Monday, March 5, 2018

SMEG is collaborating with the Institute of Marine Sciences in Barcelona

David Conesa, Joaquín Martínez Minaya and Maria Grazia Pennino, members of the SMEG, gave the course entitled “Modeling species distributions: methods and applications” in Barcelona, (Spain) from 287th of February to 2nd of March 2018 trough the BarcelonaOcean platform of the Institute of Marine Sciences (ICM/CSIC). The course described some of the most prominent SDMs methods currently in use both from a frequentist and a Bayesian approach. There was a great participation of students and researchers from the Institute of Marine Sciences (ICM/CSIC) but also from the University of Barcelona and others research centres (Spanish Oceanographic Institute - IEO- Mediterranean Institute for Advanced Studies- IMEDEA- University of Vigo).


A special thanks go to Marta Coll, also part of the SMEG group, that invite us to  teach this course.

Wednesday, February 28, 2018

Fully-funded Master (MS) position in Movement Ecology at the University of Florida starting Fall 2018

We are seeking a motivated MS student to join a large international project funded by the Human Frontiers Science Program (HFSP) aiming at understanding how seabirds navigate and the role of infrasound in their movement [1]. The MS thesis will follow one of two possible paths, requiring students with two distinct profiles: one in applied statistics/mathematics; and one in quantitative ecology. Applications are encouraged for both profiles,
but only one student will be selected.

1) Applied statistics/mathematics profile: The student will focus on the development of movement models integrating environmental cues. Applicants should have a background in mathematics or statistics, including at least a course in mathematical statistics (and not just applied statistics) and strong programming skills; fluency in R and a genuine interest in ecological applications are highly desirable. Previous experience, e.g., internships in movement ecology-related subjects or previous work with tracking or satellite data, is a plus. Interactions with the trajectometry group PathTIS in France [2] should be expected.

2) Quantitative ecology profile: The student will work to quantify and understand the associations between individual movement and abiotic conditions (such as meteorological conditions or the infrasoundscape) at multiple scales. Applicants should have a background in ecology and several courses in applied statistics, and are expected to demonstrate basic programming skills; fluency in R is highly desirable. Previous experience, e.g., internships in movement ecology-related subjects or previous work with geospatial data, is a plus.
Interactions with Dr. Samantha Patrick and Dr. Thomas Clay from the University of Liverpool in the framework of the project [3, 4] should be expected.
The student will be expected to publish the results in peer-reviewed journals and present them at national/international conferences.

The Master's degree awarded will be either in Wildlife Ecology and Conservation or in Interdisciplinary Ecology (with concentrations in Geographic Information Systems, Mathematics or Statistics). The Master’s program will consist of three semesters mainly dedicated to classwork during the first year and a second year exclusively dedicated to the Master’s thesis. This position will be completely supported for the two years (stipend + tuition) and the program is expected to start in fall 2018.

Classwork during the first two semesters will take place on the main UF campus in Gainesville. Research will be performed at Dr. Mathieu Basille's lab [5], under the supervision of Dr. Basille and Dr. Rocio Joo [6]. Dr. Basille's lab is located at the University of Florida's Fort Lauderdale Research and Education Center (FLREC), in Davie, Florida. Davie is a town within the large Miami metropolitan area in South Florida, just miles away from the Florida Everglades.

Please apply by sending an email including a cover letter describing your interest, experience and career goals, a CV, unofficial transcripts and GRE scores, and contact information for three references to Dr. Rocio Joo (rocio.joo@ufl.edu). Write “master application” as the email subject. Applications will be processed in the order they are received until April 15th or before if a suitable applicant is found.

[1] https://seabirdsound.org/
[2] https://sites.google.com/site/statistiqueettrajectoires/
[3] https://seabirdsound.org/meet-the-team/
[4] http://seguliverpool.wixsite.com/home
[5] https://mablab.org/
[6] https://mablab.org/people/rocio-joo/

Thursday, February 22, 2018

SMEG is collaborating with the University of Alicante (Spain)

As in previous years, Jose Maria Bellido, member of the SMEG, has taught several classes in the International Master of Sustainable Fisheries Management of the University of Alicante (Spain). The classes have been taught in two days of 5 hours and have been on the following aspects:

- Data sources for population dynamics;
- SIRENO, the oceanographic-fishing database of the Spanish Oceanographic Institute;
- Impact of fishing on the ecosystem;
- Discards and Bycatch in the Ecosystemic Approach for the Management;
- Biogeography and numerical ecology. The oceanographic-fishing data modeling.


In addition, as practices, a role play "Is there a future for fishing and the seas?was performed among the students. 


The Master is held in Alicante, jointly organized by the University of Alicante (UA), the Spanish Ministry of Agriculture and Fisheries, Food and Environment (MAPAMA), through the General Secretariat of Fisheries (SGP), and the International Centre for Advanced Mediterranean Agronomic Studies (CIHEAM), through the Mediterranean Agronomic Institute of Zaragoza (IAMZ). Pending final confirmation, the General Fisheries Commission for the Mediterranean (GFCM) of the Food and Agriculture Organization of the United Nations (FAO) will also be part of the organization. Furthermore, the Master counts on the collaboration of the FAO Department of Fisheries and Aquaculture. The aim of the Master is to provide a high level of specialization in fisheries management issues by providing a multidisciplinary vision of fisheries management from the perspective of various sciences such as biology, economics, law and sociology. 

Thursday, January 25, 2018

SMEG it flew to Chile

Maria Grazia Pennino, founder member of the SMEG, gave the course entitled “Modeling species distributions: methods and applications” in Concepción, (Chile) from 7th to 12th of January 2018. The course described some of the most prominent SDMs methods currently in use both from a frequentist and a Bayesian approach. There was a great participation of students from the Univeristy of Concepción, but also of researchers of the Chilean Fisheries Research Center.



The course was organised by the Evolutionary Ecology and Phyloinformatics Laboratory at the Universidad de Concepción.



The annual meeting of the Statistical Modeling Ecology Group

The Research Operations Center of the Miguel Hernández University (UMH) of Elche hosts the annual meeting of the Statistical Modeling Ecology Group (SMEG) to plan the research lines of the 2018. This Interdisciplinary research group links researchers from schools of Mathematics and Statistics, Biology and Ecology. Its activity is to develop and apply advanced mathematical and statistical methods to collect and analyze ecological/biological data and thus improve the understanding and management of natural resources and their environment.


Tuesday, October 24, 2017

Small-scale fisheries in Brazil: a not-so-invisible problem

The SMEG had the pleasure to invite the talk of Dr. Priscila F. M. Lopes through the Centro d’Investigació Operativa, Universitas Miguel Hernández (Spain). Dr. Lopes is a Professor at the Universidade of Rio Grande do Norte in Natal (Brasil) and actively collaborate with our resercah group. Take a look to this interesting talk:


Monday, June 12, 2017

A brainstorm with Bayes, Laplace, Pearson and Fisher

In our last posting session we discussed about statistical modelling.  Briefly, it was said that the main idea of statistical models relies in the quali-quantitative description of reality. If one assumes that the data sampling and modelling process is surrounded by several sources of uncertainty, it turns out that summarizing these uncertainties into probabilities is the most practical form to achieve their quantification.
Indeed, within Statistics (which may be described as the mathematics of uncertainty) the probability theory is a keystone, because through it one can quantitatively accomplish all inference about the parameters of a tested model.  Nevertheless, adopting a certain concept of probability has not always been such straightforward – Yes, there are different concepts of how to define probability and this has led to an endless debate about whether one is better than another.
Currently, the branch of statistical inference can be divided in two main strands: the frequentist (or classical) and the Bayesian inference. Here, it is not our intention to deepen philosophical discussion on both approaches. Though, we will shortly outline the main differences among them, and also give some reasons why we are inclined in using Bayesian inference in most of our studies. So, for the sake of simplicity, let’s introduce a quick way to contrast both inferences by exposing the impressive archery skills of Merida (from the Disney animation).





Figure 1: Comics contrasting frequentist and Bayesian inference. Adapted from http://faculty.washington.edu/kenrice/BayesIntroClassEpi515.pdf 


The frequentist way of thinking
What can we learn by reading these short comics? Well, firstly within the frequentist inference, the probability is defined as long-run relative frequencies of events, and for that one has to assume a purely random and well-defined experiment. For instance, in the comics the experiment would be composed by Merida shooting randomly arches at the target point (bullseye), and by you, who sits behind the target point and tries to calculate the exact position of the bullseye.
In every shoot, you draw a 10 cm circle around the arch and assume that this circle represents 95% of your confidence that it includes the bullseye. If Merida continues shooting randomly some arches (e.g. 100 arches) and you would continue drawing 10 cm circle for each shot arch, then at the end you would perceive that 95 of your circles have some degree of overlap and the remaining 5 felt completely out of the target point. What would then be your conclusions? Well, you would assume that the bullseye is contained somewhere within the 95 overlapped circles, being therefore 95% confident about your estimation since 95 of 100 shoots felt very close to each other. 
Note, however, that to estimate such outcome, Merida and you would have to repeat this experiment over and over, each of them being a new and independent experiment in relation to the previous one. In addition, although you would have 95% confidence about your estimation, it does not tell you the exact position of the bullseye, i.e., is it truly in the 95% region or could it also be within the remaining 5% region? So, what about Bayesian inference? 

The Bayesian way of thinking 
Well, under the Bayesian prism the probability is defined on an individual’s degree of belief of a particular event. Specifically, the probability quantifies the plausibility attributed to a certain proposition and whose truthfulness is uncertain in the light of available knowledge (Kinas & Andrade, 2010).
Let’s go back to the comics example: although you are sitting behind the target point, you may have some knowledge about the bullseye location due to previous experience. This experience can be attributed, for example, to the fact that you have already done the same experiment with other archers and thus may have an overall idea where the most probable target point could be, or also because you know Merida’s extraordinary abilities so well that that her first shot will probably lay very close to the bullseye (if not already over the target!).
The fact is that within Bayesian inference, you can use your previous experience and update your estimation as more and more arches are shot. Thus, a new experiment (i.e., a new shoot) is conditioned to the results of the previous experiment, and thus you will be able to continuously update your estimates until you reach the most probable outcome. As Kruschke (2014) already said, ‘’Bayesian inference is reallocation of credibility across possibilities.’’  

       So…why Bayesianism instead of Frequentism?
Most of the criticism over Bayesian inference is upon its subjective definition of probability. Such critiques have mainly increased after the 20’s, when Karl Pearson and Ronald Fisher firstly developed statistics as an information science. Nevertheless, this subjectivity is the cornerstone of Bayesian inference and represents a consequence of available information, and not merely an arbitrary quantification as usually thought. Moreover, the scientific judgment about the best choice of these probabilities is as necessary as the decision made upon choosing the most appropriate model according to tested data (Kinas & Andrade, 2010).
In conceptual terms, Bayesian inference is way much simpler than frequentist inference if we consider that all questions can be answered through the analysis of the posterior distribution, and which is obtained by means of Bayes’ Theorem (also known as inverse probability theorem)[1].  In the light of this, it can be noticed that the posterior distribution denotes the most complete way to express the state of knowledge about an investigated phenomenon.




Figure 2: Thomas Bayes (upper left panel), Pierre S. Laplace (upper right panel), Karl Pearson (lower left panel), and Ronald Fisher (lower right panel).


The essence of Bayesian inference lies precisely in the interactive dynamism between the previous experiences (also known as priors in Bayesian statistical terminology) and current experiment (known under likelihood), which jointly reallocates the credibility denoted in the posterior distribution (from which all necessary inferences are drawn). This also shows that the today’s posterior distribution can become the prior distribution of tomorrow (remember the comics), a fact that can never be assumed in the context of classical inference since all experiments are independent from each other.
Furthermore, according to Jaynes (2003), it is also at this stage where one of the major differences among both inferential branches arises, because probabilities change as we change our state of knowledge; frequencies, by contrast, do not change. This also explains why particular questions cannot be answered according to the frequency definition of probability. In environmental sciences, for example, questions such as ‘’ what is the probability that the current water use will remain sustainable’’ or ‘’what is the probability that area A presents greater potential for conservation than area B’’ can only be answered under the Bayesian prism.
Thus, knowing that uncertainty is inherent to all scientific realms, its inclusion into the decision-making process is not only desirable, but essential. Indeed, Bayesian inference has been successfully applied in a wide range of situations, such as cracking German enigmas during World War II, searching for the black box of the Air France aircraft that felt in the middle of the Atlantic Ocean in 2009, artificial intelligence, in courtrooms, and medicine[2]. Bayesian approaches has also shown to be a powerful tool in environmental sciences, as it is possible to incorporate all types of uncertainties in conservation and management decisions, and hence preventing possible catastrophes that might be irreversible.
As mentioned at the beginning of this post, the intention was not to discuss exhaustively all topics concerning the debate around frequentist and Bayesian inference. In fact, a wide range of interesting books and articles exposes an in deep discussion of this issue, highlighting pros and cons. If you are interested in further details on the incorporation of Bayesian inference into the ecological context, we recommend the articles by Dennis (1996), Ellison (2004), Clark (2005), and Cressie et al. (2009). If you are interested in more theoretical issues, you may refer to Jaynes (2003), McCarthy (2007), Gelman et al. (2013), and Kruschke (2014).



[1] Though this Theorem is named after the legacy left by the English Reverend Thomas Bayes, its practical development and application into scientific issues was mainly done by the French scientist Pierre S. Laplace – that’s also why some claims that the Theorem should be rather named as the Bayes-Laplace Theorem!

[2] For an easy-go reading about the historical application of Bayesian inference, the book The theory that would not die: how Baye’s rule cracked the enigma code, hunted down Russian submarines & emerged triumphant from two centuries of controversy, written by Sharon B. McGrayne, is highly recommended. 


References
Clark, J.S. 2005. Why environmental scientists are becoming Bayesians. Ecol. Let., 8: 2-14.
Cressie, N.; Calder, C.A., Clark, J.S., Ver Hoef, J.M. & Wilke, C.K., 2009. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecol. Appl., 19, 553-570.
Dennis, B. 1996. Should ecologists become Bayesians? Ecol. Appl., 6: 1095-1103.
Ellison, A.M. 2004. Bayesian inference in ecology. Ecol. Let., 7: 509-520
Gelman, A.; Carlin, J.B.; Stern, H.S.; Dunson, D. B.; Vehtari, A. & Rubin, D. B. 2013. Bayesian data analysis, Chapman & Hall/CRC Press, 675p.
Jaynes, E.T. 2003. Probability Theory – The Logic of Science. Cambridge University Press, 727p.
Kinas, P.G. & Andrade, H.A. 2010. Introdução à Análise Bayesiana (com R). maisQnada, 240p.
Kruschke, J.K. 2014. Doing Bayesian data analysis: a tutorial with R, JAGS and Stand. Elsevier, 759p.
McCarthy, M.A. 2007. Bayesian methods for Ecology. Cambridge University Press, 306p.



By Marie-Christine Rufener