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Peter's Research

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To study coexistence and community dynamics, we rely heavily on historical, "chart quadrat" datasets that we use to build mathematical models. We then analyze the models to test theory and explore future scenarios and we design field experiments to test the models predictions. To study species richness patterns, we often take a synthetic approach, using existing data sets from many sites or coordinated, multi-site sampling efforts. Our work on grazing uses observational field studies and exclosure experiments.

Throughout the early 1900's, ecologists at many field stations in the western US established series of 1meter-square permanent plots in which they mapped every individual plant every year for decades. Here is an example of one quadrat from a site in western Kansas:


We have digitized data like this from five sites: 1) southern mixed prairie in Kansas (public data), 2) northern mixed prairie in Montana, 3) sagebrush steppe in Idaho (public data), 4) Chihuahuan desert in New Mexico, and 5) Sonoran desert in Arizona. Using demographic data that we extract from the maps using computer algorithms (Lauenroth and Adler 2008), we build models of community dynamics to test coexistence theory and explore future climate scenarios.

The central puzzle of plant community ecology is understanding how so many species can coexist despite competing for just a few limiting resources. We use multispecies population models parametrized with chart quadrat data to quantify the role of stabilizing mechanisms and fitness differences (Adler et al. 2007). For example, at our sagebrush steppe study site, we found that stabilizing niche differences are essential for preventing competitive exclusion, but that fitness differences are small enough so that even without niches competitive exclusion would require hundreds of years (Adler et al. 2010). We have also used our models to test the role of individual coexistence mechanisms, especially the potential for interannual variability in climate to stabilize coexistence through the "storage effect." While historical climate variability played a strong role in promoting diversity in a Kansas prairie (pictured below; Adler et al. 2006; press), it had little influence on coexistence in a sagebrush steppe (Adler et al. 2009).

Hays 2002Hays 2004

An important lesson from this work is that to understand how climate change will impact biodiversity, we will need to consider changes in both climate averages and variability.

A difficult challenge in global change ecology is anticipating how species interactions will mediate the direct effects of altered climate on important species. In some cases, species interactions may overwhelm the direct effects of climate, but we currently have no way of predicting when or where such situations will occur. Our lab is now testing the idea that niche differences determine how species interactions will influence climate change impacts. Chart quadrat datasets provide a unique opportunity to model both species interactions as well as the influence of precipitation and temperature on vital rates (Adler and HilleRisLambers 2008, Dalgleish et al. 2011). We can use these models to quantify niche differences and simulate the direct and indirect effects of climate change. However, before relying on these historically-based models for ecological forecasting, they will need rigorous validation. We are collecting new chart quadrat data and initiating field experiments to test the model predictions. Another goal of this research is to predict niche differences from plant functional traits.


A second focus of our climate change work is understanding how the loss of winter snowpack will affect sagebrush steppe communities. A small increase in temperature will lead to dramatic decreases in snowpack depth and duration. Our historical data suggests that diminishing snowpacks may favor invasive annual species at the expense of the perennial grasses. PhD student Aldo Compagnoni is conducting experiments to evaluate the effect of warming and snowmelt on the demographic performance of the invasive annual cheatgrass (Bromus tectorum).


The species-area relationships (SAR), often called one of the few "laws" of ecology, shows how species richness scales with area observed. As early as 1960, Preston suggested an analogous relationship between species number and the time period of observation, a species-time relationship. My collaborators and I used the same Kansas time-series mentioned above as well as other long-term data sets to show that the SAR and STR are just two special cases of a more general species-time-area relationship (STAR), with species number increasing as a function of area and time observed, as well as their interaction (Adler and Lauenroth 2003, Adler et al. 2005). We think the STAR has important implications for biodiversity assessment in both conservation and basic research contexts. For example, I found that neutral models could reproduce an observed SARs or STRs from the Kansas dataset, but failed to reproduce both simultaneously with one set of parameters (Adler 2004).

Our current synthesis work on richness patterns is part of the Nutrient Network. We are conducting the same manipulations of soil resources and herbivory at 50 sites on 5 continents. This network approach to research should help us identify truly general patterns, something traditional site-specific studies struggle to achieve.

My dissertation research was focused on explaining why some ecosystems are so senstive to livestock grazing, while others appear quite resistant. I compared grazing effects on vegetation and soils in convergent ecosystems of North and South America--the sagebrush steppe of central Washington state, and the Patagonian steppe of southern Argentina (Adler et al. 2004, Adlet et al. 2005).


I also am interested in how grazing affects the spatial heterogeneity of vegetation. This research included field work in the shortgrass steppe of Colorado (Adler and Lauenroth 2000), along with a literature review and some basic simulation modeling (Adler et al. 2001). I also used a modeling approach to explore how herbivore foraging strategies may influence the development of patterns in forage production and utilization along distance from water gradients (Adler and Hall 2005).

Our current grazing research, led by MS student Ian Ware, is focused on bison grazing in the Henry Mountains of southern Utah. We hope that our research will help the Utah Division of Wildlife Resources and local ranchers settle a dispute about the impact of bison on cattle winter range.