Category Archives: research summary
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.