About

Masami Fujiwara received his Ph.D. in Biological Oceanography from the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program in 2002. He has a background in Biological Sciences from the University of Alaska Fairbanks, where he completed his undergraduate studies, and also holds a Master’s degree in Marine Biology from the University of Alaska Fairbanks. Fujiwara joined Texas A&M University in 2009 as an Assistant Professor in the Department of Wildlife and Fisheries Sciences and is currently an Associate Professor in the Department of Ecology and Conservation Biology. His research focuses on the integration of ecological theories, modern data analysis methods, and existing data to determine the drivers of population fluctuations and to develop predictive models for natural populations. He has made significant contributions to the development of the undergraduate curriculum for the Department of Ecology and Conservation Biology, serving as the chair of the curriculum development committee in 2020. Fujiwara teaches graduate and undergraduate courses in population ecology and has received over $2 million in external research funding from sources including the National Science Foundation and the National Oceanic and Atmospheric Administration. He is a member of the editorial board of PLoS One and has authored numerous peer-reviewed journal articles in prominent journals such as Nature, Science, and Proceedings of the National Academy of Sciences.

Research topics

• Ecology
• Geography
• Biology
• Fishery
• Environmental science
• Oceanography
• Geology
• Demography

Selected publications

• Tracking Sharks Through Time: Long-Term Trends Along the Texas Coast

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• masamifujiwara/diversity-stability: Initial release for manuscript submission

  Open MIND · 2026-04-20

  otherOpen access1st authorCorresponding

  No description provided.

  PublisherDOI

• masamifujiwara/prediction-landscapes: Initial release for manuscript submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-20

  otherOpen access1st authorCorresponding

  Time series forecasting of alpha and gamma diversity across coastal ecosystems, integrating environmental drivers and state-space, ARIMA, and machine learning models.

  PublisherDOI

• masamifujiwara/prediction-landscapes: Initial release for manuscript submission

  Open MIND · 2026-04-20

  otherOpen access1st authorCorresponding

  Time series forecasting of alpha and gamma diversity across coastal ecosystems, integrating environmental drivers and state-space, ARIMA, and machine learning models.

  PublisherDOI

• masamifujiwara/MultiDimensional-Diversity: Initial release for obtaining DOI

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-21

  otherOpen access1st authorCorresponding

  This is the initial release, but to get DOI.

  PublisherDOI

• masamifujiwara/MultiDimensional-Diversity: Initial release for obtaining DOI

  Open MIND · 2026-05-21

  otherOpen access1st authorCorresponding

  This is the initial release, but to get DOI.

  PublisherDOI

• A Predictability Framework for Conservation Strategy: Empirical Evidence from Estuarine Biodiversity Forecasting

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-23

  articleOpen access1st authorCorresponding

  ABSTRACT Conservation biology increasingly relies on ecological forecasting, yet the biodiversity components most urgently targeted by conservation, such as rare species, local assemblages, and hotspot-defined communities, are often those whose dynamics are least predictable. Understanding how predictability varies across biodiversity is therefore essential for aligning management tools with their targets. This study tests whether predictability varies along three axes, how diversity is measured, the spatial scale of observation, and the temporal forecast horizon (which together govern the effective signal-to-noise ratio of ecological dynamics), and uses these patterns to inform conservation strategies. Using long-term monitoring data from seven estuaries along the Texas Gulf Coast, forecasting performance was evaluated for Hill diversity (q = 0, 1, 2) and population-level abundance of eight dominant taxa at local (bay) and regional (coastwide) scales across near-term (1-month) and long-term (12-month) horizons. Multiple time-series model classes were assessed within a rolling-origin cross-validation framework, with performance measured as improvement in root mean square error over a seasonal naive baseline. Forecasting performance increased consistently with Hill number order, reflecting reduced stochastic variation as dominant species are emphasized. The effects of spatial aggregation differed between systems. Aggregation generally improved performance for littoral assemblages but provided limited or no benefit for demersal assemblages, consistent with differences in how predictive signals are distributed across space. Forecast skill declined from 1-to 12-month horizons, with slower decay for dominance-weighted diversity and demersal assemblages than for rare-species-weighted richness and littoral assemblages. Environmental covariates provided limited near-term gains but became an increasingly important source of predictive information at longer horizons for a subset of demersal and crustacean targets. These results define a predictability landscape structured by diversity measurement, spatial scale, and forecast horizon. Three conservation domains, stochastic, transitional, and structured, emerge from this framework, each associated with distinct predictability regimes and management strategies. Aligning conservation approaches with the predictability properties of their targets provides a principled basis for determining when forecast-based management is informative and when precautionary approaches are more appropriate.

  PublisherOA PDFDOI

• masamifujiwara/diversity-stability: Initial release for manuscript submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-20

  otherOpen access1st authorCorresponding

  No description provided.

  PublisherDOI

• Scale dependent niche conservatism in fish communities of the largest freshwater lake in China

  Oecologia · 2025-05-01

  article
  PublisherDOI

• Constructing age-structured matrix population models for all fishes

  PeerJ · 2025-01-02 · 1 citations

  articleOpen access1st authorCorresponding

  Matrix population models are essential tools in conservation biology, offering key metrics to guide species management and conservation planning. However, the development of these models is often limited by insufficient life history data, particularly for non-charismatic species. This study addresses this gap by using life history data from FishBase and the FishLife R package, complemented by size-dependent natural mortality estimates, to parameterize age-structured matrix population models applicable to most fish species. The method was applied to 30 fish species common around oil and gas platforms in the Northern Gulf of Mexico, generating seven key metrics: damping ratio, resilience, generation time, stable age distribution, reproductive value, sensitivity matrix, and elasticity matrix. The damping ratio reflects how quickly a population returns to a stable age distribution after a disturbance, while resilience indicates the speed of recovery from perturbations. Generation time captures the average age of reproduction, and the stable age distribution represents the long-term proportion of individuals in each age class. Reproductive value quantifies future reproductive potential by age class. The sensitivity matrix highlights the age-class transitions most affecting population growth, and the elasticity matrix shows the proportional influence of these factors on population growth. The results demonstrate that robust population models can be constructed with limited species-specific data and reveal notable differences in population dynamics among species. For example, species with longer generation times, like the greater barracuda ( Sphyraena guachancho ), have lower damping ratios, indicating prolonged transient dynamics. In contrast, species such as the round scad ( Decapterus punctatus ) exhibit shorter generation times and higher damping ratios, suggesting faster returns to equilibrium. These findings underscore the importance of life history variability in shaping conservation strategies. Additionally, metrics like stable age distributions and reproductive values provide insight into population structure and individual contributions to future populations, while sensitivity and elasticity matrices inform management interventions such as size limits in fisheries. By integrating extensive databases and predictive tools, this study offers a scalable approach for developing matrix population models across diverse fish species. This methodology enhances our understanding of fish population dynamics, particularly for data-deficient species, and supports more informed conservation efforts. It also promotes ecosystem-based management by enabling species comparisons through standardized metrics, contributing to the sustainability of marine ecosystems.

  PublisherDOI

Recent grants

• Effects of physical environmental conditions on the species distribution and composition of marine fish and invertebrates along the Texas coast

  NSF · $209k · 2017–2021

Frequent coauthors

• K. Ida

  National Institute for Fusion Science

  84 shared

• O. Motojima

  Chubu University

  81 shared

• K. Matsuoka

  Saitama University

  73 shared

• H. Idei

  Kyushu University

  72 shared

• S. Kubo

  71 shared

• A. Sagara

  67 shared

• K. Toi

  65 shared

• H. Iguchi

  Kyoto University

  63 shared

Education

• B.S., Biological Sciences

  University of Alaska Fairbanks

• M.S., Marine Biology

  University of Alaska Fairbanks

• Ph.D., Biological Oceanography

  Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program