Sayre’s law

Politics among academics are so vicious even though the stakes are so small.

(See investigations in variants of this on Quote Investigator.)


A coauthor on a manuscript invoked a term, ‘Taxonomic Impediment’, which was new to me. As it often happens with such things, checking it up unearthed a bunch of very interesting opinion papers, some of them going back and forth. I always like reading such comments-and-responses.

One paper was a particular delight:

After not getting the number of manuscripts I had hoped for from some team members or even progress reports from them of what taxa had been identified by them, I began to think that the only reason some participants signed on to the project was merely to get a free trip to Fiji, soak up some sun, go collecting, go back to their labs with their booty, and reminisce with digital pictures and collected specimens of the pleasant 10–14 days they had amongst the warm climes and friendly people of that South Pacific island nation… But all the participants in the Fiji project are good-natured and honest colleagues who were chosen not only for their expertise with their taxon, but because they showed a genuine interest in the Fijian arthropod fauna. What then could be the reason for their inactivity of some in publishing?… In prodding and querying team members on when they expected I might eventually see a submitted a paper, they offered a number of excuses: some reasonable, but others verging on pathetic and reminiscent of childhood excuses to their teachers for not turning in homework. It became obvious that publishing was a chore and not a priority for these latter cases. For them it was like having to eat your vegetables as a kid: Some kids ate them at the beginning so that the bad taste would not be the last taste in their mouth; while others opted to wait until the end because it the most horrible part of the meal and easiest to put it off as long as possible. I was apparently experiencing the latter school of thought on vegetable eating… Going out and collecting and even sorting and finding new taxa was reward enough for them. The drudgery of having to write and/or type up a decription was the smallest blip on their radar screens…

Evenhuis (2007) Zootaxa 1407: 3–12

For me, fieldwork are my ‘vegetables’. I love analysing data, reading papers, and thinking of new hypotheses to test, and I don’t really mind the writing.


Better air, not better alloy

I like special issues in journals. They’re like a packet of M&M’s that’s all chocolate-coloured. Whenever I see one of these special issues on a topic that I’m particularly interested in, I usually download every paper, but I rarely read them all. So just like for M&M’s, where getting excited over a colour is pretty pointless since they all taste the same, getting excited over a special issue is pretty pointless when I don’t have time to read all the papers in it.

One special issue I stumbled across recently is the Macroecology 30th Anniversary in Global Ecology & Biogeography published in 2018. As usual, I read the introductory paper, got excited about it, and downloaded the whole issue into a folder, semi-consciously aware and resigned that I wasn’t going to be able to read more than that.

The next day I reached way too early at a carpark to meet someone for fieldwork. Since I had my laptop with me, I opened up the folder with the special issue papers, and began reading.

The special issue was great. I had spent much of my young scientific career examining patterns in species diversity and composition from field data. I had a growing doubt about the theoretical basis behind much of these patterns I had been testing. Recently, however, I’ve been dabbling into more mechanistic approaches that “build up” to community ecology, e.g., from demographic processes and pairwise species interactions. McGill’s paper was especially timely for me to read to place my thoughts about bottom-up and top-down approaches to understanding communities in the context of development in the field.

But the point (and title) of this post is not really about macroecology per se. It’s about this quote in the paper by Fahrig from metallurgist Ursula Franklin. A mining company was asking her to develop a more corrosion-resistant copper alloy, to which she said,

You don’t need better alloys; what you need is better air.

The quote struck me. Not for the broader message that Fahrig cited it for, which was that we needed to try to see problems from different angles. The article should also be read for the “long and tangled tale” of habitat fragmentation research.

But the actual context of that exchange reflects the same kind of thinking of some of the people I’ve worked with. Their first reaction whenever we tell them about potential impacts of human activities or development on the environment or biodiversity is: Can you give us some solutions that allow us to have our cake and eat it?

And if we do propose solutions but they involve some common-sense actions (other than, e.g., forgoing development), such as putting up signs or educating the public, we tend to be ignored. More than once I’ve been asked if there are any “higher tech” solutions.

To which, seriously, you don’t need a better alloy. You need better air.

Science denialism

I’ve not written anything on this blog for ages. Not because I don’t have anything to say, but I’ve been so swept up since returning to Singapore in October 2017 that writing a blog post instead of writing anything else, e.g., manuscript, emails, felt like an extravagant use of limited energy. But I thought this is something worth just noting about for my own future reference.

Science denialism is an issue that I’ve become more and more aware of in recent years. Part of it has to do with seeing the post-truth world go by as a scientist and now an educator on environmental topics.

Understanding how people deal cognitively with environmental information that conflicts with their prior beliefs is part of the interdisciplinary fields of conservation psychology and environmental psychology. I just read a commentary on a paper that attempts to experimentally test, i.e., with randomised treatment-control groups, the effects of misinformation and ways of debunking the misinformation on people’s beliefs towards two common areas of science denialism: climate change and vaccination. To summarise,

  1. The “good” news: debunking works. They didn’t find any evidence of the so-called backfire effect, where people dig their heels in deeper into factually wrong beliefs when presented with the corrective information.
  2. The bad news is that after-the-fact debunking does not completely neutralise the effects of being exposed to misinformation.

This doesn’t mean that the backfire effect doesn’t exist, just that it may be less prevalent generally and more context-specific (see an accessible summary here). The backfire effect was one particular thing that worried me and made me wonder about the best practices in trying to combat misinformation.

I think at the end of the day, when advocating for certain viewpoints, it’s still strategic to categorise the potential audience into (1) people that are already aligned with your position, (2) people who have very strongly held beliefs antithetical to your position, and (3) the uncommitted middle ground. Preaching to the converted makes us feel good, but doesn’t really contribute much to getting more buy-in. The people in group (3) are the ones to prioritise efforts for.

For those in group (2), I would personally guess that a certain degree of the backfire effect may still apply. I read somewhere (and I need to find out where one day): people are more likely to transform deep-seated beliefs if they arrive (or think that they arrived) at new insights by themselves, and not from inputs by others. So engagement with this group has to take a different strategy.

All this applies not just to science denialism but dealing with misinformation and disinformation in general. The paper suggests that in light of the second finding, “innoculating” the broader public against disinformation and the tactics of their purveyors will be necessary.

How many different ways can you say “plants”?

I’ve worked on a number of inventory-based surveys for government agencies, and as the person tasked with the vegetation and plants, the use of one term always gets to me:


When exactly do we use this word?

I’ve seen it in a number of combinations that make me cringe. In an earlier project, I quickly requested a name change to prevent myself from being on the “Flora Team”. We asked to be called the Vegetation Ecology Team instead (which later got contracted to “Vegetation Team”) and spent some time correcting the phrases with the word “flora” in it to vegetation instead. I eventually decided that vegetation ecology appropriately represents most of my core research interests, and so I do like the term vegetation.

But recently I saw the phrase “vegetation species” in a report, and it feels like I’m fighting some kind of word hydra here.

Plants are, well, plants. There may be some technical debate among biologists about the exact phylogenetic delimitation of plants, most lay people know what plants are. If we wanted to be more specific, we could say “flowering plants”, “seed plants”, “vascular plants”, or “land plants” to make sure the scope is clear. Only “plant” (and only in singular form! as a modifier of a noun) go with “species” (which is the same whether singular or plural), i.e., “plant species”.

The word “flora” usually refers to flowers as a noun. As an adjective referring to flowers, it is “floral”. However, we do refer to the plants (and fungi) of a particular place as its flora, e.g., the flora of Singapore, and this collectiveness is in the term “flora and fauna”. These are the only two other safe ways to use “flora” as a substitute for plants in plural. The adjective form when used in this sense is “floristic” and not “floral”; to specify what a checklist is about, we either write “plant checklist” (clearly without fungi) or “floristic checklist” (may or may not have fungi). So “flora species” and “flora team” and “flora section” are just weird, unless you’re really referring to only flowers, in which case it is in a grammatically incorrect form because it should be “floral” instead.

“Vegetation” on the other hand is not just about what plant species there are, but also how they occur together in space and time, with the more abundant species (in terms of biomass) forming the physical structure by virtue of their greater volume. Both flora and vegetation refer to plants in the collective sense, but the former refers to a list of plant species, while the latter refers to the emergent properties of the actual occurrence of groups of plants in one place at any one time. Vegetation ecology, apparently more commonly used in Europe, is considered by some textbooks I’ve browsed through to be synonymous with plant community ecology, which is apparently more commonly used in North America, but I think vegetation ecology also includes some larger scale aspects such as ecosystem-level processes (e.g., productivity) and landscape or regional measurements (e.g., remote sensing).

There is a subtle difference when you say you study/do research on (1) plants, (2) the flora, and (3) the vegetation of an area.

If you’re an animal person, you would cringe too if someone used the word “faunal species” and “faunal community”, and that’s already fauna with an “l”. So, just call it plants and animals if you can.

Random, or random effects?

This popped up on my Twitter feed in the last few days:

If you looked at the replies and retweets, many are outraged at what appears to be a poor-fitting trend line passing haphazardly through a cloud of points.

But all is not as it seems.

Recently I’ve been scratching my head at some similar results: model parameter estimates showing strong effects plus OK-looking model diagnostics, but scatterplots with the prediction lines (and confidence bands) seemingly hinting at weaker, stronger, or even non-linear trends. I can’t show the graphs here because they’re either from a manuscript in review or confidential data until published. But here’s a simple example modified from a study we published last year (Neo et al. 2017 Applied Vegetation Science 20: 692).


At first glance, it seems like two very poor fits, or at best weak effects, of soil nutrients on species richness. But, without going into the details, the model rankings and effect sizes suggest strong (and negative) effects of soil nutrients.

What’s going on?

I think there is one thing that people forget when trying to interpret two-dimensional (X-Y) representations of regression models, especially more complex ones with either (1) multiple predictors, i.e., “multiple regression”-type models, and (2) random effects in addition to fixed effects.

1. Other predictors in the model may account for some (or much) of the apparent variation in the scatter of the points. In the graphs below, the size of the points are weighted by one of the other predictors in the best models: distance to old-growth forest. The larger the point, the further away that plot is from old-growth forest.


There is still the effect of litter depth that I didn’t show.

2. Similarly, random-effect levels, e.g., in a random-intercept model, may account for some of the variation, leading to a more precise estimate of the effect of the predictor. In the graphs below, each plotting symbol represents a different forest patch.



It’s not too easy to see, so in our paper, we represented each forest patch of five plots as a cross-hairs of standard errors of soil nutrients (along the X-axis) and species richness (along the Y-axis), rather than plot them in different symbols.

More precise estimates of the uncertainty means smaller standard errors (and also confidence intervals), which means a stronger effect size. This phenomenon is similar to the well-known “Simpson’s paradox“. This is why multiple regression and mixed-effects models are very powerful tools: they reveal effects which otherwise might be mistaken as noise.

Back to the paper being panned on Twitter: I didn’t read too much in detail, but enough to see that the authors were indeed estimating the partial correlation between the X and Y variables after accounting for a host of other effects, such as class, course, degree program, term, teacher characteristics, etc. The authors wrote that the effects they estimated were:

…equivalent to a partitioned regression model of the evaluations qdtcs on our measures of teacher effectiveness, i.e. the residuals of the regressions in Table 5, where all the observables and the fixed effects are partialled out.

…For convenience, results are reported graphically in Fig. 1.

Fig. 1(a) was the offending graph in the Tweet. They had good intentions to show the effect graphically, but “backfired” as described by some of the commentators. (I hope the same thing doesn’t happen to my manuscript(s) in review…)

Calming the mind



Too much knowledge leads to overactivity,
Better to calm the mind.
The more you consider, the greater the loss,
Better to unify the mind.


Excessive thinking weakens the will,
The more you know, the more your mind is confused.
A confused mind gives rise to vexation,
The weakened will obstructs the Tao.


Don’t say there is no harm in this,
The ensuing pain may last forever.
Don’t think there is nothing to fear,
The calamities churn like bubbles in a boiling pot.


Water dripping ceaselessly,
Will fill the four seas.
Specks of dust not wiped away,
Will become the five mountains.


Protect the branches to save the roots,
Though a small matter, it is not trivial.
Close the seven orifices,
Shut off the six senses.


Pay no heed to forms,
Do not listen to sounds.
Listening to sounds you become deaf,
Observing forms you become blind.


Literature and art
Are but busy gnats in the air.
Technique and ability,
A solitary lamp in the sun.


Those able and talented ones
Are really stupid fellows.
Discarding the pure and simple,
They drown in too much beauty.


Consciousness is an untamed horse,
The mind an unruly monkey.
If the spirit is overactive,
The body will sicken and die.


Wrong conduct ends in delusion,
Those treading this path become mired in mud.
To regard ability as precious
Is called confusion.


To exaggerate clumsiness and covet skill
Does not lead to great virtue.
Of much fame but little contribution,
Their reputations quickly crumble.


Being inwardly proud
Brings the enmity of others.


Using speech
Or written words
To gain the praise of others
Is something most repulsive.


What common people regard as auspicious
The sage takes as evil.
The enjoyment gained is fleeting
But the sorrow is everlasting.


Beware of shadows and tracks,
The farther you leave them the better.
Sitting upright in the shade of a tree,
Neither traces nor shadows remain.


Worries of birth and distress of old age
Are products of your own thoughts.
If the mind’s thinking is ended,
Birth and death are forever cut off.


Not dying, not born,
Without names and form,
The Tao is empty and tranquil,
The myriad phenomena are equal.


What is of value? What is cheap?
Where is there shame or glory?
What is excellent or inferior?
How can there be heavy and light?


The sky puts purity to shame,
No brightness compares with the brilliant sun.
Stable as Mount T’ai,
Steady as a golden wall.

(Translation adapted from The Poetry of Enlightenment: Poems by Ancient Chan Masters by Chan Master Sheng Yen.)

Putting writing in the daily schedule

If you do research, you probably enjoy it. Research is oddly fun. Talking about ideas and finding ways to test your ideas is intellectually gratifying. Data collection is enjoyable, too, especially when other people do it for you. Even data analysis is fun–it’s exciting to see if a study worked. But writing about research isn’t fun: Writing is frustrating, complicated, and un-fun. (p. 4)

Writing is grim business, much like repairing a sewer or running a mortuary. Although I’ve never dressed a corpse, I’m sure that it’s easier to embalm the dead than to write an article about it. (p. 11)

Only a fool… rewards productive writing with skipping a scheduled writing period. Never reward writing with not writing. Rewarding writing by abandoning your schedule is like rewarding yourself for quitting smoking by having a cigarette. (p. 44 – 45)

Paul J. Silvia
How to Write a Lot

Research parasites

…research synthesists in medicine have recently been described as “research parasites” of primary studies and the researchers who conduct them… ‘Research parasites’ can… serve to increase scientific diversity by adding another ‘trophic level’, thus improving the functioning of the scientific ‘ecosystem’.

Gurevitch et al. (2018)
Nature 555: 175

Whatever we still have

I was recently standing on a ridge in Borneo in a patch of forest that extended only 200 m in any direction, but I could not be sure I was not in a vast forest. If we relax our minds, forgetting temporarily that a patch is small, we can experience again the sense of wonder and desire to understand what we have at hand. These patches are the future of tropical rain forest, so let us treasure them, rather than seeing them as the dregs.

Cam Webb
Conservation Biology 19: 275

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