Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?
A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equ...
সংরক্ষণ করুন:
| প্রধান লেখক: | |
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| বিন্যাস: | article |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
2018
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://hdl.handle.net/20.500.12008/31683 |
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| _version_ | 1868890227478626304 |
|---|---|
| author | Fort, Hugo |
| author_browse | Fort, Hugo |
| author_facet | Fort, Hugo |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Fort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física. |
| dc.creator.none.fl_str_mv | Fort, Hugo |
| dc.date.none.fl_str_mv | 2018 2022-05-27T12:46:13Z 2022-05-27T12:46:13Z |
| dc.format.none.fl_str_mv | 4 h application/pdf |
| dc.identifier.none.fl_str_mv | Fort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h. 1585-8553 https://hdl.handle.net/20.500.12008/31683 10.1556/168.2018.19.2.12 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Akadémiai Kiadó, Budapest |
| dc.relation.none.fl_str_mv | Community Ecology, 2018, 19: 199–202 |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Biodiversity–ecosystem functioning experiments Quantitative Lotka-Volterra competition theory |
| dc.title.none.fl_str_mv | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_ef25b09d7cf1c4ea5eb8f8613963dc96 |
| identifier_str_mv | Fort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h. 1585-8553 10.1556/168.2018.19.2.12 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/31683 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher.none.fl_str_mv | Akadémiai Kiadó, Budapest |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?Fort, HugoBiodiversity–ecosystem functioning experimentsQuantitative Lotka-Volterra competition theoryA main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.Akadémiai Kiadó, BudapestFort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.2022-05-27T12:46:13Z2022-05-27T12:46:13Z2018Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion4 happlication/pdfFort, H. "Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?". Community Ecology. [en línea] 2018, 19: 199–202. 4 h.1585-8553https://hdl.handle.net/20.500.12008/3168310.1556/168.2018.19.2.12reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengCommunity Ecology, 2018, 19: 199–202Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/316832026-04-14T10:09:48Z |
| spellingShingle | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? Fort, Hugo Biodiversity–ecosystem functioning experiments Quantitative Lotka-Volterra competition theory |
| status_str | publishedVersion |
| title | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| title_full | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| title_fullStr | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| title_full_unstemmed | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| title_short | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| title_sort | Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters? |
| topic | Biodiversity–ecosystem functioning experiments Quantitative Lotka-Volterra competition theory |
| url | https://hdl.handle.net/20.500.12008/31683 |