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This paper uses Landsat TM/OLI images from 1987, 1993, 1999, 2005, 2011 and 2017 by the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm extracting LULC data sets, and the spatiotemporal patterns of LULC over these periods were analysed. The spatiotemporal change patterns and driving force of ESV was estimated. The effect of LULC dynamics on the ESV was evaluated. The flow chart is shown in Fig. 1.
Figure 1

The framework of this paper.

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Study area
Guangzhou is located at 112° 57′ ~ 114° 3′ E, 22° 26′ ~ 23° 56′ N in the southeast part of Guangdong Province in the northern margin of the Pearl River Delta. It has a total land area of 7434.40 km2. The topography is high in the northeast and low in the southwest, with low mountains and hills in the north and plains in the south and a rich geomorphology. The level of urbanization is high, the land use structure is complex and the land use pattern is changing rapidly. Regional public infrastructure, commercial service land and external transportation land is increasing. Export-oriented industrial agglomeration areas are developing continuously, and the characteristics of export-oriented land use are obvious. Guangzhou has a subtropical oceanic monsoon climate, with an annual average temperature of 20–22 °C and an annual rainfall of about 1720 mm. Guangzhou includes eleven districts (Fig. 2a). This paper focuses on the main urban area of Guangzhou as the research object, a decision based on the city government’s overall urban development strategy for Guangzhou (2010–2020), the main urban area of Guangzhou includes five districts—Liwan, Yuexiu, Haizhu, Baiyun and Tianhe (Fig. 2a). In 2017, the per capita GDP will exceed 136,100 yuan for the first time (Fig. 2b). The population density is large, and the intensity of development is extraordinary. The population has increased from 2.73 million in 1987 to 8.05 million in 2017 (Fig. 2c). The industrial structure has been constantly optimized and improved. The proportion of secondary industries decreased from 31.46% in 1987 to 11.28% in 2017, while the proportion of tertiary industries increased from 61.9% in 1987 to 89.61% in 2017.
Figure 2

Location of study area and GDP and population. (a) location, (b) GDP, (c) population. (Software: Arc Map 10.5.0, http://www.esri.com. OriginPro 2017C SR2, https://www.originlab.com/).

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Data and data processing
Data
The data used include Landsat series images, an administrative zoning map of Guangzhou, a local historical land use map and social and economic statistics from 1987 to 2017. To explore the socio-economic and natural factors driving the ESV change for different land use types, we examined ten factors—the normalized vegetation index (NDVI), year-end population, elevation, slope, distance from roads, distance from railways, land cover types, GDP, secondary industry GDP and investment in fixed assets.
The Landsat images were taken from the United States Geological Survey website (http://glovis.usgs.gov/), and we downloaded cloud-free Landsat 5 TM and Landsat 8 OLI images (path 122, row 44). Images taken in the dry season (October, November and December) were selected because there was less cloud cover, the change in surface reflectance was much smaller than in other seasons and the image quality was higher. The spatial resolution of imaging is 30 m, and the data of Landsat 5 TM (8 December 1987, 24 December 1993, 25 December 1999, 23 November 2005, 19 November 2011) and Landsat 8 OLI (23 October 2017) were collected.
The elevation data are provided by the Japan Aerospace Exploration Agency (http://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm); the horizontal resolution is 30 m, and the elevation accuracy is 5 m. The road and railway data are provided by Openstreetmap (download.geofabrik.de/), and the distance between roads and railways were calculated. The land use types are provided by Tsinghua University (http://data.ess.tsinghua.edu.cn/) with a resolution of 10 m. GDP, secondary industry GDP and investment in fixed assets were provided by Guangzhou Statistics Bureau28.
Considering the size of the study area and the resolution of the data, we used Fishnet in ArcGIS10.5 to establish a 500 × 500 m grid covering the study area. The values of ten factors were extracted to the grid centre points, and 3915 points were obtained.
Remote sensing extraction of LULC types
To obtain high-precision LULC data, the surface features based on the original remote sensing image were enhanced using the index model (the Index-based Built-up Index (IBI)29, the modified normalized difference water index (MNDWI)30 and the soil-adjusted vegetation index (SAVI)). The humidity, brightness and green chroma indices were transformed using the tasselled-cap method23. Using the six indices, six images were calculated and superposed into a new multi-band image in the order of IBI, SAVI, MNDWI, brightness, green chroma and humidity (referred to as the six-index image). This was superposed with the original image (band 1, 2, 3, 4, 5, 7) to create an enhanced 12-band image as the data source for LULC classification. The image stretching function was then used to stretch the composite image to better distinguish the characteristics of land use types. After feature enhancement, the LULC types in the main urban area of Guangzhou could be divided into seven types—forest, water body, grassland, cultivated land, high reflectivity building, low reflectivity building and bare land. Of these, high reflectance buildings and low reflectance buildings are sub-categories of the built-up area category. The spectrum of high reflectivity buildings is similar to that of bare land; therefore, they are classified separately to avoid erroneous classification31. Based on Google Earth and TM/OLI images, the interpretation marks of LULC types were established (Table 1). To obtain pure and representative samples, the K-means method of unsupervised classification was utilized to cluster the pixels of the ISM image into seven unlabelled classes and to eliminate abnormal clustering. We then selected uniform points and drew polygons on them. Each polygon contains more than 100 pixels, and each class contains between five and seven polygons. All polygons were saved in a shape file as a mask file, which is utilized to extract pure pixels as training samples.
Table 1 Interpretation mark of land cover types.
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Dou and Chen31 suggest using the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm for LULC classification. Compared with AdaBoost, it provides higher classification accuracy and stability. WV AdaBoost includes a C4.5 decision tree, a Naïve Bayes neural network and an artificial neural network. In this study, we use Naïve Bayes-based WV AdaBoost to classify LULC based on Landsat-enhanced images. To obtain a highly accurate LULC classification, it is necessary to post-process the image after classification, overlay the early land use map during the research period, check the incorrectly classified area, and filter and correct some segments. After image classification, the random selection of 200 pixels in each class was checked, and the correctness of the classification and the evaluated accuracy were confirmed.
The average classification accuracy of WV AdaBoost based on Naïve Bayes on the original image is 85.02%, and the kappa coefficient is 0.839. The WV AdaBoost algorithm is based on Naïve Bayes processes for the 12-band images of the new combination (enhanced 12-band image). The average classification accuracy is 88.86%, and the kappa coefficient is 0.871. The classification results still need to be strengthened by post-processing, which achieves good classification accuracy. The final average classification accuracy is 91.97%. The kappa coefficient is 0.907. These classification results agree with the ensuing analysis of LULC.
Methods
Based on Landsat TM/OLI data from 1987 to 2017, a transfer matrix, a land use change index, an ESV evaluation index, a sensitivity model (CS) and a geo-detector (P) were used to analyze the response of ESVs to LULC evolution.
Transfer matrix
The transfer matrix reflects the dynamic process information about mutual transformation LULC types at the beginning (T) and the end (T + 1) of a specified period of time in a certain region (Fig. 3). The general form is:

$${S}_{ij}=left[begin{array}{c}{s}_{11} {s}_{12}dots {s}_{1n}\ {s}_{21} {s}_{22}dots {s}_{2n}\ dots dots dots dots \ { s}_{n1} {s}_{n2}dots {s}_{nn}end{array}right]$$
(1)

where S stands for area, and i,j (i,j = 1,2,…, n) represents LULC types before and after the transfer.
Figure 3

Land use transfer process. T the beginning of land use types, T + 1 the end of land use types; A, B, C, D, E, F: different land use types.

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The LULC analyzing indices
(1)
Land use dynamics (RS)
Land use dynamics describe the rate and magnitude of LUCC. The general form is:

$$RS=frac{{U}_{b}-{U}_{a}}{{U}_{a}}times frac{1}{T}times 100mathrm{%},$$
(2)

where ({U}_{a}) is the area of a certain land class at the beginning (km2), ({U}_{b}) is the area of a certain land class at the end (km2), and T is the study period.

(2)
Spatial dynamics of land use (RSS)
Spatial dynamics of land use describe the degree of spatial change in a certain land use type. The general form is:

$$RSS=frac{{U}_{in}-{U}_{out}}{{U}_{a}}times frac{1}{T}times 100mathrm{%},$$
(3)

where ({U}_{in}) is the sum area of other types converted to this type in study period T, and ({U}_{out}) is the sum area of a certain type converted to other types in study period T. ({U}_{a}) is the area of a certain type at the beginning.

(3)
Land use change state index (PS)

The land use change state index represents the trend and state of LUCC. The general form is:

$$PS=frac{{U}_{in}-{U}_{out}}{{U}_{in}+{U}_{out}},$$
(4)

where ({U}_{in}) is the sum area of other types converted to this type in study period T, and ({U}_{out}) is the sum area of a certain type converted to other types in study period T.
Calculation of ecosystem service value
In this paper, the improved ecosystem services valuation method based on the unit area value equivalent factor proposed by Xie et al.2 is employed to evaluate ESV. Therefore, the LULC types of the main urban area of Guangzhou were associated to the corresponding representative biomes (Table 2). The most representative biomes used as a proxy for each LULC type are: (1) cropland for cultivated land, (2) tropical forest for forest, (3) grassland for grassland, and (4) water system and wetland for water body. (5) bare land for bare land.
Table 2 Parameters of ESV for different land use types in the main urban area of Guangzhou.
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The LULC types are not exactly identical with the representative biomes. For example, cultivated land in this study accounts areas used for paddy fields and dry land. Therefore, the ESV index of cultivated land is calculated by weighting the area ratio of paddy field and dry land in the statistical yearbook. The average value of the ESV index of the water system and wetland is adopted for water body. The ESV index of build-up area is 0. the ESV index of land use types is shown in Table 2. The value of the universal equivalent factor ESV (D value) of the improved ESV method is 340.65 thousand yuan/km2.
In this study, the ESV of each land use unit area is based on the research methods of Costanza3 and Xie et al.2. The calculation formula is:

$$ESV=sum {A}_{i}times {VC}_{i}$$
(5)

$${ESV}_{f}=sum {A}_{i}times {VC}_{fi},$$
(6)

where ESV is the total value (yuan) of ecosystem services, ({A}_{i}) (km2) is the area of class i, and VCi is the ESV coefficient (yuan/km· year) corresponding to class i. ESVf is the single ESV, and VCfi is the value coefficient of the single service function.
Sensitivity analysis model
The sensitivity model is used to calculate the response of ESV to the change of value coefficient (VC)14 by adjusting the 50% of the ESV coefficient of each land use type up and down and determining the change in ESV over time and the degree of dependence on the value coefficient. The calculation formula is as follows:

$$CS=frac{{(ESV}_{j}-{ESV}_{i})/{ESV}_{i}}{{(VC}_{jk}-{VC}_{ik})/{VC}_{ik}},$$
(7)

where ESV is the estimated ESV, VC is the value coefficient, i and j are the initial value and adjusted value (50% up and down adjustment) and K is the land use type. When CS ≥ 1, ESV is elastic relative to VC; when CS ≤ 1, ESV is inelastic. The larger the CS value is, the more critical the accuracy of the ESV index.
Grid analysis method
Grid analysis method is used to divided the study area into regular grid matrixes with the same size and no overlap, and takes grid as the research object to express and statistical unit in geospatial space9. It uses regular square grid as ESV’s spatial statistical unit instead of irregular land-use map spots to ensure the capacity invariance within the unit, which not only highlights the spatial distribution characteristics of ESV, but also facilitates the spatial quantitative statistics of ESV.
The premise of calculating ESV spatial differentiation is to determine the size of grid cell. In this study, based on ArcGIS10.5 software, the area of each land use type in four grid units with side length of 0.5 km, 1 km, 2 km and 3 km was extracted respectively, and then compare the degree of area change, namely the coefficient of variation. The grid of this scale is the optimal grid unit size for the spatial differentiation of ESV in the study area. Finally, the grid analysis method is introduced to construct a (0.5 × 0.5) km square grid as a geospatial statistical unit. By using the equal spacing system sampling method, the study area is divided into 2024 square grids that do not overlap each other (0.5 × 0.5) km, and the grid matrix is composed of these grids. Through the intersection operation of grid matrix and land use data of each research year, the area of various land use types in each grid is counted, and the spatial heterogeneity of land use types and ESV is analyzed.
Geo-detector
Combined with GIS spatial superposition technology and set theory, a statistical method proposed by Wang et al.32 can detect spatial heterogeneity and reveal the driving force to identify the interaction between multiple factors. This model is widely used to analyze the influence mechanism of social economic factors and natural environmental factors. The geo-detector consists of four detectors—a differentiation and factor detector, an interaction detector, a risk area detector and an ecological detector. In this paper, factor detection and interaction detection are utilized to detect and analyze the driving force of ESVs in the main urban area of Guangzhou.
(1) Factor detector: Factor detection can identify the explanatory power of each spatial driving factor in landscape type change, and its model is as follows:

$$P=1-frac{1}{{ndelta }^{2}}sum_{i=1}^{m}{n}_{i}{{delta }^{2}}_{i},$$
(8)

where P is the explanatory power index of ESV influencing factors; ni is the number of samples in the secondary area; n is the total number of samples; m is the number of samples in the secondary area; ({delta }^{2}) is the variance in land use type change in the whole area; and ({{delta }^{2}}_{i}) is the variance in land use type in the secondary area. Thus, the model is established, assuming ({{delta }^{2}}_{i}) ≠ 0.
The range of values for P is [0,1]. When P = 0, it shows that the spatial distribution of ESV changes is random. The larger the P value is, the greater the impact of longitudinal driving factors on ESV changes.
(2) Interactive detector: Interaction detection can be used to identify the interaction between different spatial drivers. When detecting the interaction of X1 and X2, the explanatory power of the dependent variable Y will increase or decrease. The evaluation method is used to calculate the q value of two factors, X1 and X2, for Y, respectively, q (X1) and q (X2); to calculate the q value of their interaction, q (X1 ∩ X2); and to compare q (X1), q (X2) and q (X1 ∩ X2). The five results of the interactive detector are given in Table 3.
Table 3 Types of interactions between two covariates.
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In this study, we used the black garden ant Lasius niger to investigate the benefits and factors of pleometrosis, the transitory association between founding queens. The monitoring of colonies founded by one or two queens showed that pleometrosis increased and accelerated offspring production. Then, the experimental pairing of L. niger founding queens revealed that in pairs of queens of different fecundity but similar size, the most fecund queen was more likely to survive. Our experiment could not detect a similar effect of size when controlling for fecundity. Finally, we found that queens associated preferentially with less fecund queens.
Our findings of pleometrosis benefiting offspring production are in line with the literature for this, and other ant species3,7,9,10,12,22,23. Interestingly, we only detected these benefits at the colony level, as pleometrosis had either no effect or a negative influence on the per capita offspring production9,12,22. However, colony-level measurements are more relevant in the case of pleometrosis, as the queen that survives the association inherits all the offspring produced during colony foundation. In the field, colonies with a faster, more efficient worker production would have a competitive advantage over neighbouring founding colonies3,4. This is especially true for L. niger, which shows high density of founding colonies that compete for limiting resources and raid the brood of other colonies10. Thus, the competitive advantage provided by pleometrosis likely enhances colony growth and survival.
The increased and faster production of workers in colonies with two queens may stem from a nutritional boost for the larvae. L. niger founding queens do not forage, and produce the first cohort of workers from their own metabolic reserves. Larvae have been observed to cannibalize both viable and non-viable (trophic) eggs24. We found that colonies with two queens produced more eggs, but that this did not translate in them having more larvae. However, more of these larvae became pupae—and ultimately workers. In addition, while the time to produce the first egg and larva did not differ between colonies with one and two queens, the first pupa and worker were produced faster when two queens were present, consistent with a shorter larval stage. We propose that larvae in pleometrotic colonies developed faster and were more likely to reach pupation because they had more eggs that provided nutrients, boosting the development rate of the first workers.
These benefits of pleometrosis are only inherited by the queens that survive, it is thus important to understand the factors that determine queen survival in pleometrotic associations. Although this question has been relatively well studied3,16,17,18,19,20,21, it has remained challenging to disentangle the effects of correlated factors. For example, we found that size, which has been reported to predict queen survival16,19, correlated with fecundity, which would itself be confounded with the parentage of workers in the first cohort produced. To address this issue, we disentangled size and fecundity experimentally, and used foreign workers that developed from pupae collected in field colonies to prevent any potential nepotistic behaviour.
We found that fecundity, but not size, determined queen survival. The finding that, despite being of similar size, more fecund queens are more likely to survive indicates that the outcome of pleometrosis is not the mere consequence of physical dominance. The higher fecundity could reflect a better health condition, which may give the advantage to the more fecund queen in direct fights3,15, or if workers initiate the fights. Natural selection may have favoured workers that skew aggression toward the less fecund queen, both because this queen would be less efficient at building a colony, and because the workers would be more likely to be the offspring of the more fecund queen. The latter would not necessarily involve direct nepotistic behaviours (the workers would not behave according to parentage, but to fecundity), which have remained elusive in social insects in general25,26,27, and in pleometrotic associations in particular16,17,20. Despite regular behavioural observations, we did not observe who initiated aggression in our experiments, and it remains unclear whether the queens and/or the workers are responsible for the onset of fights. Consistently with previous studies16,23, we found that a certain proportion of queen death occurred before worker emergence, suggesting that worker presence is not required for queen execution. Finally, we cannot rule out that the least fecund queens were more likely to die because of a weaker health status, possibly combined with the stress of being associated with another, healthier queen.
Although it has not been directly reported before, our finding that fecundity determines queen survival is consistent with previous reports of weight being associated with queen survival17, more fecund queens being more aggressive28, cuticular hydrocarbon profiles differing between surviving and culled queens21, and between more and less fecund queens28. We could not directly support previous reports of size correlating with survival16,19. This could be because in those studies, size could have been confounded with fecundity, and/or because we lacked the statistical power to detect such effect in our experiment.
Pleometrosis provides clear benefits, but these benefits are only inherited by the surviving queens, and the losing queens pay the great cost of dying without contributing to the next generation. Natural selection should thus favour queens that decide whether or not to join a pleometrotic association based on the relative benefits compared to individual foundation—these may differ across ecological contexts29—and the likelihood of surviving the association. As fecundity appears to determine queen survival in L. niger, queens may have evolved the ability to choose among potential partners according to their fecundity. Our results are consistent with this hypothesis, as queens preferentially associated with partners that would later produce fewer eggs, possibly because they were less fecund, and therefore less healthy and easier to eliminate. This suggests that founding queens may assess the fecundity of potential partners, possibly via their cuticular hydrocarbon profile28. This result further supports our finding that fecundity plays an important role in pleometrotic associations. It is important to note that this difference in egg production could have alternative explanations. First, it could stem from more fecund queens having no interest in forming an association because they are able to start a competitive colony alone. Second, it could be a consequence, rather than a cause, of the outcome of the choice experiment. We cannot rule out that entering an association with another queen and/or leaving this association prematurely at the end of the choice experiment may have been stressful for the chosen queens, and affected their later production of eggs. We could not detect any difference between chosen and not chosen queens in the number of larvae and pupae produced, which are likely influenced by factors other than fecundity (e.g., brood care behaviour). Interestingly, we did not find that queens chose according to size, consistent with our finding that size may not affect which queen survives the pleometrotic association.
Our study informs on the benefits and factors of pleometrosis, and highlights the role of fecundity in the decision to associate with another queen, and in determining which queen survives the association. As such, it contributes to a better understanding of the onset and outcome of pleometrosis, a classic case of cooperation between unrelated animals. More

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Distribution of Stentor pyriformis in Japan and its optimal culture conditions
S. pyriformis was described by Johnson in 18936. This algae-bearing Stentor has separated spherical macronuclei without pigmentation, which certainly differentiates it from other Stentor species (see Table S1, Fig. 5B). While the most common algae-bearing Stentor, S. polymorphus assumes a slender trumpet shape (often shortened), S. pyriformis never resembles such a slender trumpet, but assumes a pear or short conical shape, even when it is swimming6. Presence or absence of colored pigmentation is also a prominent characteristic for separating Stentor species. Among algae-bearing Stentor spp., S. polymorphus and S. pyriformis only are considered colorless species, whereas colored species are S. amethystinus, S. fuliginosus, S. araucanus, and S. tartari8 (Table S1). Therefore, S. pyriformis is a clearly discernible species; however, it remains underexplored. Indeed, we could only find one paper on the new habitats of S. pyriformis7, with the exception of the paper of species consolidation of this genus8. We confirmed the presence of S. pyriformis at 23 locations (Fig. 1A). This indicates that S. pyriformis is by no means a rare organism. We assume one of the reasons why S. pyriformis has been poorly studied is the difficulty of cultivation. In fact, Johnson6 noted that he could not keep them more than a month and never observed any cells in fission. In addition, after five years of failure, it was finally possible to culture S. pyriformis for more than several months. Because of objectively unfounded data that we could not include in the distribution data (Fig. 1A), we noticed the wetlands where we found S. pyriformis were limited to small ponds or bogs locating near the mountain peak or along the ridge (Fig. 1B). That is, the ponds depending on rainfall without inflowing rivers. Because there is no nutrient flowing in, waters in these ponds showed noticeable oligotrophic tendency, i.e., extremely low electric conductivity (Fig. 1A), which gave us some clues on culture.
The most important point of culture for S. pyriformis was keeping the medium lower electric conductivity. We use the KCM medium diluted by 2% with Milli-Q water, and changed medium once a week. A non-photosynthetic cryptophyte, Chilomonas paramecium was selected for food. We selected the food so that it would not itself grow in the culture medium. Growing organisms, like photosynthetic algae, seemed to cause damage to S. pyriformis. Using this culture method, S. pyriformis can be maintained for more than four years (see Table 1). For the organisms not easy to grow in culture, Professor Michael Melkonian mentioned no protist is ‘uncultivable’, there is just human failure30. Here, it just became possible to culture S. pyriformis 120 years after its discovery; however, this method does not always work. S. pyriformis appears to be extremely fragile and disintegrates when any variables are unintentionally altered, that is, the culture is still unstable. When its condition deteriorates, the cells divide unevenly in such a way that a part of the cell is broken. When this happens, the cells become spherical, and the drug drops to the bottom of the dish. It retains this shape for more than a month, but eventually disappears. The doubling time of S. pyriformis remains 3 to 4 weeks, even under favorable conditions (data not shown). We occasionally encountered the blooming of S. pyriformis all over the bottom of the ponds (Fig. 1C). S. pyriformis, therefore, does not seem to be a particularly slow growing species, but our culture method appears to be far from the optimal culture conditions for them. Three S. pyriformis strains used in this study are available from the authors upon request.
Ultrastructure
In this study, we compared the conventional chemical fixation method with the rapid-freezing fixation method for electron microscopic observation. As a result, large vacuoles were observed in the cytoplasm when chemical fixation was used, but not by rapid freezing. Instead, many multi-vesicular bodies were observed in the cytoplasm. The quick-freezing and freeze-substitution method is considered superior in that it can prevent deformation of the intracellular structure compared to chemical fixation31. Therefore, it is possible that the originally existing multi-vesicular bodies were artificially disintegrated by chemical fixation, and the constituent biological membranes fused together, eventually forming large vacuoles. To the authors’ knowledge, no intracellular structure similar to the multi-vesicular body in S. pyriformis has been reported in protists. As multi-vesicular bodies of S. pyriformis could only be observed using the freeze-substitution method, similar granules may also be found in other protists if the same technique is used for electron microscopy. In animals, on the other hand, aggregates of secretory vesicles resembling the multi-vesicular bodies of S. pyriformis are present in cardiac telocytes32. The extracellular vesicles form multi-vesicular structures of about 1 μm in diameter and contain materials for intercellular communication that are involved in cardiac physiology and regeneration. Because S. pyriformis cells often form aggregates at the bottom of the pond, some chemicals may be released from the multi-vesicular body, attracting nearby cells and forming aggregates.
Observation by the freeze-substitution method revealed that the symbiosome membrane was in close contact with the symbiotic chlorella. Furthermore, fluffy projections were observed on the cell wall of the symbiotic chlorella. These characteristics were consistent with those of C. variabilis, which is symbiotic in the cells of P. bursaria9. The only difference was that in S. pyriformis, the symbiotic chlorella cells were scattered in the cytoplasm, whereas the symbiotic Chlorella in P. bursaria were anchored directly below the cell surface.
Storage granules
The iodine in Lugol’s solution selectively binds to α-1, 4-linked glucose found in polysaccharides, such as starch33 and glycogen34. The color stained with Lugol’s solution reflects the type of glucose polymer. Starches with high amylose content stain blue-violet (cf. Fig. 4B), high amylopectin stains red–purple, and glycogen stains reddish brown (Table 2). The granules in the cytoplasm of S. pyriformis stained reddish brown with Lugol’s solution (Fig. 4A), suggesting that these granules are composed of α-1,4-linked glucans with high number of α-1,6-linked branch points, either amylopectin-rich starch or glycogen. The pyrenoid of Chlorella spp. is surrounded by a starch sheath of two large plates35. As shown in Fig. 4F,G, the image contrast formed by electron staining of the starch granule in the chloroplast (arrow) was lost by treatment with Lugol’s solution. Although the detailed mechanism is unknown, this observation suggests that electron-stained heavy metals (osmium, lead, and lanthanoid ions) bound to the granules may have been eliminated by iodine in Lugol’s solution. The cytoplasmic granules of S. pyriformis showed the same staining properties as the starch granules in the chloroplasts of symbiotic chlorella, suggesting that both types of granules share chemical characteristics as polysaccharides.
Alveolates make up one of the most diverse and largest groups of protists. They include three major taxa: dinoflagellates, ciliates, and apicomplexan protozoa. All three alveolate lineages store glucose in an α-1,4-linked glucose chain with α-1, 6 branches. Ciliates are known to synthesize glycogen granules. For example, Tetrahymena has glycogen granules between 35 and 40 nm in diameter, each granule being a collection of small γ-granules of 2–3 nm in size36. Dinoflagellates and apicomplexans typically produce more complex and larger spherical starch particles, usually greater than 1 μm in size37,38. Amylopectin-rich starch and glycogen are very similar polysaccharides, but they differ in granule size and birefringence (Table 2). Starch granules are large, birefringent, and have a high refractive index, but glycogen does not exhibit birefringence, and its granules generally have a size of 300 nm or less. When observed with a polarizing microscope, the starch granules show a Maltese cross pattern. This pattern is derived from the radial arrangement of amylose and amylopectin molecules in granules and is one of the criteria for starch identification. Since the cytoplasmic granules of S. pyriformis are large in size (1–3 μm) and show a typical Maltese cross pattern as shown in Fig. 4E, these granules are likely to be starch granules rich in amylopectin.
Phylogeny of S. pyriformis and its morphology
Relationships of Stentor species were not clearly resolved. BI and ML analyses indicated basal diverging of the S. pyriformis + S. amethystinus clade from others, but NJ analysis did not indicate so (Fig. 5). Recent phylogenetic analyses inclusive of Stentor species also indicated basal diverging of S. amethystinus from the others; however, the monophyly of the others is not highly supported21,22. Therefore, the one thing that can be said is that S. pyriformis is closely related to S. amethystinus.
For the identification of Stentor species, the shape of macronucleus, presence or absence of cortical pigmentation, and symbiotic algae are very important and iconic characteristics8,19. S. pyriformis and S. amethystinus share beaded macronuclei and the presence of symbiotic chlorella (Table S1, Fig. 5B). Pigmentation is present in S. amethystinus, but not in S. pyriformis. Pigmentation is a noticeable characteristic, which tinctures the whole body of Stentor cells. The pigment is thought to function as a defense against predators39. However, the kind of pigment compound depends on the species40, and the relationship between pigment possession and phylogeny is poor (Fig. 5). Of note, colorless vesicles exist in S. pyriformis (Fig. 2D). The short and fat shape is also a common characteristic for S. pyriformis and S. amethystinus, in this genus with many elongated trumpet shape species6,8.
Symbiotic algae in S. pyriformis
Algae-targeted PCR products from whole cells of S. pyriformis were sequenced directly, and clear peaks were obtained for each. This shows that all or nearly all of the algal symbionts in each Stentor cell are unified, regardless of samples under long-term culture or nature. In addition, all symbionts were closely related to C. variabilis (Fig. S3), which has been known as a representative symbiont of P. bursaria (Oligohymenophorea), the model organism of multi-algae retaining protists (MARP41) style symbioses. For the chlorellacean species, the diversity of ITS2 sequence comparisons has often been adopted. For two organisms to compare, ITS2 sequence differences (gaps are counted as a fifth character) usually fall either less than 2% for single species or more than 10% for different species42,43. This characteristic simply encourages a species concept. The ITS2 sequences of S. pyriformis algae differ only by one nucleotide site out of 248 sites from those of P. bursaria algae (Fig. 6A), which strongly suggests the symbiotic chlorella of S. pyriformis are also C. variabilis. Several Stentor species retain coccoid green algae8 (Table S1), but only three algal sequences have been published. Two algal sequences from S. polymorphus belonged to different clades from Chlorellaceae44,45. As for the other algal sequence of S. amethystinus, the symbiont may belong to Chlorellaceae46. This sequence (EF589816) is short (991 bp) and only covers a part of SSU rDNA; therefore, it was not included in our phylogenetic analyses (Fig. S3). The sequence differs from C. variabilis with 10 base changes and 3 indels, indicating that it is not C. variabilis.
Figure 6

Sequence differences of SSU, ITS1, 5.8S and ITS2 rRNA gene (without group I introns) among Chlorella variabilis. “PbS-gt” indicates Paramecium bursaria symbiont genotypes. Genotype 1 includes SAG 211-6, ATCC 50258 (NC64A), NIES-2541, and some other US and Japanese strains. Genotype 2 is the alga of Chinese P. bursaria strain Cs2, and genotype 3 is the alga of Australian P. bursaria strain MRBG1. For further information, see Hoshina et al.53. “SpS” indicates the algal sequence of Stentor pyriformis strains collected from Japan. (A). Different positions. Numerals represent the nucleotide number in aligned sequences (2462 aligned sites). (B). Distance tree of above four types of sequences. (C). E23_2 helix of SSU rRNA structure that includes hemi-CBC at the alignment position 656. (D). Deformation of ITS1 Helix 1 associated with the mutations including several nucleotide insertions.

Full size image

In the case of P. bursaria-C. variabilis symbiosis, C. variabilis has been shown to be vastly different from other free-living species. C. variabilis demands organic nitrogen compounds47 and leaks nearly half of the photosynthate to outside algal cells48,49. Furthermore, they are sensitive to the C. variabilis virus (CvV; so-called ‘NC64A virus’), which is abundant in natural freshwater50,51,52. Therefore, C. variabilis should be considered an already evolved species that is unable to survive without the protection of the host cell53.
Four C. variabilis rDNA sequences obtained from S. pyriformis were identical, with the exception of a nucleotide position in the S1512 intron. Here, the regions without group I introns, i.e., SSU, ITS1, 5.8S, and ITS2 rDNA, are compared among C. variabilis sequences of S. pyriformis and of P. bursaria. Several published sequences cover the above SSU-ITS region, of which varieties are shown as P. bursaria symbiont genotype (PbS-gt) 1 to 3 (Fig. 6A). Due to the small number of sequences, it is still unknown whether these genotypes depend on (or are related to) their living regions. Genotype 1 was from USA and Japan, genotype 2 was from China, and genotype 3 was from Australia. All available sequences for S. pyriformis symbionts were obtained in this study, and they were all from Japan. As a result, all sequences of S. pyriformis symbionts were aggregated into a single genotype SpS, which was distantly related to all P. bursaria symbionts, including those from Japan (Fig. 6B). Five variable sites are found in SSU rDNA among C. variabilis genotypes, of which four are concentrated to that of the symbionts of S. pyriformis (SpS) (Fig. 6A). C/T substitution at alignment position 656 will be a hemi-compensatory base change (hemi-CBC) at the E23_2 helix of SSU rRNA structure (Fig. 6C), whereas the other four sites are at single strand regions (data not shown). Mutations (1821–1828) including comparatively large indels were seen in ITS1 region (Fig. 6A). It was found that all these mutations are assembled in helix 1 (for chlorellacean ITS1 structure, see Bock et al.54,55). Thermodynamic analysis via Mfold56,57 predicted that PbS sequences form linear helix 1 similar to the other chlorellacean species, but SpS sequences including the additional nucleotides may form a dichotomous branching of helix 1 (Fig. 6D).
The group I introns inserted in SSU rDNA of S. pyriformis symbionts are identical to those of P. bursaria symbionts28,58 in terms of numbers (three introns) and insertion sites (S943, S1367 and S1512). The sequences of S943 and S1512 introns are matched more than 99%. However, with respect to the S1367 intron, a large length gap was found (168 nucleotides) at the tip of P8 (Fig. S4). This section has been indicated as a homing endonuclease gene remnant28, and those of S. pyriformis symbionts are presumed to be a more degenerated form than those of P. bursaria symbionts.
At any rate, the symbiotic algae of S. pyriformis were found to be C. variabilis. Because S. pyriformis never lost the symbiotic algae in four years of culture, and all four algae had nearly identical genetic characteristics, the symbiotic relationships between S. pyriformis and C. variabilis can be regarded as stable, or permanent. Although S. pyriformis and P. bursaria share C. variabilis as their endosymbionts, considering the genetic differences depending on their host species, the sharing event has not happened recently. Symbiont sharing among various host species has also been known for some ciliates41,59 (Carolibrandtia ciliaticola in Fig. S3), and a script to spread a particular algal symbiont has been suggested41. Given the physiological characters of C. variabilis (mentioned above), this algal species might be an ideal algal symbiont, and it will be no surprise if the other protists also retained C. variabilis as their algal partners. Research on the symbiotic algae that other Stentor spp. have and on host and regional dependencies are awaited.
Adaptation of S. pyriformis to oligotrophic environment in highland marsh
In Japan, S. pyriformis lives only in alpine ponds (Fig. 1), where the winter is cold, and the surface of the pond is always covered with ice. The water in these ponds has low electrical conductivity (~ 10 μS/cm), and there are few living organisms except S. pyriformis, meaning that only little food is available in wintertime. The reason this ciliate is rich in stored carbohydrate granules may be due to its need for nutrients to survive such harsh winter environments.
Preliminary studies suggest that many protists, especially ciliates, may make starch. Large amounts of cytoplasmic granules that show a Maltese cross were observed in chlorella-bearing ciliates such as P. bursaria, while only a small amount of such granules was observed in Euplotes aediculatus, Paramecium caudatum, Blepharisma japonicum, and Tetrahymena pyriformis. Protists with symbiotic algae seem to produce particularly large amounts of stored carbohydrate granules in the cytoplasm, but the mechanism of starch synthesis may be widely shared by ciliates.
P. bursaria has been shown to be more resistant to starvation conditions than the aposymbiotic strain of the same species13. Under food-deprived conditions, P. bursaria was interpreted to have survived by digesting symbiotic algae. Resting cyst formation and cannibalism are known as other strategies for protozoans to survive starvation conditions60. This study suggests that the use of carbohydrate granules stored in cells may be another possible strategy for ciliates to survive harsh environments such as highland oligotrophic bogs. More

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