An opportunity exists to join the international water team within CSIRO Land and Water in Canberra. Details available at https://jobs.csiro.au/job/Canberra%2C-ACT-International-River-Modeller/443576800/
Graduate Opportunity in Urban Forest Modeling
Dr. Christina Staudhammer in the Department of Biological Sciences at the
University of Alabama (http://cstaudhammer.people.ua.edu/) is now inviting
applications for a PhD or MS position starting in spring 2019. The student
will work on a project in urban forestry, partially funded by a grant from
The benefits of urban forests to city-dwelling people include recreation,
pollution, mitigation, energy savings, and water purification. However,
fundamental questions still remain about the resistance and resilience of
urban ecosystems to anthropogenic change, especially associated with
projected alterations in global climate. Hurricane Irma, while destructive,
created an opportunity to evaluate the impact of windstorms on urban
forests. Utilizing pre- and post-storm field-measured and remotely sensed
data, a student is sought to model the relationship between tree,
landscape, and socioeconomic characteristics, storm variables, and urban
forest damage. This work will fill gaps in our knowledge about the
ecosystem services provided by urban forests. The overarching goal is to
enhance our scientific understanding of the role of urban forests at local
to regional scales, and how they respond to disturbance.
It is expected that prospective graduate students will develop their own
research plans and goals, and therefore should be self-motivated and
independent. Students should be interested in combining ecology with
statistical modeling. Students should have demonstrated experience in
statistics, as well as a background forest ecology, geography, or
environmental science. A solid working knowledge of SAS and/or R is
required, and those with strong quantitative skills will be given
This position is primarily a Teaching Assistantship, supplemented by grant
funding. However students are expected to apply for additional funding.
Interested students will earn a graduate degree from the Department of
Biological Sciences. The project will also offer the opportunity to
interact with researchers from the USDA forest service, as well as
researchers from the University of Florida and University of South Florida.
The University of Alabama is located in Tuscaloosa, a college town of
~100,000, surrounded by extensive and varied forests. These forests, and
the greater region, provide a wide range of recreational amenities
including rock climbing, canoeing, kayaking, fishing, hiking and mountain
To be eligible, students must meet the graduate admission requirements of
the University of Alabama: an undergraduate GPA > 3.0 overall, 3.0 for the
last 60 semester hours in a degree program or 3.0 for a completed graduate
degree program, and a 300 on the GRE. If interested, email a short summary
of your research interests, an unofficial transcript from undergraduate
(and post-graduate, if applicable) work, as well as a CV to Dr. Christina
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.
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
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
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.
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.
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.
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.
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.
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)
1. April 9th – 13th 2018
NETWORK ANAYLSIS FOR ECOLOGISTS USING R (NTWA02
Glasgow, Scotland, Dr. Marco Scotti
2. April 16th – 20th 2018
INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01)
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre
3. April 23rd – 27th 2018
MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE
Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet
4. April 30th – 4th May 2018
QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick
5. May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL
ECOLOGICAL DATA USING R (MVSP02)
CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
6. May 14th – 18th 2018
INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS (IMBR01)
CANADA (QUEBEC), Prof Subhash Lele
7. May 21st – 25th 2018
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB05)
SCENE, Scotland, Dr. Martin Jones
8. May 21st – 25th 2018
INTRODUCTION TO REMOTE SENISNG AND GIS FOR ECOLOGICAL APPLICATIONS (IRMS01)
Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi
9. May 28th – 31st 2018
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson
10. May 28th – June 1st 2018
ADVANCED PYTHON FOR BIOLOGISTS (APYB02)
SCENE, Scotland, Dr. Martin Jones
11. June 12th – 15th 2018
SPECIES DISTRIBUTION MODELLING (DBMR01)
Myuna Bay sport and recreation, Australia, Prof. Jane Elith, Dr. Gurutzeta
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
13. June 25th – 29th 2018
SPECIES DISTRIBUTION/OCCUPANCY MODELLING USING R (OCCU01)
Glasgow, Scotland, Dr. Darryl McKenzie
14. July 2nd – 5th 2018
SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R (SNAR01)
Glasgow, Scotland, Prof James Curley
15. July 8th – 12th 2018
MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV02)
Glasgow, Scotland, Prof David Warton
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
17. July 23rd – 27th 2018
EUKARYOTIC METABARCODING (EUKB01)
Glasgow, Scotland, Dr. Owen Wangensteen
18. October 8th – 12th 2018
INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISAE01)
Glasgow, Scotland, Prof. Subhash Lele
19. October 15th – 19th 2018
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard
20. October 29th – November 2nd 2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND
PHENOTYPIC EVOLUTION (PCME01)
Glasgow, Scotland, Prof. Subhash Lele
Dr. Antigoni Kaliontzopoulou
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.
22. February 2018 TBC
MOVEMENT ECOLOGY (MOVE02)
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr
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.
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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:
- an electronic version of the paper in English,
- 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.
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
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.
Professor Roland H. Lamberson
Emeritus Professor of Mathematics
Humboldt State University
Arcata, CA 95521
- 2017 Lamberson Medal Winners
- 2018 Lamberson Medal Recipient
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