Elk decision-making rules are simplified in the presence of wolves

The risk of predation drives many behavioral responses in prey. However, few studies have directly tested whether predation risk alters the way other variables influence prey behavior. Here we use information theory (Akaike’s Information Criterion, AICc) in a novel way to test the hypothesis that the decision-making rules governing elk behavior are simplified by the presence of wolves. With elk habitat use as the dependent variable, we test whether the number of independent variables (i.e., the size of the models) that best predict this behavior differ when wolves are present vs absent. Thus, we use AICc scores simply to determine the number of variables to which elk respond when making decisions. We measured habitat use using 2,288 locations from GPS collars on 14 elk, over two winters (14 elk winters), in the Gallatin Canyon portion of the Greater Yellowstone Ecosystem. We found that the use of three major habitat components (grass, conifer, sage) was sensitive to many variables on days that wolves were locally absent, with the best models (ΔAICc≤2) averaging 7.4 parameters. In contrast, habitat use was sensitive to few variables on days when wolves were present: the best models averaged only 2.5 parameters. Because fewer variables affect elk behavior in the presence of wolves, we conclude that elk use simpler decision-making rules in the presence of wolves. This simplification of decision-making rules implies that predation risk imposes selection pressures that do not allow prey to respond to other pressures in ways that they otherwise would. If the affected processes are important, then this indirect effect of predation is likely to be important.     

Expansion of the MANAGE Database with Forest and Drainage Studies

The “Measured Annual Nutrient loads from AGricultural Environments” (MANAGE) database was published in 2006 to expand an early 1980s compilation of nutrient export (load) data from cultivated and pasture/range land at the field or farm scale. Then in 2008, MANAGE was updated with 15 additional studies, and nitrogen (N) and phosphorus (P) concentrations in runoff were added. Since then, MANAGE has undergone significant expansion adding N and P water quality along with relevant management and site characteristic data from: (1) 30 runoff studies from forested land uses, (2) 91 drainage water quality studies from drained land, and (3) 12 additional runoff studies from cultivated and pasture/range land uses. In this expansion, an application timing category was added to the existing fertilizer data categories (rate, placement, formulation) to facilitate analysis of 4R Nutrient Stewardship, which emphasizes right fertilizer source, rate, time, and place. In addition, crop yield and N and P uptake data were added, although this information was only available for 21 and 7% of studies, respectively. Inclusion of these additional data from cultivated, pasture/range, and forest land uses as well as artificially drained agricultural land should facilitate expanded spatial analyses and improved understanding of regional differences, management practice effectiveness, and impacts of land use conversions and management techniques. The current version is available at www.ars.usda.gov/spa/manage-nutrient .     

Estimating Sonoran pronghorn abundance and survival with fecal DNA and capture–recapture methods

Population abundance estimates are important for management but can be challenging to determine in low‐density, wide‐ranging, and endangered species, such as Sonoran pronghorn (Antilocapra americana sonoriensis). The Sonoran pronghorn population has been increasing; however, population estimates are currently derived from a biennial aerial count that does not provide survival or recruitment estimates. We identified individuals through noninvasively collected fecal DNA and used robust‐design capture–recapture to estimate abundance and survival for Sonoran pronghorn in the United States from 2013 to 2014. In 2014 we generated separate population estimates for pronghorn gathered near 13 different artificial water holes and for pronghorn not near water holes. The population using artificial water holes had 116 (95% CI 102–131) and 121 individuals (95% CI 112–132) in 2013 and 2014, respectively. For all locations, we estimated there were 144 individuals (95% CI 132–157). Adults had higher annual survival probabilities (0.83, 95% CI 0.69–0.92) than fawns (0.41, 95% CI 0.21–0.65). Our use of targeted noninvasive genetic sampling and capture–recapture with Sonoran pronghorn fecal DNA was an effective method for monitoring a large proportion of the population. Our results provided the first survival estimates for this population in over 2 decades and precise estimates of the population using artificial water holes. Our method could be used for targeted sampling of broadly distributed species in other systems, such as in African savanna ecosystems, where many species congregate at watering sites.     

Nitrate-nitrogen losses through subsurface drainage under various agricultural land covers

Nitrate-nitrogen (NO?-N) loading to surface water bodies from subsurface drainage is an environmental concern in the midwestern United States. The objective of this study was to investigate the effect of various land covers on NO?-N loss through subsurface drainage. Land-cover treatments included (i) conventional corn ( L.) (C) and soybean [ (L.) Merr.] (S); (ii) winter rye ( L.) cover crop before corn (rC) and before soybean (rS); (iii) kura clover ( M. Bieb.) as a living mulch for corn (kC); and (iv) perennial forage of orchardgrass ( L.) mixed with clovers (PF). In spring, total N uptake by aboveground biomass of rye in rC, rye in rS, kura clover in kC, and grasses in PF were 14.2, 31.8, 87.0, and 46.3 kg N ha, respectively. Effect of land covers on subsurface drainage was not significant. The NO?-N loss was significantly lower for kC and PF than C and S treatments (p < 0.05); rye cover crop did not reduce NO?-N loss, but NO?-N concentration was significantly reduced in rC during March to June and in rS during July to November (p < 0.05). Moreover, the increase of soil NO?-N from early to late spring in rS was significantly lower than the S treatment (p < 0.05). This study suggests that kC and PF are effective in reducing NO?-N loss, but these systems could lead to concerns relative to grain yield loss and change in farming practices. Management strategies for kC need further study to achieve reasonable corn yield. The effectiveness of rye cover crop on NO-N loss reduction needs further investigation under conditions of different N rates, wider weather patterns, and fall tillage.     

Phosphorus removal in denitrifying woodchip bioreactors varies by wood type and water chemistry

Environmental Science and Pollution Research - Denitrifying woodchip bioreactors are a practical nitrogen (N) mitigation technology but evaluating the potential for bioreactor phosphorus (P)...