Martorell, C. & R.P. Freckleton. 2014. Testing the roles of competition, facilitation and stochasticity on community structure in a species-rich assemblage. Journal of Ecology doi:10.1111/1365-2745.12173
At a given location in nature, why are some species present and others absent? Why do some species thrive and have lots of individuals and others are barely eeking out an existence? What determines how many species can live together there? These questions have fascinated (some might say obsessed) community ecologists for an almost embarrassing number of decades. They have proven difficult questions to answer and everyone has their favorite process they like to use to answer those questions. Competition for limiting resources is perennially a favorite process used to explain who gets into a community and who does well once they’re in it. But there are also a number of other processes that clearly play important roles. Theory and data are showing that the movement of species from location to location can alter what species exist where and how many individuals they have at a site. The role of facilitation (positive interactions among species) has increasingly been getting play as well, especially in stressful environments. There can also be a random component to the order that species arrive at a particular location. Because it can be difficult for very similar species to coexist, who is already at a location can influence who can then get into that location (this is sometimes referred to as historical or priority effects). I’m sure I missed some processes and I’m equally sure that someone out there right now is upset I didn’t include theirs. Others might (and by might I mean probably will) disagree with what I’m about to say, but most of the time it seems to me that we spend most of our time arguing about which process is most important. It’s competition! No it’s dispersal limitation! Niches! No niches! I have come to find this binary approach to studying communities wearisome. And here’s why. Does competition influence who exists at a particular location? Yes. Does dispersal? Yes. Does facilitation? Yes. Do stochastic processes? Yes. Do priority effects? Yes. We are at a point in ecology where I think we can feel confident that these various processes both exist and that they affect what we see in nature. Instead, we need to figure out how these processes work together to create the communities we observe. Does the role of a process stay constant through time? Or does it change depending on whether a community has been recently disturbed or is more established? Can we weave together these processes to predict how a community will look through time?
Right about now, you’re wondering if I will ever actually mention the Martorell & Freckleton paper. Here you go. Martorell & Freckleton (2014) take data from a long-term study of plants in Mexico and analyze all the pair-wise interactions among species in order to “document the intensity and demographic importance of interactions and stochasticity in terms of per capita effects, and to set them in a community context”. In effect, they used population models and the spatio-temporal data on plants to assess for each species observed how its presence and population growth/abundance was impacted by interactions with other species, interactions with individuals of the same species, variability in the environment, dispersal, and population stochasticity. If you want to know how they did this, you’ll need to read the paper. They found that both competition and facilitation between species played an important role in determining whether a new species could colonize a particular site. Once established, competition and facilitation played less important roles in explaining the abundance of species. Most of the variation in abundance between species can be explained by interactions with other members of the same species and by stochastic events influencing dynamics at a location.*
So why do I like this paper? Because it’s a step towards that integration of processes that I think we need to start doing. Their end message isn’t: process x affects ‘thing I’m interested in’ y. Their end message is about how these processes are working together and when they play a more (or less) important role for determining what species are present and how well they are doing at a site. Their results suggest a model of communities where interactions among species influences who establishes at a particular location (i.e. the species composition in community ecology lingo). However, stochastic events and interactions among members of the same species become important for understanding differences among species in abundances and population growth rates. Only time will tell if this particular integration of processes holds across different types of ecosystems. But right now it allows us to start talking about more sophisticated models of how species come together to create the diversity of species and abundances in a community.
And what does this paper say about predicting the species in a community and their abundances? My interpretation is that it says what I think a growing number of us have suspected for a while. For a specific location there is not a single expected configuration of a community. There are many possible configurations. This means that precisely predicting the species composition of a community will be difficult. But it also makes me wonder whether it might be possible to predict the space of possibilities and how probable those possibilities are. Given this disturbance rate and this pool of possible species, there’s a 60% chance of this configuration of species, but only a 10% chance for this one. I suspect many of my colleagues think that even this level of prediction or forecasting is pure science fiction thinking on my part. But like some of my other blogging colleagues (hi, Brian! hi, Peter!) I believe that pushing our field from one focused on ‘understanding’ to one focused on ‘forecasting’ or ‘predicting’ is one of the greatest challenges our science faces**. Figuring out how and when different processes operate and what aspects of community structure they are controlling is the first step towards forecasting. And that is exactly why I like this article.
* Disclaimer: I’ve distilled the paper down to the core message of what I found interesting and why. To understand what Martorell & Freckleton did, all of their results, and what they thought made their results interesting, you should really read the paper.
**Acknowledgments: Sadly, I can’t also link to the long and awesome conversations that Ethan, Allen Hurlbert and I have been having on this topic while on sabbatical. Trust me, they’ve been revolutionary experiences that you wish you were there for.
This is a guest post by Dan McGlinn, a weecology postdoc (@DanMcGlinn on Twitter). It is a Research Summary of: McGlinn, D.J., X. Xiao, and E.P. White. 2013. An empirical evaluation of four variants of a universal species–area relationship. PeerJ 1:e212 http://dx.doi.org/10.7717/peerj.212. These posts are intended to help communicate our research to folks who might not have the time, energy, expertise, or inclination to read the full paper, but who are interested in a <1000 general language summary.
It is well established in ecology that if the area of a sample is increased you will in general see an increase in the number species observed. There are a lot of different reasons why larger areas harbor more species: larger areas contain more individuals, habitats, and environmental variation, and they are likely to cross more barriers to dispersal – all things that promote more species to be able to exist together in an area. We typically observe relatively smooth and simple looking increases in species number with area. This observation has mystified ecologists: How can a pattern that should be influenced by many different and biologically idiosyncratic processes appear so similar across scales, taxonomic groups, and ecological systems?
Recently a theory was proposed (Harte et al. 2008, Harte et al. 2009) which suggests that detailed knowledge of the complex processes that influence the increase in species number may not be necessary to accurately predict the pattern. The theory proposes that ecological systems tend to simply be in their most likely configuration. Specifically, the theory suggests that if we have information on the total number of species and individuals in an area then we can predict the number of species in smaller portions of that area.
Published work on this new theory suggests that it has potential for accurately predicting how species number changes with area; however, it has not been appreciated that there are actually four different ways that the theory can be operationalized to make a prediction. We were interested to learn
- Can the theory accurately predict how species number changes with area across many different ecological systems, and
- Do the different versions of the theory consistently perform better than others
To answer these questions we needed data. We searched online and made requests to our colleagues for datasets that documented the spatial configuration of ecological communities. We were able to pull together a collection of 16 plant community datasets. The communities spanned a wide range of systems including hyper-diverse, old-growth tropical forests, a disturbance prone tropical forest, temperate oak-hickory and pine forests, a Mediterranean mixed-evergreen forest, a low diversity oak woodland, and a serpentine grassland.
Fig 1. A) Results from one of the datasets, the open circles display the observed data and the lines are the four different versions of the theory we examined. B) A comparison of the observed and predicted number of species across all areas and communities we examined for one of the versions of the theory.
Across the different communities we found that the theory was generally quite accurate at predicting the number of species (Fig 1 above), and that one of the versions of the theory was typically better than the others in terms of the accuracy of its predictions and the quantity of information it required to make predictions. There were a couple of noteworthy exceptions in our results. The low diversity oak woodland and the serpentine grassland both displayed unusual patterns of change in richness. The species in the serpentine grassland were more spatially clustered than was typically observed in the other communities and thus better described by the versions of the theory that predicted stronger clustering. Abundance in the oak woodland was primarily distributed across two species whereas the other 5 species where only observed once or twice. This unusual pattern of abundance resulted in a rather unique S-shaped relationship between the number of species and area and required inputting the observed species abundances to accurately model the pattern.
The two key findings from our study were
- The theory provides a practical tool for accurately predicting the number of species in sub-samples of a given site using only information on the total number of species and individuals in that entire area.
- The different versions of the theory do make different predictions and one appears to be superior
Of course there are still a lot of interesting questions to address. One question we are interested in is whether or not we can predict the inputs of the theory (total number of species and individuals for a community) using a statistical model and then plug those predictions into the theory to generate accurate fine-scaled predictions. This kind of application would be important for conservation applications because it would allow scientists to estimate the spatial pattern of rarity and diversity in the community without having to sample it directly. We are also interested in future development of the theory that provides predictions for the number of species at areas that are larger (rather than smaller) than the reference point which may have greater applicability to conservation work.
The accuracy of the theory also has the potential to help us understand the role of specific biological processes in shaping the relationship between species number and area. Because the theory didn’t include any explicit biological processes, our findings suggest that specific processes may only influence the observed relationship indirectly through the total number of species and individuals. Our results do not suggest that biological processes are not shaping the relationship but only that their influence may be rather indirect. This may be welcome news to practitioners who rely on the relationship between species number and area to devise reserve designs and predict the effects of habitat loss on diversity.
Harte, J., A. B. Smith, and D. Storch. 2009. Biodiversity scales from plots to biomes with a universal species-area curve. Ecology Letters 12:789–797.
Harte, J., T. Zillio, E. Conlisk, and A. B. Smith. 2008. Maximum entropy and the state-variable approach to macroecology. Ecology 89:2700–2711.
Communicating research more broadly is not only important for outreach to the public, but with the rapidly expanding literature, we think it’ll also be important for communicating to other scientists. Back in 2012 we started a post type called [Research Summary] which is based on the idea that people might not have time to read a multi-page paper but might be willing to read a <1000 word post conveying the ideas in the paper in a more casual format. Ethan did one of these for an Ecology paper his group published last year. Below, one of our graduate students, Sarah Supp, has taken up the challenge to communicate about her first-authored Ecology paper that just came out and has written the guest post below.
This is a research summary of: S. R. Supp, X. Xiao, S. K. M. Ernest, and E. P. White. 2012. An experimental test of the response of macroecological patterns to altered species interactions. Ecology 93: 2505-2511. doi:10.1890/12-0370.1
While many ecologists focus on why individuals, species, or ecological habitats differ, macroecologists are often fascinated by similarities among different groups or ecosystems. This focus on similarities emerges because macroecologists treat individuals, populations and species as ecological particles, and identify patterns in the structure of these particles to understand ecological systems and organization.
Despite a long history of documenting macroecological patterns, an understanding of what determines pattern behavior, why patterns are so easily predicted by so many different models, and how we should go about addressing real ecological problems using a macroecological approach has still not been reached.
Three common macroecological patterns, and the focus for our study, include: the species abundance distribution (SAD; distribution of abundance across species), the species-area relationship (SAR; accumulation of species across spatial scales), and the species-time relationship (STR; accumulation of species through time). Since these patterns exhibit regular behavior across taxonomic groups and ecological habitats, they are increasingly being used to make inferences about local-scale ecological processes and to inform management decisions.
Recently, discussion on how to do ecology has sometimes presented a dichotomy between two groups: “species identity matters” vs. “species identity is unimportant”. A more useful way of discussing the problem likely lies in asking more nuanced questions such as: How important are species identities for my specific question? When does species identity impact ecological organization? At what spatial/temporal scales? When are species identities necessary for prediction? For example, recent models suggest that the identity of species within a community or ecosystem is unimportant for predicting the shape of macroecological patterns. Instead, these models suggest that some macroecological patterns may only be sensitive to changes in the species richness or total abundance of the ecosystem being examined. This idea is pretty radical (in our opinion), but untested.
We took an approach that we felt could address a few problems simultaneously: 1) Does species identity play a role in determining the form of macroecological patterns, or are patterns only sensitive to changes in species richness or total abundance? 2) Can we effectively synthesize our detailed knowledge of a system with a macroscopic approach in order to link pattern with process?
We used 15 years of data from the Portal Project, our lab’s long-term research site in southeastern Arizona, to evaluate the response of the summer and winter annual plant communities to selective removal of rodent seed predators. It is not known if altered species identity alone (changes in species composition caused by manipulating an important interaction, seed predation [above]) can lead to shifts in the form of these patterns, in the absence of other changes (such as species richness and total abundance). At the Portal site, interactions within and among rodent and plant communities are well studied and we felt that this made our site an ideal experimental venue for this project. Among experimental treatments (control, kangaroo rat removal, and removal of all rodents), we compared changes in plant species composition, species richness (S), total abundance (N), and the form of each of the macroecological patterns (SAD, SAR, and STR). Below are examples of the data for each pattern from plot 22, a control plot, and a photo of a plant sampling quadrat. In the photo, California poppy (Escholtzia mexicana) and Stork’s bill dominate (Erodium cicutarium).
We found that plant species composition was always influenced by the removal of kangaroo rats and by removal of all rodent seed predators. Interestingly, we also found that removing kangaroo rats (keystone species) did not influence plant species richness or total abundance. This suggests that compensatory dynamics are at work. Finally, when we compared the macroecological patterns among the experimental treatments, we found differences in the macro-patterns only occurred when plant species richness or total abundance was altered, and not by compositional changes alone. (Below: Where the parameters do not cross the dotted line, there were significant differences among the paired manipulations [R-C: total rodent removals vs. controls; K-C: kangaroo rat removals vs. controls]. Note that the only place this occurs is in the winter annual community when species richness [S] and total abundance [N] are affected by the removal of all rodent seed predators. The rodent pictured is Merriam’s Kangaroo Rat, Dipodomys merriami.)
So what does this mean? Let’s revisit our initial questions:
1) Does species identity plan a role in determining the form of macroecological patterns, or are patterns only sensitive to changes in species richness or total abundance? Our research suggests that the key to interpreting and predicting patterns lies in our ability to make realistic predictions of community level variables, such as species richness or total abundance. Although changes in species richness or total abundance were most the important predictors for change in our macroecological patterns, we are not suggesting that changes in species identity are inconsequential. In fact, we believe that our findings suggest an important, but indirect role, for the role of species interactions in determining macroecological patterns. Species interactions may facilitate or hinder compensation dynamics, which in turn may lead to shifts in the number of species or the total number of individuals in response to manipulation. In a recent paper, Brian McGill suggests that what drives S and N is a central unanswered question in ecology.
2) Can we effectively synthesize our detailed knowledge of a system with a macroscopic approach in order to link pattern with process? We feel that our approach of using small-scale experimental field data with macroecology (which has largely relied on large-scale observational data) provides a potentially powerful framework for improving our understanding of the linkages between pattern and process. Studies using a similar approach may help bridge important gaps between pattern and process, between local and regional scale ecology and between basic and applied science.
Characterizing the species-abundance distribution with only information on richness and total abundance [Research Summary]
This is the first of a new category of posts here at Jabberwocky Ecology called Research Summaries. We like the idea of communicating our research more broadly than to the small number of folks who have the time, energy, and interest to read through entire papers. So, for every paper that we publish we will (hopefully) also do a blog post communicating the basic idea in a manner targeted towards a more general audience. As a result these posts will intentionally skip over a lot of detail (technical and otherwise), and will intentionally use language that is less precise, in order to communicate more broadly. We suspect that it will take us quite a while to figure out how to do this well. Feedback is certainly welcome.
This is a Research Summary of: White, E.P., K.M. Thibault, and X. Xiao. 2012. Characterizing species-abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology. http://dx.doi.org/10.1890/11-2177.1*
The species-abundance distribution describes the number of species with different numbers of individuals. It is well known that within an ecological community most species are relatively rare and only a few species are common, and understanding the detailed form of this distribution of individuals among species has been of interest in ecology for decades. This distribution is considered interesting both because it is a complete characterization of the commonness and rarity of species and because the distribution can be used to test and parameterize ecological models.
Numerous mathematical descriptions of this distribution have been proposed and much of the research into this pattern has focused on trying to figure out which of these descriptions is “the best” for a particular group of species at a small number of sites. We took an alternative approach to this pattern and asked: Can we explain broad scale, cross-taxonomic patterns in the general shape of the abundance distribution using a simple model that requires only knowledge of the species richness and total abundance (summed across all species) at a site?
To do this we used a model that basically describes the most likely form of the distribution if the average number of individuals in a species is fixed (which turns out to be a slightly modified version of the classic log-series distribution; see the paper or John Harte’s new book for details). As a result this model involves no detailed biological processes and if we know richness and total abundance we can predicted the abundance of each species in the community (i.e., the abundance of the most common species, second most common species… rarest species).
Since we wanted to know how well this works in general (not how well it works for birds in Utah or trees in Panama) we put together a a dataset of more than 15,000 communities. We did this by combining 6 major datasets that are either citizen science, big government efforts, or compilations from the literature. This compilation includes data on birds, trees, mammals, and butterflies. So, while we’re missing the microbes and aquatic species, I think that we can be pretty confident that we have an idea of the general pattern.
In general, we can do an excellent job of predicting the abundance of each rank of species (most abundant, second most abundant…) at each site using only information on the species richness and total abundance at the site. Here is a plot of the observed number of individuals in a given rank at a given site against the number predicted. The plot is for Breeding Bird Survey data, but the rest of the datasets produce similar results.
The model isn’t perfect of course (they never are and we highlight some of its failures in the paper), but it means that if we know the richness and total abundance of a site then we can capture over 90% of the variation in the form of the species-abundance distribution across ecosystems and taxonomic groups.
This result is interesting for two reasons:
First, it suggests that the species-abundance distribution, on its own, doesn’t tell us much about the detailed biological processes structuring a community. Ecologists have know that it wasn’t fully sufficient for distinguishing between different models for a while (though we didn’t always act like it), but our results suggest that in fact there is very little additional information in the distribution beyond knowing the species richness and total abundance. As such, any model that yields reasonable richness and total abundance values will probably produce a reasonable species-abundance distribution.
Second, this means that we can potentially predict the full distribution of commonness and rarity even at locations we have never visited. This is possible because richness and total abundance can, at least sometimes, be well predicted using remotely sensed data. These predictions could then be combined with this model of the species-abundance distribution to make predictions for things like the number of rare species at a site. In general, we’re interested in figuring out how much ecological pattern and process can be effectively characterized and predicted at large spatial scales, and this research helps expand that ability.
So, that’s the end of our first Research Summary. I hope it’s a useful thing that folks get something out of. In addition to the science in this paper, I’m also really excited about the process that we used to accomplish this research and to make it as reproducible as possible. So, stay tuned for some follow up posts on big data in ecology, collaborative code development, and making ecological research more reproducible.
*The paper will be Open Access once it is officially published but ,for reasons that don’t make a lot of sense to me, it is behind a paywall until it comes out in print.