Current and potential distributions of three non-native invasive plants in the contiguous USA

Biological invasions pose a serious threat to biodiversity, but monitoring for invasive species is time consuming and costly. Understanding where species have the potential to invade enables land managers to focus monitoring efforts. In this paper, we compared two simple types of models to predict the potential distributions of three non-native invasive plants (Geranium robertianum, Hedera spp., and Ilex aquifolium) in the contiguous USA. We developed models based on the climatic requirements of the species as reported in the literature (literature-based) and simple climate envelope models based on the climate where the species already occur (observation-based). We then compared the results of these models with the current species distributions. Most models accurately predicted occurrences, but overall accuracy was often low because these species have not yet spread throughout their potential ranges. However, literature-based models for Geranium and observation-based models for Ilex illustrated potential problems with the methodology. Although neither model type produced accurate predictions in all cases, comparing the two methods with each other and with the current species distributions provided rough estimates of the potential habitat for each species. More importantly, this methodology raised specific questions for further research to increase our understanding of invasion patterns of these species. Although these types of models do not replace more rigorous modeling techniques, we suggest that this methodology can be an important early step in understanding the potential distributions of non-native species and can allow managers of natural areas to be aware of potential invaders and implement early detection

Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201

The Santa Clara, 2018-10-11

https://scholarcommons.scu.edu/tsc/1076/thumbnail.jp