Browsing by Author "Johnson, Douglas H"

Now showing 1 - 6 of 6
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    EFFECTS OF IMPERFECT DETECTABILITY ON INFERENCES FROM AVIAN MONITORING
    (2014-10-06) Rigby, Elizabeth A; Johnson, Douglas H; Andersen, David E
    Imperfect detectability can complicate analysis of bird survey data. Adjustment methods to account for imperfect detectability exist, but it is not clear how the benefits of these methods compare to their costs. Graduate student Elizabeth Rigby is constructing a computer simulation of bird surveys to evaluate the effects of survey method on survey conclusions. The computer simulation will create simulated birds, then conduct counts of these birds, taking into account realistic parameters of factors known to affect bird counts. This project is currently in the design and coding phase. In addition to the simulation, she conducted a field study of factors affecting detectability of birds in grasslands. The field study assessed the effects of distance to sound source, wind speed and direction, habitat structure and composition, and bird species on the detection of recorded bird songs. Mock surveys with over 9,000 opportunities to detect a recorded bird song were conducted in fall 2011 and 2012 with 4 observers. Detection of recorded songs was treated as a binary variable and analyzed with logistic regression and mixed models. Distance from the observer and an index of wind speed and direction were the strongest covariates to detection. Models used to predict detections of recorded songs performed well, correctly predicting detections 68-90% of the time (depending on species). Observer effects were important; odds of detection for inexperienced observers were only 26% of those of the primary observer. Detection around a sound source was asymmetrical and heavily affected by wind direction. Design and coding for the computer simulation, as well as analysis of field data, will continue in 2014.
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    R Code and Data Supporting: A comparison of survey method efficiency for estimating densities of Zebra Mussels (Dreissena polymorpha)
    (2023-05-25) Ferguson, Jake M; Jimenez, Laura; Keyes, Aislyn A; Hilding, Austen; McCartney, Michael A; St. Clair, Katie; Johnson, Douglas H; Fieberg, John R; jfieberg@umn.edu; Fieberg, John R
    This repository contains data and R code supporting Ferguson et al. A comparison of survey method efficiency for estimating densities of Zebra Mussels (Dreissena polymorpha).
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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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    R Code and Output Supporting: Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models
    (2016-08-11) Fieberg, John R; Forester, James D; Street, Garrett M; Johnson, Douglas H; ArchMiller, Althea A; Matthiopoulos, Jason; jfieberg@umn.edu; Fieberg, John R
    Species distribution models (SDMs) are one of a variety of statistical methods that link individuals, populations, and species to the habitats they occupy. In Fieberg et al. "Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models", we introduce a new method for model calibration, which we call Used-Habitat Calibration plots (UHC plots) that can be applied across the entire spectrum of SDMs. Here, we share the Program R code and data necessary to replicate all three of the examples from the manuscript that together demonstrate how UHC plots can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity.
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    Wetland bird case study for application of habitat association models across Great Lakes and Prairie Pothole regions
    (2022-12-08) Elliott, Lisa H; Bracey, Annie M; Niemi, Gerald J; Johnson, Douglas H; Gehring, Thomas M; Gnass Giese, Erin E; Fiorino, Giuseppe E; Howe, Robert W; Lawrence, Gregory J; Norment, Christopher J; Tozer, Douglas C; Igl, Lawrence D; harnx012@umn.edu; Elliott, Lisa H; Coastal Wetland Monitoring Program; Great Lakes Marsh Monitoring Program; Dakotas Wetland Survey
    Species often exhibit regionally specific habitat associations, and, thus, habitat association models developed in one region might not be accurate or even appropriate for other regions. Three programs to survey wetland-breeding birds covering (respectively) North American wetland breeding bird survey programs in Great Lakes coastal wetlands, inland Great Lakes wetlands, and the Prairie Pothole Region offer an opportunity to test whether regionally specific models of habitat use by wetland-obligate breeding birds are transferrable across regions. This dataset includes wetland bird point count and habitat characteristics data from the Great Lakes Coastal Wetland Monitoring Program (2016-2017) and Great Lakes Marsh Monitoring Program (1995-1997 and 2016-2017). These data are then combined with publicly available data from the Dakotas Wetland Survey (1995-1997). The included code files cover the creation and selection of habitat association models, and test the transferability of these models across datasets. These data are now released to accompany publication of "Application of habitat association models across regions: useful explanatory power retained in wetland bird case study."