Effects of Leaf Litter on Amphibian Site Selection

Leaf litter plays an important role in the forest ecosystem, impacting various processes and hindering erosion. While there is variability in the chemical and nutritional properties of leaf litter, the effects these variables have on organisms within the environment are not well known. In this study, we examined the effects of leaf litter chemistry on amphibian oviposition site selection. Artificial ponds were created using small, plastic pools, and leaf litter of 15 different tree species (including two invasive species) was added. During the 60 day experiment, water quality measurements (including temperature, pH, dissolved oxygen, conductivity, and water depth) were taken weekly from each individual pool, and the amount of eggs deposited by Cope’s gray treefrog (Hyla chrysoscelis) was recorded. Using zero inflated models, results show that tree species was the most accurate predictor of the amount of eggs deposited into each pool. Frogs had a strong preference for post oak leaves, while they completely avoided southern red oak leaves. Tree species also had an effect on the amount of nitrogen, phosphorus, and tannins (a type of secondary compound in tree leaves). These results indicate that cues from tree species have a strong impact on habitat selection for amphibians, which may impact ecosystems in broader ways through changes in amphibian abundance and diversity

Amphibian Oviposition Site Selection Preferences in Response to Leaf Litter Chemical Characteristics

Rebekah Magee and Julia E. Earl are a part of the School of Biological Sciences at Louisiana Tech Universit

Effects of Leaf Litter on Amphibian Oviposition Site Selection

Rebekah Magee is a graduate student in the School of Biological Sciences at Louisiana Tech University. Julia E. Earl is an Assistant Professor in the School of Biological Sciences at Louisiana Tech University. The abstract for this presentation can be downloaded by clicking on the blue download button

Together We Rise: Reaching Inclusivity for Student Excellence

This presentation outlines the BIONIC (Believe It Or Not I Care) Program at Mattoon High School. For the past 10 years, Dr. Larson and a team of counseling interns have partnered with Mattoon High School to implement BIONIC (Believe It Or Not I Care), a school-wide peer mentoring program

Together We RISE Reaching Inclusivity for Student Excellence

This presentation outlines the data-based Freshman Connection Program at Eastern Illinois University and its impact on the student leaders who serve as mentors in the program

GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production

GrassQ is a holistic grassland decision support system (DSS) that encapsulates a range of measurement technologies to provide yield and quality data to a cloud based platform, which can provide users with real time management information in the field. GrassQ aims to promote precision agricultural concepts within the pasture based livestock industry. Accurate measurement and allocation of fresh pasture to grazing herds on a daily basis is essential in increasing efficiency. Novel systems of measuring grass yield and quality were developed at the Moorepark Animal and Grassland Research Centre in Cork, Ireland, over the grass growing seasons of 2017 and 2018. Measurement systems included ground based and remote sensing techniques. The prototype GrassQ DSS was designed to process datasets uploaded from all proposed measurement systems. Measurement parameters were compressed sward height (CSH) (mm), herbage mass (HM) (kgDM/ha), dry matter (DM) (g/kg) and crude protein (CP) (g/kg). Ground based measurements were recorded using a smart rising plate meter (RPM) and lab based near infrared spectroscopy (NIRS). Multispectral remote sensing was carried out using an unmanned aerial vehicle (UAV), and data from the European Union’s Sentinel-2 satellite (S2). Reference analyses for all prediction models were carried out at Moorpark’s Grassland Laboratory and all sample locations were geotagged to enable spatial mapping of all parameters. The GrassQ prototype DSS is currently operational, including a number of preliminary grass quantity and quality prediction models. The complete Grass DSS will be is launched upon final validation

Students' participation in collaborative research should be recognised

Letter to the editor