Hacking the principles of #openscience #workshops

In a previous post, I discussed the key elements that really stood out for me in recent workshops associated with open science, data science, and ecology. Summer workshop season is upon us, and here are some principles to consider that can be used to hack a workshop. These hacks can be applied a priori as an instructor or in situ as a participant or instructor by engaging with the context from a pragmatic, problem-solving perspective.


1. Embrace open pedagogy.
2. Use and current best practices from traditional teaching contexts.
3. Be learner centered.
4. Speak less, do more.
5. Solve authentic challenges.

Hacks (for each principle)

1. Prepare learning outcomes for every lesson.

2. Identify solve-a-problem opportunities in advance and be open to ones that emerge organically during the workshop.

3. Use no slide decks. This challenges the instructor to more directly engage with the students and participants in the workshop and leaves space for students to shape content and narrative to some extent. Decks lock all of us in. This is appropriate for some contexts such as conference presentations, but workshops can be more fluid and open.

4. Plan pauses. Prepare your lessons with gaps for contributions.  Prepare a list of questions to offer up for every lesson and provide time for discussion of solutions.

5. Use real evidence/data to answer a compelling question (scale can be limited, approach beta as long as an answer is provided, and the challenge can emerge if teaching is open and space provided for the workshop participants to ideate).

Final hack that is a more general teaching principle, consider keeping all teaching materials within a single ecosystem that then references outwards only as needed. For me, this has become all content prepared in RStudio, knitted to html, then pushed to GitHub gh-pages for sharing as a webpage (or site). Then participants can engage in all ideas and content including code, data, ideas in one place.


Fix-it Facilitation: additional resources

A super fun process exploring how empirical contributions can reshape and embrace theory by addressing gaps in better designs and clear interpretations of findings.

Fix-it Felix: advances in testing plant facilitation as a restoration tool in Applied Vegetation Science.

The original contribution was longer with a more complete set of resources. Here is the full citation list that framed and supported the story and discussion.

Literature cited

Badano, E.I., Bustamante, R.O., Villarroel, E., Marquet, P.A. & Cavieres, L.A. 2015. Facilitation by nurse plants regulates community invasibility in harsh environments. Journal of Vegetation Science: 756-767.

Badano, E.I., Samour-Nieva, O.R., Flores, J., Flores-Flores, J.L., Flores-Cano, J.A. & Rodas-Ortíz, J.P. 2016. Facilitation by nurse plants contributes to vegetation recovery in human-disturbed desert ecosystems. Journal of Plant Ecology 9: 485-497.

Barney, J.N. 2016. Invasive plant management must be driven by a holistic understanding of invader impacts. Applied Vegetation Science 19: 183-184.

Bertness, M.D. & Callaway, R. 1994. Positive interactions in communities. Trends in Ecology and Evolution 9: 191-193.

Bronstein, J.L. 2009. The evolution of facilitation and mutualism. Journal of Ecology 97: 1160-1170.

Bruno, J.F., Stachowicz, J.J. & Bertness, M.D. 2003. Inclusion of facilitation into ecological theory. Trends in Ecology and Evolution 18: 119-125.

Bulleri, F., Bruno, J.F., Silliman, B.R. & Stachowicz, J.J. 2016. Facilitation and the niche: implications for coexistence, range shifts and ecosystem functioning. Functional Ecology 30: 70-78.

Callaway, R.M. 1998. Are positive interactions species-specific? Oikos 82: 202-207.

Chamberlain, S.A., Bronstein, J.L. & Rudgers, J.A. 2014. How context dependent are species interactions? Ecology Letters 17: 881-890.

Filazzola, A. & Lortie, C.J. 2014. A systematic review and conceptual framework for the mechanistic pathways of nurse plants. Global Ecology and Biogeography 23: 1335-1345.

Gomez-Aparicio, L., Zamora, R., Gomez, J.M., Hodar, J.A., Castro, J. & Baraza, E. 2004. Applying plant facilitation to forest restoration: a meta-analysis of the use of shrubs as nurse plants. Ecological Applications 14: 1128-1138.

Holmgren, M. & Scheffer, M. 2010. Strong facilitation in mild environments: the stress gradient hypothesis revisited. Journal of Ecology 98: 1269-1275.

James, J.J., Rinella, M.J. & Svejcar, T. 2012. Grass Seedling Demography and Sagebrush Steppe Restoration. Rangeland Ecology & Management 65: 409-417.

Lortie, C.J., Filazzola, A., Welham, C. & Turkington, R. 2016. A cost–benefit model for plant–plant interactions: a density-series tool to detect facilitation. Plant Ecology: 1-15.

Macek, P., Schöb, C., Núñez-Ávila, M., Hernández Gentina, I.R., Pugnaire, F.I. & Armesto, J.J. 2017. Shrub facilitation drives tree establishment in a semiarid fog-dependent ecosystem. Applied Vegetation Science.

Malanson, G.P. & Resler, L.M. 2015. Neighborhood functions alter unbalanced facilitation on a stress gradient. Journal of Theoretical Biology 365: 76-83.

McIntire, E. & Fajardo, A. 2011. Facilitation within species: a possible origin of group-selected superoorganisms. American Naturalist 178: 88-97.

McIntire, E.J.B. & Fajardo, A. 2014. Facilitation as a ubiquitous driver of biodiversity. New Phytologist 201: 403-416.

Michalet, R., Brooker, R.W., Cavieres, L.A., Kikvidze, Z., Lortie, C.J., Pugnaire, F.I., Valiente‐Banuet, A. & Callaway, R.M. 2006. Do biotic interactions shape both sides of the humped‐back model of species richness in plant communities? Ecology Letters 9: 767-773.

Michalet, R., Le Bagousse-Pinguet, Y., Maalouf, J.-P. & Lortie, C.J. 2014. Two alternatives to the stress-gradient hypothesis at the edge of life: the collapse of facilitation and the switch from facilitation to competition. Journal of Vegetation Science 25: 609-613.

Noumi, Z., Chaieb, M., Michalet, R. & Touzard, B. 2015. Limitations to the use of facilitation as a restoration tool in arid grazed savanna: a case study. Applied Vegetation Science 18: 391-401.

O’Brien, M.J., Pugnaire, F.I., Armas, C., Rodríguez-Echeverría, S. & Schöb, C. 2017. The shift from plant–plant facilitation to competition under severe water deficit is spatially explicit. Ecology and Evolution 7: 2441-2448.

Pescador, D.S., Chacón-Labella, J., de la Cruz, M. & Escudero, A. 2014. Maintaining distances with the engineer: patterns of coexistence in plant communities beyond the patch-bare dichotomy. New Phytologist 204: 140-148.

Rydgren, K., Hagen, D., Rosef, L., Pedersen, B. & Aradottir, A.L. 2017. Designing seed mixtures for restoration on alpine soils: who should your neighbours be? Applied Vegetation Science.

Sheley, R.L. & James, J.J. 2014. Simultaneous intraspecific facilitation and interspecific competition between native and annual grasses. Journal of Arid Environments 104: 80-87.

Silliman, B.R., Schrack, E., He, Q., Cope, R., Santoni, A., van der Heide, T., Jacobi, R., Jacobi, M. & van de Koppel, J. 2015. Facilitation shifts paradigms and can amplify coastal restoration efforts. Proceedings of the National Academy of Sciences 112: 14295-14300.

Stachowicz, J.J. 2001. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51: 235-246.

von Gillhaussen, P., Rascher, U., Jablonowski, N.D., Plückers, C., Beierkuhnlein, C. & Temperton, V.M. 2014. Priority Effects of Time of Arrival of Plant Functional Groups Override Sowing Interval or Density Effects: A Grassland Experiment. PLoS ONE 9: e86906.

Went, F.W. 1942. The dependence of certain annual plants on shrubs in southern California deserts. Bulletin of the Torrey Botanical Club 69: 100-114.

Xiao, S. & Michalet, R. 2013. Do indirect interactions always contribute to net indirect facilitation? Ecological Modelling 268: 1-8.

Overdispersion tests in #rstats

A brief note on overdispersion


Poisson distribution assume variance is equal to the mean.

Quasi-poisson model assumes variance is a linear function of mean.

Negative binomial model assumes variance is a quadratic function of the mean.

rstats implementation

#to test you need to fit a poisson GLM then apply function to this model


dispersiontest(object, trafo = NULL, alternative = c(“greater”, “two.sided”, “less”))

trafo = 1 is linear testing for quasipoisson or you can fit linear equation to trafo as well


c = 0 equidispersion

c > 0 is overdispersed


  1. Function description from vignette for AER package.
  2. Excellent StatsExchange description of interpretation.

A note on AIC scores for quasi-families in #rstats

A summary note on recent set of #rstats discoveries in estimating AIC scores to better understand a quasipoisson family in GLMS relative to treating data as poisson.

Conceptual GLM workflow rules/guidelines

  1. Data are best untransformed. Fit better model to data.
  2. Select your data structure to match purpose with statistical model.
  3. Use logic and understanding of data not AIC scores to select best model.

(1) Typically, the power and flexibility of GLMs in R (even with base R) get most of the work done for the ecological data we work with within the research team. We prefer to leave data untransformed and simple when possible and use the family or offset arguments within GLMs to address data issues.

(2) Data structure is a new concept to us. We have come to appreciate that there are both individual and population-level queries associated with many of the datasets we have collected.  For our purposes, data structure is defined as the level that the dplyr::group_by to tally or count frequencies is applied. If the ecological purpose of the experiment was defined as the population response to a treatment for instance, the population becomes the sample unit – not the individual organism – and summarised as such. It is critical to match the structure of data wrangled to the purpose of the experiment to be able to fit appropriate models. Higher-order data structures can reduce the likelihood of nested, oversampled, or pseudoreplicated model fitting.

(3) Know thy data and experiment. It is easy to get lost in model fitting and dive deep into unduly complex models. There are tools before model fitting that can prime you for better, more elegant model fits.


  1. Wrangle then data viz.
  2. Library(fitdistrplus) to explore distributions.
  3. Select data structure.
  4. Fit models.

Now, specific to topic of AIC scores for quasi-family field studies.

We recently selected quasipoisson for the family to model frequency and count data (for individual-data structures). This addressed overdispersion issues within the data. AIC scores are best used for understanding prediction not description, and logic and exploration of distributions, CDF plots, and examination of the deviance (i.e. not be more than double the degrees of freedom) framed the data and model contexts. To contrast poisson to quasipoisson for prediction, i.e. would the animals respond differently to the treatments/factors within the experiment, we used the following #rstats solutions.



#deviance calc

dfun <- function(object) {

with(object,sum((weights * residuals^2)[weights > 0])/df.residual)


#reuses AIC from poisson family estimation

x.quasipoisson <- function(…) {

res <- quasipoisson(…)

res$aic <- poisson(…)$aic



#AIC package that provided most intuitive solution set####


m <- update(m,family=”x.quasipoisson”, na.action=na.fail)

m1 <- dredge(m,rank=”QAIC”, chat=dfun(m))


#repeat as needed to contrast different models



This #rstats opportunity generated a lot of positive discussion on data structures, how we use AIC scores, and how to estimate fit for at least this quasi-family model set in as few lines of code as possible.


  1. An R vignette by Ben Bolker of quasi solutions.
  2. An Ecology article on quasi-possion versus nb.regression for overdispersed count data.
  3. A StatsExchange discussion on AIC scores.


Same data, different structure, lead to different models. Quasipoisson a reasonable solution for overdispersed count and frequency animal ecology data. AIC scores are a bit of work, but not extensive code, to extract. AIC scores provide a useful insight into predictive capacities if the purpose is individual-level prediction of count/frequency to treatments.


Why read a book review when you you can read the book (for free via #oa #openscience)?


Reviews, recommendations, and ratings are an important component of contemporary online consumption. Rotten Tomatoes, Metacritic, and Amazon.com reviews and recommendations increasingly shape decisions. Science and technical books are no exception. Increasingly, I have checked reviews for a technical book on a purchasing site even before I downloaded the free book. Too much information, not too little informs many of the competing learning opportunities (#rstats ) for instance).  I used to check the book reviews section in journals and enjoyed reading them (even if I never read the book). My reading habits have changed now, and I rarely read sections from journals and focus only on target papers. This is an unfortunate. I recognize that reviews are important for many science and technical products (not just for books but packages, tools, and approaches). Here is my brief listicle for why reviews are important  for science books and tools.

benefit description
curation Reviews (reviewed) and published in journals engender trust and weight critique to some extent.
developments and rate of change A book review typically frames the topic and offering of a book/tool in the progress of the science.
deeper dive into topic The review usually speaks to a specific audience and helps one decide on fit with needs.
highlights The strengths and limitations of offering are described and can point out pitfalls.
insights and implications Sometimes the implications and meaning of a book or tool is not described directly. Reviews can provide.
independent comment Critics are infamous. In science, the opportunity to offer praise is uncommon and reviews can provide balance.
fits offering into specific scientific subdiscpline Technical books can get lost bceause of the silo effect in the sciences. Reviews can connect disciplines.

Here is an estimate of the frequency of publication of book reviews in some of the journals I read regularly.

journal total.reviews recent
American Naturalist 12967 9
Conservation Biology 1327 74
Journal of Applied Ecology 270 28
Journal of Ecology 182 0
Methods in Ecology & Evolution 81 19
Oikos 211 22

Details of journal data scrape here: https://cjlortie.github.io/book.reviews/


A good novel tells us the truth about its hero; but a bad novel tells us the truth about its author.

–Gilbert K. Chesterton