ABSTRACT
Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions;however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism. © 2022 The Author(s)
ABSTRACT
Access to and discovery of credible information is the product of numerous, interacting factors including individual characteristics and behaviors as well as features of the information environment, social, cultural, and institutional norms, policies and regulations, and more. To date, most research on information disorder has focused either on the individual or on the information environment (or on the technology that allows an individual to access the information environment), but there is a lack of systematic, theory-driven research on the dynamic relationship between the individual and their environment. In this study, we propose a novel application of Brofenbrenner's social ecological model to the study of cognitive security and resilience in the context of information disorder. First, we describe the refitting of the model from public health and human development to cognitive security. Using extant literature in the field, we identify the key factors at each level of influence - including individual-level factors such as attitudes/beliefs, knowledge/experience, and demographic characteristics, as well as higher-level factors at the interpersonal-, organizational/institutional-, community-, and policy/culture-levels - that shape susceptibility and resilience to information disorder. We also consider the dynamic interactions between individuals, groups, societies, and characteristics of the technological environment, including how algorithms and artificial intelligence interact with individual behaviors, policies, and organizational decision-making to shape access to and discoverability of credible information. Finally, we describe an application of the model to a use case involving COVID-19-related information behaviors. To our knowledge, this is the first time Brofenbrenner's social ecological model has been applied in full as a conceptual foundation for the study of cognitive security and resilience. Our findings provide important new insight into the social, cultural, and structural factors that shape information behaviors and access to credible information, as well as the impact of information disorder. The results can be used to identify vulnerabilities and targets for future information-related initiatives and interventions (such as fact-checking and journalism initiatives) and to inform evaluations of such initiatives, as well as to better understand variation in susceptibility and resilience to information disorder. Further, this study lays an important conceptual foundation for future research to expand on this use case and refine the application of the social ecological model to the information domain. © 2022 Copyright for this paper by its authors.
ABSTRACT
Rosa damascena is widely used in the perfume, cosmetics, food, and medicine industrie around the world. Especially in the last two years, the use of drugs containing Rosa damascena has been emphasized in the treatment of Covid19 patients. In order to ensure the sustainability of these studies and to get to know Rosa damascena better, first of all, the ecological demands of Rosa damascena should be determined, and to expand the production area together with planning studies. In this study, the necessary ecological criteria (soil, climate, topography) for Isparta rosa growing were determined. An ecological modeling of potential agricultural areas suitable for Isparta rosa cultivation was made using Analytical Hierarchy Process (AHP) and Remote Sensing-Geographic Information Systems (RS-GIS), Potential Isparta rosa production areas were determined spatially. With this study, it was determined that 105,637 ha (12.12%) of Isparta's land was classified as “very suitable” for rose cultivation, but rose cultivation was carried out on an area of only 2,975 ha. Physical assessment was revealed that the areas suitable for Isparta rose cultivation were significantly restricted by the socio-economic structure of the province. © 2021 Elsevier B.V.
ABSTRACT
Quantitative skills are becoming central to the undergraduate and graduate curriculum in ecology and evolutionary biology. While previous studies acknowledge that students perceive their quantitative training to be inadequate, there is little guidance on best practices. Moreover, with the recent COVID-19 sudden transition to online learning, there is even less guidance on how to effectively teach quantitative ecology online. Here, I synthesize a prescription of pedagogical best practices for teaching quantitative ecology online based on a broad review of the literature on multiple quantitative disciplines. These best practices include the following: (1) design and implement the class to meet learning goals using online strategies specifically; (2) create an open, inclusive, and welcoming online environment that promotes a sense of learning community; (3) acknowledge the diversity of talents and learning strategies; (4) use real-world examples and assessments; (5) account for gaps in knowledge; (6) emphasize the modeling cycle process; (7) focus on developing ideas rather than tools or procedures; (8) if needed, introduce computational tools thoroughly before combining them with mathematical or statistical concepts; (9) evaluate the course constantly; and (10) put your heart and soul into the class. I hope these practices help fellow instructors of quantitative ecology facing similar challenges in providing our students with the knowledge and skills needed to meet the challenges of the future.