Tuesday, September 26, 2017

When do descriptive methods exceed the sum of their points?

The last post here mused on the connection between (but also, distinctness of) the scientific goals of "understanding" and "prediction". An additional goal of science is "description", the attempt to define and classify phenomenon. Much as understanding and prediction are distinct but interconnected, it can be difficult to separate research activities between description and understanding. Descriptive research is frequently considered preliminary or incomplete on its own, meant to be an initial step prior to further analysis. (On the other hand, the decline of more descriptive approaches such as natural history is often bemoaned). With that in mind, it was interesting to see several recent papers in high-impact journals that rely primarily on descriptive methods (especially ordinations) to provide generalizations. It's fairly uncommon to see ordination plots as the key figure in journals like Nature or The American Naturalist, and it opens up the question of 'when do descriptive methods exceed description and provide new insights & understanding?'

For example, Diaz et al.'s 2016 Nature paper took advantage of a massive database of trait data (from ~46000 species) to explore the inter-relationships between 6 ecologically relevant plant traits. The resulting PCA plot (figure below) illustrates, across many species, that well-known tradeoffs between a) organ size and scaling and b) the tissue economic spectrum appear fairly universal. Variation in plant form and function may be huge, but the Diaz et al. ordination highlights that it still is relatively constrained, and that many strategies (trait combinations) are apparently untenable.

From Diaz et al. 2016.
Similarly, a new paper in The American Naturalist relies on ordination methods  to try to identify 'a periodic table of niches' of lizards (Winemiller et al. 2015 first presented this idea) – i.e. a classification framework capturing the minimal, clarifying set of universal positions taken by a set of taxa. Using the data and expert knowledge on lizard species collected over a lifetime of research by E. Pianka and L. Vitt, Pianka et al. (2017) first determine the most important life history axes -- habitat, diet, life history, metabolism, and defense attributes. They use PCoA to calculate the position of each of 134 species in terms of each of the 5 life history axes, and then combined the separate axes into a single ordination (see figure below). This ordination highlights that niche convergence (distant relatives occupy very similar niche space) and niche conservation (close relatives occupy very similar niche space) are both common outcomes of evolution. (For more discussion, this piece from Jonathon Losos is a great). Their results are less clarifying than those in Diaz et al. (2016): a key reason may simply be the smaller size of Pianka et al.'s data set and its greater reliance on descriptive (rather than quantitative) traits.

From Winemiller et al. 2017

Finally, a new TREE paper from Daru et al. (In press) attempts to identify some of the processes underlying the formation of regional assemblages (what they call phylogenetic regionalization, e.g. distinct phylogenetically delimited biogeographic units). They similarly rely on ordinations to take measurements of phylogenetic turnover and then identify clusters of phylogenetically similar sites. Daru et al.'s paper is slightly different, in that rather than presenting insights from descriptive methods, it provides a descriptive method that they feel will lead to such insights.

Part of this blip of descriptive results and methods may be related to a general return to the concept of multidimensional or hypervolume niche (e.g. 1, 2). Models are much more difficult in this context and so description is a reasonable starting point. In addition, the most useful descriptive approaches are like those seen here - where new data or a lot of data (or new techniques that can transform existing data) - are available. In these cases, they provide a route to identifying generalization. (This also leads to an interesting question – are these kind of analyses simply brute force solutions to generalization? Or do descriptive results sometimes exceed the sum of their individual data points?)

Díaz S, Kattge J, Cornelissen JH, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, Garnier E. (2016). The global spectrum of plant form and function. Nature. 529(7585):167.

Eric R. Pianka, Laurie J. Vitt, Nicolás Pelegrin, Daniel B. Fitzgerald, and Kirk O.Winemiller. (2017). Toward a Periodic Table of Niches, or Exploring the Lizard Niche Hypervolume. The American Naturalist. https://doi.org/10.1086/693781

Barnabas H. Daru, Tammy L. Elliott,  Daniel S. Park, T. Jonathan Davies. (
In press). Understanding the Processes Underpinning Patterns of Phylogenetic Regionalization. TREE. DOI: http://dx.doi.org/10.1016/j.tree.2017.08.013

Thursday, September 7, 2017

Why is prediction not a priority in ecology?

When we learn about the scientific method, the focus is usually on hypothesis testing and deductive reasoning. Less time is spent on considering the various the outcomes of scientific research, specifically: description, understanding, and prediction. Description involves parsimoniously capturing data structure, and may use statistical methods such as PCA to reduce data complexity and identify important axes of variation. Understanding involves the explanation of phenomenon by identifying causal relationships (such as via parameter estimation in models). Finally, prediction involves estimating the values of new or future observations. Naturally, some approaches in ecology orient more closely toward one of these outcomes than others and some areas of research historically have valued one outcome over others. For example, applied approaches such as fisheries population models emphasize predictive accuracy (but even there, there are worries about limits on prediction). On the other hand, studies of biotic interactions or trophic structure typically emphasize identifying causal relationships. The focus in different subdisciplines no doubt owes something to culture and historical priority effects.

In various ways these outcomes feedback on each other – description can inform explanatory models, and explanatory models can be evaluated based on their predictions. In a recent paper in Oikos, Houlahan et al. discuss the tendency of many ecological fields to under-emphasize predictive approaches and instead focus on explanatory statistical models. They note that prediction is rarely at the centre of ecological research and that this may be limiting ecological progress. There are lots of interesting questions that ecologists should be asking, including what are the predictive horizons (spatial and temporal scales) over which predictive accuracy decays? Currently, we don't even know what a typical upper limit on model predictive ability is in ecology.

Although the authors argue for the primacy of prediction ["Prediction is the only way to demonstrate scientific understanding", and "any potentially useful model must make predictions about some unknown state of the natural world"], I think there is some nuance to be gained by recognizing that understanding and prediction are separate outcomes and that their relationship is not always straightforward (for a thorough discussion see Shmueli 2010). Ideally, a mutually informative feedback between explanation and prediction should exist, but it is also true that prediction can be useful and worthy for reasons that are not dependent on explanation and vice versa. Further, to understand why and where prediction is limited or difficult, and what is required to correct this, it is useful to consider it separately from explanation.

Understanding/explanation can be valuable and inspire further research, even if prediction is impossible. The goal of explanatory models is to have the model [e.g., f(x)] match as closely as possible the actual mechanism [F(x)]. A divergence between understanding and prediction can naturally occur when there is a difference between concepts or theoretical constructs and our ability to measure them. In physics, theories explaining phenomenon may arise many years before they can actually be tested (e.g. gravitational waves). Even if useful causal models are available, limitations on prediction can be present: in particle physics, the Heisenberg uncertainty principle identifies limits on the precision at which you can know both the position of a particle and its momentum. In ecology, a major limitation to prediction may simply be data availability. In a similar field (meteorology) in which many processes are important and nonlinearities common, predictions require massive data inputs (frequently collected over near continuous time) and models that can be evaluated only via supercomputers. We rarely collect biotic data at those scales in ecology. We can still gain understanding if predictions are impossible, and hopefully eventually the desire to make predictions will motivate the development of new methods or data collection. In many ecological fields, it might be worth thinking about what can be done in the future to enable predictions, even if they aren't really possible right now.

Approaches that emphasize prediction frequently improve understanding, but this is not necessarily true either. Statistically, understanding can come at the cost of predictive ability. Further, a predictive model may provide accurate predictions, but do so using collinear or synthetic variables that are hard to interpret. For example, a macroecological relationship between temperature and diversity may effectively predict diversity in a new habitat, and yet do little on its own to identify specific mechanisms. Prediction does not require interpretability or explanatory ability, as is clear from papers such as "Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data". So it's worth being wary of the idea that a predictive model is necessarily 'better'.

With this difference between prediction and understanding in mind, it is perhaps easier to understand why ecologists have lagged in prediction. For a long time, statistical approaches used in ecology were biased toward those meant to improve understanding, such as regression models, where parameters estimate the strength and direction of a relationship. This is partially responsible for our obsession with p-values and R^2 terms. What Houlahan et al. do a great job of emphasizing is that by ignoring prediction as a goal, researchers are often limiting their ability confirm their understanding. Predictions that are derived from explanatory models Some approaches in ecology have already moved naturally towards emphasizing prediction, especially SDMs/ecological niche models. They recognized that it was not enough to describe species-environment relationships; testing predictions allowed them to determine how universal and mechanistic these relationships actually were. A number of macroecological models fit nicely with predictive statistical approaches, and could adopt Houlahan’s suggestions quite readily (e.g. reporting measures of predictive ability and testing models on withheld data). But for some approaches, the search for mechanism is so deeply integrated into how they approach science that it will take longer and be more difficult (but not impossible)*. Even for these areas, prediction is a worthy goal, just not necessarily an easy one. 

*I was asked for examples of 'unpredictable' areas of ecology. This may be pessimistic, but I think that something like accurately predicting the composition (both species' abundance and identity) of diverse communities at small spatial scales might always be difficult, especially given the temporal dynamics. But I could be wrong! 

...if the Simpsons could predict Trump, I suppose there's hope for ecologists too...
**This has been edited to correctly spell the author's name.

Wednesday, August 30, 2017

INTECOL 2017: Building the eco-civilisation

The International Association for Ecology holds their global INTECOL conference every 4 years, and it was recently held in Beijing, China. Given the location of this meeting, the theme was exceptionally appropriate: Ecology and Civilisation in a Changing World. I say that it was appropriate because no place embodies change more than China’s recent history, and I would argue that China is a prime candidate to benefit from ecological science.
One thing that was clear from the outset of the meeting was that China (both the scientists attending the meeting and the policy apparatus writ large) was serious about the notion of producing an ecological civilisation, or eco-civilisation. In 2007, the Communist Party of China adopted the idea of turning China into an eco-civilisation by incorporating ecological well-being into its constitution. In 2013, the Chinese government started implementing reforms that politically prioritised ecology and the environment. Most prominent of these was that local government officials and administrators were directed to no longer ignore the environmental consequences of development.
China is globally unique in its ability to institute change, literally with the stroke of a pen. Well documented is the ability for the major cities in China to implement drastic change in transportation policy by restricting who can drive when, and building public transit infrastructure at a torrid pace (see a commentary about this). The latest examples of cities’ power over transportation include the fact that electric cars are eligible to receive license plates immediate, while owners of conventional cars are required to wait years or spend tens of thousands of dollars to get their plates. The other example is the flooding of the market with public bicycles that can be parked anywhere and that require a phone app to unlock, and they literally cost cents to use.

A market flooded with a public bike-sharing program in China. These are all shared bikes, available everywhere, and they tend to congregate around bus stops (Photo by M. Cadotte).

I found it to be an interesting juxtaposition to see the multitude of bikes everywhere with the polluted sky that was apparent for the first two days of the conference. This was the very appropriate context for our conference. From the get go the theme of using the science of ecology to improve environmental management and policy seemed to underlie most of the talks and organised sessions. For most Chinese scientists, this is the context in which they work. To them, there is no real separation between human activities and nature, and the two have been intimately linked for millennia. The opening address was by HRH Charles Prince of Wales. Prince Charles eloquently commented on the importance of ecology in the coming decades, as humanity is testing the ecological bounds of the planet, and he encouraged attendees to use their research to affect change.

HRH Charles, Prince of Wales giving the opening address (Photo by M. Cadotte).

Representing the hosting organisation, Shirong Liu outlined all the important ecological advances in Chinese ecology, especially the development of extensive ecological experiments and research networks examining issues like climate change and nutrient deposition. Echoing Prince Charles’ call, Prof. Liu commented on the importance of ecology for Chinese policy, and the many recent policy changes in China, including the establishment of national parks, habitat restoration, climate change mitigation, and the greening of cities.
Given that most of China has been modified by humans, Gretchen Daily’s keynote address seemed incredibly poignant, even though the focus was on Costa Rica. She said that we’ve pretty much protected all the places that are likely to be protected as big parks, and that adding more is increasingly infeasible (China is an outlier). Instead, we should be looking to country sides and other human-dominated landscapes as the places to implement ecological principles to better manage these systems to benefit biodiversity and ecosystem functioning. These systems are where our science needs to pay off.

Evidence of ecosystem services in the Beijing Botanical Garden (Photo by M. Cadotte).
The talks throughout the conference echoed the themes of an ecology on and for human systems. I saw numerous talks from Chinese authors on understanding and managing human impacts, in systems from grasslands to lakes to cities. I participated in a panel discussion on how ecology could be used to create an eco-civilisation, and it was clear that there was a lot of optimism that the next decades will see a renaissance of ecology in policy, I was probably the least optimistic. I am doubtful that, having seen the United States pull out of the Paris Climate Change agreement, the political will can always be relied upon and creating an eco-civilisation depends on China’s ability to increase the standard of living without taxing ecological capacity more than it has. That said, there is currently a global leadership vacuum on the environment, created by political instability in Europe and the United States, and this is the time for China to be an environmental leader. 
Regardless, I saw inspiring talks on restoring ecosystems severely modified by human activity and invasive species, from speakers like William Bond, Carla D’Antonio, and Tom Dudley. I also ran an organised session on the importance of biodiversity in human dominated landscapes which covered topics from habitat fragmentation, to the ecology of cities, to the value of sacred groves in India for biodiversity.
After listening to talks at INTECOL 2017, one cannot help but feel that this is ecology’s time. We are entering an ecological era, and if ever there was a time to use our science to affect change, it is now.

Thursday, August 24, 2017

Novel habitat, predictable responses: niche breadth evolution in geckos

At a time of immense ecological change (such as the Anthropocene), organisms have a few options. They can move, tolerate, adapt, or, in failing to do so, face extinction. One or most of those options may not be available to most species. For example, the question of whether most species can adapt rapidly enough to maintain populations in degrading habitats, rising temperatures and increasing environmental variability has (at least in part) motivated the study of rapid or contemporary evolution. Studying the probability of successful selection and adaptation over ecological timescales may be very important for understanding the options available to species.

de Amorim et al. (2017, PNAS) describe one such example, where the result of novel environmental change provides a unique opportunity to observe rapid evolution. Beginning in 1996, a reservoir in Central Brazil was created by flooding a huge area, creating nearly 300 islands and massively affecting local wildlife. Gymnodactylus amarali was the most common lizard (a termite-specialized gecko), and the authors sought to determine the impacts of rapid isolation on the species.

Isolation on islands created an new set of biotic conditions – other termite eating lizards went extinct on islands, increasing the available diet breadth, particularly increasing the availability of larger termites. Larger termites require geckos have the physical ability to catch and processes them. One possibility is that to take advantage of this new resource, G. amarali on islands would need larger heads. Because larger heads and bodies come with increased energy requirements, the authors predicted that the island geckos would have larger heads, but no change in overall body size.
Termite size increased on average on islands; for the same body size, head length tended to be larger on islands. 
Indeed, island geckos had higher diet breadths, driven by the availability of larger termites and an increased ability to catch them via larger head lengths. Increased diet breadth was accompanied by increased head size, but not body size.

Notably, this change in diet and associated characters occurred independently across multiple reservoir islands, beginning once they were isolated from the mainland. This is an interesting example of rapid evolution precisely because evolution took the same path in every case, and because it occurred so rapidly (less than 15 years). This is not always the expectation - in many cases, human activities (e.g. fragmentation) will increase decrease population sizes and genetic diversity, thereby increasing drift and decreasing the predictability (and speed) and adaptation. Contrasts between successful and unsuccessful adaptive responses will help us understand better how and when fragmentation threatens populations.

Mariana Eloy de Amorim, Thomas W. Schoener, Guilherme Ramalho Chagas Cataldi Santoro, Anna Carolina Ramalho Lins, Jonah Piovia-Scott, and Reuber Albuquerque Brandão. 2017. Lizards on newly created islands independently and rapidly adapt in morphology and diet. PNAS. 114 (33) 8812-8816.

Monday, July 31, 2017

Novelty or prediction or something else?

There is an interesting editorial at elife from Barak Cohen on "How should novelty be valued in science?" It connects to some of the discussions on this blog and in other places concerned about the most efficient and effective path for science (Cohen suggests a focus on predictive ability).

One relevant question is how 'understanding' differs from 'predicting' and whether a focus on 'prediction' can produce perverse incentives too, as the focus on novelty has.

[This pessimistic image about perverse incentives from Edwards and Roy (2017) and the discussion from Mike Taylor seemed an appropriate addition.


Friday, July 14, 2017

Making conference talks compelling and meaningful

Langin, K. 2017. “Tell me a story! A plea for more compelling conference presentations”. The Condor 119(2):321-326.

Communicating complex ideas that rely on the accumulation of ideas, methods, and data is undeniably hard. Some people are naturals at presenting their work, but for many of us (definitely for me) it is a skill that only improves with lots of practice. With conference season in full swing, Kathryn Langin’s paper on this very topic is timely. She provides excellent advice, particularly on how to overcome the common pitfalls of “unclear questions, too much text, unreadable figures, no overarching storyline”. In particular, the appendix provides step-by-step advice on crafting talks and composing slides that should help both first timers and more experienced presenters. 

Langin notes that we treat scientists differently from other audiences: “Scientists are increasingly trained to distill research findings for audiences that lack a strong background in science (Baron 2010). However, we often fail to put those strategies to work when communicating with other scientists, which is unfortunate because many scientists lack deep knowledge of topics outside their immediate field (Pickett et al. 1991),” and “If we cannot effectively communicate our research to colleagues, then how are we going to communicate it to resource managers, policy makers, the media, and the general public?”

This is a worthy goal. But it’s also true that there isn’t perfect equivalence between these different types of talks, and while the techniques that make for public talks are useful across the board, they aren’t enough on their own. I’ve seen the odd talk where popular science video clips, overly-processed slides, or lengthy quotations took the place of substantive research, and there’s little I find more frustrating. So, to make Langin’s advice even more difficult, good science communication requires recognizing what information, and particularly what depth of information, must be communicated for a particular audience. For scientist audiences, speakers benefit from being able to make complicated ideas seem straightforward while not insulting the listener or glossing over the difficult.

Conference audiences are difficult because they tend to be a mix of different people with varied reasons for attending a particular talk. They could be specialists who sought your talk out based on the abstract, generalists in the broader area of study, or just scientists sitting randomly in the room waiting for the next talk. And while Langin says, “Science is both increasingly collaborative and increasingly specialized; an ability to communicate beyond scientists in your immediate field is important. While it may be tempting to tailor your presentation for the expert that you hope (or fear) will be in attendance (e.g., by packing it with methodological minutiae and mountains of data), such a strategy will come at the expense of communicating clearly to everyone else in the room”, I don’t completely agree. I think the people in the room that you want feedback from are the specialists and the experts. So it’s important to find a balance between losing the general audience and wasting this opportunity to communicate with your peers.

I might be in the minority here, but I would rather sit through a few methods slides that I can’t follow in detail, than to sit in a talk in which the methods are so cursory as to be uninformative. Similarly, utterances like “…and then there was some math here, but don't worry I won’t talk about it” seems counter-productive. Ignoring the anti-math sentiment (which reinforces the idea that math is hard and so should be avoided), if the math or stats are important enough to mention, they are important enough to talk about properly. With care, it is generally possible to find a balance in which you provide details for the informed listener while explaining the general logic of the mathematical approach for the rest of the audience. This is true for complicated methods of all types – all listeners should emerge feeling as though they understand what you did, even if they don’t understand it at the same level.

For new speakers this may sound overwhelming. A few points help all talks. Most importantly, every good talk has a compelling narrative that takes the listener on a journey. Even when that journey is complex or has a few twists, speakers can help by signposting important points and findings. Have important information on each slide be both written and verbalized. Get feedback from someone who is not you. And recognize – as a presenter and as a listener—that as with all things, it takes time to become an expert. And, practice makes perfect.

Thursday, July 6, 2017

Solutions to managing invasive species by combining research with local knowledge

--> *This was originally published at the Applied Ecologist's Blog

While many hurdles hamper the successful application of ecological concepts and theories to developing solutions to environmental problems, one area of ecological concern that has been especially consequential and complicated to solve has been the control of invasive species. The non-native species that end up spreading in new regions with massive impacts on local ecosystems are difficult to predict beforehand, and eradicating invasive species is a nearly impossible task. Despite hundreds of millions of dollars spent on invasive species control, there are few success stories. Realistically, the best-case scenario is finding efficient management strategies that reduce the abundance and impact of invasive species to acceptable or tolerable levels.
Image: African lovegrass (www.southeastweeds.org.au)

Part of the problem is that researchers and research organisations, which are needed to develop management strategies, are usually stretched thin and unable to devote the time and resources needed to develop evidence-based solutions. A research project into the control of invasive species requires baseline data, an understanding of basic species ecology, and a list of candidate control measures. These starting points are not trivial to satisfy and often require years of basic research before we can assess possible control measures. One of the reasons often given for this limited success is that ecological systems are inherently idiosyncratic or unpredictable. However, this lack of predictability is virtually inseparable from a lack of system specific knowledge. This lack of fundamental understanding means that we may be asking the wrong questions or pursuing inefficient management solutions based on our assumptions about an ecosystem’s behaviour.

In many systems, there exists an underutilised resource -the experience of local landowners, farmers, and ranchers. A recent paper in the Journal of Applied Ecology titled Integrating local knowledge and research to refine the management of an invasive non-native grass in critically endangered grassy woodlands by Jennifer Firn, Emma Ladouceur, and Josh Dorrough represents a new approach to incorporating local knowledge for testing invasive species management options. This paper, to my mind, constitutes one of the best and most innovative attempts to integrate detailed local non-scientist knowledge with modern research methods.

The study by Firn and colleagues takes an original approach to addressing research and invasive species control shortcomings by working with Australian landowners who have intimate knowledge of the grasslands they work in and, more importantly, how they have changed over time. Firn’s research team interviewed these landowners and developed specific hypotheses based on landowner knowledge about African lovegrass (Eragrostis curvula) growth and spread in Australia, an invasive plant introduced from southern Africa. Firn and colleagues then scientifically tested these hypotheses, showing support for some landowner perspectives, and disproving others.
This research is crucial because it shows how research and management can be made more efficient by working with local landowners. It breaks down the walls that separate academic and professional applied management from local citizens and landowners who do not work in intellectual vacuums, but rather observe, contemplate and develop questions. The scientists provide the means for landowners to test their questions.

I firmly believe that this work will change the perspective of how researchers and scientific and environmental organisations carry out their research. It shows how powerful partnerships can be, and that knowledge and expertise sharing can maximise understanding and management solutions.
Ultimately, this work will not only directly benefit Australia’s environment by providing management options for controlling African lovegrass but will also provide a template for developing solutions to any environmental problem. It is evident that researchers working on other exotic species can emulate Firn and colleague’s work, but perhaps less clear, and what should repeatedly be broadcast, is that this method should be employed for managing other environmental changes including the effects of climate change and altered land use.

Wednesday, June 21, 2017

What do we mean when we talk about the niche?

The niche concept is a good example of an idea in ecology that is continually changing. It is probably the most important idea in ecology that no one has yet nailed down. As most histories of the niche mention, the niche has developed from its first mention by Grinnell (in 1917) to Hutchinson’s multi-dimensional niche space, to mechanistic descriptions of resource usage and R*s (from MacArthur’s warblers to Tilman’s algae). Its most recent incarnation can be found in what has been called modern coexistence theory, as first proposed by Peter Chesson in his seminal 2000 paper.

Chesson’s mathematical framework has come to dominate a lot of discussion amongst community ecologists, with good reason. It provides a clear way to understand stable coexistence amongst local populations in terms of their ability to recover from low densities, and further by noting that those low density growth rates are the outcome of two types of processes: those driven by fitness differences and those driven by stabilizing effects that reduce interspecific competition relative to intraspecific competition. Many of the different specific mechanisms of coexistence can be classified in terms of this framework of equalizing and stabilizing effects. “Niche” differences between species in this framework can be defined as those differences that increase negative intraspecific density dependence compared to interspecific effects. If, as a simplistic example, two plant species have different rooting depths and so access different depths of the water table, then this increases competition for water between similar root-depth conspecifics relative to interspecific competition. Thus, this is a niche difference. Extensions on modern niche theory have offered insights into everything from invasion success, restoration, and eco-phylogenetic analyses.

But it seems as though the rise of 'modern coexistence theory' is changing the language that ecologists use to discuss the niche concept. When Thomas Kuhn talks about paradigm shifts, he notes that it is not only theory that changes but also the worldview organized around a given idea. At least amongst community ecologists, it seems as though this had focused the discussion of the niche to an increasingly local scale, particularly in terms of stabilizing and equalizing terms measured as fixed quantities made under homogenous, local conditions. A recognition of the role of spatial and temporal conditions in altering these variables seems less common, compared to the direction of earlier, Hutchinsonian-type discussions of the niche.

Note that this was not Chesson's original definition, since he is explicit that: “The theoretical literature supports the concept that stable coexistence necessarily requires important ecological differences between species that we may think of as distinguishing their niches and that often involve tradeoffs, as discussed above. For the purpose of this review, niche space is conceived as having four axes: resources, predators (and other natural enemies), time, and space.”

On a recent manuscript, an editor commented that the term 'niche processes' shouldn't be used to refer to environmental filtering since (paraphrased) “when ecologists refer to niche processes, they are usually thinking of processes that constrain species’ abundances locally, confer an advantage on rare species...” But is it fair to say that this is the only thing we mean (or should mean) when we discuss niches? I’ve had discussions with other people who’ve had this kind of response – e.g., reviewers asking for simulations to be reframed from niches defined in terms of environmental tolerances to things that fit more clearly into equalizing and stabilizing terms. That is a good description of a stabilizing process, which is termed a 'niche difference' in the modern coexistence literature. But there is still a lot of grey space we have yet to address in terms of how to integrate (e.g.) the effects of the environment (including over larger scales) into local 'niche processes' or stabilizing effects. It's a subtle argument - that we can use the framework established by Chesson, but we should try to do so without dismissing too-quickly the concepts that don't fit easily within it. In addition, elsewhere the niche is still conceptualized in varying ways from comparative evolutionary biologists who talk about niche conservatism and mean the maintenance of ancestral trait values or environmental tolerances; to functional ecologists who may refer to multidimensional differences in trait space; to species distribution modellers who thinks of large-scale environmental correlates or physiological determinants of species’ distributions. 

The niche is probably the most fundamental, yet vaguely–defined and poorly understood idea in ecology. So, formalizing the definition and constraining it is a necessary idea. And modern coexistence theory has provided great deal of insight into local coexistence and thus has allowed for a better understanding of the niche concept. But there is also a need to be careful in how quickly and how much we restrict our discussion of the niche. It's possible to gain both the strengths of modern coexistence theory as well as appreciate its current limitations. Modern coexistence theory isn’t yet complete or sufficient. It’s currently easier to estimate stabilizing and equalizing terms from experimental data in which conditions are controlled and homogenous, and this can inadvertently focus future research and discussion on those types of conditions. Models which consider larger scale processes and the impacts of changing abiotic conditions through space in time exist, but across different literatures, and these need continued synthesis. There is still a need to understand how to most realistically incorporating and understand the complex interactions between multiple species (e.g. Levine et al. 2017). The application of modern coexistence theory to observational data in particular is still limited, and such data is essential when species are slow lived or experimentally unwieldy. Further, when quantities of interest (particularly traits or phylogenetic differences) contribute to both equalizing and stabilizing effects, its still not clear how to partition their contributions meaningfully.

Friday, June 2, 2017

Image in academia

Not many seminar speakers are introduced with a discussion of their pipetting skills. When we talk about other scientists we discuss their intelligence, their rigour, their personality, above and beyond their learned skills. Most people have an image of what a scientist should be, and judge themselves against this idealized vision. There are a lot of unspoken messages that are exchanged in science and academia. It’s easy to think that the successful scientists around one interacts with are just innately intelligent, confident, passionate, and hard-working. No doubt imposter syndrome owes a lot to this one-sided internalization of the world. After all, you don’t feel like you fulfill these characteristics because you have evidence of your own personal struggles but not those of everyone else. 

"Maybe no one will notice".
The most enlightening conversation I had this year (really! Or at least a close tie with discovering that PD originally was discussed as a measure of homologous characters…) was with a couple of smart, accomplished female scientists, in which we all acknowledged that we—not infrequently—suffered from feeling totally out of our depths. It is hard to admit our failings or perceived inadequacies, for fear we’ll be branded with them. But it’s really helpful for others to see that reality is different than the image we’ve projected. If everyone is an imposter, no one is. There is something to be said for confidence when scientists are presenting consensus positions to the public, but on the other hand, I think that being open about the human side of science is actually really important. 

For those who already feel like outsiders in academia, perhaps because they (from the perspective of race, gender, orientation, social and economic background, etc) differ from the dominant stereotype of a ‘scientist’, it probably doesn’t take much to feel alienated and ultimately leave. Students have said things to me along the lines of “I love ecology but I don’t think I will try to continue in academic because academia is too negative/aggressive/competitive”. Those are legitimate reasons to avoid the field, but I always try to acknowledge that I feel the same way too sometimes. It’s helpful to acknowledge that others feel the same way, and that having this kind of feeling (e.g. that you aren’t smart enough, or you don’t have a thick enough skin) isn’t a sign that you don’t actually belong. Similarly, it’s easy to see finished academic papers and believe that they are produced in a single perfect draft and that writing a paper should be easy. But for 99% of people, that is not true, and a paper is the outcome of maybe 10 extreme edits, several rounds of peer review, and perhaps even a copy-editor. Science is inherently a work-in-progress and that’s true of scientists as well.

The importance of personal relationships and mentorship to help provide realistic images of science should be emphasized. Mentorship by people who are particularly sympathetic (by personal experience or otherwise) to the difficulties individuals face is successful precisely for this reason. This might be why blog posts on the human side of academia are so comparatively popular – we’re all looking for evidence that we are not alone in our experiences. (Meg Duffy writes nice posts along these lines, e.g. 1, 2). And though the height of the blogosphere might be over, the ability of blog posts to provide insight into humanity of academia might be its most important value.

Friday, May 19, 2017

Experimental macroevolution at microscales

Sometimes I find myself defending the value of microcosms and model organisms for ecological research. Research systems do not always have to involve a perfect mimicry of nature to provide useful information. A new paper in Evolution is a great example of how microcosms provide information that may not be accessible in any other system, making them a valuable tool in ecological research.

For example, macroevolutionary hypotheses are generally only testable using observational data. They suffer from the obvious problem that they generally relate to processes of speciation and extinction that occurred millions of years ago. The exception is the case of short generation, fast evolving microcosms, in which experimental macroevolution is actually possible. Which makes them really cool :-) In a new paper, Jiaqui Tan, Xian Yang and Lin Jiang showing that “Species ecological similarity modulates the importance of colonization history for adaptive radiation”. The question of how ecological factors such as competition and predation impact evolutionary processes such as the rapid diversification of a lineage (adaptive radiation) is an important one, but generally difficult to address (Nuismer & Harmon, 2015; Gillespie, 2004). Species that arrive to a new site will experience particular abiotic and biotic conditions that in turn may alter the likelihood that adaptive radiation will occur. Potentially, arriving early—before competitors are present—could maximize opportunities for usage of niche space and so allow adaptive radiation. Arriving later, once competitors are established, might suppress adaptive radiation.

More realistically, arrival order will interact with resident composition, and so the effects of arriving earlier or later are modified by the identities of the other species present in a site. After all, competitors may use similar resources, and compete less, or have greater resource usage and so compete more. Although hypotheses regarding adaptive radiation are often phrased in terms of a vague ‘niche space’, they might better be phrased in terms of niche differences and fitness differences. Under such a framework, simply having species present or not present at a site does not provide information about the amount of niche overlap. Using coexistence theory, Tan et al. produced a set of hypotheses predicting when adaptive radiation should be expected, given the biotic composition of the site (Figure below). In particular, they predicted that colonization history (order of arrival) would be less important in cases where species present interacted very little. Equally, when species had large fitness differences, they predicted that one species would suppress the other, and the order in which they arrived would be immaterial. ­

From Tan et al. 2017
The authors tested this using a bacterial microcosm with 6 bacterial competitors and a focal species – Pseudomonas fluorescens SBW25. SBW25 is known for its rapid evolution, which can produce genetically distinct phenotypes. Microcosm patches contained 2 species, SBW25 and one competitor species, and their order of arrival was varied. After 12 days, the phenotypic richness of SBW25 was measured in all replicates.
From Tan et al. 2017. Competitor order of arrival in general altered the final phenotypic richness of SBW25.
Both order of arrival and the identity of the competitor did indeed matter as predictors of final phenotypic richness (i.e. adaptive radiation) of SBW25. Further, these two variables interacted to significantly. Arrival order was most important when the 2 species were strong competitors (similar niche and fitness differences), in which case late arrival of SBW25 suppressed its radiation. On the other hand, when species interact weakly, arrival order had little affect on radiation. The effect of different interactions were not entirely simple, but particularly interesting to me was that fitness differences, rather than niche differences, often had important effects (see Figure below). The move away from considering the adaptive radiation hypothesis in terms of niche space, and restating it more precisely, here allowed important insights into the underlying mechanisms. Especially as researchers are developing more complex models of macroevolution, which incorporate factors such as evolution, having this kind of data available to inform them is really important.
Interaction between final phenotype richness and arrival order for B) niche differences and D) fitness differences. S-C refers to arrival of SWB25 first, C-S refers to its later arrival.