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At least 1,243 records · Page 69Linked to original sources
USGS2026

Development and assessment of fluorescent-dyed, preserved invasive grass carp (Ctenopharyngodon idella) eggs as surrogates for live eggs in transport and dispersal control experiments

Invasive species such as grass carp ( Ctenopharyngodon idella ) pose substantial ecological threats to North American freshwater ecosystems. Understanding their early life stage behavior is critical for management efforts. From spawning to hatching, invasive carp eggs must remain suspended in the water column while drifting downstream for the best chance of survival. This highly vulnerable life stage is a potential target for population control to reduce recruitment. However, studying egg transport and potential dispersal control techniques is challenging, because the availability of live eggs and time period for experimentation are extremely limited. Additionally, accurately replicating the physical characteristics and transport mechanisms of fish eggs using surrogates in laboratory and field studies is not trivial. This study presents a novel method to create fluorescein-dyed, preserved grass carp eggs as surrogates for live eggs in transport and dispersal control experiments. This technique enables year-round studies of grass carp egg transport, offering managers a reliable tool for developing and testing dispersal control and passive sampling methods for invasive carp eggs. In this study, we rehydrate and dye preserved grass carp eggs in varying concentrations of aqueous fluorescein for a range of rehydration times, evaluate dye retention and egg visibility under ultraviolet light (UV-A), and measure diameters and settling velocities for comparison with live eggs. Eggs rehydrated in 0.100 g per liter fluorescein for 30 min maintain adequate brightness for up to 40 min in mixed conditions and exhibit mean settling velocities and densities similar to live eggs, making them ideal for laboratory experiments using quantitative imaging techniques.

River Research and Applications↗
USGS2026

Estimating discharge from undular hydraulic jumps: Feasibility assessment based on flume experiments

Rapids are common in steep rivers, often forming where flow transitions from supercritical (Froude number, Fr > 1) to subcritical ( Fr < 1) through a hydraulic jump. When upstream Fr is supercritical but close to 1, this transition may occur as an undular hydraulic jump, exhibiting a train of stationary waves downstream of the jump toe. Previous studies proposed a method to estimate discharge using only UHJ wave spacing and channel width combined with a wave dispersion equation for large water depths relative to the UHJ wavelength. This method is based on the hypotheses that, by their presence, UHJs indicate near-critical flow conditions ( Fr ≈ 1) and that wave celerity c is equal to and opposite the cross-sectionally averaged flow velocity U . However, these hypotheses have not been thoroughly tested. We used data from published UHJ flume experiments to test the hypotheses that Fr ≈ 1 and c = U , compare the deep-water and general wave dispersion equations, and evaluate the accuracy of discharge estimates. In these experiments, the stationary waves exhibited shallow depths relative to wavelength and flow was subcritical ( Fr < 1) when averaged across multiple wavelengths. Additionally, wave celerity more closely approximated the surface flow velocity than U . By using a Fr representative of actual conditions and applying a coefficient to correct for c ≠ U , the accuracy of the discharge estimates improved. This finding suggests that the critical flow-based method is robust and can produce reliable streamflow estimates if the remotely observed wave trains are correctly interpreted as UHJs, without requiring in situ measurements.

Water Resources Research↗
USGS2026

Stream macroinvertebrate responses vary with region, land use and management practice type

Intensive land use alters hydrology and water quality, threatening freshwater benthic macroinvertebrates. Over 200,000 management practices (MPs) have been implemented across the Chesapeake Bay watershed since the 1980s, yet biological responses remain inconsistent. We synthesized 29 studies from 4 physiographic provinces covering 8 MP categories and evaluated macroinvertebrate responses along MP gradients using structural (richness), functional (biomass), tolerance, and biotic metrics. We hypothesized that MPs enhancing habitat complexity or restoring flow regimes would benefit taxa sensitive to sediment, hydrologic instability and organic pollution, with outcomes shaped by regional context, land use, and chosen metrics. Four themes emerged. (i) Agricultural Riparian Forest Buffers (RFBs) consistently improved sensitive metrics related to abundance, biomass and richness. (ii) Urban streams with Stream Habitat Improvement and Management (SHIM) showed improved richness and diversity, but biomass and tolerance metrics declined or remained neutral, indicating unresolved hydrologic and pollutant stress. (iii) Structural and functional responses diverged: effect sizes for total and feeding-group biomasses (functional metrics) were negative, whereas genus-level Ephemeroptera-Plecoptera-Trichoptera (EPT) richness (structural metric) was positive, indicating that structural shifts may not track underlying production changes. (iv) Physiographic comparisons showed counterintuitive patterns, as RFBs improved EPT richness in Piedmont streams but had negative effects in the Coastal Plain. Evaluating MP effectiveness requires distinguishing a no-MP pathway (stressors → instream conditions → assemblages → responses) from an MP-mediated pathway (practice regime → modified stressors → instream conditions → assemblages → responses), underscoring the need for region-specific, multi-metric monitoring and improved understanding of MP density thresholds and recovery lags.

eastern contiguous United States↗
USGS2026

Egg thiamine concentrations reflect dietary differences between four hatchery strains, including two morphotypes, of Lake Trout in Lake Ontario

Objective Test whether dietary differences among hatchery strains of Lake Trout Salvelinus namaycush were associated with variation in egg thiamine concentrations and assess the potential for using strain-specific traits to mitigate thiamine deficiency complex (TDC) in Lake Ontario restoration efforts. Methods Stomach content analysis and stable isotope mixing models were used to compare diets among four hatchery strains of Lake Trout, representing lean and humper morphotypes. Egg thiamine concentrations were measured to test whether dietary differences correlated with susceptibility to TDC. Results Significant differences in diet and egg thiamine concentrations were observed among Lake Trout strains. A humper-like morphotype strain exhibited elevated thiamine levels, which were associated with reduced consumption of prey known to degrade thiamine levels when consumed, Alewife Alosa pseudoharengus and Rainbow Smelt Osmerus mordax , and increased reliance on Round Goby Neogobius melanostomus or sculpin species (Slimy Sculpin Cottus cognatus and Deepwater Sculpin Myoxocephalus thompsonii ). These findings support the link between the consumption of Alewife and Rainbow Smelt and thiamine deficiency. Conclusions Our findings support a link between strain-specific dietary preferences and susceptibility to TDC in Lake Trout. Preferential stocking of strains less prone to TDC, such as the strain derived from a humper morph from Lake Superior, could enhance restoration success in Lake Ontario. This strategy may be especially effective given the continued dominance of Alewife in the lake's prey base and the current management priorities supporting fisheries for Pacific salmon Oncorhynchus spp.

North American Journal of Fisheries Management↗
USGS2026

Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids

Objective We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates. Methods We identified two machine learning approaches that shared the ability to model overdispersed probabilities. We compared the BBR and BMF machine learning models to a BME model to evaluate precision in detection probability prediction and model performance on bias in parameter estimation. We tested our three candidate models using a simulation study to understand the specific advantages and disadvantages of each when the data set was increasingly sparse and the capture probabilities were realistically small. We then applied the models to a case study of RST data from the Klamath River in California, United States. Results The BME and BMF outperformed BBR in all simulated scenarios, although the BMF displayed poor explanatory power. In the case study, the BME and BMF identified environmental covariates that predicted RST efficiency. Conclusions Using the BME as a benchmark for comparing machine learning approaches to trap efficiency modeling, our simulations and case study demonstrated that the BMF performed well and is a viable modeling approach with strong predictive power. The BME model would be the preferred modeling approach when its strong explanatory power is desired.

North American Journal of Fisheries Management↗
USGS2026

Working group on American Eel (WGAMEEL; outputs from 2024 meeting)

The Working Group on American Eel (WGAMEEL) met virtually three times in 2022-2024 to address the five Terms of Reference (ToRs) of its three-year term. The first two ToRs tasked WGAMEEL with listing and evaluating data on American eel landings, abundance indices, and spatial and habitat data and also to describe assessment methods used in Canada and the US. Canada-wide American eel trajectory was estimated using 12 fishery-independent datasets. Generally, longer datasets had more negative trends than shorter ones. Limiting data to the post-2000 years produced fewer negative trends that did not differ from zero, suggesting the observed declines occurred pre-2000. Spatial modelling for American eel requires knowledge and mapping that covers the breadth of habitat types occupied by the species, including freshwater, estuarine, and marine environments. In recent years there has been an expansion of online databases with data from the aquatic environment, particularly in freshwater, with estuarine and marine data less consistently documented. This report broadly compiles abiotic data series of relevance to American eel. A larger challenge for spatial modelling will be acquiring enough high quality, georeferenced biological data sets with suitable observations to assess occurrence, abundance, and trends over time in a spatial framework. The third ToR considered Indigenous Knowledge Systems for American eel. A survey reaching First Nations representatives from four Canadian provinces confirmed the cultural importance of eels in Indigenous communities, and that Indigenous knowledge possessed by the participant groups are place-based and contextual, especially regarding threats impacting eels. The final two ToRs focused on identifying stock assessment modelling approaches applicable to American and European eel, and assessing whether any of these approaches might be appropriate for American eel management moving forward. WGAMEEL evaluated the various approaches for assessing American eel or providing management advice. Two approaches that could be completed in the next few years because of their minimal data needs are index-based methods and catch-only method. A suite of approaches considered by WGAMEEL that would take more time and data were spatial or habitat models, management strategy evaluation, and spawner-per-recruit (SPR) models potentially paired with meta-population models.

ICES Scientific Reports↗
USGS2026

Evaluation of turbidity corrections for EXO fluorescent dissolved organic matter (fDOM) sensors

Executive Summary The use of field-deployable fluorescence sensors to better understand dissolved organic matter concentrations and composition has grown immensely in recent years. Applications of these sensors to critical monitoring efforts have also grown, encompassing post-fire monitoring, wastewater tracking, and use as a proxy for various contaminants. Despite the growth, it is well known that these sensors require corrections for temperature (Watras and others, 2011) and are subject to many light-field interferences caused by both scattering and absorbance due to dissolved and particulate substances (Downing and others, 2012; Lee and others, 2015; Booth and others, 2023). The most common fluorescence sensors used by the U.S. Geological Survey (USGS) include those targeting fluorescent dissolved organic matter (fDOM) and chlorophylls. Because fDOM sensors primarily measure fluorescence in the dissolved to colloidal phases, corrections to the interferences caused by particulates can be made relatively easily. By the end of 2024, the USGS had 69 fDOM sensors deployed within official water quality monitoring networks included on the USGS National Water Dashboard ( https://dashboard.waterdata.usgs.gov/app/nwd/en/ ) and numerous others used in surveys and research applications across the Nation. Although temperature corrections are widely applicable across sensor models, interference corrections can be model specific due to differences in design specifications across manufacturers and models (Booth and others, 2023). The corrections are also potentially subject to changes in manufacturing within a specific sensor model. Recently, USGS staff obtained information regarding possible changes in the manufacturing of its most widely-used fDOM sensor model, raising concerns about data consistency and quality in the USGS fDOM sensor networks. Furthermore, changes in turbidity sensors since the corrections guidance was performed may also affect the performance of the corrections. The turbidity sensor used in the original experiments (Downing and others, 2012) was determined to have a signal output approximately 1.3 times higher than the output of the turbidity sensor currently used in an extensive field comparison study (Messner and others, 2023). With these changes, it is imperative that the corrections be reevaluated to maintain data consistency and continuity across the USGS. In this study, we evaluated turbidity corrections for fDOM sensors over a range of serial numbers covering manufacturing dates 2015 through 2022 and turbidity serial numbers covering the range 2013 through 2022. The goal was to determine whether reported changes in the manufacturing process of the fDOM and turbidity sensors affected the correction approach developed by Downing and others (2012) such that additional guidance would be required to address this manufacturing change. To evaluate, we repeated a laboratory-based test similar to that performed by Downing and others (2012) in which a series of tank experiments with multiple sensors were deployed in a suspension of Elliot Silt Loam (ESL). High turbidities of the ESL suspension were maintained throughout the tank by turbulent recirculation using submersible pumps. Particulates were removed using a recirculated line equipped with a capsule filter (0.45 micron). Measurements were collected throughout the filtration until turbidities reached approximately 5 formazin nephelometric units (FNU; data available in Baxter and others, 2023). Each experimental run included a mixture of unique sensor combinations to account for variability imposed by the turbidity and temperature sensors. The fDOM correction factor was calculated for each combination of fDOM and turbidity sensors included in the test. We observed no systematic change in fDOM correction coefficients across serial numbers representing manufacturing years 2015 through 2022. However, the results highlighted questions raised about the corrections for high-turbidity samples, as noted in USGS Techniques and Methods (Booth and others, 2023). Applying the inverse of the commonly-used fDOM ratio with a quadratic fit performed better than the exponential fits when correcting fDOM data for turbidity in the ESL laboratory filtration test and generated a simple scale factor correction equation. This approach also served as a better indicator of data quality than the exponential fit approach. Similar to fDOM, more rigorous quality assurance measures may be necessary to evaluate turbidity sensor calibrations and performance. Sensors exceeding a certain age may need to be replaced despite passing quality assurance checks during calibration. Further testing of the turbidity corrections for different sediment and water types is warranted to better understand the variations in the fits and correctable ranges of turbidity in different systems.

Open-File Report↗
USGS2026

Who needs closure? Estimating abundance with a Markovian availability model for geographically open removal sampling

Removal sampling is an important method for estimating abundance, but nearly all removal models assume closure during sampling. Yet, closure may be difficult to assume, evaluate, or enforce in many settings. To address situations where populations are geographically open between each removal sample, we incorporated a Markovian availability process into an N-mixture model framework. This model relates local abundance available for sampling to a superpopulation through recruitment of new individuals to the sampling area. To test the model, we (1) conducted parameter identifiability analysis, (2) fit the model to removal data generated from a random walk movement model, and (3) analyzed a case study of empirical removal data. Parameters were increasingly identifiable as capture probability exceeded 0.25 and removal samples increased from 3 to 6. Abundance estimates were unbiased when parameters were identifiable, except for scenarios that simulated a behavioral response to sampling. For our case study, the model estimated negligible recruitment for benthic-oriented fishes, indicating closure, but we found evidence against closure for juvenile Chinook salmon, a highly mobile species. Our removal model allows researchers to formally test closure assumptions, to estimate the degree of closure, and to estimate abundance without bias when closure is violated.

California↗
USGS2026

Fragmentation as a population rate-changer: A field experiment

Experimental and observational studies of effects of fragmentation on biodiversity and population dynamics have produced mixed results, with some reviews concluding strong evidence of negative effects and others concluding small positive effects. In addition, many factors (e.g., interspecific interactions, edge effects, nutrient cycling) have been identified as potential explanations underlying the various results. We carried out a population-level fragmentation study on meadow voles, focusing on changes in vital rates caused by reduced movements in experimental 2-patch systems. We developed predictions of fragmentation effects by decomposing rates of apparent survival and recruitment (parameters directly estimated using capture–recapture models) into components that do and do not include movement. Fragmentation was predicted to reduce movement rates, and reduced movement was predicted to increase apparent survival rates, decrease immigration rates, and slightly increase population growth rates. We found evidence of increased adult and juvenile apparent survival and adult population growth rate on fragmented grids, whereas results for recruitment were ambiguous and did not support our predictions. The recruitment results led to the hypothesis that immigration into suitable habitat may not be reduced by fragmentation as much as permanent emigration from that habitat. A focus on effects of reduced movement on vital rates should be a reasonable starting point for investigations of fragmentation effects. This focus suggests that explanations underlying fragmentation effects will require additional effort devoted to isolating movement components of vital rates.

Ecology↗
USGS2026

Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar

A new oceanographic radar instrument package was developed by the U.S. Geological Survey (USGS) to measure storm waves and water levels in the nearshore, capable of being deployed rapidly and transmitting data in near real-time. To test the performance and accuracy of the sensor, multiple years of data were collected over various hydrodynamic conditions and compared to long-term monitoring data collected at the U.S. Army Corps of Engineers (USACE) Field Research Facility in Duck, North Carolina, USA. The oceanographic radars were highly reliable, with less than 1% of the record being erroneous spikes or missing data points. At the end of the pier, the radar was highly accurate, with nearly perfect agreement in water level ( r 2 = 0.997) compared to a nearby National Oceanic and Atmospheric Administration (NOAA) tide gauge, and good agreement in significant wave height ( r 2 = 0.98) and peak wave period ( r 2 = 0.65) compared to a nearby USACE sensor. This work demonstrates the potential of the USGS radar for rapid response storm deployments and collecting reliable and accurate hydrodynamic measurements in the nearshore for validating coastal impact models.

North Carolina↗
USGS2026

Climate-driven waterline variability along the North American West Coast

Sandy coasts are highly dynamic environments shaped by a myriad of hydro-sedimentary processes operating across various spatio-temporal scales. From seasonal to centennial timescales, sandy beach dynamics are strongly influenced by climate variability expressed in various forms, including seasonal cycles, climate modes (e.g., El Niño Southern Oscillation (ENSO)), and global warming. Satellite-based methods enable the observation of coastlines with unprecedented spatial and temporal coverage. However, over the past decade, as these methods have matured, coastal change studies have predominantly focused on long-term trends rather than on climate-driven cycles of shoreline change. In this study, we utilize a large dataset of monthly-sampled time series of waterline positions along the Pacific Coast of the United States and Baja California, Mexico. From these time series, we derived information on long-term trends and seasonal cycles of waterline change. Our analysis reveals clear contrasts in seasonal patterns of waterline variability across the study area, with greater amplitudes observed at higher latitudes. A shift in the dominant dynamics is evident in Southern Baja California, where shoreline variability is more influenced by intertropical dynamics and sea-level anomalies than by wave energy modulation, which dominates further north. ENSO is found to induce contrasting responses of the waterline along the study area. During El Niño events, shoreline retreat is observed in subtropical regions, while La Niña winters drive waterline retreat in higher latitudes, with southern regions either remaining stable or advancing.

Baja California, California, Oregon, Washington↗
USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗
USGS2026

Comparison of nonlethal techniques as indicators of lipid content in Lake Whitefish

Objective Energetic reserves are important indicators of the relative health of fish and fish populations. Body condition indices that relate fish weight to length are commonly used as quick, noninvasive methods for approximating lipid content and condition. A microwave meter (i.e., fat meter or energy meter) is a noninvasive method found to be more accurate in some species. The objective of this study was to evaluate the suitability of nonlethal techniques for estimating muscle lipid content in Lake Whitefish Coregonus clupeaformis . Methods We compared the sensitivity of three nonlethal indicators of lipid content to laboratory-extracted muscle lipid content in Lake Whitefish, including readings from a handheld microwave meter at several positions, Fulton’s condition factor, and relative weight. Results We found significant, positive relationships between lipid content and each estimation method, except relative weight, with weak to moderate correlations. The microwave meter was moderately correlated to lipid content when positioned anterior to the dorsal fin above the lateral line ( r 2 = 0.50), while other positions and combinations of positions had weaker correlations ( r 2 range = 0.27–0.45). Correlation was only slightly improved by including additional model variables (i.e., length and weight). Fulton’s condition factor was weakly correlated with lipid content ( r 2 = 0.19), while relative weight was not significantly correlated with lipid content. Conclusion The microwave meter provides an improvement to muscle lipid estimation compared with length–weight body condition indices; however, microwave meter readings alone do not constitute a reliable predictive measure for true muscle lipid content under the conditions tested here. We hypothesize that the low strength of correlation may be due to low muscle lipid content or the presence of thick scales in Lake Whitefish. Further investigation is needed to understand the mechanisms negatively affecting the predictive performance of the microwave meter in Lake Whitefish and other species.

Michigan↗
USGS2026

From understanding to action: Integrating new and old methodologies to manage marine infectious disease

Marine diseases can have far-reaching effects on population, community and ecosystem health; however, our ability to track, predict and manage these diseases has, historically, been poor. As a result, the fields of disease ecology and epidemiology have developed at a slower pace for marine than terrestrial systems [ 1 ]. New methodologies, including genomic tools for diagnostics [ 2 , 3 ], transcriptomic tools for measuring host and pathogen responses to infection (e.g. [ 4 , 5 ]), regional oceanic modelling systems that estimate environmental conditions influencing pathogen dispersal and disease progression [ 6 ], artificial intelligence methods for quantifying pathology from images (e.g. [ 7 ]) and advanced disease modelling techniques [ 8 , 9 ] are precipitating a rapid increase in our understanding of marine pathosystems. In 2016, these efforts led to the first special issue of Philosophical Transactions of the Royal Society B ( Marine diseases, volume 371, issue 1689) focused entirely on marine disease ecology and evolution, and in 2020, the first book, Marine disease ecology, was devoted to this topic [ 10 ]. This special issue, focused on marine disease management , is being published a decade after the first Philosophical Transactions special issue on marine diseases. The shift to a management focus reflects an urgent need for management strategies to address high-impact diseases and the rapid methodological advances that have resulted. The papers included in this issue demonstrate the value of combining classical approaches (e.g. routine disease surveillance, reductionistic pathogen challenge trials, rapid throughput diagnostics) with cutting-edge technologies (e.g. high-resolution oceanographic models, Bayesian models, replicated transcriptomic studies) to identify drivers of disease, quantify impacts and suggest management strategies.

Philosophical Transactions of the Royal Society, S↗
USGS2026

Region-specific understanding of virus transmission to support salmonid health at hatcheries in three regions of the Pacific Northwest, North America

Juvenile salmonid mortality due to infectious haematopoietic necrosis virus (IHNV) can be a major burden on fish hatcheries. We consider possible IHNV transmission routes and specialist–generalist patterns across three regions in the Pacific Northwest of North America: Coastal Washington and Oregon (CWO), Lower Columbia River Basin (LCRB) and Snake River Basin (SRB) to obtain multi-region inference about IHNV transmission and specialization. While individual regions have been studied previously, our consideration of three regions to identify consistent trends or localized patterns is novel. The most consistent patterns we found were that most exposure to IHNV was from migrating adult salmonids and that the IHNV lineage MD specialized in steelhead and rainbow trout. Our results were consistent with LCRB being a source of IHNV and the SRB and CWO being sinks. Results specific to particular regions include the role of local geography on exposure and influence of hatchery complexes on transmission, which highlights the need to understand local hatchery operations before disease ecology can be understood and suitable hatchery management can be planned. Results demonstrate the need for spatially and temporally explicit quantitative approaches to understand disease dynamics and inform management.

Oregon, Washington↗
USGS2026

Stochastic within-host dynamics and climate-sensitive traits generate predictable patterns of variation in disease outcomes

Understanding how climatic variables impact host-pathogen relationships in temperature-sensitive ectothermic host organisms is crucial under global change. Few studies have explored how temperature gradients generate inter-individual variation in epidemiological traits like host susceptibility or pathogen replication. Here, we develop a mathematical model to explore a novel hypothesis: stochastic within-host dynamics and simulated thermal mismatches between host and pathogen traits generate predictable variation in infection outcomes among hosts and across thermal gradients. Our model demonstrates that varying trait thermal optima in host immunity and pathogen replication, and stochastic within-host processes produced variation in infection outcomes. Variability was low when temperatures strongly favored host or pathogen traits, but high and diverse when their performance was similar across a broad thermal range. In contrast, when trait performance was equal across all temperatures (no mismatch) variability remained low at all temperatures. Further, the magnitude of variation, quantified by entropy, exhibited predictable patterns depending on host-pathogen thermal mismatches. We conclude that interactions between trait thermal mismatches and within-host stochasticity provide a theoretical framework to improve ectotherm disease models under climate change, providing a valuable tool for exploring the impacts of environmental change on epizootic or epidemic dynamics, particularly in vulnerable marine ecosystems.

Philosophical Transactions of the Royal Society, S↗
USGS2026

The impacts of co-circulating pathogens in Pacific herring depend on interactions between viral life-cycle traits and transmission parameters, highlighting interdependencies between pathogen epizootics

The average host susceptibility decreases as the epizootic progresses because easily infected hosts are first removed from the population. While host susceptibility is pathogen-specific, it is likely that host susceptibility is correlated between different pathogens, so that co-circulating pathogens may have reciprocal impacts on their epidemics. However, despite well-documented examples of concomitant infections in marine hosts, reciprocal epizootic effects have not been documented in wild marine organisms. We quantify reciprocal impacts between viral haemorrhagic septicaemia and viral erythrocytic necrosis in Pacific herring ( Clupea pallasii ) using field and laboratory work. We show that the causative viruses for both diseases circulate through herring populations and that infection with one pathogen has negative impacts on the epizootic and infection characteristics of the other pathogen, suggesting positive correlations in the susceptibility to infection between pathogens. We then use simulations of a two-strain pathogen model to show that the impact of the correlation is modulated by transmission parameters, such as the incubation period and the initial transmission rate. Our work shows that co-occurring epizootics pose a management challenge because single-pathogen management actions may amplify the epizootics of the non-targeted pathogen. This study provides a framework to evaluate the consequences of reciprocal epizootic impacts through field, experimental and modelling work.

Washington↗