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:

“Flora”.

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:

https://platform.twitter.com/widgets.js

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).

abaplantnwasplantk

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.

abaNdist.jpegwasKdist.jpeg

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.

abaNpatchwasKpatch

 

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

《息心铭》
僧亡名

多知多事,
不如息意;
多虑多失,
不如守一。

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

虑多志散,
知多心乱;
心乱生恼,
志散妨道。

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.

勿谓何伤,
其苦悠长;
勿言何畏,
其祸鼎沸。

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.

滴水不停,
四海将盈;
纤尘不拂,
五岳将成。

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

防末在本,
虽小不轻;
关尔七窍,
闭尔六情。

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

莫视于色,
莫听于声;
闻声者聋,
见色者盲。

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

一文一艺,
空中小蚋;
一伎一能,
日下孤灯。

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

英贤才艺,
是为愚蔽;
舍弃淳朴,
耽溺淫丽。

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

识马易奔,
心猿难制;
神既劳役,
形必损毙。

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

邪行终迷,
修涂永泥;
莫贵才能,
日益惛瞢。

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

夸拙羡巧,
其德不弘;
名厚行薄,
其高速崩。

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

内怀憍伐,
外致怨憎。

Being inwardly proud
Brings the enmity of others.

或谈于口,
或书于手;
邀人令誉,
亦孔之丑。

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

凡谓之吉,
圣谓之咎;
赏玩暂时,
悲哀长久。

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

畏影畏迹,
逾远逾极;
端坐树阴,
迹灭影沈。

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.

厌生患老,
随思随造;
心想若灭,
生死长绝。

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.

不死不生,
无相无名;
一道虚寂,
万物齐平。

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

何贵何贱,
何辱何荣;
何胜何劣,
何重何轻。

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?

澄天愧净,
皎日渐明;
安夫岱岭,
同彼金城。

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

Energy-intensive local diversity

… humans tend to sacrifice ecological and geographic heterogeneity for an artificially maintained, energy-intensive, local species diversity. Take, for example, the large numbers of plant taxa maintained in the… cities of the world. Most of these species are horticultural varieties that do well in landscaped gardens and parks. One sees a great variety of such plants… But the roses, citrus, camellias, bougainvilleas, daffodils, eucalyptus, and begonias are everywhere similar.

This combination of local variety and geographic homogeneity produces several pleasant benefits for humans. Not only are the exotic species more spectacular, but the world traveler can always feel botanically at home… But the aesthetic benefits are costly. The price is low geographic diversity and ecological complexity.

-Soulé, M. (1985)
BioScience 35: 727

Tree identification

What are the leaves of the mango tree like? It’s enough to pick up one leaf and look at it to know. Even if you look at ten thousand leaves, you won’t see much more than you do looking at one. Essentially they are all the same. By looking at one leaf, you can know all mango leaves. If you look at the trunk of the mango tree, you only have to look at the trunk of one tree to know them all. All the other mango tree trunks are the same. Even if there were a hundred thousand of them, I would just have to look at one to really see them all. The Buddha taught to practise Dhamma in this way.

– Ajahn Chah
The Key to Liberation and the Path to Peace
p. 14

The aftermath of a war for talent

[Enron] epitomized the “talent mindset” approach to management… Demanding Enron employees prove that they were smarter than everyone else inadvertently contributed to a narcissistic culture, with an overrepresentation of employees who were both incredibly smug and driven by deep insecurity to keep showing off. It was a culture that encouraged short-term performance but discouraged long-term learning and growth.

The same point comes through in the postmortem documentary on Enron called, appropriately enough, The Smartest Guys in the Room. During the company’s ascendency, it was a brash and brilliant former McKinsey consultant named Jeff Skilling who was Enron’s CEO. Skilling developed a performance review system for Enron that consisted of grading employees annually and summarily firing the bottom 15 percent. In other words, no matter what your absolute level of performance, if you were weak, relative to others, you got fired. Inside Enron, this practice was known as “rank-and-yank”. Skilling considered it one of the most important strategies his company had. But ultimately, it may have contributed to a work environment that rewarded deception and discouraged integrity.

-Angela Lee Duckworth
Grit
p. 30

Is the current (conscious or subconscious) emphasis on “talent” in academia leading us down this path?

Pascal vs. the Buddha

Pascal’s Wager is sometimes said to be the first formal use of decision theory. Blaise Pascal of the 17th century reasoned thus: if one believes in the Christian God

  • and the belief turns out correct, one is rewarded with an eternity of gain (or, conversely, avoids an eternity in hell);
  • if the belief is wrong, one loses almost nothing.

The thought struck me that the lesser known second part of the Kalama Sutta is also a similar application of decision theory. This is about 2,200 years earlier than Pascal. But it was not as formally laid out, and is also a far more complicated (but therefore more realistic) case.

The Buddha applied it to moral decisions in the context of the extent to which one believes in the cause and effect of one’s actions, or simply put, Kamma/Karma, the full extent of which is necessarily linked to rebirth.

Although how causes, moderated by conditions, lead to their effects is a highly complex matter, they are generally still apparent to us (think physics) and therefore believable… except for the catch that there might not be enough time for all effects to come to fruition within one lifetime. Therefore rebirth completes the idea of Karma by providing opportunity in the future for fruition of effects.

Ordinary people, however, do not have knowledge of their past lives or that of others’, so the idea of rebirth is not as apparent and believable.

According to the Buddha’s reasoning as re-told by the Kalama Sutta, if one acted accordingly to avoid unwholesome deeds and carry out wholesome ones throughout this life:

  • if rebirth were true, one benefits in this life as well as after the end of this life by having a better rebirth;
  • if rebirth were not true, one most probably still benefits by having lived blamelessly and avoided substantial suffering in this life.

I did a quick Google and found out that other people have linked this to Pascal’s Wager before. In fact, some folks online called this the Buddha’s Wager. There is also another scripture that is in the same spirit: the Apannaka Sutta.

The Buddha’s Wager is a far more complex application of decision theory, however, because:

  1. Pay-offs is positive (according to the formulation above; just like Pascal’s Wager, it can be formulated in the negative sense, i.e., living an unwholesome life) in both options, just that one (with afterlife) is on top of the other (this life only). In Pascal’s case, pay-off is usually taken as neutral (=zero) in the worse of the options (but see below).
  2. The degree of uncertainty is different between the two pay-offs. It seems to me that this is a critical part of the Buddha’s version. Cause and effect within this life is more believable (=lower uncertainty) because it is usually observable; rebirth (i.e., continuation of cause and effect after death) on the other hand is usually not observable and therefore highly uncertain. (In Pascal’s case, there is only one non-zero pay-off option so uncertainty is only relevant for that pay-off.)
  3. There is a cost involved, because this case, in contrast to Pascal’s in the context of Christian doctrine, is not just about belief but about taking action. Acting incurs opportunity cost in the process, e.g., effort invested, or “opportunities” missed from not taking advantage of others or not indulging oneself.

The Buddha’s case is therefore pitting a additional pay-out (P1) that has higher uncertainty (U1) together with a basic pay-out (P2) that has lower uncertainty (U2) against the cost of action (C). So one should only take action and incur the cost if one can be sure that the combined expected benefits are high enough:

P1* (1-U1)+ P2* (1-U2) – C > 0

The Buddha’s Wager is not so much of a wager as it is a statement of inequality: that P2* (1-U2) > C so that the value of U1 is irrelevant, i.e., the benefits of a wholesome life are sufficient in itself to justify us taking the trouble, whether or not there is an afterlife. In such a formulation, the Buddha portrays moral behaviour as cost-beneficial regardless of afterlife belief.

If we go strictly by the Kalama Sutta, U2 seems to be taken to be zero, which reduces the inequality condition to an even simpler one: P2 C. Herein lies the link back to the rest of the Kalama Sutta, and the difference between Pascal’s Wager and the Buddha’s version.

In Pascal’s Wager, the pay-off in choosing to believe is positive infinite. In such a system, nothing finite can be greater than positive infinite, so the pay-off for disbelief, even if positive and not zero, will never outweigh the pay-off for belief. This means that Pascal’s Wager, while logical, is still a non-falsifiable statement of theistic belief.

On the other hand, the Buddha’s inequality statement is possible to be false, and therefore can be subjected to testing; one just needs to estimate P2 and C. This is why it fits in with the first half of the Kalama Sutta: the Buddha’s exhortation to the Kalamas not to base their actions on the sole basis of belief, but to test it out for themselves and weigh the benefits and costs of their actions for themselves and others.

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