Demography of a ground nesting bird in an urban system: are populations self-sustaining?

Abstract

Urbanization poses threats to earth’s biota, and retention of remnant native habitat in protected areas within expanding urban boundaries may help alleviate threats to wildlife. However, it is unclear for nearly all nonsynanthropic (i.e., not benefiting from an association with humans) species whether vital rates in urban habitats can sustain populations or if populations persist only through immigration from outside the urban boundary. We conducted a three-year study of spotted towhees (Aves: Pipilo maculatus) breeding in four undeveloped parks in Portland, OR, USA, to measure park-specific seasonal reproductive output (F) and annual adult survival (S_A). We developed a stochastic model that combined F and S_A with an estimate of first-year survival to measure population growth rate (λ) in all parks assumed to be closed to immigration. F differed among parks but S_A did not. Relatively high F was possible because many pairs raised >1 brood/season. When combined with empirical estimates of survival through the 30-day period of post-fledging parental care (S_D = 0.645), only 2 of 4 parks were self-sustaining (i.e., λ > 1.0). However, S_D reflected substantial loss of fledglings to domestic cats (Felis catus). Assuming no loss to cats and either partial compensatory or additive mortality of fledglings substantially improved prospects of population persistence for declining (sink) populations. Moreover, allowing low levels of immigration to sinks reversed population declines in most parks even when vital rates were insufficient to maintain populations. Our results suggest that nonsynanthropic bird species can persist in urban landscapes, but also that offspring mortality in the post-fledging period may be a critical determinant of population viability.

Appendix 1. Structure of the population model

Finite rate of increase (lambda; λ) is the metric that best describes population performance (Sibly and Hone 2002). Its value lies in its use of vital rates to calculate a rate of population change that can be used to assess current population status (e.g., Arlt et al. 2008) and project future population trends (a population with λ = 1.0 just replaces itself, while values < and >1.0 represent declining and growing populations, respectively). Among others, Pulliam (1988) described a simple population model in which λ was calculated using estimates of annual adult survival (S_A), seasonal production of young (F: reduced by 50 % to represent only females [F/2]), and survival of juveniles to their first year of breeding (S_Yr1):

$$ \lambda ={\mathrm{S}}_{\mathrm{A}}+\left(\mathrm{F}/2*{\mathrm{S}}_{Yr1}\right). $$

Few good estimates of S_Yr1 exist (but see Keyser et al. 2004, Zimmerman et al. 2007, Arlt et al. 2008, Tarof et al. 2011, Tarwater et al. 2011, Anderson et al. 2012, Redmond and Murphy 2012) because of low natal philopatry in most birds (Weatherhead and Forbes 1994) and the fact that permanent emigration cannot be separated from true mortality. Depending upon the species, improvements in the estimate of S_Yr1 might be possible if S_Yr1 could be subdivided into periods during which survival is either conveniently measured and/or which reflect distinct periods when survival is likely to differ (e.g., migration vs. winter residency for a migratory bird). Tarof et al. (2011), for instance, separated first-year survival of Purple Martins (Progne subis) into the post-fledging, pre-migratory roost period, and then migration-overwinter survival and derived estimates of survival for each.

Good estimates of offspring survival for the few weeks immediately following fledging exist for an increasing number of species (see Cox et al. 2014 for review), and the usual pattern is for mortality to be relatively heavy in the week after young leave the nest, then stabilize and remain relatively high for the remainder of the period of dependence (typically 2–4 weeks). It is the period between gaining independence and the next breeding season for which the greatest uncertainty in offspring survival exists. We therefore chose to identify two periods between fledging and first opportunity to breed to estimate S_Yr1: the immediate post-fledging period when young are dependent on parents for care, and the period of independence that follows and ends with the beginning of the next breeding season. For spotted towhees (Pipilo maculatus; hereafter towhees), these encompass ~30 days and ~10 months, respectively (about one month is spent in the nest as eggs/young). Towhees fledge young from early May to early August, and therefore fledglings must survive between 11 months (May to April) and 9 (August to April) before their first opportunity to breed. Given that we used total seasonal productivity as our estimate of F, we chose the midpoint of 10 months to estimate S_I.

We modified the basic growth equation to incorporate separate estimates of survival during the period of dependence (S_D) and independence (S_I) for young in their first year,

$$ \lambda ={\mathrm{S}}_{\mathrm{A}}+\left(\mathrm{F}/2*\left[{\mathrm{S}}_{\mathrm{D}}*{\mathrm{S}}_{\mathrm{I}}\right]\right), $$

and then used STELLA (v. Stella 10.0.3) to simulate variation in all vital rates to calculate λ and assess the viability of towhee populations in all parks. Adult site fidelity was very high as we did not record a single instance of adult breeding dispersal among parks. By contrast, low natal site fidelity resulted in few detections of juvenile recruits to their natal parks. This is common in passerines (see Weatherhead and Forbes 1994); we assume that low recruitment of locally hatched juveniles represented normal dispersal patterns and that an equal number of first-year birds hatched elsewhere entered the parks The assumption that immigration and emigration were equal is equivalent to assuming that populations are closed, which is important because it enabled us to use the simulations to ask the question of whether park-specific estimates of S_A and F/2 were sufficient to maintain populations.

Our estimate of S_D came from Shipley et al.’s (2013) study of towhees breeding in SPBK. We assumed this was applicable to other parks, and then explored, through simulation, the effect on λ of a range of values for S_I that varied as a proportion of S_A. Siriwardena et al. (1998) reported survival rates for hatch-year birds captured as independent individuals from their summer of hatch to the next breeding season for 28 passerine species. Survival was reported for adults captured at the same time. After we omitted four species with small sample sizes (adult + hatch-year birds <200 individuals), the average ratio of S_I to S_A was 0.86 (SE = 0.025, N = 24), with a range from 0.70 to over 1.00. Two studies of Black-capped Vireos (Vireo atricapilla) exhibited a very similar ratio of independent hatch year survival to adult survival (0.83: Kostecke and Cimprich 2008; 0.70 to 0.89: Grzybowski 2005). We therefore chose to explore the effect of S_I on λ when S_I was 70 %, 80 %, 90 %, or 100 % of S_A.

Our model begins with the seeding of each a park with a number of individuals, which we standardized at 1/4 of “carrying capacity” (K). K was based on the highest estimated count of breeding pairs in each park over the period of study. We built K into the model to prevent unrestrained growth in a habitat that would clearly have a ceiling on population size. This added realism, but the actual value used would not affect our ability to address the question of whether the observed vital rates in a park were sufficient to maintain population size. We simulated a decade of population change, and at the start of each annual iteration of the model, population size (N_Total) was compared to K at N_B (number of breeders) in Fig. 1. When we later opened the populations to permit immigration (see below), the immigrants were added to N_Total before testing for whether K had been exceeded so that regardless of the sources, N_B could never exceed K. The excess were removed as emigrants (E) before determination of each year’s N_B.

Vital rates were not modeled to be density-dependent. Although we could have done so, the strength and form of the relationship would have been pure speculation and therefore we chose to assume that birth and death rates did not vary with population size. We suggest that this makes our models conservative as regards their ability to predict a population’s ability to sustain itself because the vital rates used were taken from populations in the parks that were likely at or near capacity, and thus already experiencing any negative effects of high density. Adding density-dependence would have led to higher F and/or S_A at lower N. The effect of this would have been to accelerate growth, but we would have ultimately arrived at the same vital rates as N approached K. The model randomly chose a value of F/2, S_A, and S_D from a range of values falling within ±2 standard errors of mean F/2 and S_A and ±20 % of mean S_D. To determine S_I for each park, the model calculated monthly survival rate (MSR) for the randomly chosen S_A from each park individually at each iteration. That monthly survival rate was raised to the power of 10 to determine the equivalent survival rate for an adult for the 10 month period preceding a recruit’s first opportunity to breed (1 July to 1 April). The latter was then multiplied by the designated proportion (0.7, 0.8, 0.9, or 1.0) to obtain S_I. Survival from the point of fledging to the following breeding season (S_Yr1) was the product of S_D and S_I. The product of F/2 and S_Yr1 equaled the number of new recruits, which when added to S_A yielded λ. Dinsmore et al. (2010) described a 2-stage (juvenile and adult) Leslie matrix for a birth pulse population censused in the postbreeding period. The towhee data conformed to these conditions, and we found that our estimates of λ were identical to that generated by Dinsmore et al.’s (2010) model when we ran our model with constant parameter values (i.e., as a deterministic model). Our STELLA model thus generated accurate estimates of λ but with the incorporation of stochasticity in all vital rates.

The 10 estimates of λ from the decade-long simulation were averaged and the population size in the final year (N_final) also averaged to produce a single data point for both S_A and N_Final. Five hundred such simulations were run for each park at all levels of S_I (0.7*S_A, 0.8*S_A, etc.), but only every fifth estimate was used so as to reduce possible serial correlation among randomly chosen values. Consequently, the number of simulations used to calculate grand mean λ and N_Final for each park at each category of S_I was 100 in our initial modelling efforts. However, we reduced the sample size to 50 in all subsequent simulations (see below) because comparison of the mean of the running average of λ at iterations 46 through 50 to that at iterations 96 through 100 showed that the differences in λ to be well below 1 % (mean difference = 0.22 [SE = 0.047 %], n = 16 comparisons). In short, iterations beyond 50 did not improve the accuracy of our estimates of λ.

As noted above, we also changed the population from being closed (i.e., immigration = emigration) to open to immigration (i.e., immigration slightly > than emigration) to incorporate the possibility of immigration in some models to evaluate whether low levels of immigration might rescue populations from local extinction. Sink populations (i.e., λ < 1.0) can persist with sufficient immigration (Schaub et al. 2010, 2013) and therefore to test for this contingency we allowed immigration to occur at a random rate. A number between 0 and 1 was generated randomly, and no immigration occurred if the number was ≤0.5, but one immigrant was allowed to enter the population if the randomly generated value fell between 0.51 and 1.0. This in in effect resulted in the addition, on average, of one female every other year. All other aspects of the model were unchanged (Fig. 4).

Fig. 4

Visual representation of STELLA model used to project population size of spotted towhees nesting in urban parks in Portland, OR USA. Circles are functions (“converters” in STELLA) that contain expressions describing seasonal fecundity (F/2), survival of adults (S_A), survival of young during the 30 day period of post-fledging parental dependence (S_D), survival of young over the ensuing 10 month period between independence and start of the next breeding season (S_I), survival over the period between fledging and start of the next breeding season (S_Yr1 = S_D*S_I), monthly survival rate (MSR) of adults, and population growth rate (λ). MSR equals 10^(logS _A ^)/12 and S_I = P*MSR¹⁰, where P represents the proportional difference between adult and first-year survival (i.e., 0.7, 0.8, 0.9, or 1.0). N_B is number of breeders, while the flow G represents growth of the population (= λ*N_B). The rectangle is a stock that contains total population size (N_Total), while flow O removes each year’s growth to prevent the summation of successive annual increments of growth. Possible immigrants are generated by the converter I by randomly generating a value between 0 and 1 at each iteration. No immigration occurs if I ≤ 0.5, but one immigrant enters the population if the randomly generated value falls between 0.51 and 1.0. At N_B, N_Total and I are summed and compared to K. If the sum is < K then N_B equals the sum. If K is exceeded the excess are removed as emigrants (“dispersers”)