Controls, controls everywhere, and change continues nonetheless

Typically, everyone chases experimental treatments – i.e. direct interventions – as the fundamental means to advance science. We do something to one set of subjects whether plants, animals, or people, and we monitor the other set designated as controls.  The paper ‘Ambient changes exceed treatment effects on plant species abundance in global change experiments‘ very nicely shows that in many if not all natural systems, the ‘controls’ change too.  We cannot ignore the fact that natural ecosystems are changing just as rapidly and just as much due to global change and climate as the treatments we might test.
Solution – do the work and check!

Significant ‘developments’ this last few days associated with how we use lands #evidence #synthesis

This last week has been busy with numerous evidence syntheses highlighting that location, location, location and land use patterns are critical issues.

(1) ‘North American diets require more lands than we have’ was published in PLOS ONE and discussed widely. A compelling map of land spared showed little remains.

(2) On the other side of the coin, retiring lands because of water regulations, limitations, and drought are an opportunity for conservation and restoration was published in Ecosphere. Tools mapped for California studying three endangered animal species highlighted that we do know enough to begin to make evidence-based decisions for strategic retirement.

(3) A compelling map of how America uses land was published at Bloomberg.

(4) Hydraulic fracking is now being considered in the region used a case study for the retired land synthesis in #2 listed above including a map of proposed lands open for leasing.

Implications

(a) Scientific synthesis rapidly advances the big picture and both different synthesis tools (maps, systematic reviews, and ideally meta-analyses too) and syntheses with different purposes facilitate a more balanced weighting of issues. Even better, reproducible syntheses provided by different sets of stakeholders would elevate discussion and decision making.

(b) Agriculture, restoration, and energy development (both sustainable and non) must be better balanced through contrasted, transparently aggregated evidence.

(c) The ecological services and functions we get from lands ‘for free’ are precarious and precious.

(d) Whilst we cannot ignore human needs (and their likelihood of continued increases),  it is hard not to imagine that a buffer for other living creatures should also be factored into proposed land use trajectories.

(e) Ecology, socioeconomics, and other fields need to much more rapidly crunch current evidence because the clock is ticking.

 

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

Assumptions

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

library(AER)

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

#interpretation

c = 0 equidispersion

c > 0 is overdispersed

Resources

  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.

Workflow

  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.

————

#Functions####

#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

res

}

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

require(MuMIn)

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

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

m1

#repeat as needed to contrast different models

————

Outcomes

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.

Resources

  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.

Fundamentals

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.