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.

INTECOL 2017: Building the eco-civilisation

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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.

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.

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.

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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.

Solutions to managing invasive species by combining research with local knowledge

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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.

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.

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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.

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. 

Problems with over-generalizing the dynamics of communities

Community ecologists talk about communities as experiencing particular processes in a rather general way. We fall into rather Clementsian language, asking whether environmental filtering dominates a community or if biotic interactions are disproportionately strong. This is in contrast to the typical theoretical focus on pairwise interactions, as it acts as though all species in a community are responding similarly to similar processes.

Some approaches to community ecology have eschewed this generality, particularly those that focus on ecological ‘strategies’ differentiating between species. For example, Grimes argued that species in a community represented a tradeoff between three potential strategies - competitive, stress-tolerant, and ruderal (CRS). Other related work describes rarity as the outcome of very strong density-dependence. The core-transient approach to understanding communities differentiates between core species, which have deterministic dynamics tied to the mean local environment, in contrast to transient species which are decoupled from local environmental conditions and have dynamics are driven by stochastic events (immigration, environmental fluctuations, source-sink dynamics). Assuming environmental stationarity, core species will have predictable and consistent abundances through time, in comparison to transient species.

If species do respond differently to different processes, then attempting to analyse all members of a community in the same way and in relation to the same processes will be less informative. Tests for environment-trait relationships to understand community composition will be weaker, since the species present in a community do not equally reflect the environmental conditions. In “A core-transient framework for trait-based community ecology: an example from a tropical tree seedling community”, Umana et al (2017) ask whether differentiating between core and transient species can improve trait-based analyses. They analyse tropical forest communities in Yunnan, China, predicting that core species "will have strong trait–environment relationships that increase the growth rates and probability of survival that will lead to greater reproductive success, population persistence and abundance".

The data for this test came from 218 1 m2 seedling plots, which differed in soil and light availability. The authors estimated the performance of individual seedlings in terms of relative growth rate (RGR). They also gathered eight traits related to biomass accumulation, and stem, root and leaf organ characteristics. They were particularly interested in how the RGR of any individual seedling differed from the mean expectation for their species. Did this RGR deviation relate to environmental differences between sites?  If a species’ presence is strongly influenced by the environment, then RGR deviation should vary predictably based on environmental conditions.

They then modelled RGR deviation as a function of the traits or environmental conditions (PCA axes). They considered various approaches for binning species based on commonness vs. rarity, but the general result was that bins containing rarer species had fewer PCA axes significantly associated with their RGR deviation and/or those relationships were weaker (e.g. see Figure below).

They conclude  that “the main results of our study show that the strength of demography-environment/trait and trait-environment relationships is not consistent across species in a community and the strength of these effects is related to abundance”. Note that other studies similarly find variation in the apparent mechanism of coexistence in communities. For example, Kraft et al. 2015  found that local fitness and niche differences only predict coexistence for a fraction of species co-occurring in their sites.

Umana et al.'s result is a reminder that work looking for general processes at the community level may be misleading. It isn't clear that there is a good reason to divide species into only two categories (e.g. core versus transients): like unhappy families, transient species may each be transient in their own way.

The most "famous" ecologists (and some time wasting links) (Updated)

(Update: This has gotten lots more attention than I expected. Since first posted, the top 10 list has been updated 2 times based on commenters suggestions. You can also see everyone we looked up here. Probably I won't update this again, because there is a little time wasting, and there is a lot of time wasting :) )

At some point my officemates Matthias and Pierre and I started playing the 'who is the most famous ecologist' game (instead of, say, doing useful work), particular looking for ecologists with an h-index greater than 100. An h-index of 100 would mean that the scientist had 100 publications with at least 100 citations  and their other papers had less than 100 citations. Although the h-index is controversial, it is readily available and reasonably capture scientists that have above average citations per paper and high productivity. We restricted ourselves to only living researchers. We used Publish or Perish to query Google Scholar (which now believes everyone using the internet in our office may be a bot).

We identified only 12 ecologists at level 100 or greater. For many researchers in specialized subfields, an h-index this high is probably not achievable. The one commonality in these names seems to be that they either work on problems of broad importance and interest (particularly, climate change and human impacts on the landscape) or else were fundamental to one or more areas of work. They were also all men, and so we tried to identify the top 12 women ecologists. (We tried as best as we could, using lists here and here to compile our search). The top women ecologists tended to have been publishing for an average of 12 years less than the male ecologists (44 vs. 56 years) which may explain some of the rather jarring difference. The m-index is the h-index/years publishing and so standardizes for differences in career age.

(It's difficult to get these kind of analyses perfect due to common names, misspellings in citations, different databases used, etc. It's clear that for people with long publication lists, there is a good amount of variance depending on how that value is estimated).

Other links: 

(I've been meaning to publish some of these, but haven't otherwise had a time or space for it.. )

Helping graduate students deal with imposter syndrome (Link). Honestly, not only graduate students suffer from imposter syndrome, and it is always helpful to get more advice on how to escape the feeling that you've lucked into something you aren't really qualified for. 

A better way to teach the Tree of Life (Link). This paper has some great ideas that go beyond identifying common ancestors or memorizing taxonomy.

Analyzing scientists are on Twitter (Link). 

Recommendation inflation (Link). Are there any solutions to an arms race of positivity?  

Biodiversity conservation in a human world: do successes involve losses?

It's become commonplace to state that the world is in the midst of a mass extinction event. And there is no doubt about the cause. Unlike previous mass extinction events, like the cretaceous extinction event that saw most dinosaurs disappear, the current extinction event is not caused by a geological or astrological event. Rather, the current extinction event is caused by a single species, humans. Through habitat destruction, wildlife harvesting, pollution, and the introduction of pest species to other regions, the current extinction rate is 100 to 1000 times higher than it should normally be. We often think of human legacy in terms of art or architecture, but a permanent scar in the biological record of the Earth is our greatest legacy.

Of course many people and some governments are very concerned about our impact, and have committed to try to conserve elements of the remaining natural world. How best to do this is largely influenced by conservation biology, a field of research and applied management that includes biology, economics, and sociology, amongst others. There are many debates within conservation biology, and a big one is about how much to involve people, and their activities, in conservation areas versus attempting to completely exclude people from protected areas.

Two conservation conversations have explored this dichotomy in meaningful ways. First is a recent paper by Elena Bennett (Bennett 2017), who argues that strategies for environment and conservation protection needs to take a human-first approach and focus on human well-being. The second is a talk I saw from Daniel Janzen the other day. Janzen is a world-renowned ecologist and has dedicated his life to conservation in Costa Rica for the past 30 years. This debate was central to his talk about the conservation successes at the Area de Conservacion Guanacaste (ACG), where Janzen developed and implemented a conservation philosophy that included local people in the managing and research in the conservation area. Before Janzen, the Park relied on the traditional approach of excluding people to protect nature and it was failing. Janzen’s approach has been immensely successful, and the Park is now considered a conservation success story.

People can be convinced to appreciate biodiversity around
-if it provides a benefit. (photo by M. Cadotte)

Including people in nature conservation is bound to have successes. People feel more familiar and involved with nature protection, which gives them a sense of ownership. If people understand the benefits of nature, economic and otherwise, then they will be invested in its protection. It all seems so logical, but as I listened to Janzen’s talk (and read Bennett’s paper), I kept thinking: “would there be any losers under a human-first approach to conservation”. I think the answer is yes, and the reason is that we are prone to use a shifting baseline to evaluate success. Let me explain what I mean.

The human-nature story is one that is about a continual 30,000 year retreat. All of our successes -our population growth, our art, our medicine, have all come at the expense of nature. Anywhere on Earth where there are humans, there are losses. Habitat alteration and destruction, and species extinctions are the defining feature of our presence. This legacy has permanently altered the biology of our planet.

Why is this important? Because we really don’t care. We don’t miss wholly mammoths in northern Europe. We don’t miss giant sloths in California. We don’t miss black bears in downtown Toronto. We don’t miss lions in Cape Town. The definition and acceptance of nature  for most people is not influenced by what is not there, but rather the critters we are familiar with and are willing to accept. Big mammals simply have no place in human dominated landscapes and we don’t bemoan their absences.

Can human-first conservation protect jaguars?
(Photo from wikipedia)

Human-first conservation strategies work simply because we accept a less valuable system as acceptable and perhaps normal because of our shifting baselines. Would a human-first conservation strategy work in Costa Rica’s ACG if there was a huge jaguar population that was attacking livestock? Not likely.

The United States government spends billions on national parks to conserve nature (among other things), but if it was up to ranchers living near Yellowstone, for example, all the top predators will be exterminated. Hunters and ranchers in Germany are similarly up in arms (literally) over the re-appearance of wolves and lynx in restored forests within Germany’s borders. Some there consider the extermination of large predators a commendable feat of an advanced society.

The point is that we like the nature we know, and the nature that is not likely to kill us. People are most often invested, familiar, and willing to conserve nature around them, which already works for them.

Costa Rica’s ACG human-first conservation works in certain contexts. It gets people involved, it protects certain facets of nature, and it has a high likelihood of long-term success. If this is the model for a successful conservation philosophy, then we must accept that not all of nature can be protected. In all likelihood, many large mammals will go extinct in my childrens’ lifetime, regardless of how well we do conservation. So perhaps, moving forward with the human-first strategy is the best option, but a part of me hopes that there is a place for real nature in our world. The rest of me knows that there isn’t.


Bennett, E. M. 2017. Changing the agriculture and environment conversation. Nature Ecology & Evolution 1:0018.

Progress on biodiversity-ecosystem function requires looking back

Williams, L. J., et al. 2017. Spatial complementarity in tree crowns explains overyielding in species mixtures. - Nature Ecology & Evolution 1: 0063.

It seems at times that the focus on whether biodiversity has a positive relationship with ecosystem functioning has been a bit limiting. Questions about the BEF relationships are important, of course, since they support arguments for protecting biodiversity and suggests a cost of failing to do so. But as a hypothesis ('higher diversity is associated with higher functioning'), they can be rather one-dimensional. It's easy to think of situations in which other types of BEF relationships (neutral, negative) exist. So is it enough to ask if positive BEF relationships exist?

It’s nice then that there is increasingly a focus on identifying mechanisms behind BEF relationships, using both theory and empirical research. A new paper along these lines is “Spatial complementarity in tree crowns explains overyielding in species mixtures” from Laura Williams et al. (2017). "Overyielding" is the phenomenon in which greater total biomass is produced in a mixture of species compared to the expectation based on their biomass production in monoculture. Overyielding would suggest a benefit in maintaining polycultures, rather than having monocultures, and is a common response variable in BEF studies.

This study focused on the production of stem biomass in monocultures vs. polycultures of forest trees. Experimental communities of young tree species were planted with orthogonal gradients of species richness and functional richness, allowing the effects of species number and trait diversity to be disentangled. Complementarity in tree canopy structure in these communities may be an important predictor of overyielding in stem biomass. Complementarity among tree crowns (that is, the extent to which they fit together spatially without overlapping, see Fig below) should reflect the ability of a set of species to maximize the efficiency of light usage as it hits the canopy. Such variation in crown canopy shapes among species could lead to a positive effect of having multiple species present in a community. 

Example of crown complementarity.
From Williams et al. 2017.

To test this, the authors estimated crown architecture for each species using traits that reflect crown shape and size. These measures were used to predict the spatial complementarity expected with different combinations of tree species. In addition, a single integrative trait – maximum growth rate – was measured for each species. The authors hypothesized that the variation in growth rate of species in a community would be associated with variation in crown heights and so also a good predictor of overyielding.

They found that crown complementarity occurred in nearly all of the experimental polycultures and on average was 29% greater in mixtures than monocultures. Controlling for the number of species, communities with greater variation in growth rate did in fact have greater crown complementarity, as predicted. Further, higher levels of crown complementarity were strongly associated (R2~0.6) with stem biomass overyielding.

Fig 2&3 from Williams et al (2017). For experimental communities:
a) the relationship between crown complementarity and variation in growth rate.
b) the relationship between crown complementarity and stem biomass overyielding.

These results provide a clear potential mechanism for a positive effect of biodiversity (particularly trait-based variation) in similar forests. (As they state, "We posit that crown complementarity is an important mechanism that may contribute to diversity-enhanced productivity in forests"). Given the importance of the sun as a limiting resource in forests, the finding that mixing species that combining shade intolerant and shade tolerant strategies are more productive (the authors note that "growth rate aligns with shade tolerance and traits indicative of a tree’s resource strategy") is not necessarily surprising. It fits within existing forestry models and practices for mixed stands. This is a reminder that we already understand many of the basic components of positive (and neutral and negative) diversity-functioning relationships. The good news is that ecology has accumulated a large body of literature on the components of overyielding (limiting resources, niche partitioning, evolution of alternate adaptive strategies, constraints on these, the strength of competition, etc). From the literature, we can identify the strongest mechanisms of niche partitioning and identify the contexts in which these are likely to be relevant. For example, sun in forests and canopy complementarity, or water limitation in grasslands and so root complementarity might be a good focal trait. 

Data management for complete beginners

Bill Michener is a longtime advocate of data management and archiving practices for ecologists, and I was lucky to catch him giving talk on the topic this week. It clarified for me the value of formalizing data management plans for institutions and lab groups, but also the gap between recommendations for best practices in data management and the reality in many labs.

Michener started his talk with two contrasting points. First, we are currently deluged by data. There is more data available to scientists now than ever, perhaps 45000 exabytes by 2020. On the other hand, scientific data is constantly lost. The longer since a paper is published, the less likely its data can be recovered (one study he cited showed that data had a half life of 20 years). There are many causes of data loss, some technological, some due to changes in sharing and publishing norms. The rate at which data is lost may be declining though. We're in the middle of a paradigm shift in terms of how scientists see our data. Our vocabulary now includes concepts like 'open access', 'metadata', and 'data sharing'. Many related initiatives (e.g.  GenBank, Dryad, Github, GBIF) are fairly familiar to most ecologists. Journal policies increasingly ask for data to be deposited into publicly available repositories, computer code is increasingly submitted during the review process, and many funding agencies now require statements about data management practices.

This has produced huge changes in typical research workflows over the past 25 years. But data management practices have advanced so quickly there’s a danger that some researchers will begin to feel that it is unobtainable, due to the level of time, expertise, or effort involved. I feel like sometimes data management is presented as a series of unfamiliar tools and platforms (often changing) and this can make it seem hard to opt in. It’s important to emphasize good data management is possible without particular expertise, and in the absence of cutting edge practices and tools. What I liked about Michener's talk is that it presented practices as modular ('if you do nothing else, do this') and as incremental. Further, I think the message was that this paradigm shift is really about moving from a mindset in which data management is done posthoc ('I have a bunch of data, what should I do with it?') to considering how to treat data from the beginning of the research process.

Hierarchy of data management needs.

One you make it to 'Share and archive data', you can follow some of these great references.

Hart EM, Barmby P, LeBauer D, Michonneau F, Mount S, Mulrooney P, et al. (2016) Ten Simple Rules for Digital Data Storage. PLoS Comput Biol 12(10): e1005097. doi:10.1371/journal.pcbi.1005097

James A. Mills, et al. Archiving Primary Data: Solutions for Long-Term Studies, Trends in Ecology & Evolution, Volume 30, Issue 10, October 2015, Pages 581-589, ISSN 0169-5347.

https://software-carpentry.org//blog/2016/11/reproducibility-reading-list.html (lots of references on reproducibility)

K.A.S. Mislan, Jeffrey M. Heer, Ethan P. White, Elevating The Status of Code in Ecology, Trends in Ecology & Evolution, Volume 31, Issue 1, January 2016, Pages 4-7, ISSN 0169-5347.

Thanks to Matthias Grenié for discussion on this topic.

Archiving the genomes of all species

There is so much bad news about global biodiversity, that it is nice to hear about new undertakings and approaches. One of these is the 'Earth BioGenome Project' which proposes to sequence the genomes of the entirety of life on earth. Given that sequencing services have never been more affordable and more available to scientists, without question, though ambitious this is a feasible undertaking. Still, with perhaps 9 million eukaryotes on the planet, a rough prediction suggests it could take 10 years and several billion dollars to achieve.

The cost suggests a certain agony of choice - what is the best use of that amount of money (in the dream world where money can be freely moved between projects)? Direct application to conservation and management activities, or a catalog of diversity which may be the only way to save some of these species? 

Leonard Eisenberg's tree of life (https://www.evogeneao.com).

When is the same trait not the same?

Different clades and traits yield similar grassland functional responses. 2016. Elisabeth J. Forrestel, Michael J. Donoghue,  Erika J. Edwards,  Walter Jetz,  Justin C. O. du Toite, and Melinda D. Smith. vol. 114 no. 4, 705–710, doi: 10.1073/pnas.1612909114

A potential benefit of trait-centric approaches is that they may provide a path to generality in community ecology. Functional traits affect growth, reproduction, and survival, and so--indirectly--should determine an organism's fitness; differences in functional traits may delineate niche differences. Since fitness is dependent on the environment, it is generally predicted that there should be strong and consistent trait–environment relationships. Species with drought-tolerant traits will be most dominant in low precipitation regions, etc, etc. Since productivity should also relate to fitness, there should be strong and consistent trait–ecosystem functioning relationships.

There are also quite general descriptions of species traits, and the life histories they imbue (e.g. the leaf economic spectrum), implying again that traits can yield general predictions about an organism's ecology. Still, as McIntyre et al. (1999) pointed out, "A significant advance in functional trait analysis could be achieved if individual studies provide explicit descriptions of their evolutionary and ecological context from a global perspective."

A new(ish) paper does a good job of illustrating this need. In Forrestel et al. the authors compare functional trait values across two different grassland systems, which share very similar environmental gradients and grass families present but entirely different geological and evolutionary histories. The North American and South African grasslands share similar growing season temperatures and the same precipitation gradient, hopefully allowing comparison between regions. They differ in grass species richness (62 grass species in SA and 35 in NA) and species identity (no overlapping species), but contain the same major lineages (Figure below).

From Forrestel et a. Phylogenetic turnover for major lineages along a
precipitation gradient differed between the 2 regions.

Mean annual precipitation (MAP) is well-established as an important selective factor and many studies show relationships between community trait values and MAP. The authors measured a long list of relevant traits, and also determined the above ground net primary productivity (ANPP) for sites in each grassland. When they calculated the community weighted mean value (CWM) of traits along the precipitation gradient, for 6 of the 11 traits measured region was a significant covariate (figure below). The context (region) determined the response of those traits to precipitation.

From Forrestel et al.

Further, different sets of traits were the best predictors of ANPP in NA versus SA. In SA, specific leaf area and stomatal pore index were the best predictors of ANPP, while in NA height and leaf area were. The upside was that for both regions, models of ANPP explained reasonable amounts of variation (48% for SA, 60% for NA).

It's an important message: plant traits matter, but how they matter is not necessarily straightforward or general without further context. The authors note, "Instead, even within a single grass clade, there are multiple evolutionary trajectories that can lead to alternative functional syndromes under a given precipitation regime" 

The removal of the predatory journal list means the loss of necessary information for scholars.

We at EEB & Flow periodically post about trends and issues in scholarly publishing, and one issue that we keep coming back to is the existence of predatory Open Access journals. These are journals that abuse a valid publishing model to make a quick buck and use standards that are clearly substandard and are meant to subvert the normal scholarly publishing pipeline (for example, see: here, here and here). In identifying those journals that, though their publishing model and activities, are predatory, we have relied heavily on Beall's list of predatory journals. This list was created by Jeffrey Beall, with the goal of providing scholars with the necessary information needed to make informed decisions about which journals to publish in and to avoid those that likely take advantage of authors.

As of a few days ago, the predatory journal list has been taken down and is no longer available online. Rumour has it that Jeffrey Beall removed the list in response to threats of lawsuits. This is really unfortunate, and I hope that someone who is dedicated to scholarly publishing will assume the mantle.

However, for those who still wish to consult the list, an archive of the list still exists online -found here.