Wednesday, August 26, 2015

Science is a maze

If you want to truly understand how scientific progress works, I suggest fitting mathematical models to dynamical data (i.e. population or community time series) for a few days.
map for science?

You were probably told sometime early on about the map for science: the scientific method. It was probably displayed for your high school class as a tidy flowchart showing how a hypothetico-deductive approach allows scientists to solve problems. Scientists make observations about the natural world, gather data, and come up with a possible explanation or hypothesis. They then deduce the predictions that follow, and design experiments to test those predictions. If you falsify the predictions you then circle back and refine, alter, or eventually reject the hypothesis. Scientific progress arises from this process. Sure, you might adjust your hypothesis a few times, but progress is direct and straightforward. Scientists aren’t shown getting lost.

Then, once you actively do research, you realize that formulation-reformulation process dominates. But because for most applications the formulation-reformulation process is slow – that is, each component takes time (e.g. weeks or months to redo experiments and analyses and work through reviews) – you only go through that loop a few times. So you usually still feel like you are making progress and moving forward.

But if you want to remind yourself just how twisting and meandering science actually is, spend some time fitting dynamic models. Thanks to Ben Bolker’s indispensible book, this also comes with a map, which shows how closely the process of model fitting mirrors the scientific method. The modeller has some question they wish to address, and experimental or observational data they hope to use to answer it. By fitting or selecting the best model for they data, they can obtain estimates for different parameters and so hopefully test predictions from they hypothesis. Or so one naively imagines.
From Bolker's Ecological Models and Data in R,
a map for model selection. 
The reality, however, is much more byzantine. Captured well in Vellend (2010)
“Consider the number of different models that can be constructed from the simple Lotka-Volterra formulation of interactions between two species, layering on realistic complexities one by one. First, there are at least three qualitatively distinct kinds of interaction (competition, predation, mutualism). For each of these we can have either an implicit accounting of basal resources (as in the Lotka-Volterra model) or we can add an explicit accounting in one particular way. That gives six different models so far. We can then add spatial heterogeneity or not (x2), temporal heterogeneity or not (x2), stochasticity or not (x2), immigration or not (x2), at least three kinds of functional relationship between species (e.g., predator functional responses, x3), age/size structure or not (x2), a third species or not (x2), and three ways the new species interacts with one of the existing species (x3 for the models with a third species). Having barely scratched the surface of potentially important factors, we have 2304 different models. Many of them would likely yield the same predictions, but after consolidation I suspect there still might be hundreds that differ in ecologically important ways.”
Model fitting/selection, can actually be (speaking for myself, at least) repetitive and frustrating and filled with wrong turns and dead ends. And because you can make so many loops between formulation and reformulation, and the time penalty is relatively low, you experience just how many possible paths forward there to be explored. It’s easy to get lost and forget which models you’ve already looked at, and keeping detailed notes/logs/version control is fundamental. And since time and money aren’t (as) limiting, it is hard to know/decide when to stop - no model is perfect. When it’s possible to so fully explore the path from question to data, you get to suffer through realizing just how complicated and uncertain that path actually is. 
What model fitting feels like?

Bolker hints at this (but without the angst):
“modeling is an iterative process. You may have answered your questions with a single pass through steps 1–5, but it is far more likely that estimating parameters and confidence limits will force you to redefine your models (changing their form or complexity or the ecological covariates they take into account) or even to redefine your original ecological questions.”
I bet there are other processes that have similar aspects of endless, frustrating ability to consider every possible connection between question and data (building a phylogenetic tree, designing a simulation?). And I think that is what science is like on a large temporal and spatial scale too. For any question or hypothesis, there are multiple labs contributing bits and pieces and manipulating slightly different combinations of variables, and pushing and pulling the direction of science back and forth, trying to find a path forward.

(As you may have guessed, I spent far too much time this summer fitting models…)

Friday, August 21, 2015

#ESA100: The next dimension in functional ecology

The third day of ESA talks saw an interesting session on functional ecology (Functional Traits in Ecological Research: What Have We Learned and Where Are We Going?), organized by Matt Aiello-Lammens and John Silander Jr.

As outlined by McGill and colleagues (2006), a functional trait-based approach can help us move past idiosyncrasies of species to understand more general patterns of species interactions and environmental tolerances. Despite our common conceptual framework that traits influence fitness in a given environment, many functional ecology studies have been challenged to explain much variation in measured functional traits using underlying environmental gradients. We might attribute this to a) measuring the ‘wrong’ traits or gradients, b) several trait values or syndromes being equally advantageous in a given environment, or c) limitations in our statistical approaches. Several talks in this organized session built up a nuanced story of functional trait diversity in the Cape Floristic Region (CFR) of South Africa. Communities are characterized by high species but low functional turnover (Matt Aiello-Lammens; Jasper Slingsby), and only in some genera do we see strong relationships between trait values and environments (Matt Aiello-Lammens; Nora Mitchell). Nora Mitchell presented a novel Bayesian approach combining trait and environmental information that allowed her to detect trait-environment relationships in about half of the lineages she investigated. These types of approaches that allow us to incorporate phylogenetic relationships and uncertainty may be a useful next step in our quest to understand how environmental conditions may drive trait patterns.

Another ongoing challenge in functional ecology is the mapping of function to traits. This is complicated by the fact that a trait may influence fitness in one environment but not others, and by our common use of ‘soft’ traits, which are more easily measurable correlates of the trait we really think is important. Focusing on a single important drought response trait axis in the same CFR system described above, Kerri Mocko demonstrated that clades of Pelargonium exhibited two contrasting stomatal behaviours under dry conditions: the tendency to favor water balance over carbon dioxide intake (isohydry) and the reverse (anisohydry). More to my point, she was able to link a more commonly measured functional trait (stomatal density) to this drought response behavior.

Turning from the macroevolutionary to the community scale, Ben Weinstein evaluated the classic assumption of trait-matching between consumer (hummingbird beak length) and resource (floral corolla length), exploring how resource availability might shape this relationship. Robert Muscarella then took a community approach to understanding species distributions, testing the idea that we are most likely to find species where their traits match the community average (community weighted mean). He used three traits of woody species to do so, and perhaps what I found most interesting about this approach was his comparison of these traits – if a species is unlike the community average along one trait dimension, are they also dissimilar along the other trait dimensions?


Thinking of trait dimensions, it was fascinating to see several researchers independently touch on this topic. For my talk, I subsampled different numbers and types of traits from a monkeyflower trait dataset to suggest that considering more traits may be our best sampling approach, if we want to understand community processes in complex, multi-faceted environments. Taking trait dimensionality to the extreme, perhaps gene expression patterns can be used to shed light on several important pathways, potentially helping us understand how plants interact with their environments across space and time (Andrew Latimer).

To me, this session highlighted several interesting advances in functional ecology research, and ended with an important ‘big picture’. In the face of another mass extinction, how is biodiversity loss impacting functional diversity (Matthew Davis)?



McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in ecology & evolution, 21(4), 178-185.

Thursday, August 13, 2015

#ESA100 The big-data era: ecological advances through data availability

Ecology is in a time of transition –from small-scale studies being the norm to large, global datasets employed to test broad generalities. Along with this ‘big data’ trend is the change in the ethical responsibility of scientists who receive public funds to share their data and ensure public access. As a result big online data repositories have been popping up everywhere.

One thing that I have been doing while listening to talks, or talking with people, is to make note of the use of large online databases. It is clear that the use of these types of data has become commonplace. So much so, that in a number of talks, the speakers simply referred to them by acronyms and we all understood what it was that they used. Here are examples of online data sources I heard referenced (and there are certainly many more):



 It seems difficult to keep track of all the different sources of available data, and these repositories differ in their openness to public access, with some requiring registration, permission requests, and the requirement to include data submitters as authors on publications. With Genbank as the gold standard for a data repository, it is inevitable that other types of ecological data will soon be required to be freely available. I've never figured out why genetic data has different accessibility expectations than, say, leaf trait data.

Despite the attractiveness of huge amounts of data available online, such data can only paint broad pictures of patterns in nature and cannot capture small scale variability very well (Simberloff 2006). We still require detailed experiments and trait measurements at small scales for things like within-species trait variability.

Ecology has grown, and will continue to do so as data is made available. Yet, the classic ecological field experiment will continue to be the mainstay for ecological advancement into the future.



Simberloff, D. (2006) Rejoinder to Simberloff (2006): don't calculate effect sizes; study ecological effects. Ecology Letters, 9, 921-922.

Wednesday, August 12, 2015

#ESA100 Have system -need science! The opportunities for green roof ecology

 Green roofs are now a mainstay of urban green infrastructure and a tool to promote sustainable urban development. A number of municipalities, including Toronto-where I live, now have bylaws or policies requiring green roofs on certain types of infrastructure. The rationale for these requirements is that green roofs provide direct energy savings, reduce albedo, reduce storm water runoff, and support other ecosystem functions and provide wildlife habitat. But it is these last two –the ecological benefits, though often touted, lack clear evidence. I attended an organized oral session on green roof biodiversity organized by Whittinghill, Starry and MacIvor, and it was clear from the presentations that people were excited by the opportunities for ecological research. More importantly, they made the case that we know so little about these systems, and research is desperately needed to guide policy –we simply need more ecologists working on this problem.

Chicago City Hall green roof, adapted from Wikipedia (CC-BY-SA 3.0)
I would argue strongly that urban systems, like green roofs, are understudied and that these systems are the very places that ecological concepts and theories can have relevance. My medical colleagues study human physiology or microbiology in order to cure sick people –their science has direct application to improving the world and human well being, and ecologists have the same opportunity. Like a sick patient, urban systems are where our science can have the greatest impact and can provide the most benefit. Urban systems are under direct management and provide ample opportunity to manipulate ecological patterns and processes in order to test theory and manage societal benefits.


Time to study cities!

Tuesday, August 11, 2015

#ESA100 Declining mysticism: predicting restoration outcomes.

Habitat restoration literature is full of cases where the outcomes of restoration activities are unpredictable, or where multiple sites diverge from one another despite identical initial restoration activities. This apparent unpredictability in restoration outcomes is often attributed to undetected variation in site conditions or history, and thus have a mystical quality where the true factors affecting restoration are just beyond our intellect. These types of idiosyncrasies have led some to question whether restoration ecology can be a predictable science.

Photo credit: S. Yasui


The oral session “Toward prediction in the restoration of biodiversity”, organized by Lars Brudvig, showed how restoration ecologists are changing our understanding of restoration, and shedding light on the mystical qualities of success. What is clear from the assembly of great researchers and fascinating talks in this session is that recent ecological theories and conceptual developments are making their way into restoration. Each of the 8 of 10 talks I saw (I had to miss the last two) added a novel take on how we predict and measure success, and the factors that influence it. From the incorporation of phylogenetic diversity to assess success (Becky Barak) to measuring dispersal and establishment limitation (Nash Turley), and from priority effects (Katie Stuble) to plant-soil feedbacks (Jonathan Bauer), it is clear that predicting success is a multifaceted problem. Further, from Jeffry Matthews talk on trajectories, even idiosyncratic restoration trajectories can be grouped into types of trajectories (e.g., increasing diversity vs plateauing) and then relevant factors can be determined.


What was most impressive about this session was the inclusion of coexistence theory and basic demography into understanding how species perform in restoration. Two talks in particular, one from Loralee Larios on coexistence theory and the other from Dan Laughlin on predicting fitness from traits by environment interactions, shed new light on predicting restoration. Both of these talks showed how species traits and local environmental conditions influence species’ demographic responses and the outcome of competition. These two talks revealed how basic ecological theory can be applied to restoration, but more importantly, and perhaps under-appreciated, these talks show how our basic assumptions about traits and interactions with other species and the environment require ground-truthing to be applicable to important applied problems.

Monday, August 10, 2015

ESA#100 Day 1: The bar is set high

Day 1! This is as good as you will feel - tomorrow and beyond sleep be in will decline and hangovers will rise. Today there was only a half day of talks, but they were a strong start to the week.


There was a great organized section on "Community and Ecosystem Effects of Rapid Evolution" that made a conclusive case for the frequent and meaningful effects of evolution over short timescales on the community or ecosystem. Examples ranged from Darwin's finches, where Sofia Carvajal Endara made at initial attempts to consider how feedbacks from finch evolution and character displacement affected species and trait diversity in seed plants. Martin Turcotte consider the issue in terms of agricultural domestication and the implications for herbivores. He obtained an impressive 29 species' pairs, representing a domesticated species and its ancestor and carried out trials, including one in which an aphid species was grown for multiple generations on the ancestral member of each pair, then switched to the domesticated species. There was no directional evolution observed for the aphid species, but their evolution slowed down on crop plants. Aphid abundances tended to be higher on crops, and simulations suggested that selection was higher on the wild plants, and so was genetic drift. As a final example, Erica Holdridge looked at protist (Colpidium) microevolution, and her results reminded us that many of the protist experiments in the past likely included both evolutionary and ecological effects. Hopefully this is a topic that will continue to grow in the future.

Random notes: I always try to see what I like in talks so I can emulate them. Today I noticed a number of speakers for whom confidence and careful, practiced wording went a long way, even on talks where the concepts were very difficult. 

Favourite quotes (unattributed for anonymity):

Use of "2 cents" to title that section where you extrapolate your results to bigger-picture discussion.

"Community ecologists always want more than two species, although, two species is a good starting point." (absolutely!)

"The data isn't very good; it doesn't support the model"

Thursday, August 6, 2015

#ESA100 - EEB & Flow blogging 100 years of ESA

We haven't quite blogged 100 years of ESA (looks like we only started in 2009), but we'll definitely be blogging our daily highlights at the 100th annual meeting in Baltimore!

If you are attending, a few links from past years for those last minute preparations:
Presentations
Survival notes
Apparently this was my take-home in 2012

I think "postdoc trying to finish their talk" should be added to this...