Monday, February 26, 2007
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...
Wednesday, February 14, 2007
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!
Monday, February 12, 2007
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!
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)
(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).
- 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)
Wednesday, February 7, 2007
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 :)
Monday, February 5, 2007
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...