Appendix C. Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.

Appendix B. A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), and estimated mean abundance per surveyed 1-km2 quadrat under the five most parsimonious models with a negative binomial or a Poisson mixture distribution of abundance.

Marc Kéry (228511)
J. Andrew Royle (111267)
Hans Schmid (650040)

August 2016

A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), an...

Appendix A. Complete results of generalized linear models to assess the significance of differences in (1) environmental parameters between experiment and control plots, (2) density of important taxa between experiment and control plots, and (3) number of taxa and number of individuals between experiment and control plots.

Simon F. Thrush (145647)
Judi E. Hewitt (145655)
Alf Norkko (126750)
Vonda J. Cummings (244635)
Greig A. Funnell (2903294)

August 2016

Complete results of generalized linear models to assess the significance of differences in (1) envir...

Appendix B. A figure showing historic precipitation data and methods for assigning frequencies to survival and breeding probabilities in our stochastic simulations.

Don R. Church (2915162)
Larissa L. Bailey (414449)
Henry M. Wilbur (2915159)
William L. Kendall (2910083)
James E. Hines (423739)

August 2016

A figure showing historic precipitation data and methods for assigning frequencies to survival and b...

Appendix A. Simulation results based on two theoretical abundance distributions and four large empirical surveys.

Anne Chao (162776)
T. C. Hsieh (753331)
Robin L. Chazdon (508740)
Robert K. Colwell (162781)
Nicholas J. Gotelli (519835)

August 2016

Simulation results based on two theoretical abundance distributions and four large empirical surveys

Appendix H. Analysis of the relationship between abundance and aggregation in the simulation model.

Anton J. Flügge (2924262)
Sofia C. Olhede (433734)
David J. Murrell (188314)

August 2016

Analysis of the relationship between abundance and aggregation in the simulation model

Appendix C. Results of Markov models, each constructed from observed data from training data sets containing 240 randomly chosen grid cells and run for predicting the community composition in the remaining independent 240 cells.

Annett Baasch (2906477)
Sabine Tischew (2906474)
Helge Bruelheide (137145)

August 2016

Results of Markov models, each constructed from observed data from training data sets containing 240...

Appendix A. Figures showing sensitivity of the results of simulations to estimates of population size, local population dynamics, and dispersal.

Zuzana Münzbergová (128398)
Mikael Mildén (2903768)
Johan Ehrlén (580333)
Tomáš Herben (474595)

August 2016

Figures showing sensitivity of the results of simulations to estimates of population size, local pop...

Appendix C. Distinguishing temporal variation of species abundance distribution from sampling and statistical artifacts.

Cheng-Han Tsai (106188)
Takeshi Miki (278993)
Chun-Wei Chang (380018)
Kanako Ishikawa (2928408)
Satoshi Ichise (2928411)
Michio Kumagai (2077648)
Chih-hao Hsieh (28626)

August 2016

Distinguishing temporal variation of species abundance distribution from sampling and statistical ar...

Appendix D. Simulation results of the three simulation scenarios to estimate trends in average arrival across years.

Tobias Roth (328891)
Nicolas Strebel (2927889)
Valentin Amrhein (328893)

August 2016

Simulation results of the three simulation scenarios to estimate trends in average arrival across ye...

Appendix F. Model summaries and parameter estimates from the linear regressions of relative abundance on road density.

Trina Rytwinski (568200)
Lenore Fahrig (540337)

August 2016

Model summaries and parameter estimates from the linear regressions of relative abundance on road de...

Appendix A. Description of Markov chain Monte Carlo (MCMC) methods used to simulate from the full conditional distribution for species classification parameters, and from the joint posterior distribution associated with seal transect data.

Paul B. Conn (143747)
Brett T. McClintock (714404)
Michael F. Cameron (2926932)
Devin S. Johnson (143756)
Erin E. Moreland (2926935)
Peter L. Boveng (318960)

August 2016

Description of Markov chain Monte Carlo (MCMC) methods used to simulate from the full conditional di...

2

Robert B. Mcqueen
Samuel L. Ellis
Jonathan D. Campbell
Kavita V. Nair
Patrick W

September 2015

This technical appendix provides further information regarding the assumptions and calculations of t...

Appendix A. Dynamics of replicated open population models with process and measurement error, and examples of empirical dynamics.

J. Timothy Wootton (203712)

August 2016

Dynamics of replicated open population models with process and measurement error, and examples of em...

Appendix C. Details of a simulation study to assess the statistical properties of the hierarchical model with multiple random effects and sparse observational sampling.

P. C. Cross (2906210)
T. G. Creech (2926671)
M. R. Ebinger (2926665)
K. Manlove (2926677)
K. Irvine (2926662)
J. Henningsen (2926659)
J. Rogerson (2926674)
B. M. Scurlock (2906207)
S. Creel (2926668)

August 2016

Details of a simulation study to assess the statistical properties of the hierarchical model with mu...

Appendix A. Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in growth and survival.

Jesse R. Lasky (2927421)
Bénédicte Bachelot (2929671)
Robert Muscarella (658399)
Naomi Schwartz (2929665)
Jimena Forero-Montaña (2923659)
Christopher J. Nytch (2929668)
Nathan G. Swenson (175117)
Jill Thompson (237870)
Jess K. Zimmerman (237877)
María Uriarte (2903960)

August 2016

Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in ...

Appendix B. A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), and estimated mean abundance per surveyed 1-km2 quadrat under the five most parsimonious models with a negative binomial or a Poisson mixture distribution of abundance.

Marc Kéry (228511)
J. Andrew Royle (111267)
Hans Schmid (650040)

August 2016

A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), an...

Appendix A. Complete results of generalized linear models to assess the significance of differences in (1) environmental parameters between experiment and control plots, (2) density of important taxa between experiment and control plots, and (3) number of taxa and number of individuals between experiment and control plots.

Simon F. Thrush (145647)
Judi E. Hewitt (145655)
Alf Norkko (126750)
Vonda J. Cummings (244635)
Greig A. Funnell (2903294)

August 2016

Complete results of generalized linear models to assess the significance of differences in (1) envir...

Appendix B. A figure showing historic precipitation data and methods for assigning frequencies to survival and breeding probabilities in our stochastic simulations.

Don R. Church (2915162)
Larissa L. Bailey (414449)
Henry M. Wilbur (2915159)
William L. Kendall (2910083)
James E. Hines (423739)

August 2016

A figure showing historic precipitation data and methods for assigning frequencies to survival and b...

Appendix A. Simulation results based on two theoretical abundance distributions and four large empirical surveys.

Anne Chao (162776)
T. C. Hsieh (753331)
Robin L. Chazdon (508740)
Robert K. Colwell (162781)
Nicholas J. Gotelli (519835)

August 2016

Simulation results based on two theoretical abundance distributions and four large empirical surveys

Appendix H. Analysis of the relationship between abundance and aggregation in the simulation model.

Anton J. Flügge (2924262)
Sofia C. Olhede (433734)
David J. Murrell (188314)

August 2016

Analysis of the relationship between abundance and aggregation in the simulation model

Appendix C. Results of Markov models, each constructed from observed data from training data sets containing 240 randomly chosen grid cells and run for predicting the community composition in the remaining independent 240 cells.

Annett Baasch (2906477)
Sabine Tischew (2906474)
Helge Bruelheide (137145)

August 2016

Results of Markov models, each constructed from observed data from training data sets containing 240...

Appendix A. Figures showing sensitivity of the results of simulations to estimates of population size, local population dynamics, and dispersal.

Zuzana Münzbergová (128398)
Mikael Mildén (2903768)
Johan Ehrlén (580333)
Tomáš Herben (474595)

August 2016

Figures showing sensitivity of the results of simulations to estimates of population size, local pop...

Appendix C. Distinguishing temporal variation of species abundance distribution from sampling and statistical artifacts.

Cheng-Han Tsai (106188)
Takeshi Miki (278993)
Chun-Wei Chang (380018)
Kanako Ishikawa (2928408)
Satoshi Ichise (2928411)
Michio Kumagai (2077648)
Chih-hao Hsieh (28626)

August 2016

Distinguishing temporal variation of species abundance distribution from sampling and statistical ar...

Appendix D. Simulation results of the three simulation scenarios to estimate trends in average arrival across years.

Tobias Roth (328891)
Nicolas Strebel (2927889)
Valentin Amrhein (328893)

August 2016

Simulation results of the three simulation scenarios to estimate trends in average arrival across ye...

Appendix F. Model summaries and parameter estimates from the linear regressions of relative abundance on road density.

Trina Rytwinski (568200)
Lenore Fahrig (540337)

August 2016

Model summaries and parameter estimates from the linear regressions of relative abundance on road de...

Appendix A. Description of Markov chain Monte Carlo (MCMC) methods used to simulate from the full conditional distribution for species classification parameters, and from the joint posterior distribution associated with seal transect data.

Paul B. Conn (143747)
Brett T. McClintock (714404)
Michael F. Cameron (2926932)
Devin S. Johnson (143756)
Erin E. Moreland (2926935)
Peter L. Boveng (318960)

August 2016

Description of Markov chain Monte Carlo (MCMC) methods used to simulate from the full conditional di...

2

Robert B. Mcqueen
Samuel L. Ellis
Jonathan D. Campbell
Kavita V. Nair
Patrick W

September 2015

This technical appendix provides further information regarding the assumptions and calculations of t...

Appendix A. Dynamics of replicated open population models with process and measurement error, and examples of empirical dynamics.

J. Timothy Wootton (203712)

August 2016

Dynamics of replicated open population models with process and measurement error, and examples of em...

Appendix C. Details of a simulation study to assess the statistical properties of the hierarchical model with multiple random effects and sparse observational sampling.

P. C. Cross (2906210)
T. G. Creech (2926671)
M. R. Ebinger (2926665)
K. Manlove (2926677)
K. Irvine (2926662)
J. Henningsen (2926659)
J. Rogerson (2926674)
B. M. Scurlock (2906207)
S. Creel (2926668)

August 2016

Details of a simulation study to assess the statistical properties of the hierarchical model with mu...

Appendix A. Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in growth and survival.

Jesse R. Lasky (2927421)
Bénédicte Bachelot (2929671)
Robert Muscarella (658399)
Naomi Schwartz (2929665)
Jimena Forero-Montaña (2923659)
Christopher J. Nytch (2929668)
Nathan G. Swenson (175117)
Jill Thompson (237870)
Jess K. Zimmerman (237877)
María Uriarte (2903960)

August 2016

Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in ...

Appendix B. A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), and estimated mean abundance per surveyed 1-km2 quadrat under the five most parsimonious models with a negative binomial or a Poisson mixture distribution of abundance.

Marc Kéry (228511)
J. Andrew Royle (111267)
Hans Schmid (650040)

August 2016

A table showing parameter estimates, number of parameters, model selection statistics (AIC, AIC), an...

Appendix A. Complete results of generalized linear models to assess the significance of differences in (1) environmental parameters between experiment and control plots, (2) density of important taxa between experiment and control plots, and (3) number of taxa and number of individuals between experiment and control plots.

Simon F. Thrush (145647)
Judi E. Hewitt (145655)
Alf Norkko (126750)
Vonda J. Cummings (244635)
Greig A. Funnell (2903294)

August 2016

Complete results of generalized linear models to assess the significance of differences in (1) envir...

Appendix B. A figure showing historic precipitation data and methods for assigning frequencies to survival and breeding probabilities in our stochastic simulations.

Don R. Church (2915162)
Larissa L. Bailey (414449)
Henry M. Wilbur (2915159)
William L. Kendall (2910083)
James E. Hines (423739)

August 2016

A figure showing historic precipitation data and methods for assigning frequencies to survival and b...

Appendix C. Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.

Abstract

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Appendix C. Simulation results for parameters associated with abundance and detection from Markov data analyzed with the Markov and log-linear trend models.

Abstract

Extracted data

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