & Wolfgang, S. The relationship between soil seed bank, above-ground vegetation and disturbance intensity on old-field successional permanent plots. Appl. Veg. Sci. 12, 415–428 (2009).Article 

Google Scholar 
Thompson, K. & Grime, J. P. Seasonal variation in the seed banks of herbaceous species in ten contrasting habitats. J. Ecol. 67, 893–921. https://doi.org/10.2307/2259220 (1979).Article 

Google Scholar 
Czarnecka, J. Microspatial structure of the seed bank of xerothermic grassland—intracommunity differentiation. Acta Soc. Bot. Pol. 73(2), 155–164. https://doi.org/10.5586/asbp.2004.022 (2004).Article 

Google Scholar 
Kalamees, R., Püssa, K., Zobel, K. & Zobel, M. Restoration potential of the persistent soil seed bank in successional calcareous (alvar) grasslands in Estonia. Appl. Veg. Sci. 15, 208–218 (2012).Article 

Google Scholar 
Skowronek, S. et al. Regeneration potential of floodplain forests under the influence of nonnative tree species: Soil seed bank analysis in Northern Italy. Restor. Ecol. 22(1), 22–30. https://doi.org/10.1111/rec.12027 (2014).Article 

Google Scholar  More

High diet overlap is assumed to cause competition between the three dominant pelagic planktivorous mesopredators in the Baltic Sea, sprat, herring, and stickleback11,24,25. Despite this assumption, stickleback populations have increased dramatically over the past decades, which raises the question of whether and how resources are partitioned26. While previous studies of fish diet overlap have mainly relied on microscopic identification of gut content, we implemented a DNA metabarcoding approach targeting two different gene regions, the 18S rRNA gene (18S) and the mitochondrial cytochrome c oxidase I gene (COI) to reveal the taxonomic diversity of prey, and a qPCR step to quantify rotifers that are at times abundant in the Baltic Sea. Our study highlights consistency between methods, with DNA metabarcoding resolving the plankton-fish link at the highest taxonomic resolution. Our results suggest a unique niche of stickleback that may enable high population growth in the open water, despite high competition between mesopredators, although this finding needs to be confirmed at larger scale. More than half of the DNA found in herring and sprat stomach contents was assigned to Pseudocalanus, supporting previous observations of high diet overlap between the two clupeids11,12. On the other hand, the diet of stickleback differed substantially from the two clupeids, with rotifers appearing as main prey DNA in spring. The high rotifer biomass in the environment and lack of competition from other predators indicate that this novel niche utilization may support the drastic increase of pelagic stickleback in the Baltic Sea.We find that copepods dominated the gut content of the two clupeids sprat and herring. Pseudocalanus and Temora occupied most of the sequence reads of the clupeid metabarcoding, two species that are often reported as preferred prey in previous studies11,12. Despite high contributions of these two copepods, Pseudocalanus was more than four times as abundant as Temora in clupeid gut contents. A strong preference for this copepod with marine origin can further confirm the increased competition between the clupeids, as Pseudocalanus has decreased due to decreased salinity12 and shares a similar vertical distribution as clupeid during daytime27. Our study using metabarcoding further reveals a large relative quantity (11%) of the ctenophore Mertensia in the gut samples of both clupeids. Similar, Clarke et al.28 reported an important contribution of gelatinous zooplankton to upper trophic levels in the Southern Ocean. Despite high abundances of ctenophores in the Baltic Sea and their assumed importance in marine food webs19, they are not reported as food for planktivorous fish. A possible explanation is the difficulty observing them microscopically, as their digestion rate is faster than crustaceans29, and no hard parts remain in the digestive system. Further, COI detected the presence of cladocerans, which was confirmed by the microscopic survey, but underrepresented with 18S that strongly amplify copepods20. Interestingly, more than twice annelid COI reads, including the benthic macroinvertebrates Bylgides and Marenzellaria, were associated to stickleback (15%) and herring (8%) than to sprat (4%), highlighting their ability to migrate vertically. These interactions suggest that together stickleback and herring contribute to benthic-pelagic coupling when oxygen is not restricting vertical migration in the southern Baltic Sea30.Sprat and herring share a similar feeding niche, which may explain previously observed declines in body mass and stomach fullness, and supports the theory of competition between the two species31. In contrast, stickleback revealed little diet overlap with the other mesopredators. The low relative abundances of Pseudocalanus (1–8%) in metabarcoding analyses indicates that the density-dependent competition may not limit the population growth of stickleback. The copepods that were shared in the diet of stickleback, sprat, and herring were Temora, Acartia, and Centropages have increased over the last decades, as opposed to Pseudocalanus32. Our results show that stickleback are able to feed on a broader spectrum of prey and highlight that stickleback utilizes the rotifer Synchaeta baltica as prey, which is an important component of the plankton community composition in the Baltic Sea18,20. Due to the difference of prey size, we can expect an overrepresentation of copepod to rotifer sequences compared with microscopic count data. High predation rate on S. baltica is supported by both the qPCR assay as well as microscopic counts, although only the eggshells were visible but not the soft-bodied rotifer. Despite the considerably lower carbon content per S. baltica (ca. 6 µg C ind−1) compared to copepods (ca. 20 µg C ind−1)33, the high number of rotifers likely act as a major food source for stickleback. These results propose that stickleback, due to their opportunistic feeding behaviour34 and smaller size35, have a distinct feeding niche from sprat and herring in the open water, as they feed on a smaller size class of zooplankton compared to the clupeids. Thus, we cannot assume the same process of competition between clupeids and stickleback.Rotifers can at times be very abundant in the Baltic Sea, reaching densities up to 25,000 ind m−3, but their natural predators are poorly studied. An increasing trend in biomass of the two main rotifer genera (Synchaeta and Keratella) was observed since the 1990s36. In a recent study, we showed that rotifers might occupy a unique feeding niche, as direct grazers of dinoflagellate spring bloom, as well as in the recycling of organic matter in summer20. The low level of predation on rotifers by clupeid adults ( More