Category Archives: Uncategorized

When to give up work that is past the expiration date anyway

Today I shipped the final revision of a manuscript I started thinking about in 2013 (the first conversation about the idea I had for this work was at dinner with Ryan Chisholm while at the 2013 SIAM meeting in San Diego – he does not remember, we were at a Mexican restaurant sipping Margaritas, maybe that's why), doing the analyses for at the end of 2016 (I believe I was in Santiago de Chile visiting the Universidad Católica when I started with coding), began writing in early 2017, and submitted to journals for publication in late spring 2017. 

The title, more descriptive than prescriptive, is: Estimates of vital rates and predictions of population dynamics change along a long-term monitoring program. In the paper, I try to figure out, using one of my model systems, how many years of monitoring we need to make reasonably accurate predictions on the future number of organisms in natural populations. Briefly, models of population dynamics use estimates of life-history and demographic processes and traits to make predictions on the future state of natural populations. Data coming from monitoring (for example, organisms can be tagged and followed through their lifetime to see how their size, weight, shape, and position change over time, along with when they die) are fed to statistical models, which give back estimates of those processes and traits; if the estimates used in the model change, the predictions are also expected to change, and more accurate estimates are expected to lead to more accurate predictions. More data should increase the accuracy of the estimates of processes and traits, but time, money, the importance of the research question, and the accuracy needed for taking decisions when the scope is applied and not theoretical are also in play when defining optimal stopping rules for monitoring programs.

In theory, and maybe in a better world, it could be a helpful paper; most field activities in biology start when researchers can get some money from funding agencies and end when there is no more money coming in. Not much time is spent beforehand on estimating for how many years the monitoring program should go on to get the information, estimates, or knowledge the researchers are looking for. Two years? Five years? The usual answers are "something like that" or "it depends". Ten, maybe. And why not twenty? A partial explanation for the dearth of analyses on the minimum or optimal duration of monitoring programs is that the scientific goals are only one of the goals of these projects, the others being funding the lab and attracting Master's and Ph.D. students and post-docs, along with the requests for collaboration and prestige coming from running long-term monitoring programs.

I am also confident that the paper that took me so long to write and revise will be read by a maximum of twenty people all over the world, and maybe five of them will put some serious thought into it. It is not a matter of being jaded, cynical, or nihilist; I have seen how many people have downloaded some of my previous papers. Quite disappointingly, not enough to warrant a celebratory cocktail despite my well-known passion for Negroni.

Academic writing (I am referring to publications in peer-reviewed journals and not to "academic" in the sense of "pedantic", although the two categories largely overlap) is valuable in the context of an academic career, where publications are the currency for positions, grants, and reputation. You send the paper to a "journal", whose prestigiousness typically depends on some measures of popularity of the papers it publishes, editorial board, and history. The article, when not "desk-rejected" by one of the associate editors, is reviewed by other academics who are working on the same topics, and after 2 to 12 months you receive an e-mail from the handling editor saying that the paper has been rejected, accepted, or most likely when not rejected, that you need to revise it before it is suitable for publication.

Outside of the ivory tower, academic writing is not read, considered, or relevant for anything of professional and personal value I can think of. Academic papers are boring to write, difficult to find, and hard to read. And yet, it's hard to let the unfinished ones go, even after leaving the academic world. More or less one year and a half after leaving academic research for better money in the tech world, I still have the following half-baked manuscripts in my soon-to-be-shut-down pipeline:

  • A paper on morphological differences in fish (that is, differences in shape) in populations of the species I worked on for 13 years and changes. Differences in shape, really? It should not be difficult to let it go. Well, unfortunately, it's not so easy. I took approximately 3000 pictures of fish in September 2012, which I then selected, digitized, and analyzed with the help of a student (heroic effort!). I was being helped by my girlfriend at the time (feelings!). It is good fun to see that you can actually tell the populations apart by looking at fish shape (meaning!). The analyses are mostly done, I am so close! (regret!). An emotional tour de force. Why should not I finish writing it then? Well, and it is enough of a reason, you can bet that almost nobody will read it and it won't advance any career I can think of. Time is a finite resource for all of us. There are other feelings, meanings, and, surely, other regrets.
  • A manuscript on the effects of extreme events on natural populations. The simulations took a few weeks to run on a cluster – that I remember. I opened years ago a fruitful research line on the effects of extreme events on population dynamics, risk of extinction, and natural selection, but it has been a few years since I started thinking about the problem and I barely remember what I was looking for at the time. I already wrote half of the manuscript anyway. It'd still require way too much effort to complete it. No way I am going to do it.
  • A blog post that could be turned into an academic paper on the usefulness of models of mathematical biology for modeling marketing, competition, and viewership predictions problems in entertainment. This paper would be a good read for a broad audience; the main problem here is that I was using Netflix examples, I am not working at Netflix anymore, and without those examples, the paper would be much more theoretical than I would like it to be. I have tried to get box office data for theater releases of movies, but I should pay more than $1000 for the data, and it is not going to happen. Next!
  • A paper on the spatial correlation among extreme rainfall events recorded in Slovenia. I paid $600 to some freelancers on Upwork in 2014 for scraping the data from the website of the Slovenian Meteorological Service. The negotiation was quite fun, she told me she had a team and the money was enough, then she asked for more money because she had medical problems – who knows whether it was true or not –, I gave her the money, and she told me I was a most excellent man. A mutually enjoyable collaboration – compliments are always well received and sometimes we have to pay for them. I have a valuable dataset for a critical research question; extreme rainfall can cause landslides and flash floods that can, in turn, wipe out populations of fish living in streams (if this were an academic paper, I would write "stream-dwelling fish populations"...). By estimating the spatial correlation of extreme rainfall events, we can make assumptions on the risk of multiple populations going extinct at the same time, and then set up conservation strategies that reduce the probability of extinction of the whole species. Of course, conservation strategies that will never be applied. It is too bad that I have neither the time nor the strength of character to resurrect data, code, and words. And it is too bad that I worked so hard for getting the data and the compliments. Oh, well.
  • A few others. A paper on the genetic structure of fish populations living in adjacent, but isolated, river basins defined by using Single Nucleotide Polymorphisms data. Another on the spatial genetic structure emerging over time within a single population due to habitat fragmentation (I presented some preliminary results at the above-cited 2013 SIAM meeting). A paper on the effects of fish movement on body growth and viceversa. All with half-written manuscripts sitting somewhere in my Dropbox folder.

I have decided that I will let those unfinished papers go, peacefully. Like a boat, leaving the shore to get lost in the foggy waters. On the one hand, I love writing, and I believe there will be quite a bit of writing in my future. I enjoy communicating my thoughts, and I have always loved the written word, both in Italian, my mother tongue, and in English. On the other hand, seriously, no more academic writing. Regret can motivate for a brief period, but long, exciting work requires aspirations for something, not flights from bad feelings and looming regrets for unfinished business. In any case, it is comforting to think that nobody on their death ever said: you know what, I regret not having written that paper on the fast estimation of hierarchical growth models using the Empirical Bayes method and parallel computation (another half-written paper, by the way). Five people would have skimmed it!

Teaching the hobbyist

My mom – 100% Italian, like me – has been studying English for a few years. She has followed the usual routine: group classes, grammar and pronunciation exercises, writing down new words and trying to remember them by sheer force of will – the vast majority of the time unsuccessfully. Despite focus, effort, and discipline, she was not improving much. This does not come as a surprise; we can look back at our high school years and guess how many of our classmates have learned a new language to reasonable proficiency – say, being able to hold a non-technical conversation that goes beyond explaining how to go from here to there with fewer than two turns – by following the learning diet described above: between almost nobody and nobody.

Since I have been spending quite some time in Italy and I have been hearing her cries for help a few too many times, I decided to take matters into my own hands and teach my mother some English. If you are wondering why my help came so late, it's because experience has taught me that teaching is much more effective when there is a certain distance between teacher and student. Parent and progeny are far from being distant enough; in the eyes of your parents, you will always be one-foot tall.

Nevertheless, I have some decent credentials. First, I have teaching experience across several fields: I taught graduate courses in ecology and statistical modeling in my Ph.D. and post-doc years, I’ve done some personal training, I currently teach a weekly Brazilian jiu-jitsu class, and I mentored a few junior colleagues when I was doing academic research. I also quite enjoy teaching, and I am not naïve anymore; I have learned the hard way that teaching beginners is hard. We tend to forget how little we knew when we started learning, and we need to change our perspective from knowing a lot to only a little or nothing. And it can be quite frustrating – do I really have to repeat the same thing for the fiftieth time?

Second, I have also remained a student myself, learning quite a few things to reasonable proficiency, and picking up a fair number of lessons along the way. I have acquired both English and Spanish, mostly on my own; I have gotten closer and closer to what I consider the measure of reasonable proficiency in Brazillian Jiu Jitsu – a black belt keeping the gi closed; I can code using a few different programming languages; when I was younger I picked up guitar (poorly, but that is another lesson in itself), and I see myself spending a few frustrating years later on in life trying to draw houses that don't look like shoeboxes with in front something rectangular resembling a window.

After four weeks of the new teaching approach, my mom's English improved quite a bit. I applied some of the lessons I learned, and I got some new perspective on teaching the hobbyist, who is someone who is learning mostly for the sake of it, not a professional student or an intellectual worker, who is frequently asking whether it would be better just to give up, forget about it, and watch some cooking shows instead – that's what I do sometimes too.

My experience and study of literature on teaching and learning told me that – hobbyist or professional – to learn something as challenging as a foreign language we need to: (1) spend a lot of time (2) doing activities that are effective for reaching our goal. It is also rare that actions that are effective for learning are not efficient (they consume less time and fewer resources for a unit of improvement) at the same time.

I will explain what I did to facilitate my mom's learning of the English language. There will be quite a bit of overlap between (1) and (2) in my description, but let's start with point (1), that is "spending a lot of time".

It takes time to learn a foreign language. If we look at the approximate learning expectations compiled by the Foreign Service Institute (FSI) of the US Department of State, it takes on average 23-24 weeks (575-600 class hours) to achieve Speaking level 3: General Professional Proficiency in Speaking (S3) and General Professional Proficiency in Reading (R3), in languages that are the most cognate with English, for example Dutch, Afrikaans, Spanish, and Italian. We can assume that the expectation for the reverse (Italian to English) is the same. There are also further bad news:

Students at the Foreign Service Institute are typically 30-40 years old, are native speakers of English with a good aptitude for formal language study, plus knowledge of one or more other foreign languages. They study in small classes of usually no more than 6. Their schedule calls for 25 hours of class per week with 3-4 hours per day of self-study.

So, if it takes people who are trained by professionals in professional settings, have strong motivation, are cognitively in good shape, and already know other foreign languages that much time to learn a foreign language, imagine how long it will take a hobbyist who is out of shape learning-wise and has little talent for foreign languages. After you have guessed the time, add one year.

It follows that first one needs to keep up the motivation – that is, the desire or willingness to study and practice – amid setbacks, sadness, gloomy feelings of improvements being too slow, too sporadic, too few. And to make it even more challenging, those feelings often go hand-in-hand with a plethora of self-defeating thoughts, such as: I am not smart enough, I do not deserve to see my efforts wasted, I will never be able to ask for directions to the nearest pub in London even if my life depended on it.

As Dave Adler writes in "The Pressure Principle" (a great book I plan to review), you can always get better at the margins of your performance/skills, that is at the very limit of what you can do. But you should keep in mind that working at the margins inevitably causes angst and frustration – we struggle, we often fail and only occasionally succeed –, and improvements are mostly non-linear. For quite a long time – days or even weeks – the performance, say, asking questions in the past tense in English, is clumsy, leaving the hobbyist full of doubts (should I use did or should? What about the third person singular? Is there an equivalent of does for the past tense? Why not?). Then, almost magically, the student gets it and internalizes the structure of the question! And then they get a bit worse, then a bit better, and so on.

In practice, when I teach, I strive to recognize that working at the margins is emotionally tough and I do my best to commend the consistent effort instead of the less predictable – in timing and magnitude – results. They will come. I make the student work at the margins frequently (every day if possible), but briefly (15-20 minutes tops) and I finish the teaching session on a positive note, scaling back to something less challenging if needed. If you think about your vacations, the last day disproportionately colors the whole ten days away. Always strive to finish on a high note and cheat (scale back) if needed.

Let's have a look at what the hobbyist should spend a lot of time on. There is abundant literature on what works for learning, though it would seem many teachers never take the time to look at it:

  • spaced repetition;
  • an appropriate level of difficulty (working at the margins and within the margins, not beyond the margins – do not work on Rachmaninoff if you can barely play Beethoven);
  • testing (gives you immediate feedback) instead of re-studying (beware of the illusion of fluency);
  • interleaving different types of material;
  • that we learn more when learning appears to be difficult and slow (because working at the margins causes angst, frustration, and improvements are non-linear).

I bet you were rarely, if ever, doing any of the above activities in high school. And I bet you barely remember anything you were supposed to learn at the time and keep with you forever. "How We Learn" and "Made To Stick" summarize most of our understanding of the learning process and the most effective learning and teaching tools and strategies. "The Pressure Principle" also has useful insights into the illusion of fluency, accountability, and other more practical tactics, such as the maximum number of repetitions one should perform consecutively before shaking things up. Too many repetitions in a row lead to mindlessness and a less-than-exceptional commitment to the task. Keep in mind that most of the above activities, along with spending a lot of time studying and practicing for some thousands of hours, can also be found in the "deliberate practice" proposed by Anders Ericsson. Individual tutoring and mastery training (students must master a topic before moving on to another one) also appear to almost miraculously speed learning up (see Bloom's 2 sigma problem). However, while mastery training can be very valuable for the school student, I believe it can make the hobbyist give up on learning because they can barely see what's at the end of the journey for far too long.

In practice, that's how I have been teaching:

  • Anki for spaced repetition. Look at this gem of an article if you want to learn more about it. It can be boring at times, but spaced repetition works. I use it too. 15-20 minutes a day, not more than that. Do too much and the risk of burning out is too high.
  • Focus on delivering information first, skip elegance and depth. Not "I feel like I would like to eat something", but "I am hungry". 
  • Make it work first, then be precise – let them feel that they can somewhat express themselves, it is a game of confidence and of keeping up the motivation. As an example, "must", "have to", "ought to" have slightly different meanings, but to keep things simple, just use "have to" for now. Action first, precise action later.
  • Let the student frequently come up with their own questions and examples and doubts. They already speak a language, they "just" need to express similar thoughts in another language.  
  • Interleave and frequently test, with the purpose of making the student internalize the structure while avoiding the dreaded "paralysis by analysis".
  • Read, listen, write, speak, do everything at the same time and with constant feedback, but only after the student has struggled on their own. It has been shown that struggling greatly increases the rate of learning.

Let me finish with an example and with the most important piece of information I think one should get from this post.

My mom had problems with the use of "went", the past tense of the verb "to go". Mostly due to the phonetic similarity of "went" and "were" and "when", which typically go at the beginning at the question, she was using strange structures such as "Went you go to the gym?". It could have been "Did you go to the gym" or "When are you going to the gym?" or "Were you going to the gym?". How to make her understand the correct structure first and then internalize the structure and make it stable, which means be confident that she will consistently use the proper structure? First, I make sure she understands, at least in theory, the structure of the question she wants to ask. Then, I test and retest (how would you ask me whether I washed the dishes yesterday night?), interleaving the tests with other question that are unrelated to the structure we are mainly focusing on (please describe what you are going to do tonight). She then has to struggle to go back to the structure we are working on when I test it again. Then I wait for the inevitable follow-up questions from her (how should I ask you how you went to the gym?), and we start again from there.

And finally, this is what I believe is the most crucial piece of information one should get from this post, in part inspired by the "The Pressure Principle" and my own experience. Frequently communicating – to ourselves and others – that working at the margins causes angst and frustration and that improvements are often non-linear, makes a tremendous difference in the motivation of the student. A positive (it's hard but worthwhile and satisfying), realistic (it will take a long time to become proficient) mindset that is moving with enthusiasm and energy toward achievement instead of away from fear makes all the difference between giving up and going on. The achievement mindset changes the struggle from a place of fear and disappointment to a place of pride and willingness to put oneself out there. Try it next time.

How I write academic papers

If there is one thing I have done plenty during my years of academic research in mathematical biology, ecology, genetics, and environmental sciences, it has been writing academic papers. As of September 2018, one year after having switched career from academia to industry, I have first-authored more than 30 papers in peer-reviewed journals. Three a year, give or take, and many others as a co-author. Some of them have been published in quite popular journals. Others, I could have spent my time enjoying the great outdoors instead of writing. 

For the vast majority of those papers, I got the idea behind the work, did the analyses, wrote the article, and dealt with revisions – all challenging tasks, especially the last one. We submit the paper along with our dreams of appreciation and the answer is: meh. Not novel; too long; who cares. Maybe I took too many papers and responsibilities on my shoulders, but it was aligned with my personality. And along the way, I learned a thing or two about writing papers. There are many useful resources out there on how to write an academic article, so I will focus on some points that have not been discussed as much as how to properly format citations. 

  1. Fewer goals are better than more goals, and concise is better than verbose. Many times I made the mistake of wasting the reader's attention by writing details that were interesting for just one person: myself – which is the same person who had spent weeks getting into rabbit holes that nobody had dared to explore before. Ask yourself: what is the most relevant result you want the reader to be aware of, among the many meandering analyses that mostly led nowhere? Skip the sensitivity analysis that took ten days to code, five days to run, and ten minutes to see that there was nothing there. Focus on the most relevant result. There is always another day for the less useful and that day is not today. I had to re-learn this lesson quite a few times.
  2. Use as little jargon as possible, and use simple words. Aside from the very little impact of much of the work we do in academia, often motivated by our research obsessions, fights over minuscule turfs, writing one more paper to beef up our CV or getting one more grant to keep the lab running, using jargon very much limits the use of our findings and insights outside of our tiny academic circle.
  3. Re-use, in spirit, paragraphs that have been already used in other papers or that were cut before publication. This point might be controversial, but a paper is not a novel, as its primary goal is to describe and teach, not entertain, and a solid paragraph that has already been used is better than a weaker one that has never been used. Don't copy, but look close. Then, being efficient is a virtue, being clear is a worthwhile aspiration, and complicating our life is a vice.
  4. Write, edit, write again, let it simmer, re-organize, be brave and cut that sentence that never seems to sound right. Writing is messy; at least, it's always been messy for me. You write a paragraph you think is brilliant and when you re-read it after a week, you ask yourself whether it is possible to write that unintelligible paragraph while sober. Like Michelangelo apocryphally said, perhaps the final draft is hidden within the gory mess of the first draft. Dig hard, let it sit, look at the creation, get some distance, dig again. The paper is never ready, but it comes the time when you should abandon it and move on. It's going to come back anyway.
  5. Introduction: 6-800 words – big picture, specific problem, how you solved it, big picture again. Cliché, but it works. Materials and Methods: no more than 1000 words – detailed where it matters and concise elsewhere. Results: maximum of 4 sub-sections, 200 words at most. Discussion: 800 to 1500 words – punch-section of 300 words, 300 words for each of the Results sub-section. These have been useful guidelines. The rest goes to Supplementary Material, but only if we strongly believe it helps us get the paper accepted. At first approximation, nobody ever downloaded any of my 20-page long Supplementary Materials.  
  6. Don't be afraid of being frustrated or disappointed or of wasting time writing, then deleting, then re-organizing. My theory of periods says that sometimes things work flawlessly and sometimes they don't work at all. The reason is often unknown and sometimes unknowable. Embrace the frustration, push through the disappointment, believe it will work out at the end. If it is not your first paper, you have already done it. If it is your first paper, many other people thought they couldn't, and then they did it.
  7. Writing a paper is a process within the process of writing many papers. Some reviews are less intelligible than abstract art; some are downright mean; some help us improve our work. And it is very human to think we have never been appreciated enough. As individuals, we can do very little to make the peer-review system fairer, but we can always focus on the process of writing that paper within the more extensive, writing-many-papers career-wide process.
  8. Read brilliant articles, save the sections you like the most, ask yourself why you like them, use the structure, words, flow in your papers. 

900 words, a bit more than needed for a concise Introduction.

A few recommendations for academic researchers who want to work in tech

I recently received an email from a former research colleague who is thinking about moving from academia to tech. The following was my answer:

I recommend moving out of academia and find more fulfilling jobs elsewhere. Tech is a good sector, especially now.
What you need is a firm grasp of Python or R (better if you know both), SQL (we do not learn it in academia, but it is used everywhere in tech), machine learning (everything in the books Applied Predictive Modeling and Elements of Statistical Learning, to give you an idea). If you are interested in experimentation (A/B tests), you also need to know about hypothesis testing in some depth.

Now, the most challenging aspect of the transition is finding the first job, since nobody knows you, you have no previous positions in tech, and what you have done in research – especially if you are not coming from either Computer Science or Economics – is mostly irrelevant to them, where them is companies, hiring managers, recruiters. Thus, everything you say or present during the interview process has to be connected in some form to business needs.

The second most challenging aspect of the transition is losing both the academic mindset and the academic jargon. In business (except for some teams in top tech companies that are doing academic research in industry), it is all applied science (more "applied" than "science"), and the jargon is coming from a mix of computer science (not predictors, but features; not estimating, but learning) and business language (not papers, but memos; and, if the model is working, it is most of the time good enough for business purposes – which means that theoretical considerations take the back seat).

The easiest – although it still requires considerable effort  way to make the transition from academic research to data science or machine learning in tech, is to take part in one of the many boot camps/incubators for data scientists. I strongly recommend Insight Data Science, since I have seen many alumni finding jobs at top tech companies right after completing the boot camp.

Some notes on "Stealing Fire" by Steven Kotler and Jamie Wheal

I occasionally write some notes on books I have been reading. On a lazy Sunday, I read the recently published "Stealing Fire: How Silicon Valley, the Navy SEALs, and Maverick Scientists Are Revolutionizing the Way We Live and Work" by Steven Kotler and Jamie Wheal and here are my observations (the two authors have recently been guests on some of my favorite podcasts too). 

The book might be decently interesting for people new to "altered states of consciousness" or "flow states", but for people like me who have been interested in altered states of consciousness, bio-feedback, movement and other tools or practices to better oneself for years (I am in the process of writing a book on autogenic training and I bought my first neuro-feedback apparatus more than 10 years ago), the book fell quite short of expectations. But it convinced me to go forward with my book, so I am not complaining too much.

The organization of the book is the one typical of non-fiction books published nowadays. You start with some anecdotes on either a single mother of 4 who's struggling to make ends meet or with some edgy Silicon Valley entrepreneur who feels a lack of motivation, is re-defining culture in the workplace, or wants to make more money while - ça va sans dire - making the world a better place. And why not, let's throw at the issue the occasional Navy Seal (am I the only one bored to death by the glorification of Special Forces?) or elite athlete involved in some obscure sport. Then, you talk about some innovative thinker (often a maverick scientist) who has in wonderful ideas what she lacks in social skills and voilà! here's the new, disruptive hypothesis on some mechanism, process, or idea whose application can better your life, save the world, or, in some not-so-rare cases, do both. Maybe that's what is needed nowadays to appeal to college-educated people who like to post platitudes or feeling-good stories on social media, but I am quite confident that in ten years very little will remain of these grab-some-money-as-soon-as-possible largely anecdotal books.

Thumbs up to the authors for including a “A Quick Note on Inside Baseball” section at the end of the book, in which they give additional details on some of the controversial aspects of the research on states of consciousness, point out that what now are experimental practices fancied by the elites will likely trickle down and reach the poor masses (Soylent anyone?) or that maybe Navy Seals are slightly exaggerating their claims. At the same time, if the reliability of up to 70% of fMRI studies should be questioned, at least one-third of the book should be deleted. Then, the authors don't discuss other questionable, when not downright disproven, research, such as Amy Cuddy and colleagues' research on the "power pose" (which was bogus also on common-sense grounds) or other research on priming effects, which have largely failed to be replicated.

What is evident after reading dozens of these Gladwellian books is that, when writing or discussing science, there are no substitutes for many years of formal study, statistical and quantitative knowledge, and a healthy dose of common sense, which is disappearing from the world faster than good manners. The now-popular broad, edgy, sometimes global thinker typically knows very little of value across many subjects (with the exception of Vaclav Smil, of course), but apparently the world is more interested in "inspiration" than in observations that make a bit of sense. For instance, the authors wrote: “Given that the percentage gains in performance from ecstasis range from 200 to 500 percent [...]”. What are we talking about when we talk about "performance"? Throwing percentage around like darts in a pub may impress the casual reader and help selling diet books when the bikini season is looming, but make the educated reader cringe a bit.

A very interesting topic, poorly treated.

 

Some excerpts here below.

When we say ecstasis we’re talking about a very specific range of nonordinary states of consciousness (NOSC)—what Johns Hopkins psychiatrist Stanislav Grof defined as those experiences “characterized by dramatic perceptual changes, intense and often unusual emotions, profound alterations in the thought processes and behavior, [brought about] by a variety of psychosomatic manifestations, rang[ing] from profound terror to ecstatic rapture . . . There exist many different forms of NOSC; they can be induced by a variety of different techniques or occur spontaneously, in the middle of everyday life.”

But once we get past the narrative wrapping paper—what researchers call the “phenomenological reporting”—we find four signature characteristics underneath: Selflessness, Timelessness, Effortlessness, and Richness, or STER for short.

When you think about the billion-dollar industries that underpin the Altered States Economy, isn’t this what they’re built for? To shut off the self. To give us a few moments of relief from the voice in our heads.
So, when we do experience a non-ordinary state that gives us access to something more, we feel it first as something less—and that something missing is us. Or, more specifically, the inner critic we all come with: our inner Woody Allen, that nagging, defeatist, always-on voice in our heads. You’re too fat. Too skinny. Too smart to be working this job. Too scared to do anything about it. A relentless drumbeat that rings in our ears.

That’s Kegan’s point. When we are reliably able to make the subject-object shift, as he points out in his book In Over Our Heads, “You start . . . constructing a world that is much more friendly to contradiction, to oppositeness, to being able to hold onto multiple systems of thinking. . . . This means that the self is more about movement through different forms of consciousness than about defending and identifying with any one form.

When our attention is focused on the present, we stop scanning yesterday for painful experiences we want to avoid repeating. We quit daydreaming about a tomorrow that’s better than today. With our prefrontal cortex offline, we can’t run those scenarios. We lose access to the most complex and neurotic part of our brains, and the most primitive and reactive part of our brains, the amygdala, the seat of that fight-or-flight response, calms down, too.

What looks inevitable in hindsight is often invisible with foresight.

Eight out of ten of us are disengaged or actively disengaged at work, despite the HR circus of incentive plans, team-building off-sites, and casual Fridays.

In a culture supposedly ruled by the pursuit of money, power, prestige, and pleasure,” Csikszentmihalyi wrote in Beyond Boredom and Anxiety, “it is surprising to find certain people who sacrifice all those goals for no apparent reason. . . . By finding out why they are willing to give up material rewards for the elusive experience of performing enjoyable acts we . . . learn something that will allow us to make everyday life more meaningful.

Across the board, from education to health care to business, motivational gaps cost us trillions of dollars a year. We know better; we just can’t seem to do better. But we can do better. Effortlessness upends the “suffer now, redemption later” of the Protestant work ethic and replaces it with a far more powerful and enjoyable drive.

It’s the same physical world, same bits and bytes, just different perception and processing. But the cascade of neurobiological change that occurs in a non-ordinary state lets us perceive and process more of what’s going on around us and with greater accuracy. In these states, we get upstream of our umwelt. We get access to increased data, heightened perception, and amplified connection. And this lets us see ecstasis for what it actually is: an information technology. Big Data for our minds.

Second, we have very little success training people to be more creative. And there’s a pretty simple explanation for this failure: we’re trying to train a skill, but what we really need to be training is a state of mind.

Throw money, people, or time at any of these and you may fix a symptom, but you create additional problems: financial aid to the developing world, for example, often breeds corruption in addition to its intended relief; adding more lanes to the highway encourages more drivers and more gridlock; fighting wars to make the world safer can make it more dangerous than ever.

As Buddhist scholar Alan Watts put it, ‘Western scientists have an underlying assumption that normal is absolutely as good as it gets and that the exceptional is only for saints, that it is something that cannot be cultivated.’

If those trauma studies demonstrated that a few instances of ecstasis can help mend what’s broken, then what happens if we deploy these techniques repeatedly, over the course of a lifetime? Can recurring access to these states really “nurture what is best within ourselves?” Can they, as Alan Watts suggested, be used to “cultivate the exceptional”?

What Newberg discovered is that extreme concentration can cause the right parietal lobe to shut down. “It’s an efficiency exchange,” he explains. “During ecstatic prayer or meditation, energy normally used for drawing the boundary of self gets reallocated for attention. When this happens, we can no longer distinguish self from other. At that moment, as far as the brain can tell, you are one with everything.”

By treating the mind like a dashboard, by treating different states of consciousness like apps to be judiciously deployed, we can bypass a lot of psychological storytelling and get results faster and, often, with less frustration.

It’s why the SEALs say “you don’t ever rise to the occasion, you sink to your level of training” and then proceed to overtrain for every scenario possible.

Academic life - Part 3

During my Ph.D., I also did quite a bit of consulting for the private industry, started teaching, and gave a number of seminars on climate change to high school students.

In Italian universities, it was common (and I believe still is) to get funding for Ph.D. scholarships, traveling, and buying tools and materials such as computers and lab instruments by doing consulting work for regional or state agencies, or for the private industry. In 2005, my Ph.D. supervisor asked me if I was willing to work on a consulting contract with a local company, which consisted in estimating the impact of activities related to cement production on atmospheric pollution. I had taken a class on atmospheric modeling during my Master's (the teacher was a researcher for the National Research Council and had a teaching style that I am going to describe using a euphemism: terrible) and that was the extent of my knowledge, which is equivalent to say I knew little about the topic. However, environmental modeling is not particularly complicated, especially when there are good software and good data, and you are a university researcher (yes, it counts). I accepted to lead the consulting work, which went on for more or less five years with some pauses here and there; it brought in very good money, which was later used to pay for my postdocs. It was time-consuming, but overall a good experience and money helped quite a bit.

In 2006, my Ph.D. supervisor asked me (he was taking parental leave for the year) to be the Instructor of record for the 2006-2007 course on "Population Dynamics and Management of Renewable Resources” for the Master's degree in Environmental Sciences. Since I rarely said no to more work or new challenges and I have never been short of self-confidence, I accepted. I was still quite young (26-27 years old) and a couple of enrolled students had been students with me some years before. It was my first experience in teaching a full course, and I did reasonably well. I would do much better now, but using today as a reference point for ten years ago is silly.

A couple of episodes. One time, I asked one of my Ph.D. colleagues to write a question for an intermediate exam, but during the exam, I recognized that there was something wrong about that problem, maybe incomplete information or the problem was ill-posed. I told students to skip that part of the exam, but some of them started complaining and protesting because now they had wasted time on that problem and it was not fair and other similar observations. I decided to use my institutional power and said: "I am the teacher and you do what I say. You have 15 minutes more". It was the correct approach, they need to know and feel I was in charge, personal power was not enough. They immediately calmed down and there were no more protests. Another time, I organized a visit to a regional park close to Parma; I made all the arrangements for transportation by bus and for lunch at the park restaurant on a lovely spring day. It was a very pleasant and informative trip, and the students told me they had a great time. The park director did not seem to believe I was the teacher, though, too young. Good times.

I was also still playing soccer at quite a high level (I was getting much more money playing soccer than by teaching and being a Ph.D. student combined). I was training 4 or 5 times a week, either in the morning or early in the afternoon, and I was going back and forth between my desk at the university and the team facilities, which were 30 km away from the university. On Sunday I was busy all day with championship matches, and when the away match was played more than 3 hours away from my town, we were leaving on Saturday and sleep in hotels. On Monday, I was dead, physically and emotionally. I was teaching, doing research, doing consulting, playing soccer, I had a girlfriend and a social life, but only a few times I felt overwhelmed (playing soccer was by far the most stressful part of my life, if you have ever played a team sport at a high level, I am sure you can relate. If not, it is difficult to describe the feeling of guilt when you make a mistake and you feel like you let your teammates down. It rarely happened to me, but if you play long enough, it happens)*. Now, I am pretty confident I would feel overwhelmed, but mostly because I would think about the situation being overwhelming. At the time, I was not thinking much, mostly doing. Better times?

Unfortunately, I started having painful physical problems; in the summer of 2006, the Achilles tendon of my right leg started to get inflamed and the situation rapidly got worse. I had problems even when walking, but I was biting the belt, as we say in Italian**. I had cortisone injections whose beneficial effects lasted for ten days, but after those effects were over, the pain was excruciating. Sometimes, it felt like I had a lighter turned on on my Achilles. I tried all kinds of conservative treatments, but in 2008 I decided to have surgery. It was an excellent decision, since I never had any problems after the surgery.

That's me showing some vertical. A few months after this photo was taken, I could barely walk. Much of my waking hours were drenched in pain and frustration. I had to warm up my Achilles for a couple of minutes before standing up

Looking back at the seminars I gave on climate change, I can only laugh. I was not prepared enough, one time there were more than 200 students listening to my talk, but somewhat I managed to answer to students' questions and gave them an idea about the effects of climate change. I believe the most important thing was to show them that I was enthusiastic about presenting the material and that I was concerned about climate change. The effects of presenting yourself as a good role model last much more than any information you can provide in one hour.

Footnotes

* I don't believe sport builds character or show character. I do believe that hugging your teammates after a victory, sharing the disappointment after a defeat, stomping the cleats on the locker room pavement before going on the pitch, are among the strongest, most fulfilling memories of my life.

** In the summer of 2007, while in Pacific Grove, CA, I was so desperate that when a physiotherapist asked me 250 dollars for a first 30-minute consultation (not even treatment), I answered: go ahead. Didn't solve the problem.

Academic life - Part 2

 

In the summer of 2005, I visited for two months Hamish McCallum and Hugh Possingham's labs at the University of Queensland in Brisbane, Australia. Hamish had done some work with my PhD advisor on diseases and I thought visiting his lab was an excellent opportunity for broadening my scientific horizons. I was also looking forward to going back to Australia after a very fun vacation in the summer of 2003 following the end of my Master's.

In Australia in 2003. I need to go back

Hamish and his wife were terrific hosts (I didn't have any interaction with them after my 2005 visit, something I regret) and I remember a nice, although brief, conversation with Hugh Possingham (Full Professor at the University of Queensland, who I had met the year before at a workshop in Trieste) about my PhD research (I met Hugh multiple times in later years, always brilliant and very gracious). Hugh is one of the most talented and relevant conservation biologists in the world (now also Chief Scientist of the Nature Conservancy) and he offered some suggestions on more conservation-motivated studies on marble trout. I still think about those suggestions, which have been a recurrent theme in my research statements since then*.

I also visited my friend Donna from Melbourne who I had met for the first time in 2003 (one of the kindest people I have ever met in my life. I have been trying forever to get back in touch with her, but her email address apparently changed and she has no presence on social media) and my former soccer teammate Gianfranco Circati, who had moved to Perth with his family the year before. Overall, I had an amazing time in Australia, personally and professionally**.

With my friend Donna, nowhere to be found, but not forgotten

My research on spatial distribution models of species was steadily going forward. The first paper I published on species distribution models for clams is still my most-cited work (~80 citations as of March 2017. Vincenzi, S. et al. 2006 A GIS-based habitat suitability model for commercial yield estimation of Tapes philippinarum in a Mediterranean coastal lagoon (Sacca di Goro, Italy). Ecol. Modell. 193, 90–104). I refined an idea of my PhD advisor about combining data and expert knowledge to get a better understanding of the spatial distribution of clams and of the commercial yield potential of different areas within coastal lagoon. Clams can grow well in certain areas, but due to constraints such as water depth or accessibility by boat, the commercial yield potential of that area could be close to zero. In applied work, I firmly believe that expert knowledge has to be seriously taken into account, as it often can provide better or faster insights (they are already there, just ask them!) that those provided by more formal methods of investigation. Fishermen are there to make money and they do not waste time harvesting or fishing where there are no clams or fish (or mushrooms, if we extend the analogy to mountain people, but don't ask them because they are not going to tell you where the mushrooms are) or where clams or fish cannot be harvested or fished.

I presented the results of my work on clams at the 2004 annual meeting of the Italian Society of Ecology (basically 4 months after the start of my PhD) and I won the award for the best talk given by a young researcher in ecology (along with 600 euros if I remember well, handed over by the at-the-time Italian Minister of Environment Altero Matteoli). Fiorenza Micheli, now Full Professor at Stanford, was in the award committee; supported by my PhD advisor, I asked her to visit her lab in the summer of 2006. It was my first trip to the US and one of the turning points of my academic career.

Let's get back to fish. Due to the strong relationship between egg production and body size of females in salmonids, I also became interested in finding out the determinants of variation in fish body size and estimating their relative importance. I still didn't have a clear vision for my research, which was truly driven by the curiosity of the month or of the week. However, one of my long-standing interests was understanding variation in phenotypes in humans and their environmental determinants, and I was reading with a certain interest about predictive adaptive responses and the famous Dutch famine study.

Predictive adaptive responses are physiological or behavioral changes in an organism that become fixed after early stimuli. For example, it is now well known that growing up in an unstable family (drugs, alcohol, violence) increases the chances of having pregnancies (too) early in life and of being sexually more adventurous. The hypothesis is that the most parsimonious prediction on the quality of the adult environment is that it will be similar to the environment experienced early in life; since life is predicted to be short and brutish when growing up in an unstable environment, it is adaptive (that is, it increases fitness) to try to reproduce as fast as possible (see Nettle, D. et al. 2010 Early-life conditions and age at first pregnancy in British women. Proc. Biol. Sci. 278, 1721–1727). Simply, if you wait too long, you might not be there anymore, so better trying to have fun earlier than never.

My research on predictive adaptive responses had also some profound implications for my view of the world. Much of our behavior is outside our conscious control and very often the results of our genetic make-up and of things that happened when we were babies (or even before when surrounded by the placenta), but easily "rationalized" a posteriori. Years later during a science retreat, I told one of my colleagues that I was surprised that so many scientists rarely apply their scientific skills, thinking, and findings to everyday problems. He did not get my observation, which kinda proved the point. On the other and darker side of the moon, I read a blog post written by a scientist a few years ago in which she was trying to motivate herself to exercise by reviewing papers that found a positive relationship between physical exercise and health, mental sharpness etc. I was both amused and appalled, but mostly appalled. It's like reviewing papers describing how water extinguishes bonfires.

Back to fish again. I saw an opportunity to test some general hypotheses on the determination early in life (we can also call it "imprinting") of adult phenotypes using trout as a model system. In this context, the main differences between humans and fish are that fish continue to grow after sexual maturity, albeit at a much slower place, and the far bigger role of environment (food, temperature) relative to genetics in determining body size in fish with respect to humans. Marble trout (mostly marble-brown hybrid) can reach 20 kg when living in lakes or reservoirs and close to 1 kg when living at higher altitudes***.

Big fish in the reservoir and big fish in the upstream reaches, quite a striking difference

As can be easily inferred by the steady increase of mean height in humans living in Western societies after WWII, environment (better food, medicine and more hygienic conditions limiting the effects of diseases, thus allowing the allocation of more energy to growth) plays a role in human growth, but current mean height of Italians is no more than 10% greater than their pre-WWII mean height. It felt like I was connecting my research on marble trout with something I had a deep interest for. Predictive adaptive responses and early determination of early phenotypes became a theme underlying - implicitly or explicitly - much of my research****. I found out that the mean body size at age 1 of trout born in the same year (a year-class) well predicted their mean size at later ages, and that mean body size of a year-class was well predicted by how many other individuals were living in the population when the year-class was born. Then, fish relatively smaller early in life were also relatively smaller later in life, there was little chance to catch-up. I published the results in two papers and years later I used much more sophisticated statistical methods to further refine my insights.

I also became interested in the role of extreme events in determining the risk of extinction of these small (typically a few hundred fish) and isolated (fish cannot move from one stream to another) marble trout populations. Alain Crivelli (a fantastic field biologist who started the marble trout project and recently retired, we had a great, stimulating, and productive collaboration over the years) and Dusan Jesensek (manager of the Tolmin Angling Association, solid leader of men, very bright, dedicated, and with great skills for field work) were also interested in the effects of flash floods. In 2004, flash floods and debris flows led to the extinction of one population of marble trout (living in a stream called Gorska) and a noticeable reduction in population density in some other populations. What if the main determinant of risk of extinction was the frequency and intensity of flash floods or debris flows, and survival, growth, and reproduction played a much smaller role?

Dusan and I in 2013 with some WWI bullets. I have been wanting for a long time to write a book on marble trout and WWI. No connection between marble trout and WWI, apart from geography (Western Slovenia was the theater of many battles fought by Italian and Austro-Hungarian armies). The inspiration, at least for the title, comes from one of my favorite books: "The Great War and Modern Memory" by Paul Fussell

Alain and I in 2012. I always find inspiration for my modeling work when spending time in the field. I tried to go to Slovenia at least once a year

The main problems were that, first, we did not have any information on water flows in the mountain streams that we were monitoring and, second, the gauges installed in the main rivers (Soca, Idrijca, Baca) could not be used to infer water flows in their tributaries, those small mountain streams in which marble trout live. Weather in Western Slovenia is highly local, and the effects of bad weather on areas only 10 km apart can be (and often are) vastly different. Although there was a bit of circular reasoning, we knew that an extreme event occurred when we observed massive mortality of fish along with visible modifications of the habitat, such as huge boulders that had moved downstream, mud everywhere, or downed trees.

This is a flash flood in Slovenia. Water rises and goes down in just a few hours

Extreme water flows and landslides move boulders and down trees. Still a magical landscape

I proceeded with a scenario analysis (what if the time of return of flash floods is 10 years? 20 years?) and I wrote a very nice paper with my colleagues that was later published in Biological Conservation (Vincenzi, S. et al. 2008 Potential factors controlling the population viability of newly introduced endangered marble trout populations. Biol. Conserv. 141, 198–210). Apart from hybridization with brown trout and flash floods or landslides, marble trout were doing well. And why should we have expected anything different? Generally, changes of state are rare (while playing soccer I was often saying that I could easily predict the injured of tomorrow by looking at the injured of yesterday), and marble trout had been living in those streams for centuries and maybe thousands of years. At that point, I expected population viability analysis for marble trout to be largely a waste of precious research time, and I started to get more and more interested in the evolution of traits following flash floods and in investigating which mechanisms were allowing fish to bounce back to normal abundance levels after they got heavily reduced in numbers by flash floods. While one population got extinct, others were recovering fast after getting hit by a flash flood.

Gorska, 2005. No fish found after the flash flood of 2004. I am at the bottom

Footnotes

* In 2004, flash floods and debris flows led to the extinction of one population of marble trout (Gorska) and a noticeable reduction in population density in other populations. Hugh suggested trying to estimate the correlated risk of extinction of multiple populations following weather extremes. In 2013, I collected a huge amount of data on daily rainfall all over Slovenia (>200 stations, records start typically in the 60s) with the goal of estimating a spatial correlation structure of rainfall extremes. Data are still there, but I never had the time to properly analyze them. It would be an amazing project for a PhD student in ecological statistics or applied math. Send me an email if you are interested.

**I still have this memory of me coding in R at the bar of the hostel in Brisbane where I was staying. Before moving to the hostel, I was staying at a cottage quite far from UQ, going there by bike was like doing the Paris-Roubaix every morning. Anyway, the R code was for a bootstrapping procedure I used for estimating survival of marble trout in the first year of life (Vincenzi, S. et al. 2007 Early survival of marble trout Salmo marmoratus: Evidence for density dependence? Ecol. Freshw. Fish 16, 116–123.). I knew very little about bootstrapping, MonteCarlo procedures and similar simulation-based methods, but it always felt intuitive to me using simulation-based approaches (also because I do not like very much to use pen and paper to find analytical solutions) for statistical inference and scenario analysis.

*** I regret not having invested more time understanding how much variation in growth in marble trout is due to variation in food. Offspring of wild trout growing in fish farm fed ad lib grow a bit faster and bigger than trout growing in the wild, but fish growing in reservoirs can become huge (up to 20 kg). Those fish living in reservoirs are marble-brown hybrid, but hybrid vigor alone cannot explain the huge variation in size. My, Dusan Jesensek, and Alain Crivelli's hypothesis to explain the variation in size between fish living in the reservoirs and fish living in mountain streams was piscivory. Although marble trout living in the wild can be cannibals (and many of them are), in reservoirs they have access to more fish and a larger variety of species (only marble trout are living in those mountain streams). They start eating fish, they become bigger, they can eat bigger fish because their mouth gets bigger and so on. However, it is difficult to understand why cannibalism cannot provide a similar positive feedback (large marble trout as far we know do not eat other marble trout that are age 4 or older) or why cannibalism does not lead the population to extinction. If they can eat one little marble, why don't they eat ten? I also found fascinating that goldfish living in small bowls become smaller than goldfish living in bigger bowls. Can "sensing the environment" explainin part why marble trout do not grow as big as they genetic potential would allow? As far as I know, there is no literature on this topic. We talked about doing isotopic analysis on trout living in the reservoirs in 2013, but there was not enough money or not enough interest. Oh well.

**** As I wrote in the Discussion of my 2014 PLoSCompBio paper: "A sizable literature on prediction of future growth exists for humans, especially in the context of early identification of pathologies [...]. An approach similar to that presented in our work for the estimation of lifetime growth trajectories given only information on growth and size during the early stages of life was proposed in [...] and [...]. In particular, in [...] Shohoji et al estimated the lifetime growth of Japanese girls using measurement up to the age of 6 years old. They first adapted to humans a parametric model previously developed to model the growth in weight of savannah baboons.". The connection of human growth was still there (I also used birds later on to investigate similar processes).

Academic life - Part 1

“Three things were more important to him: the regular improvement of his game; his rich and resuscitative returns to states of mystic calm whenever his psychic vigor flagged; and, during his seventeenth year, his first love.”

From: Trevanian: “Shibumi” (one of my top three favorite books).

I got my Master's degree in Environmental Sciences at the University of Parma (Italy, my beloved hometown) in July 2003; at the time, in Italy Bachelor's and Master's were combined in a single 5-year program. Later, following the Bologna process, the "3 + 2" (Bachelor's + Master's) became the standard also in Italian universities. For my Master's thesis, I developed a model in Visual Basic for Applications to estimate the amount of pollutants emitted by on-road transportation. The model was never used by the local city agency of environmental control that supported my thesis, but I believe it was a decent piece of software.

Following my Master's and a tremendously boring 2-month job as an environmental consultant for a local company*, Prof. De Leo (formerly my teacher in the Ecological Modeling course and then my PhD advisor, who just got back to academia full time after a one-year stint doing research for a regional institution) encouraged me to apply for the PhD program in Theoretical Ecology at the University of Parma.

The selection process included a written exam, basically a discussion of one paper selected by the committee among some one hundred foundational papers in ecology and evolutionary biology, and an oral exam. I had been an exceptional student (I won the Dean award for best student in my cohort for at least 4 out of 5 years if I remember well, maybe all five), but at the time I had zero experience in academic research, I had never been part of a lab, and maybe I had read a couple of peer-reviewed papers. Unlike many future research colleagues, I also had no long-standing, deep interest for the natural world, I did not come from an academic or professional family, and I have no recollection of ever having had the overpowering urge to find out something about the natural world. I mostly liked reading, writing, studying, doing sports, and getting the highest grades in every class. Work options were simply not so interesting and continuing to study was definitely more attractive. I was also playing semi-pro soccer and income was fine. I am confident I could have played professionally, but I was not really interested, as I very much appreciated the life of the mind.

Playing soccer in 2006

Two PhD scholarships for Ecology were offered at the Department of Environmental Sciences in 2004 and following one of the most rigged selection procedures of all time, I arrived third. There was a "beef" I was not aware of within the Department, scholarships were to be assigned to other faculties and their students, but I was too naive at the time to understand what was going on. Luckily, one of the two winners (now a local politician) gave up on his scholarship and in April 2004 I was able to start my (miserably paid, 800 euros a month) research. However, I did not have any potential research in mind for my 2.5-year PhD (they should have been 3 years, but they became 2.5 because due to funding problems the program started only in late April-early May 2004).

My PhD advisor (who has been very supportive throughout my whole academic career and still is a very good friend) suggested a couple of options for my PhD thesis, one about estimating the yield potential of clams in lagoons using species distribution models (a very profitable, largely unregulated industry in north-eastern Italy) and the other about analyzing data on trout populations living in Slovenian streams. Dr. Alain Crivelli, a biologist at Tour du Valat (France) who had been working with my PhD supervisor on the population dynamics of eels, a few years before had started collecting data on endangered populations of marble trout living in Slovenia (you can find details on the Marble Trout Project in my papers). He told my PhD advisor that he was interested in a PhD student doing some Population Viability Analyses to better understand the risk of extinction of those marble trout populations. PVAs were quite en vogue in the 90s and early 2000s, but are largely outdated now as far as I know.

Since I had no strong interest in a particular area of ecological research (i.e., I knew nothing), but I always felt accomplished when solving problems, I am mildly workaholic, and I do not like to say no, I decided to work on both topics. Marble trout soon appeared to be a much more interesting model system, and the papers on species distribution models were not included in my PhD thesis.

Marble trout eating another marble trout. They are very tasty

Since there were just 3 PhD students in my cohort, formal classes were neither required nor offered. Looking back, some classes (statistics, mathematical modeling, writing papers and analyzing literature, field- and theoretical ecology) would have been extremely beneficial for my scientific development and would have encouraged bonding among students and faculties. Soon, I started traveling taking advantage of a 50% increase in salary when abroad (field work in Slovenia, two-month visit to the University of Queensland in Brisbane in 2005, two-month visit to the Hopkins Marine Station in Pacific Grove, CA), and my research was largely self-directed.

Although I won't go into personal details, the start of my PhD coincided with the end of a relationship (it was clear that the relationship was over when I spent way too much time betting on the 2004 Olympics. However, I won money with Paolo Bettini in cycling and Karam Gaber in wrestling. I did not know about Gaber, but while in Sharm El Sheikh I watched one of his workouts on the local tv and I immediately understood he could not lose. Easy money, one of the most dominant and spectacular wins in the history of Olympic wrestling).

I tried to use standard software for PVAs, but they were clearly not meant to be used for fish. After attending a class on R in 2004 or 2005, I started using R and Matlab (I do not remember why and how I started to use Matlab, but I quickly became reasonably proficient) to estimate vital rates, developing models of population dynamics for marble trout, and developing spatial distribution models for clams. I worked long hours and spent a big fraction of my waking hours at the office (a very small space shared by 4 people, looking back it was a terrible set-up and it was surprising I was able to work),  but I never felt particularly stressed, overall work was enjoyable, I was publishing papers (5 at the end of my 2.5 years), and very proudly hanging their first page on the wall next to my desk. Quite surprisingly, I found pretty easy to publish papers at the time, despite knowing very little (it is relatively much more difficult to publish now despite me knowing much more - my knowledge climbed from 1 to 3, but on a log-10 scale - although my target journals are now way more prestigious).

However, I had very little idea of the "big questions" behind my research, little or no knowledge of the trajectory of the field (and which field? Basic ecology? Population dynamics? Animal conservation? Species distribution? Ecological statistics?), I was barely thinking about my future in academia, and I was living in almost complete scientific isolation, in part due to location as my desk space was not in the main building of the Department of Environmental Sciences. I just liked finding solutions to immediate problems (how can I estimate this vital rate?) and, being by nature very competitive, I loved publishing papers with my name in front. All mistakes for which, I later recognized, I paid a hefty price**.

* I was advising a ham-producing company for their ISO9000 and HACCP certifications. They had blood-dripping sausages in one of the refrigerators and when I looked at them, the owner's son said: "These are not here". Noped out real quick. Pay was meager, too.

** As Andrew Grove wrote in "Only the paranoid survive":  The sad news is, nobody owes you a career. Your career is literally your business. You own it as a sole proprietor. You have one employee: yourself. You are in competition with millions of similar businesses: millions of other employees all over the world. You need to accept ownership of your career, your skills and the timing of your moves. -- Then,  being less scientifically isolated would have allowed me to better understand the big questions in ecology and evolutionary biology...maybe. As I will discuss later, I was not thinking about moving abroad (I enjoyed living in my hometown, I was earning good money playing soccer, I had friends and pets and family) and to get an academic position in Italy two conditions were (and shamefully still are) necessary, the first way more important than the second: 1) being sponsored by a powerful faculty, usually your PhD advisor, and 2) publishing some papers somewhere. I simply did not see any incentives to have a big, international research vision. When I started my PhD,  word on the grapevine said that there would have been a large number of faculties retiring in 2013; after that, it would have been necessary to have a big wave of recruitment of new assistant professors to fill the empty spots. They simply downsized the personnel. Never plan further than one year in the future.

 

Are these interesting results? If yes, worthy of publication?

Memento mori

and remember that all monitoring programs end at some point

---

I don't remember exactly when I had the idea I describe in the following, but I clearly remember I had a conversation about it with Ryan Chisholm (now at the National University of Singapore) while attending the SIAM meeting in San Diego in the summer of 2013. Marc Mangel was invited, but he could not go. The organizer of the special session on spatial ecology asked Marc if he had a postdoc to recommend as a substitute for him and he recommended me. Semi-surreal experience with only the five people scheduled to give talks present in the room and a couple of them were busy checking emails. I had a very good time, though.

As a side note, I recently had a look at the slides of the talk I gave at SIAM 2013, and there are at least a couple of ideas that could become papers, one on density dependent growth at different spatial scales (which I had partially developed Vincenzi, S. et al. 2010 Detection of density-dependent growth at two spatial scales in marble trout (Salmo marmoratus) populations. Ecol. Freshw. Fish 19, 338–347) and one on rapid genetic differentiation in fish populations living in fragmented habitats. Ryan also talked about his recent (at the time) trip to Cuba and he convinced me - it was not too hard - to visit the island. I went to Cuba for the first time in November 2013, I fell in love with La Habana, and I have been there other 2 times, the last one (October-November 2016) also presenting my research at the Centro de Investigaciones Marinas of Universidad de La Habana.

In La Habana at the beginning of November 2016, humidity at 150% and not too happy about it.

The main idea is this: most monitoring programs start with no end in sight (funding is largely determining the duration of the program) and often with different goals that require vastly different amount of data, for instance estimating average survival or growth rates may require 3 to 5 years of data for salmonids (my model system), while estimating the effects on population dynamics or life-history traits of extreme events such as floods, fires, drought, hurricanes, tautologically require at least the occurrence of one extreme event across multiple populations or multiple rare events in one population (otherwise it is not possible to test hypotheses sensu Platt, J. R. 1964 Strong inferences. Science 146, 347–353.)

How can we define an event as extreme? As I wrote in a 2014 paper (Vincenzi, S. 2014 Extinction risk and eco-evolutionary dynamics in a variable environment with increasing frequency of extreme events. J. R. Soc. Interface 11, 20140441), "[...] extreme events may be defined in terms of extreme values of a continuous variable on the basis of the available climate record (e.g. temperature, precipitation levels) or in the form of a discrete (point) perturbation, such as a hurricane or a heavy storm. This latter category also includes environmental extremes such as unusually big fires, aseasonal floods or rain-on-snow events.". Extremeness includes and goes beyond rarity, and we can assume extreme events to be defined as extreme have recurrence intervals that are longer than the generation time of the species we are investigating.

It follows that if we are interested in the effects of rare events, the minimum duration of the monitoring program is dictated by the expected recurrence interval of the extreme event, but if in absence of extreme events (that is, extreme events are not expected for that species/population) we want to estimate "reliable" average survival and growth rates (which along with recruitment represent the main axes of variation in species' vitals rates and are essential for the development of any self-respecting model of population dynamics) is unclear for how long we need to go on with the monitoring program. I am not the first one to think about this problem in conservation biology, see for instance Chapter 23 of "Design and Analysis of Long-term Ecological Monitoring Studies" (many editors): "Choosing among long-term ecological monitoring programs and knowing when to stop" by Hugh P. Possingham, Richard A. Fuller, and Liana N. Joseph, or Gerber, L. R., M. Beger, M. A. McCarthy, et al. 2005. A theory for optimal monitoring of marine reserves. Ecol Lett 8, 829–837.

In practical terms, I was interested in estimating how stable were the estimates of average, maximum, and minimum (for a sampling interval) survival rates, and somatic growth rates (i.e., body growth) over time in populations of marble, brown, and rainbow trout living in Western Slovenian streams. Briefly, two populations of marble, one of brown, and one of rainbow trout were sampled bi-annually (June and September) between 2004 and 2014 (monitoring is still ongoing, but for various reasons in this study I only included data up to 2014). Those populations were not affected by extreme events since the start of the monitoring program. It is tricky to assess what's "stable" since we estimate rates or probabilities, thus we inevitably have uncertainty in the estimation, but let's put aside this thorny problem for a moment with the assumption we can at least qualitatively see or define "by look" what's stable or not.

Without getting deep into the gory details (and there are many), what I did was to fit capture-recapture models (fish were tagged when bigger than 115 mm) of survival and growth (using von Bertalanffy's growth models) with data available after 3, 4, 5 (and so on) years since the start of the monitoring program. For growth, I used two von Bertalanffy's models, one with random effects (see Vincenzi, S. et al. 2014 Determining individual variation in growth and its implication for life-history and population processes using the Empirical Bayes method. PLoS Comput. Biol. 10, e1003828) and one "classic", that is without individual random effects (I simply used the nls function in R, thus each data point is - wrongly - assumed to be coming from different individuals). For survival, I fitted models Phi(~1) (constant survival) and Phi(~time) (survival varying for each sampling occasion) with different models of probability of capture. With Phi(~time) models, I had the goal to estimate the maximum and minimum survival probabilities over sampling intervals (apart from statistical noise or use of different models of probability of capture, over time maximum survival probability for a sampling interval cannot become smaller and minimum survival probability cannot become bigger).

These are the results for growth, I took asymptotic length (of the average fish) as estimated response variable (estimates with classic nonlinear regression (dot symbol) and estimates with the model with random effects (triangle)), x-axis is the last year of sampling (for, say, 2006, I only keep the data for up to 2006, for 2010 up to 2010, and every time I re-fit models with the corresponding datasets). With the model with random effects, it takes 2 to 4 years to obtain a stable estimate. With the classic method, we need more years, and in some cases, the model gives wrong estimates, since outliers strongly pull the average growth trajectories in their direction (as I also discuss in the PLoSCompBio paper cited above and in another one currently under review). Vertical lines are standard errors of the estimate.

Estimates of asymptotic length using the classic method (dots) and the random-effects model (triangles). The size of symbols is proportional to sample size. LIdri_MT = marble trout in Lower Idrijca, UIdri_MT = marble trout in Upper Idrijca, LIdri_RT = rainbow trout in Lower Idrijca, UVol_BT = brown trout in Upper Volaja.

As I wrote in another paper currently under review: "The vBGF parameters can seldom be interpreted separately, especially when only a few older fish are measured; it follows that the analysis of the whole growth trajectories is necessary for understanding growth variation among individuals and cohorts". The second plot for growth shows the average growth trajectories using the random-effect models using data up to 2006 or up to 2014. Within populations and for all intents and purposes, the average growth trajectories are basically the same.

Average growth trajectories for the 4 salmonid populations estimated with data up to 2006 or up to 2014 with the random-effects model. LIdri_MT = marble trout in Lower Idrijca, UIdri_MT = marble trout in Upper Idrijca, LIdri_RT = rainbow trout in Lower Idrijca, UVol_BT = brown trout in Upper Volaja.

The last plot is about survival. x-axis is again the last year of simulated sampling, dot is the estimate of average survival, up triangle is the maximum estimated survival for a sampling interval, down triangle is the minimum estimated survival for a sampling interval. The estimate of average survival is very stable after just 2 or 3 years (4 to 6 sampling occasions), except for rainbow trout since the small sample size makes the estimate vary quite a bit, as expected.

Dots are average survival probabilities (Phi(~1)), up and down triangles are maximum and minimum survival for sampling occasion (max and min estimates of Phi(~time)). LIdri_MT = marble trout in Lower Idrijca, UIdri_MT = marble trout in Upper Idrijca, LIdri_RT = rainbow trout in Lower Idrijca, UVol_BT = brown trout in Upper Volaja.

The main point and message to take home from this work are that in salmonid populations 2 or 3 years of data may be enough to get "stable" estimates of survival and average growth trajectories, the latter only if using a random-effects model of growth, but > 5 years are needed to estimate maximum and minimum survival probabilities in absence of extreme events (and potentially longer for minimum survival probabilities, as it can be seen in the panels for LIdri_MT and UIdri_MT).

Interesting, surprising, useful, worthy of publication in a conservation journal such as Biological Conservation, Animal Conservation, Conservation Biology?

 

Posts on my academic life since 2004

Since my formal academic career is likely to end in a few months, I had this idea while on a car trip (San Francisco Airport to Santa Cruz, if you are interested in geographic details) of writing about my academic and research experience since 2004, the year I started my PhD in Theoretical Ecology at the University of Parma, Italy. I believe my experience has been original and interesting, and despite not having been successful in securing a permanent position (or maybe because of it), I am confident I can share valuable insights for young researchers or people outside of academia who want to understand how things work, or might work better. I also have some beautiful pictures to share.

Some brief biographical details. I am 37 years old, I do research in mathematical biology, ecology, and evolutionary biology, I won international grants and awards (in particular a Marie Curie International Outgoing Fellowship, which I started in January 2013 and finished in December 2015), I recently toured South America giving talks on my research in mathematical biology, and I have published so far 30 peer-reviewed papers (43 in total, 3 others in which I am the first author are currently under review, other 2 I may or may not write), and almost one year ago (April 25th 2016, a date that is easy to remember since it is the day in Italy in which the defeat of nazi-fascism is celebrated and remembered, la festa della Liberazione) I received a self-sponsored US Green Card for extraordinary abilities in the Sciences. I live in Santa Cruz, CA, and I have been residing here since June 2010, with a six-month interruption between December 2010 and June 2011. I have loved very much studying, learning how to think and communicate, carrying out my research, giving talks, teaching, traveling the world.