How many traits make a plant? How dimensionality simplifies plant community ecology.

Daniel C. Laughlin. 2013. The intrinsic dimensionality of plant traits and its relevance to community assembly. Journal of Ecology. Accepted manuscript online: 4 NOV. DOI: 10.1111/1365-2745.12187

Community ecology is difficult in part because it is so multi-dimensional: communities include possibly hundreds of species present, and in addition the niches of each of those species are multi-dimensional. Functional or trait-based approaches to ecology in particular have been presented as a solution to this problem, since fewer traits (compared to the number of species) may be needed to capture or predict a community’s dynamics. But even functional ecology is multi-dimensional, and many traits are necessary to truly understand a given species or community. The question, when measuring traits to delineate a community is: how many traits are necessary to capture species’ responses to their biotic and/or abiotic environment? Too few and you limit your understanding, too many and your workload becomes unfeasible.

Plant communities in particular have been approached using a functional framework (they don't move, so trait measurements aren't so difficult), but the number and types of traits that are usually measured vary from study to study. Plant ecologists have defined functional groups for plants which are ecologically similar, identified particular (“functional”) traits as being important, including SLA, seed mass, or height, or taken a "more is more" approach to measurements. There are even approaches that capture several dimensions by identifying important axes (leaf-height-seed strategy, etc.). Which of these approaches is best is not clear. In a new review, Daniel Laughlin rather ambitiously attempts to answer how many (and which) traits plant ecologists should consider. He asks whether the multi-dimensional nature of ecological systems is a curse (there is too much complexity for us to ever capture), or a blessing (is there a limit on how much complexity actually matters for understanding these systems)? Can dimensionality help plant ecologists determine the number of traits they need to measure? 

From Laughlin 2013. The various trait axes (related to plant organs) important for plant function.

Laughlin suggests that an optimal approach to dimensionality should consider each plant organ (root, leaves, height, figure above). Many of the traits regularly measured are correlated (for example, specific leaf area, leaf dry matter content, lifespan, mass-based maximum rate of photosynthesis, dark respiration rates, leaf nitrogen concentration, leaf phosphorus concentration are all interrelated), and so potentially redundant sources of information. However, there are measurements in the same organ that may provide additional information – leaf surface area provides different information than measures of the leaf economic spectrum – and so the solution is not simply measuring fewer traits per organ. Despite redundancy in the traits plant ecologists measure, it is important to recognize that dimensionality is very high in plant communities. Statistical methods are useful for reducing dimensionality (for example, principle coordinate analysis), but even when applied, Laughlin implies that authors often over-reduce trait data by retaining to only a few axes of information.

Using 3 very large plant species-trait datasets (with 16-67(!) trait measures), Laughlin applies a variety of statistical methods to explore effective dimensionality reduction. He then estimates the intrinsic dimensionality (i.e. the number of dimensions necessary to capture the majority of the information in community structure) for the three datasets (figure below). The results were surprisingly consistent for each data set – even when 67 possible plant traits were available, the median intrinsic number of dimensions was only 4-6. While this is a reasonably low number, it's worth noting that the number of dimensions analyzed in the original papers using those datasets were too low (2-3 only).

From Laughlin 2013. The intrinsic number of traits/dimensions
necessary to capture variation in community structure.

For Laughlin, this result shows that dimensionality is a blessing, not a curse. After all, it should allow ecologists to limit the number of trait measures they need to make, provided they choose those traits wisely. Once the number of traits measured exceeds 8, there appears to be diminishing returns. The caveat is that the traits that are important to measure might differ between ecosystems – what matters in a desert is different than what matters in a rainforest. As always, knowing your system is incredibly important. Regardless, the review ends on a highly optimistic note – that complexity and multi-dimensionality of plant communities might not limit us as much as we fear. And perhaps less work is necessary for your next experiment.

Exploring the intersection of conservation, ecology and human well-being

I've seen a number of articles recently that explore in different way the intersection of environment and ecology, conservation and human societies. In particular, Frontiers in Ecology and Evolution (the free ESA journal you are gifted as a member) has dedicated an entire issue to the question of climate impacts on humans and ecosystems, and the papers cover important topics relating to changing climate and its effects on biodiversity, ecosystem integrity and human societies. Economic predictions suggest costs from fires, drought, and rising sea levels: whether protecting ecosystems will preserve their function and so mediate these costs to humans and other organisms is explored in depth. Of course, scholarly papers can be impersonal, but another article about the struggles of Inuit in the north to adapt (or not) to changing ecosystems provides a smaller, more human look at climate, development, and cultural change. Another study predicts that for some cultures, climate change (and the resulting difficulties growing food, maintaining livelihoods, obtaining water and human health risks) may be too much for them to withstand.

Finally, a long-form story by Paul Voosen in The Chronicle of Higher Education asks "Who is conservation for?". While not a novel question, through interviews with Gretchen Daily and Michael Soule, Voosen does a thorough job of illuminating conservation biology in the context of real-world limitations and realities, historical precedents, ongoing tensions between new and old approaches to conservation, and economic development. In the end it asks what motivates conservation: do we conserve purely for the sake of biodiversity alone, for economic and functional benefits, for aesthetic reasons, for charismatic and at-risk species? As Voosen subtly hints in the article, if leading conservation biologists can't agree on the answer, will it ever be possible to be effective?

Slightly unrelated, but there is a great short film online about the life of Alfred Russel Wallace, the less celebrated co-discoverer of natural selection.

Managing uncertain restoration outcomes*

Human activity has impacted ecosystems around the globe, and the value of intact, functioning habitats is increasingly appreciated. One of the most important management options to maintain or increase the amount of functioning habitat is to restore destroyed, disturbed and degraded habitats. However, there is much concern about how predictable restoration efforts are and the management strategies that will maximize success. The reality that systems may reach very different, alternative ecosystem states is a problem for managers when they desire well defined outcomes. Thus the ability to understand and predict how different factors affect restoration outcomes would be an important development.

In the current issue of the Journal of Applied Ecology, Grman and colleagues examine how different factors influence prairie restoration outcomes –specifically the diversity and composition of the restored habitat. They considered several management, historical and environmental factors. For management, they compiled information on the type of planting, the diversity and density of sown seeds and fire manipulation. For local environmental variables, they considered different soil characteristics, shade levels, and site area. The historical influences included land-use history, rainfall during seed sowing and site age. Finally, they also considered the landscape context; specifically what habitats surrounded the restoration site.

Grman and colleagues show that restoration outcomes are most influenced by management decisions and site history. The density, composition and diversity of sown seeds had the greatest impact on restoration outcomes. Species richness was highest in sites sown with high diversity. High sowing density resulted in high beta diversity among sites. Site history had significant effects on non-sown diversity, but did not influence the diversity of sown species. Site characteristics failed to predict local diversity, but they were important for among site beta-diversity.

If success is measured in terms of species diversity, then this work clearly shows that management decisions directly influence success. Surprisingly, site characteristics had a minor influence on success, despite conceptual and theoretical models that predict system sensitivity to abiotic influences. This work reinforces the need to develop the best management options for prairie restoration and that the influences of site history and local conditions can be overcome by sowing decisions and site management.

Grman E., Bassett T. & Brudvig L.A. (2013). Confronting contingency in restoration: management and site history determine outcomes of assembling prairies, but site characteristics and landscape context have little effect. Journal of Applied Ecology, 50, 1234-1243.

*Note: this is from an Editor's Choice piece I wrote for Journal of Applied ecology.

Community structure - what are we missing?

Some of the most frequently used ecological concepts can be difficult to define. Sometimes this lack of clarity leads to a poor understanding and a weak base for further research. A great example is “community structure”, a concept frequently mentioned and rarely defined that probably changes a lot from use to use. The phrase “we’re interested in how communities are structured” is tossed around a lot, and I suppose an understood definition is that community structure encompasses the species that are present in a community and their abundances. Community structure may refer to  both a very simple concept (the abundances of species present in a community) and a very complicated one, connecting as it does mechanisms and models, observational data, and statistical measures. As a result, the precise way that ecologists delineate community structure and quantify it is both varied and vague.

The connection between models, community
structure and metrics.

In the literature, it seems that there are two ways of approaching “community structure”: bottom-up, in which community structure is a predicted outcome of theoretical models of different mechanisms, and top-down, in which community structure is measured in a relatively statistical or descriptive fashion. Both are valuable approaches: while statistical metrics often are interpreted as providing evidence for particular models or mechanisms, the reverse logic – that a model predicts particular results for a given metric – is rarely explicitly considered. Making connections between the model results and the descriptive metrics might actually be fairly difficult. Model predictions are often complex and multidimensional, predicting changes through time, growth rates, the combinations of species that can or cannot coexist (but only if assumptions hold), or particular relationships between measures like diversity, abundances, and range sizes. Metrics are necessarily confined to a few dimensions (or perhaps are ordination approaches), focus on straightforward observational measures like abundance and presence, and further include observational error (sampling, etc). Because community structure means something different to these two approaches, the connections between metrics and models are poorly explored. A theoretician might find it difficult to relate ordinations of communities with the typical predictions from a mathematical model (which might be something like growth rates in relation to changes in abundance), while someone collecting field data might feel that the data they can collect is difficult to relate to the predictions of models.

Part of the problem is that for a long time, the default focus was on what types of interactions structured communities (environment, competition, predation, mutualisms), and niches were assumed to be necessarily driving community structure. The type of measurements and metrics used reflected this search for niches (e.g. comparing environmental gradients with community structure). Many quantitative metrics may tell you something about how community structure relates to different variables (spatial, environment, biotic) and how much variation is still unexplained. The consideration that niches might not always be important eventually led ecologists to compare patterns in community structure to random, null, or neutral expectations. As a result, in the simplest cases the answers to questions about community structure and niches are binary – different from random (niches matter), or not. Looking for complex patterns predicted by models-for example, the relative contribution of niche based and neutral processes to community structure-is difficult using common metrics of community structure (although there are some papers that do a good job of this).

It is interesting that this problem of disconnection between theoretical models of community structure and community structure metrics received the most attention through criticisms of phylogenetic metrics of diversity. There, patterns of over- and under-dispersion were criticized for not being the necessary outcome from models of competition or environmental filtering (i.e. Mayfield and Levine 2010). While those criticisms were mostly fair, they are equally deserved in most studies of species diversity, where patterns in ordinations or beta-diversity are frequently used to infer mechanisms. In contrast, one of the best approaches thus far to integrating model predictions for community structure with metrics of community structure are null models. Though they differ greatly in ecological realism and complexity, null models suggest expected community structure or metric values if none of the expected processes are structuring a community.

One of the greatest failings of the top-down approach is that recognizing patterns outside of the expected, such as those that include stochasticity or a combination of different processes, or the effects of history, is nearly impossible. Models that can incorporate these complexities provide little suggestion of how the patterns we can easily record in communities might reflect complex structuring processes. Ecological research is limited by the poor connection between both top-down and bottom-up approaches and its vague definition of community structure. Patterns more complicated than those that the top-down approach searches for are likely being missed, while relations between models and metrics (or development of new metrics) aren’t considered often enough. One solution might be to more meaningfully define community structure, perhaps as the association (or lack thereof) between the combination of species present in a community and the combination of abiotic and/or biotic processes present. This association is generally compared to an association between species and processes that might arise from random effects alone. The difference is that structure shouldn’t be considered separately from the processes that produce it, and the connections should be explicitly rather than implicitly made.

What scares ecologists?

In the spirit of Halloween, a non-comprehensive list of a few of the things that have frightened ecologists. The things we are scared of vary, with career trajectory and stage (graduate student, post-docs, faculty, non-academic), educational background, and goals. And even if the business of ecology doesn't scare you, some of the things we study should...


A few scary things

-That ghostly thesis - will it ever solidify?

-The undead paper

-Co-authors and supervisors

-Vampire reviewers: they've been around forever, they've seen everything, and they're looking for blood. (uncommon)

The whole process is pretty frightening.

-The Bayesian conspiracy:

• "Q. What is the Bayesian Conspiracy? A. The Bayesian Conspiracy is a multinational, interdisciplinary, and shadowy group of scientists that controls publication, grants, tenure, and the illicit traffic in grad students. The best way to be accepted into the Bayesian Conspiracy is to join the Campus Crusade for Bayes in high school or college, and gradually work your way up to the inner circles. It is rumored that at the upper levels of the Bayesian Conspiracy exist nine silent figures known only as the Bayes Council."

-The job market (or lack thereof?)

-The two-body monster

Fun but troublesome

-That our work is invisible

(0?)

-That our ideas come to life and go terribly wrong

-The science news makeover

And a few (of many) ecological topics that are truly scary

Kudzu

Zombie ants

Toxoplasmosis and human mind control

And the incredibly deadly mosquitoes?

(and all the pathogens it carries)

Waste not, want not? How human food waste changes ecosystems

Daniel Oro, Meritxell Genovart, Giacomo Tavecchia, Mike S. Fowler and Alejandro Martínez-Abraín. 2013. Ecological and evolutionary implications of food subsidies from humans. Ecology Letters. Volume 16, Issue 12, pp 1501–1514. DOI: 10.1111/ele.12187

Humans have always been connected to their environment, directly and indirectly. Ecologists in particular, and people in general, have been thinking about the causes and consequences of these connections for hundreds of years. One form of interaction results when human food resources become available to other animals – for example, through waste dumps, crop waste, fishery by-catches, bird feeders, or road kill. Starting with middens and waste piles in early human settlements, our food waste has always passed t

Garbage dump in India.

o other species. And while rarely considered compared to human impacts like habitat destruction and climate change, a new review by Daniel Oro and colleagues argues that these subsidies have shaped ecosystems around the globe.

Human food waste (aka subsidies) may come from a variety of human activities, with the three most prominent being crop residuals (remnants of harvest remaining on fields), waste dumps, and fishery discards (by-catch thrown overboard). Each of these forms of subsidy occurs globally and large numbers of species rely partially or completely on them for food. For example, dumps are global in distribution, and contain enough edible waste to attract 20-30% of all mammal and bird in a region (particularly omnivorous and carnivorous species). Crop residues usually attract herbivorous or granivorous species (particularly birds), while fisheries’ waste alter marine ecosystems. Eight percent of all catch (~7 million tonnes!) is simply released back into the ocean, and this supplements species across the food web, including half of all seabirds.

Food waste from human activities may not seem so terrible – after all, they are predictably available, easy to access, fast to forage, and can lead to increased condition and fertility among species that take advantage of them. For example, seabirds foraging among fishing boats for by-catch take advantage of the predictability of boat (and food) appearances and as a result have decreased foraging time and areas, higher individual fitness and reproductive success, and ultimately increased population growth. But the authors suggest that these benefits must be considered in a more complicated web of interactions. After all, human food subsidies tend to be much more predictable than natural sources of food and quickly have large effects spanning from individuals, communities, ecosystems, and evolutionary pressures.

Individuals often, though not always, experience positive effects from subsidies – increased biomass, fertility, and survival, accompanied by changes in dispersal and ranges. If food waste draws in high densities of individuals, it may be associated with greater disease occurrence, or draw in predators attracted to easy pickings. Populations also often respond positively to food subsidies, and become larger and more stable as food waste availability increases. But this boon for one species can cascade through the food web, and have large negative effects in communities and ecosystems. For example, yellow-legged gulls are found around dumps and fishing trawlers, taking advantage of the quantities of food available there, and as a result have increased greatly in population. The downside is that in turn these larger populations increase predation pressure on other vulnerable seabird species. Seabirds in particular can create complicated interactions between human food waste and far-flung ecosystems, connecting as they do both terrestrial and marine systems, moving nutrients, pollution, and calories between systems and through trophic levels.

Snow goose exclosure in northern Canada.
Only the small green rectangle has avoided goose grazing.

A famous example of the unexpected consequences of waste subsidies is the snow goose (Chen chen caerulescens). Snow geese have moved from feeding predominantly on marsh plants to landing en masse in farmers’ fields to feed on grain residues. This new and widespread source of food lead to a population boom, and the high numbers of geese stripped away the vegetation in the arctic habitats where they summer and breed. Agriculture food subsidies in southern habitats were felt far away in the arctic, as migratory snow geese tied these systems and food webs together. Though snow geese are unlikely to lose their new source of food, other animals may face plummeting populations or extinctions if food sources disappear. Until the 1970s, in Yellowstone, grizzly bears fed nearly exclusively at a local dump that then closed: the result was both increased mortality and rapid increases in foraging distances and behaviours.

Finally, and of most concern, food waste subsidies can alter the selective pressures a population faces. Species that become reliant on dumps or fields for food may experience changes in selective pressures, leading to selection for traits necessary to exploit these subsidies, and a loss of genotypic/phenotypic variation from the population. Changes in selective pressure change with the situation, of course. In the case of Yellowstone, the dump closure and loss of food source seemed to have large effects on traits important for sexual attractiveness in males, suggesting potential effects on reproductive success. In the best known (and my favourite) example of the selective effect of human food waste, dogs eventually were domesticated from wolf ancestors. Of course dumps can also relax selective pressures, if they allow individuals in poor condition (juveniles, the elderly) to successfully feed and reproduce.

Though food waste subsidies are clearly important and can have wide ranging effects, it is worth noting that the effect and importance of food subsidies is context-dependent. Studies seem to indicate that effects are greatest when food is low naturally or habitat quality is poor; in high quality systems, food waste may only be used by juveniles or individuals in poor condition. Unfortunately, as humans degrade natural habitats, subsidies are only likely to increase in importance as a food source for species. The extent and effects of human food waste are yet another legacy of the global alterations the human species has made. Unfortunately, like so many of the changes we have made, the issues are complex and transcend political and regional boundaries. Practices in one system or nation are tied to effects in another nation, and this complexity can make it difficult to monitor and measure the effects of subsidies as thoroughly as is necessary. This review from Oro et al. certainly makes a case for why our garbage needs to receive more attention.

One example of the ecosystem wide effects of subsidies: here, fisheries inputs.

Biodiversity and ecosystem functioning: now with more spatial scales, more functions, and more measures of diversity.

Jae R. Pasari, Taal Levi, Erika S. Zavaleta, and David Tilman. 2013. Several scales of biodiversity affect ecosystem multifunctionality. PNAS. 110:25

Responses:
1) Karel Mokany, Hugh M. Burley, and Dean R. Paini. β 2013. Diversity contributes to ecosystem processes more than by simply summing the parts. PNAS. 110:43.
2) Jae R. Pasari, Taal Levi, Erika S. Zavaleta. 2013. Reply to Mokany et al: Comprehensive measures of biodiversity are critical to investigations of ecosystem multifunctionality. PNAS. 110:43.

One of the big topics in ecology in recent years is ecosystem services and functioning. In particular, the question has been how diversity (in its many forms, including species, intraspecific, phylogenetic, or functional) relates to ecosystem function (often, but not always, measured in terms of productivity). Most often, this is framed as a question about how (alpha) diversity at the local scale affects one or two functional responses. Because diversity can be measured at multiple scales (local, regional or landscape, etc), because how we measure diversity is scale-dependent (i.e. alpha, beta, and gamma diversity), and because a functional ecosystem relies on many different services, the obvious next step is to think of biodiversity and ecosystem functioning in a framework that incorporates multiple spatial scales, multiple functions, and multiple measures of diversity.

A new paper in PNAS takes advantage of David Tilman’s long running Cedar Creek biodiversity experiment to explore how multiple functions in a landscape relates to local and regional diversity, and beta-diversity. In Pasari et al. (2013), the authors use five years of data collected for 168 9x9 m plots in the Cedar Creek experiment. These plots contained 1, 2, 4, 8, or 16 perennial plant species, and had measurements for 8 ecosystem functions (invasion resistance, aboveground NPP, belowground biomass, nitrogen retention, insect richness and abundance, and change in soil C and plant N). The authors simulated combinations of these plots to create 50,000 landscapes composed of 24 local plots. Multi-functionality in this case was the (scaled) mean of each of the 8 functions minus their standard deviation, for the landscape. The authors then asked whether the average alpha-diversity of the local plots, the beta-diversity between plots, and the gamma-diversity of the landscape were important predictors of this multi-functionality.

Not surprisingly, when considering the different functional responses individually, the average alpha-diversity of plots in a landscape was the most important determinant. Past research has shown that as local diversity increases, niches may be filled, or functional redundancy may increase, and so ecosystem functioning tends to increase. When considering all 8 ecosystem functions using a single measure though, beta- and gamma-diversity also appeared to be important, although alpha diversity remains the dominant predictor (figure below). It should be noted though that the total explained variance in functionality was always low. Increasing either alpha- or gamma-diversity increased multi-functionality, while the effect of beta-diversity on ecosystem functioning was not linear. “[O]nly experimental landscapes with low β diversity were capable of achieving very high multi-functionality, whereas high β-diverse experimental landscapes more consistently achieved moderate multi-functionality”. One important conclusion suggested by these results, then, is that even at larger scales the most important determinant of ecosystem function is how local communities are assembling, since this determines local diversity.

These results are an important update to the current state of biodiversity ecosystem function research, and add to the large body of research that says that all types of diversity are important insurance for functioning natural systems. It is difficult from this study to get a clear picture of how important each type of diversity is, and when alpha, beta, and gamma diversity might be more or less important. This is in part because despite the upsides of having multiple years of tightly controlled data from the Cedar Creek data, experimental communities artificially combined into landscapes lack realism. For example, beta-diversity captures turnover between communities that may result from spatial dynamics (environmental heterogeneity, dispersal, biotic interactions). All of these characteristics may be very important for functioning at the landscape scale. The response from Mokany et al. expresses some of these concerns, noting that artificially creating landscapes like this may omit important spatial and temporal connections found in real systems.

In addition, and this is a more technical concern about how alpha, beta, and gamma diversity are defined, I’m not clear on what the implications of using all three measures as explanatory variables in the same model may be. Mostly because under the strictest definitions of diversity, these three terms should be dependent on each other – changes in alpha and beta diversity necessarily alter gamma diversity. The authors didn’t use this definition in their study, but to understand the mechanisms that relate diversity and functionality, it may be more informative to take this inter-relationship into account.

Despite any caveats, I think that a role for beta-diversity in ecosystem functioning will be shown in further work, and perhaps its role will prove to be much greater than these initial results show. As we expand our understanding of the scales at which diversity matters, unfortunately this will no doubt highlight the limitations in our conservation focuses even more.

Is ecology really failing at theory?

Sam Scheiner. 2013. The ecological literature, an idea free distribution. Ecology Letters. Early View. DOI: 10.1111/ele.12196 

“Ecology is awash with theory, but everywhere the literature is bereft”. That is Sam Scheiner's provocative start to his editorial about what he sees as a major threat to modern ecology. The crux of his argument is simple – theory is incredibly important, it allows us to understand, to predict, to apply, to generalize. Ecology began as a study rooted in system-specific knowledge or natural history in the early 1900s, and developed into a theory-dominated field in the 1960s, when many great theoreticians came to the forefront of ecology. But today, he fears that theory is dwindling in importance in ecology. To test this, he provides a small survey of ecological and evolutionary journals for comparison (Ecology Letters, Oikos, Ecology, AmNat, Evolution, Journal of Evolutionary Biology), recording papers from each journal as either containing no theory, being ‘theory motivated’, or containing theory (either tests of, development of, or reviews of theory). The results showed that papers in ecological journals on average include theory only 60% of the time, compared to 80% for evolutionary papers. Worse, ecological papers seem to be more likely to develop theory than to test it. Scheiner’s editorial (as the title makes clear) is an indictment of this shortcoming of modern ecology.

Plots made based on data table in Scheiner 2013. Results combined for all evolution and all ecology papers.
The proportion of papers in each category - all categories starting with
 "Theory" refer to theory-containing papers.

Plots made based on data table in Scheiner 2013. Results for papers from individual journals.
The proportion of papers of each type - all categories starting with
 "Theory" refer to theory-containing papers.

This is not the kind of conclusion that I find myself arguing against too often. And I mostly agree with Scheiner: theory is the basis of good science, and ecology has suffered from a lack of theoretical motivation for work, or pseudo-theoretical motivation (e.g. productivity-diversity, intermediate diversity patterns that may lack an explanatory mechanism). But I think the methods and interpretation, and perhaps some lack of recognition of differences between ecological and evolutionary research make the conclusions a little difficult to embrace fully. There are three reasons for this – first, is this brief literature review a good measure of how and why we use theory as ecologists? Second, do counts of papers with or without theory really scale into impact or harm? And third, is it fair to directly compare ecological and evolutionary literature, or are there differences in the scope, motivations, and approaches of these fields?

If we are being truly scientific, this might be a good time to point out that The 95% confidence intervals for the percentage of ecology papers with theory overlap with the confidence intervals for the percentage of evolutionary papers with theory suggesting the difference that is the crux of the paper is not significant. [Thanks to a commenter for pointing out this difference is likely significant]. While significant at the 5% level, the amount of overlap is enough that whether this difference is meaningful is less clear. (I would accept an argument that this is due to small sample sizes though). The results also show that choice of journal makes a big difference in terms of the kinds of paper found within – Ecology Letters and AmNat had more theoretical papers or theory motivated papers, while Oikos had more tests of theory and Ecology had more case studies. This sort of unspoken division of labour between journals means that the amount of theory varies greatly. And most ecologists recognize this - if I write a theory paper, it will be undoubtedly targeted to a journal that commonly publishes theory papers. So to more fully represent ecology, a wider variety of journals and more papers would be helpful. Still, Scheiner's counterargument would likely be that even non-theory papers (case studies, etc) should include more theory.

It may be that the proportion of papers that include theory is not a good measure of theory’s importance or inclusion in ecology in general. For example, Scheiner states, “All observations presuppose a theoretical context...the simple act of counting individuals and assessing species diversity relies on the concepts of ‘individual’ and ‘species,’ both of which are complex ideas”. While absolutely true, does this suggest that any paper with a survey of species’ distributions needs to reference theory related to species’ concepts? What is the difference between acknowledging theory used via a citation and more involved discussion of theory? In neither of these cases is the paper “bereft” of theory, but it is not clear from the methods how this difference was dealt with. As well, I think that ecological literature contains far more papers about applied topics, natural history, and system-specific reports than evolutionary biology. Applied literature is an important output of ecology, and as Scheiner states, builds undeniably on years of theoretical background. But on the other hand, a paper about the efficacy of existing reserves in protecting diversity using gap analysis is both important and may not have a clear role for a theoretical section (but will no doubt cite some theoretical and methodological studies). Does this make it somehow of less value to ecology than a paper explicitly testing theory? In addition, case reports and data *are* a necessary part of the theoretical process, since they provide the raw observations on which to build or refine theory. In many ways, Scheiner's editorial is a continuation of the ongoing tension between theory and empiricism that ecology has always faced.

The point I did agree strongly with is that ecology is prone to missing the step between theory development and data collection, i.e. theory testing. Far too few papers test existing theories before the theoreticians have moved on to some new theory. The balance between data collection, theory development, and theory testing is probably more important than the absolute number of papers devoted to one or the other.

Scheiner’s conclusion, though, is eloquent and easy to support, no matter how you feel about his other conclusions: “My challenge to you is to examine the ecological literature with a critical eye towards theory engagement, especially if you are a grant or manuscript reviewer. Be sure to be explicit about the theoretical underpinnings of your study in your next paper…Strengthening the ecological literature by engaging with theory depends on you.”

Science shutdown

As the United States enters the 10th day of its government shutdown, the impacts on science and scientific researchers are becoming very apparent. Ignoring the political mess, the fact is that the shutdown is having devastating effects for science, ranging from small to very large scales.

Some of the largest obvious effects relate to the cessation of highly necessary services. For example, the majority of employees the Centers for Disease Control and Prevention were furloughed. Days later experts on foodborne illness were called back to work, because, surprise, they are essential in cases of E. coli outbreaks. Similarly, nearly all of the Environmental Protection Agency’s employees were furloughed, which means that air and water quality monitoring is furloughed too. According to one employee: "No one is going to be out inspecting water discharges, or wet lands. Nobody is going to be out inspecting waste water treatment plants, drinking water treatment plants, or landfills – nothing". Most of the employees whose work relates to climate science research and related environmental controls at the EPA, NASA, and the National Oceanic and Atmospheric Administration (NOAA) are also no longer at work.

Non-government scientists of every form are also being affected in multiple ways. The overwhelming issue is that the National Science Foundation (NSF) and National Institute of Health (NIH), have been all but closed. (For a great illustration, flip through the messages currently on US gov't science webpages, here). Technically, these furloughed employees are prohibited from replying to phone calls or answering emails from their work email. From one furlough letter: “Due to legal requirements, working in any way during a period of furlough is grounds for disciplinary action, up to and including termination of employment”. If your supervisor or collaborator is a federal scientist, this is a very direct impact of the furlough. But more generally and most importantly, these agencies (particularly the NSF for EEB academics) is the primary funding source for many scientists and institutes, and that means that until the shutdown is over, there will be no new payments or grants awarded.

(From nsf.gov/outage.html, regarding new awards)

"Proposal Preparation & Submission
No new funding opportunities (program descriptions, announcements or solicitations) will be issued.
FastLane proposal preparation and submission will be unavailable."
(existing awards)

"Performance of Work
Awardees may continue performance under their NSF awards during the shutdown, to the extent funds are available, and the term of the grant or cooperative agreement has not expired. Any expenses must be allowable and in accordance with the Office of Management and Budget (OMB) cost circulars. During the shutdown, NSF cannot authorize costs exceeding available award amounts or obligate additional funds to cover such costs.
Payments
No payments will be made during the shutdown."

If your grant is up for review or you are applying for a new grant (it was DDIGs season), then you are out of luck for the moment (FastLane, necessary for submitting grants, is not available), and there undoubtedly will be delays in processing once the shutdown does end.


Even if you have money to spend, limitations may arise from the fact that permits for research activities can’t be filled, and National Parks, National Wildlife Reserves, National Forests are all closed, so planned field activities may be limited. For example, Long-term Ecological Reseach projects (LTERs), which by definition require continuity in sampling, some projects are now inaccessible. Researchers in charge of long-term studies of rodent populations in Sevilleta National Wildlife Refuge, New Mexico, found the gates locked and the combinations changed, making monitoring stations impossible to reach. If you were planning a field season in Antarctica, you are especially out of luck, since the field season has been cancelled altogether. If you planned to use the collections at a national museum (e.g. the Smithsonian), or use government websites and federally funded data repositories, you can’t. I’ve received several mass emails from researchers attempting to locate other sources of necessary data, since their government source is gone. Even if you aren’t a researcher in the US, you might notice the effect of the shutdown because the high profile open-access journal from the Public Library of Science, PLOS ONE, is also at a standstill. And if the keynote speaker at your conference is a US government employee, you’ll notice their absence, since they are not allowed to attend conferences. 

Given the global nature of science, these effects are not isolated to only just a single country in the world – scientists everywhere share US websites and institutions, museum collections, collaborate with US-located researchers, government or otherwise, do fieldwork in US national parks, or rely on the continuity of data from long-term research projects. The impacts of this shutdown are broad and deep, and the most important question is how long will it take for science and scientists to recover?


*If anyone has examples of how this shutdown is affecting their research, please leave a comment!

Why greater diversity – even of parasites – might decrease infections

Pieter T. J. Johnson, Daniel L. Preston, Jason T. Hoverman, and Bryan E. LaFonte. 2013. Host and parasite diversity jointly control disease risk in complex communities. PNAS. Early view.

(Host competence - the tendency of host species to become infected and maintain infection.)

There is often a disconnect between the reality that communities and ecosystems are diverse assemblages with numerous, often complicated and variable interactions, and ecological research, which (perhaps necessarily) focuses on interactions between at most two or three species at a time. Disease ecology similarly has often considered interactions between particular host/parasite species pairs. Some researchers have considered the diversity of host species as an important factor in explaining disease transmission and mortality, but the reality is that parasites also interact, and most hosts harbour multiple parasites. Studying disease dynamics in the context of multi-parasite, multi-host interactions is increasingly recognized as key to understanding disease transmission and severity in communities.

With this in mind, a new paper from Pieter Johnson and colleagues attempts to combine research into the effects of both parasite and host diversity on disease. Two possible hypotheses predict the effect of diversity on disease transmission: the ‘dilution effect’ suggests that the presence of multiple hosts should decrease transmission risk, if the result of additional species is a decline in community competence. It is also hypothesized, somewhat contradictorily, that increased host diversity should support a greater variety of parasites, and parasite life cycles. Both these hypotheses take a host-centric view: understanding how changes in host diversity alter disease risk also requires that we understand how changes in parasite diversity affect disease transmission.

Mutations caused by Ribeiroia infection.
From:http://www.nature.com/scitable/knowledge/library/ecological-consequences-of-parasitism-13255694

The authors looked at the contribution of host and parasite diversity to parasite transmission success using field data and laboratory experiments. First, they looked at existing data on infections of the pathogenic trematode Ribeiroia ondatrae, in amphibian species. Observations showed a positive correlation between larval trematode diversity (parasites) and the richness of free-living species (hosts). Of course the two diversities might be correlated for many unrelated reasons, like site isolation, evolutionary history, or habitat productivity. But a closer analysis showed what appeared to be an interaction between Ribeiroia infection in Pseudacris regilla (Pacific tree frog, the most common amphibian in the survey) and the total number of amphibian species at a site (figure below). Infection by Ribeiroia was highest when there was low amphibian richness and low parasite richness. It dropped significantly lower when amphibian richness was high and/or parasite richness was high.

From Johnson et al. 2013 PNAS. Results of field observations.

In addition to these observations, the authors manipulated both parasite and host species richness, first in small laboratory microcosms and then in larger and more realistic outdoor mesocosms. Results from the laboratory microcosms showed that increases in both amphibian richness (one vs. three species) and parasite richness (one vs. five species) reduced the average number of Ribeiroia in Pseudacris regilla as well as the total infection rate in the amphibian community. The mesocosms had similar results, with both host and parasite diversity negatively influencing Ribeiroia infection. In support of the generality of these results, effect sizes were comparable between the two experiments. These effects were also quite large: for example, in the mesocosm high-host, high-parasite richness treatment there were 52.4% fewer Ribeiroia per P. regilla and 38.2% fewer Ribeiroia overall compared to the low-host, low-parasite richness. Clearly multi-species interactions are crucial for understanding infection by Ribeiroia.

From Johnson et al. 2013 PNAS. Results of the microcosm and mesocosm experiments,
 showing the effects of host and parasite diversity on transmission.

The results make it clear that if you want to understand disease transmission in communities, both host and parasite diversity should be considered. To some extent, both of the initial hypotheses were supported – host and parasite diversity were correlated in the wild, but (in agreement with the dilution effect) infection rates declined as host diversity increased. One factor missing from these hypotheses is the dynamics of the parasite community: in the paper, the authors found models of transmission that included both host and parasite richness were superior. Further, past and future studies that consider only host richness may be inadvertently accounting for the effects of parasite richness on transmission as well, if those two host and parasite diversities are correlated.

There are a number of possibilities for why both host and parasite communities alter parasite transmission success. If host diversity changes the susceptibility of the community to infection (i.e. as diversity increases, the number of low competence/susceptible species increases) then low-competence hosts could act as sinks for parasite infections. Increases in parasite diversity could result in inter-parasite competition and interactions via host immunity.

One future step will be to move beyond simple measurements of species richness to understanding how species identity or characteristics are tied to the putative mechanisms. For example, how do communities of host species vary from low to high diversity sites? Do sites in fact tend to assemble with increasing numbers of low competence host species? The implications are also of interest to other types of studies of community ecology – after all, host-parasite interactions are not very different from predator-prey interactions, and similarly, despite knowing that interactions are complex and involve multiple species, we tend to focus on two or three species examples.