Friday, May 19, 2017

Experimental macroevolution at microscales

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

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

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

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

Monday, May 8, 2017

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, John 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.

Wednesday, April 12, 2017

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?  


Monday, April 3, 2017

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.

Friday, March 17, 2017

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. 

Thursday, March 9, 2017

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 GreniƩ for discussion on this topic.

Monday, February 27, 2017

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).

Friday, February 3, 2017

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" 

Tuesday, January 24, 2017

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.

Friday, January 20, 2017

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.