Kurt Yakimovich, our ‘man in Canada’, recently produced this great video on his MSc research that runs in collaboration with our group. Check it out below:
Our work on the agri-environment was recently featured on BBC’s very popular Countryfile programme. Matt Baker gave us quite big shoes to fill! You can check out the clip below:
Our first paper on the agri-environment has just appeared in this month’s issue of PLoS Biology. This is an important piece for us as it provides a foundation for empirical work being carried out by several group members.
The paper essentially makes three main points. The first is that we spend a lot more money subsidizing farming than trying to mitigate its environmental impacts. We’ve tried to plot this out below. What you can clearly see is that the purple (mitigation expenditure) is nearly invisible relative to what is spent on subsidizing farming (shown in the orange slices).
The figure now provides what is essentially a map of the ‘perversity‘ of agricultural subsidies – showing where we spend money to do things that are often bad for the environment and costly to the economy. A first step in reducing conflict between agriculture and the natural environment would be to do away with the subsidies in orange.
It has been a busy summer for our NERC-funded project RELATED (that stands for Restoring Ecosystems by Linking Aquatic and Terrestrial Ecological Dynamics). The project aims to test experimentally whether the productivity of aquatic food webs increases with the quantity and quality of terrestrial organic matter deposited in nearshore delta habitats. It builds on our previous work that showed forests fuel the growth of juvenile fish by subsidizing the base of the aquatic food web. RELATED will also feature much more work on unlocking the microbial ‘black box’ at the base of the food web as well as better understanding how greenhouse gas emissions might change with surrounding vegetation. Inland waters are major sources of atmospheric carbon and predicting their responses to future change is of major interest (some great recent work here, here, and here).
We’ve now just finished launching the experimental platform behind RELATED. Over the last 8 weeks, we’ve had a team of 8+ working tirelessly to submerge artificial lake sediments in 3 lakes. This has involved collecting, mulching, and sifting organic and inorganic materials, mixing these at an industrial scale, and outfitting nearly 300 mesocosms with the appropriate sampling gear. By replicating our experiment in 3 different lakes, we’ll be able to study terrestrial-aquatic linkages along gradients in eutrophication and climate change – the main drivers of change in the world’s inland waters. We’ve also had excellent student help from Laurentian University’s School of Architecture work to design and build a network of sampling platforms that will allow us to work without disturbing our sediments.
Here’s the main team celebrating the deployment of the experiment in Ramsey Lake:
The July 2015 issue of New Phytologist has come out and its a doozy! The entire issue features work on evolutionary plant radiations, drawing together a range of papers that summarize the current state of knowledge about “where, when, why, and how” plant radiations happened. An editorial by Colin Hughes, Reto Nyffeler Peter Linder outlines the content of what will surely be a landmark issue for years to come!
For me, this was possibly one of the most intellectually stimulating meetings that I’ve ever attended. Talks drew major figures in plant ecology, evolution, and systematics and really pushed the boundaries out on ‘diversification’ research. The field itself is still arguably quite new, with many of the key questions synthesized in a 2008 paper by Peter Linder. In fact, we have a PhD studentship available to follow-up some of these questions and build on what we talked about at the meeting.
You might have noticed that we even have a contribution in the New Phytologist Special Issue. Our paper tests the mechanisms by which plant evolutionary radiations emerge and influence ecological dynamics. We apply more of our expertise in structural equation modelling to focus on 16 species-rich genera in the alpine zone of New Zealand.
One of the most exciting aspects of our paper is that we’ve tried to reconstruct the niche space that each genus has occupied over the last 20 million years. This is fairly ambitious and has involved tasks like reconstructing sea surface temperatures through the Cenozoic from isotopic measurements of foraminifera deposited in marine sediment cores, and then using these to estimate past land temperatures. We’ve also had to consider that the alpine zone has grown immensely over time with uplift of the Southern Alps. To do so, we collated radiometric dates of rocks and tried to infer their rate of uplift since the Miocene.
Overall, our results suggest that genera that colonized New Zealand earlier encountered more ‘vacant’ environmental space, which promoted species diversification and further occupancy of the environment. Genera that occupied more environmental space were subsequently more dominant in present-day vegetation plots. The key message is that time not only explains why diversity arises, but how this diversity influences ecological dynamics. The Special Issue has many other fabulous papers, so do check it out!
It’s been a while since we’ve last blogged but we’re hoping to pick it up more regularly again over the next few months now that the group is quickly growing.
For now, I thought I’d comment on an interesting study published this month in Applied Vegetation Science by Laura Burkle and Travis Belote. It links up nicely to work we’re doing on priority effects, which is the idea that species that arrive earlier into a habitat influence the interspecific interactions of later arriving colonists. Priority effects are hardly surprising. Succession theory long predicted that communities develop along different trajectories depending on the species that are initially present. But generalizing the direction and strength of priority effects remains challenging. This is where Sgt. Pepper’s Lonely Hearts Club Band can offer some advice:
At the start of December, Andrew organized a three-day workshop in Cambridge, bringing over Brian Kielstra, John Gunn, Nikki Craig and Chris Solomon from Canada, Michael Pace, Grace Wilkinson and Stuart Jones from the USA, Jan Karlsson and Martin Breggen from Sweden, and Jon Grey from the UK. The aim was to see how terrestrially-derived organic matter (tOM) contributes to secondary production in aquatic ecosystems, by synthesizing global data collected from 594 observations on C, N and H, in over 10 zooplankton groups from 158 lakes in the northern hemisphere. Ultimately, the group would like to build a model for each consumer by lake. The ten limnologists worked all day (and all night!) but still had time to experience Cambridge, with time spent at the legendary Eagle pub (where great minds meet) and an exquisite dinner at Peterhouse, the University’s oldest college.
A few days later, while Jon, Martin, Jan, Nikki and Stuart traveled home, the rest of the group and I traveled to London to catch the Eurostar to Lille, where we attended the British Ecological Society (BES) and Société Française d’Ecologie (SFE) joint meeting. The meeting was the first of its kind and brought about 1,200 ecologists to the Lille Grand Palais convention center – mainly French and British, but also a lot of attendees from other European countries and the rest of the world.
Our latest paper in the Proceedings of the Royal Society on the “jellification” of temperate lakes has gotten an impressive amount of on-line attention. At the time of writing this blog, Altmetric scores it as the 22nd highest ranking paper ever published in the journal. You can read summaries from the Washington Post, New York Times, Daily Mail, CBC, CBC Radio, and Yahoo, among others. I’ve also done four separate interviews this week with BBC radio stations (BBC Radio 5, BBC Wales, BBC Cambridgeshire). You can catch the latest, with the BBC World Service from the 26th of Nov, below:
The main finding of the paper is that a small planktonic animal named Holopedium glacialis has been dramatically increasing in two very different lake regions of Canada as the keystone grazer in these lakes, the water flea (Daphnia spp.), has been disappearing. Our results show that this is mainly driven by declines in lake water [Ca]. Daphnia need large amounts of Ca to build their body shell, while Holopedium surround themselves in a gelatinous polysaccharide “bubble”:
This jelly also protects Holopedium from predators. By contrast, Daphnia are increasingly susceptible to predators at low [Ca] because their ability to induce evolved defences is also impaired. Our analyses show how vanishing Daphnia have now left more algae uneaten for their competitors to exploit, allowing them to multiply in number. Many media reports have picked up on this as Holopedium liking ‘pollution’, with low [Ca] somehow being the result of this. But it is more in fact a legacy of pollution. While we have curbed industrial emissions and reduced acid rain, the historical depletion of base cations from the thin soils of the boreal shield, have left behind much lower [Ca] than present prior to industrial activity. Ca concentrations have consequently been falling across much of North America and Europe.
Two things I’ve been thinking about lately.
First, outliers. A classical example of these arises in what is known as Anscombe’s quartet, four datasets with almost nearly identical properties (mean, variance, x-y correlation) yet very different when plotted:
John Kruschke at “Doing Bayesian Data Analysis” has a terrific example of how to fit robust regressions through these lines using BUGS, along with links to additional code from Rasmus Bååth. It is definitely worth taking a look at these. One of the key points is that for small sample sizes, where the population SD is really unknown, it is worth modelling data from a Student’s t distribution as opposed to a normal distribution, as it allows for more flexibility in outliers (such as in y3-y4 above). These are easy to implement in BUGS and Stan.
Second, how worried should we be about R2 values? I’ve been thinking briefly about this over the past week as I’m not getting my usual impressive 0.9 values. Does this matter though? What does a R2 really tell us? Well, consider the example below:
x1 <- rnorm(10000) y1 = 5 + 1*x1 + 1*rnorm(10000) y2 = 5 + 1*x1 + 3*rnorm(10000) summary(lm(y1 ~ x1)) summary(lm(y2 ~ x1))
If you run the code in R you will see that both models recover the estimated effects of x1 on y1 but that y2 has 3X larger error estimates around this effect. The resulting R2 is ca. 1/5 that of the y1 model. Does this mean the model is a poor fit? Not really… It speaks much more to the fact that the predictive power of the y2 model is diminished and there are wider prediction intervals, rather than there being anything wrong with the mean estimate of x1 per se. I’d be curious to hear what others make.
Finally, I thought we should drop a mention of the student blog PLANeT, run by our Part II undergraduates. It’s worth checking them out! They regularly post well written and engaging articles on the relationship between plants and society.