Crowdfunding in the Peer Reviewed Literature

(x-posted at the #SciFund Blog)

ResearchBlogging.orgThe final version of Wheat et al.’s paper Raising money for scientific research through crowdfunding is out in Trends in Ecology and Evolution. A more hefty piece of #SciFund analysis is behind it (slowed down in no small part because of my sloooow processing of new data in fancy models). The Wheat et al. paper is a lovely short piece that Rachel and Yiwei (who crowdfunded the excellent Alaska Predator Research Expedition that has it’s new website over here) were gracious enough to ask Jai and I to participate in. In it, we cover the basics of crowdfunding for the academic sciences – what is it? what are the platforms you might use? what are some strategies for success?

Overall, this is a nice, gentle introduction that you should send to any colleague who either appears interested in crowdfunding, curious as to what it is, or is highly skeptical of the entire enterprise.

So go check it out!

Wheat R.E., Wang Y., Byrnes J.E. & Ranganathan J. (2013). Raising money for scientific research through crowdfunding, Trends in Ecology & Evolution, 28 (2) 71-72. DOI:

Linkage: A field guide to privilege in marine science

As someone who, admittedly, benefitted a great deal from Privilege growing up (it definitely lowered the barriers to my becoming a succesful marine scientist), know that this is true of MANY Ecology and Evolutionary Biology folk, and now think a good deal about how I can lower those barriers for students and mentees that come under my aegis, you should all go over and check out Miriam Goldstein’s A field guide to privilege in marine science: some reasons why we lack diversity.

A Quick Note in Weighting with nlme

I’ve been doing a lot of meta-analytic things lately. More on that anon. But one quick thing that came up was variance weighting with mixed models in R, and after a few web searches, I wanted to post this, more as a note-to-self and others than anything. Now, in a simple linear model, weighting by variance or sample size is straightforward.

lm(y ~ x, data = dat, weights = 1/v)

#sample size
lm(y ~ x, data = dat, weights = n)

You can use the same sort of weights argument with lmer. But, what about if you’re using nlme? There are reasons to do so. Things change a bit, as nlme uses a wide array of weighting functions for the variance to give it some wonderful flexibility – indeed, it’s a reason to use nlme in the first place! But, for such a simple case, to get the equivalent of the above, here’s the tricky little difference. I’m using gls, generalized least squares, but this should work for lme as well.

gls(y ~ x, data=dat, weights = ~v)

#sample size
gls(y ~ x, data = dat, weights = ~1/n)

OK, end note to self. Thanks to John Griffin for prompting this.


Everyone has been pretty shocked by the devastation wreaked by Sandy. Here in New England, we also got a Nor’easter following a few days later. That’s a lot of intense storm action in a short period of time.

So I was quite curious as I ventured out into the field last weekend to see how things looked. I went on a potential field site scouting trip to UMB’s field station in Nantucket. Nantucket of course got a good dose of Sandy, although it largely passed southwest. The Nor’Easter may have been worse.

What I found while just walking about on the shoreline was pretty incredible. It was Scallapocalypse.

Let me include a video here of what one saw looking across the beach so you can get a sense of what was going on.

This was taken in Madaket. It was a bit more dramatic in other parts of the island – because scallop fishermen had come on shore, scooped up the scallops (many of which were the seed for next year, and too small for now) and taken them back out to the scallop grounds. Here’s what things looked like by the lab.

All over, the scallop grounds had come to shore.

But the huge flux of biomass onto shore was impressive. And it wasn’t just scallops, but a ton of seagrass as well, much of which was matting over fringing salt marshes.

Still, the huge amount of energy and nutrients coming into the shoreline ecosystem driven by storms gave me a lot of pause. I mean, those scallops that weren’t saved did end up in the coastal foodweb. Birds were definitely looking fat and happy, and we’d find piles like this with flocks of birds nearby:

The whole thing really got my brain going, with two big questions

1) So, what is the fate of all of this influx of stuff into the shoreline? How will the influx of energy alter the structure and dymaics of the food web? Will the smothering of the marsh matter? It is winter, when things are slower. How quickly will everything be decomposed? Will the effects be lagged until the springtime? Or will they affect the system now? I think of Gary Polis’s work on how food web structure is shaped by the influx of energy on small islands. I know this is a BIG island, but, still, the point stands, this is a big flux of biomass and nitrogen. And it’s not just plant matter, but animal protein.

2) How will climate change alter the frequency of this subsidy? What would the consequences of a regime with regular small subsidies and occasional big ones versus regular big subsidies be? This stems largely from my thinking about the increase in the size of the ‘largest storm of the year’ in California coastal systems that’s been the basis of my previous work. But, models and analysis from the Knutson group seem to show that, while hurricanes and cyclones in the Atlantic aren’t getting more frequent, the size of each one is getting bigger. So, similar pattern. If small subsidies are coming in every year now due to the occasional passing hurricane or Nor’easter, but the size of those same storms in the future is going to get larger, then having this kind of big Scallapocalypse/subsidy could get more frequent. Particular as northern Atlantic waters get warmer (which they are – Nixon 2004), this could be an interesting and perhaps not so well investigated climate effect – the increased strength of coupling between marine and terrestrial food webs.

Oh, and random 3) What role will invasive algae play in increasing the impacts of storms on the amount of material coming on land? This may lead nowhere, but I noticed a lot of material (not scallops) that had washed on land had the invasive Codium fragile attached to it. I know that subtidal kelps can do this to mussels as well (Witman’s work), but there’s no kelp here. Is Codium becoming a drag (har har) and increasing the energy and nutrient flow from sea to land?

All in all, an interesting trip with a lot to chew on for future research. And a great setting!

Knutson, T. R., J. L. McBride, J. Chan, K. Emanuel, G. Holland, C. Landsea, I. Held, J. P. Kossin, A. K. Srivastava, and M. Sugi. 2010. Tropical cyclones and climate change. Nature Climate Change 3:157–163.

Nixon, S. W., S. Granger, B. A. Buckley, M. Lamont, and B. Rowell. 2004. A one hundred and seventeen year coastal water temperature record from Woods Hole, Massachusetts. Estuaries 27:397–404.

Polis, G. A., and S. D. Hurd. 1995. Extraordinarily high spider densities on islands: flow of energy from the marine to terrestrial food webs and the absence of predation. Proceedings of the National Academy of Sciences, USA 92:4382–4386.

Polis, G. A., W. B. Anderson, and R. D. Holt. 1997. Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annual Review of Ecology and Systematics 28:289–316.

Witman, J. D., and T. H. Suchanek. 1984. Mussels in Flow – Drag and Dislodgement by Epizoans. Marine Ecology Progress Series 16:259–268.

Why I’m Teaching Computational Data Analysis for Biology

This is a x-post from the blog I’ve setup for my course blog. As my first class at UMB, I’m teaching An Introduction to Computational Data Analysis for Biology – basically mixing teaching statistics and basic programming. It’s something I’ve thought a long time about teaching – although the rubber meeting the road has been fascinating.

As part of the course, I’m adapting an exercise that I learned while taking English courses – in particular from a course on Dante’s Divine Comedy. I ask that students write 1 page weekly to demonstrate that they are having a meaningful interaction with the material. I give them a few pages from this book as a prompt, but really they can write about anything. One student will post on the blog per week (and I’m encouraging them to use the blog for posting other materials as well – we shall see, it’s an experiment). After they post, I hope that it will start a conversation, at least amongst participants in the class. I also think this post might pair well with some of Brian McGill’s comments on statistical machismo to show you a brief sketch of my own evolution as a data analyst.

I’ll be honest, I’m excited. I’m excited to be teaching Computational Data Analysis to a fresh crop of graduate students. I’m excited to try and take what I have learned over the past decade of work in science, and share that knowledge. I am excited to share lessons learned and help others benefit from the strange explorations I’ve had into the wild world of data.

I’m ready to get beyond the cookbook approach to data. When I began learning data analysis, way back in an undergraduate field course, it was all ANOVA all the time (with brief diversions to regression or ANCOVA). There was some point and click software that made it easy, so long as you knew the right recipe for the shape of your data. The more complex the situation, the more creative you had to be in getting an accurate sample, and then in determining what was the right incantation of sums of squares to get a meaningful test statistic. And woe be it if your p value from your research was 0.051.

I think I enjoyed this because it was fitting a puzzle together. That, and I love to cook, so, who doesn’t want to follow a good recipe?

Still, there was something that always nagged me. This approach – which I encountered again and again – seemed stale. The body of analysis was beautiful, but it seemed divorced from the data sets that I saw starting to arrive on the horizon – data sets that were so large, or chocked full of so many different variables, that something seemed amiss.

The answer rippled over me in waves. First, comments from an editor – Ram Meyers – for a paper of mine began to lift the veil. I had done all of my analyses as taught (and indeed even used for a class) using ANOVA and regression, multiple comparison, etc. etc. in the classic vein. Ram asked why, particularly given that the Biological processes that generated my data should in no way generate something with a normal – or even log-normal – distribution. While he agreed that the approximation was good enough, he made me go back, and jump off the cliff into the world of generalized linear models. It was bracing. But he walked me through it – over the phone even.

So, a new recipe, yes? But it seemed like something more was looming.

Then, an expiration of a JMP site license with one week left on a paper revision left me bereft. The only free tool I could turn to that seemed to do what I wanted it to do was R.

Wonderful, messy, idiosyncratic R.

I jumped in and learned the bare minimum of what I needed to know to do my analysis…and lost myself.

I had taken computer science in college, and even written the backend of a number of websites in PERL (also wonderful, messy, and idiosyncratic). What I enjoyed most about programming was that you could not hide from how you manipulated information. Programming has a functional aspect at the core where an input must be translated into a meaningful output according to the rules that you craft.

Working with R, I was crafting rules to generate meaningful statistical output. But what were those rules but my assumptions about how nature worked. The fundamentals of what I was doing all along – fitting a line to data with an error distribution – that should be based in biology, not arbitrary assumptions – was laid all the more bare. Some grumblingly lovely help from statistical denizens on the R help boards helped to bring this in sharp focus.

So, I was ready when, for whatever reason, fate thrust me into a series of workshops on Bayesian statistics, AIC analysis, hierarchical modeling, time series analysis, data visualization, meta-analysis, and last – Structural Equation Modeling.

I was delighted to learn more and more of how statistical analysis had grown beyond what I had been taught. I drank deeply of it. I know, that’s pretty nerdy, but, there you have it.

The new techniques all shared a common core – that they were engines of inference about biological processes. How I, as the analyst, made assumptions about how the world worked was up to me. Once I had a model of how my system worked in mind – sketched out, filled with notes on error distributions, interactions, and more, I could sit back and think about what inferential tools would give me the clearest answers I needed.

I had moved instead of finding the one right recipe in a giant cookbook to choosing the right tools out of a toolbox. And then using the tools of computer science – optimizing algorithms, thinking about efficient data storage, etc – to let my tools work bring data and biological models together.

It’s exciting. And that’s the core philosophy I’m trying to convey in this semester. (N.B. the spellchecker tried to change convey to convert – there’s something there).

Think about biology. Think about a line. Think about a probability distribution. Put them together, and find out what stories your data can tell you about the world.

Taking the Ecological Conversation Online

At ESA this year, I gave a talk in a symposium about changing the culture of ecology. The talk was entitled Taking the Ecological Conversation Online and summarized what I thought were the benefits of engaging in online activities. As this was a scientific audience interested in how we might change the culture of Ecology to improve our science, I focused the talk not on what I feel are the outreach & public engagement benefits of going online – those are both obvious and legion. Rather, I focused on how I felt that interacting with other scientists online via blogs, Twitter, stackexchange sites, and more actually improve your science.

I got a great positive response. A number of folk even told me they would now get on twitter – maybe just to follow a few people, maybe to talk themselves – or start following a few blogs, as they could see how this would benefit them. This warmed the cockles of my heart – particularly given that I was super nervous about giving this talk, as it was pretty much data free (with one or two exceptions).

For those not at ESA, I’ve put the slides on slideshare, and you can see them below.

Final Friday at #ESA2012

Here’s what I’m thinking of catching on Friday. Last day, and still lots of fantastic talks! What a wonderful meeting that this has been. (Eating at The Farm didn’t hurt either…)

8:00 COS 176-1 Cavanaugh, KC1, SL Davis1, JS Gosnell2, J Ahumada3 and S Andelman3, (1)University of California Santa Barbara, (2)University of California, Santa Barbara, (3)Conservation International. Interactions among climate, biodiversity, and ecosystem services in tropical forest ecosystems. C123

8am SYMP 23-1Costanza, R, Portland State University. The promise and pitfalls of ecosystem service valuation. Portland Ballroom 252

8:40 COS 194-3 Preisser, EL1 and JL Orrock2, (1)University of Rhode Island, (2)University of Wisconsin – Madison. The allometrics of fear: Interspecific relationships between body size and response to predation risk. Prtlnd Blrm 258

8:50 SYMP23-3Morse, JL, Cary Institute of Ecosystem Studies. Quantifying multiple ecosystem services and their underlying ecosystem functions in North Carolina’s largest wetlands mitigation bank. Prtlnd Bllrm 254

9:00 COS 176-4 Gilbert, B1, JM Levine2 and J Hille Ris Lambers3, (1)University of Toronto, (2)ETH Zurich, (3) University of Washington. Quantifying ecological drift in annual plant communities. C123

9:20 COS 186-5 Boettiger, C1 and A Hastings2, (1)UC Davis, (2)University of California, Davis. Unknown unknowns: Management strategies under uncertainty and alternate stable states. E143

10:10 COS 180-7 van der Plas, F1, TM Anderson2 and H Olff1, (1)University of Groningen, (2)Wake Forest University. Trait-based community assembly from a multitrophic perspective: Bottom-up or top-down regulation?. D136

10:30 COS 188-8 White, JW1, LW Botsford2, A Hastings2, ML Baskett2 and DM Kaplan3, (1)University of North Carolina Wilmington, (2)University of California, Davis, (3)Centre de Recherche Halieutique Mediterraneenne et Tropicale. Transient responses of exploited populations to establishment of no-take reserves. E145

11:10 COS194-10 Kimbro, DL, Florida State University. Tidal regime dictates the cascading consumptive and nonconsumptive effects of multiple predators on salt marshes. Prtlnd Blrm 258

Thursday #ESA2012 Schedule

ALl right – here’s my Thursday schedule (mostly for myself so I can keep track of it on my phone). I think in the morning, I just might park myself in the session on climate change & communities as one of my main interests for this meeting is…climate change and communities. Also, I’m guessing I’ll fill in the lower right hand side of my bingo card (although I already got Bingo today on the lower line…)

COS 127 – Climate Change: Communities II
F151, Oregon Convention Center

8am 123 COS 127-1 Sorte, CJB , D Blumenthal , I Ibanez ,
C D’Antonio4, JM Diez3, JS Dukes5, ED Grosholz6, SJ Jones7, LP Miller8, N Molinari4 and J Olden9, (1) University of Massachusetts – Boston, (2)USDA-ARS, (3) University of Michigan, (4)University of California Santa Barbara, (5)Purdue University, (6)University of California, (7)University of South Carolina, (8)Stanford University, (9)University of Washington. Poised to prosper: Do demographic outcomes favor non-native species in a changing climate?. F151

8:20 COS 127-2 Stuble, KL1, C Patterson1, SL Pelini2, MA Rodriguez-Cabal1, R Dunn3 and NJ Sanders1, (1) University of Tennessee, (2)Harvard University, (3)NCSU. Foraging behavior and seed dispersal mutualisms in a warmed world: The effects of experimental warming on ant assemblages and the processes they mediate.

8:40 COS 127-3 Andrew, CJ1 and EA Lilleskov2, (1) Northeastern Illinois University, (2)US Forest Service, Northern Research Station. Soil nutrient legacies surpass the effects of CO2 and O3 concentration on mycorrhizal fungal communities.

9:00 COS 127-4 Tomaszewski, T, BR Johnson, L Pfeifer- Meister, ME Goklany, LL Reynolds, HE Wilson and SD Bridgham, University of Oregon. Site-dependent versus regionally consistent effects of increased temperature and precipitation on plant community composition, productivity, and soil nutrient availability in restored Pacific Northwest prairies.

9:20 COS 127-5 Kandur, AS, University of Chicago. Climate change, sea level rise, and potential impacts on rocky intertidal populations.


9:50 COS 127-6 Barton, BT1 and AR Ives2, (1)University of Wisconsin-Madison, (2)University of Wisconsin. Experimental warming disrupts an ant-aphid mutualism.

10:10 COS 127-7 O’Connor, MI1 and JC Stegen2, (1)University of British Columbia, (2)Pacific Northwest National Laboratory. Testing the temperature dependence of stocks and fluxes in an aquatic food web.

10:30 COS 127-8 Sylvain, ZA1, DH Wall1, KL Cherwin1, DPC Peters2, OE Sala3 and LG Reichmann3, (1)Colorado State University, (2)USDA Agricultural Research Service, (3)Arizona State University. Patterns of soil community structure differ by scale and ecosystem type along a large-scale precipitation gradient.

10:50 COS 127-9 Kelly,R1, M Chipman1, PE Higuera2, LB Brubaker3 and FS Hu1, (1)University of Illinois, (2) University of Idaho, (3)University of Washington. Pushing the limits of the boreal-forest fire regime: Recent changes in a 10,000 year context.

11:10 COS 127-10 Avery, L1, AC McCall1, M Forister2 and A Shapiro3, (1)Denison University, (2)University of Nevada, Reno, (3)University of California, Davis. Butterfly community dynamics in California over 30 years.

Other talks I may skip out from the session for…

9am COS 125-4 Lefcheck, J, A Bucheister, S Chak, T Clardy, KM Laumann, PL Reynolds, K Sobocinski, M Stratton and JE Duffy, Virginia Institute of Marine Science, The College of William & Mary. Components of biodiversity in a Chesapeake Bay groundfish assemblage: A high- resolution analysis of patterns and drivers. B115

10:10 COS 139-7 Poore, AGB1, AH Campbell1, RA Coleman2, G Edgar3, V Jormalainen4, PL Reynolds5, EE Sotka6, JJ Stachowicz7, RB Taylor8, MA Vanderklift9 and JE Duffy10, (1)University of New South Wales, (2)The University of Sydney, (3)University of Tasmania, (4)University of Turku, (5)Virginia Institute of Marine Science, (6)College of Charleston, (7)University of California, Davis, (8) University of Auckland, (9)CSIRO Wealth from Oceans Flagship, (10)The College of William and Mary. Global patterns in herbivore impact on marine benthic primary producers: A comprehensive meta-analysis. Ballroom 254

10:30 OOS 41-8 Cardinale, BJ, PA Venail and A Narwani, University of Michigan. What is biodiversity’s role in providing ecosystem goods and services? A data synthesis. B116

If you can hang about for lunch, be sure to check out the following awesome workshop from 11:30-1:15
WK 47 – How to Access Ecological and Evolutionary Datasets in R
F150, Oregon Convention Center Organized by: K Ram, SA Chamberlain, C Boettiger
In the workshop we will showcase a live demonstration of several of our R packages and also lead a discussion on how you can develop similar tools for other data sources. We encourage you to bring a laptop so you can participate along (wifi permitting).
Speakers: C Boettiger, UC Davis SA Chamberlain, Rice University K Ram, University of California Berkeley

For the afternoon, there are a lot more that is great. Of course. One option might be to plant oneself at

SYMP 19 – The National Climate Assessment: Preliminary Findings, Building Assessment Capacity, and Implementing a Sustained Assessment Process
Portland Blrm 251, Oregon Convention Center Organized by: ET Cloyd (, N Grimm Endorsed by: Biogeosciences, Policy
Moderator: ET Cloyd
This session will present key findings from the draft 2013 National Climate Assessment report; discuss implementing a sustained assessment process, including developing indicators of climate change and impacts; and provide an opportunity to comment on the draft NCA report and ongoing assessment process.

But, individual talks might again be in my future. In particular, I want to check out

1:30 COS 163-1 Grosholz, ED1, DL Kimbro2 and BS Cheng3, (1)University of California, (2)Florida State University, (3)University of California, Davis. Evidence for biotic resistance and enemy release in coastal ecosystems. Portland Ballroom 254

1:50 COS153-2 Best, RJ, NC Caulk and JJ Stachowicz, University of California, Davis. Competitive outcomes and community composition in marine invertebrates are predicted by diversity in feeding traits and not by phylogenetic relatedness. D136

1:50 COS 148-2 Dee, L1, L Peavey1, S Miller1 and S Lester2, (1)University of California, Santa Barbara, (2)Sustainable Fisheries Group, University of Santa Barbara. Biodiversity is a poor predictor of fisheries production in large marine ecosystems. B112

3:20 COS 166-6 Edwards, KF1, E Litchman2 and C Klausmeier2, (1)W. K. Kellogg Biological Station, Michigan State University, (2)Michigan State University. Functional traits predict phytoplankton community structure and successional pattern in a marine ecosystem. Ballroom 258

4:00 COS 158-8 Kicklighter, CE, MK Hearl and HE Locke, Goucher College. The effects of nutrients and grazing on the estuarine marsh invader, Phragmites australis. E142