E. Carol Adair

A global synthesis reveals biodiversity loss as a major driver of ecosystem change

Evidence is mounting that extinctions are altering key processes important to the productivity and sustainability of Earth’s ecosystems. Further species loss will accelerate change in ecosystem processes, but it is unclear how these effects compare to the direct effects of other forms of environmental change that are both driving diversity loss and altering ecosystem function. Here we use a suite of meta-analyses of published data to show that the effects of species loss on productivity and decomposition—two processes important in all ecosystems—are of comparable magnitude to the effects of many other global environmental changes. In experiments, intermediate levels of species loss (21–40%) reduced plant production by 5–10%, comparable to previously documented effects of ultraviolet radiation and climate warming. Higher levels of extinction (41–60%) had effects rivalling those of ozone, acidification, elevated CO2 and nutrient pollution. At intermediate levels, species loss generally had equal or greater effects on decomposition than did elevated CO2 and nitrogen addition. The identity of species lost also had a large effect on changes in productivity and decomposition, generating a wide range of plausible outcomes for extinction. Despite the need for more studies on interactive effects of diversity loss and environmental changes, our analyses clearly show that the ecosystem consequences of local species loss are as quantitatively significant as the direct effects of several global change stressors that have mobilized major international concern and remediation efforts.

Response of decomposing litter and its microbial community to multiple forms of nitrogen enrichment

Despite the importance of litter decomposition for ecosystem fertility and carbon balance, key uncertainties remain about how this fundamental process is affected by nitrogen (N) availability. Resolving such uncertainties is critical for predicting the ecosystem consequences of increased anthropogenic N deposition. Toward that end, we decomposed green leaves and senesced litter of northern pin oak (Quercus ellipsoidalis) in three forested stands dominated by northern pin oak or white pine (Pinus strobus) to compare effects of substrate N (as it differed between leaves and litter) and externally supplied N (inorganic or organic forms) on decomposition and decomposer community structure and function over four years. Asymptotic decomposition models fit the data equally well as single exponential models and allowed us to compare effects of N on both the initial decomposition rate (ka) and the level of asymptotic mass remaining (A, proportion of mass remaining at which decomposition approaches zero, i.e., the ...

Shedding light on plant litter decomposition: advances, implications and new directions in understanding the role of photodegradation

Litter decomposition contributes to one of the largest fluxes of carbon (C) in the terrestrial biosphere and is a primary control on nutrient cycling. The inability of models using climate and litter chemistry to predict decomposition in dry environments has stimulated investigation of non-traditional drivers of decomposition, including photodegradation, the abiotic decomposition of organic matter via exposure to solar radiation. Recent work in this developing field shows that photodegradation may substantially influence terrestrial C fluxes, including abiotic production of carbon dioxide, carbon monoxide and methane, especially in arid and semi-arid regions. Research has also produced contradictory results regarding controls on photodegradation. Here we summarize the state of knowledge about the role of photodegradation in litter decomposition and C cycling and investigate drivers of photodegradation across experiments using a meta-analysis. Overall, increasing litter exposure to solar radiation increased mass loss by 23% with large variation in photodegradation rates among and within ecosystems. This variation was tied to both litter and environmental characteristics. Photodegradation increased with litter C to nitrogen (N) ratio, but not with lignin content, suggesting that we do not yet fully understand the underlying mechanisms. Photodegradation also increased with factors that increased solar radiation exposure (latitude and litter area to mass ratio) and decreased with mean annual precipitation. The impact of photodegradation on C (and potentially N) cycling fundamentally reshapes our thinking of decomposition as a solely biological process and requires that we define the mechanisms driving photodegradation before we can accurately represent photodegradation in global C and N models.

Interactive Effects of Time, CO2, N, and Diversity on Total Belowground Carbon Allocation and Ecosystem Carbon Storage in a Grassland Community

Predicting if ecosystems will mitigate or exacerbate rising CO2 requires understanding how elevated CO2 will interact with coincident changes in diversity and nitrogen (N) availability to affect ecosystem carbon (C) storage. Yet achieving such understanding has been hampered by the difficulty of quantifying belowground C pools and fluxes. Thus, we used mass balance calculations to quantify the effects of diversity, CO2, and N on both the total amount of C allocated belowground by plants (total belowground C allocation, TBCA) and ecosystem C storage in a periodically burned, 8-year Minnesota grassland biodiversity, CO2, and N experiment (BioCON). Annual TBCA increased in response to elevated CO2, enriched N, and increasing diversity. TBCA was positively related to standing root biomass. After removing the influence of root biomass, the effect of elevated CO2 remained positive, suggesting additional drivers of TBCA apart from those that maintain high root biomass. Removing root biomass effects resulted in the effects of N and diversity becoming neutral or negative (depending on year), suggesting that the positive effects of diversity and N on TBCA were related to treatment-driven differences in root biomass. Greater litter production in high diversity, elevated CO2, and enhanced N treatments increased annual ecosystem C loss in fire years and C gain in non-fire years, resulting in overall neutral C storage rates. Our results suggest that frequently burned grasslands are unlikely to exhibit enhanced C sequestration with increasing atmospheric CO2 levels or N deposition.

Single-pool exponential decomposition models: potential pitfalls in their use in ecological studies

The importance of litter decomposition to carbon and nutrient cycling has motivated substantial research. Commonly, researchers fit a single-pool negative exponential model to data to estimate a decomposition rate (k). We review recent decomposition research, use data simulations, and analyze real data to show that this practice has several potential pitfalls. Specifically, two common decisions regarding model form (how to model initial mass) and data transformation (log-transformed vs. untransformed data) can lead to erroneous estimates of k. Allowing initial mass to differ from its true, measured value resulted in substantial over- or underestimation of k. Log-transforming data to estimate k using linear regression led to inaccurate estimates unless errors were lognormally distributed, while nonlinear regression of untransformed data accurately estimated k regardless of error structure. Therefore, we recommend fixing initial mass at the measured value and estimating k with nonlinear regression (untransformed data) unless errors are demonstrably lognormal. If data are log-transformed for linear regression, zero values should be treated as missing data; replacing zero values with an arbitrarily small value yielded poor k estimates. These recommendations will lead to more accurate k estimates and allow cross-study comparison of k values, increasing understanding of this important ecosystem process.

Contrasting effects of resource availability and plant mortality on plant community invasion by Bromus tectorum L.

The positive effect of disturbance on plant community invasibility is one of the more consistent results in invasion ecology. It is generally attributed to a coincident increase in available resources (due to the disturbance) that allows non-resident plant species to establish (Davis MA, Grime JP Thompson K, J Ecol 88:528–534, 2000). However, most research addressing this issue has been in artificial or highly modified plant communities. Our goal in this study was to investigate the interactive effects of resource availability and plant mortality disturbance on the invasion of natural plant communities. We conducted a series of experiments that examined the response of Bromus tectorum L., a highly invasive annual grass, to experimentally created gradients of resource availability [nitrogen (N) and water] and resident plant species mortality. We found that B. tectorum biomass was co-limited by N and water. Biomass at the end of the growing season was a saturating function (i.e., increased to a maximum) of water, which determined maximum biomass, and N, which determined the rate at which maximum biomass was attained. Despite that fact that plant mortality increased N availability, it had a negative impact on invasion success. Plant mortality also decreased foliar cover, standing dead biomass, and soil cover by litter. In harsh environments, removing foliar and soil cover may increase germination and seedling stress by increasing soil temperatures and water loss. Across all treatments, B. tectorum success decreased with decreasing foliar cover and standing dead biomass. This, in combination with the strong limitation of B. tectorum biomass by water in this experiment, suggests that our plant mortality disturbance removed soil cover that may have otherwise aided B. tectorum invasion into this semi-arid plant community by reducing water stress.

CO-LIMITATION OF FIRST YEAR FREMONT COTTONWOOD SEEDLINGS BY NITROGEN AND WATER

Nutrient availability strongly affects the species composition and productivity of most upland ecosystems, but the importance of nutrient availability is largely undefined for riparian ecosystems in semiarid regions of the western United States. The establishment and persistence of riparian cottonwood (Populus spp.) seedlings depends largely on water availability, but this does not preclude an important role for nutrient availability. To investigate how nitrogen availability may influence the composition and productivity of riparian communities, we tested the hypothesis that the growth and survival of first-year Fremont cottonwood seedlings is limited by the availability of both water and nitrogen. Plots of naturally germinated cottonwood seedlings along the Yampa River in Northwest Colorado were randomly assigned one of four treatments: control, water, nitrogen, or water plus nitrogen. Additions of nitrogen or water doubled total (root plus shoot) seedling and shoot length. Water additions did not increase root growth, while N addition doubled the root extension of first-year cottonwood seedlings. The water-plus-nitrogen treatment doubled total seedling and root length, and tripled shoot length. Additions of water or nitrogen also more then doubled cottonwood seedling survival through the first growing season. This co-limitation of cottonwood germinants by both water and nitrogen suggests that the productivity and species composition of riparian vegetation may need to be examined in relation to supplies of resources other than water.

Modeling the Dynamics of Soil Organic Matter and Nutrient Cycling

This chapter includes a complete description of the mathematical expressions used to simulate the biological, chemical, and physical processes in existing models and a description of the computer models, which are currently being used to simulate soil carbon and nutrient cycling. The models range from analytical, substrate-enzyme-microbe, cohort, multicompartmental, nutrient dynamics, and ecosystem models. A detailed description of three of the most widely used ecosystem models is presented to show the diversity of the approaches used to simulate nutrient cycling and soil carbon dynamics. The chapter also presents a description of the analytical procedures used to classify models, compare different model results, evaluate model performance using observed data, parameterize models, and select the best model for a specific application. The conclusion section presents a critical evaluation of the limitations of the current soil organic matter (SOM) and nutrient cycling model, suggestions on how current knowledge about SOM dynamics should be incorporated into models, and a list of biological and physical processes that need to be incorporated into existing models.

Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AICc), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates as nonlinear beta regression. Our simulation results also suggest that k estimates will be most accurate when study length captures mid to late stage decomposition (50–80% mass loss) and the number of measurements through time is ≥5. Regression method and data transformation choices had the smallest impact on k estimates during mid and late stage decomposition. Estimates of k were more variable among methods and generally less accurate during early and end stage decomposition. With real data, neither model was predominately best; in most cases the models were indistinguishable based on AICc, and gave similar k estimates. However, when decomposition rates were high, normal and beta model k estimates often diverged substantially. Therefore, we recommend a pragmatic approach where both models are compared and the best is selected for a given data set. Alternatively, both models may be used via model averaging to develop weighted parameter estimates. We provide code to perform nonlinear beta regression with freely available software.