Friday, January 29, 2016

Commenting Issues, solved(?)

A few people mentioned that they have had problems commenting on the EEB & Flow for a while. I think the problem a blogger issue with the embedded comment format (if your computer doesn't accept 3rd party cookies, it looks like your comments may not post). I've changed the comment format to hopefully fix this issue.

My apologies, and if you still run into problems, please let me know!

Tuesday, January 26, 2016

Things to keep in mind when finding a PhD

A wonderful student who worked with me when I was a graduate student is in the midst of applying for graduate school, and has been going through the process of finding a suitable program and advisor. It's been nearly 7 years (!?) since I was first in graduate school and, in my case, I mostly lucked my way from undergraduate to a great lab without nearly enough due diligence (and no one I knew or in my family had been to grad school to provide advice).

If asked during grad school, I had a list of advice I would have liked to have received (admin questions, funding issues, how to get to campus on public transport). But the advice I think is important has actually changed a lot, from just “make sure you love research” (although you should, at least most of the time), to more strategic and practical considerations.

I now think the most important thing is to ask yourself while you consider graduate school is, "Why do I want to get a PhD?" Note that there is absolutely no right answer to this question, but there are some wrongs ones, e.g. "I don’t know what else to do next" or "I have good grades". The problem is that these answers aren’t enough to motivate you through a PhD program. And some people find themselves 5 years later, still not knowing what they’re going to do next or why they got a PhD. It’s okay to answer "I like the research I did as an undergrad" or "I want to develop strong quantitative skills", or "I love working with ideas", because these kind of answers mean you want something from your experience and you've thought about what that is.

Educate yourself about the opportunities that a PhD will bring, both academic and non-academic. Continue this education while you are in graduate school. [Departments, offer more opportunities for students to learn about non-academic jobs.] The reality is that getting the oft-desired research professorship is very difficult (e.g. 200+ applicants for a general ecology position is not unusual). But PhDs produce desirable skill sets and there are other opportunities, so long as you are aware of them. There are many LACs (liberal arts schools) in the US, and thus more teaching oriented professorships advertised every year than there are R1 professorships. There are NGO and government research jobs. And as many of my grad school friends leave academia, it’s a relief to see that their skills – strong quantitative abilities, good data management, a clarity of vision on how to ask questions and answer them with appropriate data – make them employable across a range of professions.

Ask questions ask questions ask questions. Don’t go into a program without knowing what it will entail. Ask the same questions of both faculty and students and see how their answers compare. 

To understand a department, you want to know what the teaching load is on average, how funding works (and for how long!). You should find out the average time to completion of a PhD program, what classwork looks like, whether there are student-lead reading or discussion groups? Is there funding for student travel to conferences or meetings?

If you have a lab in mind, you need to similarly learn about that lab. Find out, from both the PI and their students, how the lab works. What is the supervisory style? Does the PI tend to be hands on, or expect more independent research? How does your personal approach to working mesh with their style? Don't assume that if you like to have structure and feedback and the PI only is around once a month, it will just work out. How often are they physically on campus? How often would you meet? What are other students in the lab working on? Is the lab collaborative? Do students publish together? What skills are emphasized in the group? Has the PI published recently (last 2-3 years, depending on context) and, perhaps most importantly, have they graduated any students? If not, try to figure out why.

Once you’ve found a place, remember that how you feel about your PhD will rise and fall all the time. That’s normal. Avoid the worst of these dips by taking care of your mental health. The sort of unstructured, isolating, often un-rewarded work that goes into a PhD can be draining. But it is also 100% okay to change your mind, to decide a Master’s is sufficient, to hate everything you are doing and quit. Seriously. The sunk-cost fallacy will make you (and people around you) miserable.

Of course, grad school—like life—is stochastic and full of uncertainty. But its possible, with care to increase the probability that you find a supportive, nurturing lab and have a wonderful time as a graduate student. 

Monday, January 18, 2016

Have humans altered community interactions?

A recent Nature paper argues that there is evidence for human impacts on communities starting at least six thousand years ago, which altered the interactions that structure communities. “Holocene shifts in the assembly of plant and animal communities implicate human impacts” from Lyons et al. (2016, Nature) analyses data spanning modern communities through to 300 million year old fossils, to measure how the co-occurrence structure of communities has changed. The analyses look at the co-occurrence of pairs of species, and identifies those that are are significantly more likely ('aggregation') or less likely ('segregation') than a null expectation. Once the authors identified the species pairs with non-random co-occurrences, they calculated the proportion of these that were aggregated (i.e. y-axis on Figure 1). Compared to the ancient past, the authors suggest that modern species had fewer aggregated species pairs than in the past, perhaps reflecting an increase in negative interactions or distinct habitat preferences. 
Main figure from Lyons et al. 2016.
The interpretation offered by the paper is “[o]ur results suggest that assemblage co-occurrence patterns remained relatively consistent for 300 Myr but have changed over the Holocene as the impact of humans has dramatically increased.” and "...that the rules governing the assembly of communities have recently been changed by human activity". 

There are many important and timely issues related to this – changing processes in natural systems, lasting human effects, the need to use all available data from across scales, the value of cross-disciplinary collaboration. But, in my view, the paper ignores a number of the assumptions and considerations that are essential to community ecology. There are a number of statistical issues that others have pointed out (e.g. temporal autocorrelation, use of loess regression, null model questions), but a few in particular are things I was warned about in graduate courses. Such as the peril of proportions as response data (Jackson 1997), and the collapsing of huge amounts of data into an analysis of a summary of the data ("the proportion of significant pairwise associations that are aggregated"). Beyond the potential issues with calculating correct error terms, interpretation is made much more difficult for the reader. 

Most importantly, in my view, the Nature paper commits the sin of ignoring the essential role of scale in community ecology. A good amount of time and writing has been spent reconciling issues of spatial and temporal scale in ecology. These concepts are essential even to the definition of a 'community'. And yet, scale is barely an afterthought for these analyses.  (Sorry, perhaps that's a bit over-dramatic....) Fossils—undeniably an incomplete and biased sample of the an assemblage—can't be described to more than a very broad spatial and temporal scale. E.g. a 2 million year old fossil and a 2.1 million year old fossil may or may not have interacted, habitats may have varied between those times, and populations of S1 and S2 may well have differed greatly over a few thousand years. Compare this to modern data, which represents species occurring at the exact same time and in relatively small areas. The differences in scale is huge, and so these data are not directly comparable.

Furthermore, because we know that scale matters, we might predict that co-occurrences should increase at larger spatial grains (you include more habitat, so more species with the same broad requirements will be routinely found in a large area). But the authors reported that they found no significant relationship between dataset scale and the degree of aggregation observed (their Figure 2, not replicated here): this might suggest the methodology or analyses needs further consideration. Co-occurrence data is also, unfortunately, a fairly weak source of inference for questions about community assembly, without other data. So while the questions remain fascinating to me - is community assembly changing fundamentally over time? is that a frequent occurrence or driven by humans? what did paleo-communities look like? - I think that the appropriate data and analyses to answer these questions are not so easy to find and apply.

Response from Brian McGill:
My comment I was trying to post was:

Interesting perspective Caroline! As a coauthor, I of course am bound to disagree. I'll keep it short, but 3 thoughts:

1) The authors definitely agonized over potential confounding effects. Indeed spent over a year on it. In my experience paleoecologists default to assuming everything is an artefact in their data until they can convince themselves otherwise, much more than neo-ecologists do.
2) They did analyze the effects of scale (both space and time) and found it didn't seem to have much effect at all on the variable of interest (% aggregations). You interpret this as "this might suggest the methodology or analyses needs further consideration". But to me, I hardly think we know enough about scaling of species interactions to reject empirical data when it contradicts our very limited theoretical knowledge (speculation might be a better word) of how species interactions scale.
3) To me (and I think most of the coauthors) by far the most convincing point is that the pattern (a transition around 8000 years ago plus or minus after 300,000,000 years of constancy) occurs WITHIN the two datasets that span it (pollen of North America and mammal bones of North America both span about 20,000 years ago to modern times) and they have consistent taphonomies, sampling methods, etc and yet both show the transition.

I agree that better data without these issues is difficult (impossible?) to find. The question is what you do with that. Do you not anwwer certain questions. Or do you do the best you can and put it it out for public assessment. Obviously I side with the latter.

Thanks for the provoking commentary.



Tuesday, January 5, 2016

Resolutions for 2016

Having now been a postdoc for a couple of years, I think I’ve slowly developed more perspective about the day-to-day aspects of working as a researcher and the costs and benefits of various approaches. So this year, I am resolving to be proactive about the various challenges of academic life, and try some things meant to make my work life more productive and better: 

1) Carve out more time to read the literature. The busier I get, the more difficult it is to keep up with new papers that aren’t directly connected to my current projects. One of the best parts of being a grad student (without a heavy teaching load) was how much time I had to keep up with the literature. As my time is more scheduled and there are more concrete deadlines, it is harder to make time for activities like reading that don’t have an immediate pay off. I have a feed reader, but I find that I only check it monthly; I also come across interesting papers while doing lit reviews/etc and leave those open in my browser, planning to get to them eventually...

I know that braver souls than I are tackling this problem #365papers style, but I don’t think that’s what I want. Instead, I am scheduling three 1-hour slots per week, and I think that’s a manageable goal.

2) Continue to work on good project management practices (such as those described here). I use the suggestions for predictable directory structures, separation of code into different types of scripts and of course, version control, and find them very helpful. I wish that I had learnt best practices for coding and project management as a grad student, but it’s never too late.

3) Take vacations (and mean it!). Every academic I know who has good work-life balance takes vacations. That means not working—at all, including no responding to emails. This is one of the things I admire most about my European colleagues, and I look forward to enjoying the French holidays when I start a fellowship in Montpellier this spring :-)

4) Maintain relationships across distances. It can be difficult to connect with people during short postings here and there, and even harder to maintain those relationships after you move on to the next place. The tools are there (Skype, Facebook, Twitter, email, etc), so I shouldn’t forget to take advantage of them.

5) Learn a new skill. Still deciding what, given the many things I want to learn!

6) Emphasize the positive more often. In general, I think people (or at least me) can be overwhelmed by the negatives in academia – e.g. the rejections of manuscripts and applications, the difficulties in securing the next job, etc. Unlike in undergrad, we don’t get grades or quantitative measures of our success too often (and I haven’t gotten a sticker on my work in years). And when we do get praise, it is often informal (e.g. “good talk”, “I liked your paper”), or balanced with criticism (e.g. “accept but with major revisions”). This is all in pursuit of improvement, but it can be difficult to keep even constructive criticism in perspective because the brain is biased towards remembering the negative. So I’m considering keeping an explicit list of successes to help highlight the positive.

Tuesday, December 15, 2015

2015 caRd - A diveRsity of Santas

A keen observer will note that there are a number of similar taxa that are active this time of year.

Although well described in the literature, surprisingly little attention has been given to the ecology of these creatures. Observational data allows some traits to be compiled, however, and some simple exploratory analyses may allow us to better understand the 'Santa' assemblage.

                       heft transport first.appearance
Coca.cola.Santa         fat  reindeer             1900
Department.Store.Santa  fat  reindeer             1900
Salvation.Army.Santa    fat  reindeer             1890
Kriss.Kringle           fat  reindeer             1800
Santa.Claus             fat  reindeer             1700
Pere.Noel              thin    donkey             1400
Father.Christmas        fat      foot             1400
Sinterklaas            thin     horse              400
Saint.Nicholas         thin      foot              300
Ded.Moroz              thin      foot             1937

We are fortunate to also have sequence data (from DNA on milk glasses and lost beard hairs), so we can add additional information about relatedness amongst these species.

plot(xmas.tree, type = "c", FALSE, edge.color="darkgreen",  edge.lty=1, edge.width=18, label.offset = 1, direction="downward", font=3, tip.color="darkred")

The phylogeny shows that there seems to be an early divergence between European and North American santas. Indeed, there is a group of North American santas (Mall Santa, Coca-cola Santa, Salvation Army Santa) which are closely related (and also appear to share very similar traits, based on the table above). (Note that branch lengths in this phylogeny show nucleotide substitutions, and it is not time-calibrated, due to the absence of santa fossils).

One approach is to identify a few traits and plot them on the phylogeny to compare how traits vary among santas. Let's start with anatomical characteristics:

#Plot traits (fatness) against Santa
co1 = c("blue", "purple")
tiplabels(pch = 12, col = co1[as.factor(heft)], cex = 3.5, adj=c(0.5, 0), lwd=2)

#Let's see the transportation mode trait too:
co2 <- c("yellow", "gold", "darkorange", "red")
tiplabels(pch = 8, col = co2[as.factor(transport)], cex = 2, adj=c(0.5, 0), lwd=2)

legend("topleft", legend=c("reindeer", "donkey", "foot", "horse"), fill=rev(c("yellow", "gold", "darkorange", "red")))
legend("topright", legend=c("fat", "thin"), fill=c("blue", "purple"))
For a future study, we could ask whether the apparent correlation between fatness and reindeer usage is significant, once the underlying phylogenetic relationships were controlled for. 

We can also reconstruct santa traits (here, we look at the form of transportation) to explore what form of transportation ancestral santas likely used:

#reconstruct ancestral state
cc = ace(transport, xmas.tree, type="discrete")
co2 = c("yellow", "gold", "darkorange", "red")
nodelabels(pie = cc$lik.anc, piecol = co2, cex = c(1.5, rep(1, 8)))

The markers at each node show the probability that this ancestral taxa used each of the four possible types of transportation. It seems that the North American santas and their ancestors have long relied on a trusty reindeer mutualism.

Finally, we can look at the geography of all the various santas:, locales)

To run this caRd yourself, follow the link to the R code:

Monday, December 14, 2015

A bird in the hand… Worth a bunch in the bush?

Guest post by University of Toronto-Scarborough Masters of Environmental Science Student Amica Ferras
     In less than a week, Christopher Filardi achieved a level of cyber-fame worthy of this digital age— but for all the wrong reasons. If you haven’t heard of him yet, that’s okay. Not all of us peruse biodiversity articles over our morning cereal. Here’s what you’ll need to know to hold your own around the water cooler.
Photo: University of Kansas

Christopher Filardi is the director of Pacific Programs at the American Museum of Natural History’s Center for Biodiversity and Conservation. This past September he and his team were part of an international expedition to the mountains of Guadalcanal, one of the islands in the Solomon Archipelago. Lead by native islanders, the team was on a mission to assess the biodiversity and habitat constraints of this unique region in order to develop a tailored conservation strategy. It was there on those mysterious island mountains that Filardi happened upon a true legend by any biology geek’s standards — the Guadalcanal Moustached Kingfisher. Even if you have zero interest in species biology, the stats on this bird are impressive. Only three sightings of the Kingfisher have been documented in all of history: a single female captured in the 1920’s, and another two in the 1950’s. No male specimen had ever been recorded and no live animal had ever been photographed. This bird can play a mean game of Hide-and-Go-Seek.
Upon discovery of the Kingfisher colony, Filardi and his team set to work. Calls were recorded, habitat was meticulously documented, behavior and motion patterns were scrutinized and population dynamics were assessed. And then, they killed one. (Cue the angry villagers with pitchforks and hippies with signs).
The collection was purely scientific. Filardi and his team stuck to a field biology motto of collect, dissect, but ultimately respect. Filardi hoped that the Kingfisher specimen would open the door to discovering more about the elusive species and their ultra-specific habitat. But the road to media-hell is paved with good intentions, and as the story spread like wildfire Filardi’s actions fell under attack. His ‘collection’ was deemed “perverse, cruel” by a representative from PETA to the Daily News, and the UK online Daily Mail described it as “slaughter”. The story exploded, appearing in the Huffington Post, Washington Post, Nature World News and Audubon, just to name a few. For those links and more I suggest checking the wonderful world of Google, but I will personally recommend that you read Fildari’s self-defense in Audubon, and the Toronto Star’s coverage of the controversy The Star does a fabulous job of presenting both sides of the story, and also goes into detail about the rather dubious past of field biology.
In the 1700’s and 1800’s specimen collection was more sport than science. It was a my-stuffed-animal-carcass-is-bigger-than-your-stuffed-carcass race, and rare species paid the ultimate price. Great Auks, for example, upon classification as endangered in 1775, were hunted at an alarming rate by naturalists attracted to its rareness. In 1884 a final pair of Auks was caught by fishermen, and no Auk has ever been sighted since. Specimen collection has come a long way since then though, and field biology has contributed to some groundbreaking scientific discoveries. Consider eggs— comparisons of eggshell thickness from samples collected across decades was used to identify the detrimental effects of DDT and other pesticides to natural ecosystems.
So, those are the facts. And my opinion about it? I’m siding with Filardi. Science has come a long way from naturalist trophy hunting in the 1800’s. Nowadays, before even setting foot outside of the lab scientists must undergo a rigorous evaluation process to determine if collection permits will be granted. Cost-benefit analyses, potential outcomes, and fragility of a species and ecosystem are all heavily weighted in before a decision is reached. Filardi’s expedition was no exception to this rule. (And for anyone questioning the usefulness of collections at all, I suggest you read the following article I’d be happy to argue with you on that front another day).
It wasn’t as if Filardi saw the Kingfisher, pulled a net out of his pack and started swinging. After discovering the Kingfisher colony, the bird was carefully observed over several days. Input from the native islanders, assessments of habitat resilience and population robustness were all carefully analyzed before deciding to humanely collect the single male specimen. The unwilling sacrifice of the Kingfisher was honorably recognized, and the collection will be worthwhile if Filardi has anything to do with it. Scientists now have access to a complete set of genetic information for the Kingfisher. It will now be possible to undertake full molecular, toxicological and evolutionary diagnostics. Scientists may discover disease and pollutant susceptibilities that will guide Kingfisher protection efforts, or identify a direct evolutionary pressure to explain the appearance or behavior of the birds. At a more macro level, the specimen could reveal a shared trait between all high-elevation avian species or allow for an assessment of the particular environmental pressures the island ecosystem exerts over its inhabitants.  
Remember though, the point of the Guadalcanal expedition was not a Kingfisher hunt, but an internationally commissioned excursion to study the biodiversity and ecosystem threats in the Solomon Archipelago. Working with native islanders and Solomon government officials, Filardi’s team was working to establish a conservation strategy to protect the unique island system. The Pacific Island tribes have tended to their mountainous lands for decades, but recent international development has threatened the natural state of the ecosystem. Intensive mining and logging ventures have already begun transforming the lowlands of the islands, and climate change at large is effecting the delicate balance of ocean and forest features that unique species like the Kingfisher rely on. For species limited to a single isolated habitat, even minor changes in soil pH, precipitation or fluid motility can have astronomical effects on species survival. These are not the resilient squirrels and raccoons we in North America watch thrive everywhere from lush forests to derelict urban alleyways. Filardi’s collection will go a long way in identifying what needs to be done to protect these habitat-specific island species.
In fact, it already has. Discovery of the Kingfisher led Filardi to talks with local tribes and the Solomon government which culminated in formal agreements to protect the island mountain region under the recently passed Protected Areas Act. Filardi has already booked a return flight to Guadalcanal to help negotiate the next steps in this exciting conservation effort.

So, what do you think? 

Sunday, December 6, 2015

The hurdles and hardships of science in China

In my last post on China I discussed why China is becoming a scientific juggernaut. I focussed on all the things that seem to be working in its favour (funding, high expectations on scientists, etc.). While I do think that science in China is good and getting better, it is also important to point out some of the hurdles and limitations that hold back some aspects of scientific advance here.

In my previous post I noted that the expectations placed on students and researchers (i.e., to produce a minimum number of papers in journals with high impact factors, IFs) provided motivation to do good science. This is undoubtedly true, however, these strict expectations also reinforce a strategy of ‘paper-chasing’ where students are encourage to figure out how to get a paper. This is because the reward structure is so quantitative. While this type of evaluation systems has pros and cons, it does create a different sense of urgency than I’ve experienced elsewhere.

Pragmatic factors
The Great Fire Wall of China from "Cracks appear in the Great Fire Wall of China" posted by the China Daily Mail, Sep. 25th 2013.
I have never yelled at my computer or cursed the internet as much as I have in China. In the west we often hear about the ‘Great Firewall of China’ and probably do not think much about what this actually means. It sucks. The internet barely functions for significant proportions of the working day. I thought that this might have to do with the number of people and lack of infrastructure, but I no longer believe this to be true. Other countries in the region have great internet, and China has very advanced infrastructure. I’m pretty sure that when there is high traffic, the national security protocols and activity monitoring servers are the bottleneck.

Because the government policy is to block certain websites, most of the scientific internet websites and data sharing portals are not accessible here, but this may change at any given time. For example: Google Drive, Dropbox, Facebook, Blog sites, Twitter, Google Maps, and Google Scholar are all services routinely used by scientists and which are blocked in China. The reason for these to be blocked, as far as I understand it, is that they do not share users’ activities and the government cannot monitor what individuals share and download (which reinforces the value of these services to me). I also suspect that they are blocked to give local companies a chance to succeed without competition from global corporations, or perhaps simply because of disagreements with the companies.

I have had immense trouble trying to share files with my lab back in Canada (and to post this blog entry –which is why I’m doing it from Cambodia!). I am not currently engaging in social media –something that I saw as a legitimate activity for communicating science. I am having a very hard time searching for articles without Google Scholar. I also have trouble with other websites that should not be blocked, but that use third party encryption. For example, I can’t log in to my University library in Toronto, and I couldn’t connect my Canadian grant application to the Canadian Common CV (which we are required to do in Canada) because the CCV web interface was blocked (I had to get my post doc in Canada to do it for me). I have tried to go to researchers’ websites to find that they are blocked because they use a blogging site (e.g., Wordpress). The amount of time I spend doing basic online professional activities has increased 3 to 4-fold.

This is important because Chinese scientists are at a disadvantage when it comes to international collaboration and participating in online initiatives. I would encourage scientists outside of China to consider these imposed limitations to ensure that information and collaboration is barrier-free. Here are some tips:

  1. Don’t link to your Google scholar publications on the publications page of your website
  2. Don’t use a blog site to host your website (e.g., Wordpress)
  3. Don’t use Dropbox or Google drive to collaborate on papers
  4. Don’t use gmail as your work e-mail, Air China, for example, won’t send e-mails to gmail.
  5. Social media has emerged as a great way to communicate with broader communities, it is important to recognize that these dialogues exclude Chinese scientists.
  6. Ironically, as I write in this blog, blogs are blocked and while blogs provide a great platform to discuss ideas and issues, they are not available to Chinese scientists. 

These last two are interesting as journals increasingly require or request tweets or blog posts to help maximize exposure, but these forms of communication are not on scientists’ radar here.

Chinese science has been increasing by leaps and bounds despite these limitations. This is a testament to the hard work and dedication by Chinese scientists. I have no doubts that basic scientific research in China will continue to increase its stature and impact.

One thing that is interesting to me is that many of the graduate students here use VPNs (Virtual Private Networks) to mask their IP addresses. They are able to access blogs, Google Scholar, etc. In conversations with people, VPN use is extremely widespread and successful at circumventing government filters, most of the time (there seems to be an arms race between the government and VPNs). It really makes me wonder how much longer these governmental controls can be realistically maintained.

Wednesday, December 2, 2015

Paper of the lustrum*

(*lustrum = five years)

I’m co-teaching (with Kendi Davies and Julian Resasco) a graduate seminar focused on current trends and advances in community ecology. It’s been great, and having a small group with varied backgrounds (disease ecology, microbial ecology, restoration, community ecology theory, etc) allows for flexible and interesting discussions. Somehow the topic last week drifted to favourite papers, and we ended up with a plan to choose and defend the paper that was—in our opinion—the best one published in ecology in the last 5 years.

Today we described and defended our choices and tried to decide what the ‘best’ actually means, anyways. I don’t think anyone quite realised just how difficult this exercise would be. First, 5 years isn’t actually a very long time when measured in academic publishing years. That’s only the time of the average PhD, or less than the entire tenure-track period. I immediately thought of several papers I love, only to realize that sadly, they were from before 2010 (e.g. papers like these). 

Nearly everyone started their search the same way: with a Google Scholar search, looking at the most cited papers between 2010-2015. Some people looked at the most popular papers from high impact journals (Ecology Letters, Science, Nature, PNAS, etc); others looked at the output of eminent ecologists during that time period. At least one used his committee members for advice, and for the new grad students this was a nice crash course in the recent literature. Citations, quality journals, or eminent names might have been starting points for finding these papers, but it was interesting how little these actually seemed to matter. When defending their choice of paper, absolutely no one mentioned citations or journal as deciding factors. 

The papers we chose, and why: 
Conceptual synthesis in community ecology. (The Quarterly Review of Biology) Vellend 2010  
This was my choice, although I went back and forth between a short list of papers. For me, the ‘best’ paper had to either change how we do ecology, or how we think about ecology. I think Vellend 2010 has a lot of value as a pedagogical tool, and a device for organizing ecological knowledge. It has the potential to aggregate the varied, context dependent data that ecologists have been collecting for generations. Further, rather than the disjointed approach my undergraduate texts took for community ecology (productivity here, lynx-hare plot there), a single framework should help students understand community ecology as a cohesive set of ideas. And I admire papers that have big ideas.

 This was a cool choice, because it turns out to be a massively important development that many of the less molecularly-inclined knew little about. This paper introduced the use of CRISPR/Cas for gene editing. The CRISPR system is been found in archaea and bacteria, and provides a form of adaptive immunity against viruses. Importantly, it has been developed for use in incredibly precise genome editing that is heritable. It has massive implications for the study of evolution, microbial ecology, disease, population genetics, and everything in between. It is also the source of ethical concerns because it can (and has) be used to modify human embryos. 

Biodiversity loss and its impact on humanity. (Nature) Cardinale et al. 2012 
This was the choice of two students, so it may have been the de facto winner. It is a massively cited paper (>1000), and both students chose it in part because it makes a clear contribution to human welfare and society. It represents a massive undertaking (they analysed more than 1000 papers) reviewing research on how biodiversity relates to a large number of relevant ecosystem services. In particular, Table 1 (below) can be used for applied and basic research, and shows where research and data agree, disagree, or are lacking. This is certainly a must read for ecologists.

This paper helped to concentrate and inspire research on intraspecific variation and to highlight the areas of research that are still poorly studied (and it actually made my short list too). There is obvious variation within species (long acknowledged as important to evolution, starting with Darwin) but this is often ignored in community ecology. Bolnick et al. point out the many possible and important implications that arise from such variation. The writing is clear and highlights extremely well the general mechanisms that might interact with intraspecific variation. For the student who chose it, it was inspiring enough when it first came out, that they changed their research direction. 
Table 1: Bolnick et al. 
This paper was chosen in an opposite fashion: it is brand new, and rather than having inspired current research, the student thought it would inspire future approaches. The paper integrates community ecology and disease ecology in a novel and sophisticated way, advancing an area of research currently receiving a lot of attention. In this paper, mice are ‘mesocosms’ in which the importance of bottom-up versus top-down control of infection (by malaria and a nematode) could be tested. (Quote: "It's a real page-turner"). 

This was another paper chosen because it inspired the student's current studies. Ladau et al. brought together a massive data set for marine bacterial biodiversity, allowing them to map it on a global scale and develop predictive distribution models. Interestingly, they found that diversity patterns were lower at the equator, contrary to typical findings in other organisms. The student cited the careful methodology, extensive data, and comparison of results to those in macro-scale systems as the paper’s strengths. 
From Ladau et al. "Maps of predicted global marine bacterial diversity. Color scale shows relative richness of marine surface waters as predicted by SDM. Samples were rarefied to 4266 rDNA sequences to enable accurate estimation of relative richness patterns on a global scale from data sets with different sequencing depths. True richness is expected to exceed estimated values. (a) In December, OTU richness peaks in temperate and higher latitudes in the Northern Hemisphere. (b) In June, OTU richness peaks in temperate latitudes in the Southern Hemisphere..."

The final paper was Kendi’s choice. Community ecology has struggled with weak connections between pattern and process. The experimental and quantitative work coming from this research group has provided multiple examples for how to connect theory, statistics, and experimental results in a very rigourous fashion. In this paper, the focus is particularly on functional/trait approaches to community assembly and coexistence, and the authors manage to connect careful experimental data with Chessonian coexistence theory, using trait data to estimate species’ fitness and niche differences, and then using these to predict species coexistence.

After the fact, of course, lots of other great papers came to mind. It isn't really possible to choose one best paper, either. But the characteristics people looked for in a great paper were pretty similar - inspiring, providing novel approaches to particular questions, focused on big questions or ideas, and making contributions that go beyond academic ecology.

Monday, November 23, 2015

Challenges for microbial ecology

It is common in ecology for promising new areas of research to grow rapidly in terms of funding, students, and papers. Sometimes, such growth outpaces supporting development. This can lead to criticisms, which, when properly dealt with, can help such burgeoning subfields to mature. These are challenges currently facing microbial ecology as well. [Note I use the term microbial ecology here to refer to the ecology of microbes, not simply ecology that happens to use microbes as a study organism (e.g. Graham Bell or Lin Jiang’s experimental work).]

Microbes are fascinating. They are a very large and important group that has been under appreciated in ecological research until recently. Now, thanks to ever-improving molecular methods, the ecology of microbes is increasingly accessible. It has formed the basis of some great citizen science and public outreach (microbes in space, your home, your cat). And scientifically, work from this emerging subfield is often excellent, with broad implications to other areas of ecology (just as a couple of cool examples). Microbes are different from other taxa for all sorts of cool reasons - horizontal transfer of genes, tiny genomes, and immense functional plasticity – and this makes for fascinating discoveries.

However, the newness of this subfield is apparent as it attempts to mesh microbiology with the existing body of ecological knowledge and approaches. The result, at times, is that existing ecological theory and methods are applied unquestioningly to microbial datasets, but may not be appropriate. Unfortunately, the assumptions behind such analyses and their limitations with respect to microbial datasets aren’t always recognized, leading to questionable interpretations. There is sometimes also an over-reliance on “pipeline” approaches to microbial research; for example: collect samples, extract DNA, sequence, run through the QIIME pipeline, and present descriptive analyses, particularly beta-diversity metrics (Unifrac), PCoA or NMDS plots, and permutation-based statistical tests (e.g. ANOSIM) to determine whether assemblages of interest differ in composition. These pipelines originally arose because of the difficulties in handling such data sets and the need for specific software for analyses.

Of course, it is important to keep in mind that microbial ecology is in an early phase, where accumulating data and cataloging diversity is a priority. Mostly, issues arise when major questions in ecology are posed but perhaps without quite having appropriate methods or data to answer them. To provide an example, I sometimes see microbial ecology papers attempting to differentiate between niche and neutral processes as the drivers of microbial community assembly. Microbes are often thought of as lacking meaningful dispersal limitation (‘everything is everywhere; the environment decides’ is a common heuristic). As a result, it may be that communities assemble in a highly stochastic fashion (random arrival) or perhaps environmental filters and interactions do matter. But the issue of “niche” versus “neutrality” is a difficult question to answer using observational data in any system. It requires considering the many assumptions that underlie “niche” and “neutral”, making predictions about the patterns that would arise from these mechanisms, and then being able to differentiate these patterns from others that you might observe. This is a tall order for any observational data set, and I think that is especially true for microbial data sets.

Below I have listed in more detail the challenges arising when attempting to integrate ecology and microbiology. These relate to all sorts of ecological questions and analyses, including but not limited to “niche versus neutral”.

a) True measures of abundance are not typically available, and 16S copy number is incorrectly used as a measure of abundance. 16S ribosomal RNA is the typical target of studies of bacterial ecology. However, counts of 16S copies per taxa are not equivalent to abundances (as say, counts of individuals in macro-systems are): instead, different taxa can have different copy numbers. Where one taxa might have 2 copies, another might have 10. 

Despite this, it is common to see it used as a proxy for abundances; for example, to calculate beta-diversity measures such as Bray-Curtis. Since neutrality predicts patterns related to species' abundance distributions, and changes in diversity through time, when conclusions rely on 16S-based ‘abundance’ metrics, they are suspect. Some attempts are being made to address this – for example, this paper from Steve Kembel et al. (2012) recognizes that copy number is a conserved trait and so could be controlled for in a phylogenetically-informed way. qPCR can also be used to measure true abundances in samples. (See comments).

b) What spatial scale is relevant to microbes? Bacteria are very small (of course). However, sampling methods often involve fairly large samples in relation to bacterial body size. 1 g of soil, although tiny compared to many ecological samples, is a massive amount of material in the context of bacteria. There can be 10^8 cells/g of soil, and by one estimate the interaction distance between individuals is ~20um, and so it is not likely that a 1g sample is equivalent in scale to a “community”.

If a typical observational sample is not representative of a community, community ecology theory, which is dependent on assumptions about local interactions and environmental filters at particular spatial scales may not be relevant. Scale issues are an ongoing problem in ecology, and defining the ‘community’ is a thorn in our sides. It is understandable that this is a problem for a new field. Thinking about the kind of data collected as relating to macroecology may be a fruitful approach (see this paper for similar ideas on the topic). 

c) Temporal scale has similar issues. Unlike in macro-scale systems, microbial time scales are very rapid, with approximately 100-300 generations per year (with some variation between taxa). The scale of environmental variation that affects these communities should be finer as well. This is a benefit and a difficulty of the system. For examples, one can potentially observe a community assemble to equilibrium in a bacterial system. But describing changes in bacterial composition observed over 1 year as succession and placing them in the context of ecological literature on plant succession seems imprecise. The scale of observation is of particular importance.

d) There can be issues in differentiating between active and inactive taxa, since microbes may be present in a sample but dormant. Methods exist to differentiate between these taxa, but when not applied, an apparently rare taxa in an assemblage may actually be an inactive taxa.

e) Sampling artifacts and other biases can arise between labs and runs, including biases related to PCR, primers, DNA extraction, storage, rarefaction, and more. This is an issue equivalent to limitations in methodological approaches in every field, and one that is actively being worked on (for example, developing standardized approaches). Further, the existing technology is pretty amazing.

f) Limitations of the current null models and statistical methods being applied. Null models are still a work in progress for ecology, and need to continue to be developed and perfected. But I think that there are specific issues that need to be considered in applying some of these methods to microbial data in particular, and there is a need for concerted research on developing statistical methods for such massive datasets.

In particular, I suspect there is an issue regarding heightened Type 1 error rates and issues with inadequately randomizing very large data sets. Ulrich and Gotelli (2012) hint at some of these possible issues:
“null model analysis may not be well-suited to such large data sets. The general statistical problem is that with very large data sets, the null hypothesis will always be rejected unless the data were actually generated by the null model process itself. So, large data sets may often deviate significantly from null models in which row and column sums are fixed, regardless of whether species occurrences are random or not (Fayle and Manica 2010). This was not a problem in the early history of null model analysis, when ecologists worried that apparent patterns in relatively small data sets might reflect random processes”
There is not enough time here to delve into most of these issues in detail, but permutation tests/Mantel test type analyses have a number of important limitations and assumptions must be tested for appropriate usage (from Pierre Legendre). From the ANOSIM website
“Recent work…has shown distance-based methods (e.g., ANOSIM, Mantel Test, BIOENV, BEST) are inappropriate for analyzing Beta diversity because they do not correctly partition the variation in the data and do not provide the correct Type-I error rates.” 
If Type I error rates are frequently high in past analyses, or inappropriate statistical models were used, data can be re-analysed as better procedures arise. But we should also recognize that there is uncertainty in past results (particularly weak or barely significant patterns). It should also suggest that we have yet to gain a true understanding of what patterns and relationships in microbial ecology are truly significant.

Microbial research produces some of the most complex and large datasets that ecology has ever had to deal with. As a result, developing specific theory and appropriate methods for this data should be a priority alongside discovery-focused research. Fortunately, this creates opportunities for ecologists to develop methods for complex systems, which should be beneficial for the entire ecological discipline. And many people are already attempting to fill these knowledge gaps, so this is not to underplay their accomplishments. Hopefully there will continue to be developments in microbe-specific theory, with appropriate assumptions regarding temporal and spatial scale. Microbial ecologists can do better than co-opt standard ecological approaches, they can improve on them (e.g. Coyte et al. (2015)).