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Benjamin Letcher

Publications and source records attributed to Benjamin Letcher.

At least 19 recordsLinked to original sources
USGS2025

Longer exposure to warm water increases subsequent thermal tolerance of brook trout in cold water: Acclimation timing and physiology

Climate change has resulted in increased incidence and variability of warming episodes in cold-water streams that support salmonids. The capacity to acclimate to warm temperatures may allow cold-water fish to persist in spite of changing thermal regimes, but accurately predicting fish performance under fluctuating stream temperatures also requires understanding re-acclimation to cool water, which is less well understood. We tested how thermal acclimation to warm temperatures and re-acclimation to cool water affected thermal tolerance and physiological endpoints in juvenile brook trout ( Salvelinus fontinalis) . We show that an initial thermal exposure (22°C, ΔT = 7°C) of 3, 7 and 14 days (but not 1 day) improved critical thermal maximum (CT max ) after a 14-day re-acclimation to cooler temperatures (15°C). Fish growth during the re-acclimation period decreased with increasing duration of initial thermal exposure (22°C). Physiological parameters associated with thermal acclimation (cortisol, glucose, haematocrit and haemoglobin) were lower at 15°C re-acclimation temperature than at the initial thermal treatment (22°C) and in some cases, lower than the 15°C control. Muscle HSP70 protein increased early (1 day) as part of the warm acclimation process and remained elevated at lower levels for up to 14 days. During re-acclimation to 15°C, HSP70 decreased relative to initial measures at 22°C. Fish exposed to the longest thermal treatment (22°C for 14 days) maintained elevated CT max after 30 days of re-acclimation to 15°C without observed differences in the measured physiological endpoints but returned to control levels after 42 days at 15°C. This work shows that high-temperature acclimation effects in brook trout are retained for up to 30 days following re-acclimation to cool temperatures, and that isolated warming events may be expected to temporarily enhance thermal tolerance in subsequent thermal challenges.

Conservation Physiology↗
USGS2025

A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modelling framework called Streamflow Rank Estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modelled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall’s Tau rank correlation 0.75 with range 0.6–0.83) than after it (median Kendall’s Tau 0.59 with range 0.34 – 0.74). We found that annotation performance was generally consistent across the eleven camera sites and two individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1,000 annotation pairs. Our model’s estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modelling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

EGUSphere↗
USGS2025

How quickly do brook trout lose long-term thermal acclimation?

Abundances of coldwater adapted stream fish populations are declining largely due to anthropogenic influences, including increased temperature. To persist in streams with unsuitable thermal habitat, fish must move to coldwater patches, acclimate, or adapt to water temperatures above thermal optima. Brook trout, a coldwater adapted salmonid, has previously displayed physiological plasticity and the ability for reversible thermal acclimation when reared at higher temperatures. However, because stream temperatures are not static, it is important to explore the rate at which thermal acclimation occurs to evaluate whether prior thermal experience will influence future thermal performance. To determine the temporal scale in loss of thermal acclimation as water temperatures cool, we acclimated brook trout to three thermal regimes: +0 °C (ambient; mimicking the daily average water temperature of a nearby long-term study site), as well as +2 °C and +4 °C above ambient. After 2 years of being reared under those conditions, fish from the warmer treatments were moved to a common, colder temperature (ambient). We then used critical thermal maximum to measure the loss in acclimation response of fish from each treatment over time. We found that regardless of initial acclimation temperature, thermal tolerance of warm acclimated fish decreased rapidly for 1 week, then gradually decreased, and was completely lost within 42 days. This gradual loss of acclimation may be valuable to persistence in warmer streams and will be important to include in models of the impact climate change has on brook trout and other aquatic ectotherms with significant thermal plasticity.

Massachusetts↗
USGS2024

Thermal transfer rate is slower in bigger fish: How does body size affect response time of small, implantable temperature recording tags?

The recent miniaturisation of implantable temperature recording tags has made measuring the water temperatures fish experience in the wild possible, but there may be a body size-dependent delay in implanted tag response time to changes in external temperature. To determine whether fish body size affects the response rate of implanted temperature tags, we implanted 20 Salvelinus fontinalis (127–228 mm fork length (FL), 15.1–120.4 g) with temperature recording tags and subjected them to rapid temperature changes (±8°C in less than 2 seconds) in the laboratory. We found that thermal transfer rates, and the lag in temperature tag response rate, was positively correlated with fish size, but the direction of temperature change (colder or warmer) had no significant effect. In fish exposed to a slower rate of temperature change (2°C h −1 ) implanted tags did not show a response lag. Understanding the limitations of this important technology is crucial to determining the utility of the data it produces and its ability to accurately measure fish thermal experience in the wild.

Ecology of Freshwater Fish↗
USGS2024

Strong variation in Brook Trout trends across geology, elevation, and stream size in Shenandoah National Park

Objective Landscape context structures fish abundance and dynamics, and understanding trends in fish abundance across the landscape is often prerequisite for effective conservation. In this study, we evaluated the status and trends of Brook Trout Salvelinus fontinalis in Shenandoah National Park to understand how these are structured across bedrock geology, elevation, and stream size. Methods We used long-term monitoring data from 94 sites in Shenandoah National Park to evaluate trends in Brook Trout abundance over a 27-year period (1996–2022) and assess the importance of local environmental covariates using a hierarchical Bayesian N-mixture model based on depletion sampling. Focal covariates were chosen for their demonstrated importance in structuring fish populations in Shenandoah National Park and elsewhere. Bedrock geology controls sensitivity to acid deposition, watershed area is related to stream habitat features such as complexity and flow variability, and elevation creates gradients in temperature. Result Models revealed significant decreases in adult Brook Trout abundance over time (95% credible intervals < 0) for 31 of 94 sites (33%), and at least three sites exhibited apparent extirpations over the study period. Estimated Brook Trout abundance declined by 50% or more in approximately 70% of streams across the park over the study period. Sites with the warmest water temperatures exhibited the fastest declines in abundance. However, large watersheds on poorly buffered bedrock exhibited significant gains in abundance over time, suggesting some recovery from acid deposition due to improvements in air quality. Conclusion Our analysis revealed large and divergent changes in Brook Trout abundance over recent decades and suggests the importance of local water temperature and acid sensitivity as probable causal mechanisms. These results highlight the importance of considering local factors when evaluating long-term trends in stream fish populations. Results of this study can assist the development of targeted conservation actions within Shenandoah National Park and elsewhere.

Virginia↗
USGS2024

Smaller body size under warming is not due to gill-oxygen limitation in a coldwater salmonid

Declining body size in fishes and other aquatic ectotherms associated with anthropogenic climate warming has significant implications for future fisheries yields, stock assessments and aquatic ecosystem stability. One proposed mechanism seeking to explain such body-size reductions, known as the gill oxygen limitation (GOL) hypothesis, has recently been used to model future impacts of climate warming on fisheries but has not been robustly empirically tested. We used brook trout ( Salvelinus fontinalis ), a fast-growing, cold-water salmonid species of broad economic, conservation and ecological value, to examine the GOL hypothesis in a long-term experiment quantifying effects of temperature on growth, resting metabolic rate (RMR), maximum metabolic rate (MMR) and gill surface area (GSA). Despite significantly reduced growth and body size at an elevated temperature, allometric slopes of GSA were not significantly different than 1.0 and were above those for RMR and MMR at both temperature treatments (15°C and 20°C), contrary to GOL expectations. We also found that the effect of temperature on RMR was time-dependent, contradicting the prediction that heightened temperatures increase metabolic rates and reinforcing the importance of longer-term exposures (e.g. >6 months) to fully understand the influence of acclimation on temperature–metabolic rate relationships. Our results indicate that although oxygen limitation may be important in some aspects of temperature–body size relationships and constraints on metabolic supply may contribute to reduced growth in some cases, it is unlikely that GOL is a universal mechanism explaining temperature–body size relationships in aquatic ectotherms. We suggest future research focus on alternative mechanisms underlying temperature–body size relationships, and that projections of climate change impacts on fisheries yields using models based on GOL assumptions be interpreted with caution.

Journal of Experimental Biology↗
USGS2023

Aging contrast: A contrastive learning framework for fish re-identification across seasons and years.

The fields of biology, ecology, and fisheries management are witnessing a growing demand for distinguishing individual fish. In recent years, deep learning methods have emerged as a promising tool for image-based fish recognition. Our study is focused on the re-identification of masu salmon from Japan, wherein fish were individually marked and photographed to evaluate discriminative body characteristics. Unlike previous studies where fish were sampled during the same time period, we evaluated individual re-identification across seasons and years to address challenges due to aging, seasonal variation, and other factors. In this paper, we propose a new contrastive learning framework called Ag ing Co ntrast (AgCo) and evaluate its performance on the masu salmon dataset. Our analysis indicates that, unlike large changes in body size over time, the pattern of parr marks on the lateral line of the fish body remains relatively stable, despite some change in coloration across seasons. AgCo accounts for such seasonally-invariant features and performs re-identification based on the cosine similarity of these features. Extensive experiments show that our AgCo method outperforms other state-of-the-art methods.

Conference Paper↗
USGS2023

Spatial asynchrony and cross-scale climate interactions in populations of a coldwater stream fish

Climate change affects populations over broad geographic ranges due to spatially autocorrelated abiotic conditions known as the Moran effect. However, populations do not always respond to broad-scale environmental changes synchronously across a landscape. We combined multiple datasets for a retrospective analysis of time-series count data (5–28 annual samples per segment) at 144 stream segments dispersed over nearly 1,000 linear kilometers of range to characterize the population structure and scale of spatial synchrony across the southern native range of a coldwater stream fish (brook trout, Salvelinus fontinalis ), which is sensitive to stream temperature and flow variations. Spatial synchrony differed by life stage and geographic region: it was stronger in the juvenile life stage than in the adult life stage and in the northern sub-region than in the southern sub-region. Spatial synchrony of trout populations extended to 100–200 km but was much weaker than that of climate variables such as temperature, precipitation, and stream flow. Early life stage abundance changed over time due to annual variation in summer temperature and winter and spring stream flow conditions. Climate effects on abundance differed between sub-regions and among local populations within sub-regions, indicating multiple cross-scale interactions where climate interacted with local habitat to generate only a modest pattern of population synchrony over space. Overall, our analysis showed higher degrees of response heterogeneity of local populations to climate variation and consequently population asynchrony than previously shown based on analysis of individual, geographically restricted datasets. This response heterogeneity indicates that certain local segments characterized by population asynchrony and resistance to climate variation could represent unique populations of this iconic native coldwater fish that warrant targeted conservation. Advancing the conservation of this species can include actions that identify such priority populations and incorporate them into landscape-level conservation planning. Our approach is applicable to other widespread aquatic species sensitive to climate change.

Global Change Biology↗
USGS2023

A new genomic resource to enable standardized surveys of SNPs across the native range of brook trout (Salvelinus fontinalis)

Understanding how genetic diversity is distributed across spatiotemporal scales in species of conservation or management concern is critical for identifying large-scale mechanisms affecting local conservation status and implementing large-scale biodiversity monitoring programmes. However, cross-scale surveys of genetic diversity are often impractical within single studies, and combining datasets to increase spatiotemporal coverage is frequently impeded by using different sets of molecular markers. Recently developed molecular tools make surveys based on standardized single-nucleotide polymorphism (SNP) panels more feasible than ever, but require existing genomic information. Here, we conduct the first survey of genome-wide SNPs across the native range of brook trout (Salvelinus fontinalis), a cold-adapted species that has been the focus of considerable conservation and management effort across eastern North America. Our dataset can be leveraged to easily design SNP panels that allow datasets to be combined for large-scale analyses. We performed restriction site-associated DNA sequencing for wild brook trout from 82 locations spanning much of the native range and domestic brook trout from 24 hatchery strains used in stocking efforts. We identified over 24,000 SNPs distributed throughout the brook trout genome. We explored the ability of these SNPs to resolve relationships across spatial scales, including population structure and hatchery admixture. Our dataset captures a wide spectrum of genetic diversity in native brook trout, offering a valuable resource for developing SNP panels. We highlight potential applications of this resource with the goal of increasing the integration of genomic information into decision-making for brook trout and other species of conservation or management concern.

Molecular Ecology Resources↗
USGS2023

Closing the gap between science and management of cold-water refuges in rivers and streams

Human activities and climate change threaten coldwater organisms in freshwater ecosystems by causing rivers and streams to warm, increasing the intensity and frequency of warm temperature events, and reducing thermal heterogeneity. Cold-water refuges are discrete patches of relatively cool water that are used by coldwater organisms for thermal relief and short-term survival. Globally, cohesive management approaches are needed that consider interlinked physical, biological, and social factors of cold-water refuges. We review current understanding of cold-water refuges, identify gaps between science and management, and evaluate policies aimed at protecting thermally sensitive species. Existing policies include designating cold-water habitats, restricting fishing during warm periods, and implementing threshold temperature standards or guidelines. However, these policies are rare and uncoordinated across spatial scales and often do not consider input from Indigenous peoples. We propose that cold-water refuges be managed as distinct operational landscape units, which provide a social and ecological context that is relevant at the watershed scale. These operational landscape units provide the foundation for an integrated framework that links science and management by (1) mapping and characterizing cold-water refuges to prioritize management and conservation actions, (2) leveraging existing and new policies, (3) improving coordination across jurisdictions, and (4) implementing adaptive management practices across scales. Our findings show that while there are many opportunities for scientific advancement, the current state of the sciences is sufficient to inform policy and management. Our proposed framework provides a path forward for managing and protecting cold-water refuges using existing and new policies to protect coldwater organisms in the face of global change.

Global Change Biology↗
USGS2023

Community for data integration 2019 project report

The U.S. Geological Survey Community for Data Integration annually supports small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 14 projects supported in fiscal year 2019 and outlines their goals, activities, and accomplishments. Proposals in 2019 were encouraged to address the optional disciplinary theme of biosurveillance of emerging invasive species and health threats.

Open-File Report↗
USGS2023

Identifying mechanisms underlying individual body size increases in a changing, highly seasonal environment: The growing trout of West Brook

As air temperature increases, it has been suggested that smaller individual body size may be a general response to climate warming. However, for ectotherms inhabiting cold, highly seasonal environments, warming temperatures may increase the scope for growth and result in larger body size. In a long-term study of individual brook trout Salvelinus fontinalis and brown trout Salmo trutta inhabiting a small stream network, individual lengths increased over the course of 15 years. As size-selective gains and losses to the population acted to reduce body sizes and mean body size at first tagging in the autumn (<60 mm) were not observed to change substantially over time, the increase in body size was best explained by higher individual growth rates. For brook trout, increasing water temperatures during the spring (when both trout species accomplish most of their total annual growth) was the primary driver of growth rate for juvenile fish and the environmental factor which best explained increases in individual body size over time. For brown trout, by contrast, reduction in and subsequent elimination of juvenile Atlantic salmon Salmo salar midway through the study period explained most of the increases in juvenile growth and body size. In addition to these major trends, a considerable amount of interannual variation in trout growth and body size was explained by other abiotic (stream flow) and biotic (population density) factors with the direction and magnitude of these effects differing by season, age-class and species. For example, stream flow was the dominant growth rate driver for adult fish with strong positive effects in the summer and autumn, but flow variation could not explain increases in body size as we observed no trend in flow. Overall, our work supports the general contention that for high-latitude ectotherms, increasing spring temperatures associated with a warming climate can result in increased growth and individual body size (up to a point), but context-dependent change in other factors can substantially contribute to both interannual variation and longer-term effects.

Massachusetts↗
USGS2023

CGS: Coupled growth and survival model with cohort fairness

Fish modeling in complex environments is critical for understanding drivers of population dynamics in aquatic systems. This paper proposes a Bayesian network method for modeling fish survival and growth over multiple connected rivers. Traditional fish survival models capture the effect of multiple environmental drivers (e.g., stream temperature, stream flow) by adding different variables, which increases model complexity and results in very long and impractical run times (i.e., weeks). We propose a coupled survival-growth model that leverages the observations from both sources simultaneously. It also integrates the Bayesian process into the neural network model to efficiently capture complex variable relationships in the system while also conforming to known survival processes used in existing fish models. To further reduce the performance disparity of fish body length across cohorts, we propose two approaches for enforcing fairness by the adjustment of training priorities and data augmentation. The results based on a real-world fish dataset collected in Massachusetts, US demonstrate that the proposed method can greatly improve prediction accuracy in modeling survival and body length compared to independent models on survival and growth, and effectively reduce the performance disparity across cohorts. The fish growth and movement patterns discovered by the proposed model are also consistent with prior studies in the same region, while vastly reducing run times and memory requirements.

Conference Paper↗
USGS2023

Appendix D: Synthesis element 1 (revised): Water temperature effects on fisheries and stream health in nontidal waters

A limited review of relevant scientific literature related to temperature sensitivities of fish species, stream health indicators, and any related geospatial information was conducted. Based on this review, we provide a syntheses of information related to nontidal waters in the Chesapeake Bay Rising stream temperatures will have a range of impacts on nontidal aquatic ecosystems. Cold headwaters and associated species like brook trout and sculpin are especially vulnerable to higher stream temperatures. Efforts could be taken to identify and protect high quality resilient cold headwater brook trout (Salvelinus fontinalis) habitat. More information on groundwater impacts on stream temperatures and ecologically relevant temperature thresholds for species of concern could help resource managers identify temperature resilient habitats and populations. A vulnerability assessment could be valuable to better understand the drivers and stressors of rising stream temperatures, their effects on aquatic resources, and the risk to fish and other aquatic species. Further research could help in developing and fully vetting a complete list of cold/cool water benthic macroinvertebrate taxa and freshwater mussel taxa that are vulnerable to temperature change in the Chesapeake watershed.

Report↗
USGS2022

Towards continuous streamflow monitoring with time-lapse cameras and deep learning

Effective water resources management depends on monitoring the volume of water flowing through streams and rivers, but collecting continuous discharge measurements using traditional streamflow gauges is prohibitively expensive. Time-lapse cameras offer a lowcost option for streamflow monitoring, but training models for predicting streamflow directly from images requires streamflow data to use as labels, which are often unavailable. We address this data gap by proposing the alternative task of Streamflow Rank Estimation (SRE), in which the goal is to predict relative measures of streamflow such as percentile rank rather than absolute flow. In particular, we use a learning-to-rank framework to train SRE models using pairs of stream images ranked in order of discharge by an annotator, obviating the need for discharge training data and thus facilitating monitoring streamflow conditions at streams without gauges. We also demonstrate a technique for converting SRE model predictions to stream discharge estimates given an estimated streamflow distribution. Using data and images from six small US streams, we compare the performance of SRE with conventional regression models trained to predict absolute discharge. Our results show that SRE performs nearly as well as regression models on relative flow prediction. Further, we observe that the accuracy of absolute discharge estimates obtained by mapping SRE model predictions through a discharge distribution largely depends on how well the assumed discharge distribution matches the field observed data.

Conference Paper↗
USGS2022

VIMTS: Variational-based Imputation for Multi-modal Time Series

Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels, it is important to appropriately impute the high-dimensional modal missing values. However, most existing imputation methods focus on multivariate time series, fail to simultaneously consider temporal dependencies within each series and the correlations across the series, and also lack a probabilistic interpretation. In this paper, we propose a novel method, which uses a new structured variational approximation technique for the imputation of missing values in multi-modal time series. Instead of directly imputing high-dimensional modal missing values, we use the variational approximation technique to impute intermediate lower-dimensional feature representations of high-dimensional modal missing values from simple modalities related to high-dimensional modality and then feed them into a dynamical model. The dynamical model captures the temporal dependencies of the feature representations and finally predicts the target labels. In order to address the optimization difficulties caused by the lack of ground truth values of lower-dimensional feature representations, we also propose a two-stage isolated optimization strategy for better convergence. We evaluate our method on a real-world stream monitoring dataset. Our extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in both data imputation and prediction performance.

Conference Paper↗
USGS2022

Cohort strength and body size in co-occurring salmonids in a small stream network: Variation in space and time

Trout and salmon commonly coexist in stream networks. Exploring similarities and differences among species can help explain coexistence and invasive ability. Here, we describe spatial distribution, cohort strengths and size-at-age of three co-occurring species in a small stream network. Spatial distributions varied dramatically among species; native brook trout occupied all stream reaches, naturalized brown trout were found in the mainstem and lower portions of tributaries and fry-stocked Atlantic salmon were limited to the mainstem. Size-at-age also differed among species, Atlantic salmon were consistently the smallest, brook trout were intermediate in size and brown trout were the largest. Despite size differences, mean lengths of brook trout and brown trout were highly correlated among years. Cohort strengths varied considerably across years but were also highly correlated for the two trout species, suggesting strong environmental control on cohort strength and a reduced role for species interactions. At low densities, we observed strong negative effects of density on body sizes and weaker effects otherwise. Overall, these results suggest differences in spatial distribution combined with similarities in response to environmental variation contribute to species coexistence in this small steam network.

Canadian Journal of Fisheries and Aquatic Sciences↗