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.

True, False, or Neither? Hypothesis testing in ecology.

How science is done is the outcome of many things, from training (both institutional and lab specific), reviewers’ critiques and requests, historical practices, subdiscipline culture and paradigms, to practicalities such as time, money, and trends in grant awards. ‘Ecology’ is the emergent property of thousands of people pursuing paths driven by their own combination of these and other motivators. Not surprisingly, the path of ecology sways and stalls, and in response papers pop up continuing the decades old discussion about philosophy and best practices for ecological research.

A new paper from Betini et al. in the Royal Society Open Science contributes to this discussion by asking why ecologists don’t test multiple competing hypotheses (allowing efficient falsification or “strong inference” a la Popper). Ecologists rarely test multiple competing hypothesis test: Betini et al. found that only 21 of 100 randomly selected papers tested 2 hypotheses, and only 8 tested greater than 2. Multiple hypothesis testing is a key component of strong inference, and the authors hearken to Platt’s 1964 paper “Strong Inference” as to why ecologists should be adopting adopt strong inference. 

Platt

From Platt: “Science is now an everyday business. Equipment, calculations, lectures become ends in themselves. How many of us write down our alternatives and crucial experiments every day, focusing on the exclusion of a hypothesis? We may write our scientific papers so that it looks as if we had steps 1, 2, and 3 in mind all along. But in between, we do busywork. We become "method-oriented" rather than "problem-oriented." We say we prefer to "feel our way" toward generalizations.”

[An aside to say that Platt was a brutally honest critic of the state of science and his grumpy complaints would not be out of place today. This makes reading his 1964 paper especially fun. E.g. “We can see from the external symptoms that there is something scientifically wrong. The Frozen Method. The Eternal Surveyor. The Never Finished. The Great Man With a Single Hypothesis. The Little Club of Dependents. The Vendetta. The All-Encompassing Theory Which Can Never Be Falsified.”]

Betini et al. list a number of common practical intellectual and practical biases that likely prevent researchers from using multiple hypothesis testing and strong inference. These range from confirmation bias and pattern-seeking to the fallacy of factorial design (which leads to unreasonably high replication requirements including of uninformative combinations). But the authors are surprisingly unquestioning about the utility of strong inference and multiple hypothesis testing for ecology. For example, Brian McGill has a great post highlighting the importance and difficulties of multi-causality in ecology - many non-trivial processes drive ecological systems (see also). 

Another salient point is that falsification of hypotheses, which is central to strong inference, is especially unserviceable in ecology. There are many reasons that an experimental result could be negative and yet not result in falsification of a hypothesis. Data may be faulty in many ways outside of our control, due to inappropriate scales of analyses, or because of limitations of human perception and technology. The data may be incomplete (for example, from a community that has not reached equilibrium); it may rely inappropriately on proxies, or there could be key variables that are difficult to control (see John A. Wiens' chapter for details). Even in highly controlled microcosms, variation arises and failures occur that are 'inexplicable' given our current ability to perceive and control the system.

Or the data might be accurate but there are statistical issues to be concerned about, given many effect sizes are small and replication can be difficult or limited. Other statistical issues can also make falsification questionable – for example, the use of p-values as the ‘falsify/don’t falsify’ determinant, or the confounding of AIC model selection with true multiple hypothesis testing.

Instead, I think it can be argued that ecologists have relied more on verification – accumulating multiple results supporting a hypothesis. This is slower, logically weaker, and undoubtedly results in mistakes too. Verification is most convincing when effect sizes are large – e.g. David Schindler’s lake 226, which provided a single and principal example of phosphorus supplementation causing eutrophication. Unfortunately small effect sizes are common in ecology. There also isn’t a clear process for dealing with negative results when a field has relied on verification - how much negative evidence is required to remove a hypothesis from use, versus just lead to caveats or modifications?

Perhaps one reason Bayesian methods are so attractive to many ecologists is that they reflect the modified approach we already use - developing priors based on our assessment of evidence in the literature, particularly verifications but also evidence that falsifies (for a better discussion of this mixed approach, see Andrew Gelman's writing). This is exactly where Betini et al.'s paper is especially relevant – intellectual biases and practical limitations are even more important outside of the strict rules of strong inference. It seems important as ecologists to address these biases as much as possible. In particular, better training in philosophical, ethical and methodological practices; priors, which may frequently be amorphous and internal, should be externalized using meta-analyses and reviews that express the state of knowledge in unbiased fashion; and we should strive to formulate hypotheses that are specific and to identify the implicit assumptions.

87 years ago, in ecology

Louis Emberger was an important French plant ecologist in the first half of the last century, known for his work on the assemblages of plants in the mediterranean.

For example, the plot below is his published diagram showing minimum temperature of the coolest month versus a 'pluviometric quotient' capturing several aspects of temperature and precipitation from:

Emberger; La végétation de la région méditerranienne. Rev. Gén. Bot., 42 (1930)

Note this wasn't an unappreciated or ignored paper - it received a couple hundred citations, up until present day. Further, updated versions have appeared in more recent years (see bottom).

So it's fascinating to see the eraser marks and crossed out lines, this visualisation of scientific uncertainty. The final message from this probably depends on your perspective and personality:

• Does it show that plant-environment modelling has changed a lot or that plant environmental modelling is still asking about the same underlying processes in similar ways?
• Does this highlight the value of expert knowledge (still cited) or the limitations of expert knowledge (eraser marks)? 

It's certainly a reminder of how lucky we are to have modern graphical software :)

E.g. updated in Hobbs, Richard J., D. M. Richardson, and G. W. Davis. "Mediterranean-type ecosystems: opportunities and constraints for studying the function of biodiversity." Mediterranean-Type Ecosystems. Springer Berlin Heidelberg, 1995. 1-42.











Thanks to Eric Garnier, for finding and sharing the original Emberger diagram and the more recent versions.

2016 holiday caRd

Once more, tis the season! Hope you had an excellent year of science and R coding. This card requires the igraph library - it (loosely) relies on an infection (S-I model) moving through a network :-)

To view season's greetings from 2016:
Go to the gist and download the file directly ("download gist") or hit "raw" and copy/paste. Or, copy and paste the code below.

Users of Rstudio will not be able to see the animation, so base R is highly recommended.

For those not able or willing to run the card, you can view it and the past years' cards here!

150 years of 'ecology'

The word ‘ecology’ was coined 150 years ago by Ernst Haeckel in his book Generelle Morphologie der Organismen published in 1866. Mike Begon gave a fascinating talk at the British Ecological Society meeting in Liverpool on what ecology as meant over these past 150 years and what it should mean in the future. The description of ecology that follows, is largely taken from Begon’s remarks.

Ernst Haeckel, 1860

Haeckel defined ecology as ‘the science of the relations of organism to its surrounding outside world (environment)’, which is in obvious contrast to the then burgeoning science of physiology, which was concerned with the world inside of an organism. Interestingly, the first 50 years of this new field of ecology was dominated by the study of plants. In America, Clements, while in the UK, Tansley, both saw ecology as the description of patterns of plant in relation to the outside world. In many ways, this conception of ecology was what Haeckel had envisioned.

Frederic Clements

However, by the 1960s, the domain of ecology began to grow rapidly. Ecologists like Odum used ‘ecology’ to mean the structure and function of ecosystems, while others focussed on the abundance and distribution of species. By this time ecology had grown to encapsulate all aspects of organismal patterns and functions in nature.

The post-60s period saw another expansion -namely the value of ecology. While Begon points out that text books, including his, focussed on the science of ecology in its pure form, many were ignoring the fact that ecology had/has important repercussions for how humanity will need to deal with the massive environmental impacts we’ve had on Earth’s natural systems. That is, the science of ecology can provide the foundation by which applied management solutions can be built. I personally believe that applied ecology has only just begun its ascension to being the most important element of ecological science (but I’m biassed -being the Executive Editor of the Journal of Applied Ecology). Just like how human physiology has become problem oriented, often focussed on human disease, ecology will too become more problem oriented and focus on our sick patients.

Begon went on to say what ecology should be in the near future. He juxtaposed the fact and truth based necessity of science to the post-truth Brexit/Trump era we now find ourselves in. If ecologists and scientists are to engage the public, and alter self-destructive behaviours, it cannot be with logic and evidence alone. He argued that we need to message like those post-truthers. Use metaphors, simple messages that are repeated, repeated, and repeated.

Can coexistence theories coexist?

These days, the term ‘niche’ manages to cover both incredibly vague and incredibly specific ideas. All the many ways of thinking about an organism’s niche fill the literature, with various degrees of inter-connection and non-independence. The two dominant descriptions in modern ecology (last 30 years or so) are from ‘contemporary niche theory’ and ‘modern coexistence theory’. Contemporary niche theory is developed from consumer-resource theory, where organisms' interactions occur via usage of shared resources. (Though it has expanded to incorporate predators, mutualists, etc), Analytical tools such as ZNGIs and R* values can be used to predict the likelihood of coexistence (e.g. Tilman 1981, Chase & Leibold 2003). Modern coexistence theory is rooted in Peter Chesson’s 2000 ARES review (and earlier work), and describes coexistence in terms of fitness and niche components that allow positive population growth.

On the surface these two theories share many conceptual similarities, particularly the focus on measuring niche overlap for coexistence. [Chesson’s original work explicitly connects the R* values from Tilman’s work to species’ fitnesses in his framework as well]. But as a new article in Ecological Monographs points out, the two theories are separated in the literature and in practice. The divergence started with their theoretical foundations: niche theory relied on consumer-resource models and explicit, mechanistic understanding of organisms’ resource usage, while coexistence theory was presented in terms of Lotka-Volterra competition models and so phenomenological (e.g. the mechanisms determining competition coefficients values are not directly measured). The authors note, “This trade-off between mechanistic precision (e.g. which resources are regulating coexistence?) and phenomenological accuracy (e.g. can they coexist?) has been inherited by the two frameworks….”

There are strengths and weaknesses to both approaches, and both have been used in important ecological studies. So it's surprising that they are rarely mentioned in the same breathe. Letten et al. answer an important question: when directly compared, can we translate the concepts and terms from contemporary niche theory into modern coexistence theory and vice versa?

Background - when is coexistence expected? 
Contemporary niche theory (CNT) (for the simplest case of two limiting resources): for each species, you must know the consumption or impact they have on each resource; the ratio at which the two resources are supplied, and the ZNGIs (zero net growth isoclines, which delimit the resource conditions a species can grow in). Coexistence occurs when the species are better competitors for different resources, when each species has a greater impact on their more limiting resource, and when the supply ratio of the two resources doesn’t favour one species over the other. (simple!)

For modern coexistence theory (MCT), stable coexistence occurs when the combination of fitness differences and niche differences between species allow both species to maintain positive per capita growth rates. As niche overlap decreases, increasingly small fitness differences are necessary for coexistence.

Fig 1, from Letten et al. The criteria for coexistence under modern coexistence theory (a) and contemporary niche theory (b).  In (a), f1 and f2 reflect species' fitnesses. In (b) "coexistence of two species competing for two substitutable resources depends on three criteria: intersecting ZNGIs (solid red and blue lines connecting the x- and y-axes); each species having a greater impact on the resource from which it most benefits (impact vectors denoted by the red and blue arrows); and a resource supply ratio that is intermediate to the inverse of the impact vectors (dashed red and blue lines)."

So how do these two descriptions of coexistence relate to each other? Letten et al. demonstrate that:
1) Changing the supply rates of resources (for CNT) impacts the fitness ratio (equalizing term in MCT). This is a nice illustration of how the environment affects the fitness ratios of species in MCT.

2) Increasing overlap of the impact niche between two species under CNT is consistent with increasing overlap of modern coexistence theory's niche too. When two species have similar impacts on their resources, there should be very high niche overlap (weak stabilizing term) under MCT too.

3) When two species' ZNGI area converge (i.e. the conditions necessary for positive growth rates), it affects both the stabilizing and equalizing terms in MCT. However, this has little meaningful effect on coexistence (since niche overlap increases, but fitness differences decrease as well).

This is a helpful advance because Letten et al. make these two frameworks speak the same (mathematical) language. Further, this connects a phenomological framework with a (more) mechanistic one. The stabilizing-equalizing concept framework (MCT) has been incredibly useful as a way of understanding why we see coexistence, but it is not meant to predict coexistence in new environments/with new combinations of species. On the other hand, contemporary niche theory can be predictive, but is unwieldy and information intensive. One way forward may be this: reconciling the similarities in how both frameworks think about coexistence.

Letten, Andrew D., Ke, Po-Ju, Fukami, Tadashi. 2016. Linking modern coexistence theory and contemporary niche theory. Ecological Monographs: 557-7015. http://dx.doi.org/10.1002/ecm.1242
(This is a monograph for a reason, so I am just covering the major points Letten et al provide in the paper. It's definitely worth a careful read as well!).

The value of ecology through metaphor

The romanticized view of an untouched, pristine ecosystem is unrealistic; we now live in a world where every major ecosystem has been impacted by human activities. From pollution and deforestation, to the introduction of non-native species, our activity has influenced natural systems around the globe. At the same time, ecologists have largely focused on ‘intact’ or ‘natural’ systems in order to uncover the fundamental operations of nature. Ecological theory abounds with explanations for ecological patterns and processes. However, given that the world is increasingly human dominated and urbanized, we need a better understanding of how biodiversity and ecosystem function can be sustained in the presence of human domination. If our ecological theories provide powerful insights into ecological systems, then human dominated landscapes are where they are desperately needed to solve problems.

From the Spectator

This demand to solve problems is not unique to ecology, other scientific disciplines measure their value in terms of direct contributions to human well-being. The most obvious is human biology. Human biology has transitioned from gross morphology, to physiology, to molecular mechanisms controlling cellular function, and all of these tools provide powerful insights into how humans are put together and how our bodies function. Yet, as much as these tools are used to understand how healthy people function, human biologists often stay focussed on how to cure sick people. That is, the proximate value ascribed to human biology research is in its ability to cure disease and improve peoples’ lives. 

In Ecology, our sick patients are heavily impacted and urbanized landscapes. By understanding how natural systems function can provide insights into strategies to improve degraded ecosystems. This value of ecological science manifests itself in shifts in funding and publishing. We now have synthesis centres that focus on the human-environment interaction (e.g., SESYNC). The journals that publish papers that provide applied solutions to ecological and environmental problems (e.g., Journal of Applied Ecology, Frontiers in Ecology and the Environment, etc.) have gained in prominence over the past decade. But more can be done.

We should keep the ‘sick patient’ metaphor in the back of our minds at all times and ask how our scientific endeavours can help improve the health of ecosystems. I was once a graduate student that pursued purely theoretical tests of how ecosystems are put together, and now I am the executive editor of an applied journal. I think that ecologists should feel like they can develop solutions to environmental problems, and that their underlying science gives them a unique perspective to improving the quality of life for our sick patients. 

What is a community ecologist anyways?

I am organizing a 'community ecology' reading group, and someone asked me whether I didn’t think focusing on communities wasn’t a little restrictive. And no. The thought never crossed my mind. Which I realized is because I internally define community ecology as a large set of things including ‘everything I work on’ :-) When people ask me what I do, I usually say I’m a community ecologist.

Obviously community ecology is the study of ecological communities [“theoretical ideal the complete set of organisms living in a particular place and time as an ecological community sensu lato”, Vellend 2016]. But in practice, it's very difficult to define the boundaries of what a community is (Ricklefs 2008), and the scale of time and space is rather flexible.

So I suppose my working definition has been that a community ecologist researches groups of organisms and understands them in terms of ecological processes. There is flexibility in terms of spatial and temporal scale, number and type of trophic levels, interaction type and number, or response variables of interest. It’s also true that this definition could be encompass much of modern ecology…

On the other hand, a colleague argued that only the specific study of species interactions should be considered as ‘community ecology’: e.g. pollination ecology, predator-prey interactions, competition, probably food web and multi-trophic level interactions. 

Perhaps my definition is so broad as to be uninformative, and my colleague's is too narrow to include all areas. But it is my interest in community ecology that leads me to sometimes think about larger spatial and temporal scales. Maybe that's what community ecologists have in common: the flexibility needed to deal with the complexities of ecological communities.

Reviewing peer review: gender, location and other sources of bias

For academic scientists, publications are the primary currency for success, and so peer review is a central part of scientific life. When discussing peer review, it’s always worth remembering that since it depends on ‘peers’, broader issues across ecology are often reflected in issues with peer review. A series of papers from Charles W. Fox--and coauthors Burns, Muncy, and Meyer--do a great job of illustrating this point, showing how diversity issues in ecology are writ small in the peer review process.

The journal Functional Ecology provided the authors up to 10 years of data on the submission, editorial, and review process (between 2004 and 2014, maximum). This data provides a unique opportunity to explore how factors such as gender and geographic local affects the peer review process and outcomes, and also how this has changed over the past decade.

Author and reviewer gender were assigned using an online database (genderize.io) that includes 200,000 names and an associated probability reflecting the genders for each name. Geographic location of editors and reviewers were also identified based on their profiles. There are some clear limitations to this approach, particularly that Asian names had to be excluded. Still, 97% of names were present in the genderize.io database, and 94% of those names were associated with a single gender >90% of the time.

Many—even most—of Fox et al.’s findings are in line with what has already been shown regarding the causes and effects of gender gaps in academia. But they are interesting, nonetheless. Some of the gender gaps seem to be tied to age: senior editors were all male, and although females make up 43% of first authors on papers submitted to Functional Ecology, they are only 25% of senior authors.

Implicit biases in identifying reviewers are also fairly common: far fewer women were suggested then men, even when female authors or female editors were identifying reviewers. Female editors did invite more female reviewers than male editors. ("Male editors selected less than 25 percent female reviewers even in the year they selected the most women, but female editors consistently selected ~30–35 percent female").  Female authors also suggested slightly more female reviewers than male authors did.

Some of the statistics are great news: there was no effect of author gender or editor gender on how papers were handled and their chances of acceptance, for example. Further, the mean score given to a paper by male and female reviewers did not differ – reviewer gender isn’t affecting your paper’s chance of acceptance. And when the last or senior author on a paper is female, a greater proportion of all the authors on the paper are female too.

The most surprising statistic, to me, was that there was a small (2%) but consistent effect of handling editor gender on the likelihood that male reviewers would respond to review requests. They were less likely to respond and less likely to agree to review, if the editor making the request is female.

That there are still observable effects of gender in peer review despite an increasing awareness of the issue should tell us that the effects of other forms of less-discussed bias are probably similar or greater. Fox et al. hint at this when they show how important the effect of geographic locale is on reviewer choice. Overwhelmingly editors over-selected reviewers from their own geographic locality. This is not surprising, since social and professional networks are geographically driven, but it can have the effect of making science more insular. Other sources of bias – race, country of origin, language – are more difficult to measure from this data, but hopefully the results from these papers are reminders that such biases can have measurable effects.

From Fox et al. 2016a. 

References:

  Fox, C. W., Burns, C. S., Muncy, A. D., Meyer, J. A. (2016), Author-suggested reviewers: gender differences and influences on the peer review process at an ecology journal. Functional Ecology. doi: 10.1111/1365-2435.12665

Fox, C. W., Burns, C. S., Muncy, A. D. and Meyer, J. A. (2016), Gender differences in patterns of authorship do not affect peer review outcomes at an ecology journal. Funct Ecol, 30: 126–139. doi:10.1111/1365-2435.12587

Fox, C. W., Burns, C. S., Meyer, J. A. (2016), Editor and reviewer gender influence the peer review process but not peer review outcomes at an ecology journal. Functional Ecology, 30: 140–153. doi: 10.1111/1365-2435.12529

When individual differences matter - intraspecific variation in 2016

Maybe it is just confirmation bias, but there seems to have been an upswing in the number of cool papers on the role of intraspecific variation in ecology. For example, three new papers highlight the importance of variation among individuals for topics ranging from conservation, coexistence, and community responses to changing environments. All are worth a deeper read.

‘An Anthropocene map of genetic diversity’ asks how intraspecific variation is distributed globally, a simple but important question. Genetic diversity in a species is an important predictor of their ability to adapt to changing environments. For many species, however, as their populations decline in size, become fragmented, or experience strong selection related to human activities, genetic diversity may be in decline. Quantifying a baseline for global genetic diversity is an important goal. Further, with the rise of ‘big data’ (as people love to brand it) it is now an accessible one: there are now millions of genetic sequences in GenBank and associated GPS coordinates. 

Many of the global patterns in genetic diversity agree with those seen for other forms of diversity: for example, some of the highest levels are observed in the tropical Andes and Amazonia, and there is a peak in the mid-latitudes and human presence seems to decrease genetic diversity.

From Miraldo et al. (2016): Map of uncertainty. Areas in green represent high sequence availability and taxonomic coverage (of all species known to be present in a cell). All other colors represent areas lacking important data.

The resulting data set represents ~ 5000 species, so naturally the rarest species and the least charismatic are underrepresented. The authors identify this global distribution of ignorance, highlighting just how small our big data still is.

Miraldo, Andreia, et al. "An Anthropocene map of genetic diversity." Science353.6307 (2016): 1532-1535.


In ‘How variation between individuals affects species coexistence’, Simon Hart et al. do the much needed work to answer the question of how intraspecific variation fits into coexistence theory. Their results reinforce the suggestion that in general, intraspecific variation should making coexistence more difficult, since it increases the dominance of superior competitors, and reduces species' niche differentiation. (Note this is a contrast to the argument Jim Clark has made with individual trees, eg. Clark 2010)

Hart, Simon P., Sebastian J. Schreiber, and Jonathan M. Levine. "How variation between individuals affects species coexistence." Ecology letters (2016).


The topic of evolutionary rescue is an interesting, highlighting (see work from Andy Gonzalez and Graham Bell for more details) the ability of populations to adapt to stressors and changing environments, provided enough underlying additive genetic variation and time is available. It has been suggested that phenotypic plasticity can reduce the chance of evolutionary rescue, since it reduces selection on genetic traits. Alternatively, by increasing survival time following environmental change, it may aid evolutionary rescue. Ashander et al. use a theoretical approach to explore how plasticity interacts with a change in environmental conditions (mean and predictability/autocorrelation) to affect extinction risk (and so the chance of evolutionary rescue). Their results provide insight into how the predictability of new environments, through an affect on stochasticity, in turn changes extinction risk and rescue.

Ashander, Jaime, Luis-Miguel Chevin, and Marissa L. Baskett. "Predicting evolutionary rescue via evolving plasticity in stochastic environments." Proc. R. Soc. B. Vol. 283. No. 1839. The Royal Society, 2016.

The problematic effect of small effects

Why do ecologists often get different answers to the same question? Depending on the study, for example, the relationship between biodiversity and ecosystem function could be positive, negative, or absent (e.g. Cardinale et al. 2012). Ecologists explain this in many ways - experimental issues and differences, context dependence. However, it may also be due to an even simpler issue, that of the statistical implications of small effect sizes.

This is the point that Lemoine et al. make in an interesting new report in Ecology. Experimental data from natural systems (e.g. for warming experiments, BEF experiments) is often highly variable, has low replication, and effect sizes are frequently small. Perhaps it is not surprising we see contradictory outcomes, because data with small true effect sizes are prone to high Type S (reflect the chance of obtaining the wrong sign for an effect) and Type M (the amount by with an effect size must be overestimated in order to be significant). Contradictory results arise from these statistical issues, combined with the idea that papers that do get published early on may simply have found significant effects by chance (the Winner's Curse). 

Power reflects the chance of failing to correctly reject the null hypothesis (Ho). The power of ecological experiments increases with sample size (N), since uncertainty in data decreases with increasing N. However, if your true effect size is small, studies with low power have to significantly overestimate the effect size to have a significant p-value. This is the result of the fact that if the variation in your data is large and your effect size is small, the critical value for a significant z-score is quite large. Thus for your results to be significant, you need to observe an effect larger than this critical value, which will be much larger than the true effect size. It's a catch-22 for small effect sizes: if your result is correct, it very well may not be significant; if you have a significant result, you may be overestimating the effect size. 

From Lemoine et al. 2016. 

The solution to this issue is clearly a difficult one, but the authors make some useful suggestions. First, it's really the variability of your data, more than the sample size, that raises the Type M error. So if your data is small but beautifully behaved, this may not be a huge issue for you (but you must be working in a highly atypical system). If you can increase your replication, this is the obvious solution. But the other solutions they see are cultural shifts when we publish statistical results. As with many other, the authors suggest we move away from reliance on p-values as a pass/fail tool for results. In addition to reporting p-values, they suggest we report effect sizes and their error rates. Further, that this be done for all variables regardless of whether the results are significant. Type M error and power analyses can be reported in a fashion meant to inform interpretation of results: “However, low power (0.10) and high Type M error (2.0) suggest that this effect size is likely an overestimate. Attempts to replicate these findings will likely fail.” 

Lemoine, N. P., Hoffman, A., Felton, A. J., Baur, L., Chaves, F., Gray, J., Yu, Q. and Smith, M. D. (2016), Underappreciated problems of low replication in ecological field studies. Ecology. doi: 10.1002/ecy.1506