Parameterizing the robust design in the BUGS language: Lifetime carry‐over effects of environmental conditions during growth on a long‐lived bird

Abstract

Since the initial development of the robust design, this capture‐recapture model structure has been modified to estimate temporary emigration and expanded to include auxiliary information such as band recovery and live resight data using maximum likelihood approaches. These developments have allowed investigators to separately assess individual and group effects on true survival, site fidelity, and temporary emigration. Additionally, recent advances in the BUGS language have allowed researchers to develop increasingly complex, user‐specified models in Bayesian frameworks. The robust design has rarely been implemented in the BUGS language, and previous attempts to parameterize the robust design in BUGS exhibited strong bias in estimates of temporary emigration rates. Given the limitations of current parameterizations of the robust design in Bayesian frameworks, and our research objectives, we have developed a parameterization of the robust design in the BUGS language that produces unbiased estimates of all model parameters. We use this novel model structure to examine lifetime carry‐over effects of environmental conditions during early life on annual breeding probabilities of Pacific black brent Branta bernicla nigricans breeding on the Yukon–Kuskokwim River Delta in Western Alaska. We found that individuals that were more structurally developed as goslings bred at increased rates as adults (β = 0.14, f = 0.94), with no effect on adult survival (β = 0.01, f = 0.62). Additionally, we provide evidence for long‐term declines in apparent survival of breeding adult females at the population level (β = −0.01, f = 0.90). This novel model structure can be easily expanded (Gibson et al., in press) and has important implications for population modelling at broad scales, where we apply it to a declining population of Pacific black brent. Given long‐term declines in gosling growth on the Yukon–Kuskokwim Delta, we predict future declines in population trajectories as a result of lifetime carry‐over effects of environmental conditions during growth on adult fecundity and long‐term declines in adult survival.

Estimating correlations among demographic parameters in population models

“…As predicted, and indicated by previous research (Leach et al, ), we observed positive correlations between adult and juvenile survival. Brent family groups remain together from hatch through late spring (Sedinger, Nicolai, Lensink, Wentworth, & Conant, ), where we would expect environmental variation which impacts adult survival to affect juvenile survival as well, despite strong carry‐over effects of environmental conditions during growth on first‐year survival, and lifetime fitness, of juvenile brent (Riecke et al, ; Sedinger et al, , ). Long‐term declines in both adult (Leach et al, ; Riecke et al, ) and juvenile survival (Leach et al, ) have critical implications for brent populations, which are also experiencing declines in fecundity (Ward, Amundson, Stehn, & Dau, ) and population size (Sedinger, Riecke, Leach, & Ward, ).…”

“…Ecologists have both highlighted (Arnold et al, ; Péron, ; Servanty et al, ) and debated (Arnold, Afton, Anteau, Koons, & Nicolai, ; Lindberg, Boomer, Schmutz, & Walker, ) the utility of these approaches. Capture–recapture (Riecke, Leach, Gibson, & Sedinger, ) and integrated population models can also be used to examine covariation among parameters such as survival and breeding propensity. This allows researchers to examine life‐history trade‐offs at the individual or population level (Cam et al, ), as well as correlations among other demographic parameters (Kindsvater et al, ; Koons, Arnold, & Schaub, ; Link & Barker, ; Schaub, Jakober, & Stauber, ).…”

Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

“…These findings are likely due, in part, to discrepancies between MAPS and BBS data sets, which differ in sizes and spatio‐temporal distribution of sampling units. Thus, ecological interpretation of the recruitment component of our model must be tempered due to inclusion of error associated with discrepancies in the sampling processes between data sets (Riecke, Leach, Gibson, & Sedinger, ). Alternative models for the capture‐recapture data, such as Jolly Seber (JS) models (Link & Barker, ; Royle & Dorazio, ) could be incorporated to inform recruitment directly.…”

Short‐term responses of freshwater mussels to floods in a southwestern U.S.A. river estimated using mark–recapture sampling

“…A Bayesian robust design model (Nichols & Pollock, 1990; Pollock, 1982) was used to estimate abundance, survival, and site fidelity while accounting for imperfect detection using code based on Riecke et al. (2018). Specifically, detection probability ( p t ) was estimated as the probability an individual available for detection during time t was in fact detected during at least one secondary occasion, abundance ( N t ) was estimated as the number of individuals in the study area during each primary period, survival ( ϕ t ) was estimated as the probability an individual in the population at time t survived to time t + 1 and did not permanently emigrate from the study area, and site fidelity (γ t ) was estimated as the probability an individual alive and in the area at time t remained in the area at time t + 1 and was available for detection.…”