Macroecological patterns in experimental microbial communities.
Ecology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captur...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-05-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013044 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849337295251963904 |
|---|---|
| author | William R Shoemaker Álvaro Sánchez Jacopo Grilli |
| author_facet | William R Shoemaker Álvaro Sánchez Jacopo Grilli |
| author_sort | William R Shoemaker |
| collection | DOAJ |
| description | Ecology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We found that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline. |
| format | Article |
| id | doaj-art-e7b263b5c12640d1afc2baafddae0c74 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e7b263b5c12640d1afc2baafddae0c742025-08-20T03:44:45ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-05-01215e101304410.1371/journal.pcbi.1013044Macroecological patterns in experimental microbial communities.William R ShoemakerÁlvaro SánchezJacopo GrilliEcology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We found that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline.https://doi.org/10.1371/journal.pcbi.1013044 |
| spellingShingle | William R Shoemaker Álvaro Sánchez Jacopo Grilli Macroecological patterns in experimental microbial communities. PLoS Computational Biology |
| title | Macroecological patterns in experimental microbial communities. |
| title_full | Macroecological patterns in experimental microbial communities. |
| title_fullStr | Macroecological patterns in experimental microbial communities. |
| title_full_unstemmed | Macroecological patterns in experimental microbial communities. |
| title_short | Macroecological patterns in experimental microbial communities. |
| title_sort | macroecological patterns in experimental microbial communities |
| url | https://doi.org/10.1371/journal.pcbi.1013044 |
| work_keys_str_mv | AT williamrshoemaker macroecologicalpatternsinexperimentalmicrobialcommunities AT alvarosanchez macroecologicalpatternsinexperimentalmicrobialcommunities AT jacopogrilli macroecologicalpatternsinexperimentalmicrobialcommunities |