A. John Woodill

A. John Woodill

Oregon State University

H-index: 6

North America-United States

About A. John Woodill

A. John Woodill, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Oregon State University, specializes in the field of Environmental and Resource Economics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Global beta diversity patterns of microbial communities in the surface and deep ocean

Detecting illegal maritime activities from anomalous multiscale fleet behaviours

Operational forecasting system based on anomalous behaviors in complex systems

Optimal spraying strategy to combat the coffee berry borer: A dynamic approach

Ocean seascapes predict distant‐water fishing vessel incursions into exclusive economic zones

Predicting illegal fishing on the Patagonia Shelf from oceanographic seascapes

A. John Woodill Information

University

Oregon State University

Position

Postdoctoral Scholar -

Citations(all)

72

Citations(since 2020)

63

Cited By

29

hIndex(all)

6

hIndex(since 2020)

6

i10Index(all)

4

i10Index(since 2020)

2

Email

University Profile Page

Oregon State University

A. John Woodill Skills & Research Interests

Environmental and Resource Economics

Top articles of A. John Woodill

Global beta diversity patterns of microbial communities in the surface and deep ocean

Authors

Ernesto Villarino,James R Watson,Guillem Chust,A John Woodill,Benjamin Klempay,Bror Jonsson,Josep M Gasol,Ramiro Logares,Ramon Massana,Caterina R Giner,Guillem Salazar,X Anton Alvarez‐Salgado,Teresa S Catala,Carlos M Duarte,Susana Agusti,Francisco Mauro,Xabier Irigoien,Andrew D Barton

Journal

Global Ecology and Biogeography

Published Date

2022/11

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). Time period 2010–2011. Major taxa studied Prokaryotes and picoeukaryotes (eukaryotes between 0.2 and 3 μm). Methods Beta diversity was calculated from metabarcoding data on prokaryotic and picoeukaryotic microbes collected during the Malaspina expedition across the tropical and subtropical oceans. Mantel correlations were used to determine the relative contribution of environment and oceanic distance driving community beta diversity. Results …

Detecting illegal maritime activities from anomalous multiscale fleet behaviours

Authors

James R Watson,A John Woodill

Journal

Fish and Fisheries

Published Date

2022/3/1

To achieve sustainable use of our oceans globally, our ability to detect and even predict illegal maritime activities must improve. The challenge is that most vessels that commit illegal acts will not transmit their location and are in effect unobservable to enforcement agencies. To overcome this challenge, we have developed a method for inferring the location and timing of illegal maritime activities inspired by complex systems theory: by monitoring the multiscale spatial behaviour of those vessels that we can observe, we are able to monitor for anomalous spatial patterns that could be associated with the presence of illegal activities (committed by an unobserved vessel). These spatial anomalies are represented as distortions in the ‘shape’ or multiscale spatial organization of fleets, which can be quantified using methods from information theory. To explore this approach, we developed a spatially explicit agent‐based …

Operational forecasting system based on anomalous behaviors in complex systems

Published Date

2022/8/11

A general-purpose approach to solving the core problems of detecting and predicting the actions of invisible actors, and the consequential challenges of intervention and prevention. The operational forecasting system is applied to data gathered from complex systems. The operational forecasting system uses novel early-warning signals that are based on anomalous behaviors of actors/agents that are observed, as they respond to those unobserved actors that are the source of systemic change. The operational forecasting system targets predicting when an event will occur, before it does, based on the anomalous behaviors of observed actors responding to those invisible actors that are creating the perturbation (ie the murmuration).

Optimal spraying strategy to combat the coffee berry borer: A dynamic approach

Authors

A John Woodill,Stuart T Nakamoto,Andrea M Kawabata,PingSun Leung

Journal

Journal of Agriculture and Food Research

Published Date

2021/6/1

The coffee berry borer (CBB), Hypothenemus hampei, is one of the most destructive pests worldwide. In Hawaii, coffee farmers have adjusted their farm management practices to deal with CBB since its introduction in 2010. This study addresses decisions coffee farmers make to combat damage from the coffee berry borer in Hawaii. The decision to spray or not spray a biological insecticide, Beauveria bassiana, is modeled during a typical coffee growing season in Kona, Hawaii. If the expected damage to the crop from not spraying is greater than the cost to spray, then it is beneficial to spray in order to mitigate that damage. To estimate economic damage, a Markov-chain tracks changes in farm-level infestation levels from month-to-month based on whether the farm decides to spray or not. The Markov-chain is incorporated into a dynamic programming model to provide a decision path for spray decisions over the …

Ocean seascapes predict distant‐water fishing vessel incursions into exclusive economic zones

Authors

A John Woodill,Maria Kavanaugh,Michael Harte,James R Watson

Journal

Fish and Fisheries

Published Date

2021/9

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 …

Predicting illegal fishing on the Patagonia Shelf from oceanographic seascapes

Authors

A John Woodill,Maria Kavanaugh,Michael Harte,James R Watson

Journal

arXiv preprint arXiv:2007.05470

Published Date

2020/7/10

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (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 illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as 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 Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

See List of Professors in A. John Woodill University(Oregon State University)

A. John Woodill FAQs

What is A. John Woodill's h-index at Oregon State University?

The h-index of A. John Woodill has been 6 since 2020 and 6 in total.

What are A. John Woodill's top articles?

The articles with the titles of

Global beta diversity patterns of microbial communities in the surface and deep ocean

Detecting illegal maritime activities from anomalous multiscale fleet behaviours

Operational forecasting system based on anomalous behaviors in complex systems

Optimal spraying strategy to combat the coffee berry borer: A dynamic approach

Ocean seascapes predict distant‐water fishing vessel incursions into exclusive economic zones

Predicting illegal fishing on the Patagonia Shelf from oceanographic seascapes

are the top articles of A. John Woodill at Oregon State University.

What are A. John Woodill's research interests?

The research interests of A. John Woodill are: Environmental and Resource Economics

What is A. John Woodill's total number of citations?

A. John Woodill has 72 citations in total.

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