244 I. F. Valencia et al.
TABLE 3 Initial parameters used to construct the population viability analysis models for the blue-billed curassowin Yondó (Fig. 1). Lowand high initial density estimates result in two values for derived parameters density, initial population size and carrying capacity.
Parameter Density/km2
Number of populations Initial population size (N0)
Carrying capacity (k) Endogamic depression
Value(s) 1.66/2.50
1 64/97 84/126
Effect of inbreeding because of lethal recessive alleles (%) 50 Mating system
2
6 lethal equivalents Monogamy
Minimum reproductive age of males & females (years) 3 Maximum reproductive age of males & females (years) 25 Maximum number of eggs per nest Progeny sex ratio (male/female; %) Annual catastrophe frequency (%) Impact of catastrophe on survival (%)
50
Annual mortality from hunting (individuals lost/year)
50/50 2
Reference
González (2004)/Rodríguez (2006) This study1
This study2
Crnokrak & Roff (1999), O’Grady et al. (2006) Crnokrak & Roff (1999), O’Grady et al. (2006) Cuervo et al. (1999), Brooks & Fuller (2006) Brooks & Fuller (2006)
C. Holmes, pers. comm. (2016)3 Medina & Castañeda (2006) Quevedo et al. (2008) Reed et al. (2003) Reed et al. (2003)
Adults (male/female): 4/1 This study4 Juveniles (male/female): 1/1
1Estimated by multiplying the population density by the habitable area with an occupancy value of $0.7 (for details, see Methods). 2Estimated by multiplying the population density by the potentially habitable area (for details, see Methods). 3From the breeding programme at Houston Zoo. 4Estimated from our fieldwork (for details, see Methods).
These were combined with the two initial densities consid- ered, creating a total of 14 scenarios.
Results
Occupancy analysis Weobtained 918 photographs of blue-billed curassows during the 1,740-night survey period (240 occasions) in 17 of the 29 survey cells (i.e. naïve occupancy ψ= 0.59). The models with the best support for explaining the occupancy of the blue- billed curassow (ΔAICc,2) were those that included the area of lowland dense flooded forest and the area of open pas- tures within the buffer zone (Table 2), both of which have a negative effect on the species (i.e. occupancy being higher in areas with little lowland flooded forest and few pastures). To determine occupancy, we used the model that included both variables affecting detection, which resulted in a mean occupancy, of ψ= 0.83 ± SE 0.002 for the study area.
Population viability analysis
Given the results from the occupancy analysis, which informed the area occupied by the species, and the density values, we obtained two N0 values, to generate two groups of scenarios (low density, d = 1.66 individuals/km2; high density, d = 2.50 individuals/km2), corresponding to an N0 of 64 and 97 individuals, respectively. We also obtained k values for the two groups of scenarios, resulting in 84 and 126 individuals, respectively.
The simulations for the No_Intervention scenarios indi-
cated a high probability of extinction of the current popula- tion, with mean times to extinction of 16 and 36 years for the low and high initial densities, respectively (Table 6). Amongst the low-density scenarios, the only scenario with viability of .100 years was
Hunting_0.Amongst the high- density scenarios, those with viability of .100 years were Hunting_50,Hunting_0 and Protected_Area (Fig. 3, Table 6). The scenarios that decreased mortality from hunting
across the whole landscape (Hunting_50 and Hunting_0 at both low and high initial densities) were the most suc- cessful in ensuring viability for 100 years (Table 6). The Protected_Area scenarios were second in ensuring viability, indicating viability at a high initial density and increasing the mean time to extinction by 40 years compared to No_Intervention at a low initial density (Fig. 3). Sup- plementation was not effective if hunting remained con- stant, although it delayed the mean time to extinction by 11 and 7 years at a low and high density, respectively, com- pared with the corresponding No_Intervention scenarios. The scenarios affecting carrying capacity (k)(k_Constant and k_Increasing at both low and high densities) did not change the probability of extinction significantly compared to the corresponding No_Intervention scenarios (Table 6).
Discussion
Using the information available for C. alberti (both field data and secondary information) we were able to create a model reflecting the dynamics of the population of the species in Yondó. We used this model to compare possible
Oryx, 2023, 57(2), 239–247 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605322000060
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