Late summer glacial meltwater contributions to Bull Lake Creek stream flow and water quality, Wind River Range, Wyoming, USA

The Wind River Range in Wyoming contains more glacial ice than any other location within the USA’s Rocky Mountain states of Colorado, Idaho, Montana, and Wyoming. Bull Lake Creek watershed in the southeast portion of the range contains five major (0.6–1.5 km2) glaciers along with numerous smaller glaciers that contribute to the Wind River. Field measurements were made of discharge from the Knife Point and Bull Lake Glaciers to determine the contribution of glacial meltwater to the river system. Water samples were collected and analyzed for stable isotopes, major ions, nutrients, and selected trace elements. Meltwater from the two glaciers contributed 13.9% to Bull Lake Creek streamflow (site BL-3), with all glaciers within the Bull Lake Creek watershed estimated to be contributing 55.6% to the streamflow of Bull Lake Creek (United States Geological Survey gage) during the August 2015 study period. Hydrogen and oxygen stable isotope analysis indicated as much as 80% of late summer discharge in the upper Bull Lake Creek watershed was attributed to glacial meltwater. This study also found that nutrients (NO3 – NO2, total P) from glacial meltwater can be a significant source of nutrient loading to Bull Lake Creek

A comparison of drone imagery and groundbased methods for estimating the extent of habitat destruction by lesser snow geese (\u3ci\u3eAnser caerulescens caerulescens\u3c/i\u3e) in La Pérouse Bay

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P \u3c 0.0001) and shrub classes (F2,182 = 160.16, P \u3c 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P \u3c 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change

A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay.

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P < 0.0001) and shrub classes (F2,182 = 160.16, P < 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P < 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change

Pleiotropy of cardiometabolic syndrome with obesity-related anthropometric traits determined using empirically derived kinships from the Busselton Health Study

Over two billion adults are overweight or obese and therefore at an increased risk of cardiometabolic syndrome (CMS). Obesity-related anthropometric traits genetically correlated with CMS may provide insight into CMS aetiology. The aim of this study was to utilise an empirically derived genetic relatedness matrix to calculate heritabilities and genetic correlations between CMS and anthropometric traits to determine whether they share genetic risk factors (pleiotropy). We used genome-wide single nucleotide polymorphism (SNP) data on 4671 Busselton Health Study participants. Exploiting both known and unknown relatedness, empirical kinship probabilities were estimated using these SNP data. General linear mixed models implemented in SOLAR were used to estimate narrow-sense heritabilities (h 2 ) and genetic correlations (r g ) between 15 anthropometric and 9 CMS traits. Anthropometric traits were adjusted by body mass index (BMI) to determine whether the observed genetic correlation was independent of obesity. After adjustment for multiple testing, all CMS and anthropometric traits were significantly heritable (h 2 range 0.18–0.57). We identified 50 significant genetic correlations (r g range: - 0.37 to 0.75) between CMS and anthropometric traits. Five genetic correlations remained significant after adjustment for BMI [high density lipoprotein cholesterol (HDL-C) and waist–hip ratio; triglycerides and waist–hip ratio; triglycerides and waist–height ratio; non-HDL-C and waist–height ratio; insulin and iliac skinfold thickness]. This study provides evidence for the presence of potentially pleiotropic genes that affect both anthropometric and CMS traits, independently of obesity