| Literature DB >> 24910015 |
Lihua Ju1, Jun Yang, Lemian Liu, David M Wilkinson.
Abstract
Freshwater microbial diversity is subject to multiple stressors in the Anthropocene epoch. However, the effects of climate changes and human activities on freshwater protozoa remain poorly understood. In this study, the diversity and distribution of testate amoebae from the surface sediments were investigated in 51 Chinese lakes and reservoirs along two gradients, latitude and trophic status. A total of 169 taxa belonging to 24 genera were identified, and the most diverse and dominant genera were Difflugia (78 taxa), Centropyxis (26 taxa) and Arcella (12 taxa). Our analysis revealed that biomass of testate amoebae decreased significantly along the latitudinal gradient, while Shannon-Wiener indices and species richness presented an opposite trend (P < 0.05). The relationship of diversity and latitude is, we suspect, an artifact of the altitudinal distribution of our sites. Furthermore, biomass-based Shannon-Wiener index and species richness of testate amoebae were significantly unimodally related to trophic status (P < 0.05). This is the first large-scale study showing the effects of latitude and trophic status on diversity and distribution of testate amoebae in China. Therefore, our results provide valuable baseline data on testate amoebae and contribute to lake management and our understanding of the large-scale global patterns in microorganism diversity.Entities:
Mesh:
Year: 2014 PMID: 24910015 PMCID: PMC4201926 DOI: 10.1007/s00248-014-0442-1
Source DB: PubMed Journal: Microb Ecol ISSN: 0095-3628 Impact factor: 4.552
List of the 51 study lakes and reservoirs in China
| Lake code | Lake name | Region | Trophic state | Latitude (° N) | Longitude (° E) |
|---|---|---|---|---|---|
| 1 YLL | Yilong L. | YN | HEU | 23.6717 | 102.5892 |
| 2 HBR | Hubian R. | FJ | LEU | 24.4972 | 118.1536 |
| 3 FXL | Fuxian L. | YN | OM | 24.5676 | 102.8882 |
| 4 BTR | Bantou R. | FJ | LEU | 24.6747 | 118.0214 |
| 5 SDR | Shidou R. | FJ | LEU | 24.6925 | 118.0092 |
| 6 TXR | Tingxi R. | FJ | MES | 24.8031 | 118.1392 |
| 7 DCL | Dianchi L. | YN | HEU | 24.8566 | 102.7039 |
| 8 DZR | Dongzhen R. | FJ | LEU | 25.4839 | 118.9434 |
| 9 EHL | Erhai L. | YN | LEU | 25.7334 | 100.2157 |
| 10 CBL | Cibi L. | YN | MES | 26.1705 | 99.9397 |
| 11 HXH | Haixihai L. | YN | MES | 26.2871 | 99.9671 |
| 12 JHL | Jianhu L. | YN | MES | 26.4865 | 99.9300 |
| 13 SML | Shengmu L. | YN | MES | 26.6289 | 99.7091 |
| 14 ZML | Zimei L. | YN | OM | 26.6319 | 99.7115 |
| 15 TCL | Tiancai L. | YN | OM | 26.6343 | 99.7168 |
| 16 LSH | Lashi L. | YN | MES | 26.8803 | 100.1283 |
| 17 HBHU | Habahuang L. | YN | OM | 27.3465 | 100.0718 |
| 18 HBHE | Habahei L. | YN | OM | 27.3554 | 100.0701 |
| 19 LGUL | Lugu L. | YN | OL | 27.7167 | 100.8000 |
| 20 XHZ | Xiaohaizi L. | YN | MES | 27.7403 | 100.7200 |
| 21 SDL | Shudou L. | YN | MES | 27.9105 | 99.9506 |
| 22 LGAL | Longgan L. | CJ | MEU | 29.9361 | 116.1661 |
| 23 TBL | Taibai L. | CJ | HEU | 29.9555 | 115.7979 |
| 24 LZL | Liangzi L. | CJ | HEU | 30.2405 | 114.5122 |
| 25 NYL | Nianyi L. | CJ | MEU | 31.1190 | 118.9753 |
| 26 TAL | Taihu L. | CJ | HEU | 31.2199 | 120.1409 |
| 27 GCL | Gucheng L. | CJ | MEU | 31.2761 | 118.9220 |
| 28 SJL | Shijiu L. | CJ | LEU | 31.4740 | 118.8879 |
| 29 CHL | Chaohu L. | CJ | MEU | 31.5193 | 117.5583 |
| 30 LML | Luoma L. | EC | MES | 34.0534 | 118.2205 |
| 31 WSL | Weishan L. | EC | MEU | 34.6388 | 117.2817 |
| 32 DPL | Dongping L. | EC | MEU | 35.9686 | 116.1921 |
| 33 HSL | Hengshui L. | EC | MEU | 37.6199 | 115.6251 |
| 34 YHL | Yuehai L. | IM | MEU | 38.5618 | 106.2040 |
| 35 BYD | Baiyangdian L. | EC | HEU | 38.9432 | 115.9826 |
| 36 XHL | Xinghai L. | IM | MEU | 38.9857 | 106.4048 |
| 37 HSH | Hasuhai L. | IM | MEU | 40.6109 | 110.9715 |
| 38 DHZ | Donghaizi L. | IM | OM | 40.6308 | 107.0031 |
| 39 WLSH | Wuliangsuhai L. | IM | LEU | 40.8685 | 108.7931 |
| 40 QSHZ | Quansanhaizi L. | IM | LEU | 41.0679 | 107.8689 |
| 41 SLHZ | Shenglihaizi L. | IM | MEU | 41.1225 | 107.8273 |
| 42 XMP | Xinmiaopao L. | NE | HEU | 45.2120 | 124.4460 |
| 43 KLP | Kulipao L. | NE | MEU | 45.3711 | 124.4967 |
| 44 YLP | Yueliangpao L. | NE | MEU | 45.7380 | 124.0030 |
| 45 LMSP | Lamasipao L. | NE | HEU | 46.2915 | 124.0954 |
| 46 AMTP | Amutapao L. | NE | HEU | 46.6081 | 124.0612 |
| 47 QJP | Qijiapao L. | NE | MEU | 46.8240 | 124.2779 |
| 48 TIL | Tianhu L. | NE | HEU | 46.8737 | 124.4015 |
| 49 BEL | Bei’er L. | IM | LEU | 47.9336 | 117.7000 |
| 50 WLP | Wulanpao L. | IM | LEU | 48.3609 | 117.5229 |
| 51 HHNE | Huhenuo’er L. | IM | MEU | 49.2960 | 119.2344 |
YN Yunnan, Southwest China, CJ the middle and lower reaches of the Yangtze River Valley, China, EC East Central China, FJ Fujian, Southeast China, IM Inner Mongolia region, North China, NE Northeast China, OL oligotrophic, OM oligo-mesotrophic, MES mesotrophic, LEU light eutrophic, MEU middle eutrophic, HEU hypereutrophic
Fig. 1Location of the 51 study lakes and reservoirs in China
Fig. 2Number of testate amoebae taxa per genus in 51 lakes and reservoirs in China
List of the testate amoebae community parameters from the 51 study lakes and reservoirs in China
| Lake code | Abundance (ind. ml−1) | Biomass (μg C ml−1) | Species richness | Abundance-based | Biomass-based |
|---|---|---|---|---|---|
| YLL | 1,408 | 16.85 | 28 | 2.48 | 2.71 |
| HBR | 2,700 | 55.68 | 28 | 2.68 | 2.33 |
| FXL | 1,790 | 11.25 | 18 | 1.69 | 1.57 |
| BTR | 7,840 | 121.42 | 30 | 2.28 | 2.07 |
| SDR | 8,120 | 146.97 | 29 | 2.47 | 2.28 |
| TXR | 1,590 | 25.74 | 37 | 2.97 | 2.84 |
| DCL | 922 | 18.80 | 17 | 1.47 | 1.39 |
| DZR | 835 | 14.43 | 25 | 2.33 | 2.40 |
| EHL | 2,534 | 78.45 | 39 | 3.22 | 2.87 |
| CBL | 2,163 | 21.19 | 28 | 2.33 | 2.69 |
| HXH | 1,185 | 11.16 | 14 | 0.76 | 1.14 |
| JHL | 1,508 | 44.04 | 19 | 2.15 | 1.69 |
| SML | 2,567 | 157.14 | 22 | 2.35 | 0.85 |
| ZML | 2,150 | 90.31 | 31 | 2.78 | 1.25 |
| TCL | 9,500 | 54.41 | 39 | 2.90 | 3.08 |
| LSH | 1,630 | 33.30 | 20 | 2.31 | 1.81 |
| HBHU | 2,717 | 29.68 | 24 | 2.27 | 2.50 |
| HBHE | 1,450 | 48.49 | 29 | 2.71 | 1.89 |
| LGUL | 599 | 6.69 | 32 | 2.78 | 2.91 |
| XHZ | 2,917 | 31.90 | 24 | 2.08 | 1.96 |
| SDL | 655 | 96.25 | 42 | 3.18 | 1.19 |
| LGAL | 10,850 | 146.06 | 39 | 3.12 | 3.07 |
| TBL | 1,425 | 25.14 | 32 | 2.65 | 2.71 |
| LZL | 1,850 | 37.37 | 28 | 2.60 | 2.44 |
| NYL | 3,750 | 53.41 | 38 | 2.90 | 2.95 |
| TAL | 1,920 | 20.00 | 30 | 2.10 | 2.51 |
| GCL | 6,225 | 100.53 | 40 | 2.85 | 2.84 |
| SJL | 8,375 | 192.53 | 40 | 2.82 | 2.30 |
| CHL | 4,125 | 85.00 | 24 | 1.82 | 1.72 |
| LML | 989 | 17.53 | 37 | 2.78 | 2.97 |
| WSL | 536 | 6.03 | 35 | 3.09 | 3.12 |
| DPL | 2,634 | 61.11 | 37 | 3.14 | 2.40 |
| HSL | 2,025 | 35.54 | 40 | 3.31 | 2.69 |
| YHL | 795 | 10.67 | 34 | 3.16 | 2.85 |
| BYD | 3,500 | 67.46 | 34 | 2.82 | 2.67 |
| XHL | 1,620 | 11.78 | 31 | 2.96 | 3.09 |
| HSH | 1,900 | 24.50 | 27 | 2.44 | 2.72 |
| DHZ | 4,300 | 18.73 | 7 | 0.59 | 0.90 |
| WLSH | 667 | 10.10 | 34 | 2.51 | 2.78 |
| QSHZ | 2,584 | 31.71 | 33 | 2.79 | 2.76 |
| SLHZ | 970 | 7.05 | 24 | 2.31 | 2.64 |
| XMP | 2,175 | 40.96 | 46 | 3.34 | 3.11 |
| KLP | 557 | 7.64 | 41 | 3.26 | 3.07 |
| YLP | 2,400 | 51.55 | 38 | 2.92 | 2.77 |
| LMSP | 906 | 7.69 | 24 | 2.48 | 2.71 |
| AMTP | 607 | 17.42 | 33 | 3.09 | 1.83 |
| QJP | 2,120 | 33.07 | 46 | 3.22 | 2.89 |
| TIL | 1,186 | 23.66 | 21 | 2.34 | 1.30 |
| BEL | 1,350 | 22.34 | 38 | 3.01 | 3.01 |
| WLP | 591 | 11.55 | 38 | 3.21 | 2.98 |
| HHNE | 1,002 | 13.10 | 46 | 3.46 | 3.38 |
H′ Shannon-Wiener index
Fig. 3Canonical correspondence analysis (CCA) sample-environment biplot for the 51 lakes and reservoirs that yield statistically significant testate amoebae populations. a Abundance, b biomass. The sample sites are given in Table 1
Fig. 4Variation of testate amoebae community parameters along a latitudinal gradient. a Abundance, b biomass, c Shannon-Wiener index based on abundance data, d Shannon-Wiener index based on biomass data and e species richness
Fig. 5Variation of testate amoebae community parameters along a trophic status gradient. a Abundance, b biomass, c Shannon-Wiener index based on abundance data, d Shannon-Wiener index based on biomass data and e species richness