Postdoc or engineer position in Paris

The Museum National d’Histoire Naturelle (MNHN) in Paris offers a 20-month (renewable) contract to a fishery scientist with strong skills in modelling of fish stocks. This position supports the development of robust assessment methods and harvest strategies for toothfish fisheries in the Southern Ocean. He/she will contribute to fisheries management outcomes under the Convention for the Conservation of Antarctic marine Living Resources (CCAMLR) and research projects on the Southern Ocean ecosystem led by the MNHN.

Essential requirements are:
1) the completion of a PhD, Master or engineer degree in relevant discipline, such as fishery science, statistics, population dynamics, resource modelling,
2) competence in the use of databases and statistical software packages such as R,
3) effective oral and written communication of research results and their implications for scientists, industry stakeholders, government and the public, and
4) Candidates must be fluent in French, for more information, see the job description (in french here:

Applications must be submitted by email by sending a letter of motivation with justification of skills for the position and a detailed Curriculum Vitae, to Prof. Guy Duhamel (guy.duhamel) and Patrice Pruvost (patrice.pruvost) before the 30th of June 2018.

Postdoctoral opportunity with DFO on Sablefish MSE and population ecology across the NE Pacific

Using Management Strategy Evaluation to understand the consequences of mismatch between Sablefish stock structure and the scale of assessment and management across the northeast Pacific.

Natural Resources Canada Postdoctoral Research Program


Fisheries and Oceans Canada (DFO) is seeking a Postdoctoral Fellow to lead a research project on the population ecology and management of Sablefish. The Principal Investigators on the project are Drs. Brendan Connors (DFO Institute of Ocean Sciences), and Sean Cox (Simon Fraser University); key collaborators include Drs. Melissa Haltuch (NOAA NW Fisheries Science Center), and Dana Hanselman (NOAA Alaska Fisheries Science Center), as well as Carrie Holt and Sean Anderson (DFO Pacific Biological Station).


Sablefish are a long-lived and commercially-valuable deep-water species that range from Southern California to the Bering Sea. Sablefish are assessed and managed at regional scales (i.e., Alaska, British Columbia and the US West Coast) but are a highly mobile straddling stock with little genetic evidence of population structure across these management regions. The conservation and fishery consequences of this mismatch between Sablefish stock structure and the scale of assessment and management are unknown. The objective of this project is to work collaboratively with an international team of Sablefish scientists to conduct a Management Strategy Evaluation (MSE) that is based on Sablefish population dynamics and stock structure across their range. We will use the MSE to understand the potential consequences of the mismatch between Sablefish stock structure and management by simulation testing current, and potential future, management procedures (data collection scheme, stock assessment method, harvest policy rules) to quantify their performance against a range of conservation and fishery objectives. The outcomes of the proposed work will provide scientific advice to help advance international fisheries governance by improving our understanding of Sablefish population dynamics and their management implications over the full range of their distribution.

While the focus of the position is on the above research, the position will afford ample opportunity for motivated individuals to lead and/or contribute to other research on groundfish population ecology and management.


Applicants must have completed a PhD in fisheries science or a related discipline within the past three years, and have demonstrated expertise in spatial population ecology and advanced statistical and simulation modelling. Successful candidates will be self-motivated and have a proven track record of publishing their research in peer-reviewed journals. The position is available for candidates of all nationalities but those who are not Canadian citizens or permanent residents of Canada must satisfy Canadian immigration requirements.

LOCATION OF TENURE FLEXIBLE: Pacific Biological Station (PBS), Nanaimo, BC; Institute of Ocean Sciences (IOS), Sidney, BC; or School of Resource and Environmental Management at Simon Fraser University, Burnaby, BC. The west coast of Canada, is well known for its rainforests, beaches, and mountains. It is a destination for kayaking, hiking, surfing, skiing, diving, biking and camping.


This fellowship is available to start September 1, 2018 and will be completed by January 1, 2021 with a salary of $65,000 CAD per annum plus travel support. The Canadian Government Postdoctoral Research Program is administered by Natural Resources Canada (NRC). More details about the program can be found at:


Interested applicants should email: 1) CV; and 2) cover letter outlining the experience and skills they bring to the project to: Brendan Connors, Brendan.Connors

Short-listed applicants will be invited to develop a full application through the NRC system. CVs will be accepted until the position is filled.

POSEIDON team hiring two new post-docs

The POSEIDON project is hiring for two (2) new postdoctoral researchers. They will support model development to explore management of Eastern Pacific tropical tunas, along with an on-going management strategy evaluation for the Indonesian deepwater snapper fishery.

  • The first postdoc will be based at the University of Oxford, and requires deep expertise in agent-based model building and application, wider software development skills and a strong quantitative background. S/he will be tasked with coding and model development as we apply the model to the Eastern Pacific Ocean (EPO) tropical tuna fishery.
  • The second postdoc will be based at the Inter-American Tropical Tuna Commission (IATTC) in La Jolla, California and will be charged with 1) understanding and accessing relevant datasets from IATTC; 2) scoping model application and designing use cases that are supportive of IATTC policy evaluation processes; and 3) conducting statistical analyses of data to support model development. This position requires a strong background in quantitative fisheries science, though other strong quantitative backgrounds may be considered.

Both will work closely with the multidisciplinary POSEIDON team from the University of Oxford, Ocean Conservancy, George Mason University, the University of California, Santa Barbara, and Arizona State University and our partners from the IATTC, The Nature Conservancy, Indonesia, and the International Seafood Sustainability Foundation. This project provides researchers the opportunity to experience first-hand how to bring research into practical application, connect with top researchers, policy experts, practitioners and business leaders, and contribute to meaningful and timely solutions for the world’s oceans and fisheries. (more on the POSEIDON model below).

Brief descriptions of each position are below – for more information or to apply, please follow the application link.

Postdoctoral Research Associate – Agent-Based Model Development (University of Oxford, based in Oxford, England, 24 mos)

Supervisor: Dr. Richard Bailey

This postdoc will focus on building build an agent-based model (ABM) to represent the extraction of fish by fishing vessels in the eastern pacific tropical tuna fishery and the associated effects on ocean ecological systems. The position requires deep expertise in agent-based model building and application, wider software development skills and a strong quantitative background – a doctorate in a quantitative subject (e.g., mathematics, physics, engineering, quantitative ecology) is a requirement; a track record of multi-disciplinary research and experience in complementary disciplines (e.g., economics, marine biology, natural resource management) is strongly preferred.

To apply:

Postdoctoral Research Assistant – Eastern Pacific Tropical Tuna Management (Arizona State University, based in La Jolla, CA, 18 mos with possibility of extension)

Supervisor: Dr. Steven Saul

This postdoc will serve as a key member of the team to develop the POSEIDON application for EPO tropical tuna management. S/he will be based at the IATTC’s headquarters in La Jolla, California, and will be charged with 1) understanding and accessing relevant datasets from IATTC; 2) scoping model application and designing use cases that are supportive of IATTC policy evaluation processes; and 3) conducting statistical analyses of data to support model development. This researcher will work closely with the modeling team based at the University of Oxford and Ocean Conservancy to drive model design, calibration and validation of the tool and its outputs, as well as evaluation of model results. At the IATTC headquarters, this researcher will act as the liaison between the POSEIDON team and IATTC staff so that the best knowledge of the data and the fishery is well-captured in the model. The position requires a strong background in quantitative fisheries science; candidates with other strong quantitative backgrounds (e.g., a doctorate in a quantitative subject, e.g., computer science, applied mathematics, physics, engineering, quantitative ecology) may be considered. This position also requires the ability to handle large datasets, knowledge of statistics and simulation modeling, and programming skills in R (or equivalent languages). Experience with agent-based model building and application, wider software development skills, and natural resource management are strongly preferred.

To apply:

POSEIDON is a coupled human-ecological model that combines an agent-based, adaptive fishing fleet model with existing fishery models or simple biological data, to simulate vessel behavior and fishery outcomes based on policies, market influences, and environmental factors. POSEIDON provides a powerful platform for policy evaluation and decision support, with a strong focus on the spatial and human dimensions of fisheries management. POSEIDON was originally developed by a multidisciplinary team from the University of Oxford, Ocean Conservancy, George Mason University, the University of California, Santa Barbara, and Arizona State University, as part of an effort to advance innovation in fisheries management. The model has been calibrated and validated to the U.S. West Coast groundfish fishery, where it has been able to reproduce observed fishing patterns. It is now being adapted to explore MSC certification for Indonesia’s deep slope snapper fishery (in partnership with The Nature Conservancy, Indonesia), as well as for EPO tropical tuna management.

International River Modeller position at CSIRO, Canberra

An opportunity exists to join the international water team within CSIRO Land and Water in Canberra. Details available at

ECOLOG-L Graduate Opportunity in Urban Forest Modeling

Graduate Opportunity in Urban Forest Modeling

Dr. Christina Staudhammer in the Department of Biological Sciences at the
University of Alabama ( 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
Staudhammer (

ECOLOG-L Applied Bayesian modelling for ecologists and epidemiologists

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.


Check out our sister sites, (Ecology and Life Sciences) (Bioinformatics and data science) (Behaviour and cognition)

Upcoming courses

1. April 9th – 13th 2018
Glasgow, Scotland, Dr. Marco Scotti

2. April 16th – 20th 2018
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre

3. April 23rd – 27th 2018
Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet

4. April 30th – 4th May 2018
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick

CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
6. May 14th – 18th 2018
CANADA (QUEBEC), Prof Subhash Lele

7. May 21st – 25th 2018
SCENE, Scotland, Dr. Martin Jones

8. May 21st – 25th 2018
Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi

9. May 28th – 31st 2018
CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson

10. May 28th – June 1st 2018
SCENE, Scotland, Dr. Martin Jones

11. June 12th – 15th 2018
Myuna Bay sport and recreation, Australia, Prof. Jane Elith, Dr. Gurutzeta

12. June 18th – 22nd 2018
Myuna Bay sport and recreation, Australia, Dr. Jon Lefcheck

13. June 25th – 29th 2018
Glasgow, Scotland, Dr. Darryl McKenzie

14. July 2nd – 5th 2018
Glasgow, Scotland, Prof James Curley

15. July 8th – 12th 2018
Glasgow, Scotland, Prof David Warton

16. July 16th – 20th 2018
Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith

17. July 23rd – 27th 2018
Glasgow, Scotland, Dr. Owen Wangensteen

18. October 8th – 12th 2018
Glasgow, Scotland, Prof. Subhash Lele

19. October 15th – 19th 2018
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard

20. October 29th – November 2nd 2018
Glasgow, Scotland, Prof. Subhash Lele
Dr. Antigoni Kaliontzopoulou

21. November 26th – 30th 2018
Glasgow, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr.
Carlos Carmona

22. February 2018 TBC
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr
Jonathan Potts

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

Call for nomination to Lamberson Award 2018

Call for nomination to Lamberson Award 2018
The Rollie Lamberson Award celebrates the contribution of Professor 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 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 2016-2017) 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 award 2018 will be closed on February 14, 2018.

The Lamberson prize will be delivered to the winners during the 2018 RMA conference to be held in Guangzhou, China on June 9-13, 2018.

Fall 2017

Download (PDF, 746KB)

Assistant or Associate Research Professor Position, Department of Fisheries Oceanography, University of Massachusetts Dartmouth


Assistant or Associate Research Professor

Department of Fisheries Oceanography

We are accepting applications for a Research Faculty position in Fisheries Oceanography at the School for Marine Science and Technology (SMAST) at the University of Massachusetts Dartmouth in the Department of Fisheries Oceanography. Candidates with quantitative expertise in fields related to living marine resource management are encouraged to apply, including but not limited to: marine ecosystem science, fisheries stock assessment, management decision support, aquaculture, marine resource economics, socio-ecological systems, ecosystem services, and the human dimensions of environmental change and marine governance.

Students and faculty within the SMAST Department of Fisheries Oceanography conduct cutting-edge research in interdisciplinary sciences related to interactions between marine organisms, the marine environment, and fisheries. We train early-career scientists in the techniques and theory of fisheries oceanography relevant to marine resource management, with emphasis on stock assessment, population dynamics, resource economics, physical and biological interactions, ecosystem modeling, fish behavior and conservation engineering, decision support, field studies, and collaborative research with fishermen. Graduate degree programs address the growing need for adept and broadly skilled marine scientists in federal and state agencies, industry, non-governmental organizations, and academic institutions.

SMAST ( is the marine campus of the University of Massachusetts Dartmouth and the lead campus of Intercampus Marine Science (IMS) degree program of University of Massachusetts system. SMAST has twelve primary tenured and tenure-track faculty members, advising approximately 50 M.S. and Ph.D. graduate students, with a total number of 100+ faculty, students, and staff of. The SMAST marine science campus includes a two-story, 32,500 square foot teaching and research building as well as a newly opened 64,100 square foot building, both located in New Bedford, the nation’s most economically productive fishing port. These facilities provide docking, house a combined 6,400 square foot seawater lab, a 90,000 gallon acoustic-optic tank, a state-of-the-art computational facility, flexible wet, dry, and computational research labs and researcher offices, flexible modern classroom and meeting spaces, the New Bedford office of the Massachusetts Division of Marine Fisheries, and facilities to support a research diving program.

The successful candidate will be expected to develop an active and productive research program, collaborate with existing SMAST faculty and scientists at regional partner institutions, and engage with managers and public on regional resource management issues. These are primarily grant-funded positions, with normally a 12-month term of service and the opportunity for multiple year and consecutive appointments. Opportunities exist within the position for teaching and for graduate student mentoring, and individuals demonstrating an interest in incorporating these activities into their research program will be viewed favorably. Research faculty within the Department of Fisheries Oceanography are paired with a faculty mentor to assist with professional development and help foster their career goals.

The Research Faculty Position is funded by the NOAA Fisheries Quantitative Ecology and Socioeconomics Training (QUEST) program to support educating and training the next generation of ecosystem scientists, stock assessment scientists, and economists ( through the Cooperative Institute for the North Atlantic Region ( The mission of CINAR is to conduct and coordinate cutting-edge research engaging both NOAA and academic scientists to enable informed decisions by NOAA for sustainable and beneficial management of the northwestern Atlantic shelf ecosystem.

To learn more about this position and the requirements, please go to our web site at:

To apply please submit online a cover letter, curriculum vita, a statement of research and teaching goals, copies of no more than three publications, and the contact information for three professional references @

Review of applications will begin January 8, 2018 and continue until the position is filled.

Applicants must be authorized for employment in the U.S. on a full time basis. Employment-based visa sponsorship not available.

University of Massachusetts Dartmouth employees and applicants for employment are protected by federal laws, Presidential Executive Orders, and state and local laws designed to protect employees and job applicants from discrimination on the bases of race, religion, color, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, family medical history or genetic information, military service, veteran status or other non-merit based factors.

The University of Massachusetts reserves the right to conduct background checks on potential employees.