Aim: Dispersal and environmental gradients shape marine microbial communities, yet the relative importance of these factors across taxa with distinct sizes and dispersal capacity in different ocean layers is unknown. Here, we report a comparative analysis of surface and deep ocean microbial beta diversity and examine how these patterns are tied to oceanic distance and environmental gradients.Location: Tropical and subtropical oceans (30°N-40°S).
Integrated pest management strategies were adopted to combat the coffee berry borer (CBB) after its arrival in Hawaii in 2010. A decision tree framework is used to model the CBB integrated pest management recommendations, for potential use by growers and to assist in developing and evaluating management strategies and policies. The model focuses on pesticide spraying (spray/no spray) as the most significant pest management decision within each period over the entire crop season. The main result from the analysis suggests the most important parameter to maximize net benefit is to ensure a low initial infestation level. A second result looks at the impact of a subsidy for the cost of pesticides and shows a typical farmer receives a positive net benefit of $947.17. Sensitivity analysis of parameters checks the robustness of the model and further confirms the importance of a low initial infestation level vis-a-vis any level of subsidy. The use of a decision tree is shown to be an effective method for understanding integrated pest management strategies and solutions.
Int. J. Semantic Computing 2014.08:85-98. Downloaded from www.worldscientific.com by MONASH UNIVERSITY on 02/04/15. For personal use only.However, the base topic model and R implementation are generally applicable to text analytics of document databases.
Many of the world's most important fisheries are experiencing illegal, unreported and unregulated (IUU) fishing, thereby undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending IUU fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time‐consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict whether a distant‐water fishing vessel is fishing within the Argentine exclusive economic zone (EEZ) on the Patagonian Shelf, one of the world's most productive regions for fisheries. We combine vessel location data with oceanographic seascapes—classes of oceanic areas based on oceanographic variables—and other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a distant‐water fishing vessel is operating inside the EEZ with 69%–96% confidence, depending on the year and predictor variables used. These results offer a promising step towards pre‐empting illegal activities, rather than reacting to them forensically.
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