Ichthyofauna of the Los Angeles River

The Los Angeles River is a highly modified urban system. Upper tributaries of the watershed are located in Angeles National Forest and are in a relatively natural state, but below the forest boundary the tributaries and the mainstem consist of a series of completely channelized sections with a concrete bottom that includes a low-flow channel and vertical walls, sections where there are graded berms and a substrate that was either not stabilized with a concrete substrate, or where enough sediment has accumulated to provide a “soft-bottom” with vegetation, boulders, and variation in flow, and off-channel impoundments. Here we provide the results of fish surveys and both professional and community-contributed citizen science observations collected from 2007-2020 that document the presence of 29 species, of which six, found either in upper reaches (sections of the river with similar habitat and hydrologic characteristics) or in the estuary, are native to the river. To accompany these data, we also provide a novel classification schema identifying the unique reaches of the LA River

A longitudinal temperature profile of the Los Angeles River from June through October 2016

This pilot study developed a longitudinal temperature profile of the Los Angeles River by deploying temperature loggers throughout the watershed between June and October 2016. The watershed was divided into zones based on river system component, urbanization, and channelization. Channelized sites recorded the highest temperatures, tributaries recorded the lowest, and the estuary showed the most fluctuation. Overall, temperatures were too warm to support re-introduction of native fish but currently support non-native fish species. Temperature mitigation is needed for native species to re-establish. Albeit limited in scope, this study establishes a baseline of summer/fall temperatures in the Los Angeles River

Colonization and Persistence of a Southern California Steelhead (Oncorhynchus mykiss) population

Abstract.-The life history and habitat interactions of southern Oncorhynchus mykiss populations have received less attention than their Pacific Northwest counterparts. In this article we create a conceptual model describing the factors affecting O. mykiss population dynamics in Topanga Creek, Los Angeles County, California to understand the process that led to extirpation following floods in 1980 and 1983, re-colonization in the late 1990's, and continued persistence. We conclude that key factors influencing population dynamics include life-history variability with both resident and anadromous individuals, population spatial structure connecting Topanga Creek with other watersheds within the metapopulation, exclusive distribution within the mainstem Topanga Creek, high-quality summer and winter rearing habit, and food availability sufficient to maintain growth at high temperatures. Protecting the population in Topanga Creek from future extirpation should include restoration of the lagoon, and preventing changes to the flow regime and water quality

The effects of a prolonged drought on southern Steelhead Trout (Oncorhynchus mykiss) in a coastal creek, Los Angeles County, California

Long-term lifecycle monitoring of federally endangered southern steelhead trout (Oncorhynchus mykiss) in Topanga Creek provides a unique opportunity to examine the health and abundance of a steelhead population before (2008-2011) and during (2012-2016) a prolonged drought. We found that the five-year drought resulted in a substantial and significant decline in available wetted habitat suitable for rearing and upstream migratory access for anadromous adults. The response of the steelhead population has been a significant reduction in anadromous spawning, distribution of rearing, and abundance of all life stages of anadromous and resident steelhead. After five years of drought a population that exceeded 325 individuals in 2008, now numbers fewer than 50 fish, and appears to be at extremely high risk of extirpation. Acknowledging the possibility of increased drought regionally and globally, the need to bolster southern steelhead resiliency to additional disturbance is paramount

Evidence of Log Integrity in Policy-based Security Monitoring

Abstract-Monitoring systems are commonly used by many organizations to collect information about their system and network operations. Typically, SNMP, IDS, or software agents generate log data and store them in a centralized monitoring system for analysis. However, malicious employees, attackers, or even organizations themselves can modify such data to hide malicious activities or to avoid expensive non-compliance fines. This paper proposes a cloud-based framework for verifying the trustworthiness of the logs based on a small amount of evidence data. A simple Cloud Security Monitoring (CSM) API, made available on the cloud services, allows organizations operating on the cloud to collect additional "evidence" about their systems. Such evidence is used to verify system compliance against the policies set by security managers or regulatory authorities. We present a strategy for randomly auditing and verifying resource compliance, and propose an architecture that allows the organizations to prove compliance to an external auditing agency

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable