Adam M. Skibbe

Timing of climate variability and grassland productivity

Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and high-intensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing.

Fire and grazing influences on rates of riparian woody plant expansion along grassland streams.

Grasslands are threatened globally due to the expansion of woody plants. The few remaining headwater streams within tallgrass prairies are becoming more like typical forested streams due to rapid conversion of riparian zones from grassy to wooded. Forestation can alter stream hydrology and biogeochemistry. We estimated the rate of riparian woody plant expansion within a 30 m buffer zone surrounding the stream bed across whole watersheds at Konza Prairie Biological Station over 25 years from aerial photographs. Watersheds varied with respect to experimentally-controlled fire and bison grazing. Fire frequency, presence or absence of grazing bison, and the historical presence of woody vegetation prior to the study time period (a proxy for proximity of propagule sources) were used as independent variables to predict the rate of riparian woody plant expansion between 1985 and 2010. Water yield was estimated across these years for a subset of watersheds. Riparian woody encroachment rates increased as burning became less frequent than every two years. However, a higher fire frequency (1–2 years) did not reverse riparian woody encroachment regardless of whether woody vegetation was present or not before burning regimes were initiated. Although riparian woody vegetation cover increased over time, annual total precipitation and average annual temperature were variable. So, water yield over 4 watersheds under differing burn frequencies was quite variable and with no statistically significant detected temporal trends. Overall, burning regimes with a frequency of every 1–2 years will slow the conversion of tallgrass prairie stream ecosystems to forested ones, yet over long time periods, riparian woody plant encroachment may not be prevented by fire alone, regardless of fire frequency.

Blazing and grazing: influences of fire and bison on tallgrass prairie stream water quality

Abstract.  Fire and grazers (such as Bison bison) were historically among the most important agents for maintaining and managing tallgrass prairie, but we know little about their influences on water-quality dynamics in streams. We analyzed 2 y of data on total suspended solids (TSS), total N (TN), and total P (TP) (3 samples per week per stream during flow) in 3 prairie streams with fire and bison grazing treatments at Konza Prairie Biological Station, Kansas (USA), to assess whether fire and bison increase the concentrations of these water-quality variables. We quantified the spatial and temporal locations of bison (∼0.21 animal units/ha) with Global Positioning System collars and documented bison trails, paw patches, wallows, and naturally exposed sediment patches within riparian buffers. Three weeks post-fire, TN and TP decreased (t-test, p < 0.001), but TSS did not change. Bison spent <6% of their time within 10 m of the streams, increased the amount of exposed sediment in the riparian areas, and avoided wooded mainstem branches of stream (&khgr;2 test, p < 0.001). Temporal trends suggest that low discharge or increased bison density in the stream may increase TSS and TP during the summer months. Our results indicate a weak connection between TSS and nutrients with bison access to streams over our 2-y study and indicate that low TSS and nutrients characterize tallgrass prairie streams with fire and moderate bison densities relative to surrounding land uses.

Evidence of physiological decoupling from grassland ecosystem drivers by an encroaching woody shrub.

Shrub encroachment of grasslands is a transformative ecological process by which native woody species increase in cover and frequency and replace the herbaceous community. Mechanisms of encroachment are typically assessed using temporal data or experimental manipulations, with few large spatial assessments of shrub physiology. In a mesic grassland in North America, we measured inter- and intra-annual variability in leaf δ13C in Cornus drummondii across a grassland landscape with varying fire frequency, presence of large grazers and topographic variability. This assessment of changes in individual shrub physiology is the largest spatial and temporal assessment recorded to date. Despite a doubling of annual rainfall (in 2008 versus 2011), leaf δ13C was statistically similar among and within years from 2008-11 (range of −28 to −27‰). A topography*grazing interaction was present, with higher leaf δ13C in locations that typically have more bare soil and higher sensible heat in the growing season (upland topographic positions and grazed grasslands). Leaf δ13C from slopes varied among grazing contrasts, with upland and slope leaf δ13C more similar in ungrazed locations, while slopes and lowlands were more similar in grazed locations. In 2011, canopy greenness (normalized difference vegetation index – NDVI) was assessed at the centroid of individual shrubs using high-resolution hyperspectral imagery. Canopy greenness was highest mid-summer, likely reflecting temporal periods when C assimilation rates were highest. Similar to patterns seen in leaf δ13C, NDVI was highest in locations that typically experience lowest sensible heat (lowlands and ungrazed). The ability of Cornus drummondii to decouple leaf physiological responses from climate variability and fire frequency is a likely contributor to the increase in cover and frequency of this shrub species in mesic grassland and may be generalizable to other grasslands undergoing woody encroachment.

Correction: Fire and Grazing Influences on Rates of Riparian Woody Plant Expansion along Grassland Streams

The authors have found errors in this paper. These errors do not change the overall conclusions of the study. There are errors in the last two sentences of the second paragraph of the “Spatial analysis of riparian vegetation” subsection of the Methods. The correct sentences are: Due to changes in fire frequency and other management treatments over time or the confounding effect of multiple wild fires and partially burned watersheds, only data for 20 out of 54 watersheds were retained for further analyses. Of these 20 watersheds, half were grazed, but only 1 was grazed by cattle so no differences between native and cattle grazed watersheds were determined in this study. There are errors in Fig 1, “Spatial extent of woody plant species within a 30 m riparian buffer across the 4 watersheds of the Kings Creek basin monitored for stream discharge during 1985, 1991, and 2010.” The Natural Trail, N4A, and C1A are no longer included in the analysis. Please see the corrected Fig 1 and its legend here. Fig 1 Spatial extent of woody plant species within a 30 m riparian buffer across the 4 watersheds of the Kings Creek basin monitored for stream discharge during 1985, 1991, and 2010. There are errors in the last sentence of the fourth paragraph of the “Spatial analysis of riparian vegetation” subsection of the Methods. The correct sentence is: The cumulative number of burns that had taken place between 1980 and 2010 for each watershed was collected through the Konza Prairie Biological Station LTER network burn history database [34]. There are errors in the first sentence of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Analyses of 30 m riparian buffers revealed an increase in wooded vegetation over time among all watersheds except two (Watershed 2B, β = −0.06 and White Pasture β = −0.008). There are errors in the first sentence of the second paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Linear regression models indicated that the cumulative number of burns between 1980 and 2010, and the historical presence of woody vegetation, significantly predicted the rate of riparian vegetation expansion (P < 0.01, Adj. R2 = 0.51, F3,16 = 7.60; Fig 2). Fig 2 The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a multiple, linear regression model. There are errors in the second sentence of the second paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Further, the average rate of expansion of watersheds with forest present historically was significantly greater than those without forest (P = 0.06, T = −2.04, df = 17; Fig 2). There are errors in the first sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: A breakpoint was detected between burn frequency and woody expansion rate at 13 (±9.12 S.E) burns over the 30 years or at about 2.3 years between burns (Overall model: Adj. R2 = 0.37). There are errors in the second sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Only the regression model fit on the side of the breakpoint with fewer than 13 burns had a significant slope, indicating that the cumulative number of burns significantly predicts the rate of woody expansion (P = 0.01, T = -4.18, Fig 3). Fig 3 The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a segmented regression model. There are errors in the third sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: Due to the low number of watersheds which had >13 burns over the study period, the segmented regression did not indicate a significant regression slope after the break. There are errors in the last sentence of the third paragraph of the “Riparian vegetation spatial analysis” subsection of the Results. The correct sentence is: The rate of woody expansion for watersheds with a cumulative number of burns greater than 13 were significantly different from zero (Mean = 0.29, P = 0.03, T = 2.65, df = 7; Fig 3) indicating that burning regimes implemented more frequently than every 2.3 years may not necessarily prevent woody encroachment. There are errors in Fig 2, “The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a multiple, linear regression model”. Please see the corrected Fig 2 and its legend here. There are errors in Fig 3, “The association between the linear regression slopes calculated for each watershed’s change in riparian vegetation from 1985–2010 and the cumulative number of burns since 1980 using a segmented regression model”, and its legend. Please see the corrected Fig 3 and its legend here. There are errors in the third sentence of the first paragraph of the “Factors influencing riparian, woody vegetation expansion” subsection of the Discussion. The correct sentence is: The rate of riparian woody vegetation expansion was significantly predicted by the cumulative number of burns taken place between 1980 and 2010. There are errors in the second sentence of the second paragraph of the “Factors influencing riparian, woody vegetation expansion” subsection of the Discussion. The correct sentence is: Segmented regression results suggests that a threshold may be reached for woody vegetation cover at ~13 burns over the 30 year study period signifying that there is a change in the way riparian woody vegetation cover responds to fire when implemented every ~2 years (Fig 3).