I don’t know about you but as an ecologist, I am not an expert in disease dynamics nor part of the inner community rapidly exchanging ideas and data. But as an ecologist I have a better handle on notions of population growth, species interactions, individual encounter rates, etc than the average population (and probably the average scientist) and I have felt in a frustrating vacuum of information.
To address this, we’re trying something new here at Dynamic Ecology – an open thread, the main purpose of which is to have a place for the community to have a conversation. Our comments sections have long been the most interesting part of the blog, so now we’re creating a direct path to comments without your having to read 1000s of words of bloviation from me!
First, a few thoughts to give some common terminology/framing to the questions. I think ecologists all know about the power of exponential growth (although this is new and still poorly grasped to most of the world). R0 is the discrete growth rate with no immunity (naive population) and no efforts at social distancing. Best estimates I have seen for Covid 19 is about R0=2.5 which is a good bit higher than flu (and a good bit lower than measles). It seems to be becoming clearer that R0 is as high as it is because people can be infectious before they show symptoms (or even if they never show symptoms like children). Once immunities start to build up or quarantine/social distancing measures start to be put in place a lower growth rate Re (effective growth rate) is observed. So as far as I can tell there are three strategies.
- Squeeze it – extreme social distancing to reduce Re<1. This seems to be what China as well as Japan and South Korea are doing (probably not coincidentally all Asian countries that got hit most by SARS and MERS).
- Let it burn – do nothing to lower Re=2.5. Sadly many (all?) countries started down this road – with exponential growth the speed of reaction required seems to be faster than governments can handle.
- Stretch it – social distancing to get Re~1.2 (nb 1.2 is an example, not a carefully calculated number, just a wild guess proxy as it is about what influenza does) so that the case load does not exceed hospital capacity. This is what everybody is talking about as “flattening the curve”.
With the stretch it and let it burn strategies the number of people who get sick and then have immunity rises to about 1-1/R0 or about 60% of the population (assuming getting sick once confers immunity – assumed right now but a few counter examples are out there). Then the effective growth rate Re drops below 1 and “herd immunity kicks in”. Individuals can still get sick but it can’t become a self-sustaining epidemic. The primary difference between let it burn and stretch it is the rate at which people get sick which is inversely correlated with how long the epidemic lasts.
I’ve posed several questions below to get this started. I’m not an expert. So the answers to some of these may be obvious in which case, I’d love to know the answer. But I have not seen the answers to any of these despite voracious reading. If they’re not so obvious I expect we could all learn from discussing them.
If you want to respond to a question stay in the same thread (even if the nesting stops at 3 levels). If you want to pose a new question, start a new thread. This is NOT a place for politics, so anything stronger than “many governments have been incompetent at X” (e.g. naming specific individuals, blaming one party or another, or getting distracted off science) will be deleted.
As has been pointed out on this blog before, it does matter who we recognize for society awards. And one of the strongest filters on that is who is nominated. Award committees can’t give an award to somebody who isn’t nominated. It does take a little time and effort to nominate somebody, but not a lot (comparable to writing a letter of reference).
The International Biogeography Society gives out awards at its biennial conference. There is an Alfred Russell Wallace award for lifetime achievement and the MacArthur & Wilson award which targets “relatively early career” (<12 years from PhD).
You can find details on the awards and how to nominate somebody at: https://www.biogeography.org/news/news/2019-call-for-awards/
The deadline is November 29th to nominate somebody for the awards given at the next IBS meeting in Vancouver January 2021 (put it on your calendar to attend too!).
So what are you waiting for? Nominate a deserving biogeographer.
The journal Science released an article entitled “Decline of the North American avifauna” by Rosenberg et al today (Sep 19, 2019), and already disaster laden headlines are appearing in major newspapers (I’m not going to bother to link to them because they’ll probably change by tomorrow but I bet you’ve already seen this in your favorite news source).
Aside from the question about what statistical methods are appropriate to use in ecology, there is a mostly independent question about how many statistical methods is optimal for use across the field of ecology. That optimum might be driven by how many techniques we could reasonably expect people to be taught in grad school and to rigorously evaluate during peer review. Beyond that limit, the marginal benefits of a more perfect statistical technique could easily be outweighed by the fact only a very small fraction of the audience could read or critique the method. To the extent we exceed that optimum and are using too many different methods, I think it is fair to talk about statistical Balkanization. Balkanization is of course a reference to the Balkans (the region in the former Yugoslavia) and how the increasing splintering into smaller geographic, linguistic and cultural groups became unsustainable and led to multiple wars. I think there is a pretty clear case that too many statistical methods in use is bad for ecology and thus the label of that state as Balkanization is fair (I’ll make that case below). I am less sure if we are there yet or not.
If you believe the press, scientists are desperate to publish open access. Is this really true? Turning our scientific method onto ourselves and our peers, let’s see what kind of actual data there is. Every 3 years Ithaka SR (a consulting firm for non-profits) publishes a survey of US faculty for attitudes and behaviors that can help university libraries serve their faculty (https://sr.ithaka.org/publications/2018-us-faculty-survey/). The whole survey is well worth a read. There are interesting questions about social media, data storage, attitudes towards books, etc. But I want to home on their Figure 31 which summarizes data about what kind of journals faculty want to publish in.
The single biggest fact about human impact on nature is that it is highly variable. We’re net cutting down forests in the tropics. But we are net increasing forest cover in eastern North America. Farmland birds are in decline in the US and Europe, but that is because farmland – a fairly intense human land use – is decreasing in area in those countries. Eutrophication is harmful to many organisms, but helpful to some. Local biodiversity is trending down in some places but trending up in others. In North America beaver and turkeys, after having been completely eliminated from most of their ranges, have made amazing recoveries trending towards near pre-European levels. Regional diversity, especially in plants, is often increased due to invasive species. Island diversity in birds is often flat or down.
None of those statements contradict the fact that humans are massively changing nature, in many ways for the worse. We have half the tree biomass today compared to what existed pre-human. We appropriate half the fresh water and terrestrial NPP annually. Extinction rates are elevated significantly. We have doubled the rate nitrogen is being introduced to the biosphere. Deer are above pre-European levels in the eastern US with devastating impacts on the structure of forests. Scientists have gotten very good about communicating these negative impacts and maybe have even evolved to a symbiotic relationship with much of the press in communicating this (media loves a disaster whether environmental or human).
But what do we as ecologists do about those facts that can be seen as positive impacts listed in the first paragraph? Continue reading
A few weeks ago, I lamented the passing of papers like Janzen’s Why mountain passes are higher in the tropics (1969) or Janzen’s Herbivore and richness hypothesis (1970) (the Janzen half of Janzen & Connell hypothesis) or the Hairston, Smith & Slobodkin (HSS 1960) paper best known as “why is the world green” even though that is not really the title. These papers were highly speculative, waved a little bit of data around, but mostly put out a hypothesis that attracted researchers for decades. But you don’t really see these kinds of papers any more. Hence my question of whether we should assume this category of paper has come to rest in peace (RIP) (i.e. are dead). Continue reading
If you want to simplify philosophy of science down to the point of gross oversimplification, it has been a millenia long debate between rationalism and empiricism. Although both could be found among the classic Greeks, rationalism was dominate from the time of the Greeks to the Renaissance (almost 2000 years). Rationalism holds that knowledge comes from logical thought. Think Euclid who established the axioms/proofs style of geometry. Or Plato’s cave which emphasized that our senses are crude and misleading (observing mere shadows on the cave wall) in capturing the underlying true essence (the perfect objects outside the cave creating the shadows which we cannot see). Empiricism on the other hand believes that knowledge comes from our sensory experiences of the world outside our mind and mistrusts the mind. Empiricism and rationalism are endpoints of epistemology (the philosophy of how we know things). But they have also been major motivators for scientists framing how to do science.
The President of the Ecological Society of America has written a nice blog post on the ESA website about the changing nature of publishing (and how this influences societies and their finances). The short answer is it has big impacts!
As I wrote a few weeks ago, a potentially new seismic shift is happening due to Plan S which seeks to go for pure Gold OA (100% OA journals) and eliminate hybrid OA, green (post a PDF on your website) OA, and other models like JSTOR and old fashioned subscription based models.
ESA is on top of this change and is seeking your input. Read the whole blog post for lots of good thoughts. But if you are tight for time, I have excerpted their request for input:
We are of course very interested in what our members think about this complex issue! Are you currently limited in your ability to access the literature – especially recent papers – and would you be in favor of a rapid shift to open access for ecological research publications? If you are active in submitting and publishing research papers – do you normally have the financial resources to cover the costs of article processing for fully open access journals? Do you have ideas about how to subsidize or afford the publication of papers in these OA journals from authors who cannot afford the processing charges?
ESA will assuredly be affected by continued evolution of the business model for scientific publishing. In order to understand the impact on our members’ professional lives, and not just on our revenues, it is important that we hear from you. I look forward to reading your thoughts. Email (firstname.lastname@example.org), and use “publications” in the subject line. We will keep our members fully informed as Plan S and related developments move forward.
I recently surveyed our readers on what shape they thought the overall trajectory of ecology took. It was a fun post with a number of good comments. First I set up the question and polled the readers what shape they thought the overall trajectory of progress in ecology took. Then I argued in some detail that it was circular or spiral (and explored what this implied). So its hard to know how many readers truly took the poll before reading my argument, and of course the subset of our readers who chose to answer a poll is clearly not random in a scientific sense.
But here are the results.
We had just over 200 respondents. Of these about 10% answered “other” and described their own trajectory. These are interesting and I’ll mention some in a minute. But of those who went with one of the six choices I provided 37% went for circular or spiral, 35% went for some version of systematically increasing (linear, exponential or saturating) and 28% went for a random walk, so very roughly 1/3, 1/3, 1/3. Of course random walk is interesting because it contains elements of both circularity and a trendline (most random walks look like they contain a trend but also have noise and are likely to return to the beginning at some point). The spiral was far more popular than the circle, although some agreed with me that the vertical gain in the spiral was only methodological, but others felt the vertical gain of a spiral was an important feature.
I thought I would find a strong link between career stage and view of trajectory shape, but I didn’t. There might have been a weak signal of a U-shape (early career and late career being more likely to pick one of the upward trends and middle career – postdoc, early permanent position – more likely to pick a circle or a random walk), but I didn’t see a strong enough effect size to want to pursue it further (read-only link to the data here: https://docs.google.com/spreadsheets/d/12AAXpbEwTbvW9_OmRxeJZwjGZM3eLl5ZlyudwhlnOwk/edit?usp=sharing).
The suggestions under “Other” were also interesting and broadly included 4 categories:
- Variants on a systematic upward trend (e.g. logarithmic, power law with exponent of 3/4 of course, upwards staircase or very uneven steps that aren’t level, sigmoidal, step function, Kuhnian/punctuated equilibrium)
- Complexifications on random walks including multiple random walks, random walk with weak trend, chaotic, chaotic with multiple attractors (several people suggested multiple dimensions are needed)
- Several poetic versions (“More like a crystallisation process in which new nodes form and spread until they butt up against one another”)
- Pessimistic (systematic downwards) such as “downward slope” or “digging a deeper hole into a dark pit”
Probably the most comprehensive answer under “other” was “A combination of gentle progress superimposed with a lot of random noise and occasional bigger and more frequent smaller jumps (and “epicycles” within certain topics as they get rediscovered and renamed)”. It would be hard to disagree with that and that is probably a good place to end!