Geology topics

Tyler Wagner

Publications and source records attributed to Tyler Wagner.

At least 109 records · Page 6Linked to original sources

USGS2016

Transboundary fisheries science: Meeting the challenges of inland fisheries management in the 21st century

Managing inland fisheries in the 21st century presents several obstacles, including the need to view fisheries from multiple spatial and temporal scales, which usually involves populations and resources spanning sociopolitical boundaries. Though collaboration is not new to fisheries science, inland aquatic systems have historically been managed at local scales and present different challenges than in marine or large freshwater systems like the Laurentian Great Lakes. Therefore, we outline a flexible strategy that highlights organization, cooperation, analytics, and implementation as building blocks toward effectively addressing transboundary fisheries issues. Additionally, we discuss the use of Bayesian hierarchical models (within the analytical stage), due to their flexibility in dealing with the variability present in data from multiple scales. With growing recognition of both ecological drivers that span spatial and temporal scales and the subsequent need for collaboration to effectively manage heterogeneous resources, we expect implementation of transboundary approaches to become increasingly critical for effective inland fisheries management.

Fisheries↗

USGS2016

The statistical power to detect cross-scale interactions at macroscales

Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets.

Ecosphere↗

USGS2016

Scale-dependent seasonal pool habitat use by sympatric Wild Brook Trout and Brown Trout populations

Sympatric populations of native Brook Trout Salvelinus fontinalis and naturalized Brown Trout Salmo trutta exist throughout the eastern USA. An understanding of habitat use by sympatric populations is of importance for fisheries management agencies because of the close association between habitat and population dynamics. Moreover, habitat use by stream-dwelling salmonids may be further complicated by several factors, including the potential for fish to display scale-dependent habitat use. Discrete-choice models were used to (1) evaluate fall and early winter daytime habitat use by sympatric Brook Trout and Brown Trout populations based on available residual pool habitat within a stream network and (2) assess the sensitivity of inferred habitat use to changes in the spatial scale of the assumed available habitat. Trout exhibited an overall preference for pool habitats over nonpool habitats; however, the use of pools was nonlinear over time. Brook Trout displayed a greater preference for deep residual pool habitats than for shallow pool and nonpool habitats, whereas Brown Trout selected for all pool habitat categories similarly. Habitat use by both species was found to be scale dependent. At the smallest spatial scale (50 m), habitat use was primarily related to the time of year and fish weight. However, at larger spatial scales (250 and 450 m), habitat use varied over time according to the study stream in which a fish was located. Scale-dependent relationships in seasonal habitat use by Brook Trout and Brown Trout highlight the importance of considering scale when attempting to make inferences about habitat use; fisheries managers may want to consider identifying the appropriate spatial scale when devising actions to restore and protect Brook Trout populations and their habitats.

Pennsylvania↗

USGS2015

The first description of oarfish Regalecus glesne ( Regalecus russellii Cuvier 1816) ageing structures

Despite being a large, conspicuous teleost with a worldwide tropical and temperate distribution, the giant oarﬁsh Regalecus spp. remain very rare ﬁsh species in terms of scientiﬁc sampling. Subsequently, very little biological information is known about Regalecus spp. and almost nothing has been concluded in the ﬁeld of age and growth (Roberts, 2012). No studies of otoliths or temporal (annual) markings on any hard structures have been reported, and to our knowledge otoliths have never been recovered from any specimens (Tyson Roberts, personal communication),although a few texts do provide illustrations of Regalecus sp. otoliths (Lin and Chang, 2012; Nolf, 2013). Further inferential diﬃculty comes from the fact that age and growth studies of any Lampridiforme species are rare. Lampris guttatus is perhaps the only Lampridiforme species for which any biological information has been reported(Francis et al., 2004), which stems from the species commercial value. In order to begin understanding any species (for later purposes of management, conservation, etc.), basic biological information is needed. In the present study, we examine not only the ﬁrst Regalecus russellii otolith, but provide suggestions toward future work that should direct data collection that can be used to generate basic biological information for this species.

Journal of Applied Ichthyology↗

USGS2015

Effects of land use on lake nutrients: The importance of scale, hydrologic connectivity, and region

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad scales.

Michigan↗

USGS2015

Translating climate change effects on species into everyday language: an example of more driving and less fishing

Climate change is expected to result in widespread changes in species distributions (e.g., shifting, shrinking, expanding species ranges; e.g., Parmesan and Yohe, 2003), especially for freshwater fish species (Heino et al. 2009). Although anglers and other resource users could be greatly affected by changes in species distributions, predicted changes are rarely reported in ways that can be easily understood by the general public. In contrast, climate science that more directly affects human welfare or livelihoods is often more readily communicated to the general public because it is of greater concern or closely related to everyday life. For example, most people can readily interpret how increases in the number of “hot” days above a given temperature threshold might affect their lives, and property owners in coastal areas can use predictive maps to determine how they might be affected by sea level rise (for more examples, see the Third National Assessment of the U.S. Environmental Protection Agency at globalchange.gov ). However, the effects of climate change on species are usually reported to the general public using summary metrics or maps designed to communicate concepts that are not normally encountered in everyday life, including changes in habitat suitability, range shifts, or increasing risks from disease or extreme events (e.g., National Audubon Society 2009; Groffman et al. 2014). Though these metrics are necessary, meaningful, and understood by scientists, many people lack the necessary training and background to readily understand them. Further, scientists and nonscientists alike may struggle to convert these metrics into a currency that directly affects day-to-day life. Climate science is a complex issue, and we argue that when communicating potential responses of vegetation, fish, and wildlife to nonscientists, creative thinking with respect to the currency of communication will facilitate discussions between scientists, policy makers, and the public. We posit that with some additional thought and relatively simple summaries, the responses of fish and other species to climate change can be translated into everyday language that will facilitate climate science communication. Although such translations are rare, one example of this type of creativity is the translation from changes in habitat suitability for tree species to potential reductions in maple syrup production (West over 2012), which is arguably more interesting and understandable for the general public. Similar translations could be especially important for communicating climate change effects on game fish and other species that are socially and economically important to large groups of people. We demonstrate this translation by communicating the potential effects of climate change on the distribution of a coldwater fish species, the eastern Brook Trout Salvelinus fontinalis . Rather than communicating the potential forecasted contraction of the Brook Trout's distribution in terms of habitat loss, we report the predicted increases in the driving distance to streams likely offering Brook Trout angling opportunities under a climate change scenario. Travel costs based on distance have been widely used to value ecosystem services such as angling under climate change scenarios (e.g., Pendleton and Mendelsohn 1998; Mendelsohn and Markowski 1999; Ahn et al. 2000) but, to the best of our knowledge, have not been used for communicating potential changes to the public despite the intrinsic link to everyday life.

New York, Pennsylvania↗

USGS2015

Spatial and temporal movement dynamics of brook Salvelinus fontinalis and brown trout Salmo trutta

Native eastern brook trout Salvelinus fontinalis and naturalized brown trout Salmo trutta occur sympatrically in many streams across the brook trout’s native range in the eastern United States. Understanding within- among-species variability in movement, including correlates of movement, has implications for management and conservation. We radio tracked 55 brook trout and 45 brown trout in five streams in a north-central Pennsylvania, USA watershed to quantify the movement of brook trout and brown trout during the fall and early winter to (1) evaluate the late-summer, early winter movement patterns of brook trout and brown trout, (2) determine correlates of movement and if movement patterns varied between brook trout and brown trout, and (3) evaluate genetic diversity of brook trout within and among study streams, and relate findings to telemetry-based observations of movement. Average total movement was greater for brown trout (mean ± SD = 2,924 ± 4,187 m) than for brook trout (mean ± SD = 1,769 ± 2,194 m). Although there was a large amount of among-fish variability in the movement of both species, the majority of movement coincided with the onset of the spawning season, and a threshold effect was detected between stream flow and movement: where movement increased abruptly for both species during positive flow events. Microsatellite analysis of brook trout revealed consistent findings to those found using radio-tracking, indicating a moderate to high degree of gene flow among brook trout populations. Seasonal movement patterns and the potential for relatively large movements of brook and brown trout highlight the importance of considering stream connectivity when restoring and protecting fish populations and their habitats.

Pennsylvania↗

USGS2015

Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km 2 ). LAGOS includes two modules: LAGOS GEO , with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOS LIMNO , with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ma↗

USGS2015

Spatial and temporal variability in growth of southern flounder ( Paralichthys lethostigma )

Delineation of stock structure is important for understanding the ecology and management of many fish populations, particularly those with wide-ranging distributions and high levels of harvest. Southern flounder ( Paralichthys lethostigma ) is a popular commercial and recreational species along the southeast Atlantic coast and Gulf of Mexico, USA. Recent studies have provided genetic and otolith morphology evidence that the Gulf of Mexico and Atlantic Ocean stocks differ. Using age and growth data from four states (Texas, Alabama, South Carolina, and North Carolina) we expanded upon the traditional von Bertalanffy model in order to compare growth rates of putative geographic stocks of southern flounder. We improved the model fitting process by adding a hierarchical Bayesian framework to allow each parameter to vary spatially or temporally as a random effect, as well as log transforming the three model parameters ( L ∞ , K , and t 0 ). Multiple comparisons of parameters showed that growth rates varied (even within states) for females, but less for males. Growth rates were also consistent through time, when long-term data were available. Since within-basin populations are thought to be genetically well-mixed, our results suggest that consistent small-scale environmental conditions (i.e., within estuaries) likely drive growth rates and should be considered when developing broader scale management plans.

Fisheries Research↗

USGS2015

Predicting Brook Trout occurrence in stream reaches throughout their native range in the eastern United States

The Brook Trout Salvelinus fontinalis is an important species of conservation concern in the eastern USA. We developed a model to predict Brook Trout population status within individual stream reaches throughout the species’ native range in the eastern USA. We utilized hierarchical logistic regression with Bayesian estimation to predict Brook Trout occurrence probability, and we allowed slopes and intercepts to vary among ecological drainage units (EDUs). Model performance was similar for 7,327 training samples and 1,832 validation samples based on the area under the receiver operating curve (&sim;0.78) and Cohen's kappa statistic (0.44). Predicted water temperature had a strong negative effect on Brook Trout occurrence probability at the stream reach scale and was also negatively associated with the EDU average probability of Brook Trout occurrence (i.e., EDU-specific intercepts). The effect of soil permeability was positive but decreased as EDU mean soil permeability increased. Brook Trout were less likely to occur in stream reaches surrounded by agricultural or developed land cover, and an interaction suggested that agricultural land cover also resulted in an increased sensitivity to water temperature. Our model provides a further understanding of how Brook Trout are shaped by habitat characteristics in the region and yields maps of stream-reach-scale predictions, which together can be used to support ongoing conservation and management efforts. These decision support tools can be used to identify the extent of potentially suitable habitat, estimate historic habitat losses, and prioritize conservation efforts by selecting suitable stream reaches for a given action. Future work could extend the model to account for additional landscape or habitat characteristics, include biotic interactions, or estimate potential Brook Trout responses to climate and land use changes.

Connecticut, Delaware, Maine, Maryland, Massachuse↗

USGS2014

Evaluating changes in stream fish species richness over a 50-year time-period within a landscape context

Worldwide, streams and rivers are facing a suite of pressures that alter water quality and degrade physical habitat, both of which can lead to changes in the composition and richness of fish populations. These potential changes are of particular importance in the Southeast USA, home to one of the richest stream fish assemblages in North America. Using data from 83 stream sites in North Carolina sampled in the 1960’s and the past decade, we used hierarchical Bayesian models to evaluate relationships between species richness and catchment land use and land cover (e.g., agriculture and forest cover). In addition, we examined how the rate of change in species richness over 50 years was related to catchment land use and land cover. We found a negative and positive correlation between forest land cover and agricultural land use and average species richness, respectively. After controlling for introduced species, most (66 %) stream sites showed an increase in native fish species richness, and the magnitude of the rate of increase was positively correlated to the amount of forested land cover in the catchment. Site-specific trends in species richness were not positive, on average, until the percentage forest cover in the network catchment exceeded about 55 %. These results suggest that streams with catchments that have moderate to high (>55 %) levels of forested land in upstream network catchments may be better able to increase the number of native species at a faster rate compared to less-forested catchments.

North Carolina↗

USGS2014

Modeling spatially-varying landscape change points in species occurrence thresholds

Predicting species distributions at scales of regions to continents is often necessary, as large-scale phenomena influence the distributions of spatially structured populations. Land use and land cover are important large-scale drivers of species distributions, and landscapes are known to create species occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially varying parameters, including change points. Our model also allows for modeling estimated parameters in an effort to understand large-scale drivers of variability in land use and land cover on species occurrence thresholds. We use range-wide detection/nondetection data for the eastern brook trout ( Salvelinus fontinalis ), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially varying threshold parameters in species occurrence. We parameterized the model for investigating thresholds in landscape predictor variables that are measured as proportions, and which are therefore restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial variation in change point estimates, although there was spatial variability in the overall shape of the threshold response and associated uncertainty. In addition, regional mean stream water temperature was correlated to the change point parameters for the proportion of urban land use, with the change point value increasing with increasing mean stream water temperature. We present a framework for quantify macrosystem variability in spatially varying threshold model parameters in relation to important large-scale drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can easily be extended to accommodate other statistical distributions for modeling species richness or abundance.

Ecosphere↗

USGS2014

Comparative bioenergetics modeling of two Lake Trout morphotypes

Efforts to restore Lake Trout Salvelinus namaycush in the Laurentian Great Lakes have been hampered for decades by several factors, including overfishing and invasive species (e.g., parasitism by Sea Lampreys Petromyzon marinus and reproductive deficiencies associated with consumption of Alewives Alosa pseudoharengus ). Restoration efforts are complicated by the presence of multiple body forms (i.e., morphotypes) of Lake Trout that differ in habitat utilization, prey consumption, lipid storage, and spawning preferences. Bioenergetics models constitute one tool that is used to help inform management and restoration decisions; however, bioenergetic differences among morphotypes have not been evaluated. The goal of this research was to investigate bioenergetic differences between two actively stocked morphotypes: lean and humper Lake Trout. We measured consumption and respiration rates across a wide range of temperatures (4–22°C) and size-classes (5–100 g) to develop bioenergetics models for juvenile Lake Trout. Bayesian estimation was used so that uncertainty could be propagated through final growth predictions. Differences between morphotypes were minimal, but when present, the differences were temperature and weight dependent. Basal respiration did not differ between morphotypes at any temperature or size-class. When growth and consumption differed between morphotypes, the differences were not consistent across the size ranges tested. Management scenarios utilizing the temperatures presently found in the Great Lakes (e.g., predicted growth at an average temperature of 11.7°C and 14.4°C during a 30-d period) demonstrated no difference in growth between the two morphotypes. Due to a lack of consistent differences between lean and humper Lake Trout, we developed a model that combined data from both morphotypes. The combined model yielded results similar to those of the morphotype-specific models, suggesting that accounting for morphotype differences may not be necessary in bioenergetics modeling of lean and humper Lake Trout.

Transactions of the American Fisheries Society↗

USGS2014

Learning rate and temperament in a high predation risk environment

Living in challenging environments can influence the behavior of animals in a number of ways. For instance, populations of prey fish that experience frequent, nonlethal interactions with predators have a high proportion of individuals that express greater reaction to risk and increased activity and exploration—collectively known as temperament traits. Temperament traits are often correlated, such that individuals that are risk-prone also tend to be active and explore more. Spatial learning, which requires the integration of many sensory cues, has also been shown to vary in fish exposed to different levels of predation threat. Fish from areas of low predation risk learn to solve spatial tasks faster than fish from high predation areas. However, it is not yet known whether simpler forms of learning, such as learning associations between two events, are similarly influenced. Simple forms of associative learning are likely to be affected by temperament because a willingness to approach and explore novel situations could provide animals with a learning advantage. However, it is possible that routine-forming and inflexible traits associated with risk-prone and increased exploratory behavior may act in the opposite way and make risk-prone individuals poorer at learning associations. To investigate this, we measured temperament in Panamanian bishop fish ( Brachyrhaphis episcopi ) sampled from a site known to contain many predators. The B. episcopi were then tested with an associative learning task. Within this population, fish that explored more were faster at learning a cue that predicted access to food, indicating a link between temperament and basic learning abilities.

Oecologia↗

USGS2014

A hierarchical community occurrence model for North Carolina stream fish

The southeastern USA is home to one of the richest—and most imperiled and threatened—freshwater fish assemblages in North America. For many of these rare and threatened species, conservation efforts are often limited by a lack of data. Drawing on a unique and extensive data set spanning over 20 years, we modeled occurrence probabilities of 126 stream fish species sampled throughout North Carolina, many of which occur more broadly in the southeastern USA. Specifically, we developed species-specific occurrence probabilities from hierarchical Bayesian multispecies models that were based on common land use and land cover covariates. We also used index of biotic integrity tolerance classifications as a second level in the model hierarchy; we identify this level as informative for our work, but it is flexible for future model applications. Based on the partial-pooling property of the models, we were able to generate occurrence probabilities for many imperiled and data-poor species in addition to highlighting a considerable amount of occurrence heterogeneity that supports species-specific investigations whenever possible. Our results provide critical species-level information on many threatened and imperiled species as well as information that may assist with re-evaluation of existing management strategies, such as the use of surrogate species. Finally, we highlight the use of a relatively simple hierarchical model that can easily be generalized for similar situations in which conventional models fail to provide reliable estimates for data-poor groups.

North Carolina↗

USGS2014

Regional variability among nonlinear chlorophyll-phosphorus relationships in lakes

The relationship between chlorophyll a (Chl a ) and total phosphorus (TP) is a fundamental relationship in lakes that reflects multiple aspects of ecosystem function and is also used in the regulation and management of inland waters. The exact form of this relationship has substantial implications on its meaning and its use. We assembled a spatially extensive data set to examine whether nonlinear models are a better fit for Chl a —TP relationships than traditional log-linear models, whether there were regional differences in the form of the relationships, and, if so, which regional factors were related to these differences. We analyzed a data set from 2105 temperate lakes across 35 ecoregions by fitting and comparing two different nonlinear models and one log-linear model. The two nonlinear models fit the data better than the log-linear model. In addition, the parameters for the best-fitting model varied among regions: the maximum and lower Chl a asymptotes were positively and negatively related to percent regional pasture land use, respectively, and the rate at which chlorophyll increased with TP was negatively related to percent regional wetland cover. Lakes in regions with more pasture fields had higher maximum chlorophyll concentrations at high TP concentrations but lower minimum chlorophyll concentrations at low TP concentrations. Lakes in regions with less wetland cover showed a steeper Chl a —TP relationship than wetland-rich regions. Interpretation of Chl a —TP relationships depends on regional differences, and theory and management based on a monolithic relationship may be inaccurate.

Iowa, Maine, Michigan, New Hampshire, Ohio, Wiscon↗

USGS2014

Evaluation of a regional monitoring program's statistical power to detect temporal trends in forest health indicators

Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service’s Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of the Vital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year −1 for all indicators and is appropriate for detecting a 1 % trend·year −1 in most indicators.

New Jersey, Pennsylvania, Virginia, West Virginia↗

USGS2014

Long-term citizen-collected data reveal geographical patterns and temporal trends in lake water clarity

We compiled a lake-water clarity database using publicly available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938–2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity.

Illinois, Indiana, Iowa, Michigan, Minnesota, Miss↗