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Article

Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China

by
Yang-Liang Gu
1,
Qi Huang
1,
Lei Xu
1,2,
Eric Zeus Rizo
3,
Miguel Alonso
4,
Henri J. Dumont
1,5 and
Bo-Ping Han
1,*
1
Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou 510632, China
2
South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China
3
Division of Biological Sciences, College of Arts and Sciences, University of the Philippines Visayas, Iloilo 063050, Philippines
4
Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
5
Department of Biology, Ghent University, B-9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Diversity 2021, 13(10), 502; https://doi.org/10.3390/d13100502
Submission received: 29 September 2021 / Revised: 11 October 2021 / Accepted: 13 October 2021 / Published: 17 October 2021
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Abstract

:
In deserts, pond cladocerans suffer harsh conditions like low and erratic rainfall, high evaporation, and highly variable salinity, and they have limited species richness. The limited species can take advantage of ephippia or resting eggs for being dispersed with winds in such habitats. Thus, environmental selection is assumed to play a major role in community assembly, especially at a fine spatial scale. Located in Inner Mongolia, the Ulan Buh desert has plenty of temporary water bodies and a few permanent lakes filled by groundwater. To determine species diversity and the role of environmental selection in community assembly in such a harsh environment, we sampled 37 sand ponds in June 2012. Fourteen species of Cladocera were found in total, including six pelagic species, eight littoral species, and two benthic species. These cladocerans were mainly temperate and cosmopolitan fauna. Our classification and regression tree model showed that conductivity, dissolved oxygen, and pH were the main factors correlated with species richness in the sand ponds. Spatial analysis using a PCNM model demonstrated a broad-scale spatial structure in the cladoceran communities. Conductivity was the most significant environmental variable explaining cladoceran community variation. Two species, Moina cf. brachiata and Ceriodaphnia reticulata occurred commonly, with an overlap at intermediate conductivity. Our results, therefore, support that environmental selection plays a major role in structuring cladoceran communities in deserts.

Graphical Abstract

1. Introduction

A metacommunity is defined as a set of local communities in which species may exchange through dispersal [1]. Concept and theory of metacommunities not only help to explain patterns of distribution and interaction of local communities, but also provide a realistic framework for understanding ecological processes at a regional scale.
Cladocerans disperse passively over long distances with wind, water birds, and similar media [2,3]. With their abundant propagules and large population size, cladocerans easily colonize new habitats [4]. In a local community, species richness and abundance are regulated by environmental conditions, biotic interactions, and dispersal [4,5]. Environmental processes usually dominate at the finest spatial scale (e.g., a single wetland), whereas dispersal and neutral processes contribute to beta diversity at broader scales [6]. The assembly of local cladoceran communities in a well-connected landscape is determined more by environmental or ecological than by spatial processes. Predation, conductivity, primary productivity, and pH are ecological factors directly affecting cladoceran community structure [5,7,8]. As cladocerans rapidly respond to environmental change, they are used as indicators of ecological change [4,9]. The concept of indicator species has been widely applied in niche analysis and assessment of habitat quality [10,11]. It has been developed to explore complex associations between species and habitats [12,13,14,15,16].
Desert ponds are ecologically distinct. In such ponds, cladocerans suffer harsh conditions, low and erratic rainfall, high evaporation, and high and variable salinity. A few well-adapted species thrive there. For example, in a saline lake (salinity 5.5%) studied by He [17], a single species of Moina dominated the cladoceran community. As sand ponds are filled by rainwater within a very short period of the year, desert cladocerans have rarely been investigated. As a result, spatial distribution and species diversity of desert cladocerans remain unclear. Systematic surveys in such habitats are required to explore regional endemicity and understand species diversity. For example, a new ctenopod, Diaphanosoma bopingi, was identified from Sahara [18]. Given the slow evolution of this group, this species was assumed to be the result of local speciation [18,19].
Ulan Buh desert is located near Bayan Nur city, Inner Mongolia, China, and has a mean annual precipitation of around 100 mm. In this desert, a number of temporary dune ponds occur, many of which have formed artificially in the last decade. Although individual dune ponds are physically isolated, strong winds, cattle, and birds visiting the area should promote cladoceran dispersal and colonization. Although the ponds are at slightly different altitudes within the desert, they can be influenced significantly by winds with very different densities. In the perspective of metacommunities, environmental selection could explain variation in the cladoceran community structure.
In the present study, we aimed to identify cladoceran species diversity and explored processes structuring the community mainly with the detailed investigation in 2012. Our study will greatly improve our understanding of species diversity and community assembly of Cladocera in the desert, and may have important guidance for species investigation and conservation of desert aquatic systems.

2. Materials and Methods

2.1. Study Object

Ulan Buh desert is located in the east of Inner Mongolia. It harbors a series of permanent lakes fed by groundwater and a large number of temporary ponds surrounding the permanent lakes. More ponds were artificially created beside roads connecting local villages. We took surveys from 2011 to 2013 but had enough samples only in 2012. We performed our sampling after a rainfall when many temporal ponds were filled. Cladocera in a few permanent lakes, more or less, could form a species pool for the temporary ponds in some cases. Winds, water birds, and local livestock are the potential medium for dispersal.
In 2012, we investigated 37 ponds and 4 permanent lakes in an area of 15 km (west to east) × 40 km (north to south) (Figure 1). Physical coordinates ranged from 40.4019° N to 40.5264° N latitude and 106.4817° E to 106.9269° E longitude. Altitude ranged from 1025 m to 1055 m.

2.2. Field Sampling and Laboratory Work

We sampled cladocerans with a dip plankton net (diameter of the net edge was 40 cm) with a mesh size of 120 μm, one sample was taken for each pond by horizontal tows 5 m over the pond surface area. Geographical coordinates and altitude data were recorded by a hand-held GPS device. Water chemistry at 0.5–1.0 m below the water surface was measured by a portable analyzer (YSI: PRO-PLUS), including water temperature (T), pH, dissolved oxygen (DO), and conductivity (Cond). Pond surface size (S), maximum depth (D), and vegetation coverage rate (V) were measured, fish and tadpole presence were recorded visually. Two states of water quality (N) were estimated de visu: “1” represents clean or high transparency, “2” represents turbid or low transparency.
Samples were preserved in formaldehyde with a final concentration of 5%. All samples were examined under a dissecting microscope (Olympus: SZXZ-ILLB) to determine the species diversity per sampling site. Species were identified using an optical microscope (Olympus U-LH100-3). Relative abundance was counted under an optical microscope using a Sedgewick Rafter counting chamber. At least 500 individuals or at least 50% volume of each sample were counted depending on the cladoceran abundance.

2.3. Statistical Analysis

Environmental variables included lake surface size (S), depth (D), conductivity (Cond), DO, pH, tadpole presence, fish presence, vegetation coverage (V in %), and water quality (N); geographic variables including latitude, longitude, and altitude. Salinity was represented as conductivity [20]. Two types of species matrices were built based on species presence/absence and relative abundance.
We applied a classification and regression tree model (CART) to determine which environmental variables correlated with species richness [21]. The species occurrence data were further used for species richness estimation. Chao 2 and Jackknife estimators were used, as these models are particularly suitable for small sample sizes [22]. Other estimators are presented as alternative results. Log transformation was used for all estimators so that the lower bound of the resulting interval is at least the number of observed species [23]. The lowest ratio of variance/estimator was used as the best estimation [24].
Redundancy analysis (RDA) was used to examine the variation of cladoceran community structure along environmental gradients. lake or pond size (S) was log-transformed (S ranges from 10 m2 to 4000 m2). All environmental variables were used in the RDA model to fit the species occurrence matrix and relative abundance matrix. Unadjusted R2 was calculated through “Ezekiel’s equation” to create an adjusted R2 (Peres-Neto et al., 2006). Variables with VIF (variance inflation factors) <20 were used as these variables are not significantly collinear with other variables. Based on the permutation test’s p-value and AIC value, the forward selection was performed to select significant variables (p < 0.05).
A PCNM method (principal coordinate of neighbor matrices) [25,26] was used to construct spatial structures that can represent all scales. The first PCNM variables (e.g., V1) describe broad-scale processes covering the whole research area, like strong dispersal. The last variables describe fine-scale processes, which cannot be related to environmental descriptors but community dynamics, such as neutral processes. All positive eigenvectors were selected to build a PCNM variable matrix. These PCNM variables were used as spatial explanatory variables in RDA analysis with species occurrence matrix and relative abundance matrix. Variance partitioning was performed to determine how much variation was explained by each of the explanatory variables.
Classification of habitat types was performed by k-means clustering for each variable independently, including pond size, depth, conductivity, DO, pH, and vegetation coverage. The number of habitat groups was decided by using the maximal “ssi” value. The habitat types for predation were grouped into two types: presence and absence. As more species combinations create too many possibilities, only one species and two species combinations were used as species indicators. “Indval” index was calculated for the association between single species or two species combination and associated habitat types. The significance of maximum “Indval” index was tested using a permutation test (per = 999). “Indval” index contains two components: specificity and fidelity. High specificity indicates species associated with specific habitats, and high fidelity reflects a high probability of seeing a species in a specific kind of habitat.
All statistical analyses were done using Rstudio (Version 1.0.136), and the packages “indicspecies”, “rpart”, “SoDA”, “SpadeR”, and “vegan” [27,28,29,30].

3. Results

3.1. Environmental Variables and Spatial Landscape

The size of the ponds varied from 10 to 4000 m2, but only two ponds had an area of more than 1000 m2, and half of the ponds (56%) were smaller than 100 m2. Pond depth ranged from 0.3 to 3 m, with a mean of 1.01 m. Conductivity was high, from 1404 μS/cm to 12,619 μS/cm, with mean of 4243 μS/cm. pH values ranged from 7.98 and 10.06. DO was between 2.92 to 15.0 mg/L, with an average 9.76 μg/L. Tadpoles occurred in 15 ponds, and small fishes were only observed in 2 ponds. Except for one pond, aquatic plants were abundant, with an average cover 71%. Among 37 investigated ponds, 15 ponds were high-transparency or clean-water.
Altitude has a negative correlation with latitude (R2 = 0.34, p < 0.001) and a positive correlation with longitude (R2 = 0.51, p < 0.001). Thus, eastern ponds (23) were located at a higher altitude than western ponds (14) (Table 1).
The two groups significantly differed in altitude and conductivity. Eastern ponds had higher conductivity. Even though a smaller number of ponds are located in the western region, it contained more species. Moina cf. brachiata was the most common species in the ponds, but it was only found in the western group.

3.2. Species Diversity

A total of 14 Cladocera species were identified, including 5 pelagic, 8 littoral, and 1 benthic species. They represent five families, viz. Daphniidae (6), Chydoridae (5), Sididae (1), Macrothrichidae (1), and Moinidae (1). Five species had occurrence frequency above 20%: Ceriodaphnia reticulata (84%), Coronatella rectangula (84%), Daphnia longispina (38%), Scapholeberis smirnovi (35%), and Daphnia magna (24%). Diaphanosoma mongolianum, Macrothrix spinosa and Pleuroxus aduncus occurred only in one pond. Oxyurella tenuicaudis, Alona guttata, and Simocephalus exspinosus were also common (occurrence frequency >10%). C. reticulata, D. longispina and M. cf brachiata were dominant (more than 90% in relative abundance) in pelagic habitats while C. rectangula and dominated in littoral habitats (more than 50%) (See Figure 2).
Cladoceran species richness for each pond varied from 0 to 7. One of the ponds did not have any cladocerans. On average, a pond had four species. The classification and regression tree model (CART) showed that pH, DO, and conductivity were the main variables affecting cladoceran richness (Figure 3). Three ponds with the highest species richness had a high pH value (≥9.73). When pH < 9.725, ponds were classified into two groups according to DO ≥ 9.345 mg/L.
Eight estimators of species richness were applied to the incidence data (Table 2). The homogeneous model was the lowest, with 13 species (14 species were actually observed). The second-order Jackknife model produced a high estimator of 19 species, similar to Chao2 (Chao, 1987) (18 species). The Chao2 model (Chao, 1987) offered the largest 95% confidence interval: 14.480 to 53.842 species. Most estimators recovered a larger prediction than the actual observation. The 1st order Jackknife estimator showed the lowest variance/estimator: 17 species at least and 26 species at most should live in the study area.

3.3. Species Association and Coexistence

Among all 105 combinations of species, 16 showed significant associations with corresponding pond groups. These contained 6 species and 10 species combinations (Table 3). M. cf brachiata occurred in ponds with high conductivity (6630–12619 μS/cm), while C. reticulata only occurred in the ponds with lower conductivity (≤7430 μS/cm). The two species coexisted in ponds with conductivity from 6630 to 7430 μS/cm. As for species combinations, C. rectangula + M. cf brachiata are significantly associated with tadpole presence (p = 0.040), while C. rectangula + D. longispina are associated with tadpole absence (p = 0.033). Two species of Daphnia coexist in two neighboring ponds, while D. magna was dominant in a 400 m2 pond with conductivity 2943 μS/cm. D. longispina built up a dominant population in a 40 m2 pond with a conductivity of 1996 μS/cm.

3.4. Variation of Cladoceran Communities along Environmental Gradients

For the incidence data, all environmental variables together explained 12.8% of the total variance (Figure 4). The first two constrained axes explained 5.5% and 2.4% of total variance. The first constrained axis was mainly contributed to by conductivity, fish, and tadpole presence, pond size, pH, DO, and vegetation coverage. Occurrence of M. cf brachiata positively correlated with conductivity, while D. longispina negatively correlated with it; the occurrence of S. smirnovi and C. reticulata positively correlated to DO and vegetation coverage. Chydorus sphaericus positively correlated with pond size. Forward selection showed that conductivity and DO were the most significant explanatory variables, which together explained 12.4% of the variance. Conductivity explained 9% of the variance and DO explained 4.5% of the variance.
Redundancy analysis (RDA) for relative abundance data showed that the environmental variables together explained 29.2% of the variance; the first two constrained axes explained 26.8% (Figure 4). The first constrained axis is mainly related to conductivity, fish and tadpole presence, pond size, pH, DO, and vegetation coverage. Ponds dominated by M. cf brachiata were mainly situated along the first constrained axis, while ponds dominated by C. reticulata and D. longispina were situated along the second constrained axis. Relative abundance of M. cf brachiata positively correlated with conductivity, fish and tadpole presence. Relative abundance of C. reticulata were positively correlated with DO and vegetation coverage, while relative abundance of D. longispina was affected by water pH and depth. Forward selection of variables also showed conductivity and DO as significant explanatory variables. Conductivity and DO together explained 25.8% of total variance; conductivity explained 17.5%, DO explained 10.3%.

3.5. Variation of the Cladoceran Community along Spatial Gradients

The PCNM model produced 36 PCNM variables. When applied to species presence/absence data, 17 spatial eigenvectors were positive while only the first two spatial eigenvectors (V1 and V8) were significant. This suggests that broad-scale spatial processes affect cladoceran occurrence; variance partitioning shows that PCNM variables (V1 and V8) explained 5.8% of the total variance and 4.3 % was purely explained by PCNM variables (V1 and V8). Three significant PCNM variables (V1, V4, and V5) were selected for cladoceran relative abundance, indicating only broad-scale spatial processes. They explained 15.9% of the total variance (See Table 4).

4. Discussion

4.1. Cladocera Diversity

Our investigation suggests limited species richness of Cladocera in the dune ponds of the Ulan Buh desert. Temporary ponds contained more species (16) than large permanent lakes (14) (see species list in Appendix A). Generally, cladoceran species changed at both temporal and spatial scales, thus, further sampling is needed to better describe species diversity in permanent lakes in the region. However, studying adjoined temporary ponds collectively provided species information to understand species diversity and distribution in the permanent lakes. Both temporary ponds and permanent ponds play important roles in maintaining total zooplankton species diversity [31].
As 37 dune ponds were investigated, estimators could be calculated. Chao2, first-, and second-order Jackknife suggested that more species can be expected. Similar estimators were used in extrapolating species numbers for 212 samples with 72 observed species in Thailand. Some 76–82 species were expected to be present even for such a large sample size [32]. ATBI (All Taxa Biological Inventory) suggested that any lowland lake is expected to have around 50 species, and species lists should be based on a minimum of three samples through seasons and years to minimize the under/overestimation [24,33]. In the present study, we sampled all ponds we could find, yet curiously, all Bosminidae and Ilyocryptidae, widespread all over the world, were absent here.
There are a few cladoceran studies in desert waters that showed high variation in species richness. Green (1976) described a sand-dune lake from New Zealand with only 1 species, Bosmina meridionalis, but the species of Bosmina were absent in our study [34]. A desert study from Xinjiang (China) also showed low Cladoceran diversity, with 7 species from 5 lakes between Tarim floodplain and Konchedarya river [35]. In contrast, high richness (34 species) was observed in 12 wet and 2 mud samples in Lençóis Maranhenses, a tropical desert in northeast Brazil. Six species were only found by hatching resting eggs/ephippia from collected mud samples resulting in a total of 15 species [36]. The Lençóis Maranhenses is a coastal strip of 155,000 ha with thousands of temporary freshwater pools fed by seasonal rains (November–May). The case study in the Brazilian desert revealed a great diversity in sediment, partly supported by hatching methods. Our study employed plankton sampling but no sediment work. Further surveys using more comprehensive sampling methods might therefore yield additional species.
In 2005, Alonso and colleagues started a lake survey of Mongolia. Until 2017, 1123 lakes have been cataloged and investigated in fourteen expeditions, almost covering the whole country. In total, 73 cladoceran species have been found, including 1 haplopod, 3 ctenopod, 1 onychopod, 16 members of Daphniidae, 5 Moinidae, 6 Macrothricidae, 5 Bosminidae, 36 Chydoridae (http://www.geodata.es/mongolian_lakes accessed on 23 April 2018). The present study area is about 250 km away from Mongolia, and it has geomorphic features similar to that country. Its cladoceran fauna is expected to be more or less similar too. Three of the six cladoceran species found in a study of northeast Mongolia appeared in our study. Even though their sampling period was quite similar to ours, the low cladoceran diversity may be due to the high salt concentrations of lakes [37]. Long-term studies in drainless saline lakes of the Uldza-Torey basin reported 13 cladoceran species, and frequently occurring three species, D. magna, M. brachiate, and D. mongolianum all occurred in our study [38]. The study in Badain Jaran desert (located about 400 km northwest of our study area) showed diverse littoral cladocerans [39], among which four of these cladoceran species were present in our samples. A study in Jili lake located in north Xinjiang, China, reported 12 cladoceran species [40], and only three of them appeared in our samples.
Environmental variables such as conductivity, water pH, and DO were the main variables correlated with species richness in our study. Conductivity functions as one of the most important environmental filters, reflecting local environmental conditions. Environmental selection, as well as spatial processes affected cladoceran communities of the ponds. Three pelagic species, viz. C. reticulata, D. longispina, and M. cf brachiate, dominated, but with a clear niche partitioning. M. cf brachiata appears to be good indicator of high salinity and is adapted to tadpole predation, while D. longispina is absent under tadpole predation. Interspecific differences in colonization and competitive abilities may also determine the species composition, which was revealed in rock pools on the Baltic islands off the south coast of Finland [41].
Deserts are mobile, and dunes move with the wind. The maximum between-pond altitude difference was 30 m within our area (40 × 15 km2). Significant correlations between altitude and longitude as well as latitude tell that eastern ponds are at higher altitudes than western ponds. This implies that the wind effect is strong in our study area. Furthermore, Cladocera produce ephippia that can be dispersed more or less easily by wind [3,42]. A study on the rock pools of the Appledore islands showed differences between ephippia of Daphnia and Moina in the ease with which they were dispersed [3]. Large numbers of Daphnia ephippia occurred in besides rock pools, against a few of Moina. Compared to Daphnia, ephippia of Moina have lower buoyancy, resulting in low dispersal. The M. cf brachiata in the present study was limited to the western ponds by environmental selection (high conductivity), even under the prevailing northwest winds. However, M. cf brachiata was still able to colonize the east ponds with high conductivity, given enough time. Louette and De Meester (2005) measured colonization by Cladocera of 25 newly dug pools in an area in Belgium of 200 km length and 50 km width [4]. In the built-up communities, a total of 20 Cladocera species were identified after 15 months; C. sphaericus, Daphnia obtusa, and Simocephalus vetulus were in more than half of the pools, but Moinidae and Sididae were absent. Daphniids represented almost 50% of the colonization events, with Daphnia always the pioneer cladoceran species. D. mongolianum, a sidid, was only present in two permanent lakes and one linked temporary pond, implying that this species is limited to permanent lakes. Diaphanosoma tolerates fluctuations in water salinity and is found in up to 15 g/L salinity [43]. The explanation of its restricted distribution in our study might therefore be low dispersal ability.
C. rectangula, C. sphaericus, C. reticulata, and S. smirnovi were the common species in the present study, and their combinations associated with habitats with different pH values, suggesting they all can be found in a wide range of water types. Walseng (2003) identified littoral microcrustaceans as indicators of acidification in Canadian ponds, and species richness increased when aquatic ecosystems recovered from acidic to nonacidic [7]. Among 64 identified Cladoceran species, Sinobosmina sp. and Sida crystallina were good indicators of acidification. This may be the reason why these two genera were absent from our study area. Chen et al. (2010) examined the impact of lake trophic state on the Cladoceran communities in 33 Irish ponds, highlighting that cladoceran functional structure strongly shifted with nutrient enrichment and should be considered as multiple biotic indicators [8]. Even though the nutrient level was not a significant variable to Cladoceran communities in the current study, it may potentially impact Cladoceran functional structure.

4.2. Community Assembly

Forward selection in RDA analysis suggested that conductivity and DO significantly explained community variation. Ponds in the present study were all shallow water bodies, thus their DO level was controlled by phytoplankton and aquatic vegetation, and varies throughout the day. In this kind of habitat, cladoceran communities are not significantly altered by DO level. Water conductivity is usually related to salinity, which strongly affects zooplankton communities [44,45]. Zooplankton species richness decreased with lake salinization in the lakes of Tibet, and the zooplankton community shifted from the dominance of copepods and small cladoceran species to large saline filter-feeding cladocerans and phyllopod species [46] A similar pattern emerged in our study, which dominant cladoceran species in our sand ponds shifted from smaller C. reticulata to larger M. cf brachiata. Salinity is a serious stress factor for freshwater cladocerans, thus, species must either adapt to it or perish. M. cf brachiata is able to survive at high salinity, while C. reticulata only appeared in lower salinity ponds. Several species of Moina have a good tolerance to salinity. Their tolerance can even reach 15‰ and they still dominate in lakes with 5.5‰ salinity [17].
In general, mineralization, salinization, and eutrophication all had a significant effect on the cladoceran community [40]. In our study, significant environmental variables, conductivity, and DO together explained the majority (12.4%) of the variations in the cladoceran community. The other environmental variables such as pH, temperature, and water depth are important, but did not significantly contribute to the variation of the cladoceran community.

5. Conclusions

Our study presented a brief outlook on species richness, fine spatial distribution, and community structure of cladocerans in a desert setting. C. reticulata and C. rectangula were the most common cladocera in the studied region, while C. reticulata, D. longispina, and M. cf brachiata all formed dominant populations. M. cf brachiata and C. reticulata were suggested as good indicators of ponds with high conductivity and lower conductivity, respectively. The combination of these two species coexisted in ponds with moderate conductivity. Species richness is limited by harsh habitats and the community structure was shaped by environmental selection as expected. Conductivity was the most important environmental variable, and a broad-scale spatial structure was significant. Large permanent lakes function as a species pool for temporal ponds nearby. The species diversity and local community in temporal ponds demonstrate environmental filtering or species sorting of local habitats for species dispersed from the permanent lakes.

Author Contributions

Conceptualization, Y.-L.G. and B.-P.H.; methodology, Y.-L.G. and B.-P.H.; software, Y.-L.G. and Q.H.; validation, Y.-L.G. and B.-P.H.; formal analysis, Y.-L.G. and Q.H.; investigation, Y.-L.G., L.X. and B.-P.H.; resources, B.-P.H.; data curation, Y.-L.G.; writing—original draft preparation, Y.-L.G. and B.-P.H.; writing—review and editing, B.-P.H., H.J.D., M.A. and E.Z.R.; visualization, Y.-L.G.; supervision, B.-P.H., H.J.D., M.A.; project administration, B.-P.H.; funding acquisition, B.-P.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NSF of China, grant number 32171538.

Institutional Review Board Statement

Sampling and all work did not have any visible adverse effects on zooplankton in natural waters. No any permissions or ethics approval are required for sampling and monitoring zooplankton in lakes and reservoirs. Our field sampling follows and obeys Wildlife Protection Law of the People’s Republic of China (revised in 2018).

Data Availability Statement

The data presented in this study are openly available in [Researchgate] at [https://www.researchgate.net/publication/355283457_sand_pondsoriginal_data].

Conflicts of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Appendix A

Table
Table A1. Checklist of species from 37 sand ponds, species from 4 neighboring permanent lakes was also listed. Lake: cladoceran species present in ponds, Pond: cladoceran species present in ponds; + means present, - means absent.
Table A1. Checklist of species from 37 sand ponds, species from 4 neighboring permanent lakes was also listed. Lake: cladoceran species present in ponds, Pond: cladoceran species present in ponds; + means present, - means absent.
FamilySpeciesCodesLakePond
Sididae Bard, 1850Diaphanosoma mongolianum Uéno, 1938dia++
Daphniidae (Straus, 1820)Daphnia magna Straus, 1820dam++
Daphnia longispina (O. F. Müller, 1776)dal++
Simocephalus exspinosus (De Geer, 1778)sie++
Simocephalus vetulus (O. F. Müller, 1776)siv++
Scapholeberis smirnovi Garibian et al., 2020sck++
Ceriodaphnia reticulata (Jurine, 1820)cer++
Moinidae Goulden, 1968Moina cf brachiata (Leydig, 1860)mor++
Macrothricidae Norman & Brady, 1867Macrothrix rosea (Jurine, 1820)mar+-
Macrothrix spinosa King, 1853mas-+
Chydoridae Stebbing, 1902Oxyurella tenuicaudis (Sars, 1862)oxt-+
Alona guttata Sars, 1862alg++
Coronatella rectangula (Sars, 1862)cor++
Alonella nana (Baird, 1843)aln+-
Pleuroxus aduncus (Jurine, 1820)pla-+
Chydorus sphaericus (O. F. Müller, 1776)chs++

References

  1. Leibold, M.A.; Holyoak, M.; Mouquet, N.; Amarasekare, P.; Chase, J.M.; Hoopes, M.F.; Holt, R.D.; Shurin, J.B.; Law, R.; Tilman, D.; et al. The metacommunity concept: A framework from multi-scale community ecology. Ecol. Lett. 2004, 7, 601–613. [Google Scholar] [CrossRef]
  2. Figuerola, J.; Green, A.J. Dispersal of aquatic organisms by waterbirds: A review of past research and priorities for future studies. Freshw. Biol. 2002, 47, 483–494. [Google Scholar] [CrossRef] [Green Version]
  3. Sirianni, K.M. Differential wind dispersal of cladoceran ephippia in a rock pool metacommunity. Aquat. Ecol. 2017, 51, 1–16. [Google Scholar] [CrossRef]
  4. Louette, G.; De Meester, L. High dispersal capacity of cladoceran zooplankton in newly founded communities. Ecology 2005, 86, 353–359. [Google Scholar] [CrossRef]
  5. Dodson, S.I.; Newman, A.L.; Willwolf, S.; Alexander, M.L.; Woodford, M.P.; Egeren, S.V. The relationship between zooplankton community structure and lake characteristics in temperate lakes (Northern Wisconsin, USA). J. Plankton Res. 2009, 31, 93–100. [Google Scholar] [CrossRef] [Green Version]
  6. Declerck, S.A.J.; Coronel, J.S.; Legendre, P.; Brendonck, L. Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High-Andes wetlands. Ecography 2011, 34, 296–305. [Google Scholar] [CrossRef] [Green Version]
  7. Walseng, B.; Yan, N.D.; Schartau, A.K. Littoral microcrustacean (Cladocera and Copepoda) indicators of acidification in Canadian Shield lakes. AMBIO 2003, 32, 208–213. [Google Scholar] [CrossRef]
  8. Chen, G.; Dalton, C.; Taylor, D. Cladocera as indicators of trophic state in Irish ponds. J. Paleolimnol. 2010, 44, 465–481. [Google Scholar] [CrossRef]
  9. Jeppesen, E.; Jensen, J.P.; Søndergaard, M.; Lauridsen, T.; Landkildehus, F. Trophic structure, species richness and biodiversity in danish lakes: Changes along a phosphorus gradient. Freshw. Biol. 2000, 45, 201–218. [Google Scholar] [CrossRef]
  10. Canterbury, G.E.; Martin, T.E.; Petit, D.R.; Petit, L.J.; Bradford, D.F. Bird Communities and Habitat as Ecological Indicators of Forest Condition in Regional Monitoring. Conserv. Biol. 2000, 14, 544–558. [Google Scholar] [CrossRef]
  11. Valero, E.; Álvarez, X.; Picos, J. An assessment of river habitat quality as an indicator of conservation status. A case study in the Northwest of Spain. Ecol. Indic. 2015, 57, 131–138. [Google Scholar] [CrossRef]
  12. Bakker, J.D. Increasing the utility of Indicator Species Analysis. J. Appl. Ecol. 2008, 45, 1829–1835. [Google Scholar] [CrossRef]
  13. Cáceres, M.D.; Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 2009, 90, 3566–3574. [Google Scholar] [CrossRef] [PubMed]
  14. Cáceres, M.D.; Legendre, P.; Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 2010, 119, 1674–1684. [Google Scholar] [CrossRef]
  15. Cáceres, M.D.; Legendre, P.; Wiser, S.K.; Brotons, L. Using species combinations in indicator value analyses. Methods Ecol. Evol. 2012, 3, 973–982. [Google Scholar] [CrossRef]
  16. Urban, N.A.; Swihart, R.K.; Malloy, M.C.; Dunning, J.B., Jr. Improving selection of indicator species when detection is imperfect. Ecol. Indic. 2011, 15, 188–197. [Google Scholar] [CrossRef]
  17. He, Z.; An, S. The adaptation to salinity in Moina brachiata. Chin. J. Zool. 1986, 2, 27–30. (In Chinese) [Google Scholar]
  18. Guo, F.F.; Dumont, H.J. Relict populations of Diaphanosoma (Cladocera: Ctenopoda) in the Chadian Sahara, with the description of a new species. Zootaxa 2014, 3856, 135–142. [Google Scholar] [CrossRef] [Green Version]
  19. Kotov, A.A. Jurassic Cladocera (Crustacea, Branchiopoda) with a description of an extinct Mesozoic Order. J. Nat. Hist. 2007, 41, 13–37. [Google Scholar] [CrossRef]
  20. Rhoades, J.D. Salinity: Electrical conductivity and total dissolved solids. Chem. Methods 1996, 142, 31–33. [Google Scholar]
  21. Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees. Wadsworth 1984, 20, 582–588. [Google Scholar]
  22. Magurran, A.E. Measuring Biological Diversity; Blackwell Science Ltd.: Victoria, UK, 2004; pp. 1–215. [Google Scholar]
  23. Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 1987, 43, 783. [Google Scholar] [CrossRef] [PubMed]
  24. Dumont, H.J.; Segers, H. Estimating lacustrine zooplankton species richness and complementarity. Hydrobiologia 1996, 341, 125–132. [Google Scholar] [CrossRef]
  25. Borcard, D.; Legendre, P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 2002, 153, 51–68. [Google Scholar] [CrossRef]
  26. Borcard, D.; Legendre, P.; Avoisjacquet, C.; Tuomisto, H. Dissecting the spatial structure of ecological data at multiple scales. Ecology 2004, 85, 1826–1832. [Google Scholar] [CrossRef] [Green Version]
  27. Chambers, J.M. SoDA: Functions and Examples for “Software for Data Analysis”. R package version 1.0-6.1. 2020. Available online: https://CRAN.R-project.org/package=SoDA (accessed on 28 October 2020).
  28. Chao, A.; Ma, K.H.; Hsieh, T.C.; Chiu, C.H. SpadeR: Species-Richness Prediction and Diversity Estimation with R. R package version 0.1.1. 2016. Available online: https://CRAN.R-project.org/package=SpadeR (accessed on 6 September 2016).
  29. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; et al. vegan: Community Ecology Package. R package version 2.5-7. 2020. Available online: https://CRAN.R-project.org/package=vegan (accessed on 28 November 2020).
  30. Therneau, T.; Atkinson, B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-15. 2019. Available online: https://CRAN.R-project.org/package=rpart (accessed on 12 April 2019).
  31. Drenner, S.M.; Dodson, S.I.; Drenner, R.W.; Pinder, J.E. Crustacean zooplankton community structure in temporary and permanent grassland ponds. Hydrobiologia 2009, 632, 225–233. [Google Scholar] [CrossRef]
  32. Maiphae, S.; Pholpunthin, P.; Dumont, H.J. Species richness of the Cladocera (Branchiopoda: Anomopoda and Ctenopoda) in southern Thailand, and its complementarity with neighboring regions. Hydrobiologia 2005, 537, 147–156. [Google Scholar] [CrossRef]
  33. Dodson, S. Predicting crustacean zooplankton species richness. Limnol. Oceanogr. 1992, 37, 848–856. [Google Scholar] [CrossRef]
  34. Green, J.D. Plankton of lake Ototoa, a sand-dune lake in Northern New Zealand. N. Z. J. Mar. Fresh. 1976, 10, 43–59. [Google Scholar] [CrossRef]
  35. Chertoprud, E.S.; Sinev, A.Y.; Dimante-Deimantovica, I. Fauna of Cladocera and copepoda from Xinjiang Uyghur autonomous region (China). Zootaxa 2017, 4258, 561–573. [Google Scholar] [CrossRef]
  36. Van Damme, K.; Dumont, H.J. Cladocera of the Lençóis Maranhenses (NE-Brazil): Faunal composition and a reappraisal of Sars’ Method. Braz. J. Biol. 2010, 70, 755–779. [Google Scholar] [CrossRef] [PubMed]
  37. Afonina, E.Y.; Tashlykova, N.A. Plankton community and the relationship with the environment in saline lakes of Onon-Torey plain, Northeastern Mongolia. Saudi. J. Biol. Sci. 2018, 25, 399–408. [Google Scholar] [CrossRef] [PubMed]
  38. Afonina, E.Y.; Tashlykova, N.A. Plankton of Saline Lakes in Southeastern Transbaikalia: Transformation and Environmental Factors. Contemp. Probl. Ecol. 2019, 12, 155–170. [Google Scholar] [CrossRef]
  39. Li, Y.; Zhao, H.; Hu, L.; Leppänen, J.J. Cladoceran communities in soda lakes of the Badain Jaran desert, NW China. J. Arid Environ. 2020, 177, 104133. [Google Scholar] [CrossRef]
  40. Hu, L.; Li, Y.; Leppänen, J.J.; Chen, G.; Lang, W.; Wang, X.; Qiang, M. Human impacts on the cladoceran community of Jili Lake, arid NW China, over the past century. J. Paleolimnol. 2021, 1–12. [Google Scholar]
  41. Hanski, I.; Ranta, E. Coexistence in a Patchy Environment: Three Species of Daphnia in Rock Pools. J. Anim. Ecol. 1983, 52, 263–279. [Google Scholar] [CrossRef]
  42. De Meester, L.; Gómez, A.; Okamura, B.; Schwenk, K. The Monopolization Hypothesis and the dispersal–gene flow paradox in aquatic organisms. Acta Oecol. 2002, 23, 121–135. [Google Scholar] [CrossRef]
  43. Korovchinsky, N.M. Sididae & Holopediidae (Crustacea: Daphniiformes). In Guides to the Identification of the Microinvertebrates of the Continental Waters of the World 3; SPB Academic: Hague, The Netherlands, 1992; pp. 1–82. [Google Scholar]
  44. Horváth, Z.; Vad, C.F.; Tóth, A.; Zsuga, K.; Boros, E.; Vörös, L.; Ptacnik, R. Opposing patterns of zooplankton diversity and functioning along a natural stress gradient: When the going gets tough, the tough get going. Oikos 2014, 123, 461–471. [Google Scholar] [CrossRef] [Green Version]
  45. Yılmaz, G.; Çolak, M.A.; Özgencil, İ.K.; Metin, M.; Korkmaz, M.; Ertuğrul, S.; Soyluer, M.; Bucak, T.; Tavşanoğlu, Ü.N.; Özkan, K.; et al. Decadal changes in size, salinity, waterbirds, and fish in lakes of the Konya Closed Basin, Turkey, associated with climate change and increasing water abstraction for agriculture. Inland Waters 2021, 1–18. [Google Scholar] [CrossRef]
  46. Lin, Q.Q.; Xu, L.; Hou, J.Z.; Liu, Z.W.; Jeppesen, E.; Han, B.P. Responses of trophic structure and zooplankton community to salinity and temperature in Tibetan lakes: Implication for the effect of climate warming. Water Res. 2017, 124, 618–629. [Google Scholar] [CrossRef]
Diversity 13 00502 g001 550
Figure 1. Map of study area of this study: ① The geographical location of Ulanbu desert; ② The location of each sampling site (represented by back spots) in the desert. Two permanent lakes are indicated with arrows.
Figure 1. Map of study area of this study: ① The geographical location of Ulanbu desert; ② The location of each sampling site (represented by back spots) in the desert. Two permanent lakes are indicated with arrows.
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Diversity 13 00502 g002 550
Figure 2. ① Frequency of each species for all ponds; ② Bar plot of cladoceran species richness ranks. Abbreviations codes of each species, see Appendix A.
Figure 2. ① Frequency of each species for all ponds; ② Bar plot of cladoceran species richness ranks. Abbreviations codes of each species, see Appendix A.
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Diversity 13 00502 g003 550
Figure 3. Regression tree of environmental variables on cladoceran richness of dune ponds. The pond without cladocerans was excluded from the analysis.
Figure 3. Regression tree of environmental variables on cladoceran richness of dune ponds. The pond without cladocerans was excluded from the analysis.
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Diversity 13 00502 g004 550
Figure 4. Redundancy analysis of cladoceran communities with presence/absence data ① and relative abundance ② Abbreviation of species see Appendix A. Sampling sites in green font: p1–p37; species in red font, abbreviations codes, see Appendix A; blue arrows: significantly explaining environmental variables.
Figure 4. Redundancy analysis of cladoceran communities with presence/absence data ① and relative abundance ② Abbreviation of species see Appendix A. Sampling sites in green font: p1–p37; species in red font, abbreviations codes, see Appendix A; blue arrows: significantly explaining environmental variables.
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Table
Table 1. Average values of environmental variables and endemic species in western and eastern ponds. t-tests were performed for the two pond groups.
Table 1. Average values of environmental variables and endemic species in western and eastern ponds. t-tests were performed for the two pond groups.
WesternEasternp Value
Altitude1032.51044.86p < 0.001
Pond size (m2)151.43311.36p = 0.492
Depth (m)0.871.1p = 0.203
Conductivity(mS/cm)6.153.03p < 0.001
DO (mg/L)9.1210.17p = 0.238
pH9.069.22p = 0.322
Vegetation coverage0.660.75p = 0.233
Average richness 3.433.73p = 0.557
Total species richness1311
Restricted speciesDiaphanosoma mongolianum
Moina cf brachiata
Pleuroxus aduncus 		Macrothrix spinosa
Table
Table 2. Species richness estimation based on the observed species presence/absence data.
Table 2. Species richness estimation based on the observed species presence/absence data.
EstimateSE95% Lower95% Upper
Homogeneous Model12.7671.06314.10119.838
Chao2 (Chao, 1987)18.3757.00314.48053.842
Chao2-bc15.4582.53414.14528.716
iChao2 (Chiu et al., 2014)18.3755.94014.59046.445
ICE (Lee & Chao, 1994)15.9542.43714.29327.046
ICE-1 (Lee & Chao, 1994)16.4413.27414.33431.826
1st order jackknife16.9172.39814.71325.930
2nd order jackknife18.8334.08615.14634.385
Table
Table 3. Selected indicators (species and species combinations) and associated environmental variables. Significance codes: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.
Table 3. Selected indicators (species and species combinations) and associated environmental variables. Significance codes: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.
Indicator SpeciesAssociated EnvironmentSpecificityFidelityp-Value
morConductivty: 6630–12619 μS/cm0.9470.6670.005 **
cor + morConductivty: 6630–12619 μS/cm0.9330.5560.004 **
cer + morConductivty: 6630–7430 μS/cm0.8570.5000.040 *
cerConductivty: 1404–7430 μS/cm1.0000.9390.001 ***
cor + cerConductivty: 1404–7430 μS/cm1.0000.8180.025 *
dalConductivty: 1404–5415 μS/cm1.0000.5190.05 *
chs + sckpH:9.82–10.060.9091.0000.001 ***
chspH:8.92–8.94 & 9.82–10.060.8110.8000.012 *
cor + chspH:8.92–8.94 & 9.82–10.060.8110.8000.012 *
cer + chspH:8.92–8.94 & 9.82–10.06 0.8110.8000.012 *
cor + sckpH:8.36–8.5 & 9.13–9.19 & 9.82–10.060.7690.7780.028 *
cor + morTadpole present0.8750.3330.040 *
cor + dalTadpole absent0.7970.5240.033 *
cor + morFish present0.8951.0000.025 *
morFish present0.8721.0000.036 *
morVegetation coverage: 50%0.7061.0000.049 *
Notes: Abbreviations codes of each species, see Appendix A.
Table
Table 4. Variance partitioning of cladoceran community structure. (E, selected environmental variables base on forward selection in redundancy analysis including conductivity and DO; S1, significant positive PCNM variables, including V1 and V8; S2, significant positive PCNM variables, including “V1”, “V4”, and “V5”).
Table 4. Variance partitioning of cladoceran community structure. (E, selected environmental variables base on forward selection in redundancy analysis including conductivity and DO; S1, significant positive PCNM variables, including V1 and V8; S2, significant positive PCNM variables, including “V1”, “V4”, and “V5”).
DfRRadjTestable
Presence/Absence
E217.4%12.4%TRUE
S1213.9%8.7%TRUE
E + S1426.5%17.0%TRUE
Shared0 4.1%FALSE
E|S12 8.3%TRUE
S1|E2 4.6%TRUE
Residuals 83.0%FALSE
Relative abundance
E230.0%25.8%TRUE
S2323.2%15.9%TRUE
E + S2545.6%36.5%TRUE
shared0 5.2%FALSE
E|S22 20.6%TRUE
S2|E3 10.7%TRUE
Residuals 63.5%FALSE
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Gu, Y.-L.; Huang, Q.; Xu, L.; Rizo, E.Z.; Alonso, M.; Dumont, H.J.; Han, B.-P. Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China. Diversity 2021, 13, 502. https://doi.org/10.3390/d13100502

AMA Style

Gu Y-L, Huang Q, Xu L, Rizo EZ, Alonso M, Dumont HJ, Han B-P. Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China. Diversity. 2021; 13(10):502. https://doi.org/10.3390/d13100502

Chicago/Turabian Style

Gu, Yang-Liang, Qi Huang, Lei Xu, Eric Zeus Rizo, Miguel Alonso, Henri J. Dumont, and Bo-Ping Han. 2021. "Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China" Diversity 13, no. 10: 502. https://doi.org/10.3390/d13100502

APA Style

Gu, Y. -L., Huang, Q., Xu, L., Rizo, E. Z., Alonso, M., Dumont, H. J., & Han, B. -P. (2021). Species Diversity and Community Assembly of Cladocera in the Sand Ponds of the Ulan Buh Desert, Inner Mongolia of China. Diversity, 13(10), 502. https://doi.org/10.3390/d13100502

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