Background: Ecologists are interested in assessing the spatial and temporal variation in ecological surveys repeated over time. This paper compares the 1985 and 2015 surveys of the Barro Colorado Forest Dynamics plot(...Background: Ecologists are interested in assessing the spatial and temporal variation in ecological surveys repeated over time. This paper compares the 1985 and 2015 surveys of the Barro Colorado Forest Dynamics plot(BCI), Panama,divided into 1250(20 m × 20 m) quadrats.Methods, spatial analysis: Total beta diversity was measured as the total variance of the Hellinger-transformed community data throughout the BCI plot. Total beta was partitioned into contributions of individual sites(LCBD indices), which were tested for significance and mapped.Results, spatial analysis: LCBD indices indicated the sites with exceptional community composition. In 1985,they were mostly found in the swamp habitat. In the 2015 survey, none of the swamp quadrats had significant LCBDs.What happened to the tree community in the interval?Methods, temporal analysis: The dissimilarity in community composition in each quadrat was measured between time 1(1985) and time 2(2015). Temporal Beta Indices(TBI) were computed from abundance and presence-absence data and tested for significance. TBI indices can be decomposed into B = species(or abundances-per-species) losses and C = species(or abundances-per-species) gains. B-C plots were produced; they display visually the relative importance of the loss and gain components, through time, across the sites.Results, temporal analysis: In BCI, quadrats with significant TBI indices were found in the swamp area, which is shrinking in importance due to changes to the local climate. A published habitat classification divided the BCI forest plot into six habitat zones. Graphs of the B and C components were produced for each habitat group. Group 4(the swamp) was dominated by species(and abundances-per-species) gains whereas the five other habitat groups were dominated by losses, some groups more than others.Conclusions: We identified the species that had changed the most in abundances in the swamp between T1 and T2.This analysis supported the hypothesis that the swamp is drying out and is invaded by species from the surrounding area. Analysis of the B and C components of temporal beta diversity bring us to the heart of the mechanisms of community change through time: losses(B) and gains(C) of species, losses and gains of individuals of various species. TBI analysis is especially interesting in species-rich communities where we cannot examine the changes in every species individually.展开更多
Aims Beta diversity is the variation in species composition among sites in a geographic region.Beta diversity is a key concept for understanding the functioning of ecosystems,for the conservation of biodiversity and f...Aims Beta diversity is the variation in species composition among sites in a geographic region.Beta diversity is a key concept for understanding the functioning of ecosystems,for the conservation of biodiversity and for ecosystem management.The present report describes how to analyse beta diversity from community composition and associated environmental and spatial data tables.Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites.Alternatively,one can carry out a direct analysis of the community composition data table over the study sites,as a function of sets of environmental and spatial variables.These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data table with respect to environmental and spatial variables.Variation partitioning is briefly described herein.Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables.Beta diversity is an interesting‘currency’for ecologists to compare either different sampling areas or different ecological communities cooccurring in an area.Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables.The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis.After partitioning,one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.展开更多
基金support of the U.S. National Science Foundation (awards 8206992, 8906869, 9405933, 9909947, 0948585 to S.P. Hubbell)the John D. and Catherine D. McArthur Foundation+1 种基金the Smithsonian Tropical Research Institutesupported by research grant #7738 from the Natural Sciences and Engineering Research Council of Canada (NSERC) to P. Legendre
文摘Background: Ecologists are interested in assessing the spatial and temporal variation in ecological surveys repeated over time. This paper compares the 1985 and 2015 surveys of the Barro Colorado Forest Dynamics plot(BCI), Panama,divided into 1250(20 m × 20 m) quadrats.Methods, spatial analysis: Total beta diversity was measured as the total variance of the Hellinger-transformed community data throughout the BCI plot. Total beta was partitioned into contributions of individual sites(LCBD indices), which were tested for significance and mapped.Results, spatial analysis: LCBD indices indicated the sites with exceptional community composition. In 1985,they were mostly found in the swamp habitat. In the 2015 survey, none of the swamp quadrats had significant LCBDs.What happened to the tree community in the interval?Methods, temporal analysis: The dissimilarity in community composition in each quadrat was measured between time 1(1985) and time 2(2015). Temporal Beta Indices(TBI) were computed from abundance and presence-absence data and tested for significance. TBI indices can be decomposed into B = species(or abundances-per-species) losses and C = species(or abundances-per-species) gains. B-C plots were produced; they display visually the relative importance of the loss and gain components, through time, across the sites.Results, temporal analysis: In BCI, quadrats with significant TBI indices were found in the swamp area, which is shrinking in importance due to changes to the local climate. A published habitat classification divided the BCI forest plot into six habitat zones. Graphs of the B and C components were produced for each habitat group. Group 4(the swamp) was dominated by species(and abundances-per-species) gains whereas the five other habitat groups were dominated by losses, some groups more than others.Conclusions: We identified the species that had changed the most in abundances in the swamp between T1 and T2.This analysis supported the hypothesis that the swamp is drying out and is invaded by species from the surrounding area. Analysis of the B and C components of temporal beta diversity bring us to the heart of the mechanisms of community change through time: losses(B) and gains(C) of species, losses and gains of individuals of various species. TBI analysis is especially interesting in species-rich communities where we cannot examine the changes in every species individually.
基金Funding was provided by Natural Sciences and Engineering Research Council of Canada(NSERC)grant no.OGP0007738 to P.L.
文摘Aims Beta diversity is the variation in species composition among sites in a geographic region.Beta diversity is a key concept for understanding the functioning of ecosystems,for the conservation of biodiversity and for ecosystem management.The present report describes how to analyse beta diversity from community composition and associated environmental and spatial data tables.Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites.Alternatively,one can carry out a direct analysis of the community composition data table over the study sites,as a function of sets of environmental and spatial variables.These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data table with respect to environmental and spatial variables.Variation partitioning is briefly described herein.Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables.Beta diversity is an interesting‘currency’for ecologists to compare either different sampling areas or different ecological communities cooccurring in an area.Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables.The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis.After partitioning,one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.
