[ECOLOG-L] EGU 2019 (Vienna) "Microbial carbon use efficiency in soils" - Call for Abstracts

[Maria Mooshammer]

Dear Colleagues,

On behalf of the convener team, I would like to bring your attention to the
EGU Soil System Science session SSS4.8
 "*Microbial
carbon use efficiency in soils*" (EGU General Assembly 2019, 7-12 April
2019, Vienna, Austria).

This session aims to bring together contributions on modelling and
empirical CUE approaches. Please see the abstract below for more
information.

The deadline for the receipt of abstracts

is 10 January 2019, 13:00 CET. If you would like to apply for a Roland
Schlich travel support, please submit no later than 1 December 2018.
Details can be found here
.

Please feel free to forward this along to any other potentially interested
colleagues.

Best regards,
Stefano, Sergey, Anke and Maria

---
SSS4.8
Microbial carbon use efficiency in soils
Convener: Stefano Manzoni
Co-Conveners: Sergey Blagodatsky, Anke Herrmann, Maria Mooshammer

Carbon use efficiency (CUE=ratio of biomass production over carbon
substrate consumption) became a topical term in soil science only during
the last decade, although the topic of microbial carbon allocation has been
investigated for several decades. The initiative to learn more about the
partitioning of organic carbon between storage in biomass (potentially
leading to sequestration) and CO2 efflux from soil to atmosphere was partly
motivated by modellers looking for effective parameterizations of carbon
flows. Therefore, this session will cover new modelling approaches
considering possible environmental, biological and soil factors controlling
CUE in soil. These new modelling efforts are supported by novel measurement
approaches, which allow reducing uncertainties in CUE estimates. Hence,
this session also welcomes contributions showing results on CUE estimation
in soil using advanced methods – isotope labelling, kinetic studies,
isothermal calorimetry and approaches disclosing the effect of microbial
community composition and activity on CUE. One of the main difficulties in
modelling CUE is scaling the model concept from organism to soil profile,
ecosystem and finally to global level. We welcome new ideas and solutions
in this direction, including empirical approaches that allow crossing these
scales. In-depth studies showing how different microbial processes (growth,
maintenance, biomass death and recycling) influence apparent CUE and carbon
storage in soil are welcomed as well.

[ECOLOG-L] U.S. Government Review: Two IPCC Special Reports

[Jonathan Miller]

Sharing on behalf of the State Department
___

Dear Colleagues,

The U.S. Department of State seeks expert comment on the second-order drafts of 
two Intergovernmental Panel on Climate Change (IPCC) Special Reports: 

•   Climate Change and Land - An IPCC Special Report on Climate Change, 
Desertification, Land Degradation, Sustainable Land Management, Food Security, 
and Greenhouse Gas Fluxes in Terrestrial Ecosystems (SR2)
•   Ocean and Cryosphere in a Changing Climate (SR3) 

General information such as the Special Report outlines and assessment 
development timeline/procedures can be found on the USGCRP Notices page 
(https://globalchange.us11.list-manage.com/track/click?u=574d74f0666ca2db5284e3c20&id=a06cdb0c55&e=7c872b56b9);
 more detailed information — i.e., background, review instructions, 
supplementary materials, and the draft reports themselves (including the first 
drafts of respective Summary for Policymakers) — can be found on the USGCRP 
Review and Comment System 
(https://globalchange.us11.list-manage.com/track/click?u=574d74f0666ca2db5284e3c20&id=20691f5aeb&e=7c872b56b9).
 You must register on both sites to access SR2 and SR3 materials, agreeing to 
the posted terms before being granted access to the site(s).

This is an Open Call. Comments are solicited from the U.S. scientific expert 
community and interested stakeholders. All comments must be input via the 
USGCRP Review and Comment System by 11:59 p.m. ET, Wednesday, 19 December 2018, 
if they are to be considered by the Federal expert panel tasked with preparing 
the U.S. transmittal to IPCC. 

Since the IPCC is an intergovernmental body, review of IPCC documents involves 
both peer review by experts and review by governments. Experts are advised that 
they have the option to submit comments direct to the IPCC rather than 
participate in the U.S. Government Review. To register for the concurrent 
Expert Review of the second-order draft, click SR2 and/or SR3. Expert 
registrations will be accepted by the IPCC until 7 and 4 January 2019, 
respectively. 

SR2: 
https://globalchange.us11.list-manage.com/track/click?u=574d74f0666ca2db5284e3c20&id=f37e30b03b&e=7c872b56b9
SR3: 
https://globalchange.us11.list-manage.com/track/click?u=574d74f0666ca2db5284e3c20&id=4fd47c3223&e=7c872b56b9

To contribute to the USGCRP-managed process, comments must be received by 19 
December 2018. 

You are receiving this broadcast because of past involvement or exposure to 
IPCC, and/or active roles played in the suite of USGCRP sustained national 
assessment products.

Thank you for your active engagement in this important U.S. Government Review.

USGCRP National Coordination Office
on behalf of the U.S. Department of State

[ECOLOG-L] UPCOMING DEADLINE! E&E GRADUATE PROGRAM AT STONY BROOK

[Stephen Baines]

GRADUATE OPPORTUNITIES IN ECOLOGY AND EVOLUTIONARY BIOLOGY

The Graduate Program in Ecology and Evolution at Stony Brook University is
recruiting doctoral and master's level graduate students for Fall 2019.

The department has a long and distinguished history, being one of the first
of its kind.  It currently has a productive and diverse faculty working on
broad array of questions involving microbes, plants, vertebrate and
invertebrate animals and whole ecosystems. Field locales span the globe
from the old and new world tropics to the Arctic and Antarctic polar
regions, as well as the uplands, wetlands and coastal areas of Long Island
and nearby New York City.

Upon admission, PhD students are guaranteed teaching assistantships upon
acceptance, with additional support available through fellowships and
research assistantships, as they become available. The deadlines for
applications are *Dec. 1, 2018* for the PhD program. Admissions to the MA
program are rolling until *April 15, 2019.  *

Below is a listing of current local program faculty to whom questions can
be directed. It is highly recommended that PhD applicants contact potential
advisors before submitting your application.  For questions or assistance
with the application process please e-mail our Graduate Program
coordinator, Melissa Cohen. melissa.j.co...@stonybrook.edu


DEPARTMENTAL FACULTY

H. Resit Akcakaya - Population and conservation ecology
http://life.bio.sunysb.edu/ee/akcakayalab/

Stephen B. Baines - Aquatic ecosystem ecology and biogeochemistry
http://life.bio.sunysb.edu/ee/baineslab/

Michael A. Bell - Contemporary evolution and biology of fishes
http://life.bio.sunysb.edu/ee/belllab/

Liliana M. Dávalos - Vertebrate phylogenetics, biogeography and conservation
http://lmdavalos.net/lab/The_Lab.html

Walter F. Eanes - Evolutionary genetics of Drosophila
http://life.bio.sunysb.edu/ee/eaneslab/

Jessica Gurevitch - Research synthesis, plant population and invasion
ecology
https://gurevitchlab.weebly.com/

Jesse D. Hollister - Plant evolutionary genomics and epigenetics
https://genomeevolution.wordpress.com/

Jeffrey S. Levinton - Marine ecology and paleobiology
http://life.bio.sunysb.edu/marinebio/levinton.main.html

Heather J. Lynch - Quantitative ecology and conservation biology
https://lynchlab.com/

Ross H. Nehm - Science education, evolution education, cognition
https://www.stonybrook.edu/commcms/ecoevo/people/faculty_pages/nehm.html

Dianna K. Padilla -  Marine and freshwater ecology, conservation and
invasion biology
http://life.bio.sunysb.edu/ee/padillalab/

Joshua Rest - Evolutionary genomics
http://life.bio.sunysb.edu/ee/restlab/Home.html

Robert W. Thacker- Systematics, phylogenetics, and ecology
https://www.stonybrook.edu/commcms/ecoevo/people/faculty_pages/thacker.html


John R. True - Evolutionary developmental biology
https://www.stonybrook.edu/commcms/ecoevo/people/faculty_pages/true.html

Krishna R. Veeramah -  Evolutionary Genomics and Paleogenomics
http://life.bio.sunysb.edu/ee/veeramahlab/

PROGRAM FACULTY IN OTHER DEPARTMENTS

Jackie Collier - Microbial ecology
https://you.stonybrook.edu/collierlab/

Nolwenn M. Dheilly - Evolution of Host-Parasite Interactions
https://you.stonybrook.edu/dheilly/

Andreas Koenig - Behavioral ecology of primates
https://sites.google.com/a/stonybrook.edu/idpas_faculty_profile_koenig/

David Q. Matus - Evolution of Cell Invasion
https://you.stonybrook.edu/matuslab/

Catherine Markham - Behavioral ecology
https://catherinemarkham.com/

Janet Nye - Quantitative Fisheries Ecology
https://you.stonybrook.edu/jnye/

Alistair Rogers - Plant Physiology and Climate Change
www.bnl.gov/TEST

Shawn P. Serbin - Plant Physiology and Remote Sensing
www.bnl.gov/TEST

Jeroen B. Smaers - Brain Evolution, Phylogenetic Comparative Methodology,
Macroevolutionary Morphology
https://smaerslab.com/

Leslie Thorne - Ecology and Behavior of Marine Birds and Mammals
https://you.stonybrook.edu/thornelab/

Nils Volkenborn - Benthic Ecology and Sediment Biogeochemistry
https://you.stonybrook.edu/voll/

Patricia Wright - Tropical Conservation and Primatology
https://www.patwrightlab.net/pat-wright.html

-- 
Assoc. Professor, Grad. Program Director,
Dept.of Ecology and Evolution, Stony Brook University
Life Sciences Bldg 112/102, Stony Brook, NY 11794-5245
Phone (631) 632-1092/Fax (631)632-7626
http://life.bio.sunysb.edu/ee/baineslab/

[ECOLOG-L] Statistical modelling of time-to-event data using survival analysis: an introduction for animal behaviourists, ecologists and evolutionary biologists (TTED01)

[Oliver Hooker]

Statistical modelling of time-to-event data using survival analysis: an 
introduction for animal behaviourists, ecologists and evolutionary biologists 
(TTED01)

https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-data-using-survival-analysis-tted01/

This course will be delivered by Dr. Will Hoppitt for the 21st - 25th January 
2019 in Glasgow City Centre

Course Overview:
Survival analysis is a set of statistical methods initially designed to analyse 
data giving the times at which individuals die, and assess the effect that 
different predictor variables have on the rate of death. However, its 
applications are much broader than this: it can be used to analyse any 
time-to-event data. Ecologists and evolutionary biologists often encounter data 
of this kind. Often factors influencing survival itself will be of interest. 
But there are many other cases, e.g. what factors influence the time of first 
breeding? Or the time taken to reach maturity? Animal behaviourists too will 
encounter this type of data frequently, e.g. what factors influence the time it 
takes to learn a novel behaviour pattern? Or the time to respond to a stimulus? 
etc. And yet the techniques of survival analysis are not generally well known 
by researchers in these disciplines.

In this course, you will learn how to apply survival analysis models to 
quantify the effect that predictor variables (continuous or discrete) have on 
the rate at which events occur, and how to test hypotheses about these effects. 
We will focus on a flexible modelling technique called the Cox proportional 
hazards model, which makes minimal assumptions about the underlying probability 
distributions. You will learn how to fit and interpret these models, how to 
evaluate its assumptions, and how to extend it to model time dependent 
variables, random effects, multistate models and competing risks models.

Course Programme
Monday 21st – Classes from 09:30 to 17:30
Module 1: Statistical modelling of rates and times
Module 2: Parametric survival models and the Cox model

Tuesday 22nd – Classes from 09:30 to 17:30
Module 3: Fitting Cox models
Module 4: Interpreting Cox Models

Wednesday 23rd – Classes from 09:30 to 17:30
Module 5: Evaluating the proportional hazard assumption
Module 6: Stratified Cox models

Thursday 24th – Classes from 09:30 to 17:30
Module 7: Time dependent variables
Module 8: Frailty Models and Multistate models

Friday 25th – Classes from 09:30 to 17:30
Module 9: Competing risks models
Module 10: Open session

Email oliverhoo...@psstatistics.com

Check out our sister sites,
www.PRstatistics.com (Ecology and Life Sciences)
www.PRinformatics.com (Bioinformatics and data science)
www.PSstatistics.com (Behaviour and cognition)

1.November 5th – 8th 2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND PHENOTYPIC 
EVOLUTION (PCME01)
Glasgow, Scotland, Dr. Antigoni Kaliontzopoulou
https://www.prstatistics.com/course/phylogenetic-comparative-methods-for-studying-diversification-and-phenotypic-evolution-pcme01/

2.November 19th – 23rd 2018
STRUCTUAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (SEMR02)
Glasgow, Scotland, Dr. Jonathan Lefcheck
https://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr02/

3.November 26th – 30th 2018
FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION (FEER01)
Glasgow, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr. Carlos 
Carmona
http://www.prstatistics.com/course/functional-ecology-from-organism-to-ecosystem-theory-and-computation-feer01/

4.December 3rd – 7th 2018
INTRODUCTION TO BAYESIAN DATA ANALYSIS FOR SOCIAL AND BEHAVIOURAL SCIENCES 
USING R AND STAN (BDRS01)
Glasgow, Dr. Mark Andrews
https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-for-social-and-behavioural-sciences-using-r-and-stan-bdrs01/

5.January 21st – 25th 2019
STATISTICAL MODELLING OF TIME-TO-EVENT DATA USING SURVIVAL ANALYSIS: AN 
INTRODUCTION FOR ANIMAL BEHAVIOURISTS, ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS 
(TTED01)
Glasgow, Scotland, Dr. Will Hoppitt
https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-data-using-survival-analysis-tted01/

6.January 21st – 25th 2019
ADVANCING IN STATISTICAL MODELLING USING R (ADVR08)
Glasgow, Scotland, Dr. Luc Bussiere, Dr. Tom Houslay
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr08/

7.January 28th–  February 1st 2019
AQUATIC ACOUSTIC TELEMETRY DATA ANALYSIS AND SURVEY DESIGN
Glasgow, Scotland, VEMCO staff and affiliates
https://www.prstatistics.com/course/aquatic-acoustic-telemetry-data-analysis-atda01/

8.February 4th – 8th 2019
DESIGNING RELIABLE AND EFFICIENT EXPERIMENTS FOR SOCIAL SCIENCES (DRES01)
Glasgow, Scotland, Dr. Daniel Lakens
https://www.psstatistics.com/course/designing-reliable-and-effecient-experiments-for-social-sciences-dres01/

9.Februa

[ECOLOG-L] Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan

[Oliver Hooker]

Introduction to Bayesian data analysis for social and behavioural sciences 
using R and Stan (BDRS01)

This course may be suitable to anyone studying animal behaviour.

https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-for-social-and-behavioural-sciences-using-r-and-stan-bdrs01/

This course will be delivered by Dr. Mark Andrews from the 3rd - 7th December 
2018 in Glasgow City Centre.

Course Overview:
This course provides a general introduction to Bayesian data analysis using R 
and the Bayesian probabilistic programming language Stan. We begin with a 
gentle introduction to all the fundamental principles and concepts of Bayesian 
data analysis: the likelihood function, prior distributions, posterior 
distributions, high posterior density intervals, posterior predictive 
distributions, marginal likelihoods, Bayes factors, etc. We will do this using 
some simple probabilistic models that are easy to understand and easy to work 
with. We then proceed to more practically useful Bayesian analyses, starting 
with general linear models, followed by generalized linear models, including 
logistic regression and Poisson regression, followed by multilevel general and 
generalized linear models. For these analyses, we will use real world data 
sets, and carry out the analysis with Stan using the brms interface to Stan in 
R. With each example, we will explore general concepts such as model checking 
and improvement using posterior predictive checks, and model evaluation using 
cross-validation, WAIC, and Bayes factors. In the final part of the course, we 
will delve into some more advanced topics: understanding Markov Chain Monte 
Carlo in depth, Gaussian process regression, probabilistic mixture models.

Course programme
Monday 3rd – Classes from 09:30 to 17:30
Class 1: We will begin with a overview of what Bayesian data analysis is in 
essence and how it fits into statistics as it practiced generally. Our main 
point here will be that Bayesian data analysis is effectively an alternative 
school of statistics to the traditional approach, which is referred to 
variously as the classical, or sampling theory based, or frequentist based 
approach, rather than being a specialized or advanced statistics topic. 
However, there is no real necessity to see these two general approaches as 
being mutually exclusive and in direct competition, and a pragmatic blend of 
both approaches is entirely possible.
Class 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to 
calculate the probability of causes from some known effects. As such, it can be 
used as a means for performing statistical inference. In this section of the 
course, we will work through some simple and intuitive calculations using 
Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an 
application of these methods to more complex statistical models, and so 
understanding these simple cases of the application of Bayes’ rule can help 
provide a foundation for the more complex cases.
Class 3: Bayesian inference in a simple statistical model. In this section, we 
will work through a classic statistical inference problem, namely inferring the 
number of red marbles in an urn of red and black marbles. This problem is easy 
to analyse completely with just the use of R, but yet allows us to delve into 
all the key concepts of all Bayesian statistics including the likelihood 
function, prior distributions, posterior distributions, maximum a posteriori 
estimation, high posterior density intervals, posterior predictive intervals, 
marginal likelihoods, Bayes factors, model evaluation of out-of-sample 
generalization.

Tuesday 4th – Classes from 09:30 to 17:30
Class 4: Bayesian analysis of linear and normal models. Statistical models 
based on linear relationships and normal distribution are a mainstay of 
statistical analyses in general. They encompass models such as linear 
regression, Pearson’s correlation, t-tests, ANOVA, ANCOVA, and so on. In this 
section, we will describe how to do Bayesian analysis of linear and normal 
models, paying particular attention to Bayesian linear regression. One of the 
aims of this section is to identify some important and interesting parallels 
between Bayesian and classical or frequentist analyses. This shows how Bayesian 
and classical analyses can be seen as ultimately providing two different 
perspectives on the same problem.
Class 5: The previous section provides a so-called analytical approach to 
linear and normal models. This is where we can calculate desired quantities and 
distributions by way of simple formulae. However, analytical approaches to 
Bayesian analyses are only possible in a relatively restricted set of cases. 
However, numerical methods, specifically Markov Chain Monte Carlo (MCMC) 
methods can be applied to virtually any Bayesian model. In this section, we 
will re-perform the analysis presented in the previous section but using MCMC 
methods. For this, w

[ECOLOG-L] Postdoc Position on ‘Landscape-scale GHG fluxes based on tall tower eddy covariance' at the Department of Forest Ecology and Management, SLU, Umeå, Sweden

[Matthias Peichl]

Dear colleagues,


*A 2-year Postdoc position is available at the Department of Forest Ecology
and Management, SLU, **to investigate the carbon dioxide and methane fluxes
over a managed boreal forest landscape based on tall tower eddy covariance
measurements.*



*Project overview*

The project focuses on exploring tall tower eddy covariance (EC) data of CO2,
CH4 and H2O fluxes across a 68km2 forest catchment (ongoing since 2016).
This work takes advantage of the ICOS-Svartberget tall tower infrastructure
(www.icos-sweden.se/station_svartberget.html) which provides additional
data of atmospheric GHG concentration gradients along a 150m tower and all
relevant meteorological and environmental variables. Combined with a stream
monitoring network to quantify aquatic GHG fluxes across the Krycklan
catchment (www.slu.se/Krycklan), this allows for investigating the full C
and GHG balances and their primary controls at the landscape scale for a
typical managed forest catchment in boreal Sweden. The successful candidate
will be mainly responsible for processing and interpreting these tall tower
eddy covariance data and for publishing findings in relevant high-rank
scientific journals.



This project is part of a larger research framework in which a team of five
postdoctoral fellows will jointly explore and integrate terrestrial and
aquatic fluxes of C and GHGs from plot to regional scales in the boreal
region of Northern Sweden. For this purpose, the postdoc team will make use
a unique set up that integrates the well-established SITES research
infrastructure of the Degerö and Krycklan catchments (www.slu.se/Krycklan)
with the ICOS-Degerö and ICOS-Svartberget flux stations (www.icos-sweden.se)
and Svartberget Experimental Forests (
www.slu.se/en/departments/field-based-forest-research/experimental-forests/vindeln-experimental-forests/),
where research related to catchment hydrology, biogeochemistry and forestry
has been carried out for several decades. These excellent research
infrastructures include permanently employed and skilled technical staff, 8
eddy covariance sites, >500 forest inventory plots, 16 long-term monitored
streams and high-resolution Lidar scans which altogether provide valuable
data to estimate terrestrial and aquatic C fluxes across two typical boreal
catchments. Combined with tall tower EC and atmospheric concentration
records to support inverse modeling, this provides exceptional resources
for investigating and linking boreal C and GHG dynamics and their
underlying drivers spanning from the plot to the regional scale. Throughout
the entire project, the postdoctoral fellows will closely collaborate in
their research activities to create new knowledge by bridging the various
spatial scales. Thus, this project offers ample possibilities for
scientific interactions and career development.







*Qualifications:*

• The candidate must have a PhD awarded in the fields of environmental
sciences, ecology, physical geography or any other closely related subject

• *Demonstrated experience with eddy covariance data and
micrometeorological theory **including data processing and interpretation
is required*

• The candidate must be able to independently conduct field work, which
also requires a driver's license valid in Sweden.

• The candidate must be fluent in English to be able to write, communicate
and interact in an English-speaking environment.

• The candidate must have documented experience in writing and publishing
scientific articles

• Experience in either one or more of the following is considered a merit:
carbon cycle research in boreal landscapes (i.e. forests, peatlands and
aquatic systems), eddy covariance measurements with a tall tower and/or in
complex terrain, flux footprint modeling, skills in GIS, logger programming
and/or in the handling and processing of large, multiple-source, data sets



*Place of work*: The postdoc location is at the Forestry Faculty of the
Swedish University of Agricultural Sciences (SLU), Department of Forest
Ecology & Management, in Umeå, Sweden.



*Starting date*: spring/summer 2019



*To apply:*

Please send a CV, publication list, PhD diploma, copies of up to three
relevant publications and a motivation letter (max. 2 pages) outlining
previous research, current research interests and other activities of
relevance for the position. Names and contact information of at least two
reference persons are also required. All application documents should be
written in English. Please submit your application in electronic form to
Matthias Peichl (matthias.pei...@slu.se). Reviewing of applications will
start on *December 3* and will continue until a suitable candidate is found.



*For further information *about the position and the project please contact:

Matthias Peichl; Associate Professor, Dept. of Forest Ecology & Management,
SLU, matthias.pei...@slu.se

Mats Nilsson; Professor, Dept. of Forest Ecology & Management, SLU,
mats.b.nils...@slu.se

Hjalmar Laudon;