Geology topics

Tyler Wagner

Publications and source records attributed to Tyler Wagner.

At least 73 records · Page 4Linked to original sources

USGS2019

Identifying and characterizing extrapolation in multivariate response data

Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.

PLoS ONE↗

USGS2019

Individual behaviour and resource use of thermally stressed brook trout Salvelinus fontinalis portend the conservation potential of thermal refugia

Individual aggression and thermal refuge use were monitored in brook trout Salvelinus fontinalis in a controlled laboratory to determine how fish size and personality influence time spent in forage and thermal habitat patches during periods of thermal stress. On average, larger and more exploratory fish initiated more aggressive interactions and across all fish there was decreased aggression at warmer temperatures. Individual personality did not explain changes in aggression or habitat use with increased temperature; however, larger individuals initiated comparatively fewer aggressive interactions at warmer temperatures. Occupancy of forage patches generally declined as ambient stream temperatures approached critical maximum and fish increased thermal refuge use, with a steeper decline in forage patch occupancy observed in larger fish. These findings suggest that larger individuals may be more vulnerable to stream temperature rise. Importantly, even at thermally stressful temperatures, all fish periodically left the thermal refuge to forage. This indicates that the success of refugia at increasing population survival during periods of stream temperature rise may depend on the location of thermal refugia relative to forage locations within the larger habitat mosaic. These results provide insights into the potential for thermal refugia to improve population survival and can be used to inform predictions of population vulnerability to climate change.

Journal of Fish Biology↗

USGS2019

Spatial and temporal variation of ecosystem properties at macroscales

Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation – spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.

Ecology Letters↗

USGS2019

Movement dynamics of smallmouth bass (Micropterus dolomieu) in a large river-tributary system

Smallmouth bass, Micropterus dolomieu Lacepède, movement dynamics were investigated in a connected mainstem river-tributary system. Smallmouth bass moved large distances annually ( n = 84 fish, average = 24.6 ± 25.9 km, range = 0.03 to 118 km) and had three peak movement periods (pre-spawn, post-spawn and overwintering). Movement into and out of tributaries was common, but the movement between mainstem river and tributary habitats varied among tagging locations and season. In general, a large proportion of fish that were tagged in tributaries moved out of the tributaries after spawning (22/30 fish). Because of the importance of fish movement patterns on population dynamics, the observed individual variability in movement, quantified using a hierarchical model, and the potential for long-distance movements are important considerations for smallmouth bass conservation and management. In addition, mainstem river-tributary connectivity appears to play an important role for smallmouth bass during key life history events.

Pennsylvania↗

USGS2019

Winter precipitation and summer temperature predict lake water quality at macroscales

Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g. , spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.

Water Resources Research↗

USGS2019

Trends in global shark attacks

Shark attacks are a global phenomenon that attracts widespread attention and publicity, often with negative outcomes for shark populations. Despite the widespread perceptions of shark attacks, trends in human water activities and shark populations are both dynamic, resulting in variable rates of shark attacks over space and time. Understanding variable trends in shark attacks may contribute to a better understanding of risk, and a more tempered response in the wake of an attack. We found that global shark attack rates are low, yet variable across global regions and over decades. Countries with low populations were found to have the highest rates of attack, while countries with high populations (U.S.A., Australia, South Africa) tended to have overall low attack rates, but also much more interannual variability. From the 1960s to the present, those countries with the highest populations also tended to be the places where attack rates have increased. Ultimately, shark attack risk is also driven by local conditions (e.g., time of day, species present); however, a global scale understanding of attack rates helps place risk into perspective and may contribute to a more scientifically-grounded discussion of sharks, and their management and conservation.

PLoS ONE↗

USGS2019

Does incorporating gear selectivity during macroscale investigations of fish growth reduce size-selective sampling bias in parameter estimates?

Understanding of fish growth, the spatial variability in individual growth, and the potential drivers of such variability is a fundamental component of many ecological investigations. However, sampling gears are always size-selective, and this selectivity can result in biased parameter estimates that can lead to, for example, biased stock assessments that use growth estimates. Using seven flathead catfish ( Pylodictis olivaris ) populations from across the USA as an example, we investigated to what degree the incorporation of gear selectivity in growth models reduces size-selective bias in the estimation of growth parameters during macroscale investigations of fish growth. We developed a series of simulation scenarios by combining different sampling methods to obtain fish samples and different gear selectivity assumptions to estimate parameters. Results showed that the efficacy of incorporating gear selectivity in growth models to reduce size-selective sampling bias during macroscale investigations depends on multiple factors, including ( i ) the size distribution of small and large fish in the sample (which is a function of sampling methods), and ( ii ) consistency of sampling methods across populations. Incorporation of gear selectivity may reduce bias when data are lacking for large fish and when sampling methods are inconsistent across populations. Demographics of the sampled populations and the growth parameter of interest can also affect the utility of directly incorporating gear selectivity into growth models. Because multiple factors can influence the efficacy of incorporating gear selectivity into growth models, the decision to do so likely needs to be made on a case-by-case basis. This study extends the existing gear selectivity research by focusing on macroscale fish growth investigations across multiple populations and provides guidance on how to handle gear selectivity assumptions during such investigations.

Canadian Journal Fisheries and Aquatic Sciences↗

USGS2019

Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models

Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.

New York New Jersey, Pennsylvania↗

USGS2018

Spatial and temporal variability of myxozoan parasite, Myxobolus inornatus, prevalence in young of the year smallmouth bass in the Susquehanna River Basin, Pennsylvania

A myxozoan parasite, Myxobolus inornatus, is one disease agent identified in young of the year (YOY) smallmouth bass in the Susquehanna River Basin, Pennsylvania. We investigated spatial and temporal variability in M. Inornatus prevalence across the Susquehanna River Basin and at several out-of-basin sites. We examined potential land use drivers of M. Inornatus prevalence including agricultural and developed land use. In 1,267 YOY smallmouth bass collected from 32 sites during 2013–2016, M. Inornatus was documented in 43.6% of samples. Among-site variability in parasite prevalence was greater than among-year variability. The effect of agricultural land use on M. Inornatus prevalence had a high probability of being positively correlated at multiple spatial scales (probability of positive effect > 0.80). The effect of developed land use on M. Inornatus prevalence had a relatively high probability of being negatively correlated at multiple spatial scales (probability of negative effect > 0.70). Our results suggest that land use practices could be related to M. Inornatus infection of smallmouth bass. Further study will be necessary to determine whether disease dynamics are a consequence of effects on the host, alterations of instream habitat mediating invertebrate host dynamics and/or survival and dispersal of the parasite infective stage.

Pennsylvania↗

USGS2018

Combining nutrient, productivity, and landscape-based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

Empirical nutrient models that describe lake nutrient, productivity, and water clarity relationships among lakes play a prominent role in limnology. Landscape-based regressions are also used to understand macroscale variability of lake nutrients, clarity, and productivity (hereafter referred to as nutrient-productivity). Predictions from both models are used to inform eutrophication management globally. To date, these two classes of models are generally conducted separately, which ignores the known dependencies among nutrient-productivity variables. We present a statistical model that integrates nutrient-productivity and landscape-based regressions—where lake nutrients, productivity, and clarity variables are modeled jointly. We fitted a joint nutrient-productivity model to over 7000 lakes with three nutrients (total phosphorus, total nitrogen, nitrate concentrations), chlorophyll a concentrations, and Secchi disk depth as response variables and landscape features as predictor variables. Because lakes in different regions respond to landscape features differently, we focused our analysis on two subregions with different dominant land uses, the agricultural Midwest and the forested Northeast U.S. Predictive performance was enhanced by modeling nutrient-productivity variables jointly. We also found strong evidence that nutrient-productivity variables were coupled, and that only nitrate may be decoupled from other nutrient-productivity variables in the forested region. We speculate that these regional differences may be related to differences in the strength of biogeochemical cycles and stoichiometric controls between these regions. Jointly modeling nutrient-productivity variables in lakes effectively integrates the two dominant approaches for studying lakes nutrient-productivity relationships and provides novel insight into macroscale patterns of the coupling of nutrients, chlorophyll, and water clarity in lakes.

Limnology and Oceanography↗

USGS2018

Limited hatchery introgression into wild brook trout (Salvelinus fontinalis) populations despite reoccurring stocking

Due to increased anthropogenic pressures on many fish populations, supplementing wild populations with captive‐raised individuals has become an increasingly common management practice. Stocking programs can be controversial due to uncertainty about the long‐term fitness effects of genetic introgression on wild populations. In particular, introgression between hatchery and wild individuals can cause declines in wild population fitness, resiliency, and adaptive potential, and contribute to local population extirpation. However, low survival and fitness of captive‐raised individuals can minimize the long‐term genetic consequences of stocking in wild populations, and to date the prevalence of introgression in actively stocked ecosystems has not been rigorously evaluated. We quantified the extent of introgression in 30 populations of wild brook trout ( Salvelinus fontinalis ) in a Pennsylvania watershed, and examined the correlation between introgression and 11 environmental covariates. Genetic assignment tests were used to determine the origin (wild vs. captive‐raised) for 1742 wild‐caught and 300 hatchery brook trout. To avoid assignment biases, individuals were assigned to two simulated populations that represented the average allele frequencies in wild and hatchery groups. Fish with intermediate probabilities of wild ancestry were classified as introgressed, with threshold values determined through simulation. Even with reoccurring stocking at most sites, over 93% of wild‐caught individuals probabilistically assigned to wild origin, and only 5.6% of wild‐caught fish assigned to introgressed. Models examining environmental drivers of introgression explained less than 3% of the among‐population variability, and all estimated effects were highly uncertain. This was not surprising given overall low introgression observed in this study. Our results suggest that introgression of hatchery‐derived genotypes can occur at low rates, even in actively stocked ecosystems and across a range of habitats. However, a cautious approach to stocking may still be warranted, as the potential effects of stocking on wild population fitness and the mechanisms limiting introgression are not known.

Evolutionary Applications↗

USGS2018

Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations

Quantifying spatial variability in fish growth and identifying large‐scale drivers of growth are fundamental to many conservation and management decisions. Although fish growth studies often focus on a single population, it is becoming increasingly clear that large‐scale studies are likely needed for addressing transboundary management needs. This is particularly true for species with high recreational value and for those with negative ecological consequences when introduced outside of their native range, such as the Flathead Catfish Pylodictis olivaris . This study quantified growth variability of the Flathead Catfish across a large portion of its contemporary range to determine whether growth differences existed between habitat types (i.e., reservoirs and rivers) and between native and introduced populations. Additionally, we investigated whether growth parameters varied as a function of latitude and time since introduction (for introduced populations). Length‐at‐age data from 26 populations across 11 states in the USA were modeled using a Bayesian hierarchical von Bertalanffy growth model. Population‐specific growth trajectories revealed large variation in Flathead Catfish growth and relatively high uncertainty in growth parameters for some populations. Relatively high uncertainty was also evident when comparing populations and when quantifying large‐scale patterns. Growth parameters (Brody growth coefficient [ K ] and theoretical maximum average length [ L ∞ ]) were not different (based on overlapping 90% credible intervals) between habitat types or between native and introduced populations. For populations within the introduced range of Flathead Catfish, latitude was negatively correlated with K . For native populations, we estimated an 85% probability that L ∞ estimates were negatively correlated with latitude. Contrary to predictions, time since introduction was not correlated with growth parameters in introduced populations of Flathead Catfish. Results of this study suggest that Flathead Catfish growth patterns are likely shaped more strongly by finer‐scale processes (e.g., exploitation or prey abundances) as opposed to macro‐scale drivers.

Transactions of the American Fisheries Society↗

USGS2018

Strategies for effective collaborative manuscript development in interdisciplinary science teams

Science is increasingly being conducted in large, interdisciplinary teams. As team size increases, challenges can arise during manuscript development, where achieving one team goal (e.g., inclusivity) may be in direct conflict with other goals (e.g., efficiency). Here, we present strategies for effective collaborative manuscript development that draw from our experiences in an interdisciplinary science team writing collaborative manuscripts for six years. These strategies are rooted in six guiding principles that were important to our team: to create a transparent, inclusive, and accountable research team that promotes and protects team members who have less power to influence decision‐making while fostering creativity and productivity. To help alleviate the conflicts that can arise in collaborative manuscript development, we present the following strategies: understand your team composition, create an authorship policy and discuss authorship early and often, openly announce manuscript ideas, identify and communicate the type of manuscript and lead author management style, and document and describe authorship contributions. These strategies can help reduce the probability of group conflict, uphold individual and team values, achieve fair authorship practices, and increase science productivity.

Ecosphere↗

USGS2018

Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations

Detecting temporal changes in fish abundance is an essential component of fisheries management. Because of the need to understand short‐term and nonlinear changes in fish abundance, traditional linear models may not provide adequate information for management decisions. This study highlights the utility of Bayesian dynamic linear models (DLMs) as a tool for quantifying temporal dynamics in fish abundance. To achieve this goal, we quantified temporal trends of Smallmouth Bass Micropterus dolomieu catch per effort (CPE) from rivers in the mid‐Atlantic states, and we calculated annual probabilities of decline from the posterior distributions of annual rates of change in CPE. We were interested in annual declines because of recent concerns about fish health in portions of the study area. In general, periods of decline were greatest within the Susquehanna River basin, Pennsylvania. The declines in CPE began in the late 1990s—prior to observations of fish health problems—and began to stabilize toward the end of the time series (2011). In contrast, many of the other rivers investigated did not have the same magnitude or duration of decline in CPE. Bayesian DLMs provide information about annual changes in abundance that can inform management and are easily communicated with managers and stakeholders.

Maryland, Pennsylvania, West Virginia↗

USGS2018

Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout ( Salvelinus fontinalis ), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.

Global Change Biology↗

USGS2018

Prediction of lake depth across a 17-state region in the United States

Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Z max ) observations. The model was used to predict depth for all lakes ≥4 ha ( n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Z max . Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance ( R 2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

Inland Waters↗

USGS2018

Small values in big data: The continuing need for appropriate metadata

Compiling data from disparate sources to address pressing ecological issues is increasingly common. Many ecological datasets contain left-censored data – observations below an analytical detection limit. Studies from single and typically small datasets show that common approaches for handling censored data — e.g., deletion or substituting fixed values — result in systematic biases. However, no studies have explored the degree to which the documentation and presence of censored data influence outcomes from large, multi-sourced datasets. We describe left-censored data in a lake water quality database assembled from 74 sources and illustrate the challenges of dealing with small values in big data, including detection limits that are absent, range widely, and show trends over time. We show that substitutions of censored data can also bias analyses using ‘big data’ datasets, that censored data can be effectively handled with modern quantitative approaches, but that such approaches rely on accurate metadata that describe treatment of censored data from each source.

Ecological Informatics↗

USGS2018

Quantifying population-level effects of water temperature, flow velocity and chemical-induced reproduction depression: A simulation study with smallmouth bass

Evaluating stochastic abiotic factors and their combined effects on fish and wildlife populations have been challenging in environmental sciences. Contributing to this challenge is the paucity of data describing how observations made on individuals related to exposure to chemical compounds ultimately effect population vital rates, and how this exposure interacts with other abiotic drivers. Using three smallmouth bass populations in Pennsylvania as a case study, we explored both single-factor and combined effects of water temperature, flow velocity and chemical exposure on populations through a model simulation. Although there are many pathways through which chemicals can affect population vital rates, we focused on one where chemical exposure leads to reduced reproduction. Among the three populations considered, two (the Juniata and Susquehanna populations) have experienced adverse health, including the potential adverse effects of environmental stress and chemical contamination that may cause disease and mortality of young-of-year (YOY), various skin lesions and a high prevalence of intersex or testicular oocytes in adults. The third population (The Alleghany population) has not encountered mortality events of YOY and intersex prevalence is much lower. The simulation involved projecting populations using a length-based model under different environmental conditions. In the simulations, abiotic factors influenced population dynamics through their impacts on growth, survival and recruitment. Response to the same environmental stress varied by population and life-stage of the species. Factors affecting young adult and adult life-stages had great influence on proportional stock density (PSD) and the probability of having PSD within the suggested range (PSD probability). Increases in water temperature had a negative effect and dominant role in the combined effect on population size structure (e.g., PSD and PSD probability) – increases in flow velocity during the spring season also had a negative effect on abundance. Populations with high recruitment rates sustained relatively large population size, even under high water temperature and/or high flow velocity, which suggests that factors and management strategies that benefit recruitment (such as reduced chemical contaminants) may compensate for the negative effects of warming water temperatures and high spring flow velocity on population size.

Ecological Modelling↗