Bell: The "Bell" Dataset
In DImodels: Diversity-Interactions (DI) Models
Bell R Documentation
The "Bell" Dataset
Description
This dataset comes from a bacterial biodiversity experiment (Bell et al 2005). The bacterial ecosystems used were from semi-permanent rainpools that form in bark-lined depressions near the base of large European beech trees (Fagus sylvatica). Microcosms consisting of sterile beech leaf disks and 10 ml of liquid (phosphate buffer) were inoculated with random combinations of 72 bacterial species isolated from these ecosystems. A total of 1,374 microcosms were constructed at richness levels of 1, 2, 3, 4, 6, 8, 9, 12, 18, 24, 36 and 72 species. The daily respiration rate of the bacterial
community in each microcosm was measured over three time intervals (days 0-7, 7-14 and
14-28) and the average over the three time intervals was recorded.
Usage
data("Bell")
Format
A data frame with 1374 observations on the following 76 variables:
idA numeric vector uniquely identifying each row of the dataset.
communityA numeric vector identifying each unique community, i.e., two rows with the same community value also share the same set of p1 to p72 values.
richnessThe number of species included in the initial composition, i.e., the number of proportions from p1 to p72 that are >0.
p1A numeric vector indicating the initial proportion of species 1 in the community.
p2A numeric vector indicating the initial proportion of species 2 in the community.
p3A numeric vector indicating the initial proportion of species 3 in the community.
p4A numeric vector indicating the initial proportion of species 4 in the community.
p5A numeric vector indicating the initial proportion of species 5 in the community.
p6A numeric vector indicating the initial proportion of species 6 in the community.
p7A numeric vector indicating the initial proportion of species 7 in the community.
p8A numeric vector indicating the initial proportion of species 8 in the community.
p9A numeric vector indicating the initial proportion of species 9 in the community.
p10A numeric vector indicating the initial proportion of species 10 in the community.
p11A numeric vector indicating the initial proportion of species 11 in the community.
p12A numeric vector indicating the initial proportion of species 12 in the community.
p13A numeric vector indicating the initial proportion of species 13 in the community.
p14A numeric vector indicating the initial proportion of species 14 in the community.
p15A numeric vector indicating the initial proportion of species 15 in the community.
p16A numeric vector indicating the initial proportion of species 16 in the community.
p17A numeric vector indicating the initial proportion of species 17 in the community.
p18A numeric vector indicating the initial proportion of species 18 in the community.
p19A numeric vector indicating the initial proportion of species 19 in the community.
p20A numeric vector indicating the initial proportion of species 20 in the community.
p21A numeric vector indicating the initial proportion of species 21 in the community.
p22A numeric vector indicating the initial proportion of species 22 in the community.
p23A numeric vector indicating the initial proportion of species 23 in the community.
p24A numeric vector indicating the initial proportion of species 24 in the community.
p25A numeric vector indicating the initial proportion of species 25 in the community.
p26A numeric vector indicating the initial proportion of species 26 in the community.
p27A numeric vector indicating the initial proportion of species 27 in the community.
p28A numeric vector indicating the initial proportion of species 28 in the community.
p29A numeric vector indicating the initial proportion of species 29 in the community.
p30A numeric vector indicating the initial proportion of species 30 in the community.
p31A numeric vector indicating the initial proportion of species 31 in the community.
p32A numeric vector indicating the initial proportion of species 32 in the community.
p33A numeric vector indicating the initial proportion of species 33 in the community.
p34A numeric vector indicating the initial proportion of species 34 in the community.
p35A numeric vector indicating the initial proportion of species 35 in the community.
p36A numeric vector indicating the initial proportion of species 36 in the community.
p37A numeric vector indicating the initial proportion of species 37 in the community.
p38A numeric vector indicating the initial proportion of species 38 in the community.
p39A numeric vector indicating the initial proportion of species 39 in the community.
p40A numeric vector indicating the initial proportion of species 40 in the community.
p41A numeric vector indicating the initial proportion of species 41 in the community.
p42A numeric vector indicating the initial proportion of species 42 in the community.
p43A numeric vector indicating the initial proportion of species 43 in the community.
p44A numeric vector indicating the initial proportion of species 44 in the community.
p45A numeric vector indicating the initial proportion of species 45 in the community.
p46A numeric vector indicating the initial proportion of species 46 in the community.
p47A numeric vector indicating the initial proportion of species 47 in the community.
p48A numeric vector indicating the initial proportion of species 48 in the community.
p49A numeric vector indicating the initial proportion of species 49 in the community.
p50A numeric vector indicating the initial proportion of species 50 in the community.
p51A numeric vector indicating the initial proportion of species 51 in the community.
p52A numeric vector indicating the initial proportion of species 52 in the community.
p53A numeric vector indicating the initial proportion of species 53 in the community.
p54A numeric vector indicating the initial proportion of species 54 in the community.
p55A numeric vector indicating the initial proportion of species 55 in the community.
p56A numeric vector indicating the initial proportion of species 56 in the community.
p57A numeric vector indicating the initial proportion of species 57 in the community.
p58A numeric vector indicating the initial proportion of species 58 in the community.
p59A numeric vector indicating the initial proportion of species 59 in the community.
p60A numeric vector indicating the initial proportion of species 60 in the community.
p61A numeric vector indicating the initial proportion of species 61 in the community.
p62A numeric vector indicating the initial proportion of species 62 in the community.
p63A numeric vector indicating the initial proportion of species 63 in the community.
p64A numeric vector indicating the initial proportion of species 64 in the community.
p65A numeric vector indicating the initial proportion of species 65 in the community.
p66A numeric vector indicating the initial proportion of species 66 in the community.
p67A numeric vector indicating the initial proportion of species 67 in the community.
p68A numeric vector indicating the initial proportion of species 68 in the community.
p69A numeric vector indicating the initial proportion of species 69 in the community.
p70A numeric vector indicating the initial proportion of species 70 in the community.
p71A numeric vector indicating the initial proportion of species 71 in the community.
p72A numeric vector indicating the initial proportion of species 72 in the community.
responseA numeric vector giving the average daily respiration rate of the bacterial community.
Details
What are Diversity-Interactions (DI) models?
Diversity-Interactions (DI) models (Kirwan et al 2009) are a set of tools for analysing and interpreting data from experiments that explore the effects of species diversity on community-level responses. We strongly recommend that users read the short introduction to Diversity-Interactions models (available at: DImodels). Further information on Diversity-Interactions models is also available in Kirwan et al 2009 and Connolly et al 2013.
The Bell dataset is analysed using Diversity-Interactions models in both Brophy et al 2017 and Connolly et al 2013.
Source
Bell T, JA Newman, BW Silverman, SL Turner and AK Lilley (2005) The contribution of species richness and composition to bacterial services. Nature, 436, 1157-1160.
References
Brophy C, A Dooley, L Kirwan, JA Finn, J McDonnell, T Bell, MW Cadotte and J Connolly (2017) Biodiversity and ecosystem function: Making sense of numerous species interactions in multi-species communities. Ecology, 98, 1771-1778.
Connolly J, T Bell, T Bolger, C Brophy, T Carnus, JA Finn, L Kirwan, F Isbell, J Levine, A Lüscher, V Picasso, C Roscher, MT Sebastia, M Suter and A Weigelt (2013) An improved model to predict the effects of changing biodiversity levels on ecosystem function. Journal of Ecology, 101, 344-355.
Kirwan L, J Connolly, JA Finn, C Brophy, A Lüscher, D Nyfeler and MT Sebastia (2009) Diversity-interaction modelling - estimating contributions of species identities and interactions to ecosystem function. Ecology, 90, 2032-2038.
Examples
## Load the Bell data
data(Bell)
## View the first five entries
head(Bell)
## Explore the variabes in sim1
str(Bell)
## Check that the proportions sum to 1 (required for DI models)
## p1 to p72 are in the 4th to 75th columns in Bell
Bellsums <- rowSums(Bell[4:75])
summary(Bellsums)
## Check characteristics of Bell
hist(Bell$response)
summary(Bell$response)
plot(Bell$richness, Bell$response)
## This code takes around 11 seconds to run
## Fit the average pairwise model using DI and the AV tag, with theta estimated
m1 <- DI(y = "response", prop = 4:75, DImodel = "AV", estimate_theta = TRUE, data = Bell)
summary(m1)
CI_95 <- theta_CI(m1, conf = .95)
CI_95
plot(m1)
library(hnp)
hnp(m1)
## Graph the profile likelihood
library(ggplot2)
ggplot(m1$profile_loglik, aes(x = grid, y = prof)) +
theme_bw() +
geom_line() +
xlim(0,1.5) +
xlab(expression(theta)) +
ylab("Log-likelihood") +
geom_vline(xintercept = CI_95, lty = 3) +
labs(title = " Log-likelihood versus theta",
caption = "dotted vertical lines are upper and lower bounds of 95% CI for theta")
DImodels documentation built on May 29, 2024, 7:05 a.m.
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| Bell | R Documentation |
The "Bell" Dataset
Description
This dataset comes from a bacterial biodiversity experiment (Bell et al 2005). The bacterial ecosystems used were from semi-permanent rainpools that form in bark-lined depressions near the base of large European beech trees (Fagus sylvatica). Microcosms consisting of sterile beech leaf disks and 10 ml of liquid (phosphate buffer) were inoculated with random combinations of 72 bacterial species isolated from these ecosystems. A total of 1,374 microcosms were constructed at richness levels of 1, 2, 3, 4, 6, 8, 9, 12, 18, 24, 36 and 72 species. The daily respiration rate of the bacterial community in each microcosm was measured over three time intervals (days 0-7, 7-14 and 14-28) and the average over the three time intervals was recorded.
Usage
data("Bell")Format
A data frame with 1374 observations on the following 76 variables:
idA numeric vector uniquely identifying each row of the dataset.
communityA numeric vector identifying each unique community, i.e., two rows with the same community value also share the same set of p1 to p72 values.
richnessThe number of species included in the initial composition, i.e., the number of proportions from p1 to p72 that are >0.
p1A numeric vector indicating the initial proportion of species 1 in the community.
p2A numeric vector indicating the initial proportion of species 2 in the community.
p3A numeric vector indicating the initial proportion of species 3 in the community.
p4A numeric vector indicating the initial proportion of species 4 in the community.
p5A numeric vector indicating the initial proportion of species 5 in the community.
p6A numeric vector indicating the initial proportion of species 6 in the community.
p7A numeric vector indicating the initial proportion of species 7 in the community.
p8A numeric vector indicating the initial proportion of species 8 in the community.
p9A numeric vector indicating the initial proportion of species 9 in the community.
p10A numeric vector indicating the initial proportion of species 10 in the community.
p11A numeric vector indicating the initial proportion of species 11 in the community.
p12A numeric vector indicating the initial proportion of species 12 in the community.
p13A numeric vector indicating the initial proportion of species 13 in the community.
p14A numeric vector indicating the initial proportion of species 14 in the community.
p15A numeric vector indicating the initial proportion of species 15 in the community.
p16A numeric vector indicating the initial proportion of species 16 in the community.
p17A numeric vector indicating the initial proportion of species 17 in the community.
p18A numeric vector indicating the initial proportion of species 18 in the community.
p19A numeric vector indicating the initial proportion of species 19 in the community.
p20A numeric vector indicating the initial proportion of species 20 in the community.
p21A numeric vector indicating the initial proportion of species 21 in the community.
p22A numeric vector indicating the initial proportion of species 22 in the community.
p23A numeric vector indicating the initial proportion of species 23 in the community.
p24A numeric vector indicating the initial proportion of species 24 in the community.
p25A numeric vector indicating the initial proportion of species 25 in the community.
p26A numeric vector indicating the initial proportion of species 26 in the community.
p27A numeric vector indicating the initial proportion of species 27 in the community.
p28A numeric vector indicating the initial proportion of species 28 in the community.
p29A numeric vector indicating the initial proportion of species 29 in the community.
p30A numeric vector indicating the initial proportion of species 30 in the community.
p31A numeric vector indicating the initial proportion of species 31 in the community.
p32A numeric vector indicating the initial proportion of species 32 in the community.
p33A numeric vector indicating the initial proportion of species 33 in the community.
p34A numeric vector indicating the initial proportion of species 34 in the community.
p35A numeric vector indicating the initial proportion of species 35 in the community.
p36A numeric vector indicating the initial proportion of species 36 in the community.
p37A numeric vector indicating the initial proportion of species 37 in the community.
p38A numeric vector indicating the initial proportion of species 38 in the community.
p39A numeric vector indicating the initial proportion of species 39 in the community.
p40A numeric vector indicating the initial proportion of species 40 in the community.
p41A numeric vector indicating the initial proportion of species 41 in the community.
p42A numeric vector indicating the initial proportion of species 42 in the community.
p43A numeric vector indicating the initial proportion of species 43 in the community.
p44A numeric vector indicating the initial proportion of species 44 in the community.
p45A numeric vector indicating the initial proportion of species 45 in the community.
p46A numeric vector indicating the initial proportion of species 46 in the community.
p47A numeric vector indicating the initial proportion of species 47 in the community.
p48A numeric vector indicating the initial proportion of species 48 in the community.
p49A numeric vector indicating the initial proportion of species 49 in the community.
p50A numeric vector indicating the initial proportion of species 50 in the community.
p51A numeric vector indicating the initial proportion of species 51 in the community.
p52A numeric vector indicating the initial proportion of species 52 in the community.
p53A numeric vector indicating the initial proportion of species 53 in the community.
p54A numeric vector indicating the initial proportion of species 54 in the community.
p55A numeric vector indicating the initial proportion of species 55 in the community.
p56A numeric vector indicating the initial proportion of species 56 in the community.
p57A numeric vector indicating the initial proportion of species 57 in the community.
p58A numeric vector indicating the initial proportion of species 58 in the community.
p59A numeric vector indicating the initial proportion of species 59 in the community.
p60A numeric vector indicating the initial proportion of species 60 in the community.
p61A numeric vector indicating the initial proportion of species 61 in the community.
p62A numeric vector indicating the initial proportion of species 62 in the community.
p63A numeric vector indicating the initial proportion of species 63 in the community.
p64A numeric vector indicating the initial proportion of species 64 in the community.
p65A numeric vector indicating the initial proportion of species 65 in the community.
p66A numeric vector indicating the initial proportion of species 66 in the community.
p67A numeric vector indicating the initial proportion of species 67 in the community.
p68A numeric vector indicating the initial proportion of species 68 in the community.
p69A numeric vector indicating the initial proportion of species 69 in the community.
p70A numeric vector indicating the initial proportion of species 70 in the community.
p71A numeric vector indicating the initial proportion of species 71 in the community.
p72A numeric vector indicating the initial proportion of species 72 in the community.
responseA numeric vector giving the average daily respiration rate of the bacterial community.
Details
What are Diversity-Interactions (DI) models?
Diversity-Interactions (DI) models (Kirwan et al 2009) are a set of tools for analysing and interpreting data from experiments that explore the effects of species diversity on community-level responses. We strongly recommend that users read the short introduction to Diversity-Interactions models (available at: DImodels). Further information on Diversity-Interactions models is also available in Kirwan et al 2009 and Connolly et al 2013.
The Bell dataset is analysed using Diversity-Interactions models in both Brophy et al 2017 and Connolly et al 2013.
Source
Bell T, JA Newman, BW Silverman, SL Turner and AK Lilley (2005) The contribution of species richness and composition to bacterial services. Nature, 436, 1157-1160.
References
Brophy C, A Dooley, L Kirwan, JA Finn, J McDonnell, T Bell, MW Cadotte and J Connolly (2017) Biodiversity and ecosystem function: Making sense of numerous species interactions in multi-species communities. Ecology, 98, 1771-1778.
Connolly J, T Bell, T Bolger, C Brophy, T Carnus, JA Finn, L Kirwan, F Isbell, J Levine, A Lüscher, V Picasso, C Roscher, MT Sebastia, M Suter and A Weigelt (2013) An improved model to predict the effects of changing biodiversity levels on ecosystem function. Journal of Ecology, 101, 344-355.
Kirwan L, J Connolly, JA Finn, C Brophy, A Lüscher, D Nyfeler and MT Sebastia (2009) Diversity-interaction modelling - estimating contributions of species identities and interactions to ecosystem function. Ecology, 90, 2032-2038.
Examples
## Load the Bell data
data(Bell)
## View the first five entries
head(Bell)
## Explore the variabes in sim1
str(Bell)
## Check that the proportions sum to 1 (required for DI models)
## p1 to p72 are in the 4th to 75th columns in Bell
Bellsums <- rowSums(Bell[4:75])
summary(Bellsums)
## Check characteristics of Bell
hist(Bell$response)
summary(Bell$response)
plot(Bell$richness, Bell$response)
## This code takes around 11 seconds to run
## Fit the average pairwise model using DI and the AV tag, with theta estimated
m1 <- DI(y = "response", prop = 4:75, DImodel = "AV", estimate_theta = TRUE, data = Bell)
summary(m1)
CI_95 <- theta_CI(m1, conf = .95)
CI_95
plot(m1)
library(hnp)
hnp(m1)
## Graph the profile likelihood
library(ggplot2)
ggplot(m1$profile_loglik, aes(x = grid, y = prof)) +
theme_bw() +
geom_line() +
xlim(0,1.5) +
xlab(expression(theta)) +
ylab("Log-likelihood") +
geom_vline(xintercept = CI_95, lty = 3) +
labs(title = " Log-likelihood versus theta",
caption = "dotted vertical lines are upper and lower bounds of 95% CI for theta")
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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