2007 Summaries of Wildlife Research Findings - Minnesota State ...

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378 area that were accessible by public roads during spring. We randomly selected nList=135 plots from the list frame (NList=181 plots), 1 of which was excluded due to inaccessibility. Six of the plots selected from the area frame were also on the list frame, so nnonoverlap=127 plots were in the nonoverlap domain (i.e., 127=135–2–6), and noverlap=140 plots were in the overlap domain (i.e., 140=135–1+6). The AIC-best a priori model for p was the “global” model, which contained all 16 covariates (i.e., 5 for observers, recapture, day of the study, time of day, temperature, wind speed, presence of precipitation, proportion of the sky obscured by clouds, road density, density of interior roads, proportion of suitable land cover types that were visible from roads, and proportion of suitable land cover types that were under snow or temporary water). It accounted for 97% of the AIC weight in the model set for p. The 4 best a priori models for occupancy, which accounted for 93% of the AIC weight, included covariates measured at the plot scale and land cover data that was corrected in the field (Table 2). Although they contained 21–25 parameters, only 6 modelaveraged parameter estimates had confidence intervals that did not include 0 (Table 3). Wind speed, cloud cover, road density, and an observer effect were correlated with p (Figure 2; ˆp = 0.45, 95% CI=0.34–0.56). Road density was also correlated with occupancy (Figure 3). No land cover covariates, however, were correlated with occupancy within each sampling frame. In the a priori analysis, models fit equally well at both spatial scales when using uncorrected GAP land cover data (Table 2). The probability of occupancy based on model-averaged a priori models was 0.83 (95% CI=0.31–0.98) for plots in the overlap domain (i.e., from the list frame) and 0.09 (95% CI=0.01–0.46) for plots in the nonoverlap domain (i.e., from the area frame but not the list frame). Therefore, ψ ˆ = 420 (SD=270) plots in the study area were occupied by a lek. The lack of precision of ψ ˆ was acceptable, given the objectives of the study. The results, however, will be useful for evaluating the level of sampling effort necessary to estimate ψ ˆ with adequate precision at range-wide scales in the future. We started the exploratory analysis by simplifying the model for p to include only the dominant 4 covariates rather than all 16 and by using combinations of covariates for ψ that may not have been included in the a priori set of models. The AIC-best occupancy model then included domain, habitat area, density of all roads, and density of paved roads as covariates for ψ. There was still much model-selection uncertainty, and the combined-1 and disturbance-1 models for ψ were only 2.0 and 3.1 AIC-units away from the best model. We further refined the exploratory analysis by removing the domain covariate because it appeared to be an excellent discriminator between occupied and unoccupied plots and therefore potentially masking relationships between ψ and more informative landscape characteristics. Using a reduced model for p (K=5) and no domain covariate for ψ resulted in 3 models that accounted for 98% of the AIC-weight in the new exploratory model set (Table 4). As in the a priori analysis, the best-fitting models included covariates measured at the plot scale and land cover data that was corrected in the field. The model-averaged parameter estimates whose confidence intervals did not include 0 were those for the proportion of the plot covered in grass, distance to the next nearest lek observed the previous year, area of habitat in the plot, and density of roads (Figure 4). A goal of this project was to be able to predict the probability of occupancy for any or perhaps all plots in the prairie-chicken range. That would require applying an occupancy model that only included covariates that are available for all plots in the range. The sets of models based on uncorrected data for landscape characteristics meet that criterion. Comparing only models that included covariate data that is available for all plots in the range, those that included measurements at the plot scale fit much better than those that included measurements at the landscape scale (Table 4).
ACKNOWLEDGMENTS We thank N. G. Emery, T. E. Flatt, N. W. Gruber, R. J. Kleinschmidt, E. D. Peterson, and J. B. Winter for collecting data in the field; R. Wright and B. Sampson for GIS assistance; J. Fieberg and D. Pierce for helping me improve the project proposal; and L. Gilbert for map interpretation and data entry. LITERATURE CITED 379 Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer, New York. Cartwright, K. S. 2000. Influence of landscape, land use, and lek attendance on greater prairie-chicken lek surveys. Thesis, Kansas State University, Manhattan, Kansas, USA. Giudice, J. H. 2004. Minnesota prairie-chicken survey: 2004 annual report. Minnesota Department of Natural Resources, Madelia, Minnesota, USA. Haines, D. E., and K. H. Pollock. 1998. Estimating the number of active and successful bald eagle nests: an application of the dual frame method. Environmental and Ecological Statistics 5:245-256. Keating, K. A., and S. Cherry. 2004. Use and interpretation of logistic regression in habitatselection studies. Journal of Wildlife Management 68:774-789. MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248-2255. Merrill, M. D., K. A. Chapman, K. A. Poiani, and B. Winter. 1999. Land-use patterns surrounding greater prairie-chicken leks in northwestern Minnesota. Journal of Wildlife Management 63:189-198. Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. 1996. Applied linear statistical models. Fourth edition. Irwin, Chicago, Illinois. Niemuth, N. D. 2000. Land use and vegetation associated with greater prairie-chicken leks in an agricultural landscape. Journal of Wildlife Management 64:278-286. Niemuth, N. D. 2003. Identifying landscapes for greater prairie chicken translocation using habitat models and GIS: a case study. Wildlife Society Bulletin 31:145-155. Robel, R. J., J. N. Briggs, J. J. Cebula, N. J. Silva, C. E. Viers, and P. G. Watt. 1970. Greater prairie chicken ranges, movements, and habitat usage in Kansas. Journal of Wildlife Management 34:286-306. Ryan, M. R., L. W. Burger, Jr., D. P. Jones, and A. P. Wywialowski. 1998. Breeding ecology of greater prairie-chickens (Tympanuchus cupido) in relation to prairie landscape configuration. American Midland Naturalist 140:111-121. Schroeder, M. A., and C. E. Braun. 1992. Greater prairie-chicken attendance at leks and stability of leks in Colorado. Wilson Bulletin 104:273-284. Svedarsky, W. D. 1983. Reproductive chronology of greater prairie chickens in Minnesota and recommendations for censusing and nest searching. Prairie Naturalist 15:120- 124.
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Summaries of Wildlife Research Find
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September 2008 State of Minnesota,
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The value of farm programs for prov
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Understanding variations in autumn
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MANAGING BOVINE TUBERCULOSIS IN WHI
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3 the lungs or chest cavity that we
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" Winter 2007 Deer Removal Location
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METHODS The MNDNR planned to sample
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Table 1. Bird species sampled for h
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Table 2 continued. Northern Pintail
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Blood was centrifuged and serum was
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Liver and Lung Culture A total of 1
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Positive results indicate exposure
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ESTIMATING WHITE-TAILED DEER ABUNDA
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or 2 quadrats. As a result, associa
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Table 1. Deer population and densit
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OBJECTIVE • To estimate density a
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35 Figure 1. Locations of trail cam
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EA hunters, identified in MNDNR 200
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percentage of EA hunters harvesting
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Deer harvested per 100 hunters 60 5
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FACTORS AFFECTING POPULATION INDICE
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following the first listening perio
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Table 1. Pheasant crowing and roads
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Index (males/stop) 4.00 3.00 2.00 1
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feeders (52%), depredation in cattl
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Table 3. Response for question 3: S
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Table 7. Responses for question 7:
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Table 10. Responses for question 10
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4. Do you need more information on
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EVIDENCE OF WILD TURKEYS IN MINNESO
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the upper valleys of the Mississipp
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68 Hoy, P. R. 1882. The larger wild
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71 songbirds, lagomorphs, rodents,
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73 (Cleary 1994). The first method
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Function #7: Public Relations with
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77 Evrard, J.O. 2000. Overwinter fo
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79 Murphy, R.K., N.F. Payne and R.K
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METHODS 81 We selected 36 study are
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Hens were relatively abundant durin
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85 Gabbert, A. E., A. P. Leif, J. R
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Table 3. Pheasant population indice
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Pheasants/100 Miles Pheasants/100 M
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RESULTS Thirty-six respondents comp
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Table 1. Mean rank (1 most importan
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EVIDENCE OF LEAD SHOT PROBLEMS FOR
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Wildlife Species Ingesting Lead Sho
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Michigan Department of Environmenta
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102 Battaglia, A., S. Ghidini, G. C
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104 Friend, M. and J.C. Franson (ed
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106 Lemay, A., P. McNicholl, and R.
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108 Pain, D.J., I. Carter, A.W. Sai
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110 Tsuji, L.S., & N. Nieboer. 1997
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SPECIES SCIENTIFIC NAME REFERENCE L
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SPECIES SCIENTIFIC NAME REFERENCE L
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NONTOXIC AND LEAD SHOT LITERATURE R
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Brown, C.S., J.Luebbert, D. Mulcahy
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CWS (Canadian Wildlife Service). 19
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European Commission Enterprise Dire
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124 Haldimann, M., A. Baumgartner,
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Johansen, P., G. Asmund, and F. Rig
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Lance, V.A., T.R. Horn, R.M. Elsey
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Mateo, R., M. Rodríguez-de la Cruz
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Ochiai, K., T. Kimura, K. Uematsu,
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Rocke, T. E., C. J. Brand, and J. G
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Sleet, R. B., and J. H. Soares, Jr.
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USEPA (United States Environmental
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140 - Reviews the international env
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142 - Summarizes current scientific
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144 Cummings School of Veterinary M
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146 - Lead poisoning of wildlife oc
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Lance, V.A., T.R. Horn, R.M. Elsey
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150 Michigan Department of Natural
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152 - Although all visible pellets
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154 Thomas, V. G., and Owen, M. 199
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SUPPORT FOR, ATTITUDES TOWARD, AND
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Statewide Estimates The study sampl
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Table 1: Response rates for each ma
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Table 7: Mean beliefs about and eva
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Protecting wildlife from lead poiso
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The southern portion of Minnesota i
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Although our domestic sheep weighed
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1 I 'T 'l 170 Figure 1. General loc
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172 Small Game Hunter Lead Shot Stu
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Executive Summary The purpose of th
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Table S-1: Gauge of shotgun used mo
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7 6 5 4 3 2 1 Support for/Oppositio
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7 6 5 4 3 2 1 Figure S-8: Concern a
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Conclusions 182 These survey result
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184 Small Game Hunter Lead Shot Stu
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Acknowledgements This study was a c
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Introduction Study Purpose and Obje
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Bohrnstedt and Mee, 2002, p. 414).
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Section 1: Small Game Hunting Activ
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Table 1-1: Proportion of respondent
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Table 1-7: How often do you hunt wi
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Table 1-13: Involvement in small ga
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Table 1-29: Involvement With and Co
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Section 2: Shotgun and Shot Prefere
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Table 2-4: Gauge of shotgun used mo
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Table 2-10: Type of shot used most
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Table 2-16: Number of boxes of shel
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Table 2-22: Number of boxes of shel
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Section 3: Beliefs, Attitudes, and
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218 Respondents were asked to indic
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Table 3-4: Beliefs about using lead
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Table 3-10: Beliefs about using lea
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Table 3-16: Likelihood that banning
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Table 3-22: Likelihood that banning
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Table 3-28: How good or bad is the
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Table 3-34: How good or bad is the
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Table 3-40: Belief about whether MY
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Table 3-46: Belief about whether TH
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Table 3-52: I want to do what DUCKS
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Section 4: Trust in the Minnesota D
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Media Resources Table 4-4: Trust in
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Media Resources Table 4-10: Trust a
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Media Resources Table 4-16: Trust a
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Section 5: Environmental Values Env
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Table 5-5: Environmental values: Th
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Table 5-13: Environmental values: H
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Table 5-19: I am concerned about en
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References Cited Dillman, D. (2000)
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Appendix A: Survey Instrument Small
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Q4. How many boxes of shells (25 to
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Nationwide there is concern about t
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Q16. Next we would like to know how
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Q18. Next we would like to know how
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Q 21. People are generally concerne
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268 Small Game Hunter Lead Shot Com
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Acknowledgements 270 This study was
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Nationwide there is concern about t
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Figure S-2: Perceived narrative qua
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Figure S-5: Agreement with message
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Figure S-9: Importance of self-dire
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Conclusions 280 Our results suggest
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282 Small Game Hunter Lead Shot Com
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Acknowledgements 284 This study was
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Introduction Study Purpose and Obje
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Games-Howell post-hoc test over oth
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Section 1: Message Quality Responde
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Section 1: Message Quality Table 1-
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Section 1: Message Quality Figure 1
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Section 2: Factual Versus Narrative
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Section 2: Factual Versus Narrative
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Section 3: Message Involvement Tabl
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Section 3: Message Involvement Tabl
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Section 4: Message Evaluation Table
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Section 4: Message Evaluation Table
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Section 5: Agreement With Message R
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Section 6: Values Table 6-1: How im
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324 Section 7: Background Informati
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Section 7: Background Information T
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Page 338 and 339: References Cited Areni, C. S. (2003
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Page 346 and 347: Appendix D: Survey Instrument Q1. P
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Page 354 and 355: MOOSE POPULATION DYNAMICS IN NORTHE
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Page 364 and 365: ECOLOGY AND POPULATION DYNAMICS OF
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Page 380 and 381: 372 Olson, C. 2006. 2005 small mamm
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Page 393 and 394: Probability of occupancy 1.0 0.8 0.
Page 395 and 396: IDENTIFYING PLOTS FOR SURVEYS OF SH
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Page 399 and 400: preliminary descriptions of seasona
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Page 407 and 408: MANAGEMENT-FOCUSED RESEARCH NEEDS O
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Page 411 and 412: Table 2. Forest management activiti
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Page 415 and 416: Table 3 continued. 407 Fire break d
Page 417 and 418: Appendix 1. Survey of management-fo
Page 419 and 420: Percent agreed needs evaluation (%)
Page 421 and 422: TABLE OF CONTENTS 413 EXECUTIVE SUM
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Page 425 and 426: 1. INTRODUCTION The most comprehens
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Page 435 and 436: Currie (2001) and Hansen et al. (20
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Prairie Parkland (Figure 1, MNDNR 2
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undulating plains with deep glacial
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433 If conditions in the Laurentian
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435 carbon pool and should not be o
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Animal species associated with mesi
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increase the primary productivity o
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Warmer, drier conditions could excl
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443 hydroperiods for temporary wetl
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The Minnesota Department of Natural
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taken to manage the unavoidable imp
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• Dedicate 1 new FTE position at
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contributions to shallow lake eutro
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Busby, W. H., and W. R. Brecheisen.
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III to the Fourth Assessment Report
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MNDNR. 2005b. Field guide to the na
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Poiani, K. A., W. C. Johnson, and T
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Westcott, P. C. 2007. U.S. ethanol
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463 Northern Southern Eastern Weste
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Scientific Name Common Name LMF Pro
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Horned grebe Podiceps auritus Louis
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MINNESOTA’S RING-NECKED DUCK BREE
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Estimated Pair Density Mean pair de
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LITERATURE CITED 473 SAS. 1999. SAS
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Table 2. Sampling rates by Ecologic
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Table 4. Estimated indicated breedi
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479
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# # # # # # # # # # # # # # # # # #
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MOVEMENTS, SURVIVAL, AND REFUGE USE
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prior to hunting season were simila
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Figure 1. Ring-necked duck study ar
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INFLUENCE OF FISH, AGRICULTURE, AND
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Mechanisms structuring characterist
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Chemical Properties and Water Quali
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Submerged Aquatic Plants Submerged
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At a broad scale, wetland and shall
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Table 1. Relative abundance (mean w
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Number of Fish 35 30 25 20 15 10 5
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Total # Captured a) 50 40 30 20 10
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Relative Units 9 8 7 6 5 4 3 2 1 a)
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THRESHOLDS AND STABILITY OF ALTERNA
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MANAGEMENT-FOCUSED RESEARCH NEEDS O
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Table 1. Survey questions for wetla
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VARIANCE OF STRATIFIED SURVEY ESTIM
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EXPLORING MIGRATION DATA USING INTE
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Forest Research Group Publications
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Roy Nielsen, C., and C. K. Nielsen.
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