What are important directions for ecology?

I was recently asked “what is the most important problem in ecology?”. I was dissatisfied with whatever I ended up answering, so it has been on my mind. I think there is an analogy with medicine here – it’s a little like asking a medical scientist “what is the most important disease to cure?” Similarly, there are multiple possible answers, and the one you give will depend on your area of interest/what type of doctor you are. (I also assume this is a question about basic research, and the answer is not as simple as saying, stop extinctions or prevent habitat loss).

Levels of biological organisation.

The medical analogy breaks down a little because ecology is *far* more complicated than medical science. Medicine has a foundation in anatomy and physiology, which in turn rely on basic sciences like cell biology and genetics. This creates a reasonably constrained framework within which further learning/investigation can be organized. Medicine typically stops at the level of the individual, but ecology inherently involves many additional levels of organization (from individuals, to populations, to species and communities, to ecosystems, and beyond). Within any one of these higher levels of organization (population, community, ecosystem), there can be such an immense amount of variation in outcomes and dynamics that ecologists can lose sight of connections with lower and higher levels. For example, community ecology encompasses so much complexity on its own, that also considering the impacts of population level processes and on ecosystem level processes is a tall order. But, we should also appreciate, given these barriers to understanding, just how far ecology has actually advanced in the last 100 years. The combination of reductionist experiments and descriptive work at all scales has been immensely successful (e.g. see this blog post for a partial list). Many general tools have been developed that we can then use to answer specific ecological questions (the integration with statistics with ecology has been highly successful; the use of specific mathematical models). Still, the ability to reconcile multiple levels of organization and scales still limits ecology.

This is a problem that cell biology has also experienced, and is now approaching via systems biology: "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models"(1): to me, this quote rings so true for ecology as well. Systems biology uses mechanistic, mathematical and computational models to attempt to represent multi-scale complexity.

Of course, the optimism about systems biology might be premature in that it hasn’t produced many useful models yet, such that it may be “more of an agenda than a body of results.”. Some of the best “systems ecology” (e.g. meta-ecosystem models) are very system specific and data-heavy (e.g. 2). Can they inform us about generality in ecology?

All of which is to say, I think the most important problems in ecology relate to this need to make the connections between studies and systems and levels of organization. But, doing so may be difficult.

More specific problems

1. The scaling of ecological processes. Many ecologists include a line about being ‘interested in questions of scale’ on their website blurbs. Despite this, our understanding of the aggregate outcome of multiple processes that are occurring at different spatial or temporal scales remains limited, and poorly predictive. There have been a few useful starts (particularly in Peter Chesson’s scale transition papers (3, 4)), but recent theoretical interest seems to be low. We have data at the community scale, and data at the macro-scale. How do we connect these (and can we)? Models describing how processes occurring at smaller scales produce larger scale dynamics can be complex: they may include non-linearities, autocorrelation between regions, the combination of discrete and continuous events, and multiple attractors.

2. Mechanisms maintaining multi-species coexistence in the real world. Hutchinson’s paradox of the plankton remains unsolved*. Community ecologists have invested a lot of time and energy into understanding species interactions as seen in natural communities. To explore the mechanisms behind coexistence, usually (but not always) ecologists have focused on two-species interactions (or maybe 3): understanding coexistence in larger groups tends to be mostly restricted to theory. But fitting the individual pieces into the larger puzzle is exponentially more difficult: in observed large groups of interacting species, what is the relative contribution of the many coexistence mechanisms identified? Which mechanisms are most important, and how do they change through space and time?

*Perhaps not surprisingly, given it is a paradox...

3. Moving farther away from species. In so many ways, focusing on ‘species’ as the unit of measurement is limiting, because ‘species’ is a discrete term and ecology is interested in quantitative measures. Important advances have been made by redefining ecology as the outcome of species traits and species interactions (5). But I think our ability to connect these ideas more closely to species’ multidimensional niches can still improve. In particular, understanding that traits and interactions can change in context-dependent ways (plasticity, ontogeny, environment) will be important (6, 7).

4. Reproducibility of ecological research. This is more of a philosophical question - how do we achieve reproducibility in a science where context-dependence, alternative stable states, chaos and stochasticity all affect results? How do we differentiate between reproducibility (same results under identical conditions) and generality (same results under similar conditions) in results?

References:
1) Sauer, Uwe; Heinemann, Matthias; Zamboni, Nicola. Genetics: Getting Closer to the Whole Picture. Science 316 (5824): 550–551. doi:10.1126/science.1142502. PMID 17463274.

2) Dominique Gravel, Frédéric Guichard, Michel Loreau and Nicolas Mouquet. Source and sink dynamics in meta-ecosystems. Ecology 91(7): 2172-2184.

3) Chesson, Peter. Scale transition theory with special reference to species coexistence in a variable environment. Journal of biological dynamics 3.2-3 (2009): 149-163.

4) Melbourne, Brett A., and Peter Chesson. The scale transition: scaling up population dynamics with field data. Ecology 87.6 (2006): 1478-1488.

5) McGill, Brian J., et al. Rebuilding community ecology from functional traits. Trends in ecology & evolution 21.4 (2006): 178-185.

6) Poisot, T., Canard, E., Mouillot, D., Mouquet, N., Gravel, D. & Jordan, F. (2012) The dissimilarity of species interaction networks. Ecology letters, 15, 1353–61.

7) Siefert, A., Violle, C., Chalmandrier, L., Albert, C.H., Taudiere, A., Fajardo, A., Aarssen, L.W., Baraloto, C., Carlucci, M.B., Cianciaruso, M.V. and L Dantas, V. A global meta‐analysis of the relative extent of intraspecific trait variation in plant communities. Ecology letters 18.12 (2015): 1406-1419.

Identifying the correct spatial scale, a work in progress

Are ecologists conducting research at the optimal scale? Jackson, H.B. and Fahrig, L. 2014. Global Ecology and Biogeography. 24, 52–63.

It’s a truism in ecology that there is a spatial scale at which to each ecological process and interaction occurs. Competition often occurs at local scales, speciation generally occurs at biogeographic scales. Empirical evidence seems to support this - the relationship between, for example, forest cover and beetle abundance changes from strong to nonexistent as the spatial scale of analysis increases, suggesting small scales are most meaningful for the relationship (Holland et al 2004).

But do most ecological studies choose the appropriate scale for data collection and analysis? A new meta-analysis from Heather Bird Johnson and Lenore Fahrig suggests that ecological studies, even multi-scale studies, rarely do. Multi-scale studies can show how a relationship changes in strength with spatial scale, and so should be ideal for identifying the “intrinsic scale” or the “scale of effect” – the spatial extent that best predicts a particular ecological process. (Figure below)

From Jackson and Fahrig 2014. The scale of effect illustrated using a multi-scale study: the strength of the relationship between abundance and spatial scale is strongest at 4 km (radius).

Identifying the appropriate spatial scale for a question and system is of course ideal for a researcher. Researchers can then collect appropriate data, choose to focus on interactions with processes occurring at the same scale, and to correctly analyze data. However, the appropriate spatial scale may not be easy to identify: appropriate spatial scales must be included a multi-scale study. If a study includes spatial scales that cover too small or too large an extent, or has divides the extent into too few sub-scales, simply having a study with multiple scales may still be insufficient.

Theory suggests that species' traits--e.g. dispersal distances and reproductive rates--should be strongly related to the scale of effect, but empirical evidence is surprisingly inconclusive. If studies are already successfully identifying the scale of effect, the authors hypothesized that the observed scale of effect (the scale of prediction at which results are strongest) should vary with taxonomic group, body size, species’ mobility, reproductive rates, and species function. On the other hand, if the scale of effect is being inappropriately determined, perhaps due to decisions about the number of scales to include, and the minimum and maximum spatial scale considered, then these may be the primary correlate of the scale of effect.

To determine whether multi-scale studies were successfully identifying the scale of effect, the authors performed a literature review and meta-analysis. They identified studies that featured abundance or occurrence, which was measured at multiple nested spatial scales, for multiple taxonomic groups. The scales considered in these studies ranged from 10m to 100km.

The results were rather disappointing. By far the strongest predictors of the scale of effect in a study were the largest or smallest scales they considered. This suggests that the true scale of effect might be outside the scales considered by such studies. Worse, differences between taxonomic groups disappeared when you controlled for the minimum and maximum spatial scales used in a study. Where the same species appeared in several different studies, their scales of effect from each study were no more similar than if you had chosen any random species in the same taxonomic group.

From Jackson and Fahrig 2014. There were no significant differences between the observed scale of effect and taxonomic groups. Instead, the largest and smallest spatial scales evaluated in the study drove the conclusion about the scale of effect.

The good news is that the more scales that were considered in a study, the less likely it was that the minimum or maximum scales considered appeared to be driving the results.

From Jackson and Fahrig 2014. The more scales evaluated, the less likely that choice of minimum or maximum scale was driving the result.

In addition, the authors found that the relationship between observed scale of effect and species traits was weak to non-existent in most studies. This is particularly unfortunate given the recent focus on species traits as useful predictors of ecological relationships. The inability to correctly identify the scale of effect certainly may limit our ability associate spatial scale and traits. It is also likely that context modifies the effect of traits (for example, body size may have different effects on dispersal depending on the type of matrix and the environment), further weakening the observed relationship.

One of the largest issues Jackson and Fahrig identified is that in many of the papers, choice of scales was driving by methodological (data availability, precedent, etc.) issues rather than biological justifications. Questions about trait-scale relationships may well be unanswerable until studies have the data for a necessary range of spatial scales. Until then, Jackson and Fahrig recommend that studies be more forthright about their limitations, something this paper will hopefully draw attention to.

Evolution at smaller and smaller scales: a role for microgeographic adaptation in ecology?

Jonathan L. Richardson, Mark C. Urban, Daniel I. Bolnick, David K. Skelly. 2014. Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology & Evolution, 19 February 2014.

Among other trends in ecology, it seems that there is a strong trend towards re-integration of ecological and evolutionary dynamics, and also in partitioning ecological dynamics to finer and finer scales (e.g. intraspecific variation). So it was great to see a new TREE article on “Microgeographic adaptation and the spatial scale of evolution”, which seemed to promise to contribute to both topics.

In this paper, Richardson et al. attempt to define and quantify the importance of small-scale adaptive differences that can arise between even neighbouring populations. These are given the name “microgeographic adaptation”, and defined as arising via trait differences across fine spatial scales, which lead to fitness advantages in an individual’s home sites. The obvious question is what spatial scale does 'microgeographic' refer to, and the authors define it very precisely as “the dispersal neighborhood … of the individuals located within a radius extending two standard deviations from the mean of the dispersal kernel of a species”. (More generally they forward an argument for a unit--the ‘wright’--that would measure adaptive divergence through space relative to dispersal neighbourhoods.) The concept of microgeographic adaptation feels like it is putting a pretty fine point on already existing ideas about local adaptation, and the authors acknowledge that it is a special case of adaptation at scales where gene flow is usually assumed to be high. Though they also suggest that microgeographic adaptation has received almost no recognition, it is probably fairer to say that in practice the assumption is that on fine scales, gene flow is large enough to swamp out local selective differences, but many ecologists could name examples of trait differences between populations at close proximity.

From Richardson et al. (2014). One
example of microgeographic adaptations.

Indeed, despite the general disregard to fine-scale evolutionary differences, they note that there are some historical and more recent examples of microgeographic variation. For example, Robert Selander found that despite the lack of physical barriers to movement, mice in neighbouring barns show allelic differences, probably due to territorial behaviour. As you might expect, microgeographic adaptations result when migration is effectively lower than expected given geographic distance and/or selection is stronger (as when neighbouring locations are very dissimilar). A variety of mechanisms are proposed, including the usual suspects – strong natural selection, landscape barriers, habitat selection, etc.

A list of the possible mechanisms leading to microgeographic adaptation is rather less interesting than questions about how to quantify the importance and commonness of microgeographic adaptation, and especially about its implications for ecological processes. At the moment, there are just a few examples and fewer still studies of the implications, making it difficult to say much. Because of either the lack of existing data and studies or else the paper's attempt to be relevant to both evolutionary biologists and ecologists, the vague discussion of microgeographic differences as a source of genetic variation for restoration or response to climate change, and mention of the existing—but primarily theoretical—ecological literature feels limited and unsatisfying. The optimistic view is that this paper might stimulate a greater focus on (fine) spatial scale in evolutionary biology, bringing evolution and ecology closer in terms of shared focus on spatial scale. For me though, the most interesting questions about focusing on smaller and smaller scales (spatial, unit of diversity (intraspecific, etc)) are always about what they can contribute to our understanding. Does complexity at small scales simply disappear as we aggregate to larger and larger scales (a la macroecology) or does it support greater complexity as we scale up, and so merit our attention? 

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.