Publikation: Konferencebidrag › Konferenceabstrakt til konference › Forskning › peer review

**Improvement of the ID model for quantitative network data.** / Sørensen, Peter Borgen; Damgaard, Christian Frølund; Dupont, Yoko Luise.

Publikation: Konferencebidrag › Konferenceabstrakt til konference › Forskning › peer review

Sørensen, PB, Damgaard, CF & Dupont, YL 2015, 'Improvement of the ID model for quantitative network data'.

Sørensen, P. B., Damgaard, C. F., & Dupont, Y. L. (2015). *Improvement of the ID model for quantitative network data*.

Sørensen PB, Damgaard CF, Dupont YL. 2015. Improvement of the ID model for quantitative network data.

Sørensen, Peter Borgen, Christian Frølund Damgaard, og Yoko Luise Dupont *Improvement of the ID model for quantitative network data*. Konferenceabstrakt til konference, 2015. 1 s.

Sørensen PB, Damgaard CF, Dupont YL. Improvement of the ID model for quantitative network data. 2015.

Sørensen, Peter Borgen ; Damgaard, Christian Frølund ; Dupont, Yoko Luise. / **Improvement of the ID model for quantitative network data**. 1 s.

@conference{f13d07e0e8a348ad8c4e836285f1e432,

title = "Improvement of the ID model for quantitative network data",

abstract = "Many interactions are often poorly registered or even unobserved in empirical quantitative networks. Hence, the output of the statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. Such artefacts are a challenge for existing mathematical methods typically used to derive informative parameters as e.g. nestedness, or entropy. However, this problem of sampling uncertainty is tackled by the statistical method, termed the Interaction Distribution ID method (S{\o}rensen et al. (2011)1. This presentation will illustrate the application of the ID method based on a data set which consists of counts of visits by 152 pollinator species to 16 plant species. The method is based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i{\textquoteright}th pollinator species to take place in a flower of the j{\textquoteright}th plant species; (2), qi,j: the probability for a visit received by the j{\textquoteright}th plant species to be made by the i{\textquoteright}th pollinator. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.This presentation will suggest some simple but important improvements of the ID method and validate this by showing how well the method can reproduce the high number of zero valued cells in the data set and mimic the sampling distribution. 1 S{\o}rensen et al, Journal of Pollination Ecology, 6(18), 2011, pp129-139",

author = "S{\o}rensen, {Peter Borgen} and Damgaard, {Christian Fr{\o}lund} and Dupont, {Yoko Luise}",

year = "2015",

month = oct,

day = "22",

language = "English",

}

TY - ABST

T1 - Improvement of the ID model for quantitative network data

AU - Sørensen, Peter Borgen

AU - Damgaard, Christian Frølund

AU - Dupont, Yoko Luise

PY - 2015/10/22

Y1 - 2015/10/22

N2 - Many interactions are often poorly registered or even unobserved in empirical quantitative networks. Hence, the output of the statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. Such artefacts are a challenge for existing mathematical methods typically used to derive informative parameters as e.g. nestedness, or entropy. However, this problem of sampling uncertainty is tackled by the statistical method, termed the Interaction Distribution ID method (Sørensen et al. (2011)1. This presentation will illustrate the application of the ID method based on a data set which consists of counts of visits by 152 pollinator species to 16 plant species. The method is based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place in a flower of the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.This presentation will suggest some simple but important improvements of the ID method and validate this by showing how well the method can reproduce the high number of zero valued cells in the data set and mimic the sampling distribution. 1 Sørensen et al, Journal of Pollination Ecology, 6(18), 2011, pp129-139

AB - Many interactions are often poorly registered or even unobserved in empirical quantitative networks. Hence, the output of the statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. Such artefacts are a challenge for existing mathematical methods typically used to derive informative parameters as e.g. nestedness, or entropy. However, this problem of sampling uncertainty is tackled by the statistical method, termed the Interaction Distribution ID method (Sørensen et al. (2011)1. This presentation will illustrate the application of the ID method based on a data set which consists of counts of visits by 152 pollinator species to 16 plant species. The method is based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place in a flower of the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.This presentation will suggest some simple but important improvements of the ID method and validate this by showing how well the method can reproduce the high number of zero valued cells in the data set and mimic the sampling distribution. 1 Sørensen et al, Journal of Pollination Ecology, 6(18), 2011, pp129-139

M3 - Conference abstract for conference

ER -