Nesting and brood-rearing ecology of resident Canada geese in New Jersey
Date
2012
Authors
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Publisher
University of Delaware
Abstract
The Atlantic Flyway Resident Population (AFRP) of Canada geese (Branta canadensis) in New Jersey has grown so considerably during the last thirty years that it is now considered a nuisance in urban areas (United States Fish and Wildlife Service 2003). New Jersey is also the most densely human populated state in the nation, with intensive urbanization of agricultural and natural lands. Development of corporate parks and urban areas with manicured lawns and artificial ponds offer ideal nesting habitat for AFRP geese, with limited pressure from hunting or natural predators. As a result, spatial heterogeneity in reproduction and survival must be taken into account in managing the population. My objectives for this study were to 1) identify the spatial scale/s at which land use features influence nest site selection and nest success, 2) estimate nesting parameters across three decades and identify variables that influence productivity, and 3) estimate pre-fledged gosling survival from hatch until summer molt banding efforts, in order to assist in developing a spatially-explicit population model for AFRP geese in New Jersey. I conducted a two-year (2009–2010) nesting ecology study of AFRP Canada geese, and compared it to data collected in New Jersey from 1985–1989 and 1995–1997. Nest searches were conducted on 250 1-km2 plots throughout the state, and 309 nests were monitored through hatch to determine the fate. I ran a spatial correlation analysis of land use composition to nest success during 2009–2010 to identify spatial scales at which geese respond to their environment for nest site selection and nest success. All significant spatial scales were at or below 2250m for the five classified land use types. Geese responded to human dominated land uses at a smaller scale than land uses with low human density. Mean clutch size at hatch in 2009–2010 was 4.66 eggs (SE ± 0.12 eggs) and 4.76 eggs (SE ± 0.16 eggs), respectively. Mean hatchability in 2009–2010 was 0.86 (SE ± 0.02) and 0.81 (SE ± 0.02), respectively. I estimated nest success at 0.44 (SE ± 0.05) in 2009 and 0.41 (SE ± 0.05) in 2010. Variables important to nest success from 1985–1989 were the age of the nest, year, extreme high temperature, nest density, rural residential land use at the landscape scale, commercial at the site level, and daily precipitation. Variables important to nest success for 1995-1997 were the age of the nest, date of nest initiation, year, physiographic stratum, extreme high temperature, rural residential land use at the landscape level, and agricultural land use at the site level. Variables important to nest success for 2009-2010 were the age of the nest and date of nest initiation. Nest success decreased during the duration of the study, likely due to an increase in reproductive control efforts. Additionally, I conducted a two-year (2009-2010) gosling survival study from hatch until annual banding efforts in late-June at 12 known nesting and brood rearing sites. To estimate gosling survival, I used 1) mark-recapture of web tagged goslings to estimate partial brood loss, 2) radio-collared breeding adults to estimate total brood loss, and 3) observations of broods associated with marked adults and color-marked broods to quantify mortality during the first two weeks after hatch. The proportion of breeding adults that experienced total brood loss was 0.316. The remaining proportion of breeding adults was subject to partial brood loss (0.684), which was estimated at 0.465 (SE ± 0.026) for 56 days. The overall survival estimate for 56 days after hatch was 0.318 (SE ± 0.018). Select environmental and density-dependent variables were used to build candidate models to identify sources of variation in partial brood loss. The number of broods at the site was negatively related to brood survival. The percent agriculture within 215 m was positively related to brood survival. Managers are encouraged to consider scale-dependent relationships in identifying habitat-wildlife relationships, and if population control of AFRP Canada geese is of primary interest, then focus on habitat management at the local scale will most likely have the largest influence. Developing productivity trends should assist in understanding the dynamics of recruitment as a function of population size, spatial distribution, and human influence. I recommend that managers consider land use and human development as important features in identifying the driving forces of productivity in AFRP Canada geese.