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Leaf profitability
We calculated the economic profitability of leaves and analysed the patterns in relation to leaf size, environment and durability (longevity). There was wide variation in leaf profitability with a mean value (± SD) of 3.4 ± 3.5% day−1 and 5th/95th percentile range of 0.29–10.3% day−1. There are no previous studies reporting leaf profitability values for direct comparison, but they can be re-calculated based on published values for leaf payback time. For example, Williams et al.21 reported payback time values in several Piper species from a Mexican rainforest, corresponding to leaf profitability values between 0.01 and 33% day−1. Poorter et al.23 calculated payback times corresponding to leaf profitability values of 1.25–50% day−1 , depending on species type, light environment, and growth conditions (very high values were observed for seedlings grown under non-limiting, hydroponic conditions). The mean values of our study are lower, but in line with values calculated from payback time of Kikuzawa and Lechowicz25 (2.2 ± 2% day−1), because they consider the mean labour time and the favourable period length, as we also applied in our calculations (see “Methods” and Supplementary File S2 online).
One factor that could influence profitability is the size of a production unit. For example, a large leaf may imply a higher cost required for structural support; therefore, the changes in profitability will depend on how gains and expenses vary with size. As it turned out, we found that leaf profitability was positively related to leaf size (Fig. 2A), but the percentage of variance explained was not especially high (R2 = 0.07, P  More

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Impact of glacial retreat on the benthic ecosystem
Most enriched POM δ13C concentration in the inner cove location (B2) indicates a potential melt-water input near the glacier (Table S2). The δ13C signature of diatoms showed a similar spatial concentration gradient along the cove, but was slightly more enriched than POM δ13C. This signature of freshwater influence has also been detected in other Antarctic regions. For example, the enriched δ13C of POM and diatoms in Potter Cove was recently reported16. In the enclosed environment beneath glaciers, δ13C might be enriched due to increased HCO3− utilization and production of organic materials17. The POM and diatom δ15N concentrations showed the lack of parallel gradients over the study area. The POM δ15N, especially phytoplankton values, is affected by their nutrient sources. Snow melt-water input occasionally appears from the local creeks throughout the Marian Cove, and the melt-water is associated with the nutrient input as well. Thus, the POM and diatom δ15N concentrations seemed to reflect the melt-water input throughout the cove.
The coastline of the inner locations (B1–B2;  More

Nutrient, FOM, and physiochemical contents of the fecal manures
For the three main fertility nutrients (N, P and K) in the fecal manures (Table 1a) the E manure contained the highest N level at 80 µg/g, 16-fold more than the other samples. In addition, it had a high P level of 100 µg/g (along with PC1 and PC3) compared with an average of 34.66 µg/g for the others, and a moderate K level of 150 µg/g (others ranged from 125.00 to 350.00 µg/g. The FOM content ranged from 1.00 to 1.50% for all the animal manures (Table 1a). For the general physiochemical properties, chicken manures showed a relatively high salinity (Table 1b: 3.52 ± 0.28 to 4.01 ± 0.64 ppt), and these ionic salts caused a high conductivity (11.27 ± 0.69 to 12.79 ± 0.08 mS/cm) and high water saturation (73.29 ± 0.40 to 80.56 ± 3.17 mS/cm), giving less available water for plant roots. A moderate level of salinity, conductivity and water content, such as those in E, are generally preferred for farming soil. The water content of the manure was lowest in G2, goats fed with P. purpureum (23.75 ± 3.07%), but increased two-fold in G1 goats fed with D. eriantha (49.35 ± 6.08%), suggesting a different type of grass feed might confer different fecal liquid and dry contents, or the chemical composition influenced the water activity in the goat’s gut. The R manure also had a low water content (41.53 ± 5.57%).
Table 1 Characteristics of (a) NPK and FOM, and (b) physiochemical levels of the different animal fecal manures.
Full size table

The visual observation of each manure was in accord with the reported water content, with a dry and hard texture (insufficient moisture) for G1, G2, and R manures, and a wet texture for the chicken, cattle, pig, and buffalo manures. Too much water in the soil can adversely affect aeration, where the plant’s roots receive insufficient oxygen and rot. Moreover, most plants prefer a slightly acidic soil pH of 6.2–6.8, which was found in the R, MB and E composts (Table 1b). The manure pH can change through reactions such as organic matter decomposition that affects the availabilities of NPK39.
From the overall comparison of the NPK nutrients and physiochemical properties, the fecal manures from the Phuparn chicken species might be less suitable as a fertilizer because of the poor N level, and high level of salts and conductivity. Indeed, the manures from most species contained a low N level, and those from goat and rabbit also had a relatively low water content (insufficient water for a plant to absorb nutrients from the soil through the roots, to the trunk, leaves and fruits). A moderate water content in the manure was appropriate. But of course, a somewhat low water level could be adjusted by adding water. The biophysiochemical quality analyses of the manures suggested that the E manure would be appropriate as a fertilizer because of the enhanced NPK levels, low salinity, low conductivity, slightly acidic-neutral pH, and a moderate water content (Table 1b).
Quantification of total bacteria in the fecal manures
The total number of bacteria in copies/g DW fecal manure was derived from the 16S rRNA gene qPCR. Figure 1a showed the data were consistent between independent triplicate samples, and that the E and R manures had the lowest bacterial counts (at 2.1 × 109 and 2.5 × 109 copies/g DW, respectively). The other animal fecal manures contained more than 5 × 109 copies/g DW, and the greatest bacterial load was found in PC2 and MB at 2.6 × 1010 and 1.9 × 1010 copies/g DW, respectively, while PC1 and PC3 had a significantly lower level (p  98% of the sequencing coverage of taxonomic compositions at the genus and species levels, respectively (Table 2). The average Good’s coverage indices were 99.54% and 99.43% for the genus and species levels, respectively (Supplemental Table 1). This was consistent with the plateau rarefaction curves, showing the frequencies of OTUs become constant despite increasing sequencing reads, meaning a sufficient sequencing coverage was obtained (Supplemental Fig. 1).
Table 2 Good’s coverage indices (estimated sequencing coverage) and alpha diversity indices of bacterial taxonomic profiles by 16S rRNA gene sequences at the (a) genus and (b) species levels.
Full size table

Combining the count of total 16S rRNA gene copies with the OTU percent compositions gave the quantitative number of copies of each OTU in a community. These quantitative microbiota data were then used to compute the alpha and beta diversity measurements. The alpha diversity revealed that the microbiota of E had the relatively most diverse OTUs at both the genus and species levels (Chao richness, Fig. 2a,c), regardless of having the lowest total bacterial count (Fig. 1). The low total bacterial abundance but high diversity in E underlined that the various OTUs of bacteria in E might be present in small numbers when compared to the copy numbers of OTUs in the other animal manures. Note that the alpha diversity obtained by considering the distribution of general OTUs among the different animal manures were similar (Shannon diversity, Fig. 2b,d).
Figure 2

Alpha diversity measurements of OTU compositions at (a,b) genus and (c,d) species levels, by richness (Chao) and evenness (Shannon). Box plot with bar representing the mean from three sequencing replicates, and asterisk (*) indicates a statistically significant difference by one-way ANOVA at p  1% abundance). In (b), the OTUs where Mothur could not identify the genus name were denoted by small letters (p_ abbreviates phylum; o_, order; and f_, family) to the deepest taxonomic names that could be identified.

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Relative frequencies of plant symbiotic and pathogenic bacterial genera
Plant symbiosis and pathogenic bacteria were analysed across the different animal manures. Manures PC1-3, G1, G2 and CC demonstrated generally abundant symbiotic bacteria comprised of the genera Pseudomonas, Bacillus, Arthrobacter, Flavobacterium, Alcaligenes and Streptomyces (Fig. 4a). For examples, PC2 contained abundant Alcaligenes (1.16 × 1010 cells/g DW), Pseudomonas (3.35 × 109), Flavobacterium (2.82 × 108) and Arthrobacter (3.8 × 108). Streptomyces was only found in G1, G2 and E in moderate numbers (5.9–29.0 × 106 cells/g DW).
Figure 4

Comparative number of bacterial genera categorized as plant (a) symbiotic or (b) pathogenic genera across the different animal manures. Data represented mean ± S.D. List of bacteria categorized as plant symbionts and pathogens were downloaded from the Virulence Factor Database (VFDB).

Full size image

For the plant pathogenic bacteria, high levels were found in the manures that also contained high levels of plant symbiosis bacteria (PC1-3, G1, G2 and CC), except for E, plus the other manures, such as S, D and MB. The E had none of VFDB-listed plant pathogens. Thus, all manures except for E contained plant pathogenic bacteria, and these were from Escherichia, Shigella, Enterococcus, Clostridium, Acinetobacter, Treponema, Staphylococcus and Bacteroides (Fig. 4b). This finding correlated with the percent abundance of genera (Fig. 3b) where, examples, Treponema were relatively high in PCO and PC3 at 2.03 × 108 and 1.31 × 108 cells/g DW, respectively. Escherichia were relatively low in R and DC at 1.15 × 105 and 2.06 × 105 cells/g DW, respectively. In contrast, only E did not contain any VFDB-listed plant pathogenic bacteria suggesting that E offers a plant pathogen-free organic fertilizer.
Relationship among bacterial communities, and statistical correlation with the biophysiochemical properties
The NMDS demonstrated both the reproducibility of the data between the independent triplicate samples, except for R and PCO. The larger variation in bacterial communities were found generally between the manures from different animal species, while the minor variation in bacterial communities were found between the breeds, or the feeding diets. For examples, the variation in quantitative microbiota profiles between G1, G2 and E, compared with PC1-3 (p = 0.085) and MB (p = 0.59) (Supplemental Fig. 2). Indeed, the quantitative microbiota structures belonging to the six animal manures that had all the VFDB’s categorized plant pathogens (PC, S, R, D and MB, except PCO) were rather distant from E (p = 0.101, 0.052, 0.089, 0.104 and 0.099, respectively).
Seven parameters (NPK and four physiochemical properties) were analyzed for possible Pearson’s correlation with any of the quantitative microbiota structures. The N level was strongly correlated (p = 0.01) to the structures and in the same direction of the E (Fig. 5). The percent water, salinity and conductivity characteristics were significant and associated in the direction opposite to E, and also to the G1, G2 and PCO microbiota structures. On the other hand, the PC1, PC2 and MB microbiota structures were strongly associated with the water content, salinity and conductivity. These parameters are suggested to be important in controlling the diversity of microbiota structures.
Figure 5

Relationship among bacterial communities via NMDS constructed from thetayc distance coefficients among quantitative microbiota (stress value = 0.15, R2 = 0.86), and Pearson’s correlation with nutrients and physiochemical properties (AMOVA, p  More

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