Emergent patterns in nature and society


Sharing and openness

One of the features that attracts me to open online learning is the democratisation of knowledge. Science and scientific knowledge is one of the main achievements that makes us humans: from the invention of fire to the development of vaccines. Knowledge is a form of power that cannot be taken, once you know something no one can force you to unlearn it. By encouraging open education, we are empowering people to be better, but also to scrutinise, criticise and improve what others have done. But it begs the question of how to be open or what practices are out there for practicing open education?

The first thing that comes to mind are massive online open courses or MOOCs. They have revolutionised education by using technology and reach out to many more people that traditional classroom based courses. There are however some caveats. Learning occurs in the interaction with peers and teachers, through discussions, questions and exercises. Online platforms are good for putting online lectures available to many people, but are more restricted at recreating the wealth of social interactions that a real classroom or lab can offer. Although some MOOCs do offer group work and mentoring by teaching assistants, the content and platforms are not completely open. Most MOOCs are run by companies whose interest include making profit. Courses are open only for a limited period of time and by subscription, they can be free if one does not opt for a certificate, but they are not free all the time so students can follow at their pace or convenience.

But MOOCs are the product of companies like Coursera or EdX. They have state-of-the-art resources to make materials available, make use of interactivity, language translation, or other features. How does one practice open learning if your are a high school teacher or a lecturer at a university? What if you don’t have all the time or resources that companies do, but want to practice some of that openness in your classes?

That got me thinking on content types and looking for examples of teachers sharing materials. I found useful and inspiring, for example, the recorded lectures of the non-linear dynamics and chaos class by Steven Strogatz. He is a famous mathematician and excellent communicator on the topic who has authored several non-scientific books, columns on the New York Times, as well as the Joy of X podcast.

I also found a series of online materials for a Bayesian statistics class (on twitter). The author has the content open and is happy to get feedback from the larger community on how to improve it. As an R programmer myself, I find useful that many books and tutorials are available online, free, and often develop with the input of people who use it and test it as it is written. Examples of these resources I often use include:

Inspired by these people I started collecting all my teaching materials under my website and make it available when possible for students or my future self to revisit, correct and improve. I still need to add creative commons licences to it, and perhaps store the code to create them on a clean repo with a clear open licence. I got inspired by the stats course on developing a website later on if I’m leading courses. I also like how Strogatz uses video, or for example the math classes in the Khan Academy walk students through demonstrations. I’ll need to think more carefully how to create such type of content in an engaging way. For now here I collected some resources for inspiration.


Inequality and the biosphere

Soon I will be coming back to Stockholm to think about inequality and the biosphere with a group of young scholars. Here are some random notes that I want to keep in mind for the meeting. Abigail Sewell reviews the book The Dead Gap by David Ansell and writes in Nature today:

“There is a 35-year difference in life expectancy between the richest US neighbourhoods and the most deprived.”

There are links between inequality and health, education, and in general life satisfaction. If you are more likely to die because of inequality, why would you care for the biosphere if society don’t care about yourself?

The Death Gap presents a Marxian view of inequality as a societal disease that in turn produces biological disease, dispersed differentially across the sociopolitical hierarchy. Although he is a physician, Ansell uses metaphorical language that may stump readers in biomedical fields. Moving beyond epidemiological frameworks that model what would happen to an individual exposed to a single causal factor, Ansell weaves a much more complicated story, in which structures of disadvantage affect every step of the process of survival. The Death Gap provides an overarching framework for understanding the root cause of ethno-racial and economic disparities in illness that seamlessly weaves together the very best research in epidemiology, public health, medical sociology, health policy and psychology.



“One doesn’t get credit for the projects that get started but for the ones that get finished”

– César Hidalgo

Mapping regime shifts

The regime shifts database (RSDB) is to my knowledge the largest repository of information about regime shifts in the world. It synthesises scientific literature about known social-ecological regime shifts that can have impacts on ecosystem services. I always think of it as a Wikipedia of regime shifts: a public online repository where people can go and learn what are they about, but also contribute content and cases.

Lead by Oonsie Biggs and Garry Peterson at the Stockholm Resilience Centre, the database started as an internship I did as master student in 2009. Later it became my master thesis and soon after the core of my doctoral studies. Several master thesis have been published using the database and it has also been useful for international assessments of regime shifts. It contributed to Global Biodiversity Outlook 3 (2010), the Arctic Resilience Assessment (2016), and more recently I was asked to contribute a short synthesis for IPBES – the International Panel for Biodiversity and Ecosystem Services.

Despite its importance, at least on my eyes, the regime shift database is largely unknown to the scientific community. There is no published peer reviewed paper describing how it was done and how can it be used. As a result, few groups have been publishing work suggesting that a framework for comparing regime shifts is needed, in other words: reinventing the wheel. The first public available draft of that paper is included on my Licenciate thesis (2013), and a later edited pre-print was posted on bioRxive (2015) that was included on my PhD thesis. I needed to graduate. Unfortunately, it has never been submitted for peer-review and people keep using the database without knowing how to give proper recognition to our work. The draft has been sitting on Oonsie’s desk for literally years waiting for the submit button to be pressed.

To date the database describes 30 generic types of regime shifts and 324 case studies, plus a few experimental cases that if included add up to 35 regime shift types. A generic type is for example the coral reefs transitions from coral dominated reefs to macro-algae or other alternative stable states; while a case study would be the coral transitions in Jamaica. A generic type is a collection of all drivers and mechanisms that can produce a particular regime shift, while a case is an instance of its occurrence. An analogy that often helps me distinguish them is the one of a disease (generic type) and the patient (case). Such was my enthusiasm with this project that out of the 30 regime shifts currently available, 16 were contributed by me during the first year of my PhD. Most case studies were contributed by my friend and colleague Johanna Yletyinen (@jo_yletyinen) who spent a lot of time digitalising the Hypoxia database made available by the study of Diaz and Rosenberg in Science (2008). The rest of the contributions have been developed in the class room with master students or researchers who have been generous to share their work with us by filling up a data template. Roughly 1000 scientific publications have been reviewed to build the database.

Last week writing the synthesis for IPBES made me think that we need better visualisations of the RSDB. I created a map where large dots represent generic types or regime shifts. The are located on the kind of ecosystem where they would be expected to occur. So for example, fisheries collapse is located on the southern ocean close to Antarctica (a place where many fish stocks have been reported on be in decline), but a case study about salmon appears on the other side of the planet in Alaska. Case studies are the small dots on the same colour as their generic types. You can see all the hypoxia cases that Johanna coded especially on the coasts of Europe, North America and Japan. Creating the map brought memories of my days writing regime shifts reviews for the database. It also made me realise that it has been few years I have not contributed besides my teaching duty. Disappointed by the lack of commitment with the paper, my PhD student enthusiasm almost disappeared. The last draft I worked on was on desertification (2014?), it was never reviewed nor published online. Each contribution to the RSDB has been peer reviewed by an expert on the topic to make sure we do a fair assessment of the literature.  Here is the map:RSDB_map.png

Currently I live in the United States, Princeton to be precise. It’s not a secret for anyone who follow the news -or try to avoid them like me- that the political situation here is concerning, to say the least. The kind of situation when you ask yourself what can I do? How do I contribute to make this a better place. Scientist being censored, immigration policies that not only contradict the USadian heritage of immigrants but also remind us of a one of the darkest moments of human history. Whatever I could think of seems insignificant.

Then I looked the map again and tried to remember what was my driver when contributing to RSDB. It was making knowledge free for others to be used, the kind of thing that doesn’t contribute to your career but hopefully is doing some good somewhere. If you come from a place such as the one I come from, you know that scientific knowledge is locked behind paywalls and most of people do not have access to scientific literature. Documenting change in ecosystems and making available to a broader public was important to me. It was like sending wealth to someone else, no on the form of money but knowledge, making sure it’s available when is needed, for free. A lot of people is marching for life and standing up for science. My way of contributing will be by putting dots on the map, making scientific facts count and visible when needed. From now on every week I will contribute a case to RSDB. Let’s make science count. If you want to contribute just drop me a line.




Book review: Alguien tiene que llevar la contraria

alguien-tiene-que-llevar-la-contraria-500x500Mis vacaciones este año en Colombia fueron cortas y por tanto elegí un libro que se veía corto pero sustancioso. “Alguien tiene que llevar la contraria” es una colección de 12 ensayos escritos por Alejandro Gaviria, el actual ministro de salud de Colombia. El título me viene como anillo al dedo, pero en verdad lo que me cautivo fue el autor. Yo conocí a Alejando en 2008 cuando el era decano de Economía en la Universidad de los Andes y yo comenzaba a trabajar como asistente de investigación en el grupo del economista ambiental Jorge Maldonado. Alejandro Gaviria, como varios de mis profesores modelo (e.g. Garry Peterson, Juan Camilo Cárdenas) comenzó su carrera como Ingeniero. Luego hizo su doctorado en Economía en la Universidad de California, y aunque escribe y piensa como economista, también ha hecho carrera como columnista en periódicos nacionales,  ha escrito varios libros, mantiene un blog, y ha recibido premios por su labor como investigador, docente, y periodista. En resumen, es una persona que no solamente es un buen académico, también es un buen comunicador y ha tenido el coraje de pocos de hacer la transición de escribir artículos científicos a políticas publicas. El libro, ya en su cuarta edición tan solo tres meses después de la primera, está catalogado por la editorial como ‘sociología’. Ahi fue cuando me pregunte: cómo lo hace?

El libro se divide en tres secciones. La primera titulada ‘Liberalismo y cambio social’ tiene un matiz mucho mas filosófico y literario. Son un conjunto de reflexiones valiosas sobre que es la democracia, sus limitantes, el conflicto y su valor como motor de cambio social, al igual que la importancia del escepticismo. Esta primera parte muestra el aprecio que Gaviria tiene por la literatura y como el contar historias nos ayudan a imaginar futuros y criticar presentes. Me gusto mucho como resalta el valor del conflicto en la sociedad, respetuoso y necesario, al igual que el valor del escepticismo, una practica imprescindible en el quehacer científico.

La segunda parte es sobre hechos y palabras. Tiene un matiz mucho mas histórico y es muy rico en detalles del contexto colombiano. Comienza evaluando la evolución de la desigualdad en Colombia y el apogeo de las ideologías Marxistas en los países latinoamericanos. Continua con una breve reseña del Darwinismo en Colombia, de como las ideas evolutivas fueron en un principio rechazadas y finalmente aceptadas en nuestro país.  Introduce también la historia de la ‘meritocracia’, un termino acuñado por Michael Young en 1958 cuyo significado se ha transformado en algo menos negativo de lo propuesto por el autor de El ascenso de la meritocracia. Gaviria retoma su significado original y advierte de sus consecuencias negativas en la division de clases sociales y en ultimas el aumento de la desigualdad. Por ultimo, el autor revista la historia de la guerra contra las drogas en Colombia con una colección buenísimas de referencias para el lector interesado.

La tercera parte fue mi favorita. Gaviria cierra el libro con ensayos mas académicos basados en hechos y estadísticas del progreso social en Colombia y otros países latinoamericanos. Entre otros temas, trata la disminución de la pobreza, un análisis de movilidad social y por ultimo una critica a la ‘crisis’ de salud publica. Gaviria es cauto al advertir que es largo el camino por recorrer, pero a la vez sincero en dejar claro que progreso si ha habido, mas social que económico, pero definitivamente no es negligible. Lo que me gustó fue el aire de realismo optimista que se respira entre sus lineas. Llama al colombiano a criticar la realidad desde los hechos, a no darnos palo tan duro y de gratis, y darnos cuenta que si se puede. Gaviria deja ver aqui y allá su pasión por la literatura, sus gustos y disgustos ideológicos y politicos, así como los dilemas éticos que enfrenta como funcionario público. Al final de cuentas es un ser humano como cualquier otro que a travez de su escritura invita a repensar el país y la época que nos toco vivir de una manera diferente, al menos constructiva.



“Me too” social science is not fighting inequality

In late spring 2016 I joined the “Beijer Young Scholars”, a vibrant group of PhD students and junior postdocs that gathered in a small island in the Stockholm archipelago to think about inequality and the biosphere. Discussions were heated, disagreements were common, from what the concept means from different disciplinary lenses, how to measure it, how to approximate or even define a research problem, and how to be aware of our own prejudices when we approach the topic. Yet it has been a rewarding learning experience that I hope will continue to provide sources of inspiration, healthy disagreements and skepticism. A note on myths of inequality for future conversations were found on a blog by Kevin Leicht, Professor of Sociology at University of Illinois Urbana-Champaign. That’s why here are his words reblogged:

Work in Progress


by Kevin T. Leicht

Sociology is at risk of losing what credibility it has because we have latched onto ways of studying inequality that are not suited to new economic arrangements.

What are those ways? They started as truths that now represent half-truths or worse – we just repeat them and think we’re doing something to produce insights into how inequality is produced and maintained.

We can’t end inequality by closing group gaps

Let’s start with the most basic of these habits and beliefs – The belief that most social inequality is tied to race and gender. Empirically this is not true and it hasn’t been for at least thirty years.

There is far more social inequality within demographic groups than there is between them.

There is overwhelming evidence to support this claim. The ratio of mean household income in the top 5 percent to the mean household income in…

View original post 919 more words

Behavioural Experiments in Social-Ecological Systems with Thresholds

Here are the slides and abstract of my talk at the conference of complex systems in Amsterdam:


How does people behave when dealing with situations pervaded by thresholds? Imagine you’re a fisherman whose livelihoods depend on a resource on the brink to collapse, what would you do? and what do you think others will do? Here we report results form a field experiment with fishermen from four coastal communities in the Colombian Caribbean. A dynamic game with 256 fishermen helped us investigate behavioural responses to the existence of thresholds (probability =1 ), risk (threshold with a climate event with known probability of 0.5) and uncertainty (threshold with an unknown probability climate event). Communication was allowed during the game and the social dilemma was confronted in groups of 4 fishermen. We found that fishermen facing thresholds presented a more conservative behaviour on the exploration of the parameter space of resource exploitation. Some groups that crossed the threshold managed to recover to a regime of high fish reproduction rate. However, complementary survey data reveals that groups that collapsed the resource in the game come often from communities with high livelihood diversification, lower resource dependence and strongly exposed to infrastructure development. We speculate that the later translates on higher noise levels on resource dynamics which decouples or mask the relationship between fishing efforts and stock size encouraging a more explorative behaviour of fishing effort in real life. This context is brought to our artificial game and leave statistical signatures on resource exploitation patterns. In general, people adopt a precautionary behaviour when dealing with common pool resource dilemmas with thresholds. However, stochasticity can trigger the opposite behaviour.

Cascading effects of critical transitions in social-ecological systems

For those who miss the talk, here is the slides and the abstract.


Critical transitions in nature and society are likely to occur more often and severe as humans increase they pressure on the world ecosystems. Yet it is largely unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another, and whether these potential teleconnections (social and ecological) correlate critical transition in distant places. Here we present a framework for exploring three types of potential cascading effects of critical transitions: forks, domino effects and inconvenient feedbacks. Drivers and feedback mechanisms are reduced to a network form that allow us to explore drivers co-occurrence (forks). Sharing drivers is likely to increase correlation in time or space among critical transitions but not necessarily interdependence. Random walks on causal networks allow us to detect and compare communities of common drivers and feedback mechanisms across different critical transitions. Domino effects and inconvenient feedbacks were identified by mapping new circular pathways on coupled networks that have not been previously reported. The method serves as a platform for hypothesis exploration of plausible new feedbacks between critical transitions in social-ecological systems; it helps to scope structural interdependence and hence an avenue for future modelling and empirical testing of regime shifts coupling.

ESA: Regime Shifts in the Anthropocene

Last year I was supposed to present this talk at ESA100 but a delayed visa made me miss the opportunity to share the main results of my PhD with the ecological society of America. This year and with the support of the PlosONE early career travel awards, I’m presenting my talk Regime Shifts in the Anthropocene at ESA101 in Fort Lauderdale. Here are the slides and the abstract of my talk.



Human action is driving worldwide change in ecosystems. While some of these changes have been gradual, others have led to surprising, large and persistent ecological regime shifts. Such shifts challenge ecological management and governance because they substantially alter the availability of ecosystems services, while being difficult to predict and reverse. Assessing whether continued global change will lead to further regime shifts, or has the potential trigger cascading regime shifts has been a central question in global change policy. Addressing this issue has, however, been hampered by the focus of regime shift research on specific cases or types of regime shifts. To systematically assess the global risk of regime shifts we conducted a comparative analysis of 25 types of regime shifts across marine, terrestrial and polar systems; identifying their main drivers, and most common impacts on ecosystem services. We use network analysis to demonstrate that regime shifts share clusters of direct and indirect drivers that shape opportunities for management.

While climatic change and food production are common drivers of regime shifts, drivers’ diversity undermine blue print solutions. Drivers co-occurrence vary with management scale and ecosystem type. Subcontinental regime shifts have fewer drivers related to climate; aquatic regime shifts share more drivers, often related to nutrient inputs and food production; while terrestrial regime shifts have a higher diversity of drivers making their management more context dependent. Given this variety of drivers, avoiding regime shifts requires simultaneously managing multiple types of global change forces across scales. However, there are substantial opportunities for increasing resilience to global drivers, such as climate change, by managing local drivers. Such coordinated actions are essential to reduce the risk of ecological surprises in the Anthropocene. Because many regime shifts can amplify the drivers of other regime shifts, continued global change can also be expected to increase the risk of cascading regime shifts. Nevertheless, the variety of scales at which regime shift drivers operate provides opportunities for reducing the risk of many types of regime shifts by addressing local or regional drivers, even in the absence of rapid reduction of global drivers.

Writing advise

Few words of advise from Steven Pinker.


Sustainability science

There is a lot of talk about what is, what is not or how should it be. As a note to the self: Juan remind that ‘sustainability’ is an adjective, the noun is ‘science’. So make it science first and then add the adjective that comes more handy: social, natural, inter, trans… blah blah. Adjectives is a matter of taste, but first it has to be science.

Coming back to the ‘sustainability’ side, there has been a number of editorials in Nature and Science clarifying the need and challenges of the new international program Future Earth. Exciting news for a guy on the sustainability science business like me. However, the editorial of Nature (March 3, Vol. 531, p8) explains why the world remains skeptical of such effort, it is questioned whether the program will be able to deliver on something that is fashionable but conceptually unproven. In other words, it is calling for useful and empirically based science that helps making difficult decisions… not another conceptual framework but more jargon than substance.

Future sustainability research, no matter how interdisciplinary, should build on that heritage and focus on finding and closing knowledge gaps. In doing so, scientist involved in Future Earth can provide an invaluable service to society. And researchers in niche disciplines – paleoclimatology or behavioural science, say – who work to fill those gaps will get a welcome chance to put their work into a broader context.

Future Earth might also become a showcase for linking natural and social sciences – a real necessity given that human activity is altering the planet at worrying speed. But sustainability research must not become tied in the straitjacket of conceptualism and utilitarianism. Scientist are not merely service providers. As in any other field of science, sustainability research must remain at its core a curiosity-driven affair.

The author clarifies at the beginning of the editorial that sustainability science does not have the luxury of timelessness. Sustainability science is one of the urgent science that deals with issues with socio-economic stakes, where high decisions are needed (aka. a post-normal science), and where time, funding or ethics do not allow for the perfect experiment or traditional scientific methods.

What to do when you miss conferences because of visas? – Keep an eye on Twitter

Screw up visas. It arrived last night, just two weeks late to be able to attend the ESA meeting – Ecological Society of America who this year celebrate their centenary anniversary. I missed the opportunity to present my work and get feedback from the ecologist community; and more importantly, get to know what other ecologist are doing. It’s a pity. Earlier this summer I also missed the International Conference of Computational Social Sciences and another meeting in the US regarding the Arctic Resilience Assessment. Last year I also missed the European Conference of Complex Systems, my mum got sick and I had to travel home to take care of her. Luckily my mum is better now, and with so many exiting academic events that one cannot attend either due to visa restrictions, lack of funding, or unfortunate life events; one has to come with some alternative solution to get to know what is going on. Here is my solution: I mine twitter.

Twitter is not a perfect source of data, but at least is free and gives you a flavour of what the digital conversation is about. At the end of the day, humans are sensors of that reality you’re missing and leave traces of what they find interesting on the digital world. Twitter is not a perfect source of data because it’s biased: only people with access to smartphones  or internet connection tweets, twitter is mostly used on certain age groups that might not represent what is going on in the whole community (in this case of mostly ecologist), and you never have certainty on how well  is your data sampled. Anyways, is free and you don’t need a visa to play with twitter data, although some restrictions do exist.

At ESA people was asked not to tweet unless speakers allowed for tweeting at the beginning of their talks. Despite the non-twitter policy of the #ESA100 meeting (that’s was the official twitter hashtag), I managed to recover over 18000 tweets from 2589 twitter users. That’s huge!! Just to put perspective to those numbers, other conferences I’ve observed on twitter without the non-twitter policy include:

  • International Conference of Computational Social Science: #ICCSS2015, 2288 tweets from 570 users.
  • Network Science conference 2015: #NetSci15, >2000 tweets, ~550 users (of which I analysed 801 from 195 users)
  • European Conference of Complex Systems: #ECCS14, 2330 tweets, 399 users
  • EAT Forum Stockholm: #EAT2015, 897 tweets, 560 users (I missed the first day of data)
  • Resilience conference 2014: #Resilience2014, 2042 tweets, 442 users
  • World Water Week 2014: #wwweek, 1599 tweets, 793 users

So by comparison, not only was #ESA100 huge, it was also full of virtual activity despite its non-tweeting policy. Tweeting activity is nevertheless quite predictable at least in time. You would expect burst of activity around the plenary sessions in the mornings and afternoons, less so during nights and before / after the conference. Here is how it looks:


As you can see from the figure above, I don’t have data for the tweets before the conference. That’s one of the limitation of the Twitter API, you can ask for tweets but they decide which ones and which time period you get. Previously (last year) there was a window of 4 days that you could look in the past. Now it allows you to go further in the past and harvest more data but still it is not perfect. And since I only do this as hobby, I’m not up to date with the constant API terms of use changes. As expected there is peaks of activity during the day and valleys at night, in some days you can even observe the lunch break between two peaks. Is good that people mingle together and put down the phone from time to time. But who is this people? who is talking to who and about what?

The figure below depicts a mention network, one node connects to another if the first mention the second on her/his tweet. Therefore is a directed network where 16% of the links are reciprocated. The node size is scaled by the degree aka. the number of connections in the network. One could also use the number of followers in twitter but since I’m interested on the conversation and who is the interesting people to keep an eye on while missing the conference, not on how popular they are on twitter, degree on the mention network is a good proxy of the quality of the tweets content. Although is not depicted on the graph, note that there is also link weights given that one user can mention another many times through different tweets. Thus some links are stronger than others. But besides the visual appealing of the picture, is not very informative: few people have lots of links while most of people have fewer links. This could be because some people is simply more active on twitter, or they tweet more interesting stuff that is worth mention / retweeting, or simply some other underlying process that is unknown from the data alone; for example that the person tweeting is a very famous ecologist or that it mentions president Barak Obama, or both. Anyways, extracting the core of nodes that everyone is talking about is good if you want to filter the information that the network as a whole is signalling as more important, instead of reading the whole +18000 tweets. You can extract them on a list and keep an eye on the most trending stuff.


Who are they? Plotting the names on the graph would make it just messier. So here is the list of the top 50 #ESA100 twitter users given the number of times some one mention them. The number following the name is the number of links they have in the network, so the number of people who mention them.

  1. PLOS    248
  2. JacquelynGill    230
  3. leafwarbler    217
  4. srsupp    193
  5. DrEmilySKlein    174
  6. ESA_org    173
  7. ethanwhite    164
  8. SPBombaci    162
  9. ucfagls    154
  10. katteken    137
  11. DJPMoore    136
  12. openscience    134
  13. matthewgburgess    126
  14. skmorgane    123
  15. jhpantel    123
  16. annamgroves    121
  17. commnatural    113
  18. noamross    112
  19. polesasunder    108
  20. LeahAWasser    107
  21. DrNitrogen    106
  22. sjGoring    105
  23. sesync    104
  24. treebiology    102
  25. algaebarnacle    102
  26. tpoi    101
  27. ElenaBennett     98
  28. NEONInc     90
  29. jonbkoch     90
  30. MethodsEcolEvol     88
  31. PLOSEcology     86
  32. colindonihue     86
  33. tewksjj     82
  34. INNGEcologist     82
  35. jessicablois     81
  36. ESAOpenSci     81
  37. JoshGalperin     79
  38. elitabaldridge     78
  39. cjlortie     78
  40. GrunerDaniel     75
  41. MorphoFun     74
  42. JCSvenning     73
  43. bjenquist     72
  44. PLNReynolds     70
  45. fluby     69
  46. nceas     67
  47. wildwonderweb     66
  48. esanathist     66
  49. RallidaeRule     64
  50. davidjayharris     64

What were they talking about?

Since is the 100 ESA anniversary, here are the top 100 most retweeted tweets:

[1] “Theoreticians: stop telling us not to be scared of your equations. I’m not. Explain them well, like I do my methods, then continue #ESA100”          

  [2] “Watch President Obama wish the Ecological Society of America a happy 1OOth birthday on @Vimeo #ESA100 https://t.co/nhyaYmkt7C”                       

  [3] “#esa100 is a good time to announce that @uofa is looking for 5 new hires in Ecosystem Genomics | global to microbes http://t.co/OZAyoDyO86”          

  [4] “First speaker to #ESA100 recognises ESA’s contribution to the environment: President Obama. Am impressed! http://t.co/oDSMNXYPg4”                    

  [5] “Know of anyone looking for a PhD in ecology? Fully funded (!) at Wisconsin to work on bats and insects http://t.co/q3rGh9roZr #esa100”               

  [6] “#ESA100 friends, please read and RT my article on how to live-tweet scientific conferences! http://t.co/fMhDWivy9c #SciComm”                         

  [7] “Exciting news from @ESA_org Council Meeting: all ESA members will get free online access to ESA journals. #ESA100”                                   

  [8] “One of the nicest things you can do at meetings is to acknowledge the students trying to catch your eye and introduce themselves. #ESA100”           

  [9] “Tenure track job in ecological modelling with @JaneElith & the rest of us at @qaecology https://t.co/44jCNxRBiZ #ESA100”                         

[10] “weird that tweeting talks at #ESA100 with permission only. If you don’t want people discussing your work you should not present it.”                 

[11] “Scicomm resource guide to eco-communication #ESA100 http://t.co/h6nEbjaq9S http://t.co/Xv5qe0HxIS”                                                   

[12] “What were we Tweeting about at #ESA100? (H/T again @fmic_ for Twitter stats code http://t.co/SlyQHL0yDE) http://t.co/eWmUxQJDhP”                     

[13] “Of course Terry Pratchett already wrote everything I think about science and sci fi, and better than I could #ESA100 http://t.co/fMmM04NpHM”         

[14] “A few thoughts on #SciComm at #ESA100: Sharing science, stories & art; and @ESA_org’s social media confusion: http://t.co/7crijzElJ2”            

[15] “This is what students see: fewer women speaking. Imagine gender equality for ESA 2016. #ESA100 @ESA_org #WomenInSTEM http://t.co/irs3QmStKD”         

[16] “Slides from my #ESA100 talk on comparing different approaches to forecasting diversity. http://t.co/LoHIxgbidc w/links to code + grant”              

[17] “Our #ESA100 centennial paper out in Ecospehre: Climate change & microbial-plant interactions @ESA_org http://t.co/fTLU0xtTOL”                    

[18] “Top tweeps at the #ESA100 meeting.  (H/T @fmic_ for Twitter stats code http://t.co/SlyQHL0yDE) Good work, team! http://t.co/zOpkQO4PaS”              

[19] “#ESA100 \nThe world is big. Scientists are relatively small. Collaborate.”                                  

[20] “Overheard conversation by a bronycon goer: I think these are ecology people, there are a lot of Hawaiian shirts. #ESA100”                            

[21] “Test your talk in a simulator like ColorOracle first! http://t.co/PNhsJQsApv #esa100  https://t.co/aVlYUz1TED”                                       

[22] “#ESA100 1.2M publications in ecology (or more). A total of 40% captured by 4 terms: interactions, biodiversity, climate change, & gradients.”    

[23] “Happy #ESA100 & #BronyCon! Hasbro, DM me if you want to discuss marketing. #mylittlesturgeon #mylittlestudyspecies http://t.co/GhY4KD56yb”       

[24] “Too many talks that I can’t understand because the figures are not colourblind-friendly #ESA100”                                                     

[25] “Secrets to successful scientific networks: trust, time and early-career scientists. @e_seabloom @e_borer #ESA100”                                    

[26] “Let’s make ecology in the field safer for all: come to @Drew_Lab and my free workshop on Tues: http://t.co/icSjpaQE06 #ESA100 All welcome!”          

[27] “Our Postdoc Fellowship Program is now accepting applications! Pre-screening submissions due October 26: http://t.co/8EkdBzxZjX Attn: #ESA100”        

[28] “Yes, that’s @POTUS! RT @LPZ_UWI: Obama celebrates the #ESA100 centennial with us! http://t.co/M1MMpbKolD”                                            

[29] “Hello #ESA100 The @calacademy is hiring new biodiversity scientists! Lots of ’em! Do science, change the world! http://t.co/F9DvM3e1Hp”              

[30] “At our blog, you can submit your own “seed” of a Good Anthropocene: http://t.co/rIBLhUGGPF #ESA100”                                                  

[31] “A surprise birthday message to @ESA_org from @POTUS \”The health of our nation depends on the health of our environment\” #thanksobama #ESA100″      

[32] “Speakers: promote #openscience ! \nDon’t forget to tell your audience if you are OK live tweeting! #ESA100”                                          

[33] “#VirginiaTech is hiring a stream ecologist! Come talk to me at #ESA100 if you have questions: https://t.co/aNOXL7l2yN”                               

[34] “We’re seeking time-series data for a #biodiversity study. Do you have data to share? http://t.co/6PfCHWZMNI @maadornelas @mioconnor #ESA100”         

[35] “Research News at #ESA100 “Increase in red spruce growth tied to the Clean Air Act” @atkinsjeff http://t.co/yDm3w8vxUa http://t.co/rb9HO0ih9u”        

[36] “One thing clear at #esa100: The Anthropocene as an idea has won.”                                 

[37] “Dr Erwin: Change is the observable dynamic of the fossil record – there is not empirical evidence for equilibrium. \n\nYes!! #ESA100 #esapl2”        

[38] “Loss: Cat predation:  2.4 billion birds killed by cats in the US every year 70% from feral cats #ESA100″                                             

[39] “We might just need better (realistic, detailed, radical) visions of positive future. #GoodAnthropocenes #ESA100”                                     

[40] “Sketching your notes at #ESA100 ideas for creative expression from #ESASciComm @commnatural http://t.co/22kL1reSc7 http://t.co/kPYoVV74wu”           

[41] “When Science is Not Enough: Communicating the Scientific Consensus on #ClimateChange @samillingworth #scicomm #ESA100 http://t.co/eLDAzp6sr2”        

[42] “Hi #ESA100, please favorite this if you are interested in finding a way to convince the society to give a budget line to support @ESA_SEEDS.”        

[43] “\”we use statistics to hide the instability of our arguments\” http://t.co/R7BC2f1UMr #ESA100 #ecology #biology”                                     

[44] “Not sure I understand the no tweet policy at #ESA100. I mean why would you want it? You are already sharing your research w professionals”           

[45] “Scientists have a hard time talking about race. We also have a hard time listening. These are uncomfortable but vital conversations #ESA100”         

[46] “#ESA100 program change: new COS at 1050AM Fire alarm impacts on ecologist community dynamics http://t.co/hPW60xD55K”                                 

[47] “Beautiful data, carefully curated and presented, made available to the world in multiple formats. Surely this is the future. #ESA100”                

[48] “#ESA100 slide makers: allow me to recommend this color scale for your graphs in the future: http://t.co/FoTnVldbGL”                                  

[49] “Strong argument for allowing tweeting of conference talks & posters. #ESA100 #gsa2015  https://t.co/9bxHXga6dJ”                                  

[50] “You never know someone’s personal pronouns unless you ask. Some folks at #ESA100 write them on their badges. It’s always worth checking.”            

[51] “Access now! Functional Ecology Special Feature: Urban Ecology: http://t.co/IHEVetopyL  #ESA100 #UrbanEcology”                                        

[52] “Could #ESA100 moderators ask speakers if tweeting talk is okay?I bet most are okay with it but don’t know assent is required. @ESA_org”              

[53] “Diverse group of people better solve problems. Benefit of diversity to science goes up as problems get harder #esa100 @ESA_SEEDS”                    

[54] “What happens when the fire alarm goes off during talks at #ESA100 http://t.co/G1zjTLJEAA” 

[55] “My take from #ESA100 so far: Ecology is actually a loose collection of disciplinary silos that barely communicate.”                                  

[56] “Ecologists with mad data skills will catapult ecology into its next 100 years! #ESA100 #hackingecology”                                              

[57] “Coding is becoming crucial for #Ecology @MethodsEcolEvol Applications explain new software, equipment & tools #ESA100 http://t.co/FWgSo232hX”    

[58] “#ESA100: I’m adding to dataset on who asks ?’s after talks. Want to help? Just note gender of speaker & ppl asking them ?’s in your program.”    

[59] “.@KathiJoJo \”China alone is firing up a new coal plant every eight to 10 days\” #ESA100 https://t.co/ywkc5WSi4r”                                    

[60] “Anyone can tweet about my #ESA100 poster if they want: it’s up on @figshare and @github too. \nhttps://t.co/fwHlmI9o5y”                              

[61] “Slides from my #ESA100 talk on @nceas and @DataONEorg provenance tools in #rstats for reproducibility and #opendata https://t.co/vlsHXsT4YF”         

[62] “New blog post: Thoughts about #SciComm, #openscience, sharing, & social media confusion at #ESA100. http://t.co/7crijzElJ2”                      

[63] “Check out the highlights of my talk on Nitrogen fixation in tropical dry forests #ESA100 featured @PLOSEcology! \nhttps://t.co/i7kU68NcD9”           

[64] “From the audience: calculus is the *wrong* math. We’d be better off teaching stats & probability (& computing) #ESA100 #HackingEcology”      

[65] “So far very few talks have given permission to tweet. I wonder if bc they actively don’t want them shares or it’s not on their radar #ESA100”        

[66] “Conducting ecosystems research? Check out our methods, models, tools, & databases: http://t.co/2ihCI5Db1R\n#ESA100”                              

[67] “#ESA100 save a postdoc’s self esteem, live tweet a talk.”                                                       

[68] “@BarackObama helps celebrate the @ESA_org centennial! #ESA100 #POTUS http://t.co/gz0W91hujg”                                                         

[69] “Beginning wk of special #ecology #climatechange coverage for #ESA100; get the rundown at http://t.co/FNpye6kbhm http://t.co/ekg6oi4HNW”              

[70] “At #ESA100 @jagephart applies a climate change vulnerability framework to #foodsecurity in @PLOSEcology by @atkinsj http://t.co/rT5Di6yliT”          

[71] “The Gund Institute in #Vermont seeks 5 PhD students. Do great work in beautiful #BTV: http://t.co/KvXdx7jVoQ #ESA100 http://t.co/vU14Qy9MT5”         

[72] “Science is worthless unless it’s shared with others, yet academics incentivized to focus only on peer rev journals @JulieReynolds88 #ESA100”         

[73] “Fascinating ignite talk Rachel Vannette (http://t.co/lWuzTXhE1c): microbial effects on plant-pollinator interactions #ESA100”                        

[74] “Another reason for #ESA100 talks to be open to live tweeting: we have a global audience unable to attend conference! https://t.co/8kVQXsC3fR”        

[75] “Best #ESA100 fundraising #frisbee #secchidisk for @ESA_SEEDS by @duffy_ma @Drew_Lab @ESAAquatic @limnojess! http://t.co/wttjyh9Lm6”                  

[76] “All materials, slides, sources, code on @github & under CC-BY #openscience #ESA100 #rstats https://t.co/ocffOZsKL5 https://t.co/eJSMR0VmdQ”      

[77] “To tweet or not to tweet at conferences? Confusion at #esa100 http://t.co/9PA66JBVTO\n\n@Drew_Lab @ewanbirney @_Jni_ @ta_wheeler @ESA_org”           

[78] “Lenore Fahrig: \”All habitat has value, no matter how small\”. Major review shows habitat loss NOT fragmentation hurts biodiversity #ESA100″         

[79] “Powerful to hear Susan Harrison tell us her 15-yr field site was consumed by wildfires just 30 minutes ago. Here’s to new directions #ESA100”        

[80] “#BrightSpots, seeds of a #GoodAnthropocene: Pockets of a better future \nthat are already in existence today #ESA100  http://t.co/rIBLhUGGPF”        

[81] “#ESA100,Pres David Inouye,Scientific Plenary during #POTUS greeting video,Whooa, ESA and US Pres, doesn’t get better! http://t.co/i1ThOuwkyS”        

[82] “Can you guys at #ESA100 help me spread the good word on #sciart? https://t.co/opvO8isK70 Thanks! http://t.co/gG5S8gO2Q7”                             

[83] “How to educate all when we don’t value outreach &esp social justice work? When we pretend the meritocracy works? @RushHolt @ESA_org #ESA100”     

[84] “Lovejoy 2 degree Warming target chosen not for its ecological merit; means a world w/out tropical reefs e.g. #esa100 http://t.co/nQsHj8jbK9”         

[85] “Climate change shapes drought/flood frequency & severity @allingon on @PLOSONE @PLOSBiology papers & #ESA100 sessions http://t.co/B3EqPAvWx0”

[86] “New @PLOSEcology \”All Eyes on the Oceans: James Hansen & Sea Level Rise http://t.co/vIRFonX1Rb @sashajwright #ESA100 http://t.co/ZWmbW5OsSo”    

[87] “Another fun animation of an Am Nat Classic foundations of ecology in rhyme no less!  http://t.co/H6dcb2WbXb #esa100″                                 

[88] “.@ESA_org Another good way to keep important secrets is to *not* include them in presentations to groups of strangers #ESA100”                       

[89] “Best live-tweet advice so far: When tweeting 2+ times per talk, reply to your 1st to create a chain of tweets. Thx @PlantTeaching! #ESA100”          

[90] “The BronyCon people have red, yellow, & green tags on their name tags.  These are how willing they are to talk. Chat w/ green only #ESA100″      

[91] “Ecology from treetop to bedrock: human influence in earth’s critical zone #ESA100 – Ecotone (blog) http://t.co/WFSnddb6un”                           

[92] “@colindonihue #ESA100 Most favorited users (among users who tweeted 5+ times, excludes retweets). http://t.co/otms4ndCix”                            

[93] “Ecology in a Changing World: the #ESA100 centennial video http://t.co/ByaaYIhXlB”               

[94] “Conservation fuels ecological discovery, not just vice versa says Bill Fagan #ESA100”             

[95] “Slides from my #ESA100 ignite talk on \”Hacking ecology: Facilitating data-intensive research in ecology\” http://t.co/TD5BZYy3f0″                   

[96] “Fot those interested in R: a new R package called cati #ESA100  http://t.co/gVMwoKtReG”    

[97] “@Drew_Lab \”We didn’t have a tardis, but we had a museum collection!\” Going back in time to look at fish diversity in Bootless Bay. #ESA100″        

[98] “Time matters. Learn about temporal ecology and ecosystems at #ESA100 Thursday morning | http://t.co/dLmN9ZWESR http://t.co/e7c8BiVgsi”               

[99] “.@polesasunder created an #rstats package to analyze community time series data: codyn #HackingEcology #ESA100”                                      

[100] “.@ethanwhite on the cultural changes needed to get more scientists creating software tools:\nTrain\nHire\nCollaborate\nReward\n#esa100” 

The search produce slightly different results when one look on tweets that have been previously retweeted. It include tweets that are not listed above, for example:

“RT @flypod2: Know of anyone looking for a PhD in ecology? Fully funded (!) at Wisconsin to work on bats and insects http://t.co/q3rGh9roZr …”

“RT @PLNReynolds: 50 notable papers in #Ecology, all currently #OpenAccess! #ESA100 #ReadingList  http://t.co/Y1WYsYdVKA http://t.co/O6KDr3v…”

As you see, lots of job offers going on, president Obama was mentioned quite a bit, and gladly I was not the only one doing twitter analytics 🙂 The first tweet was retweeted 92 times and the last one only 11. One of the topics that got retweeted a lot was about the live-tweeting policy, see tweets 6 and 10 as example with 53 and 44 retweets respectively. Do you think people was generally happy with the conference despite this tweeting policy?

Reading the >18000 tweets to figure it out is not a pleasurable read even if you couldn’t attend the conference like me. To answer such question one can use sentiment analysis, a text mining technique that ranks pieces of text (tweets in this case) given the presence of words that have been previously labeled as common when expressing positive or negative emotions. The labelled lexicon (~6800) was developed by Minqing Hu and Bing Liu, two computer scientist from University of Illinois and Microsoft respectively. You can download their lexicon and learn more about their work here.

The figure below shows the results of the sentiment analysis for the #ESA100 dataset. If a tweet got a zero score, it’s emotion content is neutral, if the score is positive is dominated by positive words and if the score is negative the opposite. The plot shows that the distribution of tweet emotions as learned from the Hu & Liu training lexicon are skewed towards the positive side. The top 10 positive tweets are:

  1. Come and see us at Booth 328. Play our game to win an exciting prize and enter our prize draw for $100 worth of books! #ESA100
  2. Wow! What an absolute pleasure to meet @kwren88! #ESA100 keeps getting better and better!
  3. Super excited to see that #sketchyourscience happened again at #ESA100! Good work #ESASciComm people! (#WishIWasThere)
  4. Du is making it easy for us by being super clear about whether results matched his predictions. Good thing b/c it’s late on Day 4 of #ESA100
  5. RT @srsupp: Scanga: You need to find a strong support network. Family friendly work, backup at home (and money can help). #earlycareer #esa…
  6. RT @JoshGalperin: .@uedlab – Ecologists look at the way #citites work, but they can work with designers to make cities work better. #ESA100…
  7. Jackson: interdisciplinary work is tough! Takes time and the right attitude/aptitude – they do work but still a major challenge #ESA100
  8. SeJin Song’s #ESA100 ignite talk was gorgeous, w/ vivid clear visuals. #ESASciComm would love to talk w/ her re design decisions!
  9. Big thank you to @leafwarbler for the great #ESA100 live tweeting — SO great for those who can’t be there! (like me 😦 …)
  10. .@MCFitzpatrick: Realized niches overlap less and less as you go back in time. How well does this work and can it work better? #ESA100

And the top 10 + negative tweets are:

  1. @k_a_christopher Buddhism: suffering stems from greed, hatred, and/or delusion. Ecological problems often have same origins. #ESA100
  2. Comparing areas with american seagrass vs invasive asiatic sand sedge… Invasive areas are NOT more susceptible to erosion #ESA100
  3. Brown: sustainable development is thermodynamically unsustainable. A catastrophic crash seems almost inevitable #ESA100 very provocative.
  4. Cause of all environmental problems? Greed, hatred, and/or delusion says @ElBeeddha #ESA100
  5. #ESA100 poster 188: Alyssa Gehman #OdumSchool-Influences on infection by an invasive castrating parasite, 8:30-10:30 am 8/14 Exhibit Hall
  6. Scientists have a hard time talking about race. We also have a hard time listening. These are uncomfortable but vital conversations #ESA100
  7. Jim Brown: risk of a catastrophic earth collapse is >99.99%. I don’t see any way out. Time for ecologists to step up. #ESA100
  8. I see a problem with this picture: http://t.co/zMGVI42K1n hint: it’s the same problem the #ESA100 plenary suffered from …
  9. We lose minority STEM students after second univ year at alarming rates. What are we doing wrong? Focus on intro courses #esa100 @ESA_SEEDS
  10. Sorry about the fire alarm folks. The conv center sprinkler system activated; cause unknown #ESA100

15. RT @LauraEllenDee: Agreed! “@BonnieKeeler: Bummed to miss the #ESA100 Shark Tank. Hoping there will be live tweeting” https://t.co/005rayit…

As you can see (dear ESA organiser) there was not hard feelings against the policy, although tweets on both sides of the distribution point out to people sad of missing the conference and glad to see so much twitting activity. ehemmm just saying.

Another technique that I’ve used on my work to understand large amounts of unstructured data such as text is topic mining. Again, is not practical to read all tweets but thankfully there are methods out there to simplify noisy data and extract more valuable meaning. In topic mining by using the frequency distribution of words across documents one can fit the probability of a word belonging to a topic, and the probability of a topic explaining the contents of a document. A common technique to do so is called Latent Dirichlet Allocation. First I cleaned up the dataset creating a corpus without stop words, punctuation, the conference hashtags (#ESA100, #ESA2015), the twitter names of people mentioned and links to other webpages. That leave me with words that hopefully capture the topics of the twitter conversation. To better capture the variability of words I also get rid of overly popular words and extremely rare words that doesn’t contribute much when differentiating one topic from another.


Although the machine learning algorithm does its job, I’m not completely happy with the result. Each word cloud above summarises the most common words of 30 topics characterising the conversation of the 2589 twitter users. Each word is scaled according to how frequent they are in each topic. The problem with twitter data and topic models is that one ends up with more documents than words on them. Once the dataset is clean many tweets have few words or none at all, therefore the document term matrix is too sparse. A way to solve the issue would be to change the unit of analysis, the documents, from individual tweets to all tweets written by an user assuming that each person has a particular interest on the conference. If I’d have attended the conference, I’d probably look for talks related to regime shifts and methods to study them. Inherently each attendee has intentions and interest captured on their tweeting behaviour. But that’s probably for next blog post. If you want to play yourself with the topic model data, you can check this interactive visualisation.


All this work was done in R following blogs by others and also scientific papers. If you are interested on this type of analysis just drop me a line and I can point you out towards some sources. The libraries I used are:

Jeff Gentry (2015). twitteR: R Based Twitter Client. R package version
1.1.9. http://CRAN.R-project.org/package=twitteR

Jeff Gentry and Duncan Temple Lang (2015). ROAuth: R Interface For OAuth.
R package version 0.9.6. http://CRAN.R-project.org/package=ROAuth

Ingo Feinerer and Kurt Hornik (2015). tm: Text Mining Package. R package
version 0.6-2. http://CRAN.R-project.org/package=tm

Bettina Gruen, Kurt Hornik (2011). topicmodels: An R Package for Fitting
Topic Models. Journal of Statistical Software, 40(13), 1-30. URL

Jonathan Chang (2012). lda: Collapsed Gibbs sampling methods for topic
models.. R package version 1.3.2. http://CRAN.R-project.org/package=lda

Ian Fellows (2014). wordcloud: Word Clouds. R package version 2.5.

Butts C (2008). “network: a Package for Managing Relational Data in R.”
_Journal of Statistical Software_, *24*(2). <URL:

Carter T. Butts (2014). sna: Tools for Social Network Analysis. R package
version 2.3-2. http://CRAN.R-project.org/package=sna

Carson Sievert and Kenny Shirley (2015). LDAvis: Interactive
Visualization of Topic Models. R package version 0.2.

Ramnath Vaidyanathan, Karthik Ram and Scott Chamberlain (). gistr: Work
with ‘GitHub’ ‘Gists’. R package version

Without them it wouldn’t be as fun to play with Twitter data in R. Thanks guys!

Video: Ecology on a changing world

A nice video for the 100 anniversary of the Ecological Society of America. Ecology in a Changing World from Benjamin Drummond / Sara Steele on Vimeo.

‘Good’ & ‘bad’ science

I just step upon an excellent opinion article about good, bad, sound and junk science. Although is addressed to marine scientist, I think it has good lessons for every one who’s trying to make a living on the knowledge production business we call academia. If you’re interested on the full opinion paper, which I recommend, it’s entitled The good, the bad and the ugly science: examples from the marine science arena by E. Parsons and A. Wright from George Mason University. They define science as:

Science is a process. It’s the act of taking observations made in the natural world to test hypotheses, preferably in a rigorous, repeatable way. The tested hypotheses are then rejected if they fall short, rather than accepted if the data are compatible, and the results are ultimately critically reviewed by the scientific community. Concepts that work survive, whereas those that do not fit the observed data die off. Eventually, concepts that survive the frequent and repeated application of enormous amounts of observational data become scientific theory. Such theories become as close to scientific fact as is possible—nothing can be proved absolutely. This process holds for social science as much as for chemistry, physics or biology: it does not matter if the data come from surveys or observational data from humans. A study either follows this protocol or it does not. Put simply, it is science or it isn’t science.

The following passage reminded me of many conflicting opinions when faced to the regime shifts literature: what people argue what are or not regime shifts, and what constitutes a proof of their existence and / or occurrence. Not the both are different.

If a scientist were to follow the scientific method, a “good” scientist’s understanding of the environment changes as additional data are acquired, whereas a “bad” scientist sticks stubbornly to previously held beliefs despite being faced with data that suggest an alternative scenario. It is a basic tenant of scientific inquiry after all that hypotheses are rejected when not supported by data. Good scientists are willing to change their opinions quickly in the face of new evidence or in response to a good valid argument. However, opinions that are not based in data-tested hypotheses do not represent good or bad science; they are simply not scientific at all.

Sticking to an opinion or an idea despite evidence to the contrary is sadly quite common in the science community. One sees “scientists” who stubbornly resist new ideas and studies, especially those that contradict a paper that the “scientists” wrote or concepts that they have publicly supported, or even based their career on. But adapting to new evidence is a key criterion of the scientific method. When scientists stubbornly resist new evidence contrary to their opinion, it really is “bad science,” i.e., refusing to reject a hypothesis that has been shown to be false.

I hope the regime shifts database will help us to track the debate and distinguish what is ‘good’ from ‘bad’ as evidence accrue with time.

How to imagine regime shifts? – Drawings and communication of regime shift ideas

After years of reading about regime shift is really hard to explain what they are without the typical bifurcation diagram or the ball and cup metaphors. My brain is biased towards the representations that the authors I’ve read used. They have been useful for teaching but obscure certain aspects of the concept that are hidden by the representation of you choice. In what other ways can we represent and communicate the idea of regime shifts?

Every year in our master’s course on regime shifts we ask students to come the first day with a representation of their understanding of what are regime shift. It’s amazing how, after reading a couple of conceptual key papers, they come with cool examples of regime shifts, covering fields as far apart as emotions in psychology (e.g. depression) to cancer development. Through their representations students explore different aspects of regime shifts: temporal dynamics, hysteresis, flickering, tipping points, feedbacks, what structure and function of a system means. One day perhaps I make some of their artistic representation public (first I need to ask for permission), these includes drawing, painting and last year we got a poem. I’m happy to see how this week at the Ecological Society of America 100 years meeting in Baltimore, there has been a buzz about science communication and many researchers are following the same idea of explaining their research field with a drawing. Here you can find some sketches and here some tips on how better communicate science.

At SRC we faced a similar challenge last year when some students and researchers gather together to discuss what have been the models that have shaped resilience and social-ecological systems ideas. The result is the Models Calendar that you can freely download here. I was involved on the model selection and particularly on diagraming the idea of regime shift. Not surprisingly my brain is biased by the endless s-shape curves, ball and cup (potential energy of the system) or bifurcation diagrams I commonly see on the papers. However, with help of artist Elsa Wikander from Azote, we arrived to the following representation:


Browsing for good images that people relate to regime shifts today I found this one created by Seiji Ishida entitled ‘Social regime shift in this post-oil-peak world’. It caught my eye because they look like colourful networks. For more of his work check his Picasa album Eccentric art.

Social regime shift due to post oil peak

Book review: John Holland’s Signals and Boundaries (2012)

I’m soon finishing my PhD and this blog was supposed to be my research diary. I’m not sure if I will keep it after my PhD, but in case I don’t, I would like to dedicate a series of post to the authors and the books that have shaped my thinking during my doctoral research. This first post is dedicated to John Holland’s book ‘Signals and Boundaries: Building blocks for Complex Adaptive Systems‘.

UnknownResearchers on regime shifts are still debating what constitutes a ‘real’ regime shift, what can be considered evidence, and what are the best techniques for detection. However, in my opinion, much of the contestation arguments are due to ambiguous terminology and arbitrary selection of system boundaries both in space and time. When I started reading Holland’s work I was searching for rigorous methods that help distinguishing system boundaries. What I found was an elegant set of ideas about the fundamental principles of complex adaptive system and the challenges ahead when it comes to the computational techniques required to capture their dynamics.

The first two chapters outline the fundamental principles of complex adaptive systems (CAS), this is features that are shared by markets, ecosystems, cells, language, interacting atoms, or governments. CAS are characterised by: i) a diversity of interacting elements (agents), ii) recirculation mechanisms that allow multiplier effects (reproduction or trends), iii) niches and hierarchies that allow pockets of experimentation, mutation, and ultimately iv) coevolution. CAS scholars not only need to acknowledge such features on their systems of study, but they also need to investigate the mechanism that give rise to such features. On chapter two Holland introduces the need of a theory for the exploration of such mechanisms. He first reviews the existing theories used to study CAS and their shortcomings: Lodka-Volterra type of models (ordinary differential equations), cellular automata, agent-based models, artificial chemistry models, neural networks, classifier systems and network theory. I really enjoyed that part. Then he introduces the requirements for a signal / boundary theory, an ambitious theory that would take the most of the previous attempts while overcoming their shortcomings. The requirements are a formal grammar that specifies allowable combination of building blocks (genes, letters, species, etc), an underlying geometry that allow for inhomogeneities, a grammar that allow programmable agents to execute signal-processing programs that includes reproduction. Armed with these concepts the book develops by taking you step by step of what is needed to recreate features of CAS: agents and signal processing, networks and flows, adaptation, recombination and reproduction, boundaries and hierarchies. The last chapters tie all the parts together and formalise the computational procedures for creating the grammar and mathematically represent such models.

Chapters 7 and 8 were the ones relevant for my questions about system’s boundaries. Holland relies strongly on ‘billiard ball’ and urn models used in chemistry to explain reactions between agents and how membranes evolved to permeate certain elements and not others. His simple prose and clear examples helped me to better appreciate basic probability theory and how the problem of differentiating systems units are so similar to the problem of identifying network communities, or defining niches in ecology. The data should be able to reveal the system boundaries if one has complete information of the interaction of its elements. That’s seldom the case in ecology, but there is certainly growing datasets to play around in other disciplines such as cell phone data, emails, or trade. See for example language community identification for Belgium based on cell phone data: paper here and other visualisations here by Vincent Blondel’s group. Although the data driven identification of system boundaries is possible, it will never by a sharp line as one would expect.  For example, not all genes on your body are yours, microbial communities living inside us mix their genes with the ones in our cells, help us maintain a good health and even influence our mood.

Although the book applies a lot of probability theory to represent interactions, a couple of quotes draw my attention on the limits of statistical approaches to study complex systems.

In attempting to answer the questions [how do agents arise? How do agents specialise? How do agents aggregate into hierarchical organizations?], it is important to examine the formation of agent boundaries, both internal and external, and the effects of those boundaries on the flows. This emphasis directs attention to building blocks that can be combined to define boundaries. The building blocks must, of course, be based on available data. Though there are extensive data sets for most signal/boundary systems, and we can rather easily derive a great array of reliable, sophisticated statistics from such data, such statistics do not, of themselves, reveal building blocks or mechanisms. Anatoly Rapoport, one of the founders of mathematical biology, long ago pointed out that you cannot learn the rules of chess by keeping only the statistics of observed moves (Rapoport 1960). We confront the same difficulty when using statistics to study signal/boundary interactions. The interactions are just too complex (nonlinear) to allow theory to be built with the linear techniques of statistics. (Holland, 2012:38)

And then toward the end of the book he makes a nice bottom up definition of niche, contrary to the one we have in Ecology, while going back again to the argument of statistical approaches to understanding mechanisms underlying complexity:

The concept of community within a network (Newman, Barabasi, and Watts 2006) provides a starting point that leads naturally to an overarching definition of niche.  A niche isa diverse array of agents that regularly exchange resources and depend on that exchange for continued existence. Most signal/boundary systems exhibit counterparts of ecological niche interactions – symbiosis, mutualism, predation, and the like. From this definition of niche, it is relatively easy to move to an evolutionary dynamic of niches, because the conditional actions that underpin the interlocking activities can be defined and compared in a uniform way by means of tag-sensitive rules (See chapter 3).

Defining niches in terms of tag-based rules leads to an important mechanism-oriented question: Can mechanisms for manipulating tags (such as recombination), in combination with selection for the ability to collect resources, lead from simple niches to complex niches? The conditional actions of the agents lead to non-additive effects that cannot be usefully averaged, so a purely statistical approach isn’t likely to provide answers to this question. Statistical approaches wind up in the same cul-de-sac as statistical approaches to understanding computer programs. The ‘trends’ suggested by a series of ‘snapshots’ based on the average over agents’ activities rarely give reliable predictions or opportunities for control – instead of ‘clearing’ of a market, we get ‘bubbles’ and ‘crashes’. (Holland, 2012:287)

I’ve long questioning the role of statistics for identifying causality in ecosystems; most statistical methods rely on linear regression which often implies avoiding co-linearity and assuming independence. Most statistical procedures are not suitable for understanding feedback mechanisms, from neural networks to structural equation modelling. Only recently a different approach has been developed that embrace the interdependent nature of variables in ecosystems: convergent cross mapping. It still to be seen what we can learn about causality in ecosystems by using such methods.

Holland’s book was an inspiring companion on the public transportation of Stockholm while commuting to the climbing hall, that’s where I read most of the book.

Marine Regime Shifts: Drivers and Impacts on Ecosystem Services

That’s the title of the recently published paper by my colleagues and me on Philosophical Transactions of the Royal Society B.  The link takes you to the journal webpage where you can download the paper for free, we made it open access. It went online on late November, and the printed version was the first issue of 2015, an special edition on marine regime shifts just in time for the 350 anniversary of the journal. Here is the abstract:

Marine ecosystems can experience regime shifts, in which they shift from being organized around one set of mutually reinforcing structures and processes to another. Anthropogenic global change has broadly increased a wide variety of processes that can drive regime shifts. To assess the vulnerability of marine ecosystems to such shifts and their potential consequences, we reviewed the scientific literature for 13 types of marine regime shifts and used networks to conduct an analysis of co-occurrence of drivers and ecosystem service impacts. We found that regime shifts are caused by multiple drivers and have multiple consequences that co-occur in a non-random pattern. Drivers related to food production, climate change and coastal development are the most common co-occurring causes of regime shifts, while cultural services, biodiversity and primary production are the most common cluster of ecosystem services affected. These clusters prioritize sets of drivers for management and highlight the need for coordinated actions across multiple drivers and scales to reduce the risk of marine regime shifts. Managerial strategies are likely to fail if they only address well-understood or data-rich variables, and international cooperation and polycentric institutions will be critical to implement and coordinate action across the scales at which different drivers operate. By better understanding these underlying patterns, we hope to inform the development of managerial strategies to reduce the risk of high-impact marine regime shifts, especially for areas of the world where data are not available or monitoring programmes are not in place.

I have to confess it’s my first publication on a scientific journal, and that the open access rights were a bit delayed. It is very rewarding when someone you don’t know write you an email asking for your work, it means someone finds your stuff worth reading. Thus, when it was not open access, I got quite few emails of people asking for the pdf, once it was open access, emails decreased. Then I realised that open access reduces the old school asking papers directly to the author, which I enjoy both asking and giving. Open access, despite giving up the little pleasures of old school academia, does expand the horizons of your research bringing knowledge at the clicks of your fingerprints: that’s the pleasure of modern academia.

The paper has attracted the attention of few scientist and the policy making community. The European commission contacted me in late January to cover the key results of the paper and include them on their newsletter ‘Science for Environment Policy‘. They did an excellent work at summarising  the results on a pager that you can access here [PDF] in case you feel lazy of reading the full paper. Shortly, emails from the Norwegian Environmental Agency, European Marine Board, and the German Maritime and Hydrographic Agency were asking again for our piece. It might not lead to the usual referencing and h-index boosting that scientist usually look for; but knowing that your work is being used on these arenas gives a gratifying feeling. In fact, when I received those emails, I was on holiday with no access to my computer. People were happy to receive my reply saying that the pdf was available on the journal website thanks to the open access option.

The special issue has other very interesting papers on regime shifts. I haven’t read them all (I was on holiday!) but I can recommend the piece by Vasilis Dakos et al where they reflect on the prospects and limitations of early warning signals of regime shifts. Jean-Baptiste Jouffray and collaborators offer a strong empirical evidence of regime shifts in Hawaiian coral reefs, a timely response to those who don’t believe on regime shifts and have shown data without bimodality [Bruno et al 2009 in Ecology]. JB combines a battery of statistics to demonstrate that coral reefs do have alternative states. His team elegantly combine principal component analysis and bootstrapped regression trees to show which variables better characterise the alternative states. On cascading effects, Beaugrand et al and Fisher et al provide a vividly discussion on the likelihood of synchronised regime shifts. While the former shows empirical evidence of quasi-synchrony in the northern hemisphere driven by temperature and atmospheric pressure patterns; the later discusses how spatial heterogeneity in environmental characteristics may diminish the tendency of these teleconnections. Other papers on my to-read-list include the paper by Ling et al where they analyse kelp transitions to urchin barrens globally, while Gårdmark et al and Pershing et al address regime shifts in food webs. The issue closes with 4 more papers focused on the topic of management, including a piece by Henrik Österblom and Carl Folke where they show how the rise and the fall of the soviet union coincide with the global expansion of industrial fisheries, bringing into the table another important matter when studying regime shifts: understanding major social-ecological context, political and economic tensions, and power dynamics.

I leave you for now with a video from the Institute of Marine and Antarctic Studies where Scott Ling explain his work on kelps transitions to urchin barrens. If you’re curious about the special issue here is the link to the table of contents. I encourage you to read all that interesting work, but if you don’t know where to start I suggest to start with the open access ones.




Global ship traffic visualisation: 90% of all goods traded globally travel by ship.

The visualisation was developed by FleetMon.com an organisation who monitores ships traffic and sells the information according to your needs. Shame is not open access, for scientific purposes it’s an amazing dataset. However, it’s a nice reminder of the connectivity, interdependence and potential teleconnections we have in the Anthropocene. As NPR puts it:

It’s a good reminder that about 90 percent of all the goods traded globally spend at least some of their transit time on a ship.

Company and emotion: a mechanism for social cohesion

Like bits of matter floating in space, humans cluster into communities. These communities serve several purposes: they offer protection and security, they provide resources both physical and emotional, and they give a sense of meaning and belonging. They also hold an arguably even greater power: to actively influence the way we interpret the world. The most dazzling firework can seem muted if viewed alone; the most unremarkable vista inspiring with good friends. Being with others adds a Technicolor tinge to the drab mundanity of daily life. It would seem, then, that the best way to go about choosing your next concert should be to focus not on the fame of the headliner but on the quality of the company

The quote comes from Scientific American article: Without Friends or Family, even Extraordinary Experiences are Disappointing. Lot’s has been said on social networks literature about social contagion and how modularity pervades social networks. However, what is the mechanism behind is not so clear. An obvious one is preferential attachment, or ‘birds of a feather flock together’. Yet why? The enhancing emotions story behind the scientific american article can be a complementary mechanism; we not only feel with our bodies but our experience is enhanced when experienced in company – socially.

Ebola: could disease outbreaks be a case of regime shifts?

Back in summer my colleague Victor Galaz (@vgalaz) asked on twitter what are the perceived global ecological risk further than climate change related disasters. To the questions, Professor Steve Carpenter replied that the risk of global pandemics, being one ebola, was one of his worries.   Today we face the IMG_2249biggest ebola outbreak in history. Some sources speculate that we will reach the 10.000 cases before the end of the year. While the media campaigns to better communicate the risk and reduce the panic, others try to make sense of the most recent data and update current models on the likelihood of spreading and size of infected population. One of the latest is the Mobs-lab lead by Alessandro Vespignani, a famous network scientist working amongst other things on disease spreading problems. Here is the link to the lab’s website where you can keep an eye on the latest datasets, a current issue on PLOS Current Outbreaks and their ‘live’ paper on ebola. The following video is another source of information about ebola statistics that takes into account the social side of it: cultural factors that affect the likelihood of transmission, poverty, communications issues and how our best hopes to contain the outbreak reside mainly on timely information and behavioural response.     As contribution to the broader discussion on how to deal with epidemics, not only ebola, I’d like to make available a draft for the Regime Shifts Database that I wrote back in 2009. Here I explored the possibility of conceptualise outbreaks as a type of regime shifts. The draft was never reviewed or published. Any comments or suggested readings to update it is more than welcome!

Outbreaks: from low to high frequency of events

  Summary of regime shift

Outbreak is a general term to refer an abrupt increase on a particular population size, often those that produce diseases in humans, animals or crops, and invasive species. Outbreaks could be both a regime shift in itself, or a driver of other regime shifts. In the first case, it happen when a threshold of susceptible individuals is passed. In the second case, the frequency of outbreaks can lead to chaotic-like dynamics: strange attractors, chaotic cycles and boundary coalitions. The change on the frequency of outbreaks could lead to structural change in ecosystems and society. Implications for ecosystem services includes loss of agricultural production, threats to human health, and landscape configuration.

Description of the alternate regimes and reinforcing feedbacks

Outbreaks are a regime shift by itself and a driver of other regime shifts (Scheffer, 2009). The first case is some times related to a shock event. There are events that happen once, for example in the case of diseases outbreaks a lots of victims die (human, animals or crops) and then the disease disappears. It could be that the host develop immunity or that the disease is so isolated that there is not chance of new infections that trigger a new outbreak. Other example is the one of invasive species outbreaks. In the absence of natural controls, these species hardly leave their new habitat establishing two regimes: with and without invasive species. However, this is a rare case because at some point the outbreaking organism (insect, parasite, virus, etc) ran out hosts, or the invasive species ran out resources. Then, a sharp decrease is followed by a dormant phase where the population is keep in low densities (Blarer & Doebeli, 1999). Thus, outbreak dynamics tend to be locked in strange cycles between two stable states: low abundance (healthy systems) versus extremely hight abundance (sick systems). Surprisingly, such dynamics can have periodic cycles and present synchronization (Stone et al., 2007). The frequency of flu increase in spring, measles on children increase at the beginning of the scholar year (Stone et al., 2007) whereas spruce budworm has outbreaks cycles every 40 years (Ludwig et al. 1978). Outbreaks might be a driver of other regime shifts. Knew examples include the shit from coral to algae dominance trigger by a disease outbreak on urchin barrens (Mumby et al., 2007), and the shift from primary to secondary arrangements in boreal forest (Scheffer 2009). Note that a second interpretation of “regime shift” might arise when the explanatory variable is the frequency of the strange cycle, rather than the healthy versus sick states described above. In such cases, small change in a parameter or a structural variable of the system in question can lead to an acceleration or slow down of the outbreak frequency. This effect might transform completely the structure, hence identity of the system. For example, insect outbreak cycles seems to be synchronized in northern American forests. The fragmentation of primary forest due to logging activities, settlements, or road construction might break down such synchrony. Consequently, different patches present outbreaks at different times. Depending on the landscape structure, in the new regime outbreaks could jump from patch to patch, making them more frequent in time and heterogeneous in space, exacerbating their economic consequences. Under such frequency regime, the forest might be trapped on a secondary state (trees are kept in youth ages, they never reach maturity) (Brassard & Chen, 2006). Another example is the prevalence of infectious diseases in poor countries. The poverty trap is explained by the following mechanism: under lack of enough income, people cannot afford protection against infectious diseases via nutrition or sanitary conditions; therefore they spend greater time infected and consequently produce less income (Bonds et al., 2009). In the later case, the structural variable (where the threshold is) would be some indicator of welfare in the country like income per capita. The mechanism underlying outbreaks are rather simple. An outbreak happen when a threshold of susceptible individuals is passed (Scheffer 2009). However, feedback mechanism become a little bit more elaborated when regarding specific cases. Environmental conditions and timing play a fundamental role when determining whether an outbreak is about to happen (Blarer & Doebeli, 1999; Stone et al., 2007). Although it is mathematically complicated to predict when an outbreak is going to happen, the timing plus the configuration of the population right after the last outbreak event (relative proportion of infected, recovered, susceptible and exposed individuals) could determine when the next event is probable and its magnitude (Stone et al., 2007). In addition, Blarer and Doebeli (1999) found that temporal coherence and the magnitude of population’s outbreaks is maximized at intermediate levels of environmental noise. Thus, at least in some cases, evidence suggest that the frequency of outbreaks is determined by its history. In the same line of reasoning, Peterson (2002) examine the effect of ecological memory in the emergence of landscape patterns due to contagious disturbances. Although he looks specifically at fire dynamics, outbreaks are contagious disturbance processes as well. His findings support the idea that some system with high memory could exhibit persistent patterns determined by the frequency of the disturbance and the structure of the system. Another possible mechanism is basin boundary collisions (Vandermeer & Yodzis, 1999). Briefly, it refers to cases where the regime shift is provoked by a subtle change in the boundary of the basin of attraction, getting close the attractor and the separatrix with adjacent basins. In other words, structural change in the system change the point where it might jump from one regime to another, rather than changing the dynamics. Such is the case of the use of pesticides that can induce mortality of natural enemies. Outbreaks would tend to be more frequent and present chaotic transients – a sort of flickering effect when the system goes to the alternative state and comes back repeatedly before actually shifting to second regime – (Vandermeer & Yodzis, 1999). Another example is vaccination, exemplified by Janssen (1998) in a model where malaria can evolve drug resistance. Following a period of low incidence, malaria picks up due to the increase in susceptible individuals against resistant varieties. Therefore there is two threshold to bear in mind. First of all, the amount of susceptible individuals in the target population. Second, structural variables that may affect the frequency of the outbreak. The second class would depend on the system under consideration.

Drivers that precipitate the regime shift

Outbreak is a general term to refer an abrupt increase on a particular population size, often those that produce diseases in humans, animals or crops, and invasive species. Consequently, the drivers of the regime shift covers a broad set of categories. When looking at the regime shift in its most simple way, healthy versus sick states, all that is needed is to cross a threshold on the susceptible individuals (Scheffer 2009). Therefore, the income of new individuals would make the population prone to new outbreaks. This includes population growth and migration dynamics as drivers. The loss of biodiversity can drive outbreaks under the same threshold mechanism. Thus, when predators or competitors densities are reduced both due to extinction or overexploitation, other species become abundant, hence prone to outbreaks. Such is the mentioned case of sea urchins in the Caribbean (Bellwood et al., 2004) In addition, hight connectivity could facilitate the spread of outbreaks (Norberg and Cumming, 2008). Under this category can be grouped the increase of trade facilities, expansion of transport systems and the concentration or establishment of urban environments (Foley et al., 2005). On the other hand, when regarding outbreaks regimes as changes in their frequency that are able to transform the system where they are produced (e.g. poverty traps, secondary forest); drivers become more subtle. Connectivity again play a fundamental role. Such is the case of landscape patterns that facilitate or not the spread of contagious disturbances (Peterson, 2002). Additionally, environmental oscillations like ENSO may trigger outbreak dynamics through stochastic resonance mechanism, that is a nonlinear interaction between environment and organism response (Blarer & Doebeli, 1999). Climate change is thought to increase the risk of pest outbreaks, including malaria, cholera, dengue, hantavirus pulmonary syndrome, influenza, and diarrhea among others(Ford et al., 2009; Khasniss & Nettleman, 2005; Janssen, 1998). Thus, not only connectivity in space but in time can modulate the frequency of outbreak dynamics. The introduction of pesticides or intensive vaccination programs might generate basin boundary collisions (Vandermeer & Yodzis, 1999; Janssen, 1998). A system with low frequency outbreak is then attracted to a high frequency state by the subsequent increase of susceptible individuals. In social systems, the lack of infrastructure and education may trap societies in regimes where outbreaks are more frequent. As a result, this societies become poorer and less capital for infrastructure and education is produced. Then poverty is a driver of high frequency outbreaks in human systems (Bonds et al., 2009; Khasniss & Nettleman, 2005).

Impacts on ecosystems and human well-being

Outbreaks are part of ecosystem dynamics, some species have evolved to present periods of extremely high densities and periods of dormancy (Blarer & Doebeli, 1999). In this sense, outbreaks represent intermediate pulse-like disturbances ecosystems are used to deal with (Scheffer et al., 2008). Most of the cases outbreaks works as a control for species with high densities. However, their frequency and intensity might modulate the ecosystem where they are present, favoring a set of species over other. Thus, outbreaks might be linked with other regime shifts where change in species dominance is a determining factor. Since outbreaks affect all organism, ecosystem services related to food production both crops, livestock, fisheries and wild animals are affected. Timber production, woodfuel and other products forest-related are included as well. Strange and Scott (2005) clearly capture the big picture for crops: 800 million people suffer from hungry, 1.3 billion live in poverty (less than US$ 1 per day) and at least 10% of global food production is lost due to plant diseases. The Great Bengal Famine back in 1943 caused 2 million people die due to high dependence on rice crops which suffer an outbreak collapse; whereas the southern corn leaf blight epidemic leave severe economical losses in the 1970’s US agricultural economy (Strange and Scott, 2005). Oerke (2005) estimated relative global losses due to pests for major crops as follows: from 26 to 29% for soybean, wheat and cotton, 31% for maize, 37% for rice and 40% potatoes. Human related diseases are not the exception. They follow the same dynamics and causal pathways as other outbreaks cases. Moreover, climate change is predicted to increase outbreaks intensity and frequency (MEA, 2005), including human disease outbreaks like diarrhea, malaria, dengue, influenza, cholera, and hantavirus (Ford et al., 2009).

Management options for preventing or reversing regime shift

Managerial options depend in case specific features of the regime shift including the type of organism, dispersion, vectors, and interactions with noise sources like climate variability. Given the drivers mentioned above, three generic managerial options arise. First, population growth and migration control. The likelihood of an outbreak increase in high density arrangements like large scales and homogeneous crops, intensive livestock production or big cities. Therefore, arrangements with low density might reduce this likelihood. Since connectivity plays a fundamental role when it comes to outbreaks, it is convenient to manage and monitoring all possible transportation links. In the human case, it comes to traveling facilities and migration movements. When it comes to food production, it has to be with landscape connectivity, vector migration dynamics and trade of resources. For example, biological corridors has been thought to be good barriers against dispersion of plants diseases or its vectors. In that sense, the maintenance of biodiversity both in the landscape and in food webs reduce the propagation of outbreaks dynamics. Second, policies for using vaccination or pesticides should take into consideration the evolutive dynamics, or the possibility of developing resistance, of both the host and the problematic organism (Janssen, 1998). When such dynamics are not taken into consideration, extensive use of vaccination and pesticides increase the amount of susceptible individuals to new resistant organisms. Then, the threshold of susceptible individuals is easily crossed and a bigger outbreak might happen. Third, monitoring programs would help to predict and react to outbreaks. Ford et al. (2009) suggest that the use of satellite images might help to predict forthcoming outbreaks by monitoring real time changes on landscape dynamics. Most of human related disease keep strong correlation with climate variables like temperature and humidity, landscape connectivity, water fluxes and its quality or environmental factors that affect their vectors behavior. In the same line of reasoning, information technology might help us to create early warning systems that allow us to react faster than the outbreak by preparing national health systems (Galaz, 2009; Ford et al., 2009). Finally, strategies that include reduction of poverty and empowerment might help to break the poverty trap. For example, education and sanitation technology have help to reduce significantly water-borne diseases in poor areas (Khasniss & Nettleman, 2005). However, other vulnerability factors like malnourishment, access to clean drinking water and local management of vector threats need to be addressed (Khasniss & Nettleman, 2005).


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TED video on why trust science…

Video explaining the risk of West Antarctica Ice Sheet collapse

Recent papers and news reported that the West Antarctica Ice Sheets are in risk of collapse, meaning that ice is retreating with risk of sea level rise. This process will take between 200 and 900 years according with simulations, but the key point scientist make is that the process is irreversible given current climate change. This video by NASA nicely explain in plain language what is going on down there, and what are the glaciers most affected.

When to adapt or when to transform? Using network controllability to assess how manageable are regime shifts

When should we try to adapt or give up and go for transformations? These are non-trivial questions for managers. Indeed, how manageable is your system? Recent theoretical developments on network science suggest that there is fundamental principles that allow us to detect which components of a system need to be observed and manipulated in order to gain control of its dynamics. Although such findings have been tested for a variety of networks such as Internet, social networks or ecological food webs; it reminds to be seen if such theory is applicable to large-scale social-ecological processes. Here I review how manageable are regime shifts by applying network controllability to a set of regime shifts causal networks: a network map of the feedback mechanisms and causal pathways that undermine regime shifts dynamics. I present preliminary results and discuss opportunities and challenges of this area of research into the practice of ecosystem management in the face of surprise.

Causes and Consequences of Regime Shifts: A Network Analysis of Global Environmental Change

Regime shifts are large, abrupt and often hard to reverse changes in the function and structure of ecosystems. These critical transitions have been documented in a broad range of systems and scales both in marine, terrestrial and polar ecosystems. Regime shifts have attracted the attention of scientist, managers and policy makers because they substantially affect the ecosystem services that society relies upon. However, despite their relevance in the face of climate change or increasing human pressure on ecosystems, little is understood about the overall patterns of regime shifts causation and impacts for human well-being.

In this presentation, I present a recent analysis of the global change drivers of 25 general types of regime shifts at a range of different scales (e.g. eutrophication, coral transitions, bush encroachment). By using network analysis, we construct bipartite networks and explore the patterns of co-occurrence among regime shifts drivers. Overall, the main drivers of regime shifts globally are drivers related to food production and climate change. Results further suggest that marine regime shifts tend to synchronize in scale and time, sharing significantly more drivers and similar processes underlying their dynamics. Terrestrial regime shifts tend to be more idiosyncratic, challenging management practices to focus on context dependent features of each case.