About

Katie Lotterhos is an associate professor of marine and environmental sciences at Northeastern University, affiliated with the Coastal Sustainability Institute and the Marine Science Center. Her research employs eco-evolutionary genomics to understand how climate has historically shaped biodiversity and to predict how a rapidly changing climate will impact marine life in the future. Her work integrates theory and experimentation, developing novel statistical methodologies to analyze data across biological, spatial, and temporal scales. Current projects include studying the responses of marine invertebrates to ocean acidification and pollution, analyzing the population dynamics of fisheries with applications to management and marine reserve design, and advancing methods in statistical genomics. She has received notable recognition for her contributions, including a CAREER award from the National Science Foundation and a Fulbright scholarship. Dr. Lotterhos is actively involved in collaborative efforts, such as the Genomics Partnership at the Gloucester Marine Genomics Institute, and her research has been highlighted in various media outlets. Her work aims to inform conservation strategies and improve understanding of how climate change affects marine biodiversity, with a focus on practical applications like oyster enhancement and predicting species survival under changing ocean conditions.

Research topics

• Computer Science
• Biology
• Machine Learning
• Evolutionary biology
• Genetics
• Ecology
• Agroforestry

Selected publications

• Inversion invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow

  Philosophical Transactions of the Royal Society B Biological Sciences · 2022 · 80 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Biology

  outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

  DOI

• Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest

  Evolutionary Applications · 2022 · 87 citations

  Senior authorCorresponding
  • Computer Science
  • Biology
  • Agroforestry

  Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF-predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic "population genetic" model with a single environmentally adapted locus; and (3) a polygenic "quantitative genetic" model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.

  DOI

• The Importance of Genetic Redundancy in Evolution

  Trends in Ecology & Evolution · 2020 · 189 citations

  Senior authorCorresponding
  • Computer Science
  • Evolutionary biology
  • Computer Science
  DOI

Recent grants

• Testing the tests: a predictive framework to guide genome scans for locally adapted traits

  NSF · $565k · 2017–2022

• Collaborative Research: Does ocean acidification induce a methylation response that affects the fitness of the next generation in oysters?

  NSF · $715k · 2017–2020

• RCN: Evolution in Changing Seas

  NSF · $523k · 2018–2024

• CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP)

  NSF · $1.5M · 2021–2026

Frequent coauthors

• Sara M. Schaal

  NOAA National Marine Fisheries Service

  19 shared

• Sean Hoban

  14 shared

• Alan M. Downey‐Wall

  Northeastern University

  13 shared

• Yaamini R. Venkataraman

  Woods Hole Oceanographic Institution

  10 shared

• Steven Roberts

  University of Washington

  10 shared

• Matthew P. Hare

  Louisiana Department of Natural Resources

  9 shared

• Jonathan B. Puritz

  University of Rhode Island

  9 shared

• Áki J. Láruson

  Marine and Freshwater Research Institute

  8 shared

Labs

• The Lotterhos Lab at Northeastern Marine Science CenterPI

Awards & honors

• NSF CAREER Award
• Fulbright scholarship

Similar researchers at Northeastern University

• Albert-László Barabásih-234
• Lisa Feldman Barretth-169
• Herbert Levineh-119
• Eduardo Sontagh-108
• Mansoor Amijih-97