Ecology

The upside-down jellyfish, Cassiopea sp. produces several hydrodynamic effects capable of altering the ecosystem which it inhabits. Not only do Cassiopea produce feeding currents capable of turning over the water column above them several times per hour3, they are also capable of releasing interstitial porewater from the benthos5. The rate of porewater release, on the order of mL h−13, is capable of increasing water column NH4 levels by almost 30% under certain conditions3. In this study, we investigated two hypothetical mechanisms for this porewater release, and found that a combination of the morphology of the bell and the pulsing behavior of the jellyfish was responsible for releasing porewater from directly below the bell via a suction-pumping mechanism.The Bernoulli hypothesis4, a low-pressure zone surrounding the animal due to a velocity gradient between the substrate boundary and the incurrent flow of the Cassiopea sp. feeding current, predicted porewater release from the substrate surface surrounding the perimeter of the animal. While porewater is entrained from the perimeter of the bell into the feeding current4 lateral expulsion of porewater due to the suction pump mechanism would produce a visually similar flow of porewater. A horizontal flow of water does occur near the bottom1, but this flow is restricted to a narrow region near the bell and velocities were low compared to the vertical excurrent jet (Fig. 4). To test the effect of Bernoulli’s principle, we measured the effect on porewater release rates of an impermeable ring-shaped barrier surrounding the animal in order to inhibit benthic-pelagic fluid flux other than directly under the animal (Fig. 2A) using labeled fluorescein per the methods of Durieux et al.3, which were adapted from those of Jantzen et al.5 (Fig. 2). If the Bernoulli mechanism contributed to porewater liberation this treatment should have reduced the porewater release rate, but the release rates observed were not significantly different from the control treatment (2.23 mL h−1 ± 1.27 s.d., Fig. 2D).The suction pumping hypothesis5, a mechanism using the exumbrellar cavity as a suction pump that draws porewater vertically upward beneath the bell and then expels it laterally, would expect to see the majority of porewater released from directly under the bell of Cassiopea sp. This mechanism is supported by bell morphology5 and the appearance of deep porewater at the benthic surface of the exumbrellar cavity5. In our, an impermeable disk was placed underneath the animal to obstruct the flow predicted by the suction pump hypothesis (Fig. 2B). Additionally, we made a 6 mm perforation in the bells of the jellyfish to interfere with the ability to form the sub-ambient pressure in the exumbrellar space necessary for suction pumping to occur (Fig. 2C). Both treatments resulted in a significant decrease in porewater liberation, with flows indistinguishable from the absence of any animal (Fig. 2D), supporting the suction-pumping hypothesis.Since the suction pumping mechanism requires pressure fluctuations in the exumbrellar space, we also directly measured the water pressure below the jellyfish. The initiation of the power stroke of bell pulsation coincides with a sudden decrease in water pressure in the exumbrellar space (Fig. 3A,B) of a mean magnitude of 43.4 Pa (± 13.6 s.d.). These pressure fluctuations appear to be unaffected by animal size (Fig. 3D,E), although the rate of porewater release is known to scale with bell diameter3. Note that the muscles responsible for bell contraction in Cassiopea sp. are roughly 2-dimensional sheets13 with a thickness of one cell14 and therefore the cross-sectional area also does not scale with diameter. Our experiments were performed on smooth acrylic rather than sand, so that the conditions here were optimal for the formation of a tight seal with the bottom. However, the magnitude of this difference is likely to be small, as Cassiopea sp. produce copious amounts of mucus, which can compensate for small-scale surface roughness. In addition, the duration of each individual bell pulse is short1, so given the fine pore size of a sand or mud substrate, it is unlikely that subambient pressure would have the opportunity to dissipate enough to affect the high suction impulse produced.While not statistically significant, bell perforation did lead to data suggesting a decrease in exumbrellar pressure fluctuations (Fig. 3C), which could explain the reduction in porewater release observed (Fig. 2C). The fact that some pressure fluctuation was seen despite a complete lack of porewater release suggests that a minimum magnitude of pressure fluctuation might be necessary for suction pumping to occur. Furthermore, the effect may have been reduced by the ability of injured Cassiopea to produce copious amounts of mucus, which could have acted to minimize the impact of bell perforation. These parallel lines of reasoning firmly suggest that suction-pumping is, in fact, the dominant mechanism by which Cassiopea sp. release porewater.The suction-pumping mechanism for the release of porewater has broad-ranging ecological implications. Release rates should increase additively with population density, and the rate of bell pulsation should correlate with the rate of porewater liberation. The additive relationship to population density is important, since Cassiopea can occur at high densities of up to 100 animals m−23. Furthermore, while the Bernoulli mechanism predicted that interstitial water movement would be limited to the upper layers of the benthos, the suction pump mechanism has the potential to release porewater from deeper sediment strata. This deep flushing should expand the oxygen penetration depth downward, affecting factors such as respiration and sediment stability15. Given the fact that Cassiopea are capable of moving along the substrate5,16 this also means that the oxygen penetration depth is likely to fluctuate over time, favoring organisms that are able to adapt their metabolism or are able to relocate themselves17.Given that porewater at the field site in Long Key, Florida, from which the animals in this study were collected, has mean ammonium concentrations of 72 μM, 160 times higher than the surrounding water column11, any benthic-pelagic coupling mechanisms in this habitat could alter nitrogen dynamics, especially given the fact that many marine primary producers preferentially take up ammonium, the most reduced state of nitrogen available, as a nitrogen source18. Cassiopea sp. animal size and population densities are known to correlate with anthropogenic disturbances, and it is suggested that this is due to an increase in nutrient availability in these areas6. In addition to prey capture, Cassiopea sp. could be supplementing their nitrogen demand through the release of nutrient-rich interstitial porewater, from which Cassiopea sp. can directly absorb ammonium and other nutrients such as phosphate and trace metals5. In fact, jellyfish presence significantly reduced porewater ammonium levels near the animal5, suggesting that nutrient-rich porewater was replaced by down-welling low-nutrient surface water. The observed benthic locomotion of Cassiopea5,16 may be a mechanism to avoid remaining in locations where they have depleted this nutrient resource3. It has been reported that Cassiopea sp. affect benthic nutrient transport on a more general level, increasing ammonium uptake and decreasing nitrate uptake of the bottom sediments19. Water column nutrient levels also varied significantly between presence and absence of Cassiopea sp., and also between light and dark treatments in the presence of Cassiopea sp.20. The addition of jellyfish increased the efflux of ammonium from the benthos during the dark treatments, but reduced ammonium concentrations in the water column during light treatments20. It is entirely possible that absorption of nutrients by Cassiopea sp.5 in order to meet daytime metabolic demand resulted in the animals reducing water column ammonium concentrations in these experiments20.In addition, Cassiopea sp. have been shown to increase spatial heterogeneity of interstitial oxygen and nutrient fluxes20, making it comparable to other biogenic processes like bioturbation. Bioturbation typically oxygenates the upper layers of substrate, increasing the nitrification zone21, and also increases 3-dimensional heterogeneity of oxygen and nutrient concentrations, allowing for more complex nutrient dynamics21. The transport of interstitial porewater from specific regions under individual jellyfish could well produce a similar effect. The porewater release rates can also be compared to that of abiotic processes, such as wind-wave driven flow over sediment wave ripples, which have been shown to liberate porewater at rates of up to 140 L m−2 day−1, or three orders of magnitude greater than diffusion alone, on shallow, exposed coastlines such as beaches22. Environmental mixing would be lower in the sheltered mangrove habitats where Cassiopea sp. are found, since at our study site wind wave height was reduced from 5.4 cm in the bay to 0.07 cm in the mangroves3. In these coastal habitats, the sediment often acts as a nutrient sink, causing certain nutrients to become limiting to primary producers. Some fringe mangrove forests along coastlines in both Florida and Belize have been shown to be N-limited, for example23,24. If these nutrients are then released back into the water column, they potentially increase primary productivity in the system occupied by Cassiopea sp. Depending on the system, this could either increase production or cause eutrophication, potentially altering productivity on a local or regional scale, as has been observed when nutrients are released from the benthos by winds25 or bioturbation26.The mechanics of suction-pumping also imply that interstitial porewater release rate will correlate with bell pulse rate. Pulse rate correlates with water temperature (Fig. 5B), which would suggest that Cassiopea sp. can release greater quantities of nutrient-rich porewater during the summer months. This was confirmed by a recent study on the related species, Cassiopea medusa from Lake Macquarie, Australia8. By extension, our model suggests that pulsing, and therefore porewater release, should cease entirely below 18ºC. In fact, at our site in Lido Key, population densities of Cassiopea sp. declined rapidly once water temperatures dropped this low (Fig. 6). This same temperature of 18 °C was determined independently to be the threshold at which Cassiopea sp. polyp feeding was inhibited10. As such, it is likely that winter minimum temperatures of 18ºC represent a limiting condition on Cassiopea sp. range expansion. Studies on Cassiopea medusa, suggested thermal stress and bell degradation at 16 °C8. As global climates warm, we can expect both a poleward shift of Cassiopea sp. Range9,27 and an increase in transport rates of porewater and its associated benthic nutrients throughout this range, leading to increased productivity, and potentially exacerbating eutrophication in some regions.We determined that a suction-pumping mechanism is responsible for the interstitial porewater release by Cassiopea, suggesting that release rates are independent of population density, but affected by pulse rate. The potential role of bell pulse rate was investigated further, and we found correlations between bell pulse rate and both animal size and water temperature. As a result, we expect that porewater liberation would demonstrate seasonal variations, with lower rates during the winter and reaching a maximum during the summer months. Cassiopea are able to release nutrient-rich porewater in the shallow quiescent habitats they inhabit, and through their feeding current mix these nutrients throughout the water column. Since this effect varies seasonally, it is likely that further study will show that these jellyfish are responsible for a complex system of nutrient dynamics in their ecosystem. More

Traditional ecological footprint consumption accountsTo truly reflect the ecological footprint and ecological carrying capacity of Dongying city, according to the lifestyle and consumption of Dongying city and with reference to Shandong Province Statistical Yearbook and Dongying City Statistical Yearbook, the biologically productive land is divided into arable land, forestland, grassland, water, construction land and fossil energy land, and the main consumption items of each category are shown in Fig. 3.Figure 3Traditional ecological footprint consumption accounts in Dongying city. This paper uses the carbon footprint to improve the fossil energy footprint of the traditional ecological footprint.Full size imageNPP-based correction of ecological footprint parametersThe 30 m land use of the study area was resampled to 500 m, consistent with the resolution of MOD17A3H after pre-processing with MRT and other tools. Correction of ecological footprint parameter factors in Dongying City for 2015, 2018 and 2020 based on the annual average NPP of vegetation (Table 1). This method is faster and more accurate than other methods, and the implementation of NPP calculations from the vegetation light energy use efficiency (LUE) framework to correct ecological footprint parameters is more applicable and accurate than other methods.Table 1 Average annual net primary productivity per land type in the Yellow River Delta.Full size tableYield factorThe formula for calculating the yield factor for arable land in the Yellow River Delta refers to NFA 2016:$$left{ {begin{array}{*{20}c} {Y_{j1} = frac{{Sigma A_{W} }}{{Sigma A_{N} }}} \ {A_{N} = frac{{P_{N} }}{{Y_{N} }}} \ {A_{W} = frac{{P_{N} }}{{Y_{W} }}} \ end{array} } right.$$
(1)
In Eq. (1), ({Y}_{j1}) is the yield factor of the arable land in the study area, ({A}_{N}) is the harvested area ( culture area ) of agricultural products of category (N) in the study area, ({A}_{W}) is the area required to produce an equivalent amount of this type of agricultural product based on the world average yield, ({P}_{N}) is the production of agricultural products of category (N) under the region, ({Y}_{N}) is the average yield of agricultural products of category (N) under the region, and ({Y}_{W}) is the world average production of a category of agricultural products.The NPP products from MODIS supported by remote sensing were used as the base data to correct the yield factors of woodlands and grasslands in the study area under the ecological footprint model.$$Y_{{{text{j}}2}} = overline{{NPP_{local} }} /overline{{NPP_{global} }}$$
(2)
In Eq. (2), ({Y}_{mathrm{j}2}) is the yield factor for woodland and grassland in the study area, ({NPP}_{local}) is the average annual net primary productivity of woodland and grassland in the study area in the corresponding year, and ({NPP}_{global}) is the global average NPP of woodland and grassland in the corresponding year, referring to Amthor et al.24.In addition, most of the land for construction comes from cropland, so the yield factor for construction land is the same as that for cropland25. The yield factors for the watershed were derived from the Wackernagel and Rees26 study.Balancing factorThe NPP model for provincial hectares was applied to the municipal scale. Among them, the NPP of four biologically productive lands, namely cropland, woodland, grassland and water, was weighted and summed to obtain the annual average NPP within the city area.$$overline{NPP} = frac{{mathop sum nolimits_{j} left( {A_{j} times NPP_{j} } right)}}{{mathop sum nolimits_{j} A_{j} }}$$
(3)
In Eq. (3), (overline{NPP }) is the average net primary productivity of arable land, forestland, grassland and water in Dongying, ({A}_{j}) is the area of land in category (j), and ({NPP}_{j}) is the average annual NPP of productive land in category (j).Balancing factors for arable land, woodland, grassland and water in the Yellow River Delta.$$R_{j} = frac{{NPP_{j} }}{{overline{NPP} }}$$
(4)
In Eq. (4), ({R}_{j}) is a balancing factor.The sites for construction are located in areas suitable for agricultural cultivation or directly occupy arable land, so the potential ecological productivity of urban construction land is the same as that of arable land, and therefore the equilibrium factor for construction land is equal to that of arable land27.Ecological footprint principles and improvementsEcological footprint modelEcological footprint model includes ecological footprint, ecological carrying capacity and ecological deficit. As the study area is within the city limits and the statistics have their own characteristics, adjustments have been made to the methodology for calculating the national ecological footprint accounts28. Based on the biological consumption account, the ecological footprint can be calculated for any land use type.$$EF = frac{P}{{Y_{N} }} times R_{j} times Y_{j}$$
(5)
In Eq. (5), (P) is the number of biologically productive land harvesting consumption items in a category, and ({Y}_{N}) is the average production of consumption Item (N) in the region. The ecological footprint of the construction land is measured based on the area of human infrastructure land and is equal to its ecological carrying capacity.Ecological carrying capacity is the determination of the maximum carrying capacity of an ecosystem for human activity, expressed as the sum of the biologically productive land area available in an area.$$EC = N times ec = N times sum left( {a_{j} times R_{j} times Y_{j} } right)$$
(6)
In Eq. (6), (EC) is the ecological carrying capacity per capita, and ({a}_{j}) is the per capita area of biologically productive land of category j in the region. According to the recommendations of the World Commission on Environment and Development, 12% of the ecological carrying capacity should also be deducted for biodiversity conservation. The population figures for the study area were obtained from the statistical yearbook and the seventh national census data. According to the recommendations of the World Commission on Environment and Development, 12% of the ecological carrying capacity should also be deducted for biodiversity conservation.An ecological deficit is the interpolation of the ecological footprint and ecological carrying capacity.$$ED = EF – EC$$
(7)
When (ED >0) indicates an ecological deficit, the ecological environment has exceeded the carrying capacity. Conversely, when (ED More

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Brazil’s new government aims to achieve zero deforestation in the Amazon rainforest. This welcome initiative should be extended to the Cerrado, the world’s most biodiverse woodland savannah.
Competing Interests
The authors declare no competing interests. More

SettingThis study was conducted in approximately 40-year-old naturally regenerated P. densiflora stands in Wola National Experimental Forest in Gyeongnam province in South Korea (35°12′ N, 128°10′ E; Table 1). The productivity of this forest is low, with a dominant tree height of 10 m at 20 years of age. Over the last 10 years, the mean annual precipitation was 1490 mm, of which one third fell during summer (July–August), and the mean temperature was 13.1 °C. The vegetation growing season generally lasts for approximately 200 days, extending from early April to October. The soil texture is a silt loam originating from sandstone and shale (clay 13.0 ± 0.8%, silt 44.1 ± 1.3%, sand 42.9 ± 1.6%; n = 18). The given texture results in volumetric water contents at 13.4 ± 0.7% (m3 m−3) at permanent wilting point (1500 kPa) and 40.7 ± 1.2% at field capacity (10 kPa)55. The understory is covered with Lespedeza spp., Quercus variabilis, Q. serrata, Smilax china, and Lindera glauca.In 2010, we selected two adjacent P. densiflora stands approximately 100 m apart from each other (180 m and 195 m above sea level, on slopes of 15° and 33°, both stands face south). Following a completely randomized design, we established nine plots (10 × 10 m2 with a 5 m untreated buffer) within each stand, of which three were randomly assigned to annual NPK fertilization, three to PK fertilization, and the rest to a control treatment without fertilization. The fertilizer, composed of urea, fused superphosphate and potassium chloride (N3P4K1) or P4K1 was added manually by deposition on the forest floor for 3 years in April 2011, April 2012, and March 2013. Over these 3 years, the NPK plots received 33.9 g N, 45 g P, and 11.1 g K m−2, while the PK plots received 45 g P and 11.1 g K m−2.Tree and stand measurementsThe standing biomass of trees was estimated using a combination of site-specific allometric equations based on destructive harvesting56 and repeated measurements of the dimensions of all trees in each plot (5–18 trees plot−1). The stem diameter at 1.2 m (D) was measured for all trees in each plot for which D was ≥ 6 cm. Selecting a representative tree in size for each plot within the 4 × 4 m2 center of the plot, we measured the tree height (H) and crown base for the representative trees. Measurements were performed in April and September 2011, September 2012–2014, and October 2021. We observed no effect of fertilization on the relationship between D and H or between D and crown base, so we assumed no effect on the allometric functions for foliage or branch biomass. A dendrometer band (Series 5 Manual Band, Forestry Suppliers Inc., Jackson, MS, USA) was installed on 18 representative trees (one per plot) to monitor radial growth monthly.Three 0.25 m2 circular litter traps were installed 60 cm above the forest floor in each plot in April 2011. Litter was collected at 3-month intervals between June 2011 and March 2015. The litter from each trap was transported to the laboratory and then oven-dried at 65 °C for 48 h. All dried samples were separated into needles, bark, cones, branches, and miscellaneous components, and weighed separately.In September 2014, we estimated the biomass of understory vegetation, separately for woody plants and herbaceous plants. All woody plants  More

Description of the phenological, vegetative and yield traits of the accessions per habitatThe process of data management included the computation of mean squares for the assessed phenological, vegetative and yield traits of the accessions with the sampling habitats considered as the treatment factor. The error mean squares served in the multiple comparison of means reported in Table 1.Table 1 Means of the measured phonological, vegetative and flowering and yield traits of Cucurbita moschata genotypes sampled from seven habitats.Full size tableRegarding the phenological traits, the accessions from the habitat of Zh have the longest period from seeding to first male (102.39 d) and first female (108.14 d) flower appearances, and the longest period from seeding to physiological maturity (153.95 d). For those traits, the accessions from Tiassale and Soubre are not significantly different from those of Zh. And, accessions from Tiassale and Zh have the longest periods from seeding to 50% flowering. On the other hand, accessions from Korho, Ferke, Bondu and Burki develop their first male and female flowers and attain 50% flowering in a very short period. They also reach physiological maturity faster. Accessions from Korho, however, have the longest period from seeding to 50% emergence (6.07 d) and accessions from Bondu have the longest period from first female flower appearance to physiological maturity (53.04 d).
For the vegetative traits, accessions from Tiassale and Soubre have the largest girth size (4.43 cm and 4.63 cm, respectively). Accessions from Tiassale have the longest (24.98 cm) and widest (19.94 cm) leaves, the longest male (16.2 cm) and female (4.03 cm) peduncles and the longest petioles (34.94 cm). The measures for those organs on accessions from Soubre rank second to those of Tiassale. On the other hand, accessions from Korho, Ferke, Bondu and Burki are characterized by smaller girth size, smaller leaves, smaller petioles and smaller peduncles of male and female flowers. But the accessions from Bondu are the tallest (586.91 cm) followed by the accessions from Ferke (489.20 cm). And the accessions from Zh are the shortest (417.38 cm).For the flowering and yield traits, accessions from Tiassale and Soubre show the largest numbers of male (27.33 units and 22.58 units, respectively) and female (5.22 units and 6.05 units, respectively) flowers per plant, largest numbers of fruits per plant (2.78 units and 2.53 units, respectively) and largest measures of all fruit-related traits. Their seeds are very large, but in small numbers. In contrast, accessions from Korho, Ferke, Bondu and Burki have the smallest numbers of male and female flowers per plant, the smallest numbers of fruits per plant and the smallest measures of fruit-related traits. They have large numbers of seeds, but their seeds are smaller, except the seeds of the accessions from Burki. Refer to Table 1 for more detailed information.
Variability of the phenological, vegetative and yield traitsTable 2 shows the spread of the phenological and morphological traits of the assessed accessions of C. moschata. All the evaluated traits showed very wide ranges of distribution of the observations. Some conspicuously wide ranges of traits include number of days to 50% flowering (DTF) that goes from 52 to 152 d, plant height with a minimum of 48 cm and a maximum of 1510 cm, diameter of the fruit that is between 5.8 cm and 35 cm, weight of the fruit that varies between 150 g and 10,930 g and number of seeds per fruit that spreads in the interval from 32 units per fruit to 729 units per fruit. Excluding the number of days to 50% emergence (DTE), all the other assessed traits have remarkably wide ranges of phenotypic expressions (Table 2). All the traits but DTE, DTF, days from first female flower appearance to fruit maturity, fruit length and length of the dry seed, had outliers. The number of outliers ranged from 1 to 67. Except the outliers observed with the width of the dry seed, all the outliers were above 1.5*IQR + Q3 where IQR is the inter-quartile range and Q3 is the third quartile. The presence of outliers is indicative of the richness and large variability of the population of accessions. The outliers are exceptional performances that fall outside the normal distribution of the observations. They are a stock of unusual traits that can be used in a crop improvement program when beneficial. For example, the observed outliers for diameter of the fruit, weight of the fruit or thickness of the pulp can be used in a breeding program for the improvement of fruit yield. Similarly, outliers for beneficial traits related to the seed can be used to improve C. moschata crop for seed yield. Besides, the computed mean squares (data not reported) showed highly significant variations between accessions for the assessed traits. They all yielded p-values less than 0.01, providing additional support to the evidence of large variability among the accessions of C. moschata of Cote d’Ivoire. The computed standard deviation, and median absolute deviation for each trait are additional evidence. We should note that in most cases, the mean squares associated to year (data not reported) were not significant, indicating the relative stability of the assessed traits.Table 2 Minimum (Min), first quartile (Q1), median, third quartile (Q3), maximum (Max), standard deviation (SD), median absolute deviation (MAD) and outliers obtained from the phenological, vegetative and flowering and yield traits of 663 accessions of C. moschata.Full size tableThe components of variance, the quantitative genetic differentiation, the overall mean, and the coefficients of variation are reported in Table 3. The lme4 package37 used in the determination of the components of variance, does not provide p-values in the analysis of mixed or random models. The reported quantities in Table 3 are not accompanied with tests of significance. It is worth mentioning that the respective units of measure of the assessed traits are squared for the variances and the evaluated estimates will be reported without the units of measure. The phenotypic variance ((sigma_{p}^{2})) is partitioned into variance between morphotypes or genotypic variance ((sigma_{g}^{2})), and within morphotypes or residual variance ((sigma_{e}^{2})). For the class of phenological traits, considerable genotypic variances were observed with days to 50% flowering (266.21) and days to first male flower appearance (254.40), compared with their respective residual variances (148.13 and 199.50). Regarding the class of vegetative traits, only the peduncle length of male flowers had a genotypic variance (9.22) greater than its residual variance (8.86). In the class of flowering and yield traits, 8 of the 15 traits assessed showed large genotypic variances in comparison with their respective residual variances. They are number of female flowers per plant ((sigma_{g}^{2}) = 3.02 versus (sigma_{e}^{2}) = 2.36), length of the fruit ((sigma_{g}^{2}) = 53.96 versus (sigma_{e}^{2}) = 48.97), diameter of the fruit ((sigma_{g}^{2}) = 37.17 versus (sigma_{e}^{2}) = 16.76), volume of the fruit ((sigma_{g}^{2}) = 10,713,468 versus (sigma_{e}^{2}) = 3,904,590), weight of the fruit ((sigma_{g}^{2}) = 5,413,819 versus (sigma_{e}^{2}) = 1,420,187), diameter of the cavity enclosing the seed ((sigma_{g}^{2}) = 19.12 versus (sigma_{e}^{2}) = 7.75), thickness of the fruit pulp ((sigma_{g}^{2}) = 1.11 versus (sigma_{e}^{2}) = 0.94) and weight of the fruit pulp ((sigma_{g}^{2}) = 5,979,212 versus (sigma_{e}^{2}) = 1,088,750). For a trait to have a lager genotypic variance than the residual variance is synonymous to a relative ease of improvement of the crop for that trait through a breeding program.Table 3 Components of variances ((sigma_{p}^{2}), (sigma_{g}^{2}), (sigma_{e}^{2}), (sigma_{a}^{2})), quantitative genetic differentiation ((Q_{ST})), overall mean ((mu)), and coefficients of variation (%) ((CV_{p}),(CV_{g}),(CV_{e})), of the measured phenological, vegetative and yield traits of the accessions of C. moschata of Cote d’Ivoire.Full size tableThe coefficient of variation (CV) is another statistic that measures variation. It is actually the dispersion of a trait per unit measure of its mean, which can be used to compare variations of traits with different measurement units or different scales. As a rule-of-thumb, a coefficient of variation greater than 20% is indicative of large variation for the trait. The phenotypic coefficient of variation is considerably high for 25 of the 28 assessed traits. Only the number of days from seeding to physiological maturity, the first and second longest axes of the dry seed show coefficients of variation less than 20%. Traits with very large phenotypic coefficients of variation include the peduncle length of female flowers ((CV_{p}) = 93.98%), weight of the pulp ((CV_{p}) = 92.96%), volume of the fruit ((CV_{p}) = 89.17%), weight of the fruit ((CV_{p}) = 78.30%) and number of female flowers per plant ((CV_{p}) = 65.81%). With respect to the residual coefficients of variation, only the number of days from seeding to 50% emergence and number of days from first female flower appearance to physiological maturity have residual coefficients of variation greater than 20%, among the phenological traits. All the vegetative traits have residual coefficients of variation greater than 20%, and show a near-perfect linear relation (r = 0.98; p  More

Preparation of the pao cai soupFirst, 35 kg of white radish (Raphanus sativus), 35 kg of cabbage (Brassica oleracea), 2 kg of chili pepper (Capsicum frutescens), 1 kg of ginger (Zingiber officinale), 1 kg of peppercorns (Zanthoxylum bungeanum), 2.5 kg of rock sugar, and 210 kg of cold boiled water (containing 6% salt) were divided into six ceramic jars. After 7 days of natural fermentation at room temperature, the pao cai was filtered out with sterile gauze to obtain 200 kg of pao cai soup. To ensure an even distribution of microorganisms in the soup, the soup was mixed well and then left to rest for 12 h, the supernatant was taken, and the soup was left to rest for 12 h again.The plants used in this study were cultivated vegetables which purchased from the vegetable market at the study site. All local, national or international guidelines and legislation were adhered to in the production of this study.Establishment of the microcosm systemSeventy-eight for each size of 10 ml, 20 ml, 50 ml, 100 ml, 250 ml, 500 ml, and 1000 ml sterile glass culture flasks were filled with pao cai soup, the bottle mouth was sealed with sterile sealing film, and the bottle was capped without leaving any air (Fig. 1). Each flask became a microcosm and was cultured in a 25 °C incubator.Figure 1Schematic diagram of the establishment of the microcosmic system.Full size imageSample collectionBefore the microcosm system was established, a sample of well-mixed pao cai soup was taken as a reference to establish background biodiversity. The microbial community dynamics should change the fastest at the beginning of the microcosm system establishment and gradually become slower over time. Considering the workload and cost, this study collected samples daily for 1–10 day after the establishment of the microcosm and then collected every 2 days for 10–30 day and every 5 days for 30–60 day. Three different microcosms of the same volume were established. Monitoring was carried out for 60 days, and a total of 546 samples of 7 volumetric gradients were obtained at 26 time points. At the time of sampling, the pao cai soup in the microcosm was mixed, and 50 mL of sample (10 mL of sample was collected for microcosm systems with a volume of less than 50 mL) was collected. The sample was centrifuged at 8000 rpm for 10 min, the supernatant was collected for pH determination, and the pellet was stored in a − 80 °C freezer.Microbial analysesMicrobial DNA was extracted from pao cai samples using the E.Z.N.A.® Soil DNA Kit (Omega Biotek, Norcross, GA, U.S.) according to the manufacturer’s protocols. For bacteria, we targeted the V3-V4 region of the 16S ribosomal RNA (rRNA) gene, using the 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primer pairs. For fungi, we targeted the ITS1-1F region of the nuclear ribosomal internal transcribed spacer region (ITS rDNA) gene, using ITS1-1F-F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS-1F-R (5′-GCTGCGTTCTTCATCGATGC-3′). PCRs were performed in triplicate in a 20 μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase and 10 ng of template DNA. The PCR program for the 16S rRNA gene was as follows: 3 min of denaturation at 95 °C; 27 cycles of 30 s at 95 °C, 30 s of annealing at 55 °C, and 45 s of elongation at 72 °C; and a final extension at 72 °C for 10 min. For the ITS1-1F region, the PCR program was as follows: samples were initially denatured at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, primer annealing at 50 °C for 30 s, and extension at 72 °C for 30 s. A final extension step of 5 min at 72 °C was added to ensure complete amplification of the target region. The resulting PCR products were extracted from a 2% agarose gel, further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, Madison, WI, USA).Purified amplicons were pooled in equimolar amounts and paired-end sequenced (2 × 300) on an Illumina NovaSeq platform (Illumina, San Diego, CA, USA) according to standard protocols. The analysis was conducted by following the “Atacama soil microbiome tutorial” of QIIME2 docs along with customized program scripts (https://docs.qiime2.org/2019.1/). Briefly, raw data FASTQ files were imported in the QIIME2 system using the qiime tools import program. Demultiplexed sequences from each sample were quality filtered, trimmed, denoised, and merged, and then the chimeric sequences were identified and removed using the QIIME2 DADA2 plugin to obtain the feature table of amplicon sequence variants (ASVs)24. Compared with traditional OTU that clusters at 97% similarity, ASV has higher accuracy, equivalent to 99% similarity clustering. The QIIME2 feature-classifier plugin was then used to align ASV sequences to the pretrained GREENGENES 13_8 99% database (trimmed to the V3-V4 region bound by the 338F/806R primer pair for bacteria) and UNITE database (for fungi) to generate the taxonomy table25. Any contaminating mitochondrial and chloroplast sequences were filtered using the QIIME2 feature-table plugin. Based on the sequence number of the lowest sample, perform the resampling to make the sequence number equal for each sample. Due to the random nature of sequencing, ASVs specific to each sample in this study were present. To reduce the uncertainty introduced by the sequencing process, we filtered out rare ASVs with less than 0.001% of the total sequence volume.Data analysisIn this study, the data of fungi and bacteria were integrated and analyzed, and all microbial diversity appearing in the text represent the sum of all fungi and bacteria. Species richness is equal to the number of taxa, which is equal to the total number of all bacterial and fungal ASVs. The vegan package in R 4.2.1 was used to calculate the species richness of each sample based on the ASV feature table26. Using flask volume instead of area, SAR fitting was performed using a semi-logarithmic model, and its significance was tested. The semi-logarithmic model is the function S = c + b*logA, where S is species richness, A is area (in this case, volume is used instead), and b and c are fit parameters27.The microcosmic system in this study is hermetically sealed, and all microorganisms originate from a single portion of well-mixed paocai soup (ie species pool). The speciation process in the 60-day experimental system should be negligible due to the short experimental period. The extinction rate of a microcosm system is equal to the number of ASVs lost in the microcosm system compared to the species pool divided by the total number of ASVs in the species pool. The extinction rate is the number of extinct ASVs in each system compared to the species pool. Pearson correlation analysis was performed with volume as the independent variable and extinction rate as the dependent variable to determine the correlation between volume and extinction rate at each time point. When microorganisms of a microcosmic system disappear entirely or cannot be detected, the microcosm is recorded as an annihilated microcosm. The annihilation rate at a time point is equal to the number of microcosms annihilated at that time, divided by the total number of microcosms. The difference between the extinction rate and annihilation rate defined in this paper is that the extinction rate is for ASVs within each sample, and the annihilation rate is for microcosmic system at each sampling time point. The two indicators jointly characterize the local extinction of microorganisms from different perspectives. Non-linear regression with a bell-shaped form was performed with time as an independent variable and pH and annihilation rate as dependent variables, and regression lines were plotted based on R 4.2.1.According to the taxonomy table, bacterial ASVs were divided into acid-producing and non-acid-producing categories, and their extinction rates were calculated separately. The agricola, ggplot2, vegan and ggpubr packages were used to draw alpha diversity box plots and perform the Wilcoxon rank sum test for differences between groups26,28,29,30. Non-metric multidimensional scaling (NMDS) analysis was performed with the vegan package based on Bray–Curtis dissimilarity. In addition, the potential Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologue (KO) functional profiles of microbial communities were predicted with PICRUSt31. Resistance-related genes were screened using the gene function predictions. The relationship between the relative abundance of resistance-related genes and the volume of the microcosm was analysed by Pearson correlation, and a forest map was plotted to present the results. More

We investigated the complex relationships between the environmental characteristics of blue spaces and visit-related mental well-being in a multi-country study including 17 bluespace types and four facets of subjective well-being. Our aim was to operationalise, and consider the utility of, the Bratman et al.9 conceptual model that links ecosystem services (ESS) with mental health.Consistent with the proposed conceptual model, mental well-being outcomes relied on a complex interplay of individual, environmental, and visit characteristics.Summary of findingsOverall, bluespace visits were associated with better subjective mental well-being outcomes if the visits took place in nearby coastal areas or rural rivers, were perceived as safe and to have good water quality, and had a long duration. They could involve a range of activities such as playing with children, socialising, or walking. The degree to which the perceived presence of wildlife predicted visit satisfaction varied depending on the bluespace type, suggesting that the importance of ecosystem features such as biodiversity may vary by the setting.We can also identify the combination of environmental and visit characteristics associated with particularly high levels of well-being for a specific outcome. For example, an optimal visit in terms of happiness might be to sandy beaches where there are high levels of perceived safety and excellent water quality; with a visit lasting at least three hours; and possibly involving playing with children, socialising, sunbathing/paddling and/or walking with a dog; and has short travel times that do not involve public transport.RQ1—natural and environmental featuresResearch question 1a—Which bluespace type(s) were associated with the highest levels of recalled visit mental well-being?Four of the five bluespace types associated with the highest levels of visit satisfaction were coastal (sea cliffs, rocky shore, sandy beaches, rural river and seaside promenade), indicating that these environments may be particularly beneficial for well-being. Visits to these environments were also associated with the lowest levels of visit anxiety, with the exception of seaside promenade and sea cliffs, which were not significantly different to the grand mean. Seaside promenade was the only urban environment in the top five.In addition, only coastal sites were associated with significantly higher levels of visit happiness (compared to the grand mean), further highlighting the potential importance of these environments. Although not explored here, coastal scenes tend to be associated with particularly high aesthetic and scenic value25,26 which may also be positively related to subjective well-being.These findings are broadly consistent with other studies from the UK17,27, but are extended here to our international sample. White et al.28 also used data from the BlueHealth International Survey (BIS) and explored visit frequency to different environments and associations with general mental health and well-being outcomes, including the World Health Organisation five-item Well-being index referring to the two weeks prior to the survey. Consistent with the results here, they found that visit frequency to “coastal blue” environments was more strongly associated with psychological well-being in general than visit frequency to “inland blue” environments. Our study adds to these more general findings by showing that these associations may come as a direct result of the recalled well-being experienced on specific visits to these locations.Confidence in our results was strengthened as we included general mental well-being in our analysis to adjust for whether happier people tend to visit sandy beaches, for example. The results for visit anxiety were not always the inverse of the trends observed in the positive measures of well-being, supporting the need to look at multiple aspects of mental well-being when considering the effects of nature contact.Research question 1b—Which bluespace qualities were associated with the highest levels of recalled visit mental well-being?Of the range of qualities that we investigated as predictors, perceived safety and ‘excellent’ water quality (vs. ‘sufficient’) consistently exhibited the strongest relationships with subjective mental well-being. Perceived safety has been found to be important when visiting blue spaces in several qualitative studies29,30,31, as well as a quantitative study with older adults in Hong Kong14. Blue spaces have particular safety issues with respect to drowning32,33, but fear of crime29,30,33 or pedestrian safety34 may also be relevant.Water quality has also been found to be important in previous economic valuation studies of recreational use and enjoyment of lakes and estuaries in the USA and Australia35,36 as well as a contingent behaviour experiment carried out as part of the BlueHealth International Survey (in European countries only)37. We recognise that here we used a metric of perceived water quality, rather than measures based on biological or toxicological sampling. Nevertheless, perceptions have been reported to positively correlate with sampled water quality parameters38, although assessments can vary systematically such as by bluespace type39. Highly visible harmful algal blooms, for instance, have also been found to affect experiences of blue spaces40.Further, and again consistent with earlier work15,41,42, the presence of facilities and wildlife, and absence of litter, were generally associated with better subjective mental well-being. Both perceived presence of wildlife and facilities were also associated with higher levels of anxiety however, indicating complexities between environmental qualities and well-being. Some wildlife may be deemed unpleasant or an ecosystem disservice, for example. The presence of good facilities may indicate the presence of more people; and visitor density in natural environments can be related to preference43. These results highlight the importance of environmental quality and not just type, consistent with other frameworks12,37.Research question 2—How is exposure, as operationalised by visit duration, related to recalled visit mental well-being?Broadly consistent with research in the green and bluespace literature14,17,44, we found that mental well-being outcomes were generally higher with greater exposure as indicated by visit duration. For decreasing visit anxiety, this was only significant when visits were longer than an hour and a half. As we did not measure pre-visit anxiety levels, we are cautious about identifying this as a potential temporal threshold for reducing anxiety at this stage.Similarly, also using the BlueHealth International Survey, White et al.28 found that well-being outcomes were higher with greater visit exposure to green and blue spaces using a metric of visit frequency. However, in contrast to this and other research which looked at overall weekly aggregated time in nature (e.g.28,45), we have no evidence of diminishing marginal returns as the effect sizes associated with specific visit duration continued to increase with increasing duration.Research question 3—What experiences in blue spaces, in terms of activities (3a) and companions (3b), are associated with the most positive recalled visit mental well-being outcomes?Although walking was the most popular activity, the activity with the highest mental well-being ratings was playing with children, especially in certain locations such as beaches (Fig. 4). However, we also find that anxiety tended to be higher when children were present. We speculate that the purpose of the visit may be important. For example, many who go to the beach with children do so in order to play. However, if children are present on more adult-oriented activities such as hiking, this may increase adult anxiety during the visit. From a representative sample of English adults, White et al.17 found that recent nature visits with children were associated with the lowest levels of well-being. Therefore, visits with children may be associated with a more complex set of emotions, being both slightly more stressful, but also potentially more rewarding and ‘meaningful’46. Ecosystem features of beaches may be particularly supportive of high well-being activities. A qualitative study in the UK, for instance, highlighted the particular opportunities for adults and children to play together at the beach, including rock-pooling and making sandcastles as well as water-based activities47.Visits with other adults were associated with higher levels of both visit satisfaction and worthwhile-ness, and socialising as an activity was associated with better visit well-being for all outcomes compared to the grand mean. This is consistent with studies using the day reconstruction method, which link activities with experiential well-being, in the USA48 and Germany49 where socialising was associated with the highest, or second highest, levels of well-being for all the activities assessed. Further, social interactions have been recognised as an important benefit by many of those visiting freshwater blue spaces in a previous study18.Research question 4—Does the relationship between wildlife presence and recalled visit well-being vary by bluespace settings?The relationship between the presence of wildlife and visit satisfaction varied with bluespace type. The strongest positive association was found for fen, marsh and bog areas, which may also be related to the purpose of visit. For instance, those who visit places such as fens, marshes and bogs, may do so for the explicit purpose of observing wildlife (often birds) and the presence of wildlife would therefore be important for satisfaction with the visit.Perceptions towards wildlife have been found to vary by location in other studies. For example, in Sweden, greater prior experience with geese at beaches was associated with a negative attitude towards geese50. Further, the species present are likely to vary across different environments. In three urban areas in the UK, green spaces correlated with the abundance and species richness of birds considered to provide cultural services (songbirds and woodpeckers), while an abundance of birds considered to provide disservices (e.g. some gull species, feral pigeons) was independent of green spaces51. Preferences for some species over others may explain some of the negative or null relationships between the presence of wildlife at different blue spaces. These examples from the literature, alongside our own results, indicate the potential for benefits from the management of wildlife for psychological ecosystem services differentially across environments, although these should be considered alongside other conservation and ESS goals.MechanismsSeveral mechanisms potentially explain the beneficial effects of visiting blue spaces on mental health and well-being12, including the provision of opportunities for physical activity52,53; social interaction18; cognitive restoration and stress reduction17,54; emotion regulation55 and connecting with nature12. Consistent with these mechanisms, we found that respondents were using blue spaces for both physical activity and social interaction; and that playing with children and socialising were associated with particularly high levels of well-being.In addition to the positive association we find between some ESS and well-being, including presence of wildlife and water quality, additional bluespace ESS not considered here, may also affect mental health and well-being12. For example, the provision of a cooling effect56 and air pollution mitigation57.Strengths and limitationsA key strength is our operationalisation of the Bratman et al.9 conceptual model for mental health using data from a large, 18 country survey that included 17 different bluespace types, five quality metrics and four subjective mental well-being outcomes. The relatively high explanatory power of our models suggests all the variables we explored were important for subjective well-being.Despite the strengths, however, there were also several limitations. The survey was cross-sectional and causality cannot be inferred. For example, happier people may choose to visit a beach rather than another location, although we also controlled for general levels of subjective mental well-being in an attempt to control for this possibility (See Supplemental Materials). Further, although the majority of respondents (53%) recalled a visit within the last 7 days, some were recalling visits up to a month ago, with potential memory biases increasing in line with length of recall.Although our data were collected by an international market research company to be representative of age, gender and region within country, our online sample may not be fully representative across more characteristics and any country-level conclusions need to be treated with caution. We also acknowledge that there were no results from Africa, the Middle East or South America; and Hong Kong was the only representative from Asia. This suggests far more research is needed in other regions to better understand how bluespace ecosystems interact with subjective well-being globally.There may also be socioeconomic confounds that we did not include in our models which may account for some of the effects. Not everyone visits nature for recreation58, including about 4000 people here who did not visit a bluespace in the four weeks prior to responding to the survey. Some groups may therefore have been under-represented; and we should be careful in assuming that our findings generalise to all sub-population groups.Nevertheless, our visit sub-sample distributions were generally similar to that of the weighted percentages in the full sample, with the exception of age where those aged over 60 were under-represented (Table S2); therefore, we suspect these issues were not too influential for the overall results, although care needs to be extended to inferences with respect to older adults.A further limitation was that we only considered the qualities of places where people reported making recreational visits, with respondents presumably less likely to visit places where they feel really unsafe or lacking in facilities29. Further research may want to study responses to a broader range of bluespace settings, including those that are less visited, to determine the generalisability of the generally positive results found here. Such studies could use pre-existing tools to objectively assess the quality of blue spaces59.ImplicationsOur finding that coastal environments are particularly beneficial adds to the body of evidence linking coastal environments with health and well-being and suggests this is consistent across many countries. Previous research has found that greater proximity to blue spaces, especially coastal settings, predicts visit frequency14,60,61 as well as other health outcomes—e.g. reduced risk of mortality and better general health, well-being and physical activity53,62. Here, we found that shorter travel times also predict visit well-being, highlighting the importance of having equitable access to good quality natural environments near to people’s homes.We also identified that different types of coastal and inland blue spaces (e.g. seaside promande, rural rivers), with different qualities (e.g. wildlife present), involving particular types of activities in specific social configurations (e.g. playing with children), were especially good at promoting well-being. This moves beyond a simple location-based assessment of benefit to one that recognises the complex interplay between location, behavioural and social processes. Numerous commentators63 (including Bratman et al.9 on which we have based this paper) have argued that we need to go beyond the determinate effects of green and blue spaces and develop a far richer, more nuanced understanding. The approach we have taken here is intended as a step in this direction.In terms of policy applications, these results provide support for the potential health benefits of efforts to improve equitable access to high quality environments, such as the English Coast Path (https://englandcoastpath.co.uk/) and the creation of beaches in Barcelona with the Olympic project in 199264. Our results also hint at the importance of high-level legislation, such as the EU’s Bathing Waters Directive65 for mental well-being37. If conducted on a more fine-grained geographical level, results could have the potential to leverage public support for more localised conservation initiatives. Furthermore, such results could be used as a basis for integration into more systematic conservation planning66.Further researchAlthough we incorporate a range of variables in our analysis, and our pseudo-R2 values are relatively high for a social research context, considerable variation remains unexplained. Although other individual characteristics may be important, such as nature connectedness67 and memories68, further research could explore the specific ecosystem features and social contexts associated with the particular positive results from coastal spaces, which would be of interest to policy makers and environmental managers. We also speculated that purpose of visit may explain some of our findings. Further research could explore the interactions between motivations and location, experience, and well-being outcomes.The presence of wildlife was differentially important across bluespace types and further research could unpack this. Exploring similar possibilities for the other quality metrics, as well as considering additional ecosystem characteristics, would also be informative. For example, identifying which factors are important in perceptions of safety in blue spaces. Bratman et al.9 also considered effect modification by visitor characteristics and further research could include interactions, or sub-group analysis, by socio-demographic factors.Further research could also explore longer-term benefits of these features over repeated visits; the potential for ecosystem disservices, such as the relationships we find between an interaction of wildlife and ice rinks and well-being; the potential for negative outcomes associated with ecosystem degradation69; and the potential for positive mental health outcomes from ecological restoration70.We have demonstrated some of the complexities involved in the human-nature relationship and that many factors are related to the outcome from a visit. The conceptual model applied allows the investigation of a wide range of variables including natural features and other environmental qualities, and characteristics of the exposure and experience, as well as individual parameters. We suggest that other researchers can apply this conceptual model and design data collection accordingly to target specific research questions and hypotheses (as opposed to where we have fitted already collected data). More