Selected Publications

Many of the world’s fisheries are unassessed, with little information about population status or risk of overfishing. Unassessed fisheries are particularly predominant in developing countries and in small‐scale fisheries, where they are important for food security. Several catch‐only methods based on time series of fishery catch and commonly available life‐history traits have been developed to estimate stock status (defined as biomass relative to biomass at maximum sustainable yield: B/BMSY). While their stock status performance has been extensively studied, performance of catch‐only models as a management tool is unknown. We evaluated the extent to which a superensemble of three prominent catch‐only models can provide a reliable basis for fisheries management and how performance compares across management strategies that control catch or fishing effort. We used a management strategy evaluation framework to determine whether a superensemble of catch‐only models can reliably inform harvest control rules (HCRs). Across five simulated fish life histories and two harvest‐dynamic types, catch‐only models and HCR combinations reduced the risk of overfishing and increased the proportion of stocks above BMSY compared to business as usual, though often resulted in poor yields. Precautionary HCRs based on fishing effort were robust and insensitive to error in catch‐only models, while catch‐based HCRs caused high probabilities of overfishing and more overfished populations. Catch‐only methods tended to overestimate B/BMSY for our simulated data sets. The catch‐only superensemble combined with precautionary effort‐based HCRs could be part of a stepping stone approach for managing some data‐limited stocks while working towards more data‐moderate assessment methods.
In Regional Environmental Change,2018

Marine social-ecological conditions in the Arctic are rapidly changing. With many transboundary issues, such as shifting ranges of fisheries, biodiversity loss, sea ice retreat, economic development and pollution, greater pan-Arctic assessment and co-management are necessary. We adapted the Ocean Health Index (OHI) to compile pan-Arctic data and evaluate ocean health for nine regions above the Arctic Circle to assess the extent to which pan-Arctic assessment is possible and identify broad social-ecological trends. While the quality and availability of data varied, we assessed and scored nine OHI goals, including the pressures and resilience measures acting upon them. Our results show the Arctic is sustainably delivering a range of benefits to people, but with room for improvement in all goals, particularly tourism, fisheries, and protected places. Successful management of biological resources and short-term positive impacts on biodiversity in response to climate change underlie these high goal scores. The OHI assesses the past and near-term future but does not account for medium- and long-term future risks associated with climate change, highlighting the need for ongoing monitoring, dynamic management, and strong action to mitigate its anticipated effects. A general increase in and standardisation of monitoring is urgently needed in the Arctic. Unified assessments, such as this one, can support national comparisons, data quality assessments, and discussions on the targeting of limited monitoring capabilities at the most pressing and urgent transboundary management challenges, which is a priority for achieving successful Arctic stewardship.
In Regional Environmental Change,2018

Reproducibility has long been a tenet of science but has been challenging to achieve—we learned this the hard way when our old approaches proved inadequate to efficiently reproduce our own work. Here we describe how several free software tools have fundamentally upgraded our approach to collaborative research, making our entire workflow more transparent and streamlined. By describing specific tools and how we incrementally began using them for the Ocean Health Index project, we hope to encourage others in the scientific community to do the same—so we can all produce better science in less time.
In Nature Ecology & Evolution, 2017

Recent Publications

More Publications

. Trade‐offs for data‐limited fisheries when using harvest strategies based on catch‐only models. In Regional Environmental Change, 2018.

PDF Project

. A pan-Arctic assessment of the status of marine social-ecological systems. In Regional Environmental Change, 2018.

PDF Project

. Cumulative human impacts in the Bering Strait Region. In Ecosystem Health and Sustainability, 2017.

PDF Project

. Drivers and implications of change in global ocean health over the past five years. In PLoS ONE, 2017.

PDF Code Dataset Project Interactive App

. Our path to better science in less time using open data science tools. In Nature Ecology & Evolution, 2017.

PDF Project

. Aligning marine species range data to better serve science and conservation. In PLoS ONE, 2017.

PDF Code Project Interactive Shiny App

. Applying a New Ensemble Approach to Estimating Stock Status of Marine Fisheries Around the World. In Conservation Letters, 2017.

PDF Code Project

. Improving estimates of population status and trend with superensemble models. In Fish and Fisheries, 2017.

PDF Code Project

. Eastern Pacific reef fish responses to coral recovery following El Niño disturbances. In MEPS, 2014.

PDF

Recent & Upcoming Talks

Creating a personal website with blogdown
Mar 14, 2018 12:00 AM
Ocean Health Index in the US Northeast
May 2, 2017 12:00 AM

Recent Posts

This blog post was originally written for OHI-Science.org A significant portion of my work on the Ocean Health Index (OHI) involves working with raster data, a specific type of spatial data where values are held in grid cells. The data I work with varies from high resolution, remotely sensed data on sea surface temperature to coarse, modeled data on global fish catch. When I was working on the global assessment, I dealt with raster data at a global scale.

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Creating a dynamic figure using gganimate and tweenr.

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Projects

Cumulative Human Impacts

Estimating and tracking human impacts on marine ecosystems

Data-Limited Fisheries

A working group focused on developing new methods for assessing the status of data poor fish stocks globally.

Ocean Health Index

The Ocean Health Index is a framework to measure the health of the oceans. Understanding the state of our oceans is a first step towards ensuring they can continue providing humans benefits now and in the future.

Teaching

Tutorials:

Intro to Spatial Analysis in R (focus on rasters)

Software Carpentry for R at Woods Hole Oceanographic Institute Woods Hole, MA October 22 - October 23, 2018

Data Wrangling & Visualization Taught at the Monterey Bay Aquarium Research Institute Software Carpentry Moss Landing, CA November 30 - December 1, 2017

Data Wrangling and Visualization (SWC for Ecology Materials) UC Merced Software Carpentry Merced, CA August 18, 2017

Contact