Browsing by Author "Arnold, Todd W"

Now showing 1 - 5 of 5
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    Data and model code for assessing dabbling duck age ratios and corresponding environmental correlates in the North American Prairies, 1969-2015
    (2018-05-16) Specht, Hannah M; Arnold, Todd W; spech030@umn.edu; Specht, Hannah M
    Fecundity estimates for demographic modeling are difficult to acquire at the regional spatial scales that correspond to climate shifts, land use impacts or habitat management programs, yet are important for evaluating such effects. While waterfowl managers have historically used harvest-based age ratios to assess fecundity at continental scales, widely available age ratios from late-summer banding data present an underutilized opportunity to examine a regional fecundity index with broad temporal replication. We used age ratios from banding data and hierarchical mixed-effect models to examine how fecundity of five North American dabbling duck species was affected by temporal variation in hydrological cycles, intra- and inter-specific density dependence and alternate prey availability, and whether those relationships were consistent across a broad geographic area. The data and code for these analysis are included here.
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    Data and R code supporting "A Meta-Analysis of Band Reporting Probabilities for North American Waterfowl"
    (2019-11-25) Arnold, Todd W; arnol065@umn.edu; Arnold, Todd W
    This archive includes a csv formatted data set that includes 337 unique estimates of band reporting probabilities from North American waterfowl, as used in the 2020 Journal of Wildlife Management paper "A Meta-Analysis of Band Reporting Probabilities for North American Waterfowl" by Todd W. Arnold, Ray T. Alisauskas, and James S. Sedinger. Also included are two files of R and JAGS code for replicating our analyses.
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    Data, R Code, and Output Supporting: Using lorelograms to measure and model correlation in binary data: Applications to ecological studies
    (2019-09-25) Iannarilli, Fabiola; Arnold, Todd W; Erb, John; Fieberg, John R; ianna014@umn.edu; Iannarilli, Fabiola
    These files contain data, R code and associated output supporting results presented in “Iannarilli, F. , Arnold, T. W., Erb, J. and Fieberg, J. R. (2019). Using lorelograms to measure and model correlation in binary data: Applications to ecological studies. Methods Ecol Evol.”. In this paper, we introduce in the ecological literature the lorelogram, a statistical tool for quantifying correlation patterns in binary data, with novel applications to species distributional and camera-trap studies. We demonstrate the usefulness of the lorelogram via several motivating examples illustrating its use a) as a data-based method for objectively determining space- or time-to-independence between subsequent detections; and b) for describing correlation and behavioural patterns at different time scales, including short-time scales (e.g., minutes) common to camera trap data. This information can then be used to formulate an appropriate statistical modelling framework that allows researchers to explore effects of additional covariates (at different scales), while properly accounting for correlation.
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    Full Simulation Data and Worked Examples from Specht et al. Conditional Occupancy Manuscript
    (2017-02-27) Specht, Hannah S; Iannarilli, Fabiola; Edwards, Margaret R; Johnson, Michael K; Stapleton, Seth P; Weegman, Mitch; Yohannes, Brittney J; Arnold, Todd W; Reich, Henry T; spech030@umn.edu; Specht, Hannah M
    Occupancy models are widely used to describe the distribution of rare and cryptic species— those that occur on only a portion of the landscape and cannot be detected reliably during a single survey. However, occupancy models often provide inaccurate estimates of occupancy (ψ ̂) and detection probabilities (p ̂) under these circumstances. We developed a new "conditional" occupancy design that more accurately estimates occupancy for rare species. Here we provide the full simulation dataset used to compare estimation properties of standard, removal and conditional designs. Data were simulated in R and analyzed using MCMC methods in package R2jags. See Specht et al. (in review) for description of methods. Please cite Specht et al. in further use of this data set.
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    Seasonal influence on detection probabilities for multiple aquatic invasive species using environmental DNA
    (2023-12-14) Rounds, Christopher; Arnold, Todd W; Chun, Chan Lan; Dumke, Josh; Totsch, Anna; Keppers, Adelle; Edbald, Katarina; García, Samantha M; Larson, Eric R; Nelson, Jenna KR; Hansen, Gretchen JA; round060@umn.edu; Rounds, Christopher; University of Minnesota Fisheries Systems Ecology Lab
    Aquatic invasive species (AIS) are a threat to freshwater ecosystems. Documenting AIS prevalence is critical to effective management and early detection. However, conventional monitoring for AIS is time and resource intensive and is rarely applied at the resolution and scale required for effective management. Monitoring using environmental DNA (eDNA) of AIS has the potential to enable surveillance at a fraction of the cost of conventional methods, but key questions remain related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: Common Carp (Cyprinus carpio), Rusty Crayfish (Faxonius rusticus), Spiny Waterflea (Bythotrephes longimanus), and Zebra Mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season. Quantitative PCR was used with species-specific assays to determine the presence of species DNA in water samples. Using Bayesian occupancy models, we quantified the effects of lake and site characteristics and sampling season on eDNA detection probability. These results provide critical information for decision makers interested in using eDNA as a multispecies monitoring tool and highlight the importance of sampling when species are in DNA releasing life history stages.