Geology topics

Tyler Wagner

Publications and source records attributed to Tyler Wagner.

At least 55 records · Page 3Linked to original sources

USGS2021

Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management

If not managed properly, modern agricultural practices can alter surface and groundwater quality and drinking water resources resulting in potential negative effects on aquatic and terrestrial ecosystems. Exposure to agriculturally derived contaminant mixtures has the potential to alter habitat quality and negatively affect fish and other aquatic organisms. Implementation of conservation practices focused on improving water quality continues to increase particularly in agricultural landscapes throughout the United States. The goal of this study was to determine the consequences of land management actions on the primary drivers of contaminant mixtures in five agricultural watersheds in the Chesapeake Bay, the largest watershed of the Atlantic Seaboard in North America where fish health issues have been documented for two decades. Surface water was collected and analyzed for 301 organic contaminants to determine the benefits of implemented best management practices (BMPs) designed to reduce nutrients and sediment to streams in also reducing contaminants in surface waters. Of the contaminants measured, herbicides (atrazine, metolachlor), phytoestrogens (formononetin, genistein, equol), cholesterol and total estrogenicity (indicator of estrogenic response) were detected frequently enough to statistically compare to seasonal flow effects, landscape variables and BMP intensity. Contaminant concentrations were often positively correlated with seasonal stream flow, although the magnitude of this effect varied by contaminant across seasons and sites. Land-use and other less utilized landscape variables including biosolids, manure and pesticide application and percent phytoestrogen producing crops were inversely related with site-average contaminant concentrations. Increased BMP intensity was negatively related to contaminant concentrations indicating potential co-benefits of BMPs for contaminant reduction in the studied watersheds. The information gained from this study will help prioritize ecologically relevant contaminant mixtures for monitoring and contributes to understanding the benefits of BMPs on improving surface water quality to better manage living resources in agricultural landscapes inside and outside the Chesapeake Bay watershed.

Delaware, Maryland, New Jersey, New York, Pennsylv↗

USGS2021

Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems

Dimethyl sulfide (DMS) serves as an anti-greenhouse gas, plays multiple roles 7 in aquatic ecosystems, and contributes to the global sulfur cycle. The chlorophyll 8 a (CHL, an indicator of phytoplankton biomass)-DMS relationship is critical for 9 estimating DMS emissions from aquatic ecosystems. Importantly, recent research has 10 identified that the CHL-DMS relationship has a breakpoint, where the relationship 11 is positive below a CHL threshold and negative at higher CHL concentrations. 12 Conventionally, mean regression methods are employed to characterize the CHL-DMS 13 relationship. However, these approaches focus on the response of mean conditions 14 and cannot illustrate responses of other parts of the DMS distribution, which could 15 be important in order to obtain a complete view of the CHL-DMS relationship. In 16 this study, for the first time, we proposed a novel Bayesian change point quantile 17 regression (BCPQR) model that integrates and inherits advantages of Bayesian change 18 point models and Bayesian quantile regression models. Our objective was to examine 19 whether or not the BCPQR approach could enhance the understanding of shifting 20 CHL-DMS relationships in aquatic ecosystems. We fitted BCPQR models at five 21 regression quantiles for freshwater lakes and for seas. We found that BCPQR models 22 could provide a relatively complete view on the CHL-DMS relationship. In particular, 23 it quantified the upper boundary of the relationship, representing the limiting effect of 24 CHL on DMS. Based on the results of paired parameter comparisons, we revealed the 25 inequality of regression slopes in BCPQR models for seas, indicating that applying 26 the mean regression method to develop the CHL-DMS relationship in seas might not 27 be appropriate. We also confirmed relationship differences between lakes and seas at 28 multiple regression quantiles. Further, by introducing the concept of DMS emission 29 potential, we found that pH was not likely a key factor leading to the change of the 30 CHL-DMS relationship in lakes. These findings cannot be revealed using piecewise 31 linear regression. We thereby concluded that the BCPQR model does indeed enhance 32 the understanding of shifting CHL-DMS relationships in aquatic ecosystems and is 33 expected to benefit efforts aimed at estimating DMS emissions. Considering that 34 shifting (threshold) relationships are not rare and that the BCPQR model can easily 35 be adapted to different systems, the BCPQR approach is expected to have great 36 potential for generalization in other environmental and ecological studies.

Water Research↗

USGS2021

A framework for assessing the ability to detect macroscale effects on fish growth

Various abiotic and biotic factors affect fish and their habitats at macroscales. For example, changes in global temperatures will likely alter demographic rates, including growth. However, to date, there is no statistical framework for assessing the ability to detect macroscale effects on fish growth under different sampling scenarios. We provide a generalized framework for calculating the frequentist and Bayesian power of detecting macroscale effects on fish growth. We illustrate this framework for a range of sampling scenarios that varied in the number of fish sampled per lake, the number of lakes sampled, and the magnitude of the temperature effect on growth for two case study species. However, the framework can be adapted to investigate other species, sampling scenarios, and environmental drivers. The ability to detect macroscale effects was more affected by the number of lakes sampled rather than the number of fish sampled from each lake. Confidently detecting macroscale effects likely requires sampling hundreds of lakes. This was true for both case study species, despite different life histories and extents of spatial variability in growth.

Canadian Journal of Fisheries and Aquatic Sciences↗

USGS2021

Do lake-specific characteristics mediate the temporal relationship between walleye growth and warming water temperatures?

Walleye ( Sander vitreus ) population declines have been linked to climate change, but it is unclear how the growth of this cool-water species may be affected by warming water temperatures. Because warming rates vary among lakes, it is uncertain whether lake characteristics may mediate the temperature effects on walleye growth or may vary as a result of differences in lake habitat or productivity. In this study, we ( i ) quantified walleye annual growth from 1983 to 2015 in 61 lakes in midwestern United States; ( ii ) estimated the relationship between annual early life growth ( ω ; mm·year –1 ) and water growing degree days (GDD); and ( iii ) identified lake characteristics affecting log e ( ω )–GDD relationships. On average, ω estimates significantly increased with increasing GDD; however, this relationship varied in direction and magnitude among lakes. We estimated an 84% posterior probability of a negative effect of water clarity on the log e ( ω )–GDD relationship, suggesting that water clarity may mediate the effect of warming water temperatures by affecting the magnitude and direction of the log e ( ω )–GDD relationship. Our results provide insights into the conservation of cool-water species in a changing environment and identify lakes characteristics in which walleye growth may be more resilient to climate change.

Minnesota, Wisconsin↗

USGS2020

Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis)

1) Movement has been studied extensively in stream salmonids, and most data suggest that population-level behavior is best described by a leptokurtic distribution. This distribution emphasizes the large proportion of sedentary individuals in a population, which can implicitly lead to assumptions of low population connectivity and overlook the ecological significance of rare individuals with more mobile phenotypes. 2) We report findings of a multi-season radio telemetry study conducted on four adjacent populations of wild brook trout ( Salvelinus fontinalis ) connected by Loyalsock Creek in northcentral Pennsylvania. We used these data to investigate temporal and spatial patterns in movement and fitness tradeoffs associated with behavioral phenotype. 3) Similar to previous studies, we found that 59 of the 120 radio-tagged individuals (49%) were sedentary and moved less than 200 m. Only 18% of individuals dispersed more than 1 km, but the maximum distanced moved exceeded 13 km. We also found that mobile individuals had significantly higher summer and fall survival than did sedentary fish, which could indicate that there are fitness benefits associated with vagility. 4) Most long-distance movements were the result of fish migrating from small tributaries into a larger mainstem river in the days after spawning. Therefore, even though mobility was only expressed for a short duration and by relatively few individuals in the population, the behavior appears to maintain metapopulation connectivity throughout the watershed. 5) Our study highlights the ecological significance of rare phenotypes for population demography across large spatial scales and the need to understand movement across multiple temporal and spatial scales to ensure adequate conservation of critical forms of cryptic life history diversity.

Pennsylvania↗

USGS2020

A fishery after the decline: The Susquehanna River Smallmouth Bass story

The Smallmouth Bass Micropterus dolomieu fishery in the Susquehanna River basin, Pennsylvania, is one of the most socioeconomically important fisheries in the region and has recently undergone considerable changes. These changes started in 2005, when disease was documented in young-of-the-year (age-0) Smallmouth Bass. Shortly thereafter, declines in abundance of both juveniles and adults were observed. These declines in abundance coincided with disease infections in age-0, intersex in adults, and concerns regarding contaminant exposure. Natural mortality rates, particularly for age-0, increased during this period (2005–2011), and there were concerns for the overall health of this world-class fishery. However, in recent years (2012–2017), there have been decreases in both mortality rates and external observations of disease and increases in abundance across multiple size-classes. Recent changes are encouraging for the future of the Smallmouth Bass fishery in the Susquehanna River. Yet, in light of the ever changing environmental, social, and anthropogenic influences on aquatic ecosystems, there remain concerns for Smallmouth Bass health and management. Because of this, ongoing research efforts are needed to monitor population and health changes and to conduct integrative research that considers complex relationships between organisms and their environments.

Pennsylvania↗

USGS2020

Citizen scientists record novel leaf phenology of invasive shrubs in eastern U.S. forests

Invasive shrubs are an emergent concern in deciduous forests of eastern North America. Their extended leaf phenology (ELP) – earlier leaf emergence and later leaf off compared to native shrubs and the overstory canopy – can simultaneously provide photosynthetic benefits to invasive shrubs while negatively affecting native flora and fauna through providing novel understory shade when the overstory canopy is leafless. However, phenology varies geographically, and the degree to which ELP from local-scale studies applies across the range of forests experiencing shrub invasion is unknown. In partnership with the USA National Phenology Network, we developed a citizen science campaign to record broad patterns of species-specific ELP – an otherwise prohibitively large effort. Using 1,500 observations of 14 species spanning 4 years, we found that the difference in leaf phenology between native and invasive shrubs can exceed 77 days within a growing season, a gap that decreases with increasing latitude by 2.9 [1.6, 4.2] days per degree latitude in the spring, and 2.2 [0.6,1.9] in the fall. Geographic trends in ELP provide a context for interpreting local scale phenology research while bridging inconsistent findings between existing studies and can streamline management by targeting detection and removal of invasive shrubs with leaves while natives are dormant. Furthermore, even small changes to seasonal phenology can impact forest communities, especially if there is a differential response to climate cues by nativity. After exploring broad-scale environmental variables associated with leaf phenology, we find a similar correlation with pre-growing season warmth for native and invasive shrubs, but a different relationship with dormant chill days – a variable less frequently considered for modeling spring phenology for forest productivity.

Biological Invasions↗

USGS2020

Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

Connecticut, Illinois, Indiana, Iowa, Michigan, Mi↗

USGS2020

Factors affecting sampling strategies for design of an effects‐directed analysis for endocrine‐active chemicals

Effects‐directed analysis (EDA) is an important tool for identifying unknown bioactive components in a complex mixture. Such an analysis of endocrine‐active chemicals (EACs) from water sources has promising regulatory implications but also unique logistical challenges. We propose a conceptual EDA (framework) based on a critical review of EDA literature and concentrations of common EACs in waste and surface waters. Required water volumes for identification of EACs under this EDA framework were estimated based on bioassay performance (in vitro and in vivo bioassays), limits of quantification by mass spectrometry (MS), and EAC water concentrations. Sample volumes for EDA across the EACs showed high variation in the bioassay detectors, with genistein, bisphenol A, and androstenedione requiring very high sample volumes and ethinylestradiol and 17β‐trenbolone requiring low sample volumes. Sample volume based on the MS detector was far less variable across the EACs. The EDA framework equation was rearranged to calculate detector “thresholds,” and these thresholds were compared with the literature EAC water concentrations to evaluate the feasibility of the EDA framework. In the majority of instances, feasibility of the EDA was limited by the bioassay, not MS detection. Mixed model analysis showed that the volumes required for a successful EDA were affected by the potentially responsible EAC, detection methods, and the water source type, with detection method having the greatest effect on the EDA of estrogens and androgens. The EDA framework, equation, and model we present provide a valuable tool for designing a successful EDA.

Environmental Toxicology and Chemistry↗

USGS2020

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Ecological Applications↗

USGS2020

Tissue distribution and immunomodulation in channel catfish (Ictalurus punctatus) following dietary exposure to polychlorinated biphenyl Aroclors and food deprivation

Although most countries banned manufacturing of polychlorinated biphenyls (PCBs) over 40 years ago, PCBs remain a global concern for wildlife and human health due to high bioaccumulation and biopersistance. PCB uptake mechanisms have been well studied in many taxa; however, less is known about depuration rates and how post-exposure diet can influence PCB concentrations and immune response in fish and wildlife populations. In a controlled laboratory environment, we investigated the influence of subchronic dietary exposure to two PCB Aroclors and food deprivation on tissue-specific concentrations of total PCBs and PCB homologs and innate immune function in channel catfish ( Ictalurus punctatus ). Overall, we found that the concentration of total PCBs and PCB homologs measured in whole body, fillet, and liver tissues declined more slowly in food-deprived fish, with slowest depuration observed in the liver. Additionally, fish that were exposed to PCBs had lower plasma cortisol concentrations, reduced phagocytic oxidative burst activity, and lower cytotoxic activity, suggesting that PCBs can influence stress and immune responses. However, for most measures of immune function, the effects of food deprivation had a larger effect on immune response than did PCB exposure. Taken together, these results suggest that short-term dietary exposure to PCBs can increase toxicity of consumable fish tissues for several weeks, and that PCB mixtures modulate immune and stress responses via multiple pathways. These results may inform development of human consumption advisories and can help predict and understand the influence of PCBs on fish health.

International Journal of Environmental Research an↗

USGS2020

Walleye growth declines following zebra mussel and Bythotrephes invasion

Invasive species represent a threat to aquatic ecosystems globally; however, impacts can be heterogenous across systems. Documented impacts of invasive zebra mussels ( Dreissena polymorpha ) and spiny water fleas ( Bythotrephes cederströmii ; hereafter Bythotrephes ) on native fishes are variable and context dependent across locations and time periods. Here, we use a hierarchical Bayesian analysis of a 35-year dataset on two fish species from 9 lakes to demonstrate that early life growth of ecologically important fishes are influenced by these aquatic invasive species. Walleye ( Sander vitreus ) in their first year of life grew more slowly in the presence of either invader after correcting for temperature (measured by degree days), and were on average 12 or 14% smaller at the end of their first summer following invasion by Bythotrephes or zebra mussels, respectively. Yellow perch ( Perca flavescens ) growth was less affected by invasion. Yellow perch on average grew more slowly in their first year of life following invasion by zebra mussels, although this effect was not statistically distinguishable from zero. Early life growth of both walleye and yellow perch was less tightly coupled to degree days in invaded systems, as demonstrated by increased variance surrounding the degree day-length relationship. Smaller first-year size is related to walleye survival and recruitment to later life stages and has important implications for lake food webs and fisheries management. Future research quantifying effects of zebra mussels and Bythotrephes on other population-level processes and across a wider gradient of lake types is needed to understand the mechanisms driving observed changes in walleye growth.

Minnesota↗

USGS2020

A novel quantitative framework for riverscape genetics

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout ( Salvelinus fontinalis ) in north central Pennsylvania, USA. Using the fixation index ( F ST ) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Ecological Applications↗

USGS2020

Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu

Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu , as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2020

Spatiotemporal variation in occurrence and co-occurrence of pesticides, hormones, and other organic contaminants in rivers in the Chesapeake Bay Watershed, United States

Investigating the spatiotemporal dynamics of contaminants in surface water is crucial to better understand how introduced chemicals are interacting with and potentially influencing aquatic organisms and environments. Within the Chesapeake Bay Watershed, USA, there are concerns about the potential role of contaminant exposure on fish health. Evidence suggests that exposure to contaminants in surface water is causing immunosuppression and intersex in freshwater fish species. Despite these concerns, there is a paucity of information regarding the complex dynamics of contaminant occurrence and co-occurrence in surface water across both space and time. To address these concerns, we applied a Bayesian hierarchical joint-contaminant model to describe the occurrence and co-occurrence patterns of 28 contaminants and total estrogenicity across six river sites and over three years. We found that seasonal occurrence patterns varied by contaminant, with the highest occurrence probabilities during the spring and summer months. Additionally, we found that the proportion of agricultural landcover in the immediate catchment, as well as stream discharge, did not have a significant effect on the occurrence probabilities of most compounds. Four pesticides (atrazine, metolachlor, fipronil and simazine) co-occurred across sites after accounting for environmental covariates. These results provide baseline information on the contaminant occurrence patterns of several classes of compounds within the Chesapeake Bay Watershed. Understanding the spatiotemporal dynamics of contaminants in surface water is the first step in investigating the effects of contaminant exposure on fisheries and aquatic environments.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2020

The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes

The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.

Water Research↗

USGS2020

Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models

Two primary goals in fisheries research are to ( i ) understand how habitat and environmental conditions influence the distribution of fishes across the landscape and ( ii ) make predictions about how fish communities will respond to environmental and anthropogenic change. In inland, freshwater ecosystems, quantitative approaches traditionally used to accomplish these goals largely ignore the effects of species interactions (competition, predation, mutualism) on shaping community structure, potentially leading to erroneous conclusions regarding habitat associations and unrealistic predictions about species distributions. Using two contrasting case studies, we highlight how joint species distribution models (JSDMs) can address the aforementioned deficiencies by simultaneously quantifying the effects of abiotic habitat variables and species dependencies. In particular, we show that conditional predictions of species occurrence from JSDMs can better predict species presence or absence compared with predictions that ignore species dependencies. JSDMs also allow for the estimation of site-specific probabilities of species co-occurrence, which can be informative for generating hypotheses about species interactions. JSDMs provide a flexible framework that can be used to address a variety of questions in fisheries science and management.

Canadian Journal of Fisheries and Aquatic Sciences↗

USGS2019

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint-nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

Limnology and Oceanography Letters↗