Of Caterpillars and Compounds...

The theme du jour is the influence of plant defenses (particularly secondary compounds) on caterpillar growth and anti-predator defense in tropical forests (primarily at BCI and La Selva). Over the weekend, I read a proposal seeking to document the influences of mixtures of similar plant compounds in anti-herbivore (specifically, caterpillar) defense. This study focused primarily on the intraplant chemical level, looking at which compounds or compound mixtures best deterred caterpillars. I followed this up by reading an article for Journal Club today focusing on the role of plant quality (supposably including secondary compounds, though these weren't investigated directly - more on this later) in influencing caterpillar growth rates and anti-predator defenses. And I myself have focused more on the next trophic level up - namely, the role of diverse arthropod defenses (which are presumably due to some combination of bottom-up - i.e., plant-driven - and top-down - i.e., predator-driven) on limiting food availability to insectivorous birds. I found this to be interesting and thought-provoking reading - for all the time I've spent thinking about the relationship between caterpillars and birds in terms of top-down selection for anti-predator defenses and bottom-up food limitation acting to reduce metabolic and foraging rates and population densities of the birds, I've spent very little time thinking about other - namely, plant-driven - selective forces driving caterpillar defensive trait evolution. This adds a whole new level of questions to my research. Funny how ecological research feels a bit like jumping down the rabbit hole sometimes - the more you learn, the more new worlds of ideas and questions open up.

The other interesting aspect of reading these two papers back-to-back was the contrasts in methodological techniques and quality. The proposal I read was directly testing the roles of secondary compounds on herbivory, by synthesizing the compounds and adding different concentrations and mixtures to leaves which are then offered to caterpillars, allowing direct testing of causation. The second paper, however, primarily investigated the role of nutrient quality on caterpillar growth, discovered that nitrogen and water did not fully explain the regressions, and from there made a leap to saying that the residuals are likely therefore explained by secondary compounds. They did throw in a few other related experiments - e.g., exposing caterpillars fed on different leaves to ant predators - but it seemed they were confusing correlation for causation. Another problem I had with the methods of this paper is that they arbitrarily broke continuous characters (e.g., leaf-expansion rate, number of spines on a caterpillar) into dichotomous categories, and worse, did not explain the justification for the divisions. Dividing the data into discrete classes allowed them to use ANOVA and t-tests which produced significant differences between groups, but I can't help but wonder if that isn't partially just a construct, a result of their categorizations rather than reflecting biological reality. I wonder if keeping the data continuous and using single and/or multiple regressions wouldn't have better reflected reality - and if the significant results they found would still appear this way. As scientists we're taught to strive for that p<0.05 value, but I wonder if we don't sometimes rearrange and reclassify our data in ways not completely reflecting the complexity of nature in order to reach that holy grail...

Spring has sprung

Spring is here (so early!), or at least so it seemed yesterday. The great egrets are staking out territories, reuniting with or courting new mates, and gutting and refurbishing their old nests down at the rookery at Audubon Park. I love seeing the birds (displaying males, primarily) fanning out their lacy, snow-white plumes almost peacock-like. It's sad to think how we nearly lost this species - and so many others - a century ago, though I can see why people wanted those gorgeous plumes for themselves. Crows are building nests and songbirds are belting out their amorous songs from the treetops, while young couples walk arm-in-arm below, acting out their own courting rituals on this St. Valentin's Day (we like to think we're so different from the "lower animals", but are we really?)

Anyway, what does this have to do with stats? Absolutely nothing! I just don't have anything all that interesting to say about today's reading. The Verzani book is certainly practical, and is a good introduction to programming in this language - which is fairly different (less object-oriented) than some other programs I've worked with. But it's straight-forwardness (is that a word?) which makes it so useful for learning the code leaves little to comment upon!

Dissertation Hypotheses!!

So, I've spent a lot of time trying to hone down my ideas/questions regarding the causes of declines in tropical insectivorous understory birds into specific, testable hypotheses. That's a lot harder than it sounds!! Well, at least it was for me - I find coming up with the question(s) - and more than that, valid, testable, bivariate (yes/no) questions to be one of the most difficult parts of the scientific method. The field work's the fun part, and I find even the analysis (seeing the results coming together) and the writing to be easier. Anyone else find this as well?

Anyway, here are the hypotheses that I've developed at this point. I likely won't test all of these questions now (too much for one project), but will choose between the most relevant and testable. Seeing as I'm about to go present them to Tom (wish me luck!), and we're discussing hypothesis-framing in class tomorrow, this seems like a good time to put them out there for comment. So, here goes - let me know what you think!

Dissertation questions/hypotheses:

Overarching: 1) what mechanisms are responsible for the decline of understory insectivorous birds in tropical rainforest?

2) Does a trophic cascade exist (can one exist w/in such a highly diverse environment), and if so what are the strengths of the various direct and indirect effects (model this w/ path analysis, per Lee)

(note: LS = La Selva)

Hyp 1) Abundance of leaf-litter and understory arthropods is greater in absence of disturbance by large mammals (peccaries) => top-down trophic cascade.

- Test: sample leaf-litter and understory arthropods w/in and outside peccary exclosures at LS (very high density of peccaries) and Tirimbina (no peccaries).

Hyp 2) Food limitation is a primary influence on demographic rates of understory insectivores => bottom-up effects

- Test: provide supplemental food (mealworms) in proportions sufficient to equalize biomass/ha of exclosures, measure demographic parameters (I expect post-fledging success rate and adult survival rate to be most influential). Build matrix population model and do sensitivity analyses to determine which parameter most influences pop. Growth/decline rate. Prediction: post-fledging success

Hyp 3) Antwren (a group of birds which have disappeared at La Selva, but not at other sites) losses at LS are due to "loss" of vine and dead-leaf tangles (can't measure loss per se - no historic records - but can compare current biomass/cover at sites w/ and w/o full insectivore guild)

- measure vine/leaf tangle (biomass? percent cover?) at La Selva and compare with sites where antwrens still persist (Tirimbina or Pipeline Road = low density, Bartola or BCI = high density)

- Prediction: I can’t think of any mechanism that would drive loss of leaf tangles at LS and not at the other sites – unless Pentaclethra has increased it’s proportion of canopy cover in the last 40 years (which I doubt).

Hyp 4) Nest predation is a primary influence on demographic rates

- Observe nests (White-breasted Wood-wren probably - easiest to find and observe), use cameras – who the heck are the predators, since there are so few snakes there these days???

- Include fledging success as a parameter in sensitivity analyses (see hyp #2)

Hyp 5) In absence of other guild/trophic-level members, remaining species widen their realized niches and fulfill role of absent species, maintaining similar effects on next-lower trophic level (=arthropods)

- Quantify niches (foraging height, strategy, prey consumed) of insectivores (white-breasted wood-wren and/or chestnut-backed antbird) at several sites. Prediction: niche width LS > Tirimbina > Bartola/Plastico (also, or conversely, antwrens BCI > Tirimbina > Bartola/Plastico)

- Establish bird exclosures at LS (w/in and outside peccary exclosures), Plastico, Tirimbina (BCI and/or Bartola?). Quantify arthropods w/in and outside exclosures. Prediction: differences b/w avian exclosures & controls should be similar at all sites (assumes peccary exclosures do not limit avian access).

Hyp 6) Avian predators limit arthropod populations, but to a lesser degree than resource (plant/detritus/other arthropod) availability (per Dan Gruner)

- difference b/w (avian exclosure w/in peccary exclosure and avian exclosure outside) should be > than difference between (avian exclosure w/in peccary exclosure and control w/in peccary exclosure),; quantify influence of birds on arthropods

(Hyp 7 – pesticides from surrounding banana plantations drifting into forest and influencing arthropods directly and/or birds indirectly (e.g., estrogen-mimics lowering fertility?)? Want to wait to run some tests on litter/arthropod samples I collect over spring break – not sure about this as of yet)

JV Ch. 3, Bivariate data

This chapter discussed methods for plotting 2+ sample data, looking for correlations, linear regression, etc. (strange to be discussing linear regression prior to learning how to do a t-test! Usually it's the other way around...)

They discussed some odd graphical methods (odd in that I've never seen or heard of them before) - like qqplots. I still don't grasp when you would use these, or how to read them. To me, it seems easier to compare density plots or - better yet - boxplots. Lee got us into using boxplots in his stats class last fall, and I'm really beginning to see just how useful they are for viewing the spread and means/medians of data.

I'm glad to see that Pearson correlation coefficients can be calculated so easily! I'll need to calculate a bunch of them for my independent project, and I can't recall using them before. One less thing to worry about :)

G&E Ch. 3

This chapter discussed summary stats - mean, sd, se, cv, median, mode, and all that jazz. Mostly stuff we've discussed in many classes before - and in Ch. 2 of JV - but they made a few interesting points. The descriptions of the different kinds of means (arithmetic vs. geometric vs. harmonic) and when they're used was interesting - we used geo. means in a pop. ecology/analysis class I took a couple years ago, but I never really understood why. I also liked the description of degrees of freedom - finally, it makes sense. That's what I like about this book so far (as I've said before) - they give good, easy-to-understand descriptions of basic things that, generally, are assumed to be understood (yet rarely/never explained) in other stats books.

Then later in the chapter they bring up Bayesian stats again a couple times, but never follow up... Why don't we have a Bayesian stats class here? I'm not even aware of any current faculty who have much experience with Bayesian stats, with the exception of their use in systematics (correct me if I'm wrong)...I'll have to read up, because it seems that they're becoming more common...