#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):

ATBI

Bioclim

Clo-Pla

Discover Life

Dryad

eflora
FishBase

Genbank

GlopNet

Jepson Flora

LEDA

LTER

NatureServe

Treebase

TRY

USDA plants

 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.

#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!

#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.

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"

#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...

Fear of missing out

There are so many tools, techniques, communication platforms and methods out there for ecologists that it can be hard to keep track of them all. Whether it be LaTeX, Linux, or Twitter, someone has probably suggested that your research life will improve if you use their favourite tool.

Online science has allowed fantastic networking and information exchange. However, one side effect of the increased usage of social media can be the feeling that you can’t keep up. The phrase ‘Fear of Missing Out’ in social psychology was coined to describe the anxiety arising from looking at social media and fearing that you have chosen how to spend our time poorly. But this can extend to academic working life too – that nagging fear that your colleagues are doing cooler things (going to more conferences, using more cutting edge techniques, publishing more papers) than you. It can be disconcerting to see that there’s a cool R package you know nothing about, or to receive yet another invite to a work-based social media platform you’ve never heard of.

There are people who are the early adopters and the technological trendsetters; people who derive their success from their methodological/technological skill sets. These people are fantastic, since they provide the examples for others in the field to follow. But for most researchers, it is practical to recognize that knowing every new technique and tool doesn’t promise success.

Graduate students may suffer this fear of falling behind most intensely, given how closely it relates to imposter syndrome and the general skills gap that grad students have to overcome. But I’d argue for most students, that balance is key. Otherwise it can be a slippery slope: maybe you learn R, then find that people recommend knitR and R Markdown, then you learn Shiny to show your results beautifully, then someone suggests things would be faster in a lower level language – and maybe months have gone by. That’s not necessarily bad, intellectual freedom and self-teaching are some of the best parts of graduate school, and if you are going to learn a bunch of new techniques, that is the perfect time. But aficionados will make arguments for everything from Python, Linux, Emacs (apparently the one true operating system), Github, to LaTeX/BibTeX, etc, etc. And every one of these is a useful tool, but maybe not always a necessary tool.

Researchers can never learn everything, and some things will fall out of fashion as quickly as they arose. Sure, it is less than ideal to be a researcher who hasn’t learned a new approach since the advent of SAS. But we need to balance necessity (is it fundamental to my research?), the time investment, and the utility of these skills for future endeavours (e.g. if you are applying for a data analyst job after grad school, learning a few languages might be smart - but if you will rarely use it again, perhaps it is not so important). The danger for many students is that either the time investment in learning a less-than-fundamental tool is very high, or else they learn a number of tools but never master any of them. This isn't to say you can shirk on quantitative skills - on the contrary, having those skills is far more important than mastering any specific program or language. I can think of fantastic ecologists who don’t know what Twitter is, who still use SAS, who have never learned R, and who produce brilliant work. The questions, the ideas, and the knowledge matter too, after all.

(*I should note that I'm a quantitative/theoretical ecologist, and I think every ecologist should have a strong quantitative education! Just that you can do that using lots of different methods and doing it well should always be the first priority.)

The first null model war in ecology didn't prevent the second one*

The most exciting advances in science often involve scientific conflict and debate. These can be friendly and cordial exchanges, or they can be acrimonious and personal. Scientists often wed themselves to their ideas and can be quite reluctant to admit that their precious idea was wrong. Students in ecology often learn about some of these classic debates (Clements v. Gleason; Diamond v. Simberloff and Connor), but often other debates fade from our collective memory. Scientific debates are important things to study, they tell us about how scientists function, how they communicate, but more importantly by studying them we are less likely to repeat them! Take for example the debate over species per genus ratios, which happened twice, first in the 1920s, then again in the 1940s. The second debate happened in ignorance of the first, with the same solution being offered!

To understand the importance of testing species-genus ratios we can start with a prediction from Darwin:

As species of the same genus have usually, though by no means invariably, some similarity in habits and constitution, and always in structure, the struggle will generally be more severe between species of the same genus, when they come into competition with each other, than between species of distinct genera (Darwin 1859)

To test this hypotheses, the Swiss botanist, Paul Jaccard (1901) created a ‘generic coefficient’ to describe biogeographical patterns and to measure the effects of competition on diversity. The generic coefficient was a form of the species-genus ratio (S/G), calculated as G/S x 100, and he interpreted a low S/G ratio (or high coefficient) to mean that competition between close relatives was high, and a high ratio (low coefficient) meant that there was a high diversity of ‘ecological conditions’ supporting closely related species in slightly different habitats (Jaccard 1922). At the same time as Jaccard was working on his generic coefficient, the Finnish botanist, Alvar Palmgren, compiled S/G patterns across the Aland Islands and inferred the low S/G values on distant islands to reflect random chance (Palmgren 1921). Over several years, Jaccard and Palmgren had a heated exchange in the literature (across different journals and languages!) about interpreting S/G ratios (e.g., Jaccard 1922, Palmgren 1925). Palmgren’s contention was that the S/G ratios he observed were related to the number of species occurring on the islands –an argument which later work vindicates. A few years after their exchange, another Swiss scientist, Arthur Maillefer, showed that Jaccard’s interpretation was not supported by statistical inference (Maillefer 1928, 1929). Maillefer created what is likely one of the first null model in ecology (Jarvinen 1982). He calculated the expected relationship between Jaccard’s generic coefficient and species richness from ‘chance’ communities that were randomly assembled (Fig. 1 –curve II). Maillefer rightly concluded that since the number of genera increase at a slower rate than richness, the ratio between the two couldn’t be independent of richness.

Jaccard’s generic coefficients plotted by Maillefer showing the relationship between the coefficients (calculated as Genera/Species x 100) and species richness (Maillefer 1929). The four curves depict different scenarios. Curve I shows the maximum values possible, and curve IV is the minimum. Curve III is when coefficients are calculated on sampled values from a flora, which stays on a mean value. Curve II represents the first null model in ecology, where species are randomly sampled (‘hasard’ is translated as chance or luck) and the coefficient was calculated from the random assemblages.

 This example is especially poignant because it foreshadowed another debate 20 years later –and not just in terms of using a null expectation, but that S/G ratios cannot be understood without comparison to the appropriate null. Elton (1946) examined an impressive set of studies to show that small assemblages tended to have low S/G ratios, which he thought indicated competitive interactions. Mirroring the earlier debate, Williams (1947), showed that S/G ratios were not independent of richness and that inferences about competition can only be supported if observed S/G values differed from expected null values. However, the error of inferring competition from S/G ratios without comparing them to null expectations continued into the 1960s (Grant 1966, Moreau 1966), until Dan Simberloff (1970) showed, unambiguously, that, independent of any ecological mechanism, lower S/G are expected on islands with fewer species. Because he compared observationed values to null expectations, Simberloff was able to show that assemblages actually tended to have higher S/G ratios than one would expect by chance (Simberloff 1970). So not only is competition not supported, but the available evidence indicated that perhaps there were more closely related species on islands, which Simberloff took to mean that close relatives prefer the same environments (Simberloff 1970).

Darwin, C. 1859. The origin of the species by means of natural selection. Murray, London.

Elton, C. S. 1946. Competition and the Structure of Ecological Communities. Journal of Animal Ecology 15:54-68.

Grant, P. R. 1966. Ecological Compatibility of Bird Species on Islands. The American Naturalist 100:451-462.

Jaccard, P. 1901. Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Societe Vaudoise des Sciences Naturelle 37:547-579.

Jaccard, P. 1922. La chorologie selective et sa signification pour la sociologie vegetale. Memoires de la Societe Vaudoise des Sciences Naturelle 2:81-107.

Jarvinen, O. 1982. Species-To-Genus Ratios in Biogeography: A Historical Note. Journal of Biogeography 9:363-370.

Maillefer, A. 1928. Les courbes de Willis: Repar- tition des especes dans les genres de diff6rente etendue. Bulletin de la Societe Vaudoise des Sciences Naturelle 56:617-631.

Maillefer, A. 1929. Le Coefficient générique de P. Jaccard et sa signification. Memoires de la Societe Vaudoise des Sciences Naturelle 3:9-183.

Moreau, R. E. 1966. The bird faunas of Africa and its islands. Academic Press, New York, NY.

Palmgren, A. 1921. Die Entfernung als pflanzengeographischer faktor. Series Acta Societatis pro Fauna et Flora Fennica 49:1-113.

Palmgren, A. 1925. Die Artenzahl als pflanzengeographischer Charakter sowie der Zufall und die säkulare Landhebung als pflanzengeographischer Faktoren. Ein pflanzengeographische Entwurf, basiert auf Material aus dem åländischen Schärenarchipel. Acta Botanica Fennica 1:1-143.

Simberloff, D. S. 1970. Taxonomic Diversity of Island Biotas. Evolution 24:23-47.

Williams, C. B. 1947. The Generic Relations of Species in Small Ecological Communities. Journal of Animal Ecology 16:11-18.

*This text has been modified from a forthcoming book on ecophylogenetics authored by Cadotte and Davies and published by Princeton University Press