Selected Publications

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/BMSY). We also compare several other models to estimate B/BMSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/BMSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.
In Fish & Fisheries,2019

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

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

. Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index. In Fish & Fisheries, 2019.

PDF Project

. 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

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 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.


Creating a dynamic figure using gganimate and tweenr.



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.



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