ECOLOG-L Applied Bayesian modelling for ecologists and epidemiologists

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Applied Bayesian modelling for ecologists and epidemiologists (ABME04)

https://www.prstatistics.com/course/applied-bayesian-modelling-for-
ecologists-and-epidemiologists-abme04/

This course will run from the 15th – 19th October 2018 in Glasgow city
centre and will be delivered by Dr Matt Denwood.

Course Overview:
This application-driven course will provide a founding in the basic theory
& practice of Bayesian statistics, with a focus on MCMC modeling for
ecological & epidemiological problems. Starting from a refresher on
probability & likelihood, the course will take students all the way to
cutting-edge applications such as state-space population modelling &
spatial point-process modelling. By the end of the week, you should have a
basic understanding of how common MCMC samplers work and how to program
them, and have practical experience with the BUGS language for common
ecological and epidemiological models. The experience gained will be a
sufficient foundation enabling you to understand current papers using
Bayesian methods, carry out simple Bayesian analyses on your own data and
springboard into more elaborate applications such as dynamical, spatial and
hierarchical modelling.

Monday 15th
Module 1: Revision of likelihoods using full likelihood profiles and an
introduction to the theory of Bayesian statistics. Probability and
likelihood. Conditional, joint and total probability, independence, Baye’s
law. Probability distributions. Uniform, Bernoulli, Binomial, Poisson,
Gamma, Beta and Normal distributions – their range, parameters and common
uses of Likelihood and parameter estimation by maximum likelihood.
Numerical likelihood profiles and maximum likelihood. Introduction to

Bayesian statistics.
Relationship between prior, likelihood & posterior distributions.
Summarising a posterior distribution; The philosophical differences between
frequentist & Bayesian statistics, & the practical implications of these.
Applying Bayes’ theorem to discrete & continuous data for common data types
given different priors. Building a posterior profile for a given dataset, &
compare the effect of different priors for the same data.

Tuesday 16th
Module 2: An introduction to the workings of MCMC, and the potential
dangers of MCMC inference. Participants will program their own (basic)
MCMC sampler to illustrate the concepts and fully understand the strengths
and weaknesses of the general approach. The day will end with an
introduction to the bugs language.

Introduction to MCMC. The curse of dimensionality & the advantages of MCMC
sampling to determine a posterior distribution. Monte Carlo integration,
standard error, & summarising samples from posterior distributions in R.
Writing a Metropolis algorithm & generating a posterior distribution for a
simple problem using MCMC.

Markov chains, autocorrelation & convergence. Definition of a Markov chain.
Autocorrelation, effective sample size and Monte Carlo error. The concept
of a stationary distribution and burnin. Requirement for convergence
diagnostics, and common statistics for assessing convergence. Adapting an
existing Metropolis algorithm to use two chains, & assessing the effect of
the sampling distribution on the autocorrelation. Introduction to BUGS &
running simple models in JAGS. Introduction to the BUGS language & how a
BUGS model is translated to an MCMC sampler during compilation. The
difference between deterministic & stochastic nodes, & the contribution of
priors & the likelihood. Running, extending & interpreting the output of
simple JAGS models from within R using the runjags interface.

Wednesday 17th
Module 3: Common models for which jags/bugs would be used in practice, with
examples given for different types of model code. All aspects of writing,
running, assessing and interpreting these models will be extensively
discussed so that participants are able and confident to run similar models
on their own. There will be a particularly heavy focus on practical
sessions during this day. The day will finish with a discussion of how to
assess the fit of mcmc models using the deviance information criterion
(dic) and other methods. Using JAGS for common problems in biology.
Understanding and generating code for basic generalised linear mixed models
in JAGS. Syntax for quadratic terms and interaction terms in JAGS.
Essential fitting tips and model selection. The need for minimal cross-
correlation and independence between parameters and how to design a model
with these properties. The practical methods and implications of minimizing
Monte Carlo error and autocorrelation, including thinning. Interpreting the
DIC for nested models, and understanding the limitations of how this is
calculated. Other methods of model selection and where these might be more
useful than DIC. Most commonly used methods Rationale and use for fixed
threshold, ABGD, K/theta, PTP, GMYC with computer practicals. Other
methods, Haplowebs, bGMYC, etc. with computer practicals.

Thursday 18th
Module 4: The flexibility of MCMC, and precautions required for using MCMC
to model commonly encountered datasets. An introduction to conjugate priors
and the potential benefits of exploiting gibbs sampling will be given. More
complex types of models such as hierarchical models, latent class models,
mixture models and state space models will be introduced and discussed. The
practical sessions will follow on from day 3.

General guidance for model specification. The flexibility of the BUGS
language and MCMC methods. The difference between informative and diffuse
priors. Conjugate priors and how they can be used. Gibbs sampling. State
space models. Hierarchical and state space models. Latent class and mixture
models. Conceptual application to animal movement. Hands-on application to
population biology. Conceptual application to epidemiology.

Friday 19th
Module 5: Additional practical guidance for the use of Bayesian methods in
practice, and finish with a brief overview of more advanced Bayesian tools
such as Integrated Nested Laplace Approximation (INLA) and stan.
Additional Bayesian methods. Understand the usefulness of conjugate priors
for robust analysis of proportions (Binomial and Multinomial data). Be
aware of some methods of prior elicitation. Advanced Bayesian tools.
Strengths and weaknesses of INLA compared to BUGS. Strengths and weaknesses
of stan compared to BUGS.

Email oliverhooker@prstatistics.com

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

Upcoming courses

1. April 9th – 13th 2018
NETWORK ANAYLSIS FOR ECOLOGISTS USING R (NTWA02
Glasgow, Scotland, Dr. Marco Scotti
http://www.prstatistics.com/course/network-analysis-ecologists-ntwa02/

2. April 16th – 20th 2018
INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01)
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre
http://www.psstatistics.com/course/introduction-to-statistics-using-r-for-
psychologists-ipsy01/

3. April 23rd – 27th 2018
MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE
(VGNR01)
Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet
http://www.prstatistics.com/course/multivariate-analysis-of-ecological-communities-
in-r-with-the-vegan-package-vgnr01/

4. April 30th – 4th May 2018
QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES
(QGER01)
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick
http://www.prstatistics.com/course/quantitative-geographic-ecology-using-r-
modelling-genomes-niches-and-communities-qger01/

5. May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL
ECOLOGICAL DATA USING R (MVSP02)
CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-
ecological-data-theory-and-practice-mvsp03/
6. May 14th – 18th 2018
INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS (IMBR01)
CANADA (QUEBEC), Prof Subhash Lele
http://www.prstatistics.com/course/introduction-to-mixed-hierarchical-models-for-
biologists-using-r-imbr01/

7. May 21st – 25th 2018
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB05)
SCENE, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/introduction-to-python-for-biologists-
ipyb05/

8. May 21st – 25th 2018
INTRODUCTION TO REMOTE SENISNG AND GIS FOR ECOLOGICAL APPLICATIONS (IRMS01)
Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi
http://www.prinformatics.com/course/introduction-to-remote-sensing-and-gis-for-
ecological-applications-irms01/

9. May 28th – 31st 2018
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson
http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/

10. May 28th – June 1st 2018
ADVANCED PYTHON FOR BIOLOGISTS (APYB02)
SCENE, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/advanced-python-biologists-apyb02/

11. June 12th – 15th 2018
SPECIES DISTRIBUTION MODELLING (DBMR01)
Myuna Bay sport and recreation, Australia, Prof. Jane Elith, Dr. Gurutzeta
Guillera
http://www.prstatistics.com/course/species-distribution-models-using-r-sdmr01/

12. June 18th – 22nd 2018
STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS
USING R (SEMR02)
Myuna Bay sport and recreation, Australia, Dr. Jon Lefcheck
http://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-
and-evolutionary-biologists-semr02/

13. June 25th – 29th 2018
SPECIES DISTRIBUTION/OCCUPANCY MODELLING USING R (OCCU01)
Glasgow, Scotland, Dr. Darryl McKenzie
http://www.prstatistics.com/course/species-distributionoccupancy-modelling-using-r-
occu01/

14. July 2nd – 5th 2018
SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R (SNAR01)
Glasgow, Scotland, Prof James Curley
http://www.psstatistics.com/course/social-network-analysis-for-behavioral-
scientists-snar01/

15. July 8th – 12th 2018
MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV02)
Glasgow, Scotland, Prof David Warton
http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-
data-using-r-mbmv02/

16. July 16th – 20th 2018
PRECISION MEDICINE BIOINFORMATICS: FROM RAW GENOME AND TRANSCRIPTOME DATA
TO CLINICAL INTERPRETATION (PMBI01)
Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith
http://www.prinformatics.com/course/precision-medicine-bioinformatics-from-raw-
genome-and-transcriptome-data-to-clinical-interpretation-pmbi01/

17. July 23rd – 27th 2018
EUKARYOTIC METABARCODING (EUKB01)
Glasgow, Scotland, Dr. Owen Wangensteen
http://www.prinformatics.com/course/eukaryotic-metabarcoding-eukb01/

18. October 8th – 12th 2018
INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISAE01)
Glasgow, Scotland, Prof. Subhash Lele
https://www.prstatistics.com/course/introduction-to-spatial-analysis-of-
ecological-data-using-r-isae01/

19. October 15th – 19th 2018
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-
epidemiologists-abme04/

20. October 29th – November 2nd 2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND
PHENOTYPIC EVOLUTION (PCME01)
Glasgow, Scotland, Prof. Subhash Lele
Dr. Antigoni Kaliontzopoulou
https://www.prstatistics.com/course/phylogenetic-comparative-methods-for-
studying-diversification-and-phenotypic-evolution-pcme01/

21. November 26th – 30th 2018
FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION (FEER
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/

22. February 2018 TBC
MOVEMENT ECOLOGY (MOVE02)
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr
Jonathan Potts
http://www.prstatistics.com/course/movement-ecology-move01/

Media Release: Worsening Worldwide Land Degradation Now ‘Critical’,Undermining Well-Being of 3.2 Billio n People

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Barcelona 2017

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The Barcelona conference WCNRM 2017 was a great success featuring 110 talks during 3 days delivered by scientists from all around the world providing a very satisfying balance between young and senior researchers.

The central theme of the conference namely “Vulnerability and Resilience of Socio-ecological Systems” as well as the quality and
diversity of keynote speakers including Linda Nøstbakken
(Norway, Resource Economics), Mark Finney (US, Research
Forester), Marc Castellnou (Spain, Research Forester) and Frank van Langevelde (Netherlands, Ecology) have strongly contributed to the attractiveness of the conference. The awarding of both the Rollie Lamberson Medal and the prize of best student presentation during the conference also reinforces this interest of our annual conferences. Bill Reed from the University of Victoria in Canada was also honored as RMA Fellow 2017 by the Resource Modeling Association in recognition to his scientific work in natural resource modeling and for his sustained leadership regarding the resource conservation, management and economics.

The Barcelona conference was also successful in regard to social events. In particular the well-organised gala dinner held on the splendid terrace along the Barcelona port provided a friendly and very pleasant atmosphere strongly enjoyed by all participants . Many attendees had difficulties to leave this wonderful place of the Catalan city.

Key to the success of any conference is the unseen work behind the scenes, with tireless efforts from local scientists, administrative staff and organization. In this respect, Nuria Prat Guitart, Elsa Pastor, Oriol, Mariona and all Spanish colleagues in particular from the Pau Costa Foundation and the Universitat Politècnica de Catalunya did a great job and deserve warm acknowledgement for their organisation of WCNRM 2017.

Updates On No-Hassle Mail Order Bride Russia Plans

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Nominations for Lamberson Award 2019

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Call for nomination to Lamberson Award 2019
The Rollie Lamberson Award celebrates the contribution of Professor Emeritus Rollie Lamberson to the field of natural resource modeling and the growth of the Resource Modeling Association by recognizing each year the most outstanding paper in natural resource modeling in the previous two years. See http://resourcemodeling.org/lamberson/ or below for more details.

  • Only current RMA members are eligible for the award.
  • All papers published in Natural Resource Modeling during the previous two calendar years (namely 2017-2018) will be considered automatically provided at least one of the authors is a current member.
  • Papers published in other journals may be nominated for consideration provided at least one of the co-authors is a current member. To be considered, the submission must comprise:
  1. an electronic version of the paper in English,
  2. a nominating letter specifying why the paper is deserving of the Rollie Lamberson Award. Criteria below.

Send your nominations to <luc.doyen or to RMA contact from the website. The submission for the 2019 award will be closed on February 14, 2019.

The Lamberson prize will be delivered to the winners during the 2018 RMA conference to be held in Canada during June, 2019.

Fall 2017

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Postdoctoral Fellow, Gulf of Alaska (GOA) Integrated Ecosystem Assessment (IEA) Program

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Subject: Postdoctoral Fellow, Gulf of Alaska (GOA) Integrated Ecosystem Assessment (IEA) Program

Recruitment has been reopened for an exciting postdoctoral researcher opportunity.

Position: Postdoctoral Fellow, Gulf of Alaska (GOA) Integrated Ecosystem Assessment (IEA) Program

Organization: University of Alaska Fairbanks, College of Fisheries and Ocean Sciences, Juneau, Alaska, USA

Partner organization: NOAA Fisheries, Ted Stevens Marine Research Institute Alaska Fisheries Science Center, Juneau, Alaska, USA

Project summary: NOAA Fisheries has identified a need for ecosystem-based management to fully support 21st century stewardship of our oceans and coasts. Integrated Ecosystem Assessments (IEAs) are a next generation tool designed to incorporate ecological processes in decision making and transfer scientific knowledge to managers and stakeholders. When fully implemented, IEAs have the power to quantify ecosystem services and feed into Management Strategy Evaluations (MSEs). This project will involve collaboration with fisheries oceanographers, stock assessment scientists, ecosystem modelers, natural resource economists, commercial fishermen’s organizations, tribal entities, and non-profit research organizations.

Statement of Work: Dr. Gordon Kruse will supervise the post-doctoral researcher. The NOAA Program Manager (Dr. Moss) and Dr. Kerim Aydin of NOAA/NMFS will provide additional substantive guidance on this collaborative GOA IEA program effort. Primary duties of the post-doctoral researcher will involve: (1) developing a conceptual model, or series of models that emulate the major ecological functions of the GOA; (2) an overarching Gulf of Alaska-wide plan for the Alaska IEA Program; and (3) the drafting of a proposal for a regional IEA specific to Southeast Alaska. Additional analyses will focus on determining which data are the most useful for ecosystem indicators. This project will require strong quantitative/analytical skills, including a background in population dynamics, stock assessment, applied mathematics, or theoretical ecology, as well as familiarity with databases and strong programming skills (e.g., R, AD Model Builder, and Template Model Builder).

Specific Duties of the Postdoctoral Researcher (in collaboration with Alaska IEA team members):

  • Develop conceptual model(s) of the Gulf of Alaska by combining new information from the recently completed Gulf of Alaska Project (NPRB) with that existing in the literature;
  • Develop an overarching plan for the IEA Program in the Gulf of Alaska;
  • Develop a proposal for a regional IEA for coastal Southeast Alaska;
  • Contribute to innovative statistical and mathematical models to better assess and incorporate environmental drivers and/or ecosystem indicators in the ecosystem assessments, fisheries stock assessments, and management strategy evaluations;
  • Provide technical and scientific manuscripts for publication and participates in the development of scientific manuscripts for publication with Alaska IEA team members; and
  • Carry out projects as a full member of a research team and as an independent researcher.

Deliverables of the Postdoctoral Researcher:

  • Conceptual model(s) of the Gulf of Alaska
  • Comprehensive strategic plan for the GOA IEA(s)
  • Proposal for Southeast Alaska IEA
  • Secure partnerships through stakeholder involvement and outreach by hosting stakeholder workshops and in person meetings

Qualifications: PhD or advanced degree in fisheries, biological oceanography, quantitative ecology, biology, zoology, or biological oceanography, mathematics, or statistics. Appropriate experience or additional education, including evidence of statistical work such as: field sampling, computing, and analyzing statistical data.

Salary: Commensurate with experience plus benefits. See: http://www.alaska.edu/benefits/

Contact: Dr. Gordon H. Kruse. Phone: Gordon.Kruse

Apply online: http://careers.alaska.edu/cw/en-us/job/505316/post-doctoral-fellow

Include: Cover letter describing your interest and experience relevant to the position, CV with contact information for three professional references, letter of recommendation from adviser, and pdfs of publications.

Closing Date: Please submit applications by 11:55 pm AKST on 8/1/2017, when application reviews begin. Applications close on 9/1/2017.

New vacancies at DTU

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World Conference on Natural Resource Modeling

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The 2018 WCNRM held at the South China Agricultural University in Guangzhou, during 9-13 June 2018 is over and I want to sincerely thank Krishna Paudel and Yiming Liu and their colleagues for their organisational efforts in delivering a successful conference.

To all participants, please accept my sincere thanks for your individual and collective contributions either as a presenter, as a member of the audience, or as an organizer as these were greatly appreciated by the RMA Board and the broader Membership. As a follow up to the success of the conference, it would also be appreciated if you could:
  1. Follow the RMA networking activities and share scientific information on ResearchGate:https://www.researchgate.net/project/Resource-Modeling-Association,  Linkedin group:https://www.linkedin.com/groups/8578585, Facebook: https://www.facebook.com/Resource-Modeling-Association-543808299018879/, Twitter: https://twitter.com/ResModelAsso
  2. Consider submitting your manuscript to Natural Resource Modeling; our journal’s website is wileyonlinelibrary.com/journal/nrm. In particular,  Krishna Paudel will coordinate a special issue derived from the WCNRM 2018 conference.
  3. Visit RMA’s website at www.resourcemodeling.org;
  4. Plan to attend the next WCNRM conference to be held in Montreal (Canada) on May 22-24, 2019.
  5. Consider joining the RMA if you are not currently a member?  Our fees are modest. To join, visit http://ordering.onlinelibrary.wiley.com/Membership.asp?ref=1939-7445.
In Montreal, Canada in 2019 there will doubtless once again be a terrific line-up of interesting speakers and some great opportunities for social interaction among like minded researchers. I recommend that you start thinking about attending and make a commitment to participate, catching up with old acquaintances or expanding your research network. Those of you who have joined me and most of the RMA Board in conferences past can attest to the enjoyment experienced during these meetings with their relaxed, friendly, and collegiate atmosphere in locations which provide access to magnificently diverse natural environments. Students, early career researchers, and those with substantial experience and achievement,  intermingle in an unpretentious manner which is why many of us have kept coming back each year. I sincerely hope that you will join me at our next meeting and look forward to seeing you in Montreal in 2019.
Most sincerely,
Luc Doyen, President RMA.

5 new jobs!

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See 5 adverts for 5 new positions to work in my ERC team on data assimilation, ocean modelling and
ocean observations. Please send them around to potential candidates,
or post them in places you see fit.

Thanks,
Peter Jan

PS PDRA means postdoc…

PDRA Causality
https://jobs.reading.ac.uk/displayjob.aspx?jobid=1138

PDRA Data Assimilation
https://jobs.reading.ac.uk/displayjob.aspx?jobid=1137

PDRA Ocean Modelling & Data Assimilation
https://jobs.reading.ac.uk/displayjob.aspx?jobid=1136

PDRA Ocean Observation Analysis
https://jobs.reading.ac.uk//displayjob.aspx?jobid=1135

Computational Scientist
https://jobs.reading.ac.uk//displayjob.aspx?jobid=1139#

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