SUNY Oneonta Campus Invasive Plant Survey, Removal, and Replacement Plan

SUNY Oneonta Campus Invasive Plant Survey, Removal and Replacement Plan College campuses encompass a variety of habitats subject to a high level of invasive plant introductions; from frequent soil disturbance during construction projects, and non-native horticultural plantings. At the same time, campus landscapes can become outdoor laboratories for invasive plant species research, education and control efforts. Last fall, students of Plant Ecology (BIOL 381) at SUNY Oneonta researched 24 invasive plants from the New York State list of Prohibited and Regulated Invasive Species (6 NYCRR Part 575). The 250-acre campus is within the Catskill Regional Invasive Species Partnership (CRISP) region, and the project was developed in cooperation with CRISP. Students were assigned portions of campus to survey, and many of the 20 invasive species located were horticultural plantings ( e.g., Japanese Barberry, Burning Bush), but in several of the minimally managed woodlots they found extensive Norway Maple and Garlic Mustard in the understory. GPS locations were reported to the state online iMapInvasives dataset. Our current study expands the previous work to include ground-truthing of GPS locations and species identification. We are collaborating with our SUNY Oneonta Facilities Office to initiate an invasive plant removal and replacement plan that includes an outreach event to replace some of the invasive shrubs on campus with native species. One target species is the Japanese Barberry, which is prohibited for sale in New York State. Our plan will replace Japanese Barberry with suitable, deer resistant, native, and non-invasive shrubs over a period of years. To our knowledge, this project is the first full-campus invasive plant survey in the 64 campus SUNY system. We hope that our project will provide a successful template for invasive plant management, and encourage other SUNY campuses to become more sustainable by complying with 6 NYCRR Part 575

Appendix B. A table presenting observed visitors to Collinsia verna flowers in southwestern Pennsylvania, USA.

A table presenting observed visitors to Collinsia verna flowers in southwestern Pennsylvania, USA

Appendix A. A table presenting characteristics of the pollination environment for Collinsia verna at three populations in Pennsylvania, USA, 1997 and 1999.

A table presenting characteristics of the pollination environment for Collinsia verna at three populations in Pennsylvania, USA, 1997 and 1999

Developing Quantitative and Analytic Skills in Undergraduates Using Conservation Examples

This talk was given during the North American Congress for Conservation Biology

Student Interpretation of Conservation Data: Does their Reach exceed their Grasp

This study examined how well undergraduate students can develop data analysis skills relevant to conservation biology over the course of a single semester. Students completed two conservation data analysis exercises, pre and post self-assessments of confidence in data analysis skills, a classroom discussion, and pre/post content assessments. Between the first and second exercises, a data analysis teaching intervention was administered in all classes. Instructional and assessment materials were created and validated by 24 conservation educators led by the Center for Biodiversity and Conservation at AMNH. Results from one semester (100+ students) show that students scored significantly higher on post-content assessments for both exercises. We also found significant increases in student self-assessment of confidence in data analysis skills. However, when evaluated at the level of different skill dimensions, students\u27 ability to represent and interpret data improved between exercises, but ability to complete calculations and draw conclusions was significantly worse on the second exercise. While our study demonstrates that direct instruction in data analysis does improve student performance overall, there is a disconnect between student self-assessment of their data analysis skills and their actual ability. This indicates that some aspects of data analysis may require different teaching intervention approaches

Student Interpretation of Conservation Data: Does Their Reach Exceed their Grasp

Background/Question/Methods: The fast pace of biological data generated nowadays calls for our biology students to be proficient in quantitative skills such as data analysis. This study examined how well undergraduate students can develop data analysis skills relevant to ecology and conservation biology over the course of a single semester. Students completed two data analysis exercises, pre and post self-assessments of confidence in data analysis skills, a classroom discussion, and pre/post content assessments. The two data analysis exercises were adapted from the free online teaching modules on the Network of Conservation Educators and Practitioners website (www.ncep.amnh.org). Between the first exercise (a demography problem involving palm harvests and parrots) and second exercise (calculating diversity indices for spider communities), a data analysis teaching intervention was administered in all classes. Instructional and assessment materials were created and validated by 24 conservation educators led by the Center for Biodiversity and Conservation at AMNH. Results/Conclusions: Results from one semester show that students scored significantly higher on post-content assessments for both conservation exercises (N1 = 207 students; N2 = 199; P \u3c 0.01 for both). We also found significant increases in student self-assessment of confidence in data analysis skills (N = 87). However, when evaluated at the level of different skill dimensions, students’ ability to represent and interpret data improved between exercises (N = 257; P \u3c 0.01), but ability to complete calculations and draw conclusions was significantly worse on the second exercise (P \u3c 0.01). While our study demonstrates that direct instruction in data analysis does improve student performance overall, there is a disconnect between student self-assessment of their data analysis skills and their actual ability. This indicates that some aspects of data analysis may require different teaching intervention approaches

How Much Can Students Gain in Data Analysis and Critical Thinking Skills in One Semester?

Background/Question/Methods: The effective preservation and sustainable use of ecosystems is a complex endeavor that requires proficiency in skills of critical thinking, data analysis, oral communication, broad synthesis of information and teamwork across diverse groups. However, there is concern that US undergraduate science students do not currently develop these fundamental process skills they will need as professionals. In this study, we investigate how we can best ‘operationalize’ the teaching of process skills and how we can assess their development in undergraduate students. We are implementing a multi-year, multi-institutional research project to: (1) develop a set of instructional materials and assessment tools for critical thinking, oral communication, and data analysis; and (2) pilot these materials in a diversity of classroom settings under two instructional modalities: individual student reflection versus intensive classroom discussion of the skill. Results/Conclusions: Twenty-four conservation biologists have collaborated during the last year to create and validate instructional materials for process skills development, led by the Network of Conservation Educators and Practitioners (ncep.amnh.org). The instructional set for each skill consisted of pre/post student self-assessments, two exercises with rubrics for evaluation of student performance, and pre/post exercise content assessments. In fall 2011, nine professors piloted these materials in biology, ecology, and conservation biology courses. We present preliminary results from a subset of their students, from instructional units on data analysis using an intensive classroom discussion (DA; N=22) and critical thinking using individual student reflection (CT; N=20). For DA, we find significant increases in student self-confidence on data representation and interpretation (P0.05). For both skills, we find gains in content knowledge after the application of exercises (DA: g=0.22±0.082; CT: g=0.4±0.11). Observed gains in the skills vary depending on the aspect analyzed. For DA, students experienced significant gains in data representation and interpretation (V=17,