Lauchlan H. Fraser

Worldwide evidence of a unimodal relationship between productivity and plant species richness

Grassland diversity and ecosystem productivity The relationship between plant species diversity and ecosystem productivity is controversial. The debate concerns whether diversity peaks at intermediate levels of productivity—the so-called humped-back model—or whether there is no clear predictable relationship. Fraser et al. used a large, standardized, and geographically diverse sample of grasslands from six continents to confirm the validity and generality of the humped-back model. Their findings pave the way for a more mechanistic understanding of the factors controlling species diversity. Science, this issue p. 302 The humped-back model of plant species diversity is confirmed by a global grassland survey. The search for predictions of species diversity across environmental gradients has challenged ecologists for decades. The humped-back model (HBM) suggests that plant diversity peaks at intermediate productivity; at low productivity few species can tolerate the environmental stresses, and at high productivity a few highly competitive species dominate. Over time the HBM has become increasingly controversial, and recent studies claim to have refuted it. Here, by using data from coordinated surveys conducted throughout grasslands worldwide and comprising a wide range of site productivities, we provide evidence in support of the HBM pattern at both global and regional extents. The relationships described here provide a foundation for further research into the local, landscape, and historical factors that maintain biodiversity.

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r2 = 0.56–0.77 AIC = 0.16–0.30 for SR models; r2 = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVIMODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVIMSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVIMSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVIMODIS data (r2 = 0.68 for SC for non-grazed systems, modeled with SR based on NDVIMODIS data; r2 = 0.77 for SC for non-grazed systems, modeled with SR based on NDVIMSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.

Forecasting Climate Change Impacts on the Distribution of Wetland Habitat in the Midwestern United States

Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when controlling for landscape context. Inferential (linear) models identified a consistent negative association between wetland area and isothermality. This is likely the result of regular inundation in areas where precipitation accumulates as snow, then melts faster than drainage capacity. Moisture index seasonality was identified as a key factor distinguishing between emergent and forested wetland types, where forested wetland area at the landscape scale is associated with a greater seasonal variation in water table depth. Forecasting models (neural networks) predicted an increase in potential wetland area in the coming century, with areas conducive to forested wetland formation expanding more rapidly than areas conducive to emergent wetlands. Local cluster analyses identified Iowa and Northeastern Missouri as areas of anticipated wetland expansion, indicating both a risk to crop production within the Midwest Corn Belt and an opportunity for wetland conservation, while Northern Minnesota and Michigan are potentially at risk of wetland losses under a future climate.

Predicting plant trait similarity along environmental gradients

Plant traits affect the success or failure of plants to establish, grow, and reproduce. Although we have an increased understanding of certain individual plant traits and their relative effects on performance and fitness, it is a challenge to predict relative similarity of traits between neighbouring plants. Assembly rules suggest that abiotic filters will restrict the range of viable strategies, thus creating a community of plants that share a similar suite of traits. In contrast, limiting similarity predicts that segregation of species’ resource use will lead to character displacement. What is the relative strength of these two processes and do they differ depending on site condition? We know that trait similarity of plants can vary with site productivity and disturbance. In this study, we investigate the interaction of these two ecological factors and how they affect plant trait similarity. We find support for the hypothesis that trait convergence occurs at low productivity/high disturbance and high productivity/low disturbance, and trait dispersion is most likely at intermediate levels of disturbance and productivity. The relationships among evolution, plant traits, and ecology are multivariate, hierarchical, and complex making plant traits at the ecosystem level an exciting and challenging agenda for the future.

State of knowledge about energy development impacts on North American rangelands: An integrative approach.

To reduce dependence on foreign oil reserves, there has been a push in North America to develop alternative domestic energy resources. Relatively undeveloped renewable energy resources include biofuels and wind and solar energy, many of which occur predominantly on rangelands. Rangelands are also key areas for natural gas development from shales and tight sand formations. Accordingly, policies aimed at greater energy independence are likely to affect the delivery of crucial ecosystem services provided by rangelands. Assessing and dealing with the biophysical and socio-economic effects of energy development on rangeland ecosystems require an integrative and systematic approach that is predicated on a broad understanding of diverse issues related to energy development. In this article, we present a road map for developing an integrative assessment of energy development on rangelands in North America. We summarize current knowledge of socio-economic and biophysical aspects of rangeland based energy development, and we identify knowledge gaps and monitoring indicators to fill these knowledge gaps.

Short-Term Effects of Changing Precipitation Patterns on Shrub-Steppe Grasslands: Seasonal Watering Is More Important than Frequency of Watering Events

Climate change is expected to alter precipitation patterns. Droughts may become longer and more frequent, and the timing and intensity of precipitation may change. We tested how shifting precipitation patterns, both seasonally and by frequency of events, affects soil nitrogen availability, plant biomass and diversity in a shrub-steppe temperate grassland along a natural productivity gradient in Lac du Bois Grasslands Protected Area near Kamloops, British Columbia, Canada. We manipulated seasonal watering patterns by either exclusively watering in the spring or the fall. To simulate spring precipitation we restricted precipitation inputs in the fall, then added 50% more water than the long term average in the spring, and vice-versa for the fall precipitation treatment. Overall, the amount of precipitation remained roughly the same. We manipulated the frequency of rainfall events by either applying water weekly (frequent) or monthly (intensive). After 2 years, changes in the seasonality of watering had greater effects on plant biomass and diversity than changes in the frequency of watering. Fall watering reduced biomass and increased species diversity, while spring watering had little effect. The reduction in biomass in fall watered treatments was due to a decline in grasses, but not forbs. Plant available N, measured by Plant Root Simulator (PRS)-probes, increased from spring to summer to fall, and was higher in fall watered treatments compared to spring watered treatments when measured in the fall. The only effect observed due to frequency of watering events was greater extractable soil N in monthly applied treatments compared to weekly watering treatments. Understanding the effects of changing precipitation patterns on grasslands will allow improved grassland conservation and management in the face of global climatic change, and here we show that if precipitation is more abundant in the fall, compared to the spring, grassland primary productivity will likely be negatively affected.