When do descriptive methods exceed the sum of their points?

The last post here mused on the connection between (but also, distinctness of) the scientific goals of "understanding" and "prediction". An additional goal of science is "description", the attempt to define and classify phenomenon. Much as understanding and prediction are distinct but interconnected, it can be difficult to separate research activities between description and understanding. Descriptive research is frequently considered preliminary or incomplete on its own, meant to be an initial step prior to further analysis. (On the other hand, the decline of more descriptive approaches such as natural history is often bemoaned). With that in mind, it was interesting to see several recent papers in high-impact journals that rely primarily on descriptive methods (especially ordinations) to provide generalizations. It's fairly uncommon to see ordination plots as the key figure in journals like Nature or The American Naturalist, and it opens up the question of 'when do descriptive methods exceed description and provide new insights & understanding?'

For example, Diaz et al.'s 2016 Nature paper took advantage of a massive database of trait data (from ~46000 species) to explore the inter-relationships between 6 ecologically relevant plant traits. The resulting PCA plot (figure below) illustrates, across many species, that well-known tradeoffs between a) organ size and scaling and b) the tissue economic spectrum appear fairly universal. Variation in plant form and function may be huge, but the Diaz et al. ordination highlights that it still is relatively constrained, and that many strategies (trait combinations) are apparently untenable.

From Diaz et al. 2016.

Similarly, a new paper in The American Naturalist relies on ordination methods  to try to identify 'a periodic table of niches' of lizards (Winemiller et al. 2015 first presented this idea) – i.e. a classification framework capturing the minimal, clarifying set of universal positions taken by a set of taxa. Using the data and expert knowledge on lizard species collected over a lifetime of research by E. Pianka and L. Vitt, Pianka et al. (2017) first determine the most important life history axes -- habitat, diet, life history, metabolism, and defense attributes. They use PCoA to calculate the position of each of 134 species in terms of each of the 5 life history axes, and then combined the separate axes into a single ordination (see figure below). This ordination highlights that niche convergence (distant relatives occupy very similar niche space) and niche conservation (close relatives occupy very similar niche space) are both common outcomes of evolution. (For more discussion, this piece from Jonathon Losos is a great). Their results are less clarifying than those in Diaz et al. (2016): a key reason may simply be the smaller size of Pianka et al.'s data set and its greater reliance on descriptive (rather than quantitative) traits.

From Winemiller et al. 2017

Finally, a new TREE paper from Daru et al. (In press) attempts to identify some of the processes underlying the formation of regional assemblages (what they call phylogenetic regionalization, e.g. distinct phylogenetically delimited biogeographic units). They similarly rely on ordinations to take measurements of phylogenetic turnover and then identify clusters of phylogenetically similar sites. Daru et al.'s paper is slightly different, in that rather than presenting insights from descriptive methods, it provides a descriptive method that they feel will lead to such insights.

Part of this blip of descriptive results and methods may be related to a general return to the concept of multidimensional or hypervolume niche (e.g. 1, 2). Models are much more difficult in this context and so description is a reasonable starting point. In addition, the most useful descriptive approaches are like those seen here - where new data or a lot of data (or new techniques that can transform existing data) - are available. In these cases, they provide a route to identifying generalization. (This also leads to an interesting question – are these kind of analyses simply brute force solutions to generalization? Or do descriptive results sometimes exceed the sum of their individual data points?)

References:

Díaz S, Kattge J, Cornelissen JH, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, Garnier E. (2016). The global spectrum of plant form and function. Nature. 529(7585):167.

Eric R. Pianka, Laurie J. Vitt, Nicolás Pelegrin, Daniel B. Fitzgerald, and Kirk O.Winemiller. (2017). Toward a Periodic Table of Niches, or Exploring the Lizard Niche Hypervolume. The American Naturalist. https://doi.org/10.1086/693781

Barnabas H. Daru, Tammy L. Elliott,  Daniel S. Park, T. Jonathan Davies. (In press). Understanding the Processes Underpinning Patterns of Phylogenetic Regionalization. TREE. DOI: http://dx.doi.org/10.1016/j.tree.2017.08.013

Novelty or prediction or something else?

There is an interesting editorial at elife from Barak Cohen on "How should novelty be valued in science?" It connects to some of the discussions on this blog and in other places concerned about the most efficient and effective path for science (Cohen suggests a focus on predictive ability).

One relevant question is how 'understanding' differs from 'predicting' and whether a focus on 'prediction' can produce perverse incentives too, as the focus on novelty has.

[This pessimistic image about perverse incentives from Edwards and Roy (2017) and the discussion from Mike Taylor seemed an appropriate addition.

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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.

Science is a maze

If you want to truly understand how scientific progress works, I suggest fitting mathematical models to dynamical data (i.e. population or community time series) for a few days.

map for science?

You were probably told sometime early on about the map for science: the scientific method. It was probably displayed for your high school class as a tidy flowchart showing how a hypothetico-deductive approach allows scientists to solve problems. Scientists make observations about the natural world, gather data, and come up with a possible explanation or hypothesis. They then deduce the predictions that follow, and design experiments to test those predictions. If you falsify the predictions you then circle back and refine, alter, or eventually reject the hypothesis. Scientific progress arises from this process. Sure, you might adjust your hypothesis a few times, but progress is direct and straightforward. Scientists aren’t shown getting lost.

Then, once you actively do research, you realize that formulation-reformulation process dominates. But because for most applications the formulation-reformulation process is slow – that is, each component takes time (e.g. weeks or months to redo experiments and analyses and work through reviews) – you only go through that loop a few times. So you usually still feel like you are making progress and moving forward.

But if you want to remind yourself just how twisting and meandering science actually is, spend some time fitting dynamic models. Thanks to Ben Bolker’s indispensible book, this also comes with a map, which shows how closely the process of model fitting mirrors the scientific method. The modeller has some question they wish to address, and experimental or observational data they hope to use to answer it. By fitting or selecting the best model for they data, they can obtain estimates for different parameters and so hopefully test predictions from they hypothesis. Or so one naively imagines.

From Bolker's Ecological Models and Data in R,
a map for model selection. 

The reality, however, is much more byzantine. Captured well in Vellend (2010): 

“Consider the number of different models that can be constructed from the simple Lotka-Volterra formulation of interactions between two species, layering on realistic complexities one by one. First, there are at least three qualitatively distinct kinds of interaction (competition, predation, mutualism). For each of these we can have either an implicit accounting of basal resources (as in the Lotka-Volterra model) or we can add an explicit accounting in one particular way. That gives six different models so far. We can then add spatial heterogeneity or not (x2), temporal heterogeneity or not (x2), stochasticity or not (x2), immigration or not (x2), at least three kinds of functional relationship between species (e.g., predator functional responses, x3), age/size structure or not (x2), a third species or not (x2), and three ways the new species interacts with one of the existing species (x3 for the models with a third species). Having barely scratched the surface of potentially important factors, we have 2304 different models. Many of them would likely yield the same predictions, but after consolidation I suspect there still might be hundreds that differ in ecologically important ways.”

Model fitting/selection, can actually be (speaking for myself, at least) repetitive and frustrating and filled with wrong turns and dead ends. And because you can make so many loops between formulation and reformulation, and the time penalty is relatively low, you experience just how many possible paths forward there to be explored. It’s easy to get lost and forget which models you’ve already looked at, and keeping detailed notes/logs/version control is fundamental. And since time and money aren’t (as) limiting, it is hard to know/decide when to stop - no model is perfect. When it’s possible to so fully explore the path from question to data, you get to suffer through realizing just how complicated and uncertain that path actually is. 

What model fitting feels like?

Bolker hints at this (but without the angst):

“modeling is an iterative process. You may have answered your questions with a single pass through steps 1–5, but it is far more likely that estimating parameters and confidence limits will force you to redefine your models (changing their form or complexity or the ecological covariates they take into account) or even to redefine your original ecological questions.”

I bet there are other processes that have similar aspects of endless, frustrating ability to consider every possible connection between question and data (building a phylogenetic tree, designing a simulation?). And I think that is what science is like on a large temporal and spatial scale too. For any question or hypothesis, there are multiple labs contributing bits and pieces and manipulating slightly different combinations of variables, and pushing and pulling the direction of science back and forth, trying to find a path forward.

(As you may have guessed, I spent far too much time this summer fitting models…)

#ESA100 : Statistical Steps to Ecological Leaps

By Marc Cadotte and Caroline Tucker

For their centennial, ESA is asking their members identify as the ecological milestones of the last 100 years. They’ve asked the EEB & Flow to consider this question as a blog post. And there are many – ecology has grown from an amateur mix of natural history and physiology to a relevant and mature discipline. Part of this growth rests on major theoretical developments from great ecologists like Clements, Gleason, MacArthur, Whittaker, Wilson, Levins, Tilman and Hubbell. These people provided the ideas needed to move ecology to new territory. But ideas on their own aren’t enough, in the absence of necessary tools and methods. Instead, we argue that modern ecology would not exist without statistics.

The most cited paper in ecology and evolutionary biology is a methodological one (Felsenstein’s 1985 paper on phylogenetic confidence limits in Evolution – cited over 26,000 times) (Felsenstein, 1985). Statistics is the backbone that ecology develops around. Every new statistical method potentially opens the door to new ways of analyzing data and perhaps new hypotheses. To this end, we show how seven statistical methods changed ecology.

1. P-values and Hypothesis Testing – Setting standards for evidence.

Ecological papers in the early 1900s tended to be data-focused. And that data was analyzed in statistically rudimentary ways. Data was displayed graphically, perhaps with a simple model (e.g. regression) overlaid on the plot. Scientists sometimes argued that statistical tests offered no more than confirmation of the obvious.

At the same time, statistics were undergoing a revolution focused on hypothesis testing. Karl Pearson started it, but Ronald Fisher (Fisher 1925) and Pearson’s son Egon and Jerzy Neyman (Neyman & Pearson 1933) produced the theories that would change ecology. These men gave us the p-value – ‘the probability to obtain an effect equal to or more extreme than the one observed presuming the null hypothesis of no effect is true’ and gave us a modern view of hypothesis testing – i.e. that a scientist should attempt to reject a null hypothesis in favour of some alternative hypothesis.

It’s amazing to think that these concepts are now rote memorization for first year students, having become so ingrained into modern science. Hypothesis testing using some pre-specified level of significance is now the default method for looking for evidence. The questions asked, choices about sample size, experimental design and the evidence necessary to answer questions were all framed in the shadow of these new methods. p-values are no longer the only approach to hypothesis testing, but it is incontestable that Pearson and Fisher laid the foundations for modern ecology. (See Biau et al 2010 for a nice introduction).

2. Multivariate statistics: Beginning to capture ecological complexity.

Because the first emergence of statistical tests arose from agricultural studies, they were designed to test for differences from among treatments or from known distributions. They applied powerfully to experiments manipulating relatively few factors and measuring relatively few variables. However, these types of analyses did not easily permit investigations of complex patterns and mechanisms observed in natural communities.

Often what community ecologists have in hand are multiple datasets about communities including species composition and abundance, environmental measurements (e.g. soil nutrients, water chemistry, elevation, light, temperature, etc.), and perhaps distances between communities. And what researchers want to know is how compositional (multi-species) change among communities is determined by environmental variables. We shouldn’t understate the importance of this type of analysis on communities, in one tradition of community ecology, we would simply analyze changes in richness or diversity. But communities can show a lack of variation in diversity even when communities are being actively structured: diversity is simply the wrong currency.

Many of the first forays into multivariate statistics were through measuring the compositional dissimilarity or distances between communities. For example Jaccard (Jaccard, 1901), and Bray and Curtis (Bray & Curtis, 1957) are early ecologists that invented distance-based measures. Correlating compositional dissimilarity with environmental differences required ordination techniques. Principle Component Analysis (PCA) was actually invented by Karl Pearson around 1900 but computational limitations constrained its use until the 1980s. Around this time, other methods began to emerge which ecologists started to employ (Hill, 1979; Mantel, 1967). The development of new methods continues today (e.g. Peres-Neto & Jackson, 2001), and the use of multivariate analysis is a community ecology staple.

There are now full texts dedicated to the implementation of multivariate statistical tests with ecological data (e.g., Legendre & Legendre, 1998). Further, there are excellent resources available in R (more on this later) and especially in the package vegan (Oksanen et al., 2008), which implements most major multivariate methods. Going forward it is clear that multivariate techniques will continue to be reassessed and improved (e.g. Guillot & Rousset, 2013), and there will be a greater emphasis on the need to articulate multivariate hypotheses and perhaps use multivariate techniques to predict communities (Laughlin, 2014) –not just explain variation.

3. Null models: Disentangling patterns and processes.

Ecology occurs over large spatial and temporal scales, and so it is always reliant on observational data. Gathering observational data is often much easier than doing experimental work at the same spatial or temporal scale, but it is also complicated to analyze. Variation from a huge number of unmeasured variables could well weaken patterns or create unexpected ones. Still, the search for patterns drove the analysis of observational data: including patterns along environmental gradients, patterns in species co-occurrences, patterns in traits. The question of what represented a meaningful pattern was harder to answer.

It seems that ecology could not go on looking at patterns forever. But it took some heated arguments finally change this. The ‘null model wars’ revolved around Jared Diamond’s putative assembly rules for birds on islands (Diamond 1975), which relied on a “checkerboard” pattern of species co-occurrences. The argument for null models was led by Connor and Simberloff (Connor & Simberloff 1979) and later joined by Nicholas Gotelli (e.g. Gotelli & Graves 1996). A null model, they point out, was necessary to determine whether observed patterns of bird distribution were actually different from random patterns of apparent non-independence between species pairs. Further, other ecological mechanisms (different habitat requirements, speciation, dispersal limitations) could also produce non-independence between species pairs. The arguments about how to appropriately formulate null models have never completely ended (e.g., 1, 2, 3), but they now drive ecological analyses. Tests of species-area relationships, phylogenetic diversity within communities, limiting similarity of body sizes or traits, global patterns of diversity, species co-occurrences, niche overlaps, and nestedness in networks, likely all include a null model of some sort.

The null model wars have been referred to as a difficult and contentious time for ecology. Published work (representing significant amounts of time and funding) perhaps needed to be re-evaluated to differentiate between true and null ecological patterns. But despite these growing pains, null models have forced ecology to mature beyond pattern-based analyses to more mechanistic ones.

4. Spatial statistics: Adding distance and connectivity.

Spatially-explicit statistics and models seem like an obvious necessity for ecology. After all, the movement of species through space is an immensely important part of their life history, and further, most ecologically relevant characteristics of the landscapes vary through space, e.g. resources, climate, and habitat. Despite this, until quite recently ecological models tended to assume a uniform distribution of species and processes through space, and that species’ movement was uniform or random through space. The truism that points close in space, all else being equal, should be more similar than distant points, while obvious, also involved a degree of statistical complexity and computing requirements difficult to achieve.

Fortunately for ecology, the late 1980s and early 1990s were a time of rapid computing developments that enabled the incorporation of increasing spatial complexity into ecological models (Fortin & Dale 2005). Existing methods – some ecological, some borrowed from geography – were finally possible with available technology, including nearest neighbour distances, Ripley’s K, variograms, and the Mantel test (Fortin, Dale & ver Hoef 2002). Ideas now fundamental to ecology such as connectivity, edge effects, spatial scale (“local” vs. “regional”), spatial autocorrelation, and spatial pattern (non-random, non-uniform spatial distributions) are the inheritence of this development. Many fields of ecology have incorporated spatial methods or even owe their development to spatial ecology, including meta-communities, landscape ecology, conservation and management, invasive species, disease ecology, population ecology, and population genetics. Pierre Legendre asked in his seminal paper (Legendre 1993) on the topic whether space was trouble, or a new paradigm. It is clear that space was an important addition to ecological analyses.

5. Measuring diversity: rarefaction and diversity estimators.

How many species are there in a community? This is a question that inspires many biologists, and is something that is actually very difficult to measure. Cryptic, dormant, rare and microscopic organisms are often undersampled, and accurate estimates of community diversity need to deal with these undersampled species.

Communities may seem to have different numbers of species simply based on the fact some have been sampled more thoroughly. Unequal sampling effort can distort real differences or similarities in the numbers of species. For example, in some recent analyses of plant diversity using the freely available species occurrence data from GBIF, we found that Missouri seems to have the highest plant diversity –a likely outcome of the fact that the Missouri Botanical Gardens routinely samples local vegetation and makes the data available. Estimating diversity from equalized sampling effort was developed by a number of ecologists (Howard Sanders, Stuart Hurlbert, Dan Simberloff, and Ken Heck) in the 1960s and 1970s resulting in modern rarefaction techniques.

Sampling effort was one problem, and ecologists also recognized that even with equivalent sampling effort, we are likely missing rare and cryptic species. Most notably Anne Chao and Ramon Margalef developed a series of diversity estimators in the 1980s-1990s. These types of estimators place emphasis on the numbers of rare species, because these give insight into the unobserved species. All things being equal, the community with more rare species likely has more unobserved species. These types of estimators are particularly important when we need to estimate the ‘true’ diversity form a limited number of samples. For example, researchers at Sun Yat-sen University in Guangzhou, China, recently performed metagenomic sampling of almost 2000 soil samples from a 500x1500 m forest plot. From these samples they used all known diversity estimators and have come to the conclusion that there are about 40,000 species of bacteria and 16,000 species of fungi in this forest plot! This level of diversity is truly astounding, and without genetic sampling and the suite of diversity estimators, we would have no way of knowing that there is this amazing, complex world beneath our feet.

As we move forward, researchers are measuring diversity in new ways, by quantifying phylogenetic and functional diversity and we will need new methods to estimate these for entire communities and habitats. Anne Chao, and colleagues have recently published a method to estimate true phylogenetic diversity (Chao et al., 2014).

6. Hierarchical and Bayesian modelling: Understanding complex living systems.

Each previous section reinforces the fact that ecology has embraced statistical methods that allow it to incorporate complexity. Accurately fitting models to observational data might require large numbers of parameters with different distributions and complicated interconnections. Hierarchical models offer a bridge between theoretical models and observational data: they can account for missing or biased data, latent (unmeasured) variables, and model uncertainty. In short, they are ideal for the probabilistic nature of ecological questions and predictions (Royle and Dorazio, 2008). The computational and conceptual tools have greatly advanced over the past decade, with a number of good computer programs (e.g., BUGS ) available and several useful texts (e.g., Bolker 2008).

The usage of these types of models has been closely (but not exclusively) tied to Bayesian approaches to statistics. Bayesian statistics have had much written about them, and not a little controversy beyond the scope of this post (but see these blogs for lots of interesting discussion). The focus is on assigning a probability distribution to a hypothesis (the prior distribution) which can be updated sequentially as more information is obtained. Such an approach may have natural similarities to management and applied practices in ecology, where expert or existing knowledge is already incorporated into decision making and predictions informally. Often though, hierarchical models can be tailored to better fit our hypotheses than traditional univariate statistics. For example, species occupancy or abundance can be modelled as probabilities based on detection error, environmental fit and dispersal likelihood.

There is so much that can be said about hierarchical and bayesian statistical models, and their incorporation into ecology is still in progress. The promise from these methods that the complexity inherent in ecological processes can be more closely captured by statistical models and that model predictions are improving, is one of the most important developments in recent years. 

7. The availability, community development and open sharing of statistical methods.

The availability of and access to statistical methods today is unparalleled in any time in human history. And it is because of the program R. There was a time recently where a researcher might have had to purchase a new piece of software to perform a specific analysis, or that they would have to wait years for new analyses to become available. The rise of this availability of statistical methods is threefold. First, R is freely available without any fees limiting access. Second, is that the community of users contribute to it, meaning that specific analyses required for different questions are available, and often formulated to handle the most common types of data. Finally, new methods appear in R as they are developed. Cutting edge techniques are immediately available, further fostering their use and scientific advancement.

References

Bolker, B. M. (2008). Ecological models and data in R. Princeton University Press.

Bray, J. R., & Curtis, J. T. (1957). An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecological Monographs, 27(4), 325–349. doi:10.2307/1942268

Chao, A., Chiu, C.-H., Hsieh, T. C., Davis, T., Nipperess, D. A., & Faith, D. P. (2014). Rarefaction and extrapolation of phylogenetic diversity. Methods in Ecology and Evolution, n/a–n/a. doi:10.1111/2041-210X.12247

Connor, E.F. & Simberloff, D. (1979) The assembly of species community: chance or competition? Ecology, 60, 1132-1140.

Diamond, J.M. (1975) Assembly of species communities. Ecology and evolution of communities (eds M.L. Cody & J.M. Diamond), pp. 324-444. Harvard University Press, Massachusetts.
Felsenstein, J. (1985). Confidence limits on phylogenies : An approach using the bootstrap. Evolution, 39, 783–791.

Fisher, R.A. (1925) Statistical methods for research workers. Oliver and Boyd, Edinburgh.
Fortin, M.-J. & Dale, M. (2005) Spatial Analysis: A guide for ecologists. Cambridge University Press, Cambridge.

Fortin, M.-J., Dale, M. & ver Hoef, J. (2002) Spatial analysis in ecology. Encyclopedia of Environmetrics (eds A.H. El-Shaawari & W.W. Piegorsch). John Wiley & Sons.
Gotelli, N.J. & Graves, G.R. (1996) Null models in ecology. Smithsonian Institution Press Washington, DC.

Guillot, G., & Rousset, F. (2013). Dismantling the Mantel tests. Methods in Ecology and Evolution, 4(4), 336–344. doi:10.1111/2041-210x.12018
Hill, M. O. (1979). DECORANA — A FORTRAN program for Detrended Correspondence Analysis and Reciprocal Averaging.

Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Bulletin de La Societe Vaudoise Des Sciences Naturelle, 37, 547–579.

Laughlin, D. C. (2014). Applying trait-based models to achieve functional targets for theory-driven ecological restoration. Ecology Letters, 17(7), 771–784. doi:10.1111/ele.12288

Legendre, P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology, 74.

Legendre, P., & Legendre, L. (1998). Numerical Ecology. Amsterdam: Elsevier Science B. V.

Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.

Neyman, J. & Pearson, E.S. (1933) On the problem of the most efficient tests of statistical hypotheses. PHilosophical Transactions of the Royal Society A, CCXXXL.

Oksanen, J., Kindt, R., Legendre, P., O’Hara, R., Simpson, G. L., Stevens, M. H. H., & Wagner, H. (2008). Vegan: Community Ecology Package. Retrieved from http://vegan.r-forge.r-project.org/

Peres-Neto, P. R., & Jackson, D. A. (2001). How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia, 129, 169–178.

Royle and Dorazio. (2008). Hierarchical Modeling and Inference in Ecology. 

Researching ecological research

Benjamin Haller. 2014. "Theoretical and Empirical Perspectives in Ecology and Evolution: A Survey". BioScience; doi:10.1093/biosci/biu131.

Etienne Low-Décarie, Corey Chivers, and Monica Granados. 2014. "Rising complexity and falling explanatory power in ecology". Front Ecol Environ 2014; doi:10.1890/130230.

A little navel gazing is good for ecology. Although maybe it seems like it, ecology spends far less time evaluating its approach, compared to simply doing research. Obviously we can't spend all of our time navel-gazing, but the field as a whole would benefit greatly from ongoing conversations about its strength and weaknesses. 

For example, the issue of theory vs. empirical research. Although this issue has received attention and arguments ad nauseum over the years (including here, 1, 2, 3), it never completely goes away. And even though there are arguments that it's not an issue anymore, that everyone recognizes the need for both, if you look closely, the tension continues to exist in subtle ways. If you have participated in a mixed reading group did the common complaint “do we have to read so many math-y papers?" ever arise; or equally “do we have to read so many system specific papers and just critique the methods?” Theory and empirical research don't see eye to eye as closely as we might want to believe.

The good news? Now there is some data. Ben Haller did a survey on this topic that just came out in BioScience. This paper does the probably necessary task of getting some real data beyond the philosophical and argumentative about the theory/data debate. Firstly, he defines empirical research as being involved in the gathering and analysis of real world data, while theoretical research does not gather or analyze real world data, instead involves mathematical models, numerical simulations, and other such work. The survey included 614 scientists from related ecology and evolutionary biology fields, representing a global (rather North American) perspective.

The conclusions are short, sweet and pretty interesting: "(1) Substantial mistrust and tension exists between theorists and empiricists, but despite this, (2) there is an almost universal desire among ecologists and evolutionary biologists for closer interactions between theoretical and empirical work; however, (3) institutions such as journals, funding agencies, and universities often hinder such increased interactions, which points to a need for institutional reforms."

 

For interpreting the plots – the empirical group represents respondents whose research is completely or primarily empirical; the theoretical group's research is mostly or completely related to theory, while the middle group does work that falls equally into both types. Maybe the results don't surprise anyone – scientists still read papers, collaborate, and coauthor papers mostly with others of the same group. What is surprising is that this trend is particularly strong for the empirical group. For example, nearly 80% of theorists have coauthored a paper with someone in the empirical group while only 42% of empiricists have coauthored at least one paper with a theorist. Before we start throwing things at empiricists, it should be noted that this could relate to a relative scarcity of theoretical ecologists, rather than insularity on the part of the empiricists. However, it is interesting that while the responses to the question “how should theory and empiricism coexist together?” across all groups agreed that “theoretical work and empirical work would coexist tightly, driving each other in a continuing feedback loop”, empirical scientists were significantly more likely to say “work would primarily be data-driven; theory would be developed in response to questions raised by empiri­cal findings.”

Most important, and maybe concerning, is that the survey found no real effect of age, stage or gender – i.e. existing attitudes are deeply ingrained and show no sign of changing.

Why is it so important that we reconcile the theoretical/empirical issue? The paper “Rising complexity and falling explanatory power in ecology” offers a pretty compelling reason in its title. Ecological research is getting harder, and we need to marshall all the resources available to us to continue to progress. 

The paper suggests that ecological research is experiencing falling mean R² values. Values in published papers have fallen from above 0.75 prior to 1950 to below 0.5 in today's papers.

The worrying thing is that as a discipline progresses and improves, you might predict that the result is an improving ability to explain ecological phenomenon. For comparison, criminology was found to show no decline in R² values as that matured through time. Why don’t we have that? 

During the same period, however, it is notable that the average complexity of ecological studies also increased – the number of reported p-values is 10x larger on average today compared to the early years (where usually only a single p-value relating to a single question was reported). 

The fall in R² values and the rise in reported p-values could mean a number of things, some worse for ecology than others. The authors suggest that R² values may be declining as a result of exhaustion of “easy” questions (“low hanging fruit”), increased effort in experiments, or a change in publication bias, for example. The low hanging fruit hypothesis may have some merit – after all, studies from before the 1950s were mostly population biology with a focus on a single species in a single place over a single time period. Questions have grown increasingly more complex, involving assemblages of species over a greater range of spatial and temporal scales. For complex sciences, this fits a common pattern of diminishing returns: “For example, large planets, large mammals, and more stable elements were discovered first”.

In some ways, ecologists lack a clear definition of success. No one would argue that ecology is less effective now than it was in the 1920s, for example, and yet a simplistic measure (R²) of success might suggest that ecology is in decline. Any biases between theorists and empiricists is obviously misplaced, in that any definition of success for ecology will require both.  

Ecological progress, what are we doing right?

A post from Charles Krebs' blog called "Ten limitations on progress in ecology" popped up a number of times on social media last week. Krebs is a established population ecologist who has been working in the field for a long time, and he suggests some important problems leading to a lack of progress in ecology. These concerns range from lack of jobs and funding for ecologists, to the fracturing of ecology into poorly integrated subfields. Krebs' post is a continuation of the ongoing conversation about limitations and problems in ecology, which has been up for discussion for decades. And as such, I agree with many of the points being made. But it reminded me of something I have been thinking about for a while, which is that it seems much more rare to see ecology’s successes listed. For many ecologists, it is probably easier to come up with the problems and weaknesses, but I think that's more of a cognitive bias than a sign that ecology is inescapably flawed. And that’s unfortunate: recognizing our successes and advances also helps us improve ecology. So what is there to praise about ecology, and what successes we can build on?

Despite Krebs’ concerns about lack of jobs for ecologists, it is worth celebrating how much ecology has grown in numbers and recognition as a discipline. The first ESA annual meeting in 1914 had 307 attendees, recent years’ attendance is somewhere between 3000-4000 ecologists. Ecology is also increasingly diverse. Ecology and Evolutionary Biology departments are now common in big universities, and sometimes replacing Botany and/or Zoology programs. On a more general level, the idea of “ecology” has increasing recognition by the public. Popular press coverage of issues such as biological invasions, honeybee colony collapses, wolves in Yellowstone, and climate change, have at least made the work of ecologists slightly more apparent.

Long-term ecological research is probably more common and more feasible now than it has ever been. There are long-term fragmentation, biodiversity and ecosystem function studies, grants directed at LTER, and a dedicated institute (the National Ecological Observatory Network (NEON)) funded by the NSF for longterm ecological data collection. (Of course, not all long term research sites have had an easy go of things – see the Experimental Lakes Area in Canada).

Another really positive development is that academic publishing is becoming more inclusive – not only are there more reputable open access publishing options for ecologists, the culture is changing to one where data is available online for broad access, rather than privately controlled. Top journals are reinforcing this trend by requiring that data be published in conjunction with publications.

Multi-disciplinary collaboration is more common than ever, both because ecology naturally overlaps with geochemistry, mathematics, physics, physiology, and others, and also because funding agencies are rewarding promising collaborations. For example, I recently saw a talk where dispersal was considered in the context of wind patterns based on meteorological models. It felt like this sort of mechanistic approach provided a much fuller understanding of dispersal than the usual kernel-based model.

Further, though subdisciplines of ecology have at times lost connection with the core knowledge of ecology, some subfields have taken paths that are worth emulating, integrating multiple areas of knowledge, while still making novel contributions to ecology in general. For example, disease ecology is multidisciplinary, integrating ecology, fieldwork, epidemiological models and medicine with reasonable success.

Finally, more than ever, the complexity of ecology is being equalled by available methods. More than ever, the math, the models, the technology, and the computing resources available are sufficient. If you look at papers from ecology’s earliest years, statistics and models were restricted to simple regressions or ANOVAs and differential equations that could be solved by hand. Though there is uncertainty associated with even the most complex model, our ability to model ecological processes is higher than ever. Technology allows us to observe changes in alleles, to reconstruct phylogenetic trees, and to count species too small to even see. If used carefully and with understanding, we have the tools to make and continue making huge advances.

Maybe there are other (better) positive advances that I’ve overlooked, but it seems that – despite claims to the contrary – there are many reasons to think that ecology is a growing, thriving discipline. Not perfect, but successfully growing with the technological, political, and environmental realities.

Ecology may be successfully growing, but it's true that the timing is rough...

The species we’ve neglected

Species in last 3 months' papers in Ecology Letters.
"Multiple species" tended to be meta-analyses.

Browse the abstracts of a high profile ecological journal (for example, Ecology Letters, right) and one pattern you’ll notice is that high impact, hypothesis-driven ecology usually involves a small pool of focal species. Plants, for example, dominate any discussion of community ecology and have since Clements’ and Gleason’s arguments. It is not that hard to see why – plants don’t move, for one, live in speciose groups, and often complete a full lifecycle in a matter of months. They are also the lowest trophic level and so pesky multiple trophic level interactions can be omitted.

Other groups of species also show up frequently. Insects are popular for some studies of herbivory (again, it is easy to estimate damage to species that can’t move), mutualisms, and predation. Butterflies and birds, being pretty and easy to count, make a nice model for species populations and climate change studies. And while it is easy to sound critical of this kind of system-based myopia, it exists for perfectly good reasons. Immobile plants, after all, are a major source of experimental knowledge upon which much of modern ecology relies. They are easy to work with and manipulate, and their responses are relatively easy to measure (phenology, fitness, biomass, herbivory). Further, once an experimental system is established, using that system becomes increasingly attractive. You have a growing literature to base decisions on, to put your results into context, and against which to prove the novelty or importance of your work. In contrast, if you do your work on the rare bunny-toed sloth-monkey, the novelty of the system may overwhelm the generality of the work. And so the short-term limitation is that established systems allow immediate in-depth studies, while novel systems, though necessary to broaden ecological knowledge, may (initially) relatively be shallow in their returns.

Establishing a new system may be a time-consuming activity with the possibility of failure. But these under-utilized species have something new to tell ecology. This is not to say that the popular systems of species have nothing to tell us anymore – not at all, given all the complexities of ecological dynamics – but they bias the story. The ecological processes at play are not likely much different between novel systems and traditional ones. But the same processes interact in different ways and differ in importance across systems, and so we may have unrealistic expectations about the importance of, say, competition, if we only focus on 1 or 2 systems. To follow Vellend’s (2011) framework, the processes of selection, drift, speciation, and dispersal are part of any ecological system. What differs is their importance, and their importance differs for reasons related to the ecological context and evolutionary history a species experiences. This is the reason that comparing Mark McPeek’s work on neutrality in damselflies with Jonathan Losos’ findings about adaptive radiation in anoles is so interesting. No one questions that adaptive radiations may drive one set of species and neutrality another, the real question is what about their contexts produces to this result. Unfortunately, if our current set of focal species is small, we are limited in our ability to make such informative comparisons.

Many of the limitations on species have been methodological: popular systems tend to involve amenable species. Other species may be very small, very mobile, very difficult to identify, or highly specialized in their habitats. This creates difficulties. But when we overcome them, the results are often revolutionary. For example, consider the current burst of interest in belowground interactions, once their incredible importance to plant community interactions became clear (e.g. Klironomos 2002, Nature). Further, techniques are continually improving in ways which make new systems tenable.

So we should continue to focus on a few well-understood systems, attempting to perfect our understanding and predictive abilities. There is much value in understanding a system as completely as possible. But on the other hand, we can limit ourselves by focusing too much. It seems like one of the big areas for growth in modern ecology is simply to expand into novel ecological systems.

(**It's probably too general and a bit unfair to refer to all plants and all insects as though they are monolithic groups, since they are each large and varied (which is part of the reason they've been useful thus far). And some of their great representation may in fact relate to the number of species available to study. But I do think the general point about the problem of focusing too much holds.**)

#esa2013 What ESA tells us about where ecology is going

The annual ESA meeting functions in a lot of different ways. There are the obvious: the sharing of ideas and work, the discovery of new ideas, methods or sources of inspiration, networking and job finding, social reunions. But it also functions as a kind of report on the state of the field (and that's not even considering sessions meant to explicitly do this, like the panel “Conversations on the Future of Ecology”). The topics and methods presented say a lot about what ideas and methods are timeless, what is trendy, and over many meetings, where ecology appears to be going. If you go to enough ESAs, you are participating in a longitudinal study of ecology (or at least your subfield).

I went to my first ESA five years ago in Albuquerque, NM. One of the things that struck me was that there were two Community Assembly and Neutral Theory sessions and many talks in those focused on tests of neutral theory, particularly looking at species abundance distributions (SADs) and various iterations of neutral models. There are usually still one to two sessions called Community Assembly and Neutral Theory, but five years later, I don't think I saw a single talk that looked at SADs for evidence of neutral theory (and only one or two talks that were named to explicitly include neutral theory). Instead, the concept first introduced by Hubbell has morphed from "neutral theory" in to something slightly more general, designated "neutral dynamics". This gets used in a lot of ways – most precisely, neutral dynamics are in the spirit of neutral theory, suggesting that population demographic rates are similar, allowing long-term co-occurrence. Sometimes this is cited with reference to equalizing fitness effects in a Chessonian framework, where similarity in fitnesses prevents exclusion despite overlap in species niches. But it also seemed to get used in a default sort of way, as the explanation for why niche differences between species weren't discovered by a study, or else "neutral" was used interchangeably with "stochastic". In any case, the pattern appeared to be a move from highly specialized and precisely defined usage of the term, to broader incorporation of the concept that had suddenly acquired several, often less precisely defined meanings. Instead of being the central focus of a few specialized talks, neutrality was commonly invoked as a minor theme or explanation in many more talks. It is not what I expected, but its continuing usage suggests that neutrality has developed a life of its own.

Other topics similarly seem to have taken on separate lives from their initial application; even over the short time I've been attending ESA. For example, sessions focused on simple applications of ecophylogenetics methods (overdispersion, clustering, using different systems) were relatively common 3-4 years ago, while there wasn't a single contributed session specifically named for phylogenetics this year. There was however many sessions in which phylogenetic work formed the backbone of talks that were about broader questions, including in the "Evolution, Biodiversity, and Ecosystem Function" session and the “Coexistence of Closest Relatives: Synthesis of Ecological and Evolutionary Perspectives”. In the best case scenarios, it seems like even over-hyped approaches may be used with more nuance in time, as people recognize what information these methods can and cannot provide.

Sometimes it did seem that there is a lag between when critiques of certain methods or ideas are expressed and when they actually get incorporated into research. I could be wrong, but it seems this is most common where the research is focused on particular study systems or species, and methodology may be driven more by precedent in the literature and criticisms may take longer to infiltrate (since they aren’t the main focus of the work anyways). And unfortunately, the topics and sessions which appear to be timeless are those on human-related applications (restoration, climate change, invasion). Those pressures are sadly unchanging.

*The great thing to do would be map out changes in keyword frequency over the ESAs that have archived programs. Unfortunately, I don’t have the time/motivation.

Reinventing the ecological wheel – why do we do it?

Are those who do not learn from (ecological) history are doomed to repeat it?

A pervasive view within ecology is that discovery tends to be inefficient and that ideas reappear as vogue pursuits again and again. For example, the ecological implications of niche partitioning re-emerges as an important topic in ecology every decade or so. Niche partitioning was well represented in ecological literature of the 1960s and 1970s, which focused theoretical and experimental attention on how communities were structured through resource partitioning. It would be fair to say that the evolutionary causes and the ecological consequences of communities structured by niche differences were one of the most important concepts in community ecology during that time. Fast-forward 30 years, and biodiversity and ecosystem functioning (BEF) research slowly  has come to the conclusion that niche partitioning to explains the apparent relationship between species diversity and ecosystem functioning. Some of the findings in the BEF literature could be criticized as simply being rediscoveries of classical theory and experimental evidence already in existence. How does one interpret these cycles? Are they a failure of ecological progress or evidence of the constancy of ecological mechanisms?

Ecology is such a young science that this process of rediscovery seems particularly surprising. Most of the fundamental theory in ecology arose during this early period: from the 1920s (Lotka, Volterra), 1930s (Gause) to 1960s (Wilson, MacArthur, May, Lawton, etc). There are several reasons why this was the foundational period for ecological theory – the science was undeveloped, so there was a void that needed filling. Ecologists in those years were often been trained in other disciplines that emphasized mathematical and scientific rigor, so the theory that developed was in the best scientific tradition, with analytically resolved equations meant to describe the behaviour of populations and communities. Most of the paradigms we operate in today owe much to this period, including an inordinate focus on predator-prey, competitive interactions, and plant communities, and the use of Lotka-Volterra and consumer-resource models. So when ecologists reinvent the wheel, is this foundation of knowledge to blame, is it flawed or incomplete? Or does ecology fail in education and practice in maintaining contact with the knowledge base that already exists? (Spoiler alert – the answer is going to be both).

Modern ecologists face the unenviable task of prioritizing and decoding an exponentially growing body of literature. Ecologists in the 1960s could realistically read all the literature pertaining to community ecology during their PhD studies –something that is impossible today with an exponentially growing literature. Classic papers can be harder to access than new ones: old papers are less likely to be accessible online, and when they are, the quality of the documents is often poor. The style and accessibility of some of these papers is also difficult for readers used to the succinct and direct writing more common today. The cumulative effect of all of this is that we read very little older literature and instead find papers that are cited by our peers.

True, some fields may have grown or started apart from a base of theory that would have been useful during their development. But it would also be unfair to ignore the fact that ecology’s foundation is full of cracks. Certain interactions are much better explored than others. Models of two species interactions fill in for complex ecosystems. Lotka-Volterra and related consumer-resource models make a number of potentially unrealistic assumptions, and parameter space has often been incompletely explored. We seem to lack a hierarchical framework or synthesis of what we do know (although a few people have tried (Vellend 2010)). When models are explored in-depth, as Peter Abrams has done in many papers, we discover the complexity and possible futility of ecological research: anything can result from complex dynamics. The cynic then, would argue that models can predict anything (or worse, nothing). This is unfair, since most modelling papers test hypotheses by manipulating a single parameter associated with a likely mechanism, but it hints at the limits that current theory exhibits.

So the bleakest view of would be this: the body of knowledge that makes up ecology is inadequate and poorly structured. There is little in the way of synthesis, and though we know many, many mechanisms that can occur, we have less understanding of those that are likely to occur. Developing areas of ecology often have a tenuous connection to the existing body of knowledge, and if they eventually connect with and contribute to the central body, it is through an inefficient, repetitive process. For example a number of papers have remarked that invasion biology has dissociated itself from mainstream ecology, reinventing basic mechanisms. The most optimistic view, is that when we discover similar mechanisms multiple times, we gain increasing evidence for their importance. Further, each cycle of rediscovery reinforces that there are a finite number of mechanisms that structure ecological communities (maybe just a handful). When we use the same sets of mechanisms to explain new patterns or processes, in some ways it is a relief to realize that new findings fit logically with existing knowledge. For example niche partitioning has long been used to explain co-occurrence, but with a new focus on ecosystem functioning, it has leant itself as an efficacious explanation. But the question remains, how much of what we do is inefficient and repetitive, and how much is advancing our basic understanding of the world?

By Caroline Tucker & Marc Cadotte

Rumors of community ecology’s death were greatly exaggerated: reflections on Lawton 1999

In 1999, John Lawton, eminent British ecologist, published a lament for the state of community ecology entitled “Are there general laws in ecology?” Cited more than 600 times, Lawton’s paper forced a re-evaluation of community ecology’s value, success, and even future existence. Other scientists at the time seemed to agree, with papers starting with phrases like “Although community ecology is a struggling science…” and “Given the lack of general laws in ecology…”. Lawton appeared to be suggesting that community ecology be abandoned for the generality of macroecology or the structure of population ecology.

An important point to be made is that Lawton was simply making a particularly public expression of ecology’s growing pains. In 1999, ecology was at a crossroads between the traditional approach of in-depth system-based study, and a fairly single-minded focus on competition as an explanation for patterns (e.g., Cooper 1993 ‘The Competition Controversy in Community Ecology’ Biology and Philosophy 8: 359-384), while at the same time there were emergent approaches and explanations like neutrality, macroecology, spatial ecology, ecophylogenetics, and improved computer and molecular methods. There was also growing dissent about ecology’s philosophical approach to ecology (e.g., Peters 1991 ‘A Critique for Ecology’; Haila and Heininen 1995 ‘Ecology: A New Discipline for Disciplining’ Social Text 42: 153-171): ecologists tended to ignore the Popperian approach, which required falsification of existing hypothesis, instead tending to look for support for an existing hypothesis, or at least advocated looking for patterns without considering alternative mechanisms. Not only this, but the applications for ecology were more clear than ever – the Intergovernmental Panel for Climate Change was meeting , and the ecological consequences of human actions were perhaps more obvious they had ever been. But ecologists were failing at providing solutions –Lawton argued-correctly-that in 1999 ecologists could provide little insight into how a community might change in structure and function in response to changing climate.

Although everyone should read Lawton’s paper, a simple synthesis of his concerns would be this – that community ecology is too contingent, communities are too complex, and therefore community ecology cannot formulate any laws, cannot make predictions, cannot be generalized from one system to another. This makes community ecology suspect as a science (physics being the most common example of an “ideal” science), and certainly not very useful. Lawton suggests that population ecology, where only a few models of growth could explain the majority of species’ dynamics, or macroecology, which focuses on the most general, large-scale patterns, were a better example of how ecology should be practiced.

Community ecology, rather than dying, has experienced an incredible surge in popularity, with a large contingent represented at meetings and in journal publications. Ecology itself is also thriving, as one of the fastest growing departments in universities. So what, if anything, has changed? Has ecology addressed Lawton’s criticisms?

Two major things happened in the late 1990’s and early 2000’s, which helped ecologists see beyond this general malaise. The first was that a number of well-thought out alternative ecological mechanisms explaining community membership were published. Before the late 90’s community ecologists looked for evidence of competition in patterns of community composition, either among locales or through time following disturbance. When local competition was insufficient to explain patterns, researchers likely cited, but did not test other mechanisms. Or if they did test other mechanisms, say predation, it was as an alternative, mutually exclusive mechanism. The new publications, drawing on previous ideas and concepts formalized assembly mechanisms like neutral processes or metacommunity dynamics where uneven fitnesses in a heterogeneous landscape can affect local coexistence. More than these as solely alternative mechanisms, these allowed for a synthesis where multiple mechanisms operate simultaneously to affect coexistence. Probably the most emblematic paper of this renewed excitement is Peter Chesson’s 2000 ‘Mechanisms of maintenance of species diversity’ published in Annual Reviews of Ecology and Systematics. This paper, cited over a thousand times, offers a way forward with a framework that includes competitive and niche differences but can also account for neutral dynamics.

A second major development that rejuvenated ecology was the formation of technological and statistical tools engendering broad-scale synthetic research. Suddenly the search for general explanations – Lawton’s most piercing criticism - became more common and more successful. With the advent of on-line databases, meta-analytic procedures and centers (e.g., the National Center for Ecological Analysis and Synthesis) that foster synthetic research, ecologists routinely test hypotheses that transcend local idiosyncrasies. Often, the capstone publication on a particular hypothesis is no longer a seminal experiment, but rather a meta-analysis that is combines all the available information to assess how strongly and how often a particular mechanism affects patterns.

While these theoretical and technological developments have been essential ingredients in this ecological rejuvenation, there has also been a subtle shift the philosophical approach to what it is ecological theory can and should do. Criticism in the 1990’s (e.g., Peters 1991 ‘A Critique for Ecology’) centered on the inability of ecological theory to make accurate predictions. The concept of science common in ecology in the 1990’s was that a rigorous, precise science (i.e., with laws) results in the ability to accurately predict species composition and species abundances given a set of mechanisms. This view of ecological science has been criticized as simplistic ‘physics-envy’ (e.g., see Massimo Pigliucci’s PhD dissertation ‘Dangerous habits: examining the philosophical baggage of biological research’published by the University of Tennessee in 2003). The subtle philosophical change has been a move from law=prediction to law=understanding. This is as true for physics as it is for ecology. We don’t expect a physicist to predict precisely where a falling feather will land, but we do expect to totally understand why it landed where it did based on fundamental processes. (for more on the contrast of prediction and understanding, see Wilhelm Windelband’s nomothetic and idiographic knowledge)


While the feather example above is simplistic, it is telling. In reality a physicist can produce probability contours of where the feather is likely to land, which could be very focused on a calm day or broad on a windy one. This is exactly what ecologists do. Once they understand how differing mechanisms come together to shape diversity, they make probabilistic predictions about the outcome of a set of known mechanisms.

Ecology today is as vibrant as ever. This is not a result of finding new laws that proved Lawton incorrect. Rather, ecologists now have a more sophisticated understanding of how various mechanisms operate in concert to shape diversity. Moreover, conceptual, technological and philosophical revolutions have fundamentally changed what ecologists do and what they are trying to explain. It is a great time to be an ecologist.

Lawton, J. H. (1999). Are there general laws in ecology? Oikos, 84(2), 177-192.


By Marc Cadotte and Caroline Tucker

The empirical divide

Has there been a shift in how ecology is done? In an interesting editorial in the most recent ESA Bulletin, titled “Losing the Culture of Ecology”, David Lindenmayer and Gene Likens wrote that “empirical and place-based research”, such as field studies and taxonomy, appear to be falling out of favor. They suggest that ecological modeling, meta-analysis, and data-mining (the three M’s) are more lucrative (and popular) approaches today, because these methods are faster, cheaper, and “easier” to perform, allowing more rapid publication. While they recognize the important advancements resulting from these methods, the result—they suggest—is that field-based empirical research is becoming less prevalent, to the detriment of ecology.

This is a polarizing issue, and the response of those ecologists we spoke to depended on where they position themselves on the field/theoretical divide. Those who define themselves as field ecologists tended to feel embattled in the face of long, expensive months of fieldwork, with slow returns in terms of data and publications. Some felt there is a subtle insinuation that fieldwork is less generalizable and so less valuable than techniques such as meta-analysis and ecological modeling, which by their nature tend to be theory-based and general.

On the other side, some theoretical ecologists we spoke to felt the need to defend the validity of doing “indoor” ecology, noting that theory and modeling can link pattern and process, without the confounding variation common in field experiments/observations. Although field ecologists felt that they have a more difficult time obtaining funding, theoretical ecologists noted that they often receive far less money because the assumption is that theory is “free”. Further, with the exception of very specialized funding opportunities (e.g., NCEAS), meta-analyses do not typically get funded as stand-alone projects.

It’s important to note that in its short history, ecology has frequently struggled with the balance between the field and lab. The primary criticism of field-based research at the turn of the 20th century was that it was “unscientific”, inseparable from natural history, producing lists of species names rather than furthering understanding, while labwork was considered to be too divorced from natural systems to be informative (producing so-called “armchair ecologists”). These conflicts split some of the first organismal departments in the United States (*) and tensions exist to this day. No doubt these criticisms are not unfamiliar to many modern ecologists.

There needs to be a balance between the production and consumption of data. Obviously abandoning fieldwork and using only meta-analysis, modeling, and data-mining is not sustainable, but these are important methods for modern ecology. In addition, the perceptions of bias against fieldwork may be due to a general decline in funding and greater overall competitiveness for the rewards of academic labour (jobs, grants, publishing in top journals, etc.), rather than a true decline in field ecology. As we discussed this article, it became clear that our own perceptions, and perhaps those of the broader community, have formed in the absence of empirical data. We examined the last few issues of some highly-ranked ecological journals that publish primary research (Ecology Letters, Molecular Ecology, American Naturalist), and recorded the number of papers that used empirical data, and further the number of those that collected their own data (versus using data from databases, literature, etc). Surprisingly, the vast majority of studies were based on empirical data, mostly data collected by the authors. In Molecular Ecology, 27 out of 28 papers were empirical, and 26 of these used data collected by the author(s); in Ecology Letters, 17 out of 20 papers were empirical, and 12 of these used data collected by the author(s). Even in American Naturalist, which is known for its theoretical bent, 44 out of 70 papers were empirical, and 32 used the author(s)’ own data. Overall, these journals, where competition for space is most severe, primarily publish empirical research.

It appears then, that neither grants nor publications systemically bias towards the three M’s. But is there still a cost to researchers on either side of the data producer-consumer divide? The answer is likely yes. The three M’s result in quicker publications, which means these researchers look more productive on paper, resulting in greater visibility. With more publications, they are likely to make it to the top of hiring committee lists. Conversely, unless a specific job has been advertised as a modeling position, candidates giving job talks focusing on the three M’s do not come across as knowledgeably as a very skilled field person. One of us (MWC) has seen job searches at four different institutions, and the unadvertised stipulation for many departmental faculty or committee members is that the candidate will come and establish a field program. Another common criticism of 3-M candidates is that they will not be able to secure large amounts of research funding.

Given this double-edged sword, what is the optimal strategy? The glib, easy answer is that ecologists need to become less specialized, to do both theory and empirical work, if they want a successful career. And maybe this is the solution, at least for some ecologists. But is having everyone become a generalist really the answer? Most field ecologists will tell you that they do fieldwork in part because they love being in the field and they’re good at it; most theoretical ecologists are adept at manipulating ideas and theory. Perhaps there is still a role for the specialist: after all quantitative ecology—which produces data—and theoretical ecology—which consumes it—are inseparable. They have a complementary relationship, in which field observations and data fuel new models and ideas, which in turn provides new hypotheses to be tested in the field. It’s obvious that people should be able to specialize, and that the focus should be on increasing collaboration between the two groups.

Despite the hand-wrenching, perhaps this collaboration is already happening. Many of the very best 3-M papers unite theoretically-minded with empirically-grounded ecologists. The working-group style funding by NCEAS (and its emulates) explicitly links together data producers and data consumers. These papers may be deserving of greater visibility. If collaboration is the future of ecology, why does the tension still exist between lab and field? The historical tension was not really about the laboratory vs. the field, but rather about scientific philosophy, and we think this holds true today. Ecology has tangibly moved towards hypothesis-driven research, at the expense of inductive science, which was more common in the past. The tensions between “indoor ecology” and field ecology have been conflated with changes in the philosophy of modern ecology, in the difficulties of obtaining funding and publishing as a modern ecologist, and some degree of thinking the “grass is always greener” in the other field. In fact, the empirical divide may not be as wide as is often suggested.

By Caroline Tucker and Marc Cadotte


* Robert E. Kohler. Landscapes and labscapes: Exploring the lab-field border in biology. 2002. University of Chicago Press. (This is a fascinating book about the early years of ecology, and definitely worth a read).

The regional community, maximum entropy, and other ideas in ecology

Looking through my feed of community ecology papers this month, I couldn’t help but notice that while most tested well-established concepts–density-dependence, niche partitioning, metacommunities, competition, dispersal limitation–there was also–as I suppose is usually true–a subset of papers championing newer, less established ideas.

For example, the article “Applying a regional community concept to forest birds of eastern North America” by Robert Ricklefs, furthers the regional community concept he introduced in 2008. Ricklefs is uncomfortable with how ecologists typically define local communities – i.e as spatially and ecologically discreet entities – and the predominant focus in community ecology on local coexistence. He argues that communities make sense as entities only at a larger scale, taking into account that local communities are not isolated, but instead interact as a function of overlapping ranges and species dispersal. In this paper he applies this concept to Breeding Bird Survey data to examine the distribution and abundance of birds in eastern NA.

Partel, Szava-Kovats, and Zobel are also critical of the predominant focus on local diversity. In their paper “Dark diversity: shedding light on absent species”, they pitch the idea of “dark diversity” as a valid diversity metric. Dark diversity accounts for the number of species which belong to the species pool for a particular habitat in a region but are not actually present in a local community of that habitat type. The resulting value can be used to calculate a dimensionless ratio of local to dark diversity, suitable for comparison of diversity components in dissimilar regions.

Lastly, in “A strong test of a maximum entropy model of trait-based community assembly”, Shipley et al. further test Shipley’s model of Entropy Maximization, using it to predict the composition of communities in the South African fynbos. The model predicts community composition (species identity and relative abundances) through an assumption of random assembly (or entropy maximization) within environmental constraints on species traits.

New ideas are a constant in ecology, but they face stiff competition in an already crowded field. The possible mechanisms of local coexistence, for example, are already a long list. What determines which of these–or any–ideas become entrenched in ecology? The likelihood of a concept becoming established must be a complex function relying on a cost-benefit analysis–what does applying this idea cost compared to the gain in understanding it produces?–further adjusted by intangible variables like timing and the skill and prestige of an idea’s advocate. After all, some ideas require decades to establish properly, requiring changes in the theoretical climate or technical capabilities, for example, neutral theory or spatial ecology. Others seem to catch on immediately. Philosophers have written more cogently on how scientific ideas change and paradigms shift, but as participants in the process, we have a rather unique perspective. After all, as scientists we play an active role in driving these shifts in thought and action. You might argue that the merit of the ecological ideas that become established are as much a reflection on those who accept and institute them, as on those who propose them.