New paper by Jaideep Joshi: Demographic noise and cost of greenbeard can facilitate greenbeard cooperation

We are delighted to announce a new paper from the lab!

Jaideep Joshi and Vishwesha Guttal, 2018, Demographic noise and cost of greenbeard can facilitate greenbeard cooperation, Evolution, DOI:

This is the second paper of our former PhD student Jaideep Joshi. Fantastic work, involving some hard-core analytical and simulational work to address an interesting problem on the evolution of greenbeard cooperation.

Congratulations to Jaideep!

While you are here, you must also check the previous paper of Jaideep on how mobility promotes cooperation, published last year in Plos Computational Biology.


Advise: On requesting recommendation letters

Many students ask me to write recommendation letters for a variety of purposes, like internship abroad, summer school and most importantly – graduate schools. It’s an important part of our job as academics to encourage younger and aspiring students. Therefore, in general, I am happy to write supporting letters if I know you ‘sufficiently well’. Here are some general tips to follow when you request me (or more generally it may apply to others whom you approach) to send recommendation letters – this information will help me to write better-supporting letters.

  • First, do write a short note requesting if the person you think is suitable to write a letter is willing to do so. When you do so, state the purpose of your application, attach your cv and mention the deadline.
  • In general, write at least a few weeks ahead of the deadline and clearly state what is the deadline. If the deadline is very short – you can still write, but be aware that many mentors may decline even if they feel you are a fantastic candidate.

Once I have agreed to send you the letter:

  • If it’s a graduate school application season, send a list of university, department, their respective deadlines, the program (PhD vs Masters), etc all in a single email.
  • Do send an updated CV every time you ask for a letter – even if I had sent a letter last year for a similar purpose. I would like to know how your cv has improved since last time, so that any modifications to the letter can be done.
  • Provide as much information on what is the recommendation for? And any other relevant information about the application. For example, if you wrote a proposal or statement of interest that is not confidential – share it with me. Share any pdf/link to details of what the application is asking. Sometimes, some advertisements are aimed for specific candidates with certain background (for ex: this conf is aimed for physicists interested in biology, or vice versa). Do point out them to me. A generic letter won’t help the selection to committee decide whether you are suitable.
  • A short note on any specific points that you would like me to highlight about you. It could be about your project/work done with me, about your grades, a new publication/work of yours that I am unaware of, or anything that you think will help your application. If you are applying to unusual programs (e.g. Masters in conservation biology but your background is in mathematics), tell me reasons for the same. Such information is very important and useful for me to write a good supporting letter.
  • Do not hesitate to remind me whether I have submitted the letter. Check the status at least a week, and a few days before, the deadline.


Finally, let me know the outcome of your application because I am curious to know, and am also interested in making sure you succeed. Moreover, its a basic courtesy to inform the outcome (even if its negative) to someone has invested time in writing a letter of recommendation. A negative outcome may also help your referee to improve the letter the next time you ask him/her.

New paper: Jaideep Joshi’s paper on mobility and cooperation

We have a bunch of papers from the lab that I haven’t time to announce on the website (but I do active tweet about them!). Here, I briefly post about the first thesis chapter of Jaideep Joshi is now published in Plos Computational Biology. It’s a really cool theory paper on mobility can actually promote cooperation.


(The above picture is from Figure 1 of the manuscript Joshi et al 2017, Mobility can promote the evolution of cooperation via emergent self-assortment dynamics, PLoS Computational Biology, 13(9): e1005732).

The way we set up the problem is that can we have cooperation in mobile organisms if we exclude well known mechanisms that facilitate the evolution of cooperation. Yes, indeed, we can find cooperation via emergent assortment of cooperators. This paper shows this counter-intuitive using heavy simulations of active or self-propelled particles, simulations of passive particles in turbulent media, and an analytical theory. All of it packed into a single paper.

Here is a nice summary of this work written by Ananya from Research Matters, a popular science communication webpage:

Classically, it has been argued that cooperative interactions evolve mostly among genetic relatives or individuals in close-knit environments – like the lions or the buffaloes. There is also the factor that these animals are mobile and often split and merge depending on the availability of food. What, then, could be the motivation for cooperative interactions to emerge among such dynamic groups that are not genetically related?

“Much of the earlier research on cooperation thought that mobility was a hindrance to the evolution of cooperation. This is because mobility allows defectors to invade and destroy clusters of co-operators, which are necessary for cooperation to sustain”, says Mr. Joshi. In their study, published in the journal PLOS Computational Biology, the researchers have considered two scenarios for mobility – one, where the individuals move through self-propulsion such as fishes and birds, and second, where the individuals move due to the flow of the medium they live in such as microbes.

The study demonstrates that, rather than hinder it, mobility can help animals evolve cooperation to form groups even among unknown individuals without any kinship. “Our study is like a thought-experiment, but aided by sophisticated theoretical and computational tools. However, our model can easily be adapted to real systems by incorporating features specific to those systems. These could include cancer cells, quorum sensing bacteria, mixed species bird flocks, or even grouping mammals such as spotted deer, baboons and elephants”, signs off Dr. Joshi.

We have been awarded an SERB grant to study collective behaviour

We are delighted that we have been awarded an extramural research grant from Science and Engineering Research Board, Govt of India, to a project titled “Inferring nonlinear and stochastic dynamics of flocking systems from real data”.

We will get around Rs 36 lakhs plus overheads over a period of three years. The grant will support conducting experiments using fish schools and to use tools from physics to infer nonlinear dynamics of schooling fish.

I would like to thank our former project assistant Amith Kumar and the PhD student Jitesh Jhawar for offering immense help  in the process of this grant writing and application.

This is funded by the Physical Sciences panel of SERB. It is interesting to note that two of our other grants to study collective behaviour too are funded by non-biology panels — one of them an Applied Mathematics section of CSIR and the other a Mathematics section of SERB (via Mathbio program at IISc).



Our new paper testing theory of spatial indicators of ecological transitions published in Global Ecology and Biogeography


geb12609-toc-0001We are happy to announce that our new paper on testing theory of spatial indicators of critical slowing down and ecological transitions has appeared in the online early version of the journal Global Ecology and Biogeography. The paper was highlighted via a cover image (left) taken by Stephanie.

Congratulations to Amit Agrawal (former project assistant of our lab) and Sabiha Majumder (PhD student) for their first research publication! This project began during conversations between Stephanie Eby, Andrew P. Dobson and myself. That was back in 2010, when I was a post-doc at Princeton. They had this excellent high resolution data that made sense in the context of a theory paper from my thesis in 2009.

So what is this paper about? 

Ecosystems like clear lakes or forests can abruptly collapse to ‘unhealthy’ states like toxic-turbid lakes or deserts with low vegetation. Our lab uses ideas from physics of phase transitions (e.g., water boiling to become vapour) to develop statistical tools of early warnings of such abrupt transitions. For example, two papers of my PhD thesis (Guttal and Jayaprakash 2008 and 2009) were on developing such tools to analyse time series and spatial data from ecosystems. The underlying theory is now popularly known as ‘Early warning signals of critical transitions’, “Theory of Critical slowing down”, etc.

In this paper, we were testing such tools using spatial data of large scale ecosystems. There were earlier efforts to test these quantitative tools, but mostly in simple laboratory conditions or controlled lake experiments.

Specifically, we test the prediction that the following metrics show stronger signatures of transitions ‘before a collapse of an ecosystem’

(A) Spatial variance (proposed in Guttal and Jayaprakash, 2009)

(B) Spatial skewness (proposed in Guttal and Jayaprakash, 2009)

(C) Spatial autocorrelation (proposed in Dakos et al 2010)

(D) Spatial spectra (proposed in Carpenter and Brock 2010).

This graph below shows how theoretical predictions and real data match.


There were a few subtle and insightful aspects related to analysing this dataset. First, we didn’t have an ideal dataset, so we had to make an approximation called ‘space-for-time substitution’ to compare theory with real data. We justified this approach using a model. Second, the data were discrete-state (occupied/unoccupied), unlike what the models typically assumes continuous variables (like biomass density) in each of the above papers. We developed a data preprocessing method called coarse-graining, inspired from the physics literature. We thought the method to be sufficiently important and hence the details of the method will be published separately. Analysing this dataset has motivated various thesis chapters in our lab’s PhD student Sabiha Majumder, who is from Physics department and works jointly with me and Prof. Sriram Ramaswamy.

I should add that reviewers gave detailed comments that helped the manuscript a lot. This was also our first manuscript where we used services of Axios.

All codes and some data associated with this manuscript are available on our Github page:

We have been awarded a CSIR grant to study coevolutionary models of cooperation and collective movement

We are delighted that we have been awarded an extramural research grant from Centre for Scientific and Industrial Research, Govt of India, to a project titled “A mathematical and computational model to investigate coevolutionary dynamics of cooperation in particle based models”.

The grant covers a small equipment (laptop) and salary for postdoc for two years. This grant is given by the (Applied) Mathematics section of the CSIR extramural grants.