About

Dr. Rusty Feagin is a Professor in the Department of Ecology and Conservation Biology, and the Department of Ocean Engineering at Texas A&M University. His research centers on how living materials moderate and respond to erosion, with a focus on coastal ecology, geomorphology, and engineering. His lab investigates the interactions between biological and physical processes in sand dunes, salt marshes, and beaches, translating scientific findings into restoration and engineering projects aimed at the sustainable management of coastlines. Dr. Feagin has authored over 100 publications, including articles in prominent journals such as Nature, Science, and PNAS. His work has garnered public media attention through NPR and the New York Times. He has received notable awards, including the Robert Dean Coastal Academic Award from the American Shore and Beach Preservation Association, and the highest environmental honor in Texas. His contributions extend to policy and academia, having served on the US President’s US National Greenhouse Gas Inventory and held visiting positions at esteemed institutions such as the University of Oxford, University of Cambridge, and others. His research significantly advances understanding of coastal erosion processes and the role of vegetation in coastal protection.

Research topics

• Environmental science
• Biology
• Ecology
• Geology
• Oceanography
• Geography
• Environmental resource management
• Meteorology

Selected publications

• Long-Term Trends and Interannual Variability in Tidal Wetland Gross Primary Production Across the Conterminous United States

  2026-01-20

  articleOpen access

  Tidal wetlands are critical carbon sinks, yet their response to ongoing environmental change remains uncertain across the conterminous United States. To address this gap, we quantified long-term trends and interannual variability in tidal wetland gross primary production (GPP; g C m⁻² d⁻¹) using a 20-year (2001–2020) satellite-based dataset. We also examined regional differences and the relative influence of climate drivers versus vegetation canopy on GPP dynamics. At the continental scale, GPP increased by approximately 6% over two decades, with the strongest gains in the South Atlantic and Gulf regions. Gulf wetlands exhibited the highest productivity, while Pacific and northern Atlantic wetlands were substantially lower, reflecting climatic gradients. Decomposition analysis indicates that rising shortwave radiation and air temperature are the primary drivers of productivity increases, outweighing declines in vegetative canopy coverage and apparent greenness. Interannual variability was modest overall but greatest in the Western Gulf, where episodic disturbances such as hurricanes and drought exert strong influence. These findings suggest that recent productivity gains are driven largely by climate forcing rather than vegetation changes, underscoring the need to incorporate climatic drivers into tidal wetland carbon models and management strategies.

  PublisherOA PDFDOI

• A fundamental trade‐off among resilience, resistance, efficiency, and redundancy in tidal wetlands

  Ecology · 2026-01-01

  articleOpen access

  In an era of change, the survival and adaptability of ecosystems will be tested. An optimal ecosystem would be both resistant and resilient to negative disturbance but also efficient and redundant in its growth when given positive subsidies. However, initial evidence has suggested that these properties cannot all be maximized at the same time, and so we sought to quantitatively assess whether there are fundamental trade-offs between them at the ecosystem level. To achieve this aim, we used a 250-m resolution NASA MODIS dataset of gross primary productivity (GPP) to monitor 145,871 tidal wetland locations across the conterminous United States every 16 days from March 2000 to December 2020. We quantified the size and duration of the perturbation events in tidal wetland GPP (n = 13,754,386) and modeled their frequency distributions. Event sizes and recurrence intervals were exponentially distributed and event durations were closely modeled by an inverse power law. This scale-free manner through which tidal wetlands dissipated perturbations to their GPP flux provided them with long-term stability across a wide range of geography. We also found that a tidal wetland's positive event responses traded off between properties of efficiency and redundancy, its negative events traded off between resistance and resilience, and that all four properties were orthogonally related to one another. We then constructed a conceptual model to help understand the potential mechanism behind this four-quadrant trade-off. The trade-off appeared to be driven by a feedback between the waiting time and magnitude of positive and negative events, the duration of their effects, and the environmental and physical constraints limiting an ecosystem's growth and productivity. In summary, we detail an emergent pattern of trade-offs and constraints associated with how tidal wetland ecosystems respond to both positive and negative perturbations in carbon flux.

  PublisherDOI

• Episodic Salinity Management to Counter Climate Change Effects on Tidal Brackish and Fresh Wetlands

  Environmental Management · 2026-01-06

  articleOpen access

  The capacity of brackish and freshwater tidal marshes to accrete vertically in response to sea level rise is threatened where drought and salinity intrusion are being amplified by climate change. Episodic salinity management with purchased augmented freshwater is an option for two modest-sized tracts in southeast Texas, where drought and hydrologic modifications threaten wetland resiliency and the Mottled Duck. We developed a transferable methodology to assess biophysical benefits in a spatially explicit manner for these heterogeneous wetlands. Four salinity objectives reflected zonal geography of the wetland plant communities and Mottled Duck brood-rearing needs. A calibrated daily wetlands hydrologic-salinity model contrasted scenarios of severe drought with those of freshwater augmentation. The volume of freshwater available, up to 12.33 M m3 per year, could be effective at moderating salinity over significant wetland areas, but benefits were sensitive to management approach, as well as delivery rates and duration of augmentation. Additionally, fixed freshwater application rates could depress salinities to suboptimal ranges and waste a purchased resource. Feedback scenarios based on in-marsh salinity conditions elevated the ratio of benefits to delivered water volumes but would entail additional monitoring and management cost. Compared to the extremely deleterious conditions of severe drought, most freshwater augmentation approaches would greatly benefit the Mottled Duck and the productivity of the wetland vegetation within the tracts. However, portions of a fragile brackish zone dominated by Spartina patens would remain at risk from elevated salinity, suggesting a need for complementary restoration actions.

  PublisherOA PDFDOI

• Climate‐Driven Long‐Term Increase in Tidal Wetland Gross Primary Production in the United States

  Global Biogeochemical Cycles · 2026-05-01

  articleOpen access

  Abstract Tidal wetlands are critical carbon sinks, yet their response to ongoing environmental change remains uncertain across the conterminous United States. To address this gap, we quantified long‐term trends and interannual variability in tidal wetland gross primary production (GPP; g C m −2 d −1 ) using a 20‐year (2001–2020) satellite‐based data set. We also examined regional differences and the relative influence of climate drivers versus vegetation canopy on gross primary productivity (GPP) dynamics. At the continental scale, GPP increased by approximately 6% over two decades, with the strongest gains in the South Atlantic and Gulf of Mexico regions. Gulf wetlands exhibited the highest productivity, while Pacific and northern Atlantic wetlands were substantially lower, reflecting climatic gradients. Decomposition analysis indicates that rising shortwave radiation and air temperature are the primary drivers of productivity increases, outweighing declines in vegetative canopy coverage and apparent greenness. Interannual variability was modest overall but greatest in the Western Gulf, where episodic disturbances such as hurricanes and drought exert strong influence. These findings suggest that recent productivity gains are driven largely by climate forcing rather than vegetation changes, underscoring the need to incorporate climatic drivers into tidal wetland carbon models and management strategies.

  PublisherDOI

• A general pattern of trade-offs between ecosystem resistance and resilience to tropical cyclones

  UNC Libraries · 2025-09-11

  articleOpen access

  Tropical cyclones drive coastal ecosystem dynamics, and their frequency, intensity, and spatial distribution are predicted to shift with climate change. Patterns of resistance and resilience were synthesized for 4138 ecosystem time series from <em>n</em> = 26 storms occurring between 1985 and 2018 in the Northern Hemisphere to predict how coastal ecosystems will respond to future disturbance regimes. Data were grouped by ecosystems (fresh water, salt water, terrestrial, and wetland) and response categories (biogeochemistry, hydrography, mobile biota, sedentary fauna, and vascular plants). We observed a repeated pattern of trade-offs between resistance and resilience across analyses. These patterns are likely the outcomes of evolutionary adaptation, they conform to disturbance theories, and they indicate that consistent rules may govern ecosystem susceptibility to tropical cyclones.

  PublisherDOI

• Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning

  arXiv (Cornell University) · 2025-09-30

  preprintOpen access

  Positive-Unlabeled (PU) learning aims to train a binary classifier (positive vs. negative) where only limited positive data and abundant unlabeled data are available. While widely applicable, state-of-the-art PU learning methods substantially underperform their supervised counterparts on complex datasets, especially without auxiliary negatives or pre-estimated parameters (e.g., a 14.26% gap on CIFAR-100 dataset). We identify the primary bottleneck as the challenge of learning discriminative representations under unreliable supervision. To tackle this challenge, we propose NcPU, a non-contrastive PU learning framework that requires no auxiliary information. NcPU combines a noisy-pair robust supervised non-contrastive loss (NoiSNCL), which aligns intra-class representations despite unreliable supervision, with a phantom label disambiguation (PLD) scheme that supplies conservative negative supervision via regret-based label updates. Theoretically, NoiSNCL and PLD can iteratively benefit each other from the perspective of the Expectation-Maximization framework. Empirically, extensive experiments demonstrate that: (1) NoiSNCL enables simple PU methods to achieve competitive performance; and (2) NcPU achieves substantial improvements over state-of-the-art PU methods across diverse datasets, including challenging datasets on post-disaster building damage mapping, highlighting its promise for real-world applications. Code: Code will be open-sourced after review.

  PublisherOA PDFDOI

• Navigating water scarcity: rainfall considerations to optimize native vegetation growth in arid lands

  Environment Development and Sustainability · 2024-05-31 · 3 citations

  article
  PublisherDOI

• Can we coevolve with <scp>AI</scp>?

  Frontiers in Ecology and the Environment · 2024-04-01 · 3 citations

  reviewOpen accessSenior author

  Ecologists have been using AI in research for decades (machine-learning is a more boring name for it), and today it is not uncommon for ecology graduate students to run their statistics using iterative, problem-solving AI algorithms. At its core, AI-based prediction is simply an automated version of the scientific method, designed to be an iterative learning process that becomes more refined with each iteration based on feedback and experience. In machine learning, selection for an optimized solution occurs with every iteration, somewhat similar to how natural selection operates on each generation of a species. With each iteration, the model attempts to minimize differences between its output and what it was trained to believe should be the “correct” output. Ultimately, humans control the inputs and impose artificial selection pressures (such as model parameters, thresholds, and goals for the training) that drive evolution of the outputs in a desired direction. A relevant question is whether humans can sensibly guide this evolution in a manner that parallels evolution and adaptation by natural selection. Those worried about AI fear that we humans will end up on the wrong side of this selection process, in a zero-sum game between biology and technology. But the reality is that selection is driving biology and computing more closely together, toward an obligate symbiosis rather than a divergence. One could argue that this coevolution has already commenced and that we are already part human, part machine. For example, many of us have instant and unrestricted access to the vast knowledge of the internet via smartphones in the palms of our hands. It is relatively easy to imagine that humans will become more integrated with and dependent on AI in the future, because AI can help humans optimize solutions for complex problems (whether for morally good or bad reasons). If a hypothetical tipping point is crossed in which AI surpasses human intelligence and gains some degree of autonomy and sentience, it is unlikely that AI will annihilate humans, because that would be akin to attacking itself. Instead, the more likely risk is that humans are becoming, and will continue to become, something new. Who better to understand the limits than ecologists, with their understanding of the fundamental principles of adaptation and evolution? In The Origin of Species, Darwin described natural selection as a process analogous to selective breeding in domesticated pigeons and horses, and this analogy can be further generalized to our coevolution with AI. If humanity becomes entangled within a mutualistic association with AI, its outputs and capabilities will be refined and its early forms will eventually either become extinct or morph into better adapted versions. This evolution is likely to be slow, though punctuated by moments of rapid and drastic change. Are there risks? Of course, but they are more likely of the variety that we currently face. Just as any successful technological innovation increasingly becomes a part of daily life, there will be initial winners and losers. Even the best adapted species of AI code and bioengineered networks will still be susceptible to disease and disorders, malfunctions, and inefficiencies. However, over time, selection will drive the evolution of better adapted forms of AI. We simply argue that this process will more closely resemble coevolution, rather than an existential battle, between humans and machines. Ecologists should find comfort in knowing that we will not soon become subject to our AI-enhanced machine overlords. We should find familiarity in the idea that human–machine coevolution will likely be guided by the same principles and processes that govern the natural systems that we study. An improved understanding of how AI can help us better use, conserve, repair, and build the natural world is where we are heading. Ecologists are well-positioned and uniquely qualified to lead in this endeavor.

  PublisherOA PDFDOI

• Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection

  Earth Surface Dynamics · 2024-01-03 · 5 citations

  articleOpen access

  Abstract. The frequency and intensity of coastal flooding is expected to accelerate in low-elevation coastal areas due to sea level rise. Coastal flooding due to wave overtopping affects coastal communities and infrastructure; however, it can be difficult to monitor in remote and vulnerable areas. Here we use a camera-based system to measure beach and back-beach flooding as part of the after-storm recovery of an eroded beach on the Texas coast. We analyze high-temporal resolution images of the beach using convolutional neural network (CNN)-based semantic segmentation to study the stochastic properties of flooding events. In the first part of this work, we focus on the application of semantic segmentation to identify water and overtopping events. We train and validate a CNN with over 500 manually classified images and introduce a post-processing method to reduce false positives. We find that the accuracy of CNN predictions of water pixels is around 90 % and strongly depends on the number and diversity of images used for training.

  PublisherOA PDFDOI

• Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis

  Earth Surface Dynamics · 2024-01-10 · 2 citations

  articleOpen access

  Abstract. Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. Partially driven by wave runup, this type of flooding is difficult to predict due to the complexity of the processes involved. Here, we present the results of a probabilistic analysis of flooding events measured on an eroded beach at the Texas coast. A high-resolution time series of the flooded area was obtained from pictures using convolutional neural network (CNN)-based semantic segmentation methods, as described in the first part of this contribution. After defining flooding events using a peak-over-threshold method, we found that their size follows an exponential distribution. Furthermore, consecutive flooding events were uncorrelated at daily timescales but correlated at hourly timescales, as expected from tidal and day–night cycles. Our measurements confirm the broader findings of a recent multi-site investigation of the probabilistic structure of high-water events that used a semi-empirical formulation for wave runup. Indeed, we found a relatively good statistical agreement between our CNN-based empirical flooding data and predictions using total-water-level estimations. As a consequence, our work supports the validity of a relatively simple probabilistic model of high-frequency coastal flooding driven by wave runup that can be used in coastal risk management and landscape evolution models.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Physics of Dune Erosion during Extreme Wave and Storm-Surge Events

  NSF · $226k · 2018–2022

• Collaborative Research: Eco-geomorphic coupling: Vegetation transition and sedimentary responses to faulting

  NSF · $113k · 2009–2013

Frequent coauthors

• Thomas P. Huff

  Texas A&M University

  52 shared

• Kevin M. Yeager

  23 shared

• James M. Kaihatu

  Texas A&M University

  18 shared

• Kuang‐An Chang

  Texas A&M University

  17 shared

• Jin Young Kim

  Korea Basic Science Institute

  17 shared

• M. Luisa Martínez

  16 shared

• Audra Hinson

  Agricultural Research Service

  15 shared

• Shih‐Heng Sun

  Texas A&M University

  14 shared

Education

• B.A., Environmental Studies

  University of California, Santa Cruz

• Ph.D., Rangeland Ecology and Management

  Texas A&M University

Awards & honors

• Robert Dean Coastal Academic Award by the American Shore and…
• Highest environmental honor in the State of Texas
• Texas State Senate proclamation in his honor