Monitoring and modeling of population dynamics for the harvest management of scaly-naped pigeons in Puerto Rico

Abstract

The scaly‐naped pigeon (Patagioenas squamosa) is threatened by hunting in the Caribbean. At present, the pigeon is abundant in Puerto Rico, but overharvesting is a major concern; therefore, the development of a sustainable harvest strategy is a management priority. The management objective of the harvest strategy is to maximize hunting opportunity while keeping the population above an abundance threshold (NT) of 260,000 pigeons. To facilitate operational development and implementation of the harvest strategy, we conducted point‐transect distance sampling to estimate population size, and mail and telephone hunter surveys to estimate total harvest. We used monitoring data and a Bayesian state‐space model to estimate population and harvest management parameters, and predicted changes in population size as a function of expected total harvest. Population size averaged 262,899 pigeons (SE = 122,087) in April–June 1986–2012, and total harvest averaged 40,760 pigeons (SD = 43,405) in September–November 1986–2011. Intrinsic rate of growth was 0.442 (SD = 0.142), carrying capacity was 524,900 pigeons (SD = 119,200), maximum sustainable harvest rate was 0.221 (SD = 0.071) with a total harvest of 57,988 pigeons (SD = 14,640), and equilibrium population size was 262,500 pigeons (SD = 59,620). Because the population recently recovered from deforestation and has been affected by hurricanes, a conservative harvest level was prescribed at 50,311 pigeons (SD = 24,939). However, harvest in 2008–2011 was 2.1 times larger than the maximum sustainable yield. Assuming 122,905 pigeons were harvested per year in 2012 and 2013, we predicted a population size of 216,000 pigeons (SD = 90,770) in 2014, suggesting that restrictive regulations may need to be prescribed to meet the management objective. Our monitoring and modeling framework is an important first step in the development and implementation of a sustainable harvest strategy for the scaly‐naped pigeon, and the approach can be applied to the management of other columbids in Puerto Rico. © 2014 The Wildlife Society.

Monitoring, modeling and harvest management of non-native invasive green iguanas on Grand Cayman, Cayman Islands

“…1). Six-minute counts were conducted by teams of three observers at a minimum of 157 and a maximum of 212 points established along and away from roads using systematic sampling with random starts (Rivera-Milán et al 2014a, 2016a. Because on-road points may not be representative of green iguana abundance at interior habitats, between 50 and 75 points (32-35%) from the total of 157-212 points were located away from roads.…”

“…1). The surveys also included areas of special interest, such as the Queen Elizabeth II Botanic Park and Colliers Wilderness Reserve in the east, where green iguanas may have negative interactions with the endangered endemic blue iguana (Cyclura lewisi: Burton and Rivera-Milán 2014, Conley et al, in press); and Safe Haven Golf Course and urban developments in the west, where green iguanas caused economic losses and were harvested before and during -2019(Rivera-Milán et al 2014a, 2016a.…”

“…The green iguana (Iguana iguana) was most likely introduced as a pet on Grand Cayman, Cayman Islands (Echternacht et al 2011). First sightings in the wild occurred in the early 1990s (Burton, personal observation), with green iguanas becoming overabundant during the last 20 years (Rivera-Milán et al 2014a, 2016avan Veen et al 2015; Department of Environment [DoE] 2018[DoE] , 2019. Although quantitative data about negative impacts are lacking for Grand Cayman, green iguana overabundance caused economic losses (e.g., damage to buildings and other infrastructure in urban areas), posed health and safety and risks, and triggered negative interactions with native plants and animals (Engeman et al 2005;Krysko et al 2007;Meshaka et al 2007;Falcón et al 2012;van Veen et al 2015;Vuillaume et al 2015;Moss et al 2017).…”

“…In addition, natural resource managers had an incomplete understanding of green iguana population dynamics, and recognized that harvest mortality could have unforeseen outcomes due to the release from density dependence and overcompensation through high survival and fecundity rates (Courchamp et al 2003;Krysko et al 2007;Meshaka et al 2007;Zipkin et al 2009;Alisauskas et al 2011;Koons et al 2014;Pasko and Goldberg 2014;Cortez and Abrams 2016;McIntire and Juliano 2018). Under these circumstances, monitoring and modeling were deemed essential to assess population response and guide harvest management decisions (Nichols and Williams 2006;Lyons et al 2008;Runge et al 2009;Lindenmayer and Likens 2010;Lindenmayer et al 2011;Pasko and Goldberg 2014;Rivera-Milán et al 2014b, 2016b.…”

“…Not accounting for individuals wounded but not retrieved for processing (i.e., crippling loss), registered hunters shot with air guns or trapped with snares and other devices over 874,252 green iguanas between October 2018 and August 2019 (for more information, see https://doe.ky/green-iguana-cull-updates/). Assuming the peak of reproduction occurred between April and July, when courtship and other reproductive behaviors were observed (e.g., dominant males defending territories and nesting females), distance sampling surveys were conducted to estimate abundance in February 2019 and annually in August 2014-2019, using methods that accounted for changes in detection probability (Buckland et al 2001(Buckland et al , 2015Royle 2004;Thomas et al 2010;Thomas and Marques 2012;Burton and Rivera-Milán 2014;Rivera-Milán et al 2014b, 2016b. To assess population response and guide harvest management decisions, distance sampling abundance estimates in August 2014-2019 were used to develop a Bayesian state-space logistic model, generate the posterior distributions of population and harvest management parameters (i.e., maximum population growth rate, carrying capacity, equilibrium abundance, maximum sustainable total harvest and maximum sustainable harvest rate), and make future predictions of abundance with harvest rates from 0.100 to 0.800 for August 2020-2030, using methods that accounted for the variation associated with observation and process errors (Millar and Meyer 2000;Kéry and Schaub 2012;Hobbs and Hooten 2015;Rivera-Milán et al 2014b, 2016b.…”