journal article·2026-05Open access

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

Zhencheng Ye, Jialiang Kuang, Colin T. Bates, Arthur Escalas, Daliang Ning, Liyou Wu, Suo Liu, Sihang Deng, Jiesi Lei, Xiangwen Chen, Jennifer Pett‐Ridge, Malay Saha, Lauren Hale, Gangsheng Wang, Renmao Tian, Ying Fu, Yu Tang, Mary Firestone, Jizhong Zhou, Yunfeng Yang

Advanced Science· Volume 13· Issue 29

Abstract

ABSTRACT Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species‐time‐area relationships (STARs) and phylogenetic‐time‐area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%‐14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Microbial Fuel Cells and Bioremediation · Soil Carbon and Nitrogen Dynamics · Microbial Community Ecology and Physiology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing China

Hainan Institute of National Park Haikou China

The Institute for Environmental Genomics University of Oklahoma Norman Oklahoma USA

College of Environment and Energy South China University of Technology Guangzhou China

MARBEC University of Montpellier Montpellier France

Physical and Life Sciences Directorate Lawrence Livermore National Laboratory Livermore CA USA

Life & Environmental Sciences Department University of California Merced Merced CA USA

Innovative Genomics Institute University of California Berkeley Berkeley CA USA

Noble Research Institute Ardmore Oklahoma USA

US Department of Agriculture (USDA) Agricultural Research Service (ARS) Water Management Research Parlier CA USA

State Key Laboratory of Water Resources Engineering and Management and Institute for Water‐Carbon Cycles and Carbon Neutrality Wuhan University Wuhan China

Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

Department of Environmental Science Policy and Management University of California Berkeley California USA

Earth and Environmental Sciences Area Lawrence Berkeley National Laboratory Berkeley California USA

School of Biological Sciences University of Oklahoma Norman Oklahoma USA

School of Civil Engineering and Environmental Sciences University of Oklahoma Norman Oklahoma USA

School of Computer Sciences University of Oklahoma Norman Oklahoma USA

Funding

National Natural Science Foundation of China · Office of Science · University of California Berkeley · University of Oklahoma · Lawrence Berkeley National Laboratory

References (73)

Show source-provided references
1. 10.1046/j.1461‐0248.2003.00497.x10.1046/j.1461‐0248.2003.00497.x ↗
2. 10.1126/science.111131810.1126/science.1111318 ↗
3. 10.1126/science.118782010.1126/science.1187820 ↗
4. 10.1016/j.tree.2006.06.01210.1016/j.tree.2006.06.012 ↗
5. 10.1038/s41396‐021‐00976‐010.1038/s41396‐021‐00976‐0 ↗
6. 10.1890/05‐006710.1890/05‐0067 ↗
7. 10.1038/ismej.2013.5410.1038/ismej.2013.54 ↗
8. 10.1038/nature1122610.1038/nature11226 ↗
9. 10.1038/s41559‐019‐0848‐810.1038/s41559‐019‐0848‐8 ↗
10. 10.1073/pnas.070901610510.1073/pnas.0709016105 ↗
11. 10.1111/geb.1382510.1111/geb.13825 ↗
12. 10.1038/nature0307310.1038/nature03073 ↗
13. 10.1038/ismej.2011.3210.1038/ismej.2011.32 ↗
14. 10.1073/pnas.080361010510.1073/pnas.0803610105 ↗
15. 10.1111/j.1461‐0248.2012.01795.x10.1111/j.1461‐0248.2012.01795.x ↗
16. 10.1111/j.1461‐0248.2010.01563.x10.1111/j.1461‐0248.2010.01563.x ↗
17. 10.1111/ecog.0311110.1111/ecog.03111 ↗
18. 10.1111/geb.1276210.1111/geb.12762 ↗
19. 10.1038/s41598‐018‐24881‐310.1038/s41598‐018‐24881‐3 ↗
20. 10.1038/s41558‐020‐0885‐y10.1038/s41558‐020‐0885‐y ↗
21. 10.1038/s41467‐019‐11974‐410.1038/s41467‐019‐11974‐4 ↗
22. 10.1038/s41396‐021‐00916‐y10.1038/s41396‐021‐00916‐y ↗
23. 10.1016/j.soilbio.2023.10917810.1016/j.soilbio.2023.109178 ↗
24. 10.2307/354410910.2307/3544109 ↗
25. 10.1126/sciadv.170086610.1126/sciadv.1700866 ↗
26. 10.1038/s41396‐023‐01496‐910.1038/s41396‐023‐01496‐9 ↗
27. 10.1016/j.biombioe.2004.05.00610.1016/j.biombioe.2004.05.006 ↗
28. 10.1126/science.113330610.1126/science.1133306 ↗
29. 10.1016/j.plantsci.2009.09.00310.1016/j.plantsci.2009.09.003 ↗
30. 10.1111/1365‐2745.1327210.1111/1365‐2745.13272 ↗
31. 10.1111/j.1461‐0248.2009.01328.x10.1111/j.1461‐0248.2009.01328.x ↗
32. White E. P. · 2007
33. 10.1890/08‐0377.110.1890/08‐0377.1 ↗
34. 10.1890/es10‐00117.110.1890/es10‐00117.1 ↗
35. 10.1128/mmbr.00002‐1710.1128/mmbr.00002‐17 ↗
36. 10.1038/nmicrobiol.2016.16010.1038/nmicrobiol.2016.160 ↗
37. 10.1038/s41467‐021‐27857‐610.1038/s41467‐021‐27857‐6 ↗
38. 10.1111/gcb.1559310.1111/gcb.15593 ↗
39. 10.1007/s00203‐004‐0696‐y10.1007/s00203‐004‐0696‐y ↗
40. 10.1094/pbiomes‐04‐20‐0029‐fi10.1094/pbiomes‐04‐20‐0029‐fi ↗
41. 10.1002/ecs2.397010.1002/ecs2.3970 ↗
42. 10.1016/j.soilbio.2020.10776610.1016/j.soilbio.2020.107766 ↗
43. 10.1007/s11104‐019‐04208‐510.1007/s11104‐019‐04208‐5 ↗
44. 10.1186/s40793‐024‐00638‐610.1186/s40793‐024‐00638‐6 ↗
45. 10.1111/nph.1692410.1111/nph.16924 ↗
46. 10.3390/agronomy1406133910.3390/agronomy14061339 ↗
47. 10.1186/s40168‐025‐02075‐010.1186/s40168‐025‐02075‐0 ↗
48. 10.1038/s41579‐023‐00980‐510.1038/s41579‐023‐00980‐5 ↗
49. 10.1002/ecs2.207910.1002/ecs2.2079 ↗
50. 10.1038/s41467‐024‐47348‐810.1038/s41467‐024‐47348‐8 ↗
51. 10.1021/acs.est.4c1279010.1021/acs.est.4c12790 ↗
52. 10.1038/s41467‐024‐48248‐710.1038/s41467‐024‐48248‐7 ↗
53. 10.1038/s41586‐020‐2387‐510.1038/s41586‐020‐2387‐5 ↗
54. 10.1111/nph.1869510.1111/nph.18695 ↗
55. 10.1038/ncomms1208310.1038/ncomms12083 ↗
56. 10.1038/ismej.2015.7810.1038/ismej.2015.78 ↗
57. 10.1034/j.1600‐0587.2002.250303.x10.1034/j.1600‐0587.2002.250303.x ↗
58. 10.1111/j.1461‐0248.2005.00848.x10.1111/j.1461‐0248.2005.00848.x ↗
59. 10.1002/ece3.52610.1002/ece3.526 ↗
60. 10.1128/msystems.00307‐2410.1128/msystems.00307‐24 ↗
61. 10.2307/354671210.2307/3546712 ↗
62. 10.1111/j.0030-1299.2006.14223.x10.1111/j.0030-1299.2006.14223.x ↗
63. 10.1073/pnas.122022811010.1073/pnas.1220228110 ↗
64. 10.1016/j.soilbio.2019.10767910.1016/j.soilbio.2019.107679 ↗
65. 10.1890/03‐900010.1890/03‐9000 ↗
66. 10.1126/science.106196710.1126/science.1061967 ↗
67. 10.1128/mBio.00240‐1510.1128/mBio.00240‐15 ↗
68. 10.1038/s41564‐022‐01147‐310.1038/s41564‐022‐01147‐3 ↗
69. 10.1186/s40168‐021‐01042‐910.1186/s40168‐021‐01042‐9 ↗
70. 10.1890/06‐1889.110.1890/06‐1889.1 ↗
71. 10.1111/j.1462‐2920.2007.01550.x10.1111/j.1462‐2920.2007.01550.x ↗
72. 10.3732/ajb.100049810.3732/ajb.1000498 ↗
73. 10.1111/j.1462‐2920.2005.00956.x10.1111/j.1462‐2920.2005.00956.x ↗