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Estimate Waterfowl Nests on Monte Vista National Wildlife

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Principal Investigator

Nicole J. Traub, College of Arts and Sciences, University of Colorado at Boulder, 275 UCB,

A Research Proposal


Measuring nest success is extremely important in order to determine the well-being of avian populations. Biologists have been attempting to infer the status of avian species by estimating rates of births and deaths to determine population growth and stability (Johnson, 1979; Newson et al., 2008). One measure of avian birth rate that is easy to gauge is the percentage of nests that hatch, which is used as an indirect measure of reproduction (Johnson, 1979). Nest success rates can also be used to hypothesis causes for declines in avian populations, i.e. habitat degradation, predation, overhunting, disease, environmental contaminants, etc. (Beauchamp et al., 1996). Nest success is defined as a nest in "which at least one egg hatched" and the "presence of detached shell membranes is the best evidence that eggs hatched" (Klett et al., 1986). Nest failure usually results from predation but they may have been abandoned if the hens are disturbed during the early stages of egg laying (Klett et al., 1986).

Transect sampling is widely used by wildlife managers and researchers to estimate population sizes of inanimate and animate objects (Newson et al., 2008). Transect studies designed to estimate inanimate object population size, such as waterfowl nests, usually proceed as follows: the area to be sampled is defined; random (or systematic) transect lines are placed throughout the area; transects are searched to record the detection of the study object (Anderson and Pospahala, 1970). Bias is unavoidable in population size (density) estimates; therefore, it is important to recognize the source(s) of bias and adjust for them. An important source of bias lies in the transect sampling methods themselves. If some objects are not detected, then the expanded population estimate will be lower than the true population size unless adjustments are made (Burnham et al., 1980; Buckland et al., 2001). This source of bias is very important when detecting objects that are small, secretive, or well concealed; however, when detecting large or inanimate objects, this source of bias may be of little importance (Anderson and Pospahala, 1970).

The basic output from line transect sampling is the encounter rate, which is the number of detections per distance walked. This method can be used to estimate relative density but it does not account for detectability which can vary depending on the study object and habitat (Marshall et al., 2008). In order to compensate for incomplete counts and problems with detectability, one can measure the distance from the transect to each observation (distance sampling) (Burnham and Anderson, 1984). The sample population is then the area sampled rather than the objects of interest. For example, the population sampled is a population of line transects in a given area, each line transect is a sample unit, and the object of interest (waterfowl nests) is the variate associated with each transect (Anderson and Posahala, 1970; Marshall et al., 2008).

Four assumptions must be met in order to make valid inferences about population densities using distance sampling (in order of importance): (1) all objects that fall on the transect line are detected with certainty; (2) objects do not move either away from or towards the observer prior to detection; (3) perpendicular distance data are measure accurately; and (4) all detections are independent of each other (Burnham and Anderson, 1984; Buckland et al., 2001). These assumptions can be violated in many ways including, but not limited to, inexperienced or untrained observers, lack of interest in the observer, fatigue, speed of travel down the transect, transect width, habitat type, time of day, season, sun angle, inclement weather, object size, shape, coloration, and habits (Burnham and Anderson, 1984; Buckland et al., 2001; Marshall et al., 2008).

Both strip transects and line transects can be useful measures of population density. However, the key difference between them is that density can be estimated using line transects based on distance without some of the bias innate to strip transects. Line transects require only the perpendicular distance to the object. In contrast, strip transect density estimates are usually low because not all objects in the strip are detected (Burnham et al., 1980; Burnham and Anderson, 1984; Buckland et al., 2001).

A previous study completed on the Monte Vista National Wildlife Refuge (Anderson and Posahala, 1970) estimated waterfowl nest density using strip transects with a narrow width (8.25 ft. each side). This method is impractical and inefficient for sampling large areas since an insufficient number of objects may be detected after covering great distances (Anderson and Posahala, 1970). In contrast, this project proposes to utilize distance sampling with systematically placed line transects to obtain a full waterfowl nest census in order to determine nest distribution, nest success, and nest density.


The purpose of this study is to test the possibility of employing a distance-based sampling protocol utilizing line transects to estimate waterfowl nest density. Specifically, the objectives are to:

  1. 1. Evaluate and expand upon previous density estimates of waterfowl nests in the Monte Vista National Wildlife Refuge.
  2. 2. Determine if line transect sampling is more efficient than strip transect sampling for calculating waterfowl nest density.
  3. 3. Implement a distance-based line transect approach to calculating:

a. Number of successful nests b. Number of depredated nests c. Total number of nests

Methods and Study Design

The general survey design will follow Anderson and Pospahala (1970). Thus, the survey design will involve at least 20 transects that will be oriented north to south across the Refuge and spaced 150 feet apart. Total transect length will depend on the desired coefficient of variation (described below). Transects will be systematically overlaid a map of the Refuge prior to the start of the project to avoid bias in the way of vegetation or land use gradients (Figure 1). A transect will be randomly selected and a subsequent transect 150 feet away will be walked. This method will be followed in a sequential manner until all transects have been walked (Anderson and Pospahala,

1970; Buckland et al., 2001).

Figure 1: Potential configuration of line transects throughout the Monte Vista National Wildlife Refuge

The Monte Vista National Wildlife Refuge is home to several species of migratory waterfowl such as ducks and geese that rely on the refuge for breeding. Some species arrive on the refuge earlier than others. To mitigate the possibility of not detecting nests due to waterfowl arrival, this project will collect data twice a year, once during mid-May and once between mid-July to mid- August (Monte Vista, 2017).

Sampling effort, and consequently cost, depends on the acceptable amount of uncertainty (randomness) in the density estimates. The coefficient of variation (CV) measures the uncertainty of the density estimate. Meaning that it measures how much the density estimate would change if the data were collected again (Burnham et al., 1980; Buckland et al., 2001; Schnupp, 2017a). The greater the variation in the estimate, the farther the estimate is from the true value. To control for fluctuations in variation, this project will utilize a systematic survey design with many transects (large sample size) and each transect will aim to have similar encounter rates (Figure 1).

For ease of navigation and repeatability, pre-established transects will be uploaded through Mapwel 2016 to Garmin Etrex GPS units (Garmin International Incorporated, Olathe, Kansas). For each nest detected, the perpendicular distance from the center of the nest to the transect line, nest state (depredated or successful), and waterfowl type (duck or non-duck) will be recorded. Program DISTANCE 7.0 (Buckland et al., 2001) will be used to calculate overall nest density, density of successful nests, and density of depredated nests for both waterfowl types. If strong habitat differences are encountered during the survey, stratification will be used in post- processing of the data to reduce variation and improve the precision of density estimates. Data will be pooled from all transects to increase model robustness. Data pooling helps even out minor fluctuations in object density between transects and lead to more precise density estimations (Fewster et al., 2005). Various detection functions will be evaluated in DISTANCE, including uniform, half-normal, hazard rate, and negative exponential with simple polynomial, hermite polynomial, or cosine adjustments. A detection function will be selected from the competing models using Akaike's Information Criterion (AIC) values and goodness of fit using Chi-square analysis (Buckland et al., 2001).

ExpecteResults and Benefits

Given that nest success is viewed as empirical evidence for reproduction success and population status, it is imperative that estimates of density be as accurate as possible. The proposed research will (1) analyze the effectiveness of line transect distance sampling versus strip transect sampling and (2) provide an accurate, efficient, and cost-effective method to determine waterfowl nest success and distribution on the Monte Vista National Wildlife Refuge, Colorado, USA.

Upon confirmation of funding, research protocols will be refined in consultation with Monte Vista National Wildlife Refuge personnel and Colorado Parks and Wildlife. Annual progress reports will be submitted and a final report detailing findings and recommendations will be submitted within 1 year of contract completion. Research results will be presented at professional scientific meetings and published in peer-reviewed scientific journals where Monte Vista

National Wildlife Refuge will be acknowledged as a major funding contributor. Additionally, if desired, one or more Monte Vista National Wildlife Refuge employees will be listed as a coauthor in all presentations and publications.

Project deliverables will include:

  1. Ph.D. dissertation and corresponding scientific publications
  2. Scientific presentations at state, regional, and international conferences (undergraduate and graduate)
  3. Spreadsheets for calculation of density estimates
  4. Technical bulletin comparing the efficacy of estimating nest density using distance sampling with line transects and strip transects.


This section is not applicable to the proposed project. 

Necessity and EthicaUsof Animals

This study will determine nest success and estimate of density of waterfowl on the Monte Vista

National Wildlife Refuge, Colorado, USA. All necessary precautions will be utilized to avoid harm to waterfowl during this study; however, an Animal Care and Use Form is being submitted with this proposal for research approval.


The principal investigator of this study will be Nicole J. Traub, M.S. and the project will involve

1 Ph.D. candidate. Additionally, 5 part-time student workers will be hired to assist with research activities and data collection.



All items are budgeted for 2x year sampling







Transect miles




Sampling hours





Sampling time

(in days)











(5 undergraduates)










Fringe (0.7% salary)










Field supplies










Expected mileage





Mileage reimbursement





Yearly Budget





Total Expenses





(3 Yearbudget)


Anderson, D.R. and R.S. Pospahala. 1970. Correction of bias in belt transect studies of immotile objects. The Journal of Wildlife Management 34(1):141-146.

Beauchamp, W. D., R.R. Koford, T. D. Nudds, R. G. Clark, and D.H. Johnson. 1996. Long-term declines in nest success of prairie ducks. The Journal ofWildlife Management 60 (2):


Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001.

Introduction to distance sampling estimating abundance of biological populations. Oxford

University Press, New York, USA. 432p.

Burnham, K. P., D. R. Anderson. 1984. The need for distance data in transect counts. The

Journal ofWildlife Management 48 (4):1248-1254.

Burnham, K. P., D. R. Anderson, and J. L. Laake. 1980. Estimation of density from line transect sampling of biological populations. Wildlife Monographs. (72):3-202.

Fewster, R.M., J. L. Laake, and S. T. Buckland. 2005. Line transect sampling in small and large regions. Biometrics. 61 (3):856-859.

Johnson, D.H. 1979. Estimating nest success: The Mayfield Method and an alternative. TheAuk

96 (4):651-661.

Klett, A.T., H.F. Duebbert, C. A. Faanes, and K.F. Higgins. 1986. Techniques for studying nest success of duck in upland habitats in the Prairie Pothole region. Resource Publication

158. 24 p.

Marshall, A.R., J. C. Lovett, and P.C.L. White. Selection of line-transect methods for estimating the density of group-living animals: lessons from primates. 2008. AmericanJournal of Primatology70:452-462.

Monte Vista. 2017. Monte Vista National Wildlife Refuge. https://www.fws.gov/refuge/Monte_Vista/wildlife_and_habitat/index.html.

Newson, S. E., K. L. Evans, D. G. Noble, J. J. D. Greenwood, and K. J. Gaston. 2008. Use of distance sampling to improve estimates of national population sizes for common and widespread breeding birds in the UK. Journal of Applied Ecology45:1330-1338.

Schnupp, M. 2017a. Sample units and transect design. PowerPoint presentation. Estimating Wildlife Populations course-WSCI 6390. http://schnuppconsulting.com/wp- content/uploads/2017/01/2-Sample-Units-Transect-Design.pdf.

Schnupp, M. 2017b. Distance Sampling Assumptions. PowerPoint presentation. Estimating Wildlife Populations course-WSCI 6390. http://schnuppconsulting.com/wp- content/uploads/2017/01/4-Distance-Sampling-Assumptions.pdf.

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