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Making sense of multivariate community responses in global change experiments

文献类型：期刊论文

作者Avolio, Meghan L.; Komatsu, Kimberly J.; Koerner, Sally E.; Grman, Emily4; Isbell, Forest5; Johnson, David S.; Wilcox, Kevin R.; Alatalo, Juha M.; Baldwin, Andrew H.; Beierkuhnlein, Carl19
刊名ECOSPHERE
出版日期2022
卷号13期号:10
ISSN号2150-8925
关键词centroids data synthesis dispersion dissimilarity metrics rank abundance curves richness
DOI10.1002/ecs2.4249
文献子类Article
英文摘要Ecological communities are being impacted by global change worldwide. Experiments are a powerful tool to understand how global change will impact communities by comparing control and treatment replicates. Communities consist of multiple species, and their associated abundances make multivariate methods an effective approach to study community compositional differences between control and treated replicates. Dissimilarity metrics are a commonly employed multivariate measure of compositional differences; however, while highly informative, dissimilarity metrics do not elucidate the specific ways in which communities differ. Integrating two multivariate methods, dissimilarity metrics and rank abundance curves (RACs), have the potential to detect complex differences based on dissimilarity metrics and detail the how these differences came about through differences in richness, evenness, species ranks, or species identity. Here we use a database of 106 global change experiments located in herbaceous ecosystems and explore how patterns of ordinations based on dissimilarity metrics relate to RAC-based differences. We find that combining dissimilarity metrics alongside RAC-based measures clarifies how global change treatments are altering communities. We find that when there is no difference in community composition (no distance between centroids of control and treated replicates), there are rarely differences in species ranks or species identities and more often differences in richness or evenness alone. In contrast, when there are differences between centroids of control and treated replicates, this is most often associated with differences in ranks either alone or co-occurring with differences in richness, evenness, or species identities. We suggest that integrating these two multivariate measures of community composition results in a deeper understanding of how global change impacts communities.
学科主题Ecology
出版地HOBOKEN
WOS关键词ABUNDANCE DISTRIBUTIONS ; CLIMATE-CHANGE ; BIODIVERSITY ; RICHNESS ; ADAPTATION ; MIGRATION ; DIVERSITY ; EVENNESS ; INDEXES ; PLANTS
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
语种英语
出版者WILEY
WOS记录号WOS:000865428900001
资助机构LTER Network [NSF EF 1545288, NSF EF 0553768]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/28819]  
专题植被与环境变化国家重点实验室
作者单位1.Reich, Peter B.] Univ Michigan, Inst Global Change Biol, Ann Arbor, MI 48109 USA
2.[Avolio, Meghan L.] Johns Hopkins Univ, Dept Earth & Planetary Sci, Baltimore, MD 21218 USA
3.Komatsu, Kimberly J.] Smithsonian Environm Res Ctr, POB 28, Edgewater, MD 21037 USA
4.Koerner, Sally E.] Univ N Carolina, Dept Biol, Greensboro, NC USA
5.Eastern Michigan Univ, Dept Biol, Ypsilanti, MI 48197 USA
6.McLaren, Jennie R.] Univ Texas El Paso, Dept Biol Sci, El Paso, TX 79968 USA
7.Reich, Peter B.] Univ Minnestoa, Dept Forest Resources, St Paul, MN USA
8.Reich, Peter B.] Univ Michigan, Inst Global Change Biol, St Paul, MN USA
9.Reich, Peter B.] Univ Michigan, Sch Environm & Sustainabil, Ann Arbor, MI 48109 USA
10.Reich, Peter B.] Western Sydney Univ, Hawkesbury Inst Environm, Sydney, NSW, Australia
推荐引用方式
GB/T 7714		 Avolio, Meghan L.,Komatsu, Kimberly J.,Koerner, Sally E.,et al. Making sense of multivariate community responses in global change experiments[J]. ECOSPHERE,2022,13(10).
APA Avolio, Meghan L..,Komatsu, Kimberly J..,Koerner, Sally E..,Grman, Emily.,Isbell, Forest.,...&Zhang, Yunhai.(2022).Making sense of multivariate community responses in global change experiments.ECOSPHERE,13(10).
MLA Avolio, Meghan L.,et al."Making sense of multivariate community responses in global change experiments".ECOSPHERE 13.10(2022).

入库方式： OAI收割

来源：植物研究所

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