Browsing by Author "Vitense, Kelsey"

Now showing 1 - 11 of 11
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    Data and R code supporting "A hidden Markov model for ecosystems exhibiting alternative stable states"
    (2021-01-20) Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John R; viten003@umn.edu; Vitense, Kelsey
    This repository contains the data and R code used to conduct the analyses in the article "Using hidden Markov models to inform conservation and management strategies in ecosystems exhibiting alternative stable states" in Journal of Applied Ecology.
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    Data and R code supporting "Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression"
    (2017-10-03) Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John R; viten003@umn.edu; Vitense, Kelsey
    This repository contains the data and R code used to conduct the analyses in the article "Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression" in Ecological Applications.
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    Data and R code supporting “Non-linear water clarity trends and impacts on littoral area in Minnesota lakes”
    (2021-04-19) Vitense, Kelsey; Hansen, Gretchen J A; viten003@umn.edu; Vitense, Kelsey; University of Minnesota Fisheries Systems Ecology Lab
    This repository contains the data and R code used to conduct the analyses in the article "Non-linear water clarity trends and impacts on littoral area in Minnesota lakes" in Limnology and Oceanography Letters.
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    Data in support of Connecting habitat to species abundance: the role of light and temperature in the abundance of walleye in lakes
    (2022-01-25) Mahlum, Shad; Vitense, Kelsey; Corson-Dorsch, Hayley; Platt, Lindsay; Read, Jordan; Schmalz, Patrick; Treml, Melissa; Hansen, Gretchen J A; smahlum@umn.edu; Mahlum, Shad; Department of Fish, Wildlife, and Conservation Biology
    This repository contains the data used to conduct the analyses in the article " Connecting habitat to species abundance: the role of light and temperature in the abundance of walleye in lakes.”
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    Data in support of: Quantifying resilience of coldwater habitat to climate and land use change to prioritize watershed conservation
    (2021-08-06) Hansen, Gretchen JA; Wehrly, Kevin E; Vitense, Kelsey; Walsh, Jacob R; Jacobson, Peter C; ghansen@umn.edu; Hansen, Gretchen; University of Minnesota Department of Fisheries, Wildlife, Conservation Biology; Minnesota Department of Natural Resources; Michigan Department of Natural Resources
    Data for 12,450 lakes in the Upper Midwestern United States used to predict coldwater, oxygenated habitat and how it is predicted to change under scenarios of climate and land use change. Specific fields include lake size, depth, watershed landuse, air temperature characteristics, and presence of the coldwater fish Cisco (Coregonus artedi). Also included are projected air temperatures under mid-Century conditions for each lake.
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    Data supporting "Predicting total phosphorus levels as indicators for shallow lake management"
    (2018-07-18) Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John R; kelsey.vitense@gmail.com; Vitense, Kelsey
    This repository contains data supporting "Predicting total phosphorus levels as indicators for shallow lake management" in Ecological Indicators.
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    Digitization of Minnesota and Wisconsin bathymetric maps resulting in hypsographic data
    (2020-09-09) Rounds, Christopher I; Hansen, Gretchen JA; Vitense, Kelsey; Van Pelt, Amanda; round060@umn.edu; Rounds, Christopher; University of Minnesota Fisheries Ecosystem Ecology Lab
    The data set includes hypsographic data (area-at-depth) for over 750 Minnesota and Wisconsin lakes throughout the states. The majority of these lakes (650+) did not have publicly available hypsography. The hypsography was derived by digitizing bathymetric DNR maps using ImageJ. One hundred Minnesota lakes were selected that had DNR hypsographic data (in the form of a DEM) available and a comparison between the hypsographic data derived from DEMs and ImageJ was completed. These results, as well as code and hypsographic data is all available. The purpose of this work was to release broad scale lake area-at-depth data for limnological and aquatic biology studies.
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    Minnesota lake ice phenology
    (2024-06-04) Walsh, Jake R; Vitense, Kelsey; Rounds, Christopher I; Peter, Boulay; Blumenfeld, Kenneth; Hansen, Gretchen JA; round060@umn.edu; Rounds, Christopher I; University of Minnesota Fisheries Systems Ecology Lab
    This dataset contains ice in, ice out and ice duration data for Minnesota lakes that have been collated by the Minnesota Department of Natural Resources State Climatology Office. Lake ice has been recorded by lake associations, community members and scientists throughout Minnesota. The definition of lake ice in and out can vary from lake to lake but observers generally use consistent criteria for determining the day ice formation occurs or ice melts for a lake. For more information see the Minnesota DNR lake ice in (https://www.dnr.state.mn.us/ice_in/index.html) and ice out (https://www.dnr.state.mn.us/ice_out/index.html) websites.
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    R Code and Output Supporting: Resampling-Based Methods for Biologists
    (2020-03-02) Fieberg, John R; Vitense, Kelsey; Johnson, Douglas H; Jfieberg@umn.edu; Fieberg, John R
    This repository contains data, R code, and associated output from running R code supporting results reported in: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ [In Revision]
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    R Code and Output Supporting: Computational reproducibility in The Wildlife Society's flagship journals
    (2019-06-05) ArchMiller, Althea A; Johnson, Andrew D; Nolan, Jane; Edwards, Margaret; Elliot, Lisa H; Ferguson, Jake M; Iannarilli, Fabiola; Velez, Juliana; Vitense, Kelsey; Johnson, Douglas H; Fieberg, John R; ALTHEA.ARCHMILLER@GMAIL.COM; ArchMiller, Althea A
    The goal of this study was to gauge the level of computational reproducibility, which is the ability to reach the same results using the same data and analysis methods, in the field of wildlife sciences. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those for which we could obtain data, we attempted to reproduce their quantitative results using the original methods and data. The dataset shared in this repository is the de-identified results of our review, and the code provided here produces the results and figures in our published manuscript.
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    Shallow lakes in Minnesota: Can we predict the good, the bad, and the vulnerable?
    (2018-12) Vitense, Kelsey
    Shallow lakes (i.e., lakes with maximum depth <5 m) provide critical habitat for wildlife and afford recreational opportunities for the public, including fishing and waterfowl hunting. However, many shallow lakes have degraded conditions resulting from anthropogenic disturbances, such as excessive nutrient inputs from land conversion to agriculture and alteration of natural hydrology leading to increased connectivity of surface waters and colonization by disruptive fish species. These degraded shallow lakes experience frequent and sometimes toxic algal blooms, which decrease the utility of lakes to the public. Additionally, reduced water clarity leads to the loss of submerged aquatic vegetation (SAV), which provide an important food source for waterfowl. Mathematical models tell us that the changing conditions of shallow lakes are reflective of alternative stable states, where lakes can be in either a turbid, algae-dominated state with little to no SAV or a clear, healthy state supporting abundant SAV. These states are stable in the sense that lakes will stay in one of the two states due to strong positive feedback loops (e.g., SAV take up nutrients that algae need, release chemicals toxic to algae, and provide a home to zooplankton that eat algae) unless: (1) there is a sudden disturbance to the system that forces the lake into the other state, or (2) a key component of the system slowly but steadily changes until a threshold is reached, at which point the lake “snaps.” The first case is akin to swinging a hammer to break a pencil in half and the second is like bending a pencil with increasing pressure until it breaks. Much is known about the causes of shifts between clear and turbid states in shallow lakes. For instance, we know that increases in phosphorus (P) inputs to lakes (i.e., bending the pencil) and colonization of bottom-feeding fish species (i.e., the hammers) are associated with shifts from the clear to turbid state. Conversely, we have observed that reducing P inputs and eradicating problematic species can sometimes, but not always, cause the reverse shift back to the clear state. Mathematical models have helped us understand that the critical P threshold at which a lake transitions from the clear to turbid state is not the same as the critical P threshold at which the lake transitions from the turbid state back to the clear state. That is, once a lake transitions to the turbid state, P must be reduced far below its previous level in the clear state before SAV reappears and restores water clarity. This phenomenon makes shallow lake restoration challenging, and efforts to force lakes into the clear state from the turbid state frequently fail or have only short-term effects. Although we have a deep qualitative understanding of the mechanisms driving these shifts, we lack essential quantitative knowledge to improve our ability to manage shallow lakes. For instance, there has not previously been a formal statistical framework grounded in mathematical theory for alternative stable states to classify the state of a lake, nor to estimate critical P thresholds. We lack models to quantify the vulnerability of lakes to state shifts given various risk factors, including proximity to P thresholds and the abundance and composition of fish populations. And we do not have a way to quantitatively prioritize lakes for management, especially without expending significant resources to physically visit and sample lakes. For this dissertation, I partnered with researchers at the Minnesota Department of Natural Resources and the University of St. Thomas who collected a wealth of data on approximately 130 lakes around Minnesota to address these knowledge gaps. I developed methods to accurately classify lake states, identify key drivers of state transitions, and quantify state transition risk based on these drivers. In Chapter 1, I develop a modeling framework that provides the foundation for the approaches I use in subsequent chapters. I use relative abundances of algae and SAV, as well as differing relationships between P and algal abundance within each state, to classify lake states as clear or turbid. The model explicitly incorporates the structure of a cusp catastrophe bifurcation diagram to estimate critical P thresholds where shallow lakes transition from the clear to turbid state and vice-versa. Using data simulated from a theoretical model describing shallow lake processes, I show that not only does this framework classify lake states and estimate critical P thresholds with high accuracy, it also performs better than existing methods. Chapter 2 uses P threshold estimates from Chapter 1 to provide a way to categorize lakes that has direct management implications: lakes that have low enough P levels such that only the clear state is possible vs. lakes with P levels where the lake may exist in the turbid state. I developed a model to predict the category of a lake using only geospatial predictor variables, such as the amount of agriculture in a lake’s watershed. This model provides a first step for managers to prioritize lakes for management and future sampling efforts without visiting lakes to collect water samples or conduct plant surveys. In Chapter 3, I extend the modeling framework in Chapter 1 to allow for temporal dynamics and estimation of state transition probabilities. I assess how transition risk depends on both resilience variables (e.g., current nutrient levels) and perturbation variables (e.g., change in fish biomass). The model identifies top predictors and combinations of predictors for anticipating state transitions, informing essential data needs for future lake surveys. Finally, Chapter 4 describes the development of an R shiny application that allows users to input lake data and receive a predicted state classification and estimate of transition risk based on the modeling work of Chapter 3. This tool can be used by shallow lake managers to help prioritize lakes for management actions based on estimated transition risk according to observed or hypothetical changes to nutrient levels and biological communities, including fluctuations in fish abundance.