One ecologist's view

The Cockroach

2012-01-22T03:44:00.000-08:00

(Inspired by my wife's habit, on Fridays in October, of providing ghastly Halloween treats and poetry in our kids' school lunches. On this day, she provided rubber cockroaches, and we had to come up with some poetry to match the theme. Lacking true creativity, I usually provide different words to poems our kids already knew. Here I update the Raven for my daughter, in the school lunchroom.)
...
Open here I flung the lunchbox, when, so quickly it did outfox,
In there was a stately Cockroach, of the saintly days, once happier.
Not the least obeisance made he; not a minute stopped or stayed he;
But with mien of lord or lady, perched upon my Twinkie wrapper.
Perched upon this food-like substance, digging at my Twinkie wrapper,
Perched, and sat, and nothing more.

Then this tawny roach beguiling, my sad fancy into smiling,
By the buzzing stern decorum of the countenance it wore,
"Though your back is made of rubber, thou," I said, "art sure no tubber!
Ghastly, creepy, and icky cockroach, wand'ring from the lunchroom store.
Tell me what the insect name is on thy lunchroom's Plutonian floor."
Quoth the cockroach, "Nevermore."

Startled at the stillness broken by reply so aptly spoken,
"Doubtless," said I, "what it utters is its only stock and store,
'Scaped from some unhappy master, whom unmerciful disaster
Taught him speech, not love nor laughter, till his songs one burden bore,
Till the words bereft of hope, so that melancholy burden bore
Of life, "No---nevermore."

"Prophet!" said I, "thing of evil--prophet still, if roach or devil!
By the old school building 'round us--by these four walls we both adore--
Tell this soul with sorrow laden, if, within the distant snacktime,
It shall consume a chocolate iced cake, whom the angels name Ding Dong---
Clasp and covet a rare delicious iced cake, whom the angels name Ding Dong?
Quoth the cockroach, "Nevermore."

And the cockroach, never skitt'ring, still is sitting, still is sitting
On the pallid Twinkie food-like substance, me wishing I had more;
And his 'tennae moving, sensing, like a demon's sword in fencing.
Flourescent lights o'er him dancing throws his shadow on the floor;
Like my hope (to eat that Ding Dong) is also dashed on the floor,
To be lifted---nevermore!

Too much or too little?

2011-11-23T12:11:00.001-08:00

Ecology seems to me like a hodge-podge of different ideas. A result of this (or a manifestation or a cause?) is that we teach and learn ecology as a hodge-podge. The single overarching theme is levels of organization, typically as individuals, populations, communities, ecosystems, landscapes, and global issues, with a generous dose of climate, geology, and geography at the beginning. Applications, statistics, experimental design, and primary literature are scattered throughout.

Quick perusal of two evolution textbooks (Ridley, and Freeman & Herron) showed me that they do not have chapters on "The Physical Environment" or "Biomes" or "The Earth's Climate System." The evolution textbooks instead focus on the math and biology that is universal.

What if a book laid out ecology completely independently of natural history and environment? Would the books look the same? Do we need context? If we lead with context (e.g., a pond, a forest, a grassland) what do we gain, what do we lose?

Why do we lead with the physical environment? Perhaps because we have been ecologists for at least the past 2 my, and we know a lot?

What if we learned B = aM^z and dX/dt = aX - bX^2 before we learned that trees dominate the eastern US, and deep water bodies are dark?

This same question plagues the niche vs. neutral debate...in ecology. Evolutionary biologists learned long ago that it is both, in different measure. Ecologists and humans generally are plagued with the notion that niche matters. It makes it hard for us to think outside the box.

US Government as Central Dogma of Molecular Biology

2011-11-23T09:30:00.001-08:00

Spoiler Alert: There is nothing new here. However, writing it helps to form the thoughts in my own head ....

Complex adaptive systems
A colleague of mine -- a very successful molecular biologist -- recently gave a Sigma Xi Researcher of the Year presentation. In it, he made the relative specific analogy relating the central dogma of biology to the operation of the U.S. federal government. It blew my mind. It was is SO cool to me, because I take seriously those "far fetched" analogies between different complex adaptive systems. I realize that others have made these analogies before, but they are cool to me, because I rarely hear them.

Social institutions, such as governments, are complex adaptive systems under selective pressures. Each governing institution acquires mutations which maybe retained or discarded. Each governing institution competes with other governing institutions for limiting resources. Different institutions exhibit different levels of survival and growth and spread. These institutions tend to be passed on from generation to generation because humans have written records, and also simply and more importantly, humans remember what they did yesterday and twenty years ago, and change is both intellectually challenging, and financially restricted.

Governments exhibit
-- phenotypic variation,
-- heritable phenotypic variation, insofar as governments persist and self-replicate,
-- fitness differences among variants.

As a consequence of these phenomena (1), governments tend to evolve. As you know, evolution does not always optimize performance. Rather, they undergo probabilistic responses to selective pressures,. It is possible for these responses to result in objects which are poorly suited for future conditions. Evolution is always backward-looking.

Social Darwinism? No, not in the original sense.

The above smacks a bit of "Social Darwinism." However, the earlier incarnation of that phenomenon was used as an excuse for greed and imperialism (2). In the past 50 years, however, strong evidence has accrued that cooperation can easily evolve and is an evolutionary stable state (3). All successful societies or nations rely heavily on within-group cooperation. It seems further that cooperation among nation-states provides increased fitness as well. This seems like a no-brainer, given that nation-states are themselves composed of interacting groups that cooperate as well as compete.

One of the primary requirements of the evolution of cooperation is that fitness of individuals within groups is increased through the cooperation. This central criterion is often easily met.

Another important criterion for the emergence and maintenance of cooperation is repeated interactions among the same agents so that "learning" can occur. Repeated interactions is the key difference between the standard Prisoner's dilemma game, where cooperation is not advantageous vs. games in which cooperation is advantageous. [I put "learning" in quotes, because it need not be learning in the usual sense of a cognitive process by an individual, but rather can be an adaptation to respond to cues given by cheaters that they are cheating. "Cheating" is defined as the receipt of benefits of cooperation without incurring the costs of cooperation and reciprocity.]

The ease with which cooperation can arise, and become a stable equilibrium does not exclude the possibility that cheating cannot also arise. However, under easily met conditions, if a "mutation" does give rise to cheating, it can be eliminated, or kept at low levels, depending on the conditions.

References cited

1. Endler, J. Natural Selection in the Wild. Princeton monographs.
2. Wikipedia, 2011, http://en.wikipedia.org/w/index.php?title=Social_Darwinism&oldid=461877577
3. Nowak, 8 December 2006, Science; Nowak et al. 26 August 2010. Nature.

Oops. Another poorly implemented assignment

2011-11-09T19:40:00.000-08:00

For a graduate class I asked grad students to prepare for a Monday class by reading a text book chapter and writing down two research ideas complete with a very short literature based rationale. In class on Monday they began collaborating, and for Wednesday's class they wrote 2-3 page preproposals. In class on Wednesday, they reviewed each others' preproposals. I had given them more guidance (see Week 12 in the linked document), but this was the gist of it.

The preproposals were horrible. Although the grammar was fine, and some of the ideas might have been adequate, but the ideas were not well-supported by the weekly readings nor based on deep thinking about the material I had assigned. I think some of their ideas came from their own research projects, but they did not construct convincing arguments as to why anyone would invite a full proposal.

I need to break down the assignment into smaller, more explicit pieces. For example:
"From the material that you have read for this week,
1. What are the important topics in this area of this sub-discipline of ecology?
2. Of the important topics that you identified (for this week, within this area of this sub-discipline), which topics have a sufficient literature upon which you can build, that is, to build a convincing case that your new idea will also be important? [Cool ideas are cool, but they have to be based upon evidence, and evidence is presented in the literature. Mere cool ideas don't get published or funded. A well-reasoned cool idea gets both published and funded.]
3. How do you convince a reader that (a) this area of ecology is important and interesting, and (b) our research idea(s) is likely to bear fruit (i.e., become an important contribution)?"

I had students work in fairly large groups 3-5, and I think it is hard for each member to contribute in a substantial way to the writing. I think the groups should be 1-3 students in size. I will have to pick a size for next week. Perhaps individuals....

When I asked for feedback, students expressed the concern that, while they enjoyed it, they would have gotten more out of lecture. I think that is because they are used to being lectured to by bright, engaged faculty (my colleagues), often on topics not well covered in the reading. In contrast, I am letting Peter Morin lecture (through his text book), and I want the students to grapple -- get sweaty -- with the reading. That is why I assigned both exploratory and formal writing exercise, in order to enable them to dig into it. They did a poor job of it, because I did not give them enough guidance.

I think that they each need to do their own next week, and bag the group work. We will use class time for that. Maybe I will make pairs (but not 3's) optional....

Pathogen-mediated promiscuity

2011-11-06T07:15:00.000-08:00

If the spread of (human) sexually transmitted diseases requires humans to have sex, wouldn't selection favor pathogens which increase our promiscuity? Very interesting...someone smart must have already thought of this and figured out the math...at some level...maybe there is something to add?

Can joy be modelled with a SIR disease model?

2011-11-06T07:12:00.000-08:00

I guess the better question is how, and whether it would make sense or lead to interesting hypotheses. I just like the idea of modelling the spread of something wonderful using a model of something we think of as bad. :-)

Learning environments (more from "How Students Learn...")

2011-05-16T12:05:00.000-07:00

Donovan and Bransford (2005) describe four types of environments, "centered on" learners, content, assessment, and community.

Learner-centered environment. Here we start with what the learner knows, and help the student expand beyond that. Typically, we connect to that existing knowledge as a base, and build outward and upward. Occasionally, we have to carefully remove what was already built before building onward. Related to this, we have to provide manageable, yet challenging tasks, and give them the tools, so students feel challenged and empowered rather than hopeless and frustrated.
Content- or Knowledge-centered environment. Here we begin with three questions: (i) what is important for students to know and be able to do? (ii) what are the core concepts we use for organization, and what are the case studies or detailed knowledge that embody those concepts? (iii) How will we know that students have mastered this knowledge and these concepts? Although items (i) and (iii) overlap with the Learner- and Assessment-centered approaches, item (ii) is the core. It appears critical that specific case studies be understood as exemplars of more general concepts, and that concepts provide a framework for understanding other specific cases. Here I will suggest that students understand that there are usually multiple conceptual frameworks by which we might perceive and understand a specific phenomenon. The authors contend that textbooks tend to focus on the facts and less on the conceptual frameworks. I observe that that is true for the ecology texts I am most familiar with.
Assessment-centered environment. Formative assessment is essential because it makes the success and failure of learning clear to both students and teachers. Such assessments can help both students and teachers identify preconceptions, and to track change in understanding over time. Seeing this change over time helps students understand better where they are and how they got there. These assessments are tools students and teachers need to use in the service of building knowledge.
Community-centered environment. In this environment, we create a place or context that rewards participation rather than correctness, because mistakes, preconceptions, and dogma are all good starting places for real learning. In addition, students are more engaged when participating, and this participation results in a positive feedback loop wherein participation begets enjoyment, enjoyment begets participation, and it all facilitates learning.

Principles of how students learn (from Donovan and Bransford (editors). 2005. How Students Learn....)

2011-05-16T11:26:00.000-07:00

Notes to myself:

The introductory chapter of this NRC book summarizes an earlier NRC report How People Learn: Brain, Mind, Experience and School.

They describe three key principles:
1. New knowledge must connect to existing knowledge already learned.
2. Facts and conceptual framework go together, hand-in-hand. A framework with facts is relatively meaningless (an empty framework) and facts without a framework make no sense, cannot be retained, or recalled.
3. Metacognition (understanding tips, tricks, and principles of learning) helps facilitate learning.

One common trap that I fall into is that I fail to appreciate what a limited experience most students students have of the natural world. Therefore, I fail to connect to their existing knowledge base. To connect this to the principles above, I fail to give students enough facts for a new conceptual framework. I assume that they already have lots of facts in hand (what a maple tree looks like, or what a sow bug acts like). What I may want to do is say or ask:

"Here is a new conceptual framework, and here is how it works and what it is good for."
"Here is a specific example of an empirical experiment that helped confirm the utility of this framework. This is how this example fits into this framework."
"Here is another example...can you figure out how this example fits into the framework?"
"Here are more examples. Go for it."
"Can you find other examples?"
"Can you imagine other ways to investigate the natural world using this framework?"
"What do you like about this framework? What do you find confusing or frustrating about this framework?"
"How might you modify this framework?"

(I might not get around to #7)

Reflections on mathematical modeling (II)

2011-04-26T10:23:00.000-07:00

Brain data dump...

levels of formalization:

what do previous data tell us - deterministic models (e.g., average, linear regression)
what do previous data tell us - stochastic models (e.g., range, standard dev., standard error)
increased sophistication (e.g., non-normal forms of stochasticity: null models, interesting parametric distributions).
meta-analysis - combining previous empirical studies
models with and without feedback or loops

Learning a language, learning modeling concepts.

Modeling data, modeling dynamics.

Learning by,

copying,
applying,
combining,
creating.

Discussing scientific papers in classes - what do we DO?

2011-04-22T08:39:00.000-07:00

Should we demonstrate understanding during class time, or should we just jump ahead? I think we need to demonstrate understanding in class, if only to make sure people actually work at reading the assigned papers. However, we could even read the paper out loud, but that would not guarantee understanding. So, it seems to me that in each class we should address at least the following questions:
1. Is the question addressed in the paper interesting?
2. Do the data address the hypotheses?
3. Do the results support the conclusions?
4. What are the implications of the conclusions (or of the results)?

In class, we might start with #2, then #3, #4, and then maybe return to #1.

Pedagogical and scientific goals

2011-04-22T08:16:00.000-07:00

I posit that understanding is the core value of mathematical modeling. There are (at least) two levels of understanding, the understanding of our own questions. The first aspect of understanding enhanced by modeling is making our spoken language precise with the aid of mathematics. The second aspect of understanding is providing an unambiguous structure to our ideas that the scientific community can use, that is, the development of useful theory.

I like to think of the scientific process of knowledge creation as a 3D spring, coil, or spiral, where a single loop represents a complete cycle of the scientific process (question, hypothesis, test, interpretation), and progress occurs as we repeat the process through multiple cycles, traveling down length of the coils. Mathematical modeling can help us at different phases of a single coil.

I think that making ourselves formalize our conceptual models helps us see and understand our ideas to a greater degree. Formalization helps us become ever more specific and thereby operationalize our hypotheses and thereby generate more testable predictions. Going through the formalization process helps us understand what a mathematical model is and and how mathematical models provide structure to theory. The process helps show us and convince us of how models are used in Science.

Reflection on teaching modeling, or why should non-modelers try to model?

2011-04-21T11:26:00.000-07:00

At the moment, I believe that non-modelers (students or faculty) benefit from attempting to model simple systems. I believe that it helps them become better scientists.

I am near the end of a semester in which we tried to incorporate a little bit of modeling into an otherwise basic graduate level ecosystems course. I think I would like to reflect a bit.

For years I have helped teach a population/community grad course, where we included basic population and food web models, and a smidgen of other stuff. In that course, we started everyone out making the same assumption of ignorance for all, and we taught just enough for students to implement simple models in R. I am not sure how satisfactory it is. I think I want to teach more basic R so that students learn about R in a modeling context, not just their stats classes. I think by learning R they will learn about models even more effectively.

This semester (Winter/Spring 2011), in the ecosystems course, we started students thinking along two tracks, one of conceptual models of ecosystems and the other learning the R language. Our thought was that by the time they had learned enough about ecosystems, to create conceptual models, they would have learned enough R to begin formalizing their conceptual models. However, that has not been the case, for at least two reasons.

The first cause of sub-optimal pedagogy may have been that students new to a language (e.g., R) need to work with it at least three days/week (preferably 4-6), but I did not structure the assignments that way. They need both carrots and sticks, and assignments that require daily turn-around (e.g., automated release and deadlines, or email with 24 hours to upload answers). I would not even have to grade every one of them - just mark them turned in or not, perform spot checks, and provide detailed answers. Why didn't I do this? Several not-very-good reasons:

I felt sorry for them,
I wasn't 100% convinced that I should push programming and math that hard,
it would have been more work for me,
not everyone needed that kind of practice,
those that needed that kind of practice COULD have done self-study.

The second reason for suboptimal pedagogy was that I tried to be more flexible with the modeling assignments than I was easily capable of -- I could create stuff, but some of it took longer than was convenient. In brief, we asked students to come up with a scientific question, explain what is known and unknown regarding that question and their study system, and design a conceptual model that captures the essence of their question and/or system. Students were then asked to formalize their conceptual model using mathematics or computer code or both. The students conceptual models were not all ecosystem models with merely pools and fluxes of all the same units and element(s). Rather, most were a hodge-podge of different sorts of variables that related typically in a mechanistic fashion, but were not comprised of, for instance, pools and fluxes of carbon. Therefore, the relatively low programming ability of the students (see first reason, above) and my desire to be flexible with regard to acceptable topics meant that I had to invent lots of unique code for each different student. And that, Virignia, is the second reason why my pedagogy was sub-optimal.

However, I think that forcing students to formalize their conceptual models has helped them see and understand their own conceptual models to a greater degree. Formalization helps them become ever more specific with regard to their conceptual model and this helps them generate more testable predictions. Formalization helps them understand what a mathematical model is and and how mathematical models provide structure to theory. The process helps show them how models are used in Science, and last, it helps them see indirect connections more clearly and accurately. Well, ... I hope it does all that.

What is your Bayesian prior on non-human animal emotion?

2010-04-16T05:06:00.000-07:00

Although humans have a strong ability to communicate with other humans, we lack the ability to communicate as well with non-human animals. As a result, we know far more about the internal emotional states of other humans than we know about the internal emotional states of non-human animals. Where do we begin to make inferences about the internal states of non-human animals? What is the appropriate Bayesian prior for making inferences about the unknown internal states of non-human animals?

A long time ago, Rene Descartes (1649) posited that non-human animals are akin to machines (have no soul or mind and couldn't feel pain). Since then, it has been considered "scientific" to assume non-human animals actually are machines unless there is overwhelming evidence to the contrary. I propose that this is highly unscientific, and that it is an example of argumentum ad ignorantiam, that the absence of evidence is the evidence of absence. That is, Descartes, and millions since then, have found it convenient to presume that an absence of evidence about non-human animal emotion constitutes evidence of absence of non-human animal emotion.

Another approach to understanding non-human animal emotions, universally taken by infants, is to assume that "all others are like me." Indeed, this is how humans learn about each other. In the absence of a lot of knowledge, we assume that other humans (especially those that look like us) think and feel as we do. This is our Bayesian prior for interactions with other humans. Like Descartes's proclamation that non-humans are machines, this could also constitute a prior for understanding non-human animal emotion.

Is emotion likely to be a synapomorphy shared with a wide range of taxa? In my simplistic way of thinking, I propose two lines of reasoning that suggest it is. First, emotion in humans appears to be regulated in a primitive part of the central nervous system, whose structure is shared with a wide variety of taxa. If it looks like beer, smells like beer, tastes like beer, it might be beer. Second, emotions are useful. Fear and pain are widely recognized for their fitness benefit, for their adaptive value. I suggest that other emotions are likely to have similar fitness benefit. If a behavior generates joy or euphoria or happiness, organisms would be inclined to continue that behavior. Emotions could help provide mechanistic links between biochemistry and behavior.

Bayesian inference is useful in that it requires we think hard and think carefully about our prior beliefs. In that sense, it helps us become more scientific and maybe even more moral.

To P or not to P, that is the question...

2010-04-16T02:42:00.001-07:00

I think that at least part of the reason most of us have a hard time stating what a frequentist P-value is (and is not) is because we do not know the difference between statisticians' correct definition and our common but erroneous definitions. In addition, or maybe another way of looking at it, is that the ambiguity of words in general (as opposed to math) contributes substantially to the confusion.

Here I take a stab at it.

A frequentist P-value (say, of a t-test for a difference between means) is the probability that a difference as large or larger than the observed difference would occur if our two samples were drawn from the same distribution, and the same experiment were conducted repeatedly ad infinitum (or ad nauseum). "A P value is often described as the probability of seeing results as or more extreme as those actually observed if the null hypothesis were true" (2).

A frequentist P-value of such a t-test is apparently not lots of things we wish it were (1):

it is not the probability that our null hypothesis is true.
it is not the probability that such a difference could occur by chance alone.
it is not the probability of falsely rejecting the null hypothesis.

Each of these leave information out. Important elements of a correct definition include

The probability of observing data as extreme or more extreme in ...
repeated identical experiments and analyses, given ...
the a priori belief that the null hypothesis is true (this is a Bayesian "prior").

Thus we see that the incorrect definitions typically share something with a complete definition, and that under most circumstances, analyses that fit incorrect definitions (e.g., Bayesian posteriors) will be correlated frequentist P-values (see comment by Bolker comment below).

A frequentist confidence interval is a region calculated from our data and our selected confidence level, α, in a manner which would include the "true" population parameter (e.g., the "true" mean) 100(1-α)% of the time if the same experiment and analysis were conducted repeatedly ad infinitum. It is a region which, so calculated, would include the true population parameter in 95% of all hypothetically repeated identical experiments. Thus, the population parameter of interest is fixed (i.e., a "true" value exists), and the interval is random (because it is based on a randomized experiment).

A frequentist confidence interval is not an interval which we are 100(1-α)% certain contains the true parameter. I don't even know what this statement (i.e., 95% certain) means -- what is "certainty"?

A 95% Bayesian credible interval (a.k.a. Bayesian confidence interval) is the a continuous subset (i.e., an interval) of a posterior probability distribution of a parameter of interest (e.g., a mean). A posterior probability distribution is the probability distribution which results from combining of our prior beliefs about the parameter, and a conditional probability distribution of our data, given all possible relevant data sets. It contains all possible values of our parameter of interest (given our priors and our data, and our model). The credible interval is merely an interval which contains a most likely subset. That is, we are not sure what is was while the world turned and our data were collected, but the safest bet is inside the credible interval.

Key differences between frequentist and Bayesian statistics:

Parameter of interest (e.g., a mean): fixed (the Platonic archetype exists) vs. random (i.e., subject to the whims of the gods of stochasticity)
Prior beliefs: implicit and sometimes hard to discern vs. explicit and plainly stated.
Statements of probability: Pr(data|null) vs. Pr(h|data). That is, frequentist P-values are the probability of observing your data (or more extreme data) given that the null hypothesis is true, whereas Bayes posterior probability distributions describe the probability of your scientific hypothesis (not the null), given that your data are true.
"P-values": Exist vs. not typically presented (usually misinterpreted; Fisher used it as a "weight of evidence," whereas Neyman used it as a basis to make decisions (yes/no) but not necessarily true/false - I do not understand how or why they can do all that; in the Bayesian context, it is not always clear what such a beast would be because of differences in underlying interpretations).

My problem is that I do not have an intuition about how these things all differ or under what conditions they are likely to differ substantially. Therefore, I cannot keep them clearly differentiated in my head. All I can do is repeat them. It would help to explore the pathological cases where frequentist and Bayesian methods result in very different outcomes differ strongly.

1. http://en.wikipedia.org/wiki/P-value#Frequent_misunderstandings
2. http://www.jerrydallal.com/LHSP/pval.htm

Damschen et al. 2006

2009-12-04T02:32:00.000-08:00

This amazing study implements at a landscape scale the type of experimental design more commonly observed in microcosms or old-fields. It is awesome!

An important difference between this experiment and those of microcosms is that microcosms run for many (10-100+) generations of the organisms, whereas this experiment ran (at the time of publication) for probably less than one generation for most of the organisms (recall generation = average age at parenthood). In that sense, it is less likely to confound evolutionary and ecological processes, so in that sense it is analogous to a pulse experiment.

The stats nerd appreciated Fig. 2C (y=Difference in richness between connected and unconnected, x=year). I wish that they reported more results in the text in terms of linear coefficients or effect sizes or actual differences, instead of the F-stats and P values. I would rather see them state biologically meaningful values, such as "The species richness of native species increased over time in connected (ave=4 spp/y +/- 1 spp) but not unconnected plots (ave=0 spp/y +/- 2 spp)."

I know it is a Science paper, but I wanted a lot more info in supplementary docs about which species were where. I am not sure why I want to know -- I should come up with hypotheses before I see the species list.

I could not figure out how they did their Chi-squared analysis (Table S5) which tests differences from expected frequencies of rare and common species in connected vs. unconnected patches. My guess is they did it right, but if not, it wouldn't be the first time if someone published bad stats in a prominent journal.

Adding a connector is simply increasing island area by doubling the size of the island from one patch to two patches. It need not be true that increasing island size increases average &alpha diversity of small samples on an island, but it would be true for a well-mixed island. So -- is this just the effect of area?

Suzan et al. 2009 - more benefits of biodiversity

2009-11-18T02:55:00.000-08:00

It was a pleasure to read this paper on the effects of biodiversity (Suzan et al. 2009, "Experimental evidence for reduced rodent diversity causing increased hantavirus prevalence." PLoS ONE 4(5):e5461.) Investigators tested whether reduced biodiversity would lead to higher disease prevalence (proportion of rodents that are infected) via higher density-dependent transmission (through higher density), or via frequency-dependent transmission (via higher host-to-host encounters). Their experimental treatment "removed" non-competent rodent hosts via trapping, allowing the remaining two species (competent hosts) to increase in abundance via competitive release. They found evidence for both mechanisms: removing non-competent host species increased both the density and frequency of hosts, and prevalence in the experimental removal areas increased significantly with both host density and frequency.

Questions and comments:
-- To where did they remove the non-competent rodents? To "rodent heaven"? another forest patch?
-- They got their interpretation of Simpson's index of diversity and index of dominance backwards. Dominance, D, is the probability that to individuals drawn at random of the SAME species (not different species, as stated by Suszan et al.). It is 1-D (diversity) that is the probability that two individuals belong to different species. 1/D (inverse: used by Suzan et al. as diversity) has no probabilistic interpretation, but is a measure of entropy, like richness, Shannon-Weiner and Simpson's Index as 1-D (complement).
-- A standard approach to testing their hypotheses would have been to compare mean prevalences in control vs. treatment. This approach would have required that their treatments consistently increased host density and frequency. Instead, they compared the continuous linear relationships between prevalence (y) and density and relative density (x). This required only that their treatment (removals) increased maximum abundance. In this fashion, their treatment merely extended the range of the x-variables, which is a good way to increase the power of the regression. An alternative approach would have been to test mean prevalence, but with a more appropriate error distribution, like Gamma distribution, that tends to hang on to zero, and increase the variance and the mean together.
-- I cannot interpret their Table 1. What are the rows? If rows are treatments, I cannot see what they see. I see no interaction - I simply don't believe it.
-- The caption in figure 4 is incorrect. They have it backwards.
-- This seemed like a really long paper for this journal, although I am not all that familiar with their format. It is seven pages of the smallest font imaginable. I was expecting a Science or Nature-type paper. They included info that I did not think was necessary (e.g., "...and several weeks can go by with no rain at all.")

Very interesting, and the number of mistakes makes me (i) wonder what all of the NINE authors were doing, (ii) helps me relax and know that other people make mistakes too.

Sir Ronald Fisher vs. Rev. Bayes -- a comment on Kremen, Williams, and Thorp. 2002. Crop pollination...

2009-11-10T19:43:00.000-08:00

A great little PNAS paper from 2002 (Kremen et al. 2002 Crop pollinaiton from native bees at risk from agricultural intensification. PNAS 99:16812-16816). They compared management (organic vs.conventional farms) and isolation from natural habitat (near vs. far), with regard to pollinator visitation rates and efficacy.

This is would be a nice paper for any undergrad class in ecology (nonmajors or majors).

My only issue is very minor: they state that "the effect of isolation from natural habitat appeared potentially to be more important than that of management" and cite a bunch of P-values from pairwise comparisons. I would argue that importance should be judged be effect size (or possible effect size) and definitely NOT based on P-values. Clearly the two are related, and in this case appear consistent with each other. However, confidence intervals, or better yet, credible intervals, would be better.

A totally uninspiring post....

Seminar schedule

2009-10-02T16:53:00.001-07:00

Ceballos and Ehrlich (2009) Discoveries of new mammal species .... PNAS 106:3841–3846

2009-09-15T03:59:00.000-07:00

Hank's "Harper's Index" of Mammal Discoveries

Number of categories of discovering new species: 3 (completely new finds (morphologically distinct), discovery that a well-known organism was actually > 1 species, and third, the elevation of subspecies to species. These last two are very similar, but the authors do not even address the > 600 cases of the third.)
Number of new mammals found since 1993: 408
Number of missing spellings of limestone forms: 1 (don't put the karst before the horse).
Percentage of the land surface exploited, for crops, rangeland, building, and other: 70%
Magnitude of the underestimate of unnoticed extinctions: gross (could we use range size to model this and actually quanitfy it?).
Number of actual lemur species once thought to be only two species: 13
Average range of previously known land mammals: 400,000 sq.km
Average range of newly discovered land mammals: 84,000 sq.km
Percentage of cells (cell=10,000sq.km) with rare species with low human population densities: 46%
Percentage of cells (cell=10,000sq.km) with rare species with "relatively high" human population densities: >20%
Number of commentators suggesting that the discovery of new species is a problem for conservation: 3
Number of authors asserting that the discovery of new species is a not problem for conservation: 4

Sinclair, T. R. (2009) Taking the measure of biofuel limits. American Scientist 97:400-407.

2009-09-02T07:49:00.000-07:00

I am enjoying greatly Sinclair's concise treatment of basic plant physiology, biochemistry, and the physical environment in which C3 and C4 crops are grown. It is the height of back-of-the-envelope artistry and clear thinking, which are hallmarks of strong quantitative, empirical biologists.

Sinclair starts with the loaming problem: the US Energy Independence and Security Act (currently) mandates that by 2022, the US should be producing 144 billion barrels of ethanol, roughly 25% percent or one barrel of ethanol for every three barrels of gasoline/diesel. This is the daunting task - it is a shit-load (my word, not his) of ethanol. Sinclair then asks whether the physical limits to plant growth will allow this mandate to be met by growing plants.

Having set up the problem, he goes about describing the elements of the puzzle:

Total annual ethanol production =
g Sugar / MJ of light intercepted by the canopy per day (C3 vs. C4) X
MJ incident light / sq. m. (max vs. average) X
days in the growing season X
grain vs. whole plant harvest X
gal ethanol / tonnes feedstock (corn vs. stalk) X
water use efficiency (C3 vs. C4 in dry vs. humid env.) X
Leaf area / land area (LAI) X
LAI / g nitrogen in tissue (C3 vs. C4) X
g N available in soil

I don't think that the above is a perfect rendering of Sinclair's elucidation, but it is close enough for now.

Sinclair next goes on to describes the sustainability of biomass harvest, in terms of N flux and the pool in the soil. He points out that the

rate of annual change in available soil N (g) =
annual application - harvest - runoff - leaching
+ production(cyanobacteria, thunderstorms)
+ mineralization(dead biomass)
+ N sequestration (perennials only)

Last, he considers land available, pointing out that most fertile land in humid regions is already in production. He concludes cautiously with

"... realistic assessments of the production challenges and costs ahead impose major limits."

This approach is a great companion to the work of Searchinger et al., Fargione et al., and Tilman et al. that have focused on land use and biodiversity issues. Much work lies ahead, and Sinclair has been of great help to me.

PNAS - "Science for managing ecosystem services..."

2009-08-27T10:49:00.000-07:00

A pile of folks (Carpenter et al.) provided a blueprint, or rather an eight page precis of a blueprint, for what ecologists and their collaborators should be doing now to help humankind (Carpenter et al., 2009, PNAS 106:1305-1312). I find the whole thing rather overwhelming, but I must be strong, and take heart.

First, some of the overwhelming bits. It seems as though we are supposed to know and understand everything about the current state of the natural environment and its processes, in every location, over time, current social (cultural, political, and economic) institutions, policies and practices, AND how they all interact, so that we can predict unpredictable future events. "Oh," I said. "Is that all?"

Every question seems to spin out of control with a huge number of factors and feedback loops that need to be included.

It sounds as though a "School of Sustainability" would include the business school, college of arts and sciences, the school of architecture, the med school ... . This will be like the IPCC committees, on steroids.

OK, so what can I do? How do we move forward? Carpenter et al. were kind enough to suggest a few research areas, including (i) the analysis of biodiversity in a social-ecological context, (ii) match quantitative models to conceptual goals, and (iii) figure out how to predict the unpredictable ... oops, scratch that last one -- I mean "address nonlinear and abrupt changes," and (iv) expand the quantification, understanding and communication of uncertainty. I must admit, these feel helpful because they have the appearance of being tractable, and happen to interest me.

Another aspect of this report that I cling to is "place-based" research. This seems to suggest the assumption (or hypothesis) that spatial variation in social and environmental drivers will require local assessment, testing, evaluation, etc., of any science or policy. Thus, we should be able to argue, for instance that a set of feedback loops operate in Ghana, Peru, and Sweden, but Ohio is different for reasons A, B, and C, and so we need to test whether these feedback loops operate here in Ohio, USA. Perhaps I am scared or lazy, but I hope that experiments replicated in place and time are valued by funding agencies. They should be, but sometimes novelty seems more important than utility.

It seems essential, and a great opportunity, to "Learn from existing management programs." I think that current efforts of monitoring and evaluation of past and current practices are probably woefully inadequate. It may be quite productive to simply ask agencies and programs how we can help. How can we bring our expertise to bear on doing jobs that are already identified as important.

Tscharntke et al. 2005

2009-08-27T10:48:00.001-07:00

Tscharntke and colleagues provide a nice framework for understanding the upsides and downsides of agricultural intensification for biodiversity, ecosystem services and their interaction. The framework combines the functioning of the local ecosystems, their spatial arrangement, and the consequences of the arrangement. They cover a lot of ground, and should raise lots of questions. Post your questions (comments) here.

Testosterone Levels in Dominant Sociable Males Are Lower than in Solitary Roamers

2009-06-17T19:13:00.000-07:00

As a pet owner, and scientist, I often shake my head at how much credit we give our species. "Oh, we're just SOOO complicated and special --- not like those 'animals'!"

Our program recently had the great fortune of a visit by Carsten Schradin, who gave a nice seminar on the sociobiology of the social striped mouse (Rhabdomys
pumilio). Many of the findings he discussed reminded me nothing so much as stereotypes of our own species. As an outsider looking in, I find the parallels between non-human and human behavior are wonderfully ironic.

I don't anthropomorphize non-human behavior. Rather, I prefer to think that I re-animate human behavior.

[the title of this blog comes from Schradin et al. 2009, Am Nat, v. 173).]

Quantitative training in EEEB and R

2009-06-11T10:04:00.000-07:00

To train our EEEB students (grad students in Ecology, Evolution and Environmental Biology) in quantitative methods, I have been putting a lot of effort into teaching the R language to willing and sometimes unwilling students.

This coming Spring (2010), I will lead a 1 credit seminar here at Miami using Ben Bolker's recent book, "Ecological Models and Data in R" (Bolker, 2008). This is a fabulous tome, by a gentle and insightful teacher. It fits into the general mood in the field of academic ecology, that measuring real quantities (estimation) is very important (as opposed to only hypothesis testing). Ben's book captures this perfectly, and further, shows how those real quantities are often the parameters in simple models of populations and ecosystems. As Bolker states, "The idea behind realistic static models is that they link together simple deterministic and stochastic models of each process in a chain of ecological processes..." [italics mine].

I anticipate that the book will be very well received in the seminar, because it is practical and clearly written, and sufficiently comprehensive to, as Bolker states, "...pose, and answer, ecological questions in a quantitative way." Thus, I anticipate that his book will be helping us to design and analyze experiments, and ultimately publish papers and finish dissertations.

Anthromes

2009-05-29T14:09:00.000-07:00

Check it out:

http://www.eoearth.org/article/Anthropogenic_biomes

It is about time.

I teach "Ecology of North America," and I have been itching (read "too lazy") to add urban landscapes to the course. Sure, we talk about natural wild fires hurting rich people in So. California, and fights over prairie dogs, and depleted aquifers, but we haven't gone the next step -- we haven't crossed that threshold into a new paradigm. Now we will.

Thanks Erle and Navin!

Reverand Bayes

2009-05-21T13:36:00.000-07:00

May the gods bless the good Reverend. As a practitioner and teacher of quantitative methods, I have been conflicted about Bayesian statistical methods. They have seemed really neato cool, but can I ask students to learn them on top of everything else? Should I invest the time?

I have found that Bayesian methods actually simplify my life, because model construction and end-user implementation for designs of any complexity are relatively transparent. In addition, they can represent nearly any ecological process. Sure, there is a bit of a learning curve, but much of that learning goes right to the core of statistical thinking and interpretation. I am currently using Bayesian methods to reinterpret an old data set, this time in a manner that mirrors the eco/evo interpretation so directly and precisely that I am still giddy.

I guess I will encourage students to consider these methods ... on a case by case basis.

Peepers

2009-05-17T05:03:00.000-07:00

My 7 year old and I saw our first Spring Peepers (Hyla crucifer) over the weekend -- a couple of optimistic boys hoping girls might still be interested this late in the Spring (the peepers, not us). We had been very excited, because this was the first year we've heard peepers in our backyard (a .4 acre parcel in 1950's era suburbia). What an amazing experience to hear these critters for so long and finally see them. It was awe-inspiring and calming -- a religious experience. Like eastern skunk cabbage (Symplocarpus foetidus), peepers have always been a wonderful harbinger of Spring for me. We are glad to see them.

Socioeconomics drive urban plant diversity (Hope et al. 2003)

2009-05-15T13:59:00.000-07:00

What a cool paper (Hope et al. 2003, PNAS 100:8788-8792). Like other before and after this folks describe how the resource and non-resource effects of a keystone species (H. sapiens) is influencing plant communities. What I find so intriguing about this paper is the effect of income and all that goes with that. Obviously income is not the proximate mechanism, but I am guessing it may be a good level of aggregation for a suite of correlated effects. These more distant connections (non-reductionistic) is part of why I got into academic ecology in the first place.