Damschen et al. 2006

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

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...

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

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

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.

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..."

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

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

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

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

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

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

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)

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.