BOHAN, David Vacher, Corinne Tamaddoni-Nezhad, Alireza Raybould, Alan Dumbrell, Alex J Woodward, Guy

We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machi...

Schiebold, Julienne M.-I. Bidartondo, Martin I. Karasch, Peter Gravendeel, Barbara Gebauer, Gerhard

Background and Aims Partially mycoheterotrophic plants are enriched in (13)C and (15)N compared to autotrophic plants. Here, it is hypothesized that the type of mycorrhizal fungi found in orchid roots is responsible for variation in (15)N enrichment of leaf tissue in partially mycoheterotrophic orchids. Methods The genus Epipactis was used as a cas...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Chernonozhkin, Stepan Costas Rodriguez, Marta Claeys, Philippe Vanhaecke, Frank

The advantages and disadvantages of using cold plasma conditions in combination with both the standard and the high-transmission ('jet') plasma interface and under dry and wet plasma conditions were evaluated in the context of high-precision isotopic analysis of Fe via multi-collector inductively coupled plasma-mass spectrometry (MC-ICP-MS). When u...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...

Brooks, Mollie Elizabeth Kristensen, Kasper van Benthem, Koen J. Magnusson, Arni Berg, Casper Willestofte Nielsen, Anders Skaug, Hans J. Machler, Martin Bolker, Benjamin M.

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inf...