High Natality Rates of Endangered Steller Sea Lions in Kenai Fjords, Alaska and Perceptions of Population Status in the Gulf of Alaska

Steller sea lions experienced a dramatic population collapse of more than 80% in the late 1970s through the 1990s across their western range in Alaska. One of several competing hypotheses about the cause holds that reduced female reproductive rates (natality) substantively contributed to the decline and continue to limit recovery in the Gulf of Alaska despite the fact that there have been very few attempts to directly measure natality in this species. We conducted a longitudinal study of natality among individual Steller sea lions (n = 151) at a rookery and nearby haulouts in Kenai Fjords, Gulf of Alaska during 2003–2009. Multi-state models were built and tested in Program MARK to estimate survival, resighting, and state transition probabilities dependent on whether or not a female gave birth in the previous year. The models that most closely fit the data suggested that females which gave birth had a higher probability of surviving and giving birth in the following year compared to females that did not give birth, indicating some females are more fit than others. Natality, estimated at 69%, was similar to natality for Steller sea lions in the Gulf of Alaska prior to their decline (67%) and much greater than the published estimate for the 2000s (43%) which was hypothesized from an inferential population dynamic model. Reasons for the disparity are discussed, and could be resolved by additional longitudinal estimates of natality at this and other rookeries over changing ocean climate regimes. Such estimates would provide an appropriate assessment of a key parameter of population dynamics in this endangered species which has heretofore been lacking. Without support for depressed natality as the explanation for a lack of recovery of Steller sea lions in the Gulf of Alaska, alternative hypotheses must be more seriously considered

Interaction between bottlenose dolphin (Tursiops truncatus) and trammel nets in the Archipelago de La Maddalena, Italy

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Domain-Specific Use Cases for Knowledge-Enabled Social Media Analysis

Social media provides a virtual platform for users to share and discuss their daily life, activities, opinions, health, feelings, etc. Such personal accounts readily generate Big Data marked by velocity, volume, value, variety, and veracity challenges. This type of Big Data analytics already supports useful investigations ranging from research into data mining and developing public policy to actions targeting an individual in a variety of domains such as branding and marketing, crime and law enforcement, crisis monitoring and management, as well as public and personalized health management. However, using social media to solve domain-specific problem is challenging due to complexity of the domain, lack of context, colloquial nature of language, and changing topic relevance in temporally dynamic domain. In this article, we discuss the need to go beyond data-driven machine learning and natural language processing, and incorporate deep domain knowledge as well as knowledge of how experts and decision makers explore and perform contextual interpretation. Four use cases are used to demonstrate the role of domain knowledge in addressing each challenge

Domain-Specific Use Cases for Knowledge-Enabled Social Media Analysis

Social media provides a virtual platform for users to share and discuss their daily life, activities, opinions, health, feelings, etc. Such personal accounts readily generate Big Data marked by velocity, volume, value, variety, and veracity challenges. This type of Big Data analytics already supports useful investigations ranging from research into data mining and developing public policy to actions targeting an individual in a variety of domains such as branding and marketing, crime and law enforcement, crisis monitoring and management, as well as public and personalized health management. However, using social media to solve domain-specific problem is challenging due to complexity of the domain, lack of context, colloquial nature of language, and changing topic relevance in temporally dynamic domain. In this article, we discuss the need to go beyond data-driven machine learning and natural language processing, and incorporate deep domain knowledge as well as knowledge of how experts and decision makers explore and perform contextual interpretation. Four use cases are used to demonstrate the role of domain knowledge in addressing each challenge