Some reflections on my science

Since I had to change the links to my publications due to some obscure passage of pdfs from one folder to another, I had a chance to have a look at all the papers I published so far. 40 total, 28 as first author, 4 under review (2 as first author, other 2 under review). Surprisingly (or not, upon further reflection) I barely remember the content of most of my papers and I have little idea on how they were originally thought, what was the development, what was the contribution of co-authors, why I used certain methods and not others. I saw big tables I did not remember I had prepared. I saw a Figure in which fish are one year older than what they should be (I also thought I sent the correct Figure during the revision process, apparently not). I read long Introductions and longer Discussions (I write a lot, no doubt). I remember long struggles to get papers accepted even if I currently do not remember the major contentious points.

Just to be clear, I do not have any memory disorder. However, I have published in many different areas, in part because I prefer to zig-zag than follow a straight-ish line, in part because I have been supported by soft money throughout all my career and I haven't been too rigid in my research/grant choices. I also tried to use novel methods (for me or in general), since I like to challenge myself and expand my research tools. I tend to go very deep and very fast in my research and this - like cramming for a test - is not conducive to long-term retention of information.

This "discovery" made me think about my research trajectory, what kind of tools and skills I have acquired, and whether production of science is like the production of eggs in fish: you give your contribution and you let it find its way.

Akin's Laws of Spacecraft Design

A great read with wider application http://spacecraft.ssl.umd.edu/akins_laws.html

Some of my favorites:

1. Engineering is done with numbers. Analysis without numbers is only an opinion.

4. Your best design efforts will inevitably wind up being useless in the final design. Learn to live with the disappointment.

6. (Mar's Law) Everything is linear if plotted log-log with a fat magic marker.

9. Not having all the information you need is never a satisfactory excuse for not starting the analysis.

16. The previous people who did a similar analysis did not have a direct pipeline to the wisdom of the ages. There is therefore no reason to believe their analysis over yours. There is especially no reason to present their analysis as yours.

17. The fact that an analysis appears in print has no relationship to the likelihood of its being correct.

19. The odds are greatly against you being immensely smarter than everyone else in the field. If your analysis says your terminal velocity is twice the speed of light, you may have invented warp drive, but the chances are a lot better that you've screwed up.

21. (Larrabee's Law) Half of everything you hear in a classroom is crap. Education is figuring out which half is which.

24. It's called a "Work Breakdown Structure" because the Work remaining will grow until you have a Breakdown, unless you enforce some Structure on it.

29. (von Tiesenhausen's Law of Program Management) To get an accurate estimate of final program requirements, multiply the initial time estimates by pi, and slide the decimal point on the cost estimates one place to the right.

32. (Atkin's Law of Demonstrations) When the hardware is working perfectly, the really important visitors don't show up.

34. (Roosevelt's Law of Task Planning) Do what you can, where you are, with what you have.

37. (Henshaw's Law) One key to success in a mission is establishing clear lines of blame.

41. Space is a completely unforgiving environment. If you screw up the engineering, somebody dies (and there's no partial credit because most of the analysis was right...)

 

Links to R-related stuff

Git and GitHub http://r-pkgs.had.co.nz/git.html

Predicting Baseball Game Attendance with R https://r-dir.com/blog/2015/02/predicting-baseball-game-attendance-with-r.html

Data wrangling process as seen by Hadley Wickham http://blog.ouseful.info/2015/02/11/code-as-magic-and-the-vernacular-of-data-wrangling-verbs/

Bayesian Rugby http://springcoil.github.io/Bayesian_Model.html

Practical Data Science by Sebastian Raschka http://www.slideshare.net/SebastianRaschka/nextgen-talk-022015

10 things statistics taught us about big data analysis http://www.kdnuggets.com/2015/02/10-things-statistics-big-data-analysis.html

Writing Scientific Papers Using Markdown https://danieljhocking.wordpress.com/2014/12/09/writing-scientific-papers-using-markdown/

Data Processing with dplyr & tidyr http://rpubs.com/bradleyboehmke/data_wrangling

Hierarchical Bayesian Survival Analysis for CVD Risk Prediction in Diabetic Individuals http://becs.aalto.fi/en/research/bayes/diabcvd/

RStudio & git/github Demonstration (Video) https://vimeo.com/119403806

Using generalized linear models to compare group means in R http://stackoverflow.com/questions/28614798/using-generalized-linear-models-to-compare-group-means-in-r

R graph catalog http://shinyapps.stat.ubc.ca/r-graph-catalog/

 

Paper submitted

With Scott Hatch, Thomas Merkling, Sasha Kitaysky

Title: Food supplementation early in life delays viability selection in a long‑lived animal

Abstract

Supplementation of food to wild animals is extensively applied as a conservation tool to increase local production of young. However, the effects of food supplementation on the subsequent recruitment as breeders of long-lived migratory animals into natal populations and their lifetime reproductive success are largely unknown. We examine how experimental food supplementation affects (a) recruitment as breeders of kittiwakes Rissa tridactyla born in a colony on Middleton Island (Alaska) between 1996 and 2006 (n = 1629) that bred in the same colony through 2013 (n = 235); and (b) breeding success of individuals that have completed their life cycle at the colony (n = 56). Birds were raised in nests that were either supplemented with food (Fed) or unsupplemented (Unfed). Fledging success was higher in Fed compared to Unfed nests. After accounting for hatching rank, growth, and oceanic conditions at fledging, Fed fledglings had a lower probability of recruiting as breeders in the Middleton colony than Unfed birds, but the per-nest contribution of breeders was still significantly higher for Fed nests. Lifetime reproductive success of a subset of breeders that completed their life cycle was not affected by the food supplementation during development. Our results cast light on the interaction between intrinsic quality and early food conditions in determining fitness of long-lived animals.

Keywords: Individual quality; supplemental feeding; long-lived animals; viability selection.

 

Paper submitted to Axios Review

A few months ago, my colleagues and I submitted to Fish and Fisheries a manuscript on the trade-offs between complexity and accuracy in random-effects models of body growth.

The paper was rejected mostly on the basis of lack of fit (i.e. the topic was only marginally interesting for the journal's readership). One Reviewer found the paper interesting and valuable, and recommended the submission of the manuscript to a more general journal, such as Ecology or Oikos. The other Reviewer commented on some unclear technical aspects of the work (the review was quite detailed and the recommendations/suggestions/critiques were valuable, thanks anonymous Reviewer).

I believe the paper should be of interest for a large audience of biologists, ecologists, computational scientists/statisticians. The main motivation of the paper is quite simple and very general: "We often face trade-offs between model complexity, biological interpretability of parameters, and goodness of fit." Then, with reference to models of growth: "Depending on formulation, parameters of some growth models may or may not be biologically interpretable. For instance the parameters of the widely used von Bertalanffy growth function (von Bertalanffy 1957) to model growth of fish may be considered either curve fitting parameters with no biological interpretation (i.e. providing phenomenological description of growth) or parameters that describe how anabolic and catabolic processes govern the growth of the organism (i.e. mechanistic description); see Mangel (2006). The classic von Bertalanffy growth function has 3 parameters: asymptotic size, growth coefficient, and theoretical age at which size is equal to 0. In the original mechanistic formulation of von Bertalanffy, asymptotic size results from the relationship between environmental conditions and behavioral traits and the growth coefficient is closely related to metabolic rates and behavioral traits (i.e. the same physiological processes affects both growth and asymptotic size). However, in the literature asymptotic size and growth rate are commonly treated as independent parameters with no connection to physiological functions, thus offering just a phenomenological description of growth."

However, I understand Editors may not fully grasp the relevance of the paper for their journal. For instance, the manuscript was previously submitted to another journal, but the Editor wrote: "I feel that the work is too specialised, as relatively few researcher work on growth curves". I might disagree on the claim that few researchers work on growth curves. I am sure that lots of scientists use growth models in their work, but I might agree on the number of people working on the development of growth models or methods for the estimation of growth model parameters (it is also quite hard).

My colleagues and I (my idea, my colleagues agreed) decided to submit the manuscript to Axios Review, a new service that should help authors publish their papers in higher profile journals. This is how it works: "Axios Review solves this problem by putting papers through rigorous external peer review and then referring them to the appropriate journal. When a journal asks the authors to revise and submit, the journal has effectively said that: i) the paper is within their scope, ii) that it is not fatally flawed, and iii) that it could be published in their journal. The Axios Review process effectively eliminates rejections on the grounds of novelty and significantly reduces the chances of rejection on quality. It’s similar to getting a ‘reject, encourage resubmission’ decision from the journal itself; for comparison, about 75% of resubmissions to top tier evolution journals get accepted. Authors submitting to Axios Review can have the reviewers comment on the suitability of their paper for any journal they choose, allowing them to aim for a high profile journal without the effort of formally submitting."

I submitted the manuscript to Axios Review a couple of days ago (target journals following an order that may or may not be the one I chose: Oikos, Ecology, Journal of Theoretical Biology, Ecological Applications). So far, communication with the Editorial staff has been excellent.

I did not upload the manuscript on arxiv or bioRxiv (I don't know where the manuscript will end up and thus which policy related to uploading of pre-print should I follow), please send an email if you'd like to read a pre-print.

 

Marie Curie Fellowship Periodic Report

At the end of each of the 3 years of the Fellowship, the European Research Council requests to submit a report detailing the various activities of the projects, main results, future plans, problems etc. Here below I show my Periodic Report (end of the second year) for the project RAPIDEVO, it may help some of the fellows who are struggling to write their report (clearly in addition to people closely following my research) - guidelines are in italics

1. PUBLISHABLE SUMMARY

This section normally should not exceed 2 pages.
It shall be of suitable quality to enable direct publication by the REA or the Commission. You may extract this wholly or partially from the website of the project, if suitable, but please ensure that this is set out and formatted so that it can be printed as a stand-alone paper document.
Please include:
• a summary description of the project objectives,
• a description of the work performed since the beginning of the project,
• a description of the main results achieved so far,
• the expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far).
You should update this publishable summary at the end of each reporting period.
Please include also, as appropriate, diagrams or photographs illustrating and promoting the work of the project, the project logo and relevant contact details.
Please ensure that all publishable reports submitted to the REA for publication are of a suitable quality to permit direct publication without any additional editing. By submitting the publishable reports to the REA, you are also certifying that they include no confidential material.
The address of the project public website should also be indicated, if applicable.
The internet address should be active.

• Summary description of the project objectives

Climate change poses a serious threat to species persistence, as it will force species to experience natural selection in new directions and at new and greater intensities. The application of models of rapid evolutionary responses to climate change integrating genetics, demography and climate prediction is essential to understand the future genetic composition and shaping of life histories of species in a climate change scenario. In particular, theoretical and empirical investigations on the complex eco-evolutionary dynamics associated with adaptations and responses to extreme events – which are predicted to increase in frequency and intensity with climate change - are urgent and essential, both to advance theory and to inform management of species.
I use marble trout (Salmo marmoratus) as a model system in the Marie Curie IOF-project RAPIDEVO, which has the main goal of understanding and predicting the genetic and demographic consequences of extreme events on natural populations. Marble trout live in Western Slovenian streams that are frequently affected by extreme flood events causing massive mortalities. However, despite very low population sizes (from 30 to 1000 individuals), little genetic variability and no immigration, these populations have surprisingly persisted for centuries. Whether marble trout will be able to cope with the predicted increase in the frequency and intensity of flood events is unclear. This model system provides a unique opportunity to investigate the effects of extreme events across populations of the same species, due to the long-term monitoring and sampling of multiple populations, the collection of an exceptional dataset of demographic and genetic information at the individual level, the occurrence of multiple flood events in the last 15 years, integration of field and experimental data, and my deep knowledge of the system.

• Description of the work performed since the beginning of the project

In 2013-2014, I carried out research on genetic and demographic aspects of marble trout along with purely theoretical studies that provided testable predictions on genetic and life-history adaptations, and demographic responses to extreme events. In particular, during the first two years of the project RAPIDEVO, I carried out (not necessarily in chronological order) (i) SNP discovery in three marble trout populations using Next Generation Sequencing (NGS)-generated data (8 individuals per population have been sequenced so far), (ii) analysis of genetic differentiation among populations and inbreeding, (iii) estimation of vital traits such as survival, movement and growth in all seven remnant marble trout populations (plus two newly created populations), (iv) prediction of demographic and genetic dynamics in an environment characterized by the occurrence of extreme events using one of the marble trout populations (Zakojska) as model population, (v) theoretical work on adaptive processes in response to extreme events.
I provide updates on my research related to the RAPIDEVO project at simonevincenzi.com/blog/ (tag: Marie Curie)

• Description of the main results achieved so far

I have selected population-specific panels of SNPs (from 70 to 100) that have been used to develop assays that provide multi-locus genotypes for three marble trout populations (Lipovscek, Trebuscica, Zadlascica). In the first months of 2015, assays will be developed for the populations of Idrijca, Svenica and Studenc. In addition, I have developed a genetic marker for determining sex in marble trout. Morphologically, male and female marble trout are indistinguishable, thus the development of the genetic marker allowed us to determine the sex of individuals. Knowing the sex of the fish is important for pedigree reconstruction as well to test for differences in growth and survival between males and females.
I have analyzed genetic differentiation at the SNP loci at local geographic scales using population samples of marble trout from the seven remnant pure marble trout populations. I found a very high (and almost unique for such a narrowly endemic species) genetic differentiation between populations (pairwise FST ranging from 0.4 to 0.84) and generally high inbreeding (population-specific coefficient of inbreeding ranging from 0.55 to 0.85) (the work is still unpublished).
Using a combination of empirical research, mathematical modeling and computer simulations, I have found that for marble trout - given a growth-mortality trade-off - extreme events (i.e. floods inducing massive mortalities) tend to select for phenotypes with faster life histories (i.e. earlier reproduction, faster growth and higher mortality risk due to the growth-mortality trade-off), as predicted by life-history theory (Vincenzi et al. 2014a).
I also obtained exceptional insights on the determinants of individual variation in growth and its implication for life-history and population processes (Vincenzi et al. 2014b). In this work, I developed a novel statistical approach using the empirical Bayes method to estimate and separate the contribution of intrinsic and environmental factors to lifetime growth trajectories of marble trout, and generate hypotheses concerning the life-history strategies of organisms. I showed that using the novel method I developed, the growth model predicts the future growth of organisms with substantially greater accuracy than using historical information on growth at the population level, and help identify year-class effects, probably associated with climatic vagaries, as the most important environmental determinant of growth in marble trout.
In addition to empirical research, I have also carried out theoretical studies with the goal of identifying key pieces of empirical information that are required for advancing understanding on the demographic and genetic consequences of extreme events on natural populations. In particular, I used numerical simulations to understand and predict the consequences of directional trend and increased variability of a climate variable, increased probability of occurrence of point extreme events (e.g. floods), selection pressure and effect size of mutations on a quantitative trait determining individual fitness, as well as the their effects on the population and genetic dynamics of a population of moderate size (Vincenzi 2014). I found that the interaction among climate trend, variability and probability of point extremes had a minor effect on risk of extinction, time to extinction and distribution of the trait after accounting for their independent effects. The survival chances of a population strongly decreased with increasing strength of selection, as well as with increasing climate trend and variability. Climate trend and strength of selection largely determined the shift of the mean phenotype in the population.

References

Vincenzi, S. (2014). Extinction risk and eco-evolutionary dynamics in a variable environment with increasing frequency of extreme events. Journal of the Royal Society, Interface / the Royal Society, 11, 20140441.
Vincenzi, S., Crivelli, A.J., Satterthwaite, W.H. & Mangel, M. (2014a). Eco-evolutionary dynamics induced by massive mortality events. Journal of Fish Biology, 85, 8–30.
Vincenzi, S., Mangel, M., Crivelli, A.J., Munch, S. & Skaug, H.J. (2014b). Determining individual variation in growth and its implication for life-history and population processes using the Empirical Bayes method. PLoS Computational Biology, 10, e1003828.

• Expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far)

So far, only a few studies have explored the adaptive mechanisms helping population recovery after massive mortality events as well as the role of extreme events (i.e. floods, fires, diseases) in shaping the genetic traits and life histories of affected populations. Given the predicted intensification of weather extremes with climate change, theoretical and empirical investigations on the complex eco-evolutionary dynamics associated with adaptations and responses to extreme events are urgent and essential, both to advance theory and to inform management of species. These studies require a combination of genomic and demographic data, statistical and eco-evolutionary modeling, and characterization of weather extremes.
The expected final result of the project is to provide - to my knowledge for the first time - an overarching study of the historical and future consequences of extreme events on population and genetic dynamics of an animal species. As explained above, this is a crucial – albeit understudied - aspect of the scientific research on the consequences of climate change on natural populations. In addition, the basic research questions motivating RAPIDEVO have led to the development of innovative statistical and mathematical methods, such as the application of the Empirical Bayes method to the estimation of individual variation in growth and its consequences for life-history and population processes in fish populations. I expect my model to be widely used by other biologists who are trying to better estimate within-population individual variability in growth and vital traits and understand their consequences for population and genetic dynamics.

2.PROJECT OBJECTIVES FOR THE PERIOD

Please provide an overview of the project objectives for the reporting period in question, as included in Annex I of the Grant Agreement. These objectives are required so that this report is a stand-alone document.
Please include a summary of the recommendations from the previous reviews (if any) and indicate how these have been taken into account.
The interdisciplinary approach proposed in the project RAPIDEVO combines (1) molecular genetics, (2) demographic analysis and characterization of temporal and spatial patterns of flood events, and (3) life-history, demographic and eco-evolutionary modeling. Goals for the reporting period in question (first 2 years of the project, i.e. years 2014 and 2015) are reported below.
1. Genetic markers.
1A. Investigate adaptive evolution in space (adaptive divergence) in marble trout populations living in Slovenian streams using molecular genetic markers
1B. Test adaptive evolution in marble trout, particularly after the occurrence of severe flood events
1C. Parentage analysis using molecular data
2A. Analysis of population structure, population dynamics, traits and compensatory responses of marble trout
2B. Analysis of common-garden experiment
2C. Analysis of flood events
3A. Development of a model of marble trout population dynamics living in Slovenian streams, with only a demographic module

There were no recommendations from previous reviews.

3.WORK PROGRESS AND ACHIEVEMENTS DURING THE PERIOD

Please provide a concise overview of the progress of the work in line with the structure of Annex I of the Grant Agreement - except project management, which will be reported in section 6.
• A summary of progress towards objectives and details for each task;
• A summary of the progress of the researcher training activities/transfer of knowledge activities/integration activities (as it applies for the MC action);
• Highlight clearly significant results;
• If applicable, explain the reasons for deviations from Annex I and their impact on other tasks as well as on available resources and planning;
• If applicable, explain the reasons for failing to achieve critical objectives and/or not being on schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be coherent with the declaration by the scientist in charge) ;
• A statement on the use of resources, in particular highlighting and explaining deviations between actual and planned researcher-months in Annex 1 (Description of Work)
• If applicable, propose corrective actions.

• A summary of progress towards objectives and details for each task

1. Genetic markers: I have selected population-specific panels of SNPs (from 70 to 100) that have been used to develop assays that provide multi-locus genotypes for three marble trout populations (Lipovscek, Trebuscica, Zadlascica). In the first months of 2015, assays will be developed for the populations of Idrijca, Svenica and Studenc. In addition, I have developed a genetic marker for determining sex in marble trout. Morphologically, male and female marble trout are indistinguishable, thus the development of the genetic marker allowed us to determine the sex of the individual analyzed. Knowing the sex of the fish is important for pedigree reconstruction, and test for differences in growth and survival between males and females. The work is still unpublished.
1A. Investigate adaptive evolution in space (adaptive divergence) in marble trout populations living in Slovenian streams using molecular genetic markers: I have analyzed genetic differentiation at the SNP loci at local geographic scales using population samples of marble trout from the seven pure marble trout populations (on average, 8 individuals per population have been sequenced using the Illumina MiSeq sequencer). I have calculated pairwise genetic distances (FST), inbreeding, and carried out model-based clustering (i.e., structure software, PCA, multidimensional scaling). I also used state-of-the-art software, such as BayeScan, DetSel, Arlequin 3.5, Lositan, to identify SNPs characterized by higher or lower levels of population divergence than strictly neutral loci, suggestive of diversifying or balancing selection, respectively. The work is still unpublished.
1B. Test adaptive evolution in marble trout, particularly after the occurrence of severe flood events: I am testing for rapid adaptive shifts in genetic composition and diversity within marble trout populations (and evaluate the possible existence of bottlenecks/population declines) by estimating genetic changes in samples collected during the study period, in particular in the population of Lipovscek, namely: prior to and right after the 2007 floods, and prior to and right after the 2009 floods. The work is completed, but still unpublished.
1C. Parentage analysis using molecular data: I used (and still using) the SNP multilocus genotypes for parentage inference to identify maternity, paternity and other relationships and construct multi-generation pedigrees. So far, I have reconstructed multi-generation pedigrees in Lipovscek and I am in the process of reconstructing pedigrees in Trebuscica and Zadlascica. This will allow us to: (i) infer mating patterns and average family size; (ii) infer heritability of life-history traits; (iii) study the association between fitness traits (survival and growth, in particular) and particular genotypes at the individual and family level.
2. Demographic analysis and statistical characterization of temporal and spatial patterns of flood events
2A. Analysis of population structure, population dynamics, traits and compensatory responses of marble trout: I used the extensive data set provided by the field monitoring program to investigate demographic trends, age- and size-distribution of the seven marble trout populations and to estimate survival probabilities, body growth rates and compensatory processes for each population. In addition, I am in the process of investigating morphological differences in individuals living in different populations. I presented results on body growth dynamics of marble trout in a talk I gave at the International Statistical Ecology Conference in Montpellier, France (July 2014) and preliminary results on variation in survival probabilities among marble trout populations in a talk I was invited to give at UC Berkeley in October 2014.
2B. Analysis of common-garden experiment: So far, I prepared the data set provided by the common garden experiment to test for differences among and within the marble trout populations in age and size at maturity, timing of spawning, occurrence of semelparity or iteroparity, size-dependent fecundities.
2C. Analysis of flood events: I acquired from the Slovenian Agency for Environment the available rainfall data for more than 200 meteorological stations, starting from the 1960s.
3. Life-history, demographic and eco-evolutionary modelling
3A. Development of a model of marble trout population dynamics living in Slovenian streams, with only a demographic module: I developed and parameterized a model, including a genetic module, to predict the evolution of life histories and the consequences of evolutionary processes on population persistence. The genetic module was parameterized without specific reference to the insights provided by the research activities 1A,B,C and 2A,B,C.

• A summary of the progress of the researcher training activities/transfer of knowledge activities/integration activities (as it applies for the MC action)

At UCSC, under the expert guidance of Prof. Marc Mangel and Dr. Carlos Garza I complemented my expertise in basic ecology and population dynamics through training with new genetic, data analysis and statistical methods.
In particular, in 2013-2014 I have been trained for the genetic part in genomics, Next-Generation Sequencing and following downstream analyses (SNP identification and validation, methods to estimate genetic structure among populations and inbreeding within populations, pedigree reconstruction using SNPs, etc.), and classical genetics. For the data analysis and statistical methods part of my training program, I have been mostly trained in Bayesian methods for parameter estimation and model selection.
More in detail, for the genetic part I have received hands-on training on the use to Next-Generation sequencer Illumina MiSeq, on software for SNPs identification (mostly the Stacks suite, which has been chosen as the default option for the analysis of sequencing data at the Southwest Fisheries Science Center in Santa Cruz), software for data manipulation, summary statistics, and estimation of parameters in genetic studies (e.g. Arlequin 3.5, Genepop, plink), outlier locus detection (e.g. BayeScan, DetSel 1.0, Lositan), genetic structure (e.g. structure, GeneLand, plink).
For the data analysis and statistical methods part, I have been trained in Bayesian methods (in particular the Empirical Bayes method) and the use of software ADMB-RE by Prof. Hans Skaug of the University of Bergen, Norway, who visited the Southwest Fisheries Science Center in Santa Cruz during a sabbatical year (year 2013). I received additional training in the use of ADMB-RE at a two-day workshop organized by Hans Skaug and other statisticians during the International Statistical Ecology Conference in Montpellier (July 2014).
In 2013-2014, I took part to the weekly meetings of the Mathematical Biology Research Group led by Marc Mangel at UCSC and of the Molecular Ecology and Genetic Analysis team led by Carlos Garza at the Southwest Fisheries Science Center (Santa Cruz, CA). In 2014, I took part to the monthly joint meetings of the molecular ecology and genetic analysis team of the Southwest Fisheries Science Center and the Paleogenomics group led by Beth Shapiro and Ed Green at USCS.
In 2013, I took part to the weekly Applied Mathematics Club meetings led by Steve Munch at the Southwest Fisheries Science Center.
In October-December 2014 I mentored PhD student Camille Musseau (University of Toulouse, France), who is studying the trophic niche of marble trout in her PhD studies.

• Highlight clearly significant results

I have found that for marble trout - given a growth-mortality trade-off - extreme events (i.e. floods inducing massive mortalities) tend to select for phenotypes with faster life histories (i.e. earlier reproduction, faster growth and higher mortality risk due to the growth-mortality trade-off), as predicted by life-history theory (Vincenzi et al. 2014a). However, the evolution of faster life histories does not increase the resilience of marble trout populations to massive mortality events with respect to a scenario in which life histories are fixed and cannot evolve (Vincenzi et al. 2014a). This happens because the relaxation of density dependence after massive mortality events increases growth and decreases early mortality and possibly age at sexual maturity in both scenarios. Then, given the relatively high egg production of marble trout at low densities with respect to the number of fish needed to reach a safe population size, a few females may be sufficient in either scenario for a fast recovery to a safe population size in a few years. Thus, this is a very interesting scenario in which the adaptive evolution of a trait does not confer an increased resilience to extreme events, since the environment in selective just after the massive mortality event.
I also obtained exceptional insights on the determinants of individual variation in growth and its implication for life-history and population processes (Vincenzi et al. 2014b). The paper describing the research has been published on PLoS Computational Biology in September 2014 and in less than four months it has been viewed almost 2,000 times (http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003828). In this work, I developed a novel statistical approach using the empirical Bayes method to estimate and separate the contribution of intrinsic and environmental factors to lifetime growth trajectories of marble trout, and generate hypotheses concerning the life-history strategies of organisms. I showed that using the novel method I developed, the growth model predicts the future growth of organisms with substantially greater accuracy than using historical information on growth at the population level, and help identify year-class effects, probably associated with climatic vagaries, as the most important environmental determinant of growth in marble trout. In this context, I am also currently investigating trade-offs between model complexity, biological interpretability of parameters, and goodness of fit in random-effects models of growth of fish, using marble trout as my model system. In particular, I am exploring how different formulations of the von Bertalanffy growth function with individual random effects and environmental predictors of function’s parameters affect these trade-offs. Studying the determinants of growth – and thus developing appropriate models and methods to estimate model parameters – is crucial, since growth is one of the traits in which adaptation to extreme events is more likely to occur. I found that adding additional complexity in the form of individual variation in multiple parameters of the von Bertalanffy growth function may offer advantages in terms of model accuracy, although the interpretation of model parameters may become more challenging. The manuscript is currently under review at Fish and Fisheries, which is - according to Impact Factor - the journal ranked number one in Fisheries.
In addition to empirical research, I have also carried out theoretical studies with the goal of identifying key pieces of empirical information that are required for advancing understanding on the demographic and genetic consequences of extreme events on natural populations. In particular, I used numerical simulations to understand and predict the consequences of directional trend and increased variability of a climate variable, increased probability of occurrence of point extreme events (e.g. floods), selection pressure and effect size of mutations on a quantitative trait determining individual fitness, as well as the their effects on the population and genetic dynamics of a population of moderate size (Vincenzi 2014). Briefly, I found that the interaction among climate trend, variability and probability of point extremes had a minor effect on risk of extinction, time to extinction and distribution of the trait after accounting for their independent effects. The survival chances of a population strongly and linearly decreased with increasing strength of selection, as well as with increasing climate trend and variability. Climate trend and strength of selection largely determined the shift of the mean phenotype in the population.
In addition, due to my broad interests on the effects of extreme events, I was invited to submit a contribution to a special issue of the journal Plant Ecology on the effects of extreme events on plants (Vincenzi & Piotti 2014). Although this contribution was not among the goals of the RAPIDEVO project, I consider it helpful for advancing the scientific dialogue on the effects of extreme events on evolutionary and demographic processes in natural populations.

References

Vincenzi, S. (2014). Extinction risk and eco-evolutionary dynamics in a variable environment with increasing frequency of extreme events. Journal of the Royal Society, Interface / the Royal Society, 11, 20140441.
Vincenzi, S., Crivelli, A.J., Satterthwaite, W.H. & Mangel, M. (2014a). Eco-evolutionary dynamics induced by massive mortality events. Journal of Fish Biology, 85, 8–30.
Vincenzi, S., Mangel, M., Crivelli, A.J., Munch, S. & Skaug, H.J. (2014b). Determining individual variation in growth and its implication for life-history and population processes using the Empirical Bayes method. PLoS Computational Biology, 10, e1003828.
Vincenzi, S. & Piotti, A. (2014). Evolution of serotiny in maritime pine (Pinus pinaster) in the light of increasing frequency of fires. Plant Ecology, 215, 689-701.

• If applicable, explain the reasons for deviations from Annex I and their impact on other tasks as well as on available resources and planning

There were no deviations from Annex I.

• If applicable, explain the reasons for failing to achieve critical objectives and/or not being on schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be coherent with the declaration by the scientist in charge)

Not Applicable

• A statement on the use of resources, in particular highlighting and explaining deviations between actual and planned researcher-months in Annex 1 (Description of Work)

There were no deviations between actual and planned researcher-months

• If applicable, propose corrective actions

No corrective actions are proposed.

4. ADDITIONAL INFORMATION

5. DISSEMINATION ACTIVITIES

Use this section to summarise all dissemination activities executed during the reporting period as well as activities planned for next period.

In terms of dissemination activities, I have mostly contributed to make the results of my work public thorough my presentations, seminar, and talks at department seminar series and international scientific meetings. Some of my talks can be found at http://simonevincenzi.com/talks/

Main talks

- Invited talk at the Wildlife & Conservation Biology Seminar series at UC Berkeley. Genetic and life-history variation in small populations living in stochastic environments. October 2014.
- Talk at the International Statistical Ecology Conference in Montpellier, France. Determining individual variation in growth and its implication for life history and population processes using the Empirical Bayes method. July 2014.
- Invited talk at EURAXESS event in New York City. Shaken, burned, drowned, but still there: how species survive catastrophic events in an increasingly extreme world. March 2014. Talk is online at https://www.youtube.com/watch?v=sp6xW67sC0I
- Invited talk at the 2013 SIAM meeting in San Diego. Spatial features of density dependence and weather extremes in population models. July 2013.

Others

Since winning my Marie Curie IOF in late 2011, I have been helping several scientists preparing their applications to European Union research calls. I use my blog to offer advice on how to apply to MC Actions and ERA Starting Research Grants (http://simonevincenzi.com/category/marie-curie/), including how to prepare applications and suggestions on the number and quality of publications allowing the applicant to be competitive. I also uploaded on my website my winning Marie Curie IOF application, free for the public and fellow scientists.

6. PROJECT MANAGEMENT

Please provide a concise overview of the progress of the work in line with the structure of Annex I of the Grant Agreement - except project management, which will be reported in section 6.
• A summary of progress towards objectives and details for each task;
• A summary of the progress of the researcher training activities/transfer of knowledge activities/integration activities (as it applies for the MC action);
• Highlight clearly significant results;
• If applicable, explain the reasons for deviations from Annex I and their impact on other tasks as well as on available resources and planning;
• If applicable, explain the reasons for failing to achieve critical objectives and/or not being on schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be coherent with the declaration by the scientist in charge) ;
• A statement on the use of resources, in particular highlighting and explaining deviations between actual and planned researcher-months in Annex 1 (Description of Work)
• If applicable, propose corrective actions.

• Project planning and status – from management point of view

According to the GANTT presented in Annex I of the RAPIDEVO project, the project is respecting deadlines and milestones. Here below are reported the planned activities for the third and last year of the project (year 2015).
1. Genetic markers: I am currently selecting a panel of SNPs for other 3 populations (Idirijca, Svenica, Studenc), while for one population (Huda Grapa) we have faced great challenges in the development assays that have determined by the exceptionally low genetic variability found among fish in Huda Grapa. With my colleagues, I am currently considering using another sequencer (Illumina HiSeq) for sequencing Huda Grapa individuals, although it is still unclear whether we will be able to find sufficient genetic variability to develop assays for pedigree reconstruction. In the first months of 2015, SNP assays will be developed for the populations of Idirijca, Svenica, Studenc. I will submit a manuscript for publication before the end of June 2015 of SNP discovery in marble trout.
1A. Investigate adaptive evolution in space (adaptive divergence) in marble trout populations living in Slovenian streams using molecular genetic markers: The work presented in Section 4 of this report is still unpublished; a manuscript on adaptive and neutral genetic differences marble trout populations living in Slovenian streams will be submitted for publication before the end of March 2015.
1B. Test adaptive evolution in marble trout, particularly after the occurrence of severe flood events: The work described in Section 4 of this report is still unpublished, although preliminary results have been presented in a talk I was invited to give at UC Berkeley in October 2014; I plan to submit a manuscript before the end of February 2015.
1C. Parentage analysis using molecular data: The work described in Section 4 of this report will be finished in the first 6 months of 2015 and a manuscript will be submitted for publication shortly after finishing the parentage analysis.
2. Demographic analysis and statistical characterization of temporal and spatial patterns of flood events
2A. Analysis of population structure, population dynamics, traits and compensatory responses of marble trout: I plan to submit a manuscript on differences in survival, growth, morphology and compensatory responses among marble trout populations for publication before the end of April 2015.
2B. Analysis of common-garden experiment: I will proceed with the analysis of data from the common-garden experiment in the second half of 2015.
2C. Analysis of flood events: I will proceed with the analysis of rainfall and flood data in the second half of 2015.
3. Life-history, demographic and eco-evolutionary modelling
3B. Development of an eco-evolutionary model for marble trout with genetic and life-history modules: In the second half of 2015 I plan to develop an eco-evolutionary model on the basis of the results and feedbacks provided by the research activities 1A,B,C and 2A,B,C. The model will be parameterized with population-specific genetic (e.g., alleles, heterozigosity, etc.) and life-history attributes. In particular, I will use Forward Stochastic Simulations to estimate the future evolutionary trajectories of marble trout living in the monitored streams, taking into account the likely climate change-induced intensification and altered timing of flood events.

• Problems which have occurred and how they were solved or envisaged solutions

No particular problems have occurred in 2013-2014.

• Changes to the legal status of any of the beneficiaries, in particular, SME status

There were no changes in legal status of any of the beneficiaries

• Impact of possible deviations from the planned milestones and deliverables, if any

There were no deviation from the planned milestones and deliverables

• Development of the project website (if applicable)

The project website is at www.simonevincenzi.com. Updated on research are provided at www.simonevincenzi.com/blog/ (tag: Marie Curie)

• Gender issues; Ethical issues

There were neither gender nor ethical issues.

• Justification of subcontracting (if applicable)

Not applicable.

• Justification of real costs (management costs)

Not applicable.

19th Dec 2014 - Update on research

I am currently working on pedigree reconstruction in a marble trout population (Lipovscek) that was affected by two big, destructive flash floods in 2007 and 2009, focusing in particular on the processes that helped the population bounce back to pre-flood density.

After SNPs discovery, we have successfully genotyped all the samples (~800) collected from 2006 until September 2014. The first step of the analysis is to merge together data coming from samples with different IDs, but that refer in reality to the same individual. My colleague field biologist Alain Crivelli uses Carlin tags (a metal tag that is attached with a piece of wire under the dorsal fin of the trout) only on fish longer than 110 mm; if the fish is shorter than 110 mm, a piece of the adipose fin is collected and the tube with the tissue sample is IDed. Every time a fish is provided with a Carlin tag, a piece of adipose fin is collected and the tube with the tissue sample has the same ID of the Carlin tag. Thus, the genotypes coming from the tissue sample of a fish that has first been sampled when shorter than 110 mm and later sampled when longer than 110 mm (and thus a Carlin tag was provided) should be the same, except for genotyping errors. Another instance of genotype matching occurs when a fish has lost the Carlin tag and it is later sampled and retagged with another Carling tag, since it is not possible to establish the previous ID of the fish (several fish lose their tags between sampling occasions). However, when two genotypes match, it is possible to merge together the demographic histories of the fish with different IDs, but that in reality refer to the same fish. It may happen that four different IDs refer to the same fish. For instance, a fish might have been sampled when aged 0 (i.e. before the first winter, first ID) and shorter than 110 mm, then sampled again the following year as age-1 fish shorter than 110 mm (second ID), then sampled the following year as age-2 longer than 100 mm and tagged (third ID), then sampled the following year as a fish that has already been tagged (when fish lose the tag, the scar is visible) and re-tagged (fourth ID).

Merging together fish histories and genotypes referring to the same fish is important for multiple reasons:

  • if the fish is a potential parent, it avoids not being able to assign the offspring to a parent since 2 potential parents (i.e. the same fish) have the same probability of being the true parent;
  • it avoids overestimating the production of young;
  • it helps for estimating more accurately  growth (it helps having longer fish histories) and survival probabilities (tag loss is a tricky problem, since the fish that lost the tag is "apparently" dead).

Parts of the "matching genotypes" analysis can be automated (e.g., given ~160 alleles per fish and allowing up to 2 or 3 mismatches, it is quite easy to write a script that extracts fish IDs with the same genotype), but then the demographic histories of the fish with the same genotype should be checked one by one. This was necessary as some demographic histories of fish with the same genotype made little sense (two different cohorts or IDs referred to fish sampled in the same year, but with vastly different length and weight), and thus I had to find out whether the mistake occurred in the lab or in the field.

I am getting closer to having a final, semi-clean, dataset allowing me to proceed with pedigree reconstruction.

Notes on "How Google Works" by Eric Schmidt and Alan Eagle

Some notes from "How Google Works" by Eric Schmidt and Alan Eagle. Very interesting book, definitely recommended.

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Over time I’ve learned, surprisingly, that it’s tremendously hard to get teams to be super ambitious. It turns out most people haven’t been educated in this kind of moonshot thinking. They tend to assume that things are impossible, rather than starting from real-world physics and figuring out what’s actually possible. It’s why we’ve put so much energy into hiring independent thinkers at Google, and setting big goals. Because if you hire the right people and have big enough dreams, you’ll usually get there. And even if you fail, you’ll probably learn something important.

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It’s also true that many companies get comfortable doing what they have always done, with a few incremental changes. This kind of incrementalism leads to irrelevance over time, especially in technology, because change tends to be revolutionary not evolutionary.

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We both came to Google as seasoned business executives who were pretty confident in our intellects and abilities. But over the humbling course of a decade, we came to see the wisdom in John Wooden’s observation that “it’s what you learn after you know it all that counts.”

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But it wasn’t Google’s culture that turned those five engineers into problem-solving ninjas who changed the course of the company over the weekend. Rather it was the culture that attracted the ninjas to the company in the first place.

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As former General Electric CEO Jack Welch said in Winning: “No vision is worth the paper it’s printed on unless it is communicated constantly and reinforced with rewards.”

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But these, as we shall see, would become integral to creating—and sustaining—a culture where a simple statement like “These ads suck” is all that’s needed to make things happen.

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One last organizational principle: Determine which people are having the biggest impact and organize around them.

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Bill Campbell, the former Intuit CEO and ongoing coach and mentor to us both, often quotes Debbie Biondolillo, Apple’s former head of human resources, who said, “Your title makes you a manager. Your people make you a leader.”

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Once you identify the people who have the biggest impact, give them more to do.

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Life is something like that island, only more complicated. For not only are knaves in real life devoid of integrity, they are also sloppy, selfish, and have a sneaky way of working their way into virtually any company. Arrogance, for example, is a knavish tendency that is a natural by-product of success, since exceptionalism is fundamental to winning. Nice humble engineers have a way of becoming insufferable when they think they are the sole inventors of the world’s next big thing. This is quite dangerous, as ego creates blind spots.

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(Tom Peters: “There is no such thing as a minor lapse of integrity.”) (my note: I there is)

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There are tipping points in knave density. It approaches a critical mass—which is smaller than you think—and people start to believe they need to be knave-like to succeed, which only exacerbates the problem. Smart creatives may have a lot of good traits, but they aren’t saints, so it’s important to watch your knave quotient.

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Knaves need to be dealt with as quickly as possible. But as long as their contributions match their outlandish egos, divas should be tolerated and even protected. Great people are often unusual and difficult, and some of those quirks can be quite off-putting. Since culture is about social norms and divas refuse to be normal, cultural factors can conspire to sweep out the divas along with the knaves.

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Marissa Mayer, who became one of Silicon Valley’s most famous working mothers not long after she took over as Yahoo’s CEO in 2012, says that burnout isn’t caused by working too hard, but by resentment at having to give up what really matters to you.

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If someone is so critical to the company’s success that he believes he can’t unplug for a week or two without things crashing down, then there is a larger problem that must be addressed. No one should or can be indispensable. Occasionally you will encounter employees who create this situation intentionally, perhaps to feed their ego or in the mistaken belief that “indispensability” equals job security. Make such people take a nice vacation and make sure their next-in-line fills in for them while they are gone. They will return refreshed and motivated, and the people who filled their shoes will be more confident. (This is a huge hidden benefit of people taking maternity and paternity leaves too.)

==========

A great start-up, a great project—a great job, for that matter—should be fun, and if you’re working your butt off without deriving any enjoyment, something’s probably wrong. Part of the fun comes from inhaling the fumes of future success. But a lot of it comes from laughing and joking and enjoying the company of your coworkers.

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There’s a problem with these Fun events: They aren’t fun.

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Everyone’s fun when they’re dancing to Billy Idol and swigging an Anchor Steam. (my note: Agreed)

==========

Here’s our idea for off-sites: Forget “team building” and have fun. Jonathan’s criteria for his excursions included doing outdoor group activities (weather permitting) in a new place far enough from the office to feel like a real trip, but still doable in a day, and providing an experience that people couldn’t or wouldn’t have on their own.

==========

Sheryl Sandberg ran a book club for her sales team that was so popular in our India office that every single person participated.

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(Eric doesn’t adhere to Satchel Paige’s advice to “dance like nobody’s watching.” When you’re a leader, everyone is watching, so it doesn’t matter that you dance poorly, it matters that you dance.)

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A defining mark of a fun culture is identical to that of an innovative one: The fun comes from everywhere. The key is to set the boundaries of what is permissible as broadly as possible. Nothing can be sacred. In 2007, a few of our engineers discovered that Eric’s profile photo in our intranet system was in a public folder. They altered the background of the photo to include a portrait of Bill Gates, and, on April Fools’ Day, posted the updated image on Eric’s page.

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It’s fun, and can only occur in a permissive environment that trusts its employees and doesn’t defer to the “what happens if this leaks?” worrywarts. It’s impossible to have too much of that kind of fun. The more you have, the more you get done.

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“When you are in a turnaround,” the man told him, “find the smart people first. And to find the smart people, find one of them.”

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(Eric was once asked at a company meeting what the Google dress code was. “You must wear something” was his answer.)

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To do all this, you have to be crazy enough to think you will succeed, but sane enough to make it happen. This requires commitment, tenacity, and most of all, single-mindedness. When Israeli tank commanders head into combat, they don’t yell “Charge!” Rather, they rally their troops by shouting “Ah’cha’rye,” which translates from Hebrew as “Follow me.”

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We have no idea what your venture is or even your industry, so we won’t presume to tell you how to create a business plan. But we can tell you with 100 percent certainty that if you have one, it is wrong.

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(Henry Ford: “If I had listened to customers, I would have gone out looking for faster horses.”)

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Companies have always built networks, but historically those networks were internal and designed to reduce costs. In this way, they followed the tenets of University of Chicago economist and Nobel laureate Ronald Coase, who argued that it often makes sense for firms to do things internally rather than externally, because the transaction costs of finding vendors, negotiating contracts, and making sure the work gets done right are high.

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Coase was right: The internal management costs were lower than the transaction costs of outsourcing. This led them to do as much as they could within the organization, and, when they did go outside their four walls, they worked with a small group of tightly controlled partners.

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Today, Coase’s framework still holds true—but it leads to radically different outcomes than it did in much of the twentieth century. Rather than growing the biggest possible closed networks, companies are outsourcing more functions and working with a bigger and more diverse network of partners. Why? Don Tapscott put it well in Wikinomics, when he wrote that “the Internet has caused transaction costs to plunge so steeply that it has become much more useful to read Coase’s law, in effect, backward: Nowadays firms should shrink until the cost of performing a transaction internally no longer exceeds the cost of performing it externally.

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we didn’t choose to specialize in that area because our crystal ball told us it would ultimately be more lucrative and impactful than the alternate, more popular portal business model. Rather, we focused on search because it was something we felt we were better at than anyone else. So in those early days of the Internet, while these leaders of the industry were busy tending to their business of building Internet portals, Google search got better and better at providing great answers for users.

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Open also allows you to harness the talents of thousands of people, because, as Sun cofounder Bill Joy noted, “no matter who you are, most of the smartest people work for someone else.”

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For a manager, the right answer to the question “What is the single most important thing you do at work?” is hiring.

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A workforce of great people not only does great work, it attracts more great people. The best workers are like a herd: They tend to follow each other. Get a few of them, and you’re guaranteed that a bunch more will follow. Google is renowned for its fabulous amenities, but most of our smart creatives weren’t drawn to us because of our free lunches, subsidized massages, green pastures, or dog-friendly offices. They came because they wanted to work with the best smart creatives.

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Their deep interest made them more interesting, which is why in an interview context our philosophy is not “Don’t get them started.” When it comes to the things they care most about, we want to get them started.

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our experience raw brainpower is the starting point for any exponential thinker. Intelligence is the best indicator of a person’s ability to handle change.

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Henry Ford said that “anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young. The greatest thing in life is to keep your mind young.” Our ideal candidates are the ones who prefer roller coasters, the ones who keep learning.

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Favoring specialization over intelligence is exactly wrong, especially in high tech. The world is changing so fast across every industry and endeavor that it’s a given the role for which you’re hiring is going to change.

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Once you hire those learning animals, keep learning them! Create opportunities for every employee to be constantly learning new things—even skills and experiences that aren’t directly beneficial to the company—and then expect them to use them. This won’t be challenging for true learning animals, who will gladly avail themselves of training and other opportunities. But keep an eye on the people who don’t; perhaps they aren’t quite the learning animals you thought they were.

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So, passion is crucial in a potential hire, as is intelligence and a learning-animal mindset.

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Another crucial quality is character. We mean not only someone who treats others well and can be trusted, but who is also well-rounded and engaged with the world. Someone who is interesting. Judging character during the interview process used to be fairly easy, since job interviews often included lunch or dinner at a restaurant and perhaps a drink or two, Mad Men style. Such a venue allowed the hiring executive to observe how the candidate comported himself “as a civilian.” What happens when he lets his guard down? How does he treat the waiter and bartender? Great people treat others well, regardless of standing or sobriety.

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We institutionalized the LAX test by making “Googleyness” one of four standard sections—along with general cognitive ability, role-related knowledge, and leadership experience—on our interview feedback form. This includes ambition and drive, team orientation, service orientation, listening & communication skills, bias to action, effectiveness, interpersonal skills, creativity, and integrity. (Larry and Sergey took the LAX test one step further when they were looking for a CEO: They took candidates away for a weekend. Eric played it a bit more conservatively: “Look, guys, I don’t need to go to Burning Man with you. How about dinner?”)

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Supercomputer pioneer Seymour Cray used to deliberately hire for inexperience because it brought him people who “do not usually know what’s supposed to be impossible.”

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John Dewey, an American philosopher and writer, said that “a problem well put is half solved.”

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It should go without saying—but it usually doesn’t, so we’ll say it—that data is best understood by those closest to the issue, which is often not management. As a leader, it is best not to get lost in details you don’t understand, but rather trust the smart people who work for you to understand them.

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As General Patton famously said, “If everyone is thinking alike, then somebody isn’t thinking.”

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They may be dissenters who are afraid to disagree with you in public (but need to get over that fear), or they may be of the shy but brilliant type. Or perhaps they truly have nothing to say, in which case maybe they shouldn’t be at the meeting in the first place. One technique is to throw out a few “stupid softballs” that let people dip their toe in the water of disagreeing with the boss.

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“Do something,” he told the executive, “even if it’s wrong.” Tom Peters would call Bill’s attitude in this situation a “bias for action,” and his book In Search of Excellence lists it as a top common attribute of the companies he studied.

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there is real signaling value in using your convening power as a leader to hold regular meetings. If the decision is important enough, the meetings should be daily.

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People complain about meetings and how they are a great waste of time, but in fact a well-run meeting is a great thing. It’s the most efficient way to present data and opinions, to debate issues, and yes, to actually make decisions.

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One of the most important decisions any business leader makes is how to spend his or her time. When Eric became CEO of Novell in 1997, he got some great advice from Bill Gates: Spend 80 percent of your time on 80 percent of your revenue.

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That shiny new stuff can be much more interesting than the boring old core business stuff, but it’s the core stuff that pays the bills, and if you make a mistake there, you probably won’t be able to recover.

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If someone is in charge of a business and can’t rattle off the key issues she faces in a matter of ten seconds, then she’s not up to the job. A hands-off approach to leadership doesn’t cut it anymore. You need to know the details.

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This doesn’t just apply to questions. It is one of the most universal of human truths: No one wants to be the bearer of bad news. Yet as a leader it is precisely the bad news that you most need to hear. Good news will be just as good tomorrow, but bad news will be worse.

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of our favorite Urs quotes from the user manual: “I didn’t grow up in the US, and I tend to be more direct than others when I talk about something.… I tend to overstate points for clarity of argument—it’s easier to summarize something in black and white vs in shades of gray.… If you think I am wrong, you need to tell me. I’ll never blame anyone for speaking up.… If you feel I’m beating you up all the time and all you’re getting is negative feedback, then it’s very likely that this wasn’t intentional.”

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many companies (universities too), the incorrect, knee-jerk management reaction is to discourage employees from connecting with company rock stars. After all, they might waste their time with stupid questions, right? Yes, that does happen, but it turns out that most rock stars have very little patience for people wasting their time and they make doing so a very unpleasant experience. The inexperienced smart creative who does it once quickly learns not to do it again.

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One of Eric’s most basic rules is sort of a golden rule for management: Make sure you would work for yourself.

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Think about the late novelist Elmore Leonard’s response to a question about his success as a writer: “I leave out the parts that people skip.”

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This isn’t just handy for emails, but important documents too. Jonathan scans his family’s passports, licenses, and health insurance cards and emails them to himself along with descriptive keywords. Should any of those things go missing during a trip, the copies are easy to retrieve from any browser. (My note: just did it)
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(Champion racecar driver Mario Andretti: “If everything seems under control, you’re just not going fast enough.”)

Updating the blog and present/future research

I decided to start providing regular updates on my Marie Curie research on this blog.

These are my plans for the next 6 months, the content is basically coming from email exchanges with my collaborators.

- Pedigree reconstruction in the marble trout population of Lipovesck. We have recently genotyped the individuals that have been sampled in 2013 (June and September) and 2014 (June and September). All samples have been sexed and I am ready to carry out the pedigree reconstruction, with particular focus on the post-2009 generation. In 2009, a huge flood hit Lipovesck and just a handful of marble trout survived. The main goal is to understand the recovery process after the flood, who reproduced and whether certain traits were related with higher chances of post-flood reproduction. Then, I'd like to test whether my predictions on post-flood recovery (younger age at reproduction due to the relaxation of density-dependent processes after an episode of massive mortality) were right. If it is true, we should expect the 2011 cohort to have reproduced (at least some individuals) in 2013, thus anticipating one year reproduction (at age 3 or 4 under normal conditions).

- Phylogeny of marble trout living in Western Slovenia. We are sequencing right now additional fish from the populations of Idirijca, Svenica, and Studenc. For Idirijca, the sequencing of additional individuals was motivated by the lack of a sufficient number of SNPs for pedigree reconstruction (96 SNPs recommended, ~80 should be enough). Fish from Svenica and Studenc have never been sequenced. In order to save some money, we tried to sequence fish from some of the populations part of the cluster identified by Fumagalli et al. 2002 (14 microsatellite loci were used). However, since almost none of the SNPs found in the populations of Trebuscica and Idrijca were found to be variable also in Svenica and Studenc, we now have the suspicion that Svenica and Studenc are not as genetically close to Trebuscica and Idrijca as reported by Fumgalli et al. After this sequencing run, we should have all the elements for studying the phylogeny of marble trout, inbreeding, loci under selection etc.

- Writing a technical paper on SNP discovery for marble trout. While we are still discovering and characterizing SNPs for the populations of Idirjca, Svenica and Studens, I am confident we will discover the population-specific panel of SNPs soon. The only real problem is the population of Huda, for which we found very little to almost non-existent polymorphism. Given money, we might try to sequence some fish from Huda using an Hi-seq machine (we are currently using a Mi-seq with size selection at 500 base pairs).

- Differences in life-histories (growth, survival, morphology) in marble trout, including density-dependent patterns, with the main focus on the relationship between growth and survival as possibly mediated by cannibalism. Most of the analysis have been done.