A population model for management of Atlantic flyway resident population Canada geese

Abstract

Highly abundant resident Canada geese (Branta canadensis) cause property damage throughout their range. Effective reduction and management of these populations requires knowledge of their population dynamics and responses to management actions. We used data from New Jersey, USA, and other resident Canada goose populations to produce stage-structured matrix models for resident Canada geese from both urban and rural landscapes. We ran stochastic simulations to assess 3 management activities for Atlantic Flyway Resident Population Canada geese: harvest, nest treatment, and cull. Unrealistic harvest rates, in excess of 10% for urban geese, would be needed to reduce the urban population to target levels within 10 years in the absence of other management activities. Nest treatment to prevent hatching is less controversial than culling adults, but as many as 62% of eggs in urban areas would need to be treated annually to sufficiently reduce the mean stochastic population growth rate. Cull would be the most effective way to achieve the population goal, but current cull rates are insufficient to reduce the urban population. Although reduction of urban geese was a challenge, current management activities in rural populations appeared to be sufficient to reduce populations. We also provide a simple spreadsheet tool for managers who want to explore management options for other resident Canada goose populations by inserting relevant vital rate estimates for their populations and manipulating management activities. Ó 2016 The Wildlife Society.

Long‐Term Survival and Harvest of Resident Canada Geese in Virginia

“…Studies should continue to take advantage of the robust USGS BBL mark‐recapture data set (Smith 2013) to estimate survival, recovery, harvest, and population estimation. Putting long‐term survival patterns of resident geese in Virginia in context with population growth rates similar to previous studies (Powell et al 2004) helps disentangle factors influencing demographic parameters and how adaptive management strategies can target particular life stages to maximize efficacy for attaining population goals (Heusmann 1999, Klimstra and Padding 2012, Beston et al 2016). Our results showing that survival estimates from this study were not influenced by reducing the data set by as much as 95% are valuable in helping optimize the Virginia Department of Game and Inland Fisheries' sampling efforts to maintain the ability to calculate representative estimates of annual survival, recovery, and harvest, even when these data are further subset into several categorical factors.…”

“…Beyond providing estimates of annual survival rates of resident geese, these studies have demonstrated the importance in estimating survival rates to evaluate the efficacy of adaptive management strategies that may incorporate harvest management (Klimstra and Padding 2012) and culling or recruitment reduction efforts such as egg oiling or addling (Coluccy et al 2004) to control growth rates of over‐abundant populations. Beston et al (2016) simulated the independent effects of harvest, culling, and nest treatments (e.g., egg oiling or addling) for urban and rural resident geese using a population model that was empirically based with data from New Jersey (Beston et al 2014). This simulation exercise indicated that although culling would have the greatest effect on decreasing populations of resident geese in New Jersey, extending harvest season durations (i.e., late‐season harvest) and increased daily bag limits would also succeed in limiting resident goose populations (Beston et al 2016).…”

“…Beston et al (2016) simulated the independent effects of harvest, culling, and nest treatments (e.g., egg oiling or addling) for urban and rural resident geese using a population model that was empirically based with data from New Jersey (Beston et al 2014). This simulation exercise indicated that although culling would have the greatest effect on decreasing populations of resident geese in New Jersey, extending harvest season durations (i.e., late‐season harvest) and increased daily bag limits would also succeed in limiting resident goose populations (Beston et al 2016).…”

Management options for a resident Barnacle Goose (Branta leucopsis) population in Flanders. A comparison of different scenarios using population modelling

“…However, for decisions on active population management through fertility reduction or culling, as applied for Canada Goose and other species in Flanders (Reyns et al 2018), demographic population models are applied more widely to gain insight in population development and the effectiveness of management options (e.g. Schekkerman et al 2000, Gauthier & Lebreton 2004, Klok et al 2010, Beston et al 2016. In stage structured populations, stage specific transition matrices are widely used to model population growth (Caswell 2001).…”

Hatch date influences pre‐fledging survival of temperate‐nesting Canada geese

“…Therefore, we believe that using data from brood mates did not cause significant estimation issues in our analyses. Beston et al (2016) acknowledged the difficulty of measuring pre-fledging survival in Canada geese. Based on total and partial brood loss of marked young and females, pre-fledging survival was estimated between 0.32 and 0.52 in New Jersey and Pennsylvania, respectively (Jacobs andDunn 2004, Guerena 2012) while Conover (1998) reported pre-fledging survival of 0.75 in Connecticut based on the proportion of successfully fledged birds.…”