Compositional variation in grassland plant communities

Jonathan D. Bakker; Jodi N. Price; Jeremiah A. Henning; Evan E. Batzer; Timothy J. Ohlert; Claire E. Wainwright; Peter B. Adler; Juan Alberti; Carlos Alberto Arnillas; Lori A. Biederman; Elizabeth T. Borer; Lars A. Brudvig; Yvonne M. Buckley; Miguel N. Bugalho; Marc W. Cadotte; Maria C. Caldeira; Jane A. Catford; Qingqing Chen; Michael J. Crawley; Pedro Daleo; Chris R. Dickman; Ian Donohue; Mary Ellyn DuPre; Anne Ebeling; Nico Eisenhauer; Philip A. Fay; Daniel S. Gruner; Sylvia Haider; Yann Hautier; Anke Jentsch; Kevin Kirkman; Johannes M. H. Knops; Lucíola S. Lannes; Andrew S. MacDougall; Rebecca L. McCulley; Rachel M. Mitchell; Joslin L. Moore; John W. Morgan; Brent Mortensen; Harry Olde Venterink; Pablo L. Peri; Sally A. Power; Suzanne M. Prober; Christiane Roscher; Mahesh Sankaran; Eric W. Seabloom; Melinda D. Smith; Carly Stevens; Lauren L. Sullivan; Michelle Tedder; G. F. (Ciska) Veen; Risto Virtanen; Glenda M. Wardle

doi:10.1002/ecs2.4542

Compositional variation in grassland plant communities

Jonathan D. Bakker^* (Co-auteur), Jodi N. Price, Jeremiah A. Henning, Evan E. Batzer, Timothy J. Ohlert, Claire E. Wainwright, Peter B. Adler, Juan Alberti, Carlos Alberto Arnillas, Lori A. Biederman, Elizabeth T. Borer, Lars A. Brudvig, Yvonne M. Buckley, Miguel N. Bugalho, Marc W. Cadotte, Maria C. Caldeira, Jane A. Catford, Qingqing Chen, Michael J. Crawley, Pedro DaleoChris R. Dickman, Ian Donohue, Mary Ellyn DuPre, Anne Ebeling, Nico Eisenhauer, Philip A. Fay, Daniel S. Gruner, Sylvia Haider, Yann Hautier, Anke Jentsch, Kevin Kirkman, Johannes M. H. Knops, Lucíola S. Lannes, Andrew S. MacDougall, Rebecca L. McCulley, Rachel M. Mitchell, Joslin L. Moore, John W. Morgan, Brent Mortensen, Harry Olde Venterink, Pablo L. Peri, Sally A. Power, Suzanne M. Prober, Christiane Roscher, Mahesh Sankaran, Eric W. Seabloom, Melinda D. Smith, Carly Stevens, Lauren L. Sullivan, Michelle Tedder, G. F. (Ciska) Veen, Risto Virtanen, Glenda M. Wardle

^*Bijbehorende auteur voor dit werk

NIOO - Terrestrial Ecology (TE)

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgave › Artikel › Wetenschappelijk › peer review

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Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.

Originele taal-2	Engels
Artikelnummer	e4542
Aantal pagina's	17
Tijdschrift	Ecosphere
DOI's	https://doi.org/10.1002/ecs2.4542
Status	Gepubliceerd - 01 jun. 2023

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10.1002/ecs2.4542Licentie: CC BY

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Bakker, J. D., Price, J. N., Henning, J. A., Batzer, E. E., Ohlert, T. J., Wainwright, C. E., Adler, P. B., Alberti, J., Arnillas, C. A., Biederman, L. A., Borer, E. T., Brudvig, L. A., Buckley, Y. M., Bugalho, M. N., Cadotte, M. W., Caldeira, M. C., Catford, J. A., Chen, Q., Crawley, M. J., ... Wardle, G. M. (2023). Compositional variation in grassland plant communities. Ecosphere, [e4542]. https://doi.org/10.1002/ecs2.4542

@article{3a804dee183f40079b2933d7db4e907b,

title = "Compositional variation in grassland plant communities",

abstract = "Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.",

author = "Bakker, {Jonathan D.} and Price, {Jodi N.} and Henning, {Jeremiah A.} and Batzer, {Evan E.} and Ohlert, {Timothy J.} and Wainwright, {Claire E.} and Adler, {Peter B.} and Juan Alberti and Arnillas, {Carlos Alberto} and Biederman, {Lori A.} and Borer, {Elizabeth T.} and Brudvig, {Lars A.} and Buckley, {Yvonne M.} and Bugalho, {Miguel N.} and Cadotte, {Marc W.} and Caldeira, {Maria C.} and Catford, {Jane A.} and Qingqing Chen and Crawley, {Michael J.} and Pedro Daleo and Dickman, {Chris R.} and Ian Donohue and DuPre, {Mary Ellyn} and Anne Ebeling and Nico Eisenhauer and Fay, {Philip A.} and Gruner, {Daniel S.} and Sylvia Haider and Yann Hautier and Anke Jentsch and Kevin Kirkman and Knops, {Johannes M. H.} and Lannes, {Luc{\'i}ola S.} and MacDougall, {Andrew S.} and McCulley, {Rebecca L.} and Mitchell, {Rachel M.} and Moore, {Joslin L.} and Morgan, {John W.} and Brent Mortensen and {Olde Venterink}, Harry and Peri, {Pablo L.} and Power, {Sally A.} and Prober, {Suzanne M.} and Christiane Roscher and Mahesh Sankaran and Seabloom, {Eric W.} and Smith, {Melinda D.} and Carly Stevens and Sullivan, {Lauren L.} and Michelle Tedder and Veen, {G. F. (Ciska)} and Risto Virtanen and Wardle, {Glenda M.}",

note = "Data archiving: no NIOO data",

year = "2023",

month = jun,

day = "1",

doi = "10.1002/ecs2.4542",

language = "English",

journal = "Ecosphere",

issn = "2150-8925",

publisher = "Ecological Society of America",

}

Bakker, JD, Price, JN, Henning, JA, Batzer, EE, Ohlert, TJ, Wainwright, CE, Adler, PB, Alberti, J, Arnillas, CA, Biederman, LA, Borer, ET, Brudvig, LA, Buckley, YM, Bugalho, MN, Cadotte, MW, Caldeira, MC, Catford, JA, Chen, Q, Crawley, MJ, Daleo, P, Dickman, CR, Donohue, I, DuPre, ME, Ebeling, A, Eisenhauer, N, Fay, PA, Gruner, DS, Haider, S, Hautier, Y, Jentsch, A, Kirkman, K, Knops, JMH, Lannes, LS, MacDougall, AS, McCulley, RL, Mitchell, RM, Moore, JL, Morgan, JW, Mortensen, B, Olde Venterink, H, Peri, PL, Power, SA, Prober, SM, Roscher, C, Sankaran, M, Seabloom, EW, Smith, MD, Stevens, C, Sullivan, LL, Tedder, M, Veen, GF, Virtanen, R & Wardle, GM 2023, 'Compositional variation in grassland plant communities', Ecosphere. https://doi.org/10.1002/ecs2.4542

TY - JOUR

T1 - Compositional variation in grassland plant communities

AU - Bakker, Jonathan D.

AU - Price, Jodi N.

AU - Henning, Jeremiah A.

AU - Batzer, Evan E.

AU - Ohlert, Timothy J.

AU - Wainwright, Claire E.

AU - Adler, Peter B.

AU - Alberti, Juan

AU - Arnillas, Carlos Alberto

AU - Biederman, Lori A.

AU - Borer, Elizabeth T.

AU - Brudvig, Lars A.

AU - Buckley, Yvonne M.

AU - Bugalho, Miguel N.

AU - Cadotte, Marc W.

AU - Caldeira, Maria C.

AU - Catford, Jane A.

AU - Chen, Qingqing

AU - Crawley, Michael J.

AU - Daleo, Pedro

AU - Dickman, Chris R.

AU - Donohue, Ian

AU - DuPre, Mary Ellyn

AU - Ebeling, Anne

AU - Eisenhauer, Nico

AU - Fay, Philip A.

AU - Gruner, Daniel S.

AU - Haider, Sylvia

AU - Hautier, Yann

AU - Jentsch, Anke

AU - Kirkman, Kevin

AU - Knops, Johannes M. H.

AU - Lannes, Lucíola S.

AU - MacDougall, Andrew S.

AU - McCulley, Rebecca L.

AU - Mitchell, Rachel M.

AU - Moore, Joslin L.

AU - Morgan, John W.

AU - Mortensen, Brent

AU - Olde Venterink, Harry

AU - Peri, Pablo L.

AU - Power, Sally A.

AU - Prober, Suzanne M.

AU - Roscher, Christiane

AU - Sankaran, Mahesh

AU - Seabloom, Eric W.

AU - Smith, Melinda D.

AU - Stevens, Carly

AU - Sullivan, Lauren L.

AU - Tedder, Michelle

AU - Veen, G. F. (Ciska)

AU - Virtanen, Risto

AU - Wardle, Glenda M.

N1 - Data archiving: no NIOO data

PY - 2023/6/1

Y1 - 2023/6/1

N2 - Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.

AB - Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.

U2 - 10.1002/ecs2.4542

DO - 10.1002/ecs2.4542

M3 - Article

SN - 2150-8925

JO - Ecosphere

JF - Ecosphere

M1 - e4542

ER -

Compositional variation in grassland plant communities

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