My research interests are rooted in two areas – using population genetics and genomics to inform fisheries management and conservation and investigating the impact of conservation actions on nature and people. The ultimate goal of my scientific endeavors is to provide greater understanding and a scientifically backed tool kit for management and conservation of marine ecosystems.
I have had the opportunity to engage in many research projects with a whole number of terrific and amazing scientists all over the world. A brief synopsis of my ongoing and past work:
Impact of nature-based conservation interventions on human well-being
Increasingly, global conservation policy is shifting away from solely nature-based conservation actions to those emphasizing human dimensions of conservation. Specifically, this shift is based on the assumption that conserving nature has overall positive impacts on human well-being. Thus, understanding the nature of these linkages between a specific intervention and human well-being outcomes is critical for effective conservation decision-making. However, the existing evidence supporting these linkages is very widely distributed, not collated or synthesized effectively, and oftentimes inaccessible and/or esoteric for researchers and practitioners. In order to address this, through systematic mapping and evidence synthesis, our Science for Nature and People (SNAPP) working group on Evidence-Based Decision Making has created a map of the evidence base for these links between nature conservation and human well-being (see Bottrill et al. 2014, McKinnon et al. 2015, 2016). We are continuing now to investigate the patterns of evidence revealed in the map and conduct in-depth examinations into specific linkages in order to understand pathways to successful nature-people outcomes. Specifically, my interest, given my overall focus on marine ecosystems, is to investigate these questions within marine-based conservation and communicate the results to pertinent ongoing projects. This is the focus of my current postdoctoral research.
Role of technology in advancing evidence-based decision-making
We are in an unprecedented time in the history of scientific research and our ability to monitor the natural world. The pace of knowledge generation and data gathering is exponentially rising by the year, aided, in part, by the rise of technological advances that allow us to collect unparalleled fine-scale data over broad spatial and temporal ranges. The conservation and development research fields alone have witnessed an explosion of research in the past few decades. This massive volume of research presents a unique and critical opportunity for ensuring that key decisions in environmental and development policies and programs are informed by the best available evidence. However, despite the potential wealth of information available – policymakers and practitioners in conservation still do not use more evidence in decision-making, relying primarily on personal experience and anecdote (see Walsh et al. 2015, Cook et al. 2010, Pullin and Knight 2005). Commonly, the difficulty of locating information both due to the volume of potential evidence and the time it takes to sort through, is cited as a primary barrier to pursuing evidence-based conservation.
Technology and sorting through evidence
On average, a systematic map or review of evidence takes anywhere from 2-4 years to complete from searching for literature to sorting through it and synthesizing key patterns. Research teams are sorting through tens of thousands of search results, typically only including ~2.5% of titles. This is a massive undertaking that can be improved with new technologies. The advent of machine learning capabilities has seen the emergence of artificial neural networks that simulate how a human brain processes information, on a digital computer, allowing for artificial intelligence systems to sort through massive amounts of data and performing tasks that humans would perform. Artificial neural networks form the processing framework behind image recognition (e.g. facial recognition, processing of geospatial images), to converting verbal to written speech, to semi-autonomous vehicles. Similarly, machine learning algorithms like this, combined with natural language processing, can be used to rapidly process through data such as scientific articles, to find relevant articles based on what it learns from input from a human reviewer.
Working with a data science non-profit volunteer organization, DataKind, I am currently working on developing an open access machine learning application for conducting systematic maps and reviews that is domain agnostic. We plan on launching a beta version of the app, Colandr, in late Spring 2017.
Technology and finding synthesized evidence
Systematic maps and reviews produce collated lists of relevant literature with associated metadata on impacts and outcomes on a policy relevant topic. However, this data is often disseminated in the form of a PDF article and attached spreadsheets. When the list of included literature grows larger, encompassing hundreds if not thousands of papers – exploring these lists and utilizing them to find desired information can be time-consuming and difficult. Thus, any efforts to bridge the science-policy gap with systematic evidence synthesis must incorporate effective and efficient communication of evidence and utilization of evidence by all audiences.
Data visualization is increasingly widely used to allow users to dynamic explore data for multiple purposes, from learning and hypothesis exploration, to detecting patterns and validating theories (for more, see this article in the Harvard Business Review). Scientists often present their data in charts and figures, allowing readers to quick discern patterns and findings with visual tools. However, charts and figures in PDFs are static. With the level of complex social and ecological data captured in a typical environmental systematic map or review, delving into the different layers and iterations of the data based on how you filter the information – would be impossible in static representation. Data visualizations allow for dynamic exploration of information – a boon to increasing accessibility and intelligibility of data from systematic evidence synthesis.
Phylogenetics and population genomics of Loliginid squids
Globally, rampant harvesting practices have left vital marine resources in sharp decline precipitating a dramatic loss of the biodiversity and threatening the health and viability of natural populations. To protect these crucial resources and ecosystems, a comprehensive assessment of biodiversity, as well as a rigorous understanding of the mechanisms underlying it, is urgently needed. As global finfish fisheries decline, harvest of cephalopod fisheries, squid, in particular, has exponentially increased. However, while much is known about the evolution and population dynamics of teleost fishes, much less is understood about squids. For my dissertation research, I conducted a robust, in-depth examination of these mechanisms in commercially important squids using a novel approach combining genetics and genomics methods.
- Big fin reef squid (Sepioteuthis cf. lessoniana)
Cephalopod fisheries in Southeast Asia and the Coral Triangle constitute a major source of income and protein to thousands of coastal communities. However, little basic information is known about these fisheries, much less about their evolution and popualtion dynamics. In particular, the big fin reef squid (S. cf. lessoniana, Family: Loliginidae) is a heavily harvested species throughout the Indian and Pacific Oceans. Existing evidence suggests that there are actually multiple morphologically similar species that are lumped together as S. cf. lessoniana. Understanding how many species there are and what distinguishes them is critical for informing effective management and conservation of these commercial stocks. Using a suite of molecular markers, we examined the distribution and evolution of a species complex of bigfin reef squid (Sepioteuthis cf. lessoniana) throughout the global center of marine biodiversity, the Coral Triangle, and adjacent areas. Phylogenetic analyses and species delimitation methods unequivocally demonstrate the presence of at least three reciprocally monophyletic cryptic lineages sympatrically distributed throughout the region (see Cheng et al. 2014). This information indicates that management of these species must take into account species identity.Differences in life history, particularly in dispersal life history, is a major factor in driving different patterns of connectivity, or the exchange of genes, over space and time. Understanding patterns of connectivity is vital to informing spatial management of these squid stocks, particularly as they range over the fishing regions of multiple nations. Using genome-wide markers, we examine patterns of population structure over the Coral Triangle and adjacent regions to identify important processes shaping both genetic and demographic connectivity in two of these cryptic species. We are using a RAD sequencing method called 2b-RAD (see Misha Matz and Eli Meyer‘s lab sites) to generate these markers.
- California market squid (Doryteuthis opalescens)
Within California, one of the most highly valued fisheries is for market squid (Doryteuthis opalescens). This fishery has long been hypothesized to be two separate stocks due to different spawning peaks and areas. However, previous studies using various methodologies have not been able to conclusively delineate populations structure. Using genome-wide SNPs (generated with 2b-RAD) and a rigorous temporal sampling scheme, we are examining this question in collaboration with California Fish and Wildlife. This project is funded by Save Our Coasts.
Evolution of diversity in reef fishes – phylogeny of Chrysiptera damsels
One of the most well-studied groups in coral reef ecosystems are the colorful Pomacentridae reef fishes. These fishes are characterized by a wide range of coloration patterns, ecological niches, and morphological diversity. Within this group, the genus Chrysiptera has at least 35 known species, but whose evolutionary history is rather complex. Previous phylogenetic studies of Pomacentridae indicate that Chrysiptera is not a monophyletic group, despite indications from morphology. Furthermore, genetic evidence suggests that multiple species complexes exist, with many cryptic species featuring subtle color polymorphisms and possible endemism. Given this complex evolutionary scenario, we are conducting an in-depth, multi-gene phylogenetic examination into Chrysiptera in order to illuminate the evolutionary history of this extremely diverse group. This project is being conducted in collaboration with Hayley Nuetzel at University of California-Santa Cruz.
Phylogeography of co-distributed mantis shrimps
The Coral Triangle is an area of the Indo-Australian Archipelago that hosts the world’s greatest concentration of marine biodiversity. This small region lies at the confluence of the Pacific and Indian Oceans, and is characterized by complex oceanographic currents, thousands of islands, and a tumultuous geological history – creating a diverse fabric of evolutionary processes. Researchers have long debated the origins of this biodiversity hotspot – as a center of origin, center of overlap or center of survival (see Bellwood et al. 2001, Briggs 2000, Barber 2009). The long-standing assumption in marine divergence and speciation theory posits that due the prevalence of a pelagic larval dispersal stage in a vast and fluid environment, very strong barriers are necessary for divergence to occur in marine populations. Using three species of co-distributed mantis shrimp (Stomatopoda) species, we take a phylogeographic approach to understanding processes that drive diversification in the Coral Triangle. We find a shared genetic break over the marine Wallace’s line as proposed by Barber 2000 in all three species, but evidence of dramatically different fine-scale population divergence patterns indicating that subtle differences in life history likely play a much larger role that previously expected. – Barber, P.H., Cheng, S.H., Erdmann M.V., Tenggardjaja, K., Ambariyanto 2011.