Capture-recapture workshop in Montpellier – 17-21 March 2014

ImageWe’ll be running our annual workshop in March 2014 in Montpellier. It’ll be about capture-recapture models seen under the hidden Markov modelling prism (aka multievent models) and their implementation in the E-SURGE software. The usual team is involved, plus Dave Koons as our guest star.

Check out the website http://multievent.sciencesconf.org/ !!

 

ISEC 2014 abstract submission now open!

logo_isec_rvb_100dpiDear all,

Authors are now invited to submit an abstract for their presentation or poster for the fourth biennial International Statistical Ecology Conference (ISEC2014), to be held 1-4 July 2014 in Montpellier France, at: http://isec2014.sciencesconf.org/

This conference will convene experts from around the world to present and discuss issues of interest to ecological statisticians and biologists.
We will hold sessions focused upon mark-recapture methods, distance sampling methods, other abundance estimation techniques, monitoring of biodiversity, survey design and analysis for estimating population trends, modelling of spatial trends in animal density, integrated population modelling, stochastic population dynamics modelling, stochastic multispecies modelling, individual-based model fitting, and stochastic modelling of animal movement.

We have a tremendous group of plenary speakers:
Marti Anderson (New Zealand). Some solutions to the Behrens-Fisher problem for multivariate ecological data.
Mark Beaumont (UK). Statistical inference for complicated models in ecology and evolutionary biology.
Ben Bolker (Canada). Statistical machismo vs common sense: when are new methods worthwhile?
Nicholas Gotelli (USA). The Well-Tempered Assemblage: Reducing Bias in the Estimation of Species Rank Abundance Distributions.
Jean-Dominique Lebreton (France). The interplay of relevance and generalization in Biostatistics.
Perry de Valpine (USA). Bayesians, frequentists, and pragmatists: the interaction of methods and software
Chris Wikle (USA). Ecological Prediction with High-Frequency ‘Big Data’ Covariates.
Simon Wood (UK). Statistical methods for non-linear ecological dynamic models.

There will also be pre-conference workshops within the area of ecological statistics taking place 28 June to 1 July.

Please place the dates of this conference into your diaries (1-4 July 2012) and visit the conference website for updates.

Best regards,
The ISEC2014 Local Organizing Committee

4-month internship (M1): To breed or not to breed – A dilemma in the Dalmatian Pelican’s life

SupervisionOlivier Gimenez & Alain Crivelli

Description: Life history theory predicts that individuals balance costs and benefits associated with trade-offs between current and future reproduction. If breeding early does not reduce future reproduction, then individuals reproducing early in life should have a better fitness than individuals delaying their reproduction. This delayed reproduction is often associated with long life or limited ressources.

Here, we will study the costs of reproduction as a function of age at first reproduction in the Dalmatian Pelican (Pelecanus crispus) as well as other potential drivers such as density and food availability. The study site is Amvrakikos in Greece. More than 900 chicks have been marked, and almost 800 nesting individuals have been detected. For almost 300 of these individuals, we were able to determine breeding success over life. Over 25 years of study, more than 4000 observations have been recorded.

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References:

Cubaynes, Doherty, Schreiber & Gimenez (2011). To breed or not to breed: seabirds response to extreme climatic events. Biology Letters 7: 303-306.

Desprez, Pradel, Cam, Monnat & Gimenez (2011). Now you see him, now you don’t: Experience, not age, is related to reproduction in Kittiwakes. Proc. of the Royal Soc. B 278: 3060-3066

Doxa, Theodorou, Hatzilacou, Crivelli & Robert (2010). Joint effects of inverse density-dependence and extreme environmental variation on the viability of a social bird species. EcoScience 17: 203-2015.

Gimenez & cie (2013). How can quantitative ecology be attractive to young scientists? Balancing computer/desk work with fieldwork. Animal Conservation 16:134-136.

PhD position in Biodemography

Below is a PhD position in which our team will be largely involved. Feel free to apply!

PhD Position in Biodemography at Centre d’Etudes Biologiques de Chizé ‐ CNRS, France
We are looking for one PhD student – funded by CNRS through the European Research Council (ERC) program EARLYLIFE (PI H. Weimerskirch) – to contribute to our research program investigating the foraging behaviour and demography of the early life of long lived marine mammals and seabirds.

A major goal in biodiversity conservation is to predict responses of populations to environmental change. To achieve this goal, quantitative information on juvenile and immature stages is essential because their mortality controls recruitment to reproductive stages and the future of populations, but also because it is young individuals that disperse most and have the potential to emigrate and colonise new environments. In this research program (EARLYLIFE) we investigate how young individuals respond to environmental changes in terms of foraging skills, foraging ecology and demography and how this affects the population dynamics. For this, we employ biodemographic analysis of long‐term data from natural populations of long‐lived marine top predators (seabirds ands seals) and extensive tracking data on juveniles and adults.

Towards these goals, a PhD position will take the lead on the analysis of juvenile survival and recruitment processes and on the effects of juvenile individual characteristics (body size, body condition, foraging skills, habitat use) and environmental factors on these demographic rates. Telemetry data will allow making inferences on the spatio‐temporal mortality of juveniles and on the variables of the physical environment affecting juvenile mortality. In the light of these results a retrospective analysis of our long term demographic database will allow to test the effects of environmental conditions encountered during early life on juvenile survival and recruitment processes and to estimate the genetic component of foraging tactics.

The student will work with advanced statistical models to investigate juvenile survival and recruitment processes (e.g., multistate, multievent and known‐fate capture recapture models, integrated population models, state‐space models), with long‐term capture recapture time series (from 20 to 40 years) of seabirds (albatrosses and petrels), and with tracking data (newly developed loggers using the GPS and Argos technology). Possibility to contribute to fieldwork on albatrosses and petrels, although not compulsory.

Qualifications
• MSc degree (or equivalent) in population ecology, biostatistics, evolutionary biology, or a relevant field.
• Solid knowledge of and demonstrated interest in population ecology and population dynamics in changing environments.
• Strong quantitative skills, proficiency in statistical analysis and demographic modelling in R or Matlab, and good experience in capture recapture modelling.

The PhD will be based at Centre d’Etudes Biologiques de Chizé (CNRS, Chizé, France) under the supervision of Christophe Barbraud and Henri Weimerskirch with ample collaboration with biostatisticians from Centre d’Ecologie Fonctionnelle et Evolutive (CNRS, Montpellier, France) and with ecologists at CNRS Chizé. Net Salary will be c. 1500€.

Please send the following material in a single PDF file to Christophe Barbraud, Henri Weimerskirch and Olivier Gimenez. Screening of applicants will start October 2nd, 2013 and continue until the position is filled.
• Cover letter indicating your motivation and expectations from this PhD
• Detailed CV
• One page summary of your MSc degree
• Contact information for two references

Being a French postdoc abroad; the language barrier

MrRudeBy Guillaume Péron, Bénédicte Madon, Sabrina Servanty, Lucile Marescot and Sarah Cubaynes.

Some of you may have noticed, lots of French postdocs are in labs around the world. For us it’s more and more a pre-requisite in order to get academic positions (meaning recruitment starts at a good 10 years post high school in the best case scenario, but more on this later).

We thought it would be amusing to write a few posts about this life that we are leading or have led; based on our experience this will involve only anglo-saxon countries (US, Australia, UK). OK, so it’s going to be mostly mishaps, criticisms, and complaints, but we hope those posts will be at least some times funny. Maybe they can be of use to either graduates entering the postdoc life (and comparing it to full time unemployment), or to PIs considering hiring one of those funny-speaking people.

The language barrier

So let’s start with the obvious; English is an acquired taste for us. Although we are confident saying that in most cases, our working English is largely good enough, we also readily acknowledge that there is a difference between working English and fluent English! Social life can be a whole lot different when small talk is an effort and requires 100% of your brain power! The first couple of months are exhausting and it takes time usually to realize that it’s mainly due to the language. If you end up doing a postdoc in an area where there aren’t that many foreigners, people might even think you do not speak English. Having a phone conversation is usually the worst. It can be quite a challenge if you’re going to have a lot of conference calls during your postdoc, especially when it’s involving a big group. And some days, it can be very frustrating and make you feel less capable at your job than native speakers.

We are often asked for examples of funny misunderstandings. Those do happen, so here are a few that we remember right off the bat. Repeating the same word trying different pronunciations hoping to be eventually understood (Dee-sai-FER? Deh-SSAI-fer? Come one, DEE-ssi-feur?)… Ending up writing down this damn word on a piece of paper (decipher everyone). Asking someone to spell a word (e.g. a street name) just to get lost after the 3 first letters, too ashamed to ask again, ending up trying to match up with whatever info we already had. And let’s end with a funny anecdote. You can read the dialogue with a French accent in your mind for added fun. One of us had been in the US for a week, and was looking around for rental apartments. She got lost after the first place she visited. So, she asked a guy in the street.

“Hi, excuse me, I’m lost. Do you know where is the New-York metro station please?”

“Sure, go straight, pass the bridge and when you see five guys, turn left.”

“OK, thanks a lot! Have a good one.” Thinking at the same time, “Well I’m not sure I get it right. How the hell does he know that there is going to be 5 guys after the bridge ?!!!!”

But anyway, straight she went, passed the bridge and planned to ask the next passerby for more info. 5 Guys turned out to be a fast food chain 🙂

After several months or even years, you’re thinking that your English is better, which is true in a sense (especially after some beers at the pub), until you’re making a phone call or meeting someone for the first time and this person is struggling to understand what you’re saying. You then realize that actually people with whom you have been interacting every day are just used to your accent and your way of speaking. Or they just grew tired and act as if they understand you. But don’t put too much pressure on you because if you witness a conversation between an Australian and an American, you will realize that they’re also struggling to understand each other. #WhereIzDeNirestBakeryPlize

Frequently asked questions on E-SURGE

We have just released a list of frequently asked questions (FAQ) on E-SURGE, a software application that is developed by Rémi Choquet in our team. E-SURGE allows the estimation of demographic parameters based on capture-recapture data. It relies on the multievent modeling framework developed by Roger Pradel to deal with uncertainty in the assignment of states to individuals. We will do our best to keep a list of applications updated. Do not hesitate to get back to us if you have any comments on the FAQ.

Complex decisions made simple

A new paper by a former PhD student of the team, Lucile Marescot, now a post-doc at UC Davis.

Marescot, L., Chapron, G., Chadès, I., Fackler, P., Duchamp, C., Marboutin, E. and O. Gimenez (2013). Complex decisions made simple: A primer on stochastic dynamic programming. Methods in Ecology and Evolution. In press. DOI: 10.1111/2041-210X.12082. PDF on request.

This review and tutorial paper (with R code) is about dynamic programming, a powerful mathematical technique to make decisions in presence of uncertainty. This paper would not have been born without the help of Guillaume Chapron, population modeller and large carnivores specialist, as well as Iadine Chadès and Paul Fackler both world experts in the field of decision making.

Abstract
1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time.
2. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this.
3. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code.
4. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. Our results show how the determination of optimal policies is sensitive to the incorporation of uncertainty.
5. SDP is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.

How (not to) write boring scientific literature 2.0

I’m pretty sure that you recall this paper, which, in a nutshell, listed all what makes a boring paper (and proposed more constructive insights). Certainly, as a keen-motivated-enthusiastic-ambitious-original (etc) researcher, you approved the message beyond its ironic tone. But you never applied the recommendations yourself, fearing coauthors’, Editors or Referees’ ultra-academic mind which, roughly, consists in requiring that you remove any kind of originality in the writing of your manuscript. Honestly, I did the same, and definitively gave up with any fun writing no later than after the first draft of my first manuscript (or so). Now that we can publically take on our will to have fun when doing / writing science without being systematically ashamed by a chilling PhD comics-like comment (need examples?), let me add a positive update to Sand-Jensen’s “ten recommendations for boring scientific writing” (if not already done check the link above).

1. Be focused

“What did that paper deal with actually?”

To start simply, could you please ensure that we stupid readers fully understand what you’re talking about?

2. Be original and personal

“Does he really believe what he wrote?”

What of starting your paper with a personal field observation or research experience, rather than by platitudes including “climatic changes affect biodiversity”, “competition is key to assembly rules”, … We all remember about that an apple falling on Newton’s head made the gravitation theory (no matters whether that’s true). If you’ve imagined your new metapopulation model while looking at a motorway interchange, or if your discussion includes experience acquired during sunny birding / hiking days, there’s no reason not to state it (together with standard scientific support of course). We researchers are like anybody, we love fun stories and recall them better than complicated sentences. And one or two such anecdote won’t harm scientific rigor – just make it more digestible.

3. Write short contributions

“Will I still be there on page 15?”

We need to manage our literature reading accounting for (in order of priority) the shortened size of coffee mugs, the high speed of public transports (don’t you also use your train or flight trips to check your >50 pdf that permanently crowd your desktop?), the (not that bad) preference for hypothetico-deductive research, and the crazy amount of literature to go through in general. You’d save everybody’s time by struggling to make your paper as concise as possible – which would also somehow make the reviews shorter… A wise journal editor suggested me a maximum of 3-4 questions / predictions to test and a maximum of 20% of the total text allocated to the discussion. A bit academic, but good to be recalled.

4. Promote some implications and speculation

“Is my paper proposing anything novel at all?”

I liked this paper, and it’s not because it’s always better to like famous people’s stuff. Rather, there is something inspiring in proposing original explanation to ecological patterns (here, that pathogens contribute to the spatial structure of bird communities). No one seriously thinks that science is working like Legos, nicely building ideas over ideas in a nicely imbricated way. Sometimes you need challenging ideas to go forwards, and an unexplained / controversial result could be a nice occasion to make a proposal, even if you don’t have quantitative support for it – simply acknowledge that you’re speculating a bit and that no-one will die of it.

5. Emphasize illustrations, particularly good ones

“Would I be able to explain that figure as part of an undergrad exam?”

It looks stupid but it’s not that much. I won’t cite any example here but we all know at least one of these papers with longish tables, weird figures (if any), which we wonder about how the production staff let it be published. Do figures, and preferably, do figures that anyone can understand at a glance without an extensive outlook to the methods. A simple X and simple Y is not a shame even for high-level statisticians – complexity should be in ideas, not in the way we present it.

6. List all necessary steps of reasoning

“I don’t know where I’m going but I’m on my way”

Conceptual diagrams (like here) may critically determine readers’ ability to follow your predictions and methods –it’s also a good exercise to ensure that you’ve not missed a step somewhere yourself. I’m often criticized about unclear introduction outlines in which the flow is too implicit or blurred by unnecessary elements. I also make such comments very often when being on the other side of the review process, so this is clearly one of the major points to check, even for self-proclaimed good writers.

7. Use few abbreviations and technical terms

“WTF?”

… unless you really don’t want any reader to understand what you’ve done, forget this website when writing. And recall that not everybody in ecology knows what a PCA, a MCMC, a ML, a GLMM are…

8. Try humor and flowery language

Conquer all mysteries by rule and line, / Empty the haunted air and gnomed mine” (J. Keats)

In a recent manuscript, I started a (rather standard) sentence by: “From the other side of the lens “. Although this is by no way comparable to Robert Burns or Shakespeare’s style, a coauthor (whom I value a lot) made this comment: “This is an unusual expression in scientific writing. I’d prefer something less poetic. ». Should I conclude that because we are scientists, we should stick with trivial writing? Some (even good) papers are terribly flat and this does not help anything but making them unduly boring. Take the most of English literature just as you take the most of mathematics or stats. Have a look at the top cited papers in the ecological literature. They are all nicely written, make good use of stylistic techniques, and some even attempt to be fun. In fact, many of us add cool stuff in our congress talks, and the public appreciates. Why should we refrain to do the same in papers?

9. Recall that species and biology are not only statistical elements

“Fish counts are Poisson distributed”

ESA journals now encourage authors to publish photos of field designs as Supplementary Materials. It’s always a good idea to visualize what you’re talking about: one or two relevant plant, mammals or landscape photos could help illustrate your data, and also a good way to meet point 2. Not necessarily because you may learn something from a picture, but also because “you’ll only recall what you’ve actually seen in the wild” (wise advice from an entomology teacher – verified since then)…

10. Quote numerous papers for self-evident statements

“Science is fun (Boulet Team, 2013)”

Do we still need to cite many papers to justify that there are global changes ongoing? Or that forest birds live in forests? Or that dispersal is a major parameter in metapopulation dynamics? Citing just to cite is nice for H factors but not very informative. We could wonder whether a given citation will call the attention of the reader. If we don’t expect that anyone will refer to the full reference in the bibliographical list, then the citation might not be that useful.

Well, it’s easier to state the above than apply it within the highly static academic world of scientific publication. Maybe that’s also because we are self-refraining to try new, fun ways of publishing papers. We won’t get rejected because our paper is concise, well structured, understandable and well illustrated. A photo, humorous sentence or personal experience statement can’t be that harmful – at worse, the Referees will ask to trash it. There are good chances however that a slightly different, personal, colorful paper will be remembered by the readership. Isn’t it what we actually want?