Emergent patterns in nature and society

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Future practice

On the previous post I’ve already introduced what I am going to do with the lessons learnt on my ONL course. I’ve been charged with the challenge of co-designing an online asynchronous introduction to sustainability science. My goal is to make it as accessible as if my mum would take it, people with full time jobs or young parents. It will be leveraging online tools, but also activities that students can perform in their local realities that can teach us what sustainability means in different cultural contexts.

I initially took the ONL course to sharpen up skills for teaching online given the whole pandemic crises and the need to move all courses online in a very short period of time. My motivation was how to do better next year if courses continue being zoom based, how to improve the experience of my students and mine as teacher. But the lessons were far beyond that initial motivation. I’ve learn about learning networks, democratisation of knowledge, or the different meanings and practices of open learning. I’ve also learnt that online courses are a way to enable long term learning for people that cannot afford going to university, either lack of funding, lack of time, or simply difficult circumstances. Online courses do however nurture curiosity. No-one is too old to learn something, online courses make it possible for many.

I’m myself a decent consumer of online content for the sake of learning new skills and keeping my curiosity alive. From computer languages, coursera refreshments in old methods (statistics) and new ones (QCA), to patching up my incomplete knowledge of linear algebra (Khan Academy, good one!). I appreciate enormously when teachers have their teaching materials freely available online, slides, exercises, even videos. One of my favourites, as you already know, is Steven Strogatz. It’s time now to pay back to the community of teachers and people who have made that experience possible for me. So I’ve started doing the same and now when designing content for my classes, I’ll make sure they are available and under some sharable format e.g. under creative commons.

The last closing remark is that there is plenty of resources and science that it is being done on how to teach better, both online and in the classroom. I’ll keep an eye on the education literature, looking for inspiration and ideas of new things to try. Teaching is part of my job, but when I do read science I typically focus on my research questions but not necessarily on teaching methods, or what practices do work well under which conditions. Keeping myself literate about teaching is catching up with those developments as well.


Designing blended learning

On topic 4 of my online networking course we discussed blended learning and how to best design for it. Blended learning has components of face-to-face activities with online ones. The advantage of the face-to-face interactions are perhaps obvious given the covid times we’re in, but worth remembering. They facilitate social bonding, stimulates trust, and an environment where people feel comfortable sharing, listening, and disagreeing. Online activities, on the other hand, allow for some additional freedoms such as asynchronous activities, creative use of other media, or the expansion of resources through the internet. That being said, a few caution has to be taken in mind regarding the digital literacy of students and teachers, privacy concerns, and making sure that the virtual environment enable learning from failure, that is, people do not feel exposed if doing an exercise wrong or by supporting / opposing certain points of view.

The topic could not have been more relevant. We brainstormed on how to design a blended course the same week I was informed that I’ll be co-leading a fully asynchronous online course on sustainability science at my department. Exciting and challenging at the same time. It will be open to all countries, all time zones, all context and local realities. The previous professor had it designed fully asynchronous, so no group activities or face-to-face time. It is an introductory course, so it should speak to the prospect student interested on the topic without lots of technicality, as well as professionals who want to update their knowledge in that area of knowledge. It needs to work for parents with kids, people with full time jobs, everyone! It is not the typical course for freshman or sophomores, it should be a course that my mum would enjoy it!

Our discussions centred on how to make that possible. Some of my colleagues (with kids) suggested that instead of lectures with slides and kind of monotonous faces, why not trying a podcast style lectures? Something more conversational that only uses audio, so people can listen to it while commuting or doing the dishes. The average adult with a job does not necessarily have all the time of a normal student in front of a screen. My mind immediately jumped to who can I invite and that would be cool for the student to listen to? Bringing famous professors to the classroom for a lecture is hard, but perhaps more likely if it is a short conversation over zoom.

We also discussed the design of assignments, whether they should be private and perhaps only shared with the student’s consent after it has been reviewed by the teacher. Either way, I’d love to bring components of the student reality to the class, design exercises or “field experiences” that one could do in your city or neighbourhood and that others could learn from. I thought of using perhaps images (e.g. pictures of plants, or picturing ecosystem services) or something that gives the taste of the different realities of the group. One does not only learn from teachers and lecturers, but the experience of other students shared. How do we make sure to have that as well in an asynchronous mode? Last, this very ONL course I’m taking has some good examples to follow of activities and ideas to play around: the website, the webcasts, the twitter conversations, etc.

I’m excited with the challenge of an asynchronous online based course. It will be very different from the teaching I’m used to, but hopefully enable many people to be interested in sustainability science that otherwise would have not made it to the classroom. That democratisation of knowledge and expanding accessibility are two aspects that keep me motivated.

Dust bowl: symptoms of desertification

The dust bowl was a critical problem in the 1930’s US where large clouds of dust formed over the great plains, accelerating erosion and diminishing agricultural production. Scientist believe it could repeat again with higher likelihood of droughts brought by climate change, and expansion of crops over areas where the soils are already vulnerable. A really nice overview of recent literature is summarised in this Science note.


The discussion topic on my online learning class was networks. Personal learning networks. That was interesting because most of my current research work uses network theory in one way or another, I use networks to study complex systems. But never stop to think too carefully on their role in education. Some of my classmates were a bit irritated by the amount of jargon around some of these terms in pedagogics, which to some extent I agree with. But pass the jargon barrier –where educators call everything a network– and start thinking how networks are created, evolved or nurtured in the physical class room as opposed to the virtual reality.

When I was at MIT I took a class taught by Cesar Hidalgo, it was an introduction to networks. The class was open to students from MIT, Harvard and other universities in the area; and their backgrounds were all over the place, from physics and engineering all the way to the humanities. I knew about networks at the time, at least the introductory material. But I was interested on how Cesar teaches, because sometimes I teach similar content, to a way less diverse groups of students. Cesar, at the time, was the leader of a research group on collective learning. Cesar is a physicist by training, but he has always be passioned about problems in the social sciences: from how economics and information grows, to how people perceive artificial intelligence. I was inspired by Cesar research, but also curious of his teaching style.

We learn in networks, Cesar knows that. In fact, he researches how countries, cities and businesses form networks to hold knowledge, skills, and develop new ones. What economist term a knowledge economy. In the classroom we would always start by discussing 2-3 papers that were supposed to be read before the class. Given the diversity of backgrounds, and the usual one or two free-riders that didn’t read, every one had a different level of understanding of the content. We would reflect on some questions for 10-15 minutes and report back to the class what the group thought of the topic. That exercise would catch up the lazy ones, and also start a collective conversation on what is the fuzz about. After some discussion and scoping a few key questions, he will then go on lecturing for 20mins or so before the next exercise. By the time the lecture arrived, we were all warmed up and collectively tackled questions, problems, and different interpretations.

But how do one reproduce such high quality discussions online? How does one deal with the zoom fatigue or the cameras off? The conversation is not the same. On the other hand, there are alternative channels where conversation can happen. Twitter is one I often use, not in the context of class, but in the context of following scientist whose work I find interesting. I use twitter to keep myself updated with papers, opinions, and sometimes ask the sources what they think about some scientific question. It is a network. This blogging exercise, for example, is how the Online Network Learning course creates the sandbox for casual but content oriented conversations.

There are creative ways of nurturing such networks. In my class at Stockholm University we heavily use flipped classroom to make sure the conversation gets ongoing within groups, and then with the larger class. Similar to the discussion reading groups Cesar uses. On a different teaching class I’m taking, we use Athena, an online platform where we can upload resources and multimedia. It has chat functionality and people can comment on each other’s contributions (e.g. an essay, an exam). The class space is restricted to students and teachers, so it is less public that then blog format for example. Another option are online forums. They are widely use in the programming communities to exchange problems, tips and solutions. They can be both open or semi-closed like stackoverflow, stack exchange, the studio community, or the Earth System Data lab forum. All of them are networks that people use to learn, exchange knowledge, or just trigger curiosity.

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.

Open learning

This was the blog I maintained during my PhD. It has been 6 years since I graduated and the journey have brought me on research adventures to Princeton, MIT, and back to Sweden. Now as a “grown up” researcher I’m still learning, everyday. And one of the joyful challenges of my career is learning how to become a good teacher.

Motivated by the covid-19 pandemic, the working from home situation, and the all-zoom-teaching that we have to endure either as teachers or students, I’ve recently enrolled on a course about open networked learning. It is highly recommended, all materials are online and you can study at your own pace. Or take the course for credits through one of the offering institutions (Stockholm University in my case), which gives you additional access to mentors and a network of students/teachers facing the same challenges as you. I’ll be blogging a bit some reflections triggered by the course.

The first part of the course catered around digital literacy. Technology has changed the way we communicate and connect to each other. Only in the last few decades, there has been an overwhelming boom of different ways to socially engage: e-mail, facebook, twitter, instagram, tik tok; you name it. It is changing the way people relate to each other, how information is consumed and produced, and even influenced political debates, health, and businesses. An excellent documentary about its impact is The Social Dilemma (Netflix).

But it also change the way we do education, the way we teach, learn. The meaning of the word classroom. It presents us with challenges and opportunities, and they are shaped by digital literacy. This is, how a person engage with digital tools, platforms, and how she interacts online. It defines the learning style of students, and challenge the teaching style of physical class room teachers. Learning occurs on the embedded networks of social interactions. In the classroom, it takes place in between conversations between teachers and students, but most importantly: in between students. Such conversations point out different understandings, rise questions, and offer opportunities to get our head around new problems, new concepts or re-evaluate old knowledge.

Online the same magic can happen but the networks of social interactions can take different forms. Working on an shared online document in google could become the place of asynchronic discussions. Questions can rise on twitter, longer discussions on slack, while teaching material can be delivered through different media such as video, or interactive exercises. Are we taking advantage of this new horizon of possibilities? Are we testing what works best or not for teaching? Are we helping students to feel comfortable within their digital literacy? And as teachers, are we using a set of digital tools that facilitates learning and perhaps provides a diversity of tools that satisfy the different learning styles?

The pandemic has force us to move the classrooms online. We all have suffered from zoom fatigue, and I can only wonder how tired students are from a lecture after another a year in a row. Research shows the rate of learning has decreased when learning from home. But it is also an opportunity to experiment, try out things and learn how to be a better teacher; how to help our students to have a joyful learning journey.

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…

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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.

Book review: Phase Transitions


Phase Transitions by Ricard Solé was one of those books that nurtured my curiosity and motivated me to carry on with my PhD. Ana, my girlfriend at the time (2011), always suggested me to bring nice books for holidays that would distract me from work, books with stories or authors from the places we were visiting . But with Solé it was difficult to leave it at home. Most of the book was read in 2012-13 on the beaches and bars of Barcelona, Solé’s home; and believe or not, it did distracted me from work by making me looking it from a different perspective.

Phase Transitions is the concept that physicist like Solé use to describe changes in dynamic systems with bifurcations – changes between different states of organisation in complex systems. It’s the same as ‘critical transitions’ or critical phenomena, as other authors like Marten Scheffer prefer to use; or ‘regime shifts’ as ecologist often call them. But that’s just jargon. I read the book too long ago to be able to give a fair summary and highlight its most important lessons. However, this review will be more from an emotional perspective, what I like and dislike from that bunch of math.

The book is an amazing resource for teaching. It’s structured in 16 very short chapters, most of them don’t exceed the 10 pages. Yet they cover as many disciplines as you can imagine, it’s like brain candy for an interdisciplinary inclined mind. Chapter 1-5 set up the basics: what are phase transitions, analysis of stability and instability, bifurcations, percolations and random graphs. Solé keeps the mathematics to a minimum, any student without a strong maths background like me follows and enjoy more the story that the mathematical subtleties. He also guide you on how the math or the set of equations that helps you understand something, say percolations, are also useful to understand what looks like unrelated topics  such as cancer dynamics or lexical change in a language.

And that is exactly what I like of the book. Chapters 6 – 16 takes you on a journey of where phase transitions have applications in different fields in science: the origins of life (6), virus dynamics (7), and cell structure (8) for the biology inclined.  For the medicine inclined: epidemic spreading (9), gene networks (10), and cancer (11). For someone like me: ecological shifts (12), social collapse (16), information and traffic jams (13) and collective intelligence (14). And my absolute favourite: language (15) because it surprised me how phase transitions can be used to understand change in language, and also because it introduced a very peculiar model called the hypercube. Now what I dislike of the book was the incomplete list of references, imagine if the one missing is the one you want to follow up!

I took the book out of the shelf today and look at it with nostalgia. Last week I read a paper that studies depression as a critical transition using models of symptoms networks with thresholds (co-authored by Scheffer, the author of the book that inspired this blog), and today I accidentally ended up watching the video below on how music can also have basins of attraction. That feeling of déjà vu, that two disparate fields can have something fundamental in common, that we can learn music and better understand depression or cancer and viceversa; that’s what makes me in love with science. That’s what I enjoyed the most of Solé’s book, it opened the horizon of what I was actually doing on my PhD and helped me feel less afraid of exploring; otherwise how does one make the nice connections?


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.

What are the main drivers of regime shifts globally?

That was one of the key questions that inspired my PhD work. There is a buzz both in the media and the scientific literature that we are approaching a dangerous zone where the stability of the world ecosystems are at stake. Coral reefs are struggling and under a 2ºC warming scenario they will most likely disappear from many areas of the world. Every summer we hear of new ice free records in the Arctic while last few months there has been a consensus that Antarctica is also warming at a higher rate than previously expected. As today, boreal forest in Canada is burning at remarkably higher rates than usual. This year droughts have impacted strongly California and Brazil, with potential impacts on US food production and carbon storage in the Amazon respectively. Things are happening as ‘we speak’ and yet our knowledge about critical transitions in ecosystems is limited and often confined to well understood case studies (e.g. Jamaican coral reefs) and theoretical models. To the best of my knowledge, comparison of regime shifts exist for a handful of systems such as climate, agricultural landscapes, hydrological regime shifts, coral reefs and marine ecosystems.

Yesterday our paper Regime Shifts in the Anthropocene: Drivers, Risks, and Resilience was published in PlosONE. It address the question ‘What are the main drivers of regime shifts?’ by studying co-occurrence patterns of drivers reported by the regime shift database. It is the first large comparison of regime shift and their drivers, in fact we analysed 25 regime shifts types in marine (blue), terrestrial (green) and polar/subcontinental (orange) ecosystems. The figure below shows a network of drivers (57) on the left and regime shifts (25) on the right. The bigger the dot, the higher is the number of connections, which is is a proxy of the number of drivers a regime shifts has reported, or the number of regime shifts a driver is reported to cause. While nodes in the bottom show idiosyncratic drivers and regime shifts, the ones on the top are generalist, this is the most common drivers and the regime shifts with higher drivers diversity.

Screen Shot 2015-08-13 at 11.46.46

The main results of our work is that drivers related to climate change (e.g. droughts, floods, green house emissions) and food production (e.g. fishing, crops, use of fertilisers) are the main responsible for regime shifts globally. They co-occur together in patterns that one wouldn’t expect by pure chance, and this associations help us envisage management opportunities and challenges. The opportunities center around the knowledge base. We found that if two regime shifts share certain attributes such as occurring on the same ecosystem type, similar space and temporal scales, and impact similar ecosystem services; we can assume that they are caused by similar sets of drivers and therefore transfer successful management strategies from well-understood regime shifts to less understood ones. The challenge is to embrace drivers diversity. Addressing only well understood variables won’t preclude regime shifts from happening. Our work shows that these phenomena are often caused by a diversity of drivers and addressing them imply co-ordinated actions across scales, especially at the international level.

If you want to know more about our work, just follow the link above. The paper is on a open access journal and the data is also publicly available both in the regime shifts database and the public scientific repository Figshare. I happy to reply to questions or comments here or on the journal’s website.

‘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

One third of Earth’s largest groundwater basins are being rapidly depleted by human consumption

NASA and CalTech report two studies that quantify the depletion rate of major aquifers in the planet. Here are the links to the papers, both open access:

  1. Quantifying renewable groundwater stress with GRACE
  2. Uncertainty in global groundwater storage estimates in a Total Groundwater Stress framework

This means that significant segments of Earth’s population are consuming groundwater quickly without knowing when it might run out, the researchers conclude […]

The studies are the first to comprehensively characterize global groundwater losses with data from space, using readings generated by NASA’s twin GRACE satellites. GRACE measures dips and bumps in Earth’s gravity, which are affected by the mass of water. In the first paper, researchers found that 13 of the planet’s 37 largest aquifers studied between 2003 and 2013 were being depleted while receiving little to no recharge.

Eight were classified as “overstressed,” with nearly no natural replenishment to offset usage. Another five were found to be “extremely” or “highly” stressed, depending upon the level of replenishment in each. Those aquifers were still being depleted but had some water flowing back into them.

The most overburdened aquifers are in the world’s driest areas, where populations draw heavily on underground water. Climate change and population growth are expected to intensify the problem.

“What happens when a highly stressed aquifer is located in a region with socioeconomic or political tensions that can’t supplement declining water supplies fast enough?” asked Alexandra Richey, the lead author on both studies, who conducted the research as a UCI doctoral student. “We’re trying to raise red flags now to pinpoint where active management today could protect future lives and livelihoods.”

The research team — which included co-authors from NASA, the National Center for Atmospheric Research, National Taiwan University and UC Santa Barbara — found that the Arabian Aquifer System, an important water source for more than 60 million people, is the most overstressed in the world.

The Indus Basin aquifer of northwestern India and Pakistan is the second-most overstressed, and the Murzuk-Djado Basin in northern Africa is third. […]

In a companion paper published today in the same journal, the scientists conclude that the total remaining volume of the world’s usable groundwater is poorly known, with estimates that often vary widely. The total groundwater volume is likely far less than rudimentary estimates made decades ago. By comparing their satellite-derived groundwater loss rates to what little data exist on groundwater availability, the researchers found major discrepancies in projected “time to depletion.” In the overstressed Northwest Sahara Aquifer System, for example, time to depletion estimates varied between 10 years and 21,000 years.

“We don’t actually know how much is stored in each of these aquifers. Estimates of remaining storage might vary from decades to millennia,” said Richey. “In a water-scarce society, we can no longer tolerate this level of uncertainty, especially since groundwater is disappearing so rapidly.”

The study notes that the dearth of groundwater is already leading to significant ecological damage, including depleted rivers, declining water quality and subsiding land.

Both papers draw the attention to yet another driver of ecological regime shifts that might be occurring unnoticed by the challenges of data gathering. The recharging of aquifers could be thought of as a regime shift where the dominant feedbacks relate to the recharging rate but also through the coupling of vegetation and rain patterns produced by moisturising recycling. Far fetched idea that worth keep an eye on. For the time being, both papers go to the ‘potential regime shifts’ folder.

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.