Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms

Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-s...
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Autor*in:	Vishwakarma, Rishabh [verfasserIn] Byrne, Laura Connolly, John de Andrade Moral, Rafael Brophy, Caroline

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Biodiversity and ecosystem function relationship Diversity-Interactions modelling Model selection Non-linear model selection Simulation study

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Environmental and ecological statistics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 30(2023), 3 vom: 06. Aug., Seite 555-574
Übergeordnetes Werk:	volume:30 ; year:2023 ; number:3 ; day:06 ; month:08 ; pages:555-574

Links:	Volltext

DOI / URN:	10.1007/s10651-023-00563-w

Katalog-ID:	SPR053074963

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100	1		\|a Vishwakarma, Rishabh \|e verfasserin \|4 aut
245	1	0	\|a Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
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520			\|a Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures.
650		4	\|a Biodiversity and ecosystem function relationship \|7 (dpeaa)DE-He213
650		4	\|a Diversity-Interactions modelling \|7 (dpeaa)DE-He213
650		4	\|a Model selection \|7 (dpeaa)DE-He213
650		4	\|a Non-linear model selection \|7 (dpeaa)DE-He213
650		4	\|a Simulation study \|7 (dpeaa)DE-He213
700	1		\|a Byrne, Laura \|4 aut
700	1		\|a Connolly, John \|4 aut
700	1		\|a de Andrade Moral, Rafael \|4 aut
700	1		\|a Brophy, Caroline \|4 aut
773	0	8	\|i Enthalten in \|t Environmental and ecological statistics \|d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 \|g 30(2023), 3 vom: 06. Aug., Seite 555-574 \|w (DE-627)312625294 \|w (DE-600)2000906-9 \|x 1573-3009 \|7 nnns
773	1	8	\|g volume:30 \|g year:2023 \|g number:3 \|g day:06 \|g month:08 \|g pages:555-574
856	4	0	\|u https://dx.doi.org/10.1007/s10651-023-00563-w \|z kostenfrei \|3 Volltext
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allfields	10.1007/s10651-023-00563-w doi (DE-627)SPR053074963 (SPR)s10651-023-00563-w-e DE-627 ger DE-627 rakwb eng Vishwakarma, Rishabh verfasserin aut Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213 Byrne, Laura aut Connolly, John aut de Andrade Moral, Rafael aut Brophy, Caroline aut Enthalten in Environmental and ecological statistics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 30(2023), 3 vom: 06. Aug., Seite 555-574 (DE-627)312625294 (DE-600)2000906-9 1573-3009 nnns volume:30 year:2023 number:3 day:06 month:08 pages:555-574 https://dx.doi.org/10.1007/s10651-023-00563-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2023 3 06 08 555-574
spelling	10.1007/s10651-023-00563-w doi (DE-627)SPR053074963 (SPR)s10651-023-00563-w-e DE-627 ger DE-627 rakwb eng Vishwakarma, Rishabh verfasserin aut Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213 Byrne, Laura aut Connolly, John aut de Andrade Moral, Rafael aut Brophy, Caroline aut Enthalten in Environmental and ecological statistics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 30(2023), 3 vom: 06. Aug., Seite 555-574 (DE-627)312625294 (DE-600)2000906-9 1573-3009 nnns volume:30 year:2023 number:3 day:06 month:08 pages:555-574 https://dx.doi.org/10.1007/s10651-023-00563-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2023 3 06 08 555-574
allfields_unstemmed	10.1007/s10651-023-00563-w doi (DE-627)SPR053074963 (SPR)s10651-023-00563-w-e DE-627 ger DE-627 rakwb eng Vishwakarma, Rishabh verfasserin aut Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213 Byrne, Laura aut Connolly, John aut de Andrade Moral, Rafael aut Brophy, Caroline aut Enthalten in Environmental and ecological statistics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 30(2023), 3 vom: 06. Aug., Seite 555-574 (DE-627)312625294 (DE-600)2000906-9 1573-3009 nnns volume:30 year:2023 number:3 day:06 month:08 pages:555-574 https://dx.doi.org/10.1007/s10651-023-00563-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2023 3 06 08 555-574
allfieldsGer	10.1007/s10651-023-00563-w doi (DE-627)SPR053074963 (SPR)s10651-023-00563-w-e DE-627 ger DE-627 rakwb eng Vishwakarma, Rishabh verfasserin aut Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213 Byrne, Laura aut Connolly, John aut de Andrade Moral, Rafael aut Brophy, Caroline aut Enthalten in Environmental and ecological statistics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 30(2023), 3 vom: 06. Aug., Seite 555-574 (DE-627)312625294 (DE-600)2000906-9 1573-3009 nnns volume:30 year:2023 number:3 day:06 month:08 pages:555-574 https://dx.doi.org/10.1007/s10651-023-00563-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2023 3 06 08 555-574
allfieldsSound	10.1007/s10651-023-00563-w doi (DE-627)SPR053074963 (SPR)s10651-023-00563-w-e DE-627 ger DE-627 rakwb eng Vishwakarma, Rishabh verfasserin aut Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213 Byrne, Laura aut Connolly, John aut de Andrade Moral, Rafael aut Brophy, Caroline aut Enthalten in Environmental and ecological statistics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 30(2023), 3 vom: 06. Aug., Seite 555-574 (DE-627)312625294 (DE-600)2000906-9 1573-3009 nnns volume:30 year:2023 number:3 day:06 month:08 pages:555-574 https://dx.doi.org/10.1007/s10651-023-00563-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 30 2023 3 06 08 555-574
language	English
source	Enthalten in Environmental and ecological statistics 30(2023), 3 vom: 06. Aug., Seite 555-574 volume:30 year:2023 number:3 day:06 month:08 pages:555-574
sourceStr	Enthalten in Environmental and ecological statistics 30(2023), 3 vom: 06. Aug., Seite 555-574 volume:30 year:2023 number:3 day:06 month:08 pages:555-574
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Biodiversity and ecosystem function relationship Diversity-Interactions modelling Model selection Non-linear model selection Simulation study
isfreeaccess_bool	true
container_title	Environmental and ecological statistics
authorswithroles_txt_mv	Vishwakarma, Rishabh @@aut@@ Byrne, Laura @@aut@@ Connolly, John @@aut@@ de Andrade Moral, Rafael @@aut@@ Brophy, Caroline @@aut@@
publishDateDaySort_date	2023-08-06T00:00:00Z
hierarchy_top_id	312625294
id	SPR053074963
language_de	englisch
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author	Vishwakarma, Rishabh
spellingShingle	Vishwakarma, Rishabh misc Biodiversity and ecosystem function relationship misc Diversity-Interactions modelling misc Model selection misc Non-linear model selection misc Simulation study Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
authorStr	Vishwakarma, Rishabh
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topic_title	Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms Biodiversity and ecosystem function relationship (dpeaa)DE-He213 Diversity-Interactions modelling (dpeaa)DE-He213 Model selection (dpeaa)DE-He213 Non-linear model selection (dpeaa)DE-He213 Simulation study (dpeaa)DE-He213
topic	misc Biodiversity and ecosystem function relationship misc Diversity-Interactions modelling misc Model selection misc Non-linear model selection misc Simulation study
topic_unstemmed	misc Biodiversity and ecosystem function relationship misc Diversity-Interactions modelling misc Model selection misc Non-linear model selection misc Simulation study
topic_browse	misc Biodiversity and ecosystem function relationship misc Diversity-Interactions modelling misc Model selection misc Non-linear model selection misc Simulation study
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title	Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
ctrlnum	(DE-627)SPR053074963 (SPR)s10651-023-00563-w-e
title_full	Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
author_sort	Vishwakarma, Rishabh
journal	Environmental and ecological statistics
journalStr	Environmental and ecological statistics
lang_code	eng
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author_browse	Vishwakarma, Rishabh Byrne, Laura Connolly, John de Andrade Moral, Rafael Brophy, Caroline
container_volume	30
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author-letter	Vishwakarma, Rishabh
doi_str_mv	10.1007/s10651-023-00563-w
title_sort	estimation of the non-linear parameter in generalised diversity-interactions models is unaffected by change in structure of the interaction terms
title_auth	Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
abstract	Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. © The Author(s) 2023
abstractGer	Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. © The Author(s) 2023
abstract_unstemmed	Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures. © The Author(s) 2023
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title_short	Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms
url	https://dx.doi.org/10.1007/s10651-023-00563-w
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author2	Byrne, Laura Connolly, John de Andrade Moral, Rafael Brophy, Caroline
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doi_str	10.1007/s10651-023-00563-w
up_date	2024-07-03T16:52:56.407Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053074963</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230915064643.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230915s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10651-023-00563-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053074963</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10651-023-00563-w-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vishwakarma, Rishabh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Many approaches for modelling the biodiversity and ecosystem function (BEF) relationship have been developed over recent decades. Diversity-Interactions modelling, a regression-based approach, models the BEF relationship by expressing ecosystem functions as a linear combination of species-specific effects, species’ proportions, and species’ interactions. The species interactions in a Diversity-Interactions model can take different forms (e.g., a unique interaction term for each pair of species, or a single interaction term for any pair of species) and may include a non-linear parameter (%$\theta%$) as an exponent to the species interactions to capture non-linear relationships, giving rise to Generalized Diversity-Interactions (GDI) modelling. The structure of the interaction terms describes the underlying biological processes in the ecosystem, while the value of %$\theta%$ can determine the shape of the BEF relationship. When fitting GDI models, it is unclear whether one should choose the interaction structure first and then estimate θ, or vice versa. It is also unknown whether the estimate of %$\theta%$ is robust to changes in the structure of the linear interaction terms of the model. Using a simulation study, we test the robustness of %$\theta%$ and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for GDI models. Results show that the estimate of %$\theta%$ is robust and remains unbiased regardless of changes in the underlying structure of interaction terms, and that the most efficient model selection procedure is to first estimate %$\theta%$ for one interaction structure and then reuse this estimate for the other interaction structures.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biodiversity and ecosystem function relationship</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diversity-Interactions modelling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-linear model selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation study</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Byrne, Laura</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Connolly, John</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">de Andrade Moral, Rafael</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brophy, Caroline</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environmental and ecological statistics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994</subfield><subfield code="g">30(2023), 3 vom: 06. 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Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms

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