Rangeland Resources Research Unit

Fort Collins, CO, United States

Rangeland Resources Research Unit

Fort Collins, CO, United States
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Mueller K.E.,Rangeland Resources Research Unit | Blumenthal D.M.,Rangeland Resources Research Unit | Carrillo Y.,University of Western Sydney | Cesarz S.,German Center for Integrative Biodiversity Research iDiv Halle Jena Leipzig | And 10 more authors.
Soil Biology and Biochemistry | Year: 2016

Climate change can alter soil communities and functions, but the consequences are uncertain for most ecosystems. We assessed the impacts of climate change on soil nematodes in a semiarid grassland using a 7-year, factorial manipulation of temperature and [CO2]. Elevated CO2 and warming decreased the abundance of plant-feeding nematodes and nematodes with intermediate to high values on the colonizer-persister scale (cp3-5), including predators and omnivores. Thus, under futuristic climate conditions, nematode communities were even more dominated by r-strategists (cp1-2) that feed on bacteria and fungi. These results indicate that climate change could alter soil functioning in semiarid grasslands. For example, the lower abundance of plant-feeding nematodes could facilitate positive effects of elevated CO2 and warming on plant productivity. The effects of elevated CO2 and warming on nematode functional composition were typically less than additive, highlighting the need for multi-factor studies. © 2016


Mueller K.E.,Rangeland Resources Research Unit | Eisenhauer N.,German Center for Integrative Biodiversity Research iDiv Halle Jena Leipzig | Eisenhauer N.,University of Leipzig | Eisenhauer N.,University of Minnesota | And 22 more authors.
Soil Biology and Biochemistry | Year: 2016

Management of biodiversity and ecosystem services requires a better understanding of the factors that influence soil biodiversity. We characterized the species (or genera) richness of 10 taxonomic groups of invertebrate soil animals in replicated monocultures of 14 temperate tree species. The focal invertebrate groups ranged from microfauna to macrofauna: Lumbricidae, Nematoda, Oribatida, Gamasida, Opilionida, Araneida, Collembola, Formicidae, Carabidae, and Staphylinidae. Measurement of invertebrate richness and ancillary variables occurred ~34 years after the monocultures were planted. The richness within each taxonomic group was largely independent of richness of other groups; therefore a broad understanding of soil invertebrate diversity requires analyses that are integrated across many taxa. Using a regression-based approach and ~125 factors related to the abundance and diversity of resources, we identified a subset of predictors that were correlated with the richness of each invertebrate group and richness integrated across 9 of the groups (excluding earthworms). At least 50% of the variability in integrated richness and richness of each invertebrate group was explained by six or fewer predictors. The key predictors of soil invertebrate richness were light availability in the understory, the abundance of an epigeic earthworm species, the amount of phosphorus, nitrogen, and calcium in soil, soil acidity, and the diversity or mass of fungi, plant litter, and roots. The results are consistent with the hypothesis that resource abundance and diversity strongly regulate soil biodiversity, with increases in resources (up to a point) likely to increase the total diversity of soil invertebrates. However, the relationships between various resources and soil invertebrate diversity were taxon-specific. Similarly, diversity of all 10 invertebrate taxa was not high beneath any of the 14 tree species. Thus, changes to tree species composition and resource availability in temperate forests will likely increase the richness of some soil invertebrates while decreasing the richness of others. © 2015.


Augustine D.J.,U.S. Department of Agriculture | Booth D.T.,Rangeland Resources Research Unit | Cox S.E.,USDI BLM | Derner J.D.,U.S. Department of Agriculture
Rangeland Ecology and Management | Year: 2012

We used very large scale aerial (VLSA) photography to quantify spatial patterns in bare soil in the northeastern Colorado shortgrass steppe. Using three pairs of pastures stocked at moderate (0.6 animal unit months [AUM]·ha -1) versus very heavy (1.2 AUM·ha -1) rates, we detected greater bare soil under very heavy (mean=22.5%) versus moderate stocking (mean=13.5%; P=0.053) and a lower coefficient of variation across pastures under very heavy (0.48) versus moderate stocking (0.75; P=0.032). Bare soil exhibited significant positive spatial autocorrelation across distances of 60120 m under moderate stocking (Moran's I=0.14), while patchiness at this scale was eliminated under very heavy grazing (I=-0.05). Across distances of 120480 m, we observed no spatial autocorrelation with either stocking rate. Spatial autocorrelation was greatest at a separation distance of 2 m (I=0.480.58) but was unaffected by stocking rate at this scale. Thus, very heavy grazing did not increase spatial autocorrelation in bare soil across scales of 2480 m. Means and variability in the distribution of bare soil were not influenced by ecological site. Bare soil increased primarily at the scale of individual plant clusters through both increases in the density of small (220 cm) bare patch intercepts and increases in the frequency of bare patch intercepts of 2060 cm (rather than <20 cm). Our approach demonstrates the utility of VLSA for analyzing interactions between grazing and other landscape features and highlights the importance of spatially explicit sampling across broad scales (pastures) while testing for potential shifts in patchiness of bare soil at the scale of plant interspaces. © Society for Range Management.


Mueller K.E.,Rangeland Resources Research Unit | Blumenthal D.M.,Rangeland Resources Research Unit | Pendall E.,University of Western Sydney | Carrillo Y.,University of Western Sydney | And 4 more authors.
Ecology Letters | Year: 2016

It is unclear how elevated CO2 (eCO2) and the corresponding shifts in temperature and precipitation will interact to impact ecosystems over time. During a 7-year experiment in a semi-arid grassland, the response of plant biomass to eCO2 and warming was largely regulated by interannual precipitation, while the response of plant community composition was more sensitive to experiment duration. The combined effects of eCO2 and warming on aboveground plant biomass were less positive in ‘wet’ growing seasons, but total plant biomass was consistently stimulated by ~ 25% due to unique, supra-additive responses of roots. Independent of precipitation, the combined effects of eCO2 and warming on C3 graminoids became increasingly positive and supra-additive over time, reversing an initial shift toward C4 grasses. Soil resources also responded dynamically and non-additively to eCO2 and warming, shaping the plant responses. Our results suggest grasslands are poised for drastic changes in function and highlight the need for long-term, factorial experiments. © 2016 John Wiley & Sons Ltd/CNRS.


Sanderson M.A.,Northern Great Plains Research Laboratory | Liebig M.A.,Northern Great Plains Research Laboratory | Hendrickson J.R.,Northern Great Plains Research Laboratory | Kronberg S.L.,Northern Great Plains Research Laboratory | And 3 more authors.
Journal of Soil and Water Conservation | Year: 2016

A century ago, Johnson Thatcher Sarvis and scientists at Mandan set out to determine the area needed to sustainably support a steer during the grazing sea-son. In addition to answering the original question, scientists gathered some of the first data on grazing resilience of native grasses, determined the critical role of soil moisture in maintaining rangeland productivity on the semiarid northern plains, and generated applied ecological insights on the persistence and resilience of native prairie during the worst drought of the last millennium. Because of the foresight of Sarvis and others, this long-term study continues to serve as a unique and valuable resource. Important long-term ecological and resource management questions, such as vegetation, soil, and cattle weight gain changes with respect to weather, management, etc., simply cannot be answered with short-term data. Leveraging existing long-term data with formation of the LTAR network and NEON can allow us to peer into the future of the northern Great Plains. The question in the twentyfirst century is a similar one: how do we sustainably intensify agroecosystems in an era of climatic and social changes? Our challenge is to exhibit the same foresight and develop research that is still relevant in 100 years. © 2016 Soil and Water Conservation Society. All rights reserved.


Augustine D.J.,Rangeland Resources Research Unit | Derner J.D.,Rangeland Resources Research Unit
Sensors (Switzerland) | Year: 2013

Advances in global positioning system (GPS) technology have dramatically enhanced the ability to track and study distributions of free-ranging livestock. Understanding factors controlling the distribution of free-ranging livestock requires the ability to assess when and where they are foraging. For four years (2008-2011), we periodically collected GPS and activity sensor data together with direct observations of collared cattle grazing semiarid rangeland in eastern Colorado. From these data, we developed classification tree models that allowed us to discriminate between grazing and non-grazing activities. We evaluated: (1) which activity sensor measurements from the GPS collars were most valuable in predicting cattle foraging behavior, (2) the accuracy of binary (grazing, non-grazing) activity models vs. models with multiple activity categories (grazing, resting, traveling, mixed), and (3) the accuracy of models that are robust across years vs. models specific to a given year. A binary classification tree correctly removed 86.5% of the non-grazing locations, while correctly retaining 87.8% of the locations where the animal was grazing, for an overall misclassification rate of 12.9%. A classification tree that separated activity into four different categories yielded a greater misclassification rate of 16.0%. Distance travelled in a 5 minute interval and the proportion of the interval with the sensor indicating a head down position were the two most important variables predicting grazing activity. Fitting annual models of cattle foraging activity did not improve model accuracy compared to a single model based on all four years combined. This suggests that increased sample size was more valuable than accounting for interannual variation in foraging behavior associated with variation in forage production. Our models differ from previous assessments in semiarid rangeland of Israel and mesic pastures in the United States in terms of the value of different activity sensor measurements for identifying grazing activity, suggesting that the use of GPS collars to classify cattle grazing behavior will require calibrations specific to the environment and vegetation being studied. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


PubMed | Rangeland Resources Research Unit
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2013

Advances in global positioning system (GPS) technology have dramatically enhanced the ability to track and study distributions of free-ranging livestock. Understanding factors controlling the distribution of free-ranging livestock requires the ability to assess when and where they are foraging. For four years (2008-2011), we periodically collected GPS and activity sensor data together with direct observations of collared cattle grazing semiarid rangeland in eastern Colorado. From these data, we developed classification tree models that allowed us to discriminate between grazing and non-grazing activities. We evaluated: (1) which activity sensor measurements from the GPS collars were most valuable in predicting cattle foraging behavior, (2) the accuracy of binary (grazing, non-grazing) activity models vs. models with multiple activity categories (grazing, resting, traveling, mixed), and (3) the accuracy of models that are robust across years vs. models specific to a given year. A binary classification tree correctly removed 86.5% of the non-grazing locations, while correctly retaining 87.8% of the locations where the animal was grazing, for an overall misclassification rate of 12.9%. A classification tree that separated activity into four different categories yielded a greater misclassification rate of 16.0%. Distance travelled in a 5 minute interval and the proportion of the interval with the sensor indicating a head down position were the two most important variables predicting grazing activity. Fitting annual models of cattle foraging activity did not improve model accuracy compared to a single model based on all four years combined. This suggests that increased sample size was more valuable than accounting for interannual variation in foraging behavior associated with variation in forage production. Our models differ from previous assessments in semiarid rangeland of Israel and mesic pastures in the United States in terms of the value of different activity sensor measurements for identifying grazing activity, suggesting that the use of GPS collars to classify cattle grazing behavior will require calibrations specific to the environment and vegetation being studied.

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