Tipping Points Comment In Nature

On Wednesday my comment piece with Alan, Tipping points: from patterns to predictions appeared in Nature (next to a nice comment by Heather Piwowar on NSF’s move to recognizing products of research more broadly than publications alone).

Heather has written an excellent post on the process of writing, which rings true with my own experience. Working with an editor with clear expertise in science writing and communication who was otherwise unfamiliar with the details of this field was an immensely rewarding experience, and her contributions really ought to be recognized more clearly. She stuck with me as I struggled to explain concepts I wasn’t even sure were well understood in my subfield, and then helped me rephrase towards a larger audience. I learned how specific examples and even potentially extraneous details could help anchor and clarify a description. Of course, some compromise was inevitable. I later had some explaining to do when the fact-checker questioned some of my citations. Yes, I know Sebastian doesn’t actually talk about salemanders in Schreiber and Randolf 2008, and Stephen Lade and Thilo Gross don’t talk about cod collapse; but their models do, I promise!

I doubt any scholar takes on a topic of close personal interest in the space of two pages and feels completely happy with the resulting message. While the piece will no doubt be received as a pushback against the bandwagon, our goal was more road-map to interesting open questions than critique of the early steps. In the piece, I aimed to raise two important issues or gaps in the existing literature, and then to propose two potential ways forward for each issue. The goal is not to close the door on early warning signals, but to further unroll the map and draw more attention to the uncharted lands.

Warning signals without controls

The first gap we highlight is the need to understand how early warning signals might be applied in the real world, rather than in experimental designs that rely on replicates and controls to identify what is and what isn’t a warning signal. We propose two potential directions, one primarily empirical, one primarily theoretical:

  1. Establish baselines. The challenges to doing this well are manifold, but we won’t know if or how to address them without more active research.

  2. Statistical Models In the absence of controls, appropriate null models might be able to simulate comparison groups we do not have. I have begun to explore this in my own work, see Boettiger and Hastings (2012).

Inability to leverage context-specific information

In our comment, this comes off as condeming the generality of warning signals. Early warning signals such as variance (Carpenter and Brock 2006) or spatial autocorrelation (Dakos et al 2011) may incerase before a catastrophic transition, but they can also decrease (Schreiber and Rudolf 2008, Bel et al 2012).

  1. Use all the data. While a lack of detailed mechanistic understanding of complex systems is more often the rule then the exception, we mustn’t give into the huberis that we can ignore context-specific information we know. When facing problems as challenging as detecting sudden shifts are no time to go battle with one hand tied behind our backs. By working with domain experts in areas where transitions are best understood, we may learn how best to do this.

  2. Follow the data A more widely appreciated issue for any signal is the concern of an adequately long and well sampled data set. While the globe ushers in an era of “big data”, long time series aren’t easy to come by. Perhaps this is a case in which searching under the street light for the keys can pay off. Early warning signal research has already shown us once how to find weak patterns if we know what to look for. A promising line of of work would identify (potentially system-specific) signals in the kind of data that is most likely to be available, such as high-resolution spatial imagery. Ignoring such patterns because they do not generalize to other systems will only slow us down.


Unfortunately the piece is not available open access (though do write to me for a reprint if you are interested). I can distribute the original text in 6 months. As an advocate for open science, my 0/9 record of publishing in open access journals might raise eyebrows, if also unequivocally demonstrating my commitment to pragmatism ;-). All my other papers are available through as green open access/preprints on arXiv, and I hope this one can join them when the terms permit.


  1. Lade, S. J. & Gross, T. Early Warning Signals for Critical Transitions: A Generalized Modeling Approach. PLoS Computational Biology 8, e1002360 (2012). doi:10.1371/journal.pcbi.1002360
  2. Schreiber, S. J. & Rudolf, V. H. W. Crossing habitat boundaries: coupling dynamics of ecosystems through complex life cycles. Ecology Letters 11, 576-87 (2008). doi:10.1111/j.1461-0248.2008.01171.x
  3. Dakos, V., Kéfi, S., Rietkerk, M., Nes, E. H. Van & Scheffer, M. Slowing Down in Spatially Patterned Ecosystems at the Brink of Collapse. The American Naturalist (2011).doi:10.1086/659945
  4. Bel, G., Hagberg, A. & Meron, E. Gradual regime shifts in spatially extended ecosystems. Theoretical Ecology 5, 591-604 (2012). doi:10.1007/s12080-011-0149-6