Complex trait ‒ environment relationships underlie the structure of forest plant communities
1. Traits differentially adapt plant species to particular conditions generating compositional shifts along environmental gradients. As a result, community-scale trait values show concomitant shifts, termed trait‒environment relationships. Trait‒environment relationships are often assessed by evalua...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , |
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Materias: | |
| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2021rolhauser1.pdf Material complementario LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 05021cab a22004217a 4500 | ||
|---|---|---|---|
| 001 | 20220926140853.0 | ||
| 003 | AR-BaUFA | ||
| 005 | 20230405100337.0 | ||
| 008 | 220926t2021 xxkd||||o|||| 00| 0 eng d | ||
| 999 | |c 54996 |d 54996 | ||
| 999 | |d 54996 | ||
| 999 | |d 54996 | ||
| 999 | |d 54996 | ||
| 999 | |d 54996 | ||
| 999 | |d 54996 | ||
| 022 | |a 0022-0477 | ||
| 024 | |a 10.1111/1365-2745.13757 | ||
| 040 | |a AR-BaUFA |c AR-BaUFA | ||
| 100 | 1 | |9 12512 |a Rolhauser, Andrés Guillermo |u University of North Carolina at Chapel Hill. Department of Biology. Chapel Hill, NC, USA. |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. | |
| 245 | 0 | 0 | |a Complex trait ‒ environment relationships underlie the structure of forest plant communities |
| 520 | |a 1. Traits differentially adapt plant species to particular conditions generating compositional shifts along environmental gradients. As a result, community-scale trait values show concomitant shifts, termed trait‒environment relationships. Trait‒environment relationships are often assessed by evaluating community-weighted mean (CWM) traits observed along environmental gradients. Regression-based approaches (CWMr) assume that local communities exhibit traits centred at a single optimum value and that traits do not covary meaningfully. Evidence suggests that the shape of trait‒abundance relationships can vary widely along environmental gradients—reflecting complex interactions—and traits are usually interrelated. We used a model that accounts for these factors to explore trait‒environment relationships in herbaceous forest plant communities in Wisconsin (USA). 2. We built a generalized linear mixed model (GLMM) to analyse how abundances of 185 species distributed among 189 forested sites vary in response to four functional traits (vegetative height—VH, leaf size—LS, leaf mass per area—LMA and leaf carbon content), six environmental variables describing overstorey, soil and climate conditions, and their interactions. The GLMM allowed us to assess the nature and relative strength of the resulting 24 trait‒environment relationships. We also compared results between GLMM and CWMr to explore how conclusions differ between approaches. 3. The GLMM identified five significant trait‒environment relationships that together explain ~40% of variation in species abundances across sites. Temperature appeared as a key environmental driver, with warmer and more seasonal sites favouring taller plants. Soil texture and temperature seasonality affected LS and LMA; seasonality effects on LS and LMA were nonlinear, declining at more seasonal sites. Although often assumed for CWMr, only some traits under certain conditions had centred optimum trait‒abundance relationships. CWMr more liberally identified (13) trait‒environment relationships as significant but failed to detect the temperature seasonality‒LMA relationship identified by the GLMM. 4. Synthesis. Although GLMM represents a more methodologically complex approach than CWMr, it identified a reduced set of trait‒environment relationships still capable of accounting for the responses of forest understorey herbs to environmental gradients. It also identified separate effects of mean and seasonal temperature on LMA that appear important in these forests, generating usefulinsights and supporting broader application of GLMM approach to understand trait‒environment relationships. | ||
| 653 | |a CLIMATE SEASONALITY | ||
| 653 | |a COMMUNITY ASSEMBLY | ||
| 653 | |a FUNCTIONAL TRAIT ANALYSIS | ||
| 653 | |a GENERALIZED LINEAR MIXED MODEL | ||
| 653 | |a LEAF TRAITS | ||
| 653 | |a MEAN ANNUAL TEMPERATURE | ||
| 653 | |a PLANT HEIGHT | ||
| 653 | |a SOIL TEXTURE | ||
| 700 | 1 | |a Waller, Donald M. |u University of Wisconsin - Madison. Department of Botany. Madison, WI, USA. |9 74069 | |
| 700 | 1 | |a Tucker, Caroline M. |u University of North Carolina at Chapel Hill. Department of Biology. Chapel Hill, NC, USA. |u University of North Carolina at Chapel Hill. Environment, Ecology and Energy Program. Chapel Hill, NC, USA. |9 74070 | |
| 773 | 0 | |t Journal of ecology |w (AR-BaUFA)SECS000112 |g Vol.109, no.11 (2021), p.3794–3806, grafs. | |
| 856 | |f 2021rolhauser1 |i En reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2021rolhauser1.pdf |x ARTI202210 | ||
| 856 | |z Material complementario |f 2021rolhauser1-c1 |i En reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2021rolhauser1-c1.pdf | ||
| 856 | |u http://www.journalofecology.org/ |z LINK AL EDITOR | ||
| 942 | |c ARTICULO |n 1 | ||
| 942 | |c ENLINEA | ||
| 976 | |a AAG | ||