Some of the best advice on the internet: several years of links

I started off the New Year with a much-needed bookmark reorganization and deletion, which also gave me a chance to re-read some of the links I've held onto (sometimes for years). There's an ever-increasing amount of useful content on the internet, but these have proven some of the most helpful, concrete, and lasting guides for navigating a scientific life.

I thought I'd collate the list here with the hope others might find some of these useful.

How to make it as early career researcher and new faculty: 

• There's no one way to be successful (in fact there are many ways), but hearing what not to do can be as important as all the other advice you will get: Mistakes I've made as an early career researcher (Times Higher Education)
• Lots of what-to-do and what-not-to-do advice here! How to be a new professor (Eco-Evo Evo-Eco)
• It's hard enough that Berkley's career centre has written pages on the topic: Transitioning from PhD to Prof (Careers at Berkley)

Identity and academia:

• I think most of us took different and often interesting routes to science (for example, I grew up in an evangelical Christian family, took a number of years to finally start my undergrad, and had no particular knowledge of ecology when I started my BSc. I wanted to be a vet, but now I'm an ecologist. Close enough :) ) and so I like to hear the many different routes by which scientists found science (SEAS).
• Overcoming imposter syndrome - there are many websites devoted to the topic, but this one provides particularly concrete steps to overcoming this common problem. 
• No one is perfect, and feedback can hurt - why feedback hurts and how to over come that. And no, it isn't enough to say, 'grow a thicker skin' (The Thesis Whisperer).
• Diversify EEB - a useful list of women and minorities working in EEB, worth keeping in mind when making nominations, selecting reviewers, and making various invitations. 
• And it's worth remembering that there is a dark side (one slightly bitter take on it). (Fear and Loathing in Academia)

Mentoring and leadership:

• Mentoring plans - this is super-useful whether you are faculty or a student. In a lot of places there may not be a tradition of having written plans and this provides examples of how to put them into practice. (Meg Duffy, Dynamic Ecology)
• Running successful lab meetings from all perspectives. I like the idea of explicitly deciding what you value and then modelling that when leading lab meetings. (Neuromechanics Lab at Emory)
• Discussing and leading successful journal clubs especially for graduate students.  (Tulane University, Dept of EEB) 
• Running successful scientific working groups and workshops (Conservation Bytes) 
• Preparing PhD students for non-academic careers (The Versatile PhD)

Computing/Data management:

• How to organize your computational projects (PlosBiol)
• Ten commandments for good data management (Brian McGill, Dynamic Ecology)
• Proselytizing about version control (Jennifer Bryan, Peer J)
• A list of lists of links for R statistical resources on Google Documents  
• Data sources worth checking out at The ecological data wiki

Data visualization:

• There are some really beautiful infographics about science from Eleanor Lutz here (Tabletop Whale).
• Information is Beautiful - infographics for inspiration
• Show me Shiny - some great examples of how R Shiny has transformed data visualization and interaction.
• If you are familiar with Edward Tufte's influential work on data visualization, you can use R to produce similar plots here. (Lukasz Piwek)

Teaching:

• The Eco-Evo Evo-Eco blog has a great how-to series, but this one especially has really concrete examples and suggestions for EEB lecturers: How to Teach (Eco-Evo Evo-Eco). 
• An article on how to use new learning methods for quantitative biology and models at Active learning and quantitative biology (Integrative and Comparative Biology)  
• The National Centre for Case Study Teaching in Science for already developed case studies and activities. 
• Common mistakes using active learning - nice to see legit critiques and ways to improve.
• Smart advice on avoiding bad teaching reviews (Terry McGlynn, Small Pond Science)

Miscellaneous links:

• Now part of the PhD canon: The infamous Snake Fight for your thesis to distribute to your PhD students (McSweeney's Internet Tendency).
• Wall of the Dead - poignant memorial list of ecologists lost while working  (Richard Conniff).
• The original text for Gause's The Struggle for Existence and plots. Ecology has changed so much (and so little).
• Visualize the tree of life beautifully and interactively (and get your name on it (sort of)!) (One Zoom)

Holiday caRd 2017!

Here is this year's card, with best wishes from both of us at the EEB & Flow!

It gets a little harder every year to figure these out. R's plotting capabilities improve every year, but usually via specialized packages. I've tried more and more to use as few additional packages beyond base, and to produce a script that is hopefully compatible across platforms.

• For best performance, users must install the 'deldir' package and the 'RCurl' package. This lets you download the necessary data file with as little effort as possible. 
• If you have trouble accessing the file via the URL, you can just download the data file from Github directly, making sure to load the file into R using the hashed out code in Lines6-7.

Then to run, copy the full code (below), OR download the source file from github ,
OR, the easiest way, run this quick code directly:

Full script here.
(Bonus points for those who can guess which species of McArthur's warblers these are meant to be ;) )

More authors, more joy?

It seems that ecologists have been complaining that no one writes single author manuscripts anymore since at least the 1960s. de Solla Price predicted in 1963:

"…the proportion of multi-author papers has accelerated steadily and powerfully, and it is now so large that if it continues at the present rate, by 1980 the single-author paper will be extinct”

Fortunately, an interesting new editorial in the Journal of Applied Ecology has the data (from their archives of published and submitted papers) to evaluate to ask whether this disastrous outcome has actually occurred.

It turns out that Price was wrong about single-author extinction, although he hadn't misread the trends. Since the 1970s, the proportion of single-authored papers at the journal have declined to less than 4% and the mean number of authors has risen to more than 5 (Figure 1).

Fig 1. 

It's also notable that single-authored papers are cited significantly less often and are 2.5x less likely to be accepted (!). (If that statistic doesn't make you want to gather some coauthors, nothing will). These trends agree with others reported in the literature.

The authors hypothesize that a number of factors drive this result. Ecology has gotten 'bigger' in many ways - analyses are less likely to focus on single populations or species and more likely to be replicated through space and or time. This increased breadth requires more students or assistants to aid with experimental or field work, or collaborations with other labs to bring such data together. Similarly, ecological data collection and analyses often require multiple types of specialized knowledge, whether statistical, mathematical, technological, or systems-based. And by relying on multiple researchers to play specialized roles, the overall quality of a manuscript might be higher (as compared to a jack-of-all trades). The authors also suggest that factors including the growing number of ecologists, the more international scope of many research activities, and more democratic approaches to authorship have increased the mean number of co-authors.

What makes these results particularly interesting is that I think there is still something of a cachet for the sole-authored paper. The conceit is that writing a sole authored paper means that you have a fully realized research plan, and you're accomplished enough to bring it to fruition by yourself. But these stats at least seem to suggest that you're better off with a few friends :)


Barlow, Jos, Stephens, Philip A., Bode, Michael, Cadotte, Marc W., Lucas, Kirsty, Newton, Erika, Nuñez, Martin A., Pettorelli, Nathalie. On the extinction of the single-authored paper: The causes and consequences of increasingly collaborative applied ecological research. J Appl Ecol. 55(1): 1365-2664. doi.org/10.1111/1365-2664.13040

Of course we need to save endangered species: a response

I spend a lot of time thinking about the related topics of conservation, biodiversity, and evolution, so I was interested to see an editorial in the Washington Post on precisely those issues. The article, "We don’t need to save endangered species. Extinction is part of evolution" by Alex Pyron, presents a misrepresentative and potentially harmful position about the future of the earth's biota.

Pyron begins by stating that "Evolution loves death." Selection necessarily means the success of one variant at the expense of others, and today's living creatures are the survivors of an ongoing battle for existence. Extinction is not a modern phenomenon by any means. There have been five mass extinctions, including the glaciation of Gondwana and the impact of an asteroid that lead to the loss of the dinosaurs.

But the 6th great extinction (the Anthropocene extinction - the one we are currently living in) shares little in common with these past events. This is the only extinction that a single species (humans) are primarily responsible for, through activities from habitat conversion or degradation, land fragmentation, warming climate, ocean acidification, and human consumption of natural resources. In this context, Pyron's argument seems to be that we ought to retain an anthropocentric viewpoint of conservation as well. That is, we are simply selecting for species that can survive in our wake, and we should feel concern only for those species that we need.

"But the impulse to conserve for conservation’s sake has taken on an unthinking, unsupported, unnecessary urgency. Extinction is the engine of evolution, the mechanism by which natural selection prunes the poorly adapted and allows the hardiest to flourish. Species constantly go extinct, and every species that is alive today will one day follow suit. There is no such thing as an “endangered species,” except for all species. The only reason we should conserve biodiversity is for ourselves, to create a stable future for human beings. Yes, we have altered the environment and, in doing so, hurt other species. This seems artificial because we, unlike other life forms, use sentience and agriculture and industry. But we are a part of the biosphere just like every other creature, and our actions are just as volitional, their consequences just as natural. Conserving a species we have helped to kill off, but on which we are not directly dependent, serves to discharge our own guilt, but little else."

This is hardly an original viewpoint (hastening to the Bible's 'Then God said, “Let Us make man in Our image, according to Our likeness; let them have dominion over the fish of the sea, over the birds of the air, and over the cattle, over all the earth and over every creeping thing that creeps on the earth.'). But it is a short-sighted one. Ignoring more philosophical arguments about the intrinsic value of all species, the arguments presented are problematic and incomplete, and the potential cost could be huge.

Pyron notes that we may be over-estimating the loss of species:

"According to some studies, it’s not even clear that biodiversity is suffering. The authors of another recent National Academy of Sciences paper point out that species richness has shown no net decline among plants over 100 years across 16,000 sites examined around the world."

The study cited by Pyron here does not support the assertion that biodiversity is fine. In fact, Vellend et al (2013) show that at local scales, plant diversity (i.e., the number of plant species; species number being only way of characterizing biodiversity) has been stable. This isn't the same as saying species are not being lost at a global scale. In a follow-up piece (Vellend et al. 2016), the same author notes that at the global scale, "Nonetheless, if we take 142 and 592 as somewhere in the ballpark of extinctions that have occurred between 1600 and 2016, we get extinction rates of 0.98–4.1, 1–2 orders of magnitude higher than the background rate." Outside of plants, Pimm et al. (2014)'s comprehensive review of extinctions in birds, amphibians, and mammals show extinction rates have at least doubled since 1900. These are rates much higher than considered 'natural'. Even when no extinctions have occurred yet, populations are declining rapidly (Ceballos and Ehrlich 2014, Ceballos et al 2017).

An anthropocentric approach also requires complete understanding and control of our environment. Preventing the loss of the species we need or the ecosystems we rely on is not straightforward (as seen by the rarity with which species become 'non-endangered'). Humans are still under-informed about ecosystem services and goods, and what biotic and abiotic interactions are essential to maintain them. The existence of IPBES is a good indicator of how essential and lacking this information is. To confidently state that "Conserving a species we have helped to kill off, but on which we are not directly dependent, serves to discharge our own guilt, but little else" ignores the indirect linkages that might matter, and our lack of knowledge of them.

Further, the philosophy that humans will survive somehow, in the face of losses of biodiversity and changing planetary climate is probably mostly true for the richest members of the planet. Elsewhere, food shortage associated with climate change (eg.) and water shortages (eg.) already threaten individuals in less wealthy countries.

Ironically, Pyron suggests that all we need to make this reality is "moderation".

"The solution is simple: moderation. While we should feel no remorse about altering our environment, there is no need to clear-cut forests for McMansions on 15-acre plots of crabgrass-blanketed land. We should save whatever species and habitats can be easily rescued (once-endangered creatures such as bald eagles and peregrine falcons now flourish), refrain from polluting waterways, limit consumption of fossil fuels and rely more on low-impact renewable-energy sources....We cannot thrive without crops or pollinators, or along coastlines as sea levels rise and as storms and flooding intensify."

But the anthropocentric view of the world that he presents is the opposite of moderation. It favours only humans. In many ways it's the other extreme of the Half-Earth proposal that suggests we set aside half the planet made free of humans. Having been told we don't need to value species beyond our current needs and interests assumes that we will capably and correctly identify those needs and goals, including for time frames beyond our own myopic lifespans. This uncertainty means that a human-centric view may be just as harmful to humans as approaches that ascribe value for biodiversity more value. And humans have proven willing and capable of taking much broader and more effective actions, that accommodate both humans and other organisms. (As FDR said and did: "We have fallen heirs to the most glorious heritage a people ever received, and each one must do his part if we wish to show that the nation is worthy of its good fortune.")

It's frustrating to see this kind of description of biodiversity as though the earth is simply a plus-minus ledger of species – a few lost here, a few gained there.

A conservation baseline is meant to capture an idealized Eden is of course unreasonable. But Pyron's view looks like Hell. ("If this means fewer dazzling species, fewer unspoiled forests, less untamed wilderness, so be it. They will return in time.")

Edit (Nov. 24): the TL:DR is that 
a) I thought the author cherrypicked the ecological literature and downplayed what we know about the loss of biodiversity and the complex/negative effects of human actions; 
b) if the argument is that we should think about biodiversity over timescales of millions of years, humans don't matter anyways; 
c) if we do care about humans, utility values of biodiversity are an acceptable focus of conservation. But it would be misguided to think that we have a perfect understanding of how ecosystems work or a perfect ability to forecast our impacts. For reasons of uncertainty, sampling effects and option value argue that we preserve as much diversity as we can;
d) Non-economic utility values (aesthetic, cultural values) are a good argument for conservation too. Most of us want to leave our children a beautiful planet that is full of life. 

Decomposing diversity effects within species

The relationship between biodiversity and ecosystem functioning is so frequently discussed in the ecological literature that it has its own ubiquitous acronym (BEF). The literature has moved from early discussions and disagreements about mechanism, experimental design, and species richness to ask how different components of biodiversity might contribute differentially to functioning. The search is for mechanisms which hopefully will lend predictability to biodiversity-function relationships. One approach is to independently manipulate different facets of biodiversity – whether species, phylogenetic, trait-based, or genetic diversity – to help disentangle the relative contribution of each.

A new paper extends this question by considering how within-species diversity – including genotypic richness, genetic differences, and trait differences – contribute to functioning. Abbott et al. (2017, Ecology) use a field-based eelgrass system to explore how independent manipulations of genotypic richness and genetic relatedness affected biomass production and invertebrate community richness. They collected 41 unique genotypes of eelgrass (Zostera marina), and used 11 species-relevant loci to determine the relatedness of each genotype pair. The authors also measured 17 traits relevant to performance including "growth rate, nutrient uptake, photosynthetic efficiency, phenolic content, susceptibility to herbivores, and detrital production ".

Eelgrass meadow.
From
http://www.centralcoastbiodiversity.org/
eelgrass-bull-zostera-marina.html

Each of these of these measures are inter-related, but not necessarily in clear, predictable fashions. Genotypes likely differ functionally, but some traits and some genotypes will vary more than others. Genetic distances or relatedness between species similarly may be proxies for trait differences, but this depends on the underlying evolutionary processes. The relationship between any of these measures and functions such as biomass production are no doubt varied and dependent on the mechanism.

The authors established plots with two levels of genotypic richness, either 2 genotypes or 6 genotypes, where genotypes varied among the 41 available. Fully crossed with the genotypic richness treatment was a genetic relatedness treatment: genotypes were either more closely related than a random selection, less closely related, or as closely related as random. At the end of the experiment, above and belowground biomass were collected, and epifaunal invertebrates were collected, and modelled as a component of the biodiversity components.

Because of early die-offs in many plots, planted genotype richness differed from final richness greatly (very few plots had 6 genotypes remaining, for example). For that reason, final diversity measures were used in the models. The relationship between aboveground biomass or belowground biomass and biodiversity were similar: both genotypic richness and genotypic evenness were positively related to total final biomass, but genetic relatedness was negatively correlated. That is, plots with more related genotypes were less productive. Other variables such as trait diversity was not as important, and in fact they did not find any relationship between trait differences and degree of genetic relatedness between genotypes. Since relatedness seemed unrelated to functional similarities, between genotypes, the authors suggested that possibly that reduced biomass among related genotypes is due to self-recognition mechanisms. Most interestingly, the best predictors of invertebrate grazer diversity were opposite -  – the best predictor was trait diversity, not genotypic richness or genetic relatedness.

Even in this case, where Abbott et al. were able to separate different diversity components experimentally, it's clear that simplistic predictions as to how they contribute to functioning are insufficient. The contributions of genotypic versus trait diversity were not strongly related. Further, trait diversity performed best on the function for which genotypic diversity performed worst. Understanding what this means is difficult - are the traits relevant for understanding intraspecific interactions (resource usage, etc) so incredibly different from those relevant for interspecific interactions with herbivores? Are the 17 traits too few to capture all differences, or too many irrelevant traits? Do we expect different biodiversity facets have unique independent effects on ecosystem functions, or does the need to consider multiple facets simply mean we have an imperfect understanding of how different facets are related? 

Positive cost-benefit analysis for conservation spending

In a time when most news about human impacts on the Earth's biodiversity seems to be negative, a new paper in Nature provides a glint of good news about our ability to change the current trend of loss. Encouraging new conservation efforts and funding may be contingent on providing evidence that such efforts will actually be effective.

The new report from Waldron et al. (2017) provides evidence for a predictable relationship between conservation spending and reduction of biodiversity loss. They focused on signatory countries of the Earth Summit's Convention on Biological Diversity and Sustainable Development Goals, and developed a pressures-and-conservation-impact’ (PACI) model to predict how biodiversity loss changed in these countries between 1996-2008. Improvements were driven by conservation spending (relativized to reflect differences in buying power between nations) and were counteracted by GDP growth and agricultural expansion. 

Using this model, the authors could predict how the conservation investments made in these nations had affected their loss of biodiversity, as compared to the scenario in which no investment had been made. Amazingly, the median loss of biodiversity per nation was 29% lower than would otherwise have been expected. Over 1996-2008, seven countries even had net biodiversity improvements: Mauritius, Seychelles, Fiji, Samoa, Tonga, Poland and Ukraine.

Fig 1. Map of biodiversity decline scores (BDS) for signatory nations.
"Colours show percentage of all global declines (total BDS) associated with each country. Pie charts show the predicted reduction in decline (in black) if spending had been I$5 million higher (for selected countries); pie size represents the square root of the BDS. Inset shows predicted versus observed BDS (log-transformed) for the continuous model".

They discuss a number of interactions among model terms that capture greater socio-economic complexity - for example, the impacts of GDP growth on biodiversity loss are lower when a country's base GDP is very low. Such large scale studies naturally face data limitations - here, they use mammal and bird Red List status changes to develop a quantitative measure of biodiversity loss. Other taxa presumably show similar trends, but we lack the data to incorporate them at this moment.

Hopefully by demonstrating this cost-benefit analysis for conservation actions, Waldron et al. (2017) encourage future 'investors' as to the payoff of spending on conservation. 

Reductions in global biodiversity loss predicted from conservation spending. Anthony Waldron, Daniel C. Miller, Dave Redding, Arne Mooers, Tyler S. Kuhn,  Nate Nibbelink, J. Timmons Roberts, Joseph A. Tobias & John L. Gittleman. Nature (2017). doi:10.1038/nature24295. 

Blogging about science for yourself

In case you missed it, a new paper in Royal Society Open Science from seven popular ecology blogs discusses the highlights and values of science community blogging. It provides some insights into the motivations behind posting and the reach and impacts that result. It's a must-read if you've considered or already have a blog about science.

It was nice to see how universal the 'pros' of blogging seem to be – the things I most appreciate about contributing to a blog are pretty similar to the things the authors here reported on too. According to the archives, I've been posting here since 2010, when I was a pretty naïve PhD student interacting with the ecological literature for the first time. I had a degree of enthusiasm and wonder upon interacting with ideas for the first time that I miss, actually. I just started a faculty job this fall, and I think that the blog allowed me to explore and experiment with ideas as I figured out where I was going as a scientist (which is still an ongoing process).

As Saunders et al. note, one of the other major upsides to blogging is the extent to which it produces networking and connections with colleagues. In a pretty crowded job market, I think it probably helped me, although only as a complement to the usual suspects (publications, 'fit', research plans, interviewing skills). Saunders et al. also mentioned blogging as relevant to NSF's Broader Impacts section, which I actually hadn't considered. Beyond that, the greatest benefit by far for me is that forcing oneself to post regularly and publicly is amazing practice for writing about science.

Despite these positives, I don't necessarily think a science blog is for everyone and there are definitely things to consider before jumping in to it. It can be hard to justify posting on a blog when your to-do list overflows, and not everyone will –understandably- think that's a good use of their time. There is a time commitment and degree of prioritisation required that is difficult. This is one reason that having co-bloggers can be a lifesaver. It is also true that while writing a blog is great practice, it probably selects for people able to write quickly (and perhaps without perfectionistic tendencies).

When students ask me about blogging, they often hint at concerns in sharing their ideas and writing. It can be really difficult to put your ideas and writing out there (why invite more judgement and criticism?) and this is can feedback with imposter syndrome (speaking from my own experience). For a long time, minorities, women, students have been under-represented in ecology blogs, and I think this may be a contributor to that. It's nice to see more women blogging about these days, and hopefully there is a positive feedback from increasing the visibility of under-represented groups.

In any case, this paper was especially timely for me, because I've been re-evaluating over the past few months about whether to keep blogging or not, and this provided a reminder of the positive impacts that are easy to overlook.

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?)

References:

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

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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