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FACTORS AFFECTING NESTING SUCCESS OF COEXISTING SHOREBIRDS AT GREAT SALT LAKE, UTAH John F. Cavitt, Department of Zoology, Weber State University The Great.

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Presentation on theme: "FACTORS AFFECTING NESTING SUCCESS OF COEXISTING SHOREBIRDS AT GREAT SALT LAKE, UTAH John F. Cavitt, Department of Zoology, Weber State University The Great."— Presentation transcript:

1 FACTORS AFFECTING NESTING SUCCESS OF COEXISTING SHOREBIRDS AT GREAT SALT LAKE, UTAH John F. Cavitt, Department of Zoology, Weber State University The Great Salt Lake (GSL) serves as a critical inland shorebird site of hemispheric importance. One reason for this designation is that the breeding population densities of American Avocets (Recurvirostra americana) and Black-necked Stilts (Himantopus mexicanus) are among the highest in North America. Despite the importance of the GSL to these populations, little information is available on their current productivity. Nesting success is of critical interest because fecundity is an important demographic parameter with the potential of influencing population dynamics. The conservation of bird populations depends on acquiring productivity information to estimate population health and predict the vulnerability of species to habitat change and contamination. Consequently, I studied the factors affecting nesting success of both American Avocets (AMAV) and Black-necked Stilts (BNST) at four study sites within the GSL ecosystem during the 2003 – 2005 breeding seasons. Introduction Methods This study was conducted from late April to late July 2003 - 2005. Study plots were located and monitored in each of four sites bordering the eastern shore of the GSL (Figure 1) - Bear River Migratory Bird Refuge (BEAR), Great Salt Lake Shorelands Preserve (SHORE), Farmington Bay Wildlife Management Area (FARM), and Salt Lake Sewer Canal (SL CANAL). Each site includes replicated study plots (3 – 15). Plots were searched twice weekly for the presence of nests. Nests were checked every three to four days to determine nest status. To minimize the probability of nest predators locating nests, 1) nests were checked from as great a distance as possible, 2) adults and nests were disturbed as little as possible and different routes were taken within each site to avoid making trails, and 3) visits to nests were quick, minimizing the amount of time spent at the nest. Nests were classified as failed if they disappeared prior to the expected date of hatching and the eggshell evidence was consistent with a failed nest (e.g. Mabee 1997). I examined nesting success by estimating daily survival rates (DSR) and their associated SE according to Mayfield’s (1961, 1975) method as modified by Hensler and Nichols (1981). Variation in DSR was complared using the program CONTRAST (Sauer and Williams 1989). Program CONTRAST generates a chi-square statistic that is based on a linear contrast of the DSRs. Results and Discussion A total of 1,222 AMAV and 631 BNST nests were located and monitored for this study. Nest predation was the most important source of nest failure at all sites (Figure 2). DSR differed significantly between sites (AMAV X 2 = 15.41; df = 3; P = 0.002; BNST X 2 = 26.71; df = 2; P = 0.001). AMAV had the lowest DSR at SHORE relative to the other sites and BNST had the lowest DSR at BEAR (Figure 3). Both FARM and BEAR utilize mammalian predator control. DSR also differed significantly between years (AMAV X 2 = 19.63; df = 2; P = 0.002; BNST X 2 = 33.7; df = 2; P < 0.001). The DSR for both species was significantly lower in 2003 relative to 2004 and 2005 (Figure 4). This difference in DSR may be related to reduced precipitation and spring runoff during 2003. DSR increased throughout the breeding season for both AMAV and BNST (Figure 5). I also broadly classified nests according to their location (island, dike, marsh). For AMAV, nest location significantly affected DSR (X 2 = 17.57; df = 2; P < 0.002). Nests placed in open marshes had significantly higher DSR relative to those on dikes and islands (Figure 6). Nest location did not significantly affect BNST DSR (X 2 = 0.124; df = 2; P = 0.940). Although both BEAR and FARM utilize mammalian predator control, predation continues to be a substantial source of nest failure. The primary predators are difficult to identify, but California Gull (Larus californicus) breeding populations are large (particularly at BEAR) and have increased over the last three years. Figure 5. Relationship between DSR and nest initiation date (AMAV 2004 – F = 10.5; df = 1,4; P = 0.032; 2005 – F = 3.9; df = 1, 6; P = 0.11; BNST 2004 – F = 4.4; df = 1,4; P = 0.13; 2005 – F = 7.3; df = 1, 5; P = 0.5) BEAR SHORE FARM SL CANAL Figure 1. Locations of study sites Figure 2. Sources of nest failure for AMAV and BNST. Figure 3. Comparison of DSR (+ SE) between sites for AMAV and BNST (means with the same letter are not significantly different P > 0.05). Figure 6. The effect of AMAV nest location on DSR (+ SE; means with the same letter are not significantly different P > 0.05). Figure 4. Comparison of DSR (+ SE) between year for AMAV and BNST (means with the same letter are not significantly different P > 0.05). Acknowledgements This research would not have been possible without the assistance and diligence of the entire WSU shorebird crew. Support for this project was provided by NSF (DBI-0353505), US Fish and Wildlife Service Challenge Grants, and the Utah Division of Water Quality (060358). Thanks also to Bridgette Olson, US Fish and Wildlife Service, and Rich Hansen, UT Division of Wildlife Resources. Works Cited Sauer, T.W., and B.K. Williams. 1989. Generalized procedures for testing hypotheses about survival or recovery rates. Journal of Wildlife Management 53:137-142. Mabee, T.J. 1997. Using eggshell evidence to determine nest fate of shorebirds. Wilson Bulletin 109:307-313. Mayfield, H. 1961. Nesting success calculated from exposure. Wilson Bulletin 73:255-261. Mayfield, H. 1975. Suggestions for calculating nest success. Wilson Bulletin 87:456-466. Hensler, G.L., and J.D. Nichols. 1981. The Mayfield method of estimating nesting success: a model, estimators and simulation results. Wilson Bulletin 93:42-53.


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