Recruitment processes in long‐lived species with delayed maturity: estimating key demographic parameters

Abstract

Long‐lived species show delayed maturity and generally skip breeding sites on a given year, causing difficulties in estimating demographic parameters. A novel multi‐state capture – recapture model (model G for general) is proposed to estimate survival and recruitment. Model G considers long and short periods of non‐attendance at the breeding site. Model G is compared against a reduced model (model R) to test if pre‐breeders skip the studied site a given year, potentially prospecting other breeding habitats. This model is applied to the vulnerable Cory's shearwaters seabird species. As for many species, natal philopatry, pre‐breeding attendance and prospecting play an important role for recruitment into the breeding population. Model G shows that all Cory's shearwaters come back to their natal colony, and attend the colony before recruiting into the breeding population. Moreover, model G is preferred against model R, highlighting that prospecting plays a potentially role in the recruitment processes. Return and recruitment probabilities show similar time variation, suggesting an environmental forcing. The maximum probability of the first breeding is reached at the age of seven years. During the first year the annual survival rate is 0.52 (IC95%=[0.40, 0.64]) and pre‐breeders and adult annual survival rates do not differ, being respectively 0.88, IC95%=[0.83, 0.92] and 0.89, IC95%=[0.88, 0.9]. Model G provides an improvement to estimate demographic parameters for long‐lived species life cycle and an adequate framework to test the influence of covariates. Model G is structured by age and breeding status categories, allowing easy further population models.

Extreme climate events and individual heterogeneity shape life‐history traits and population dynamics

“…Because Southern Fulmars come back to the colony for the first time at 8 6 3 yr of age (mean 6 SD; Jenouvrier et al 2003), the detection probabilities of pre-breeders from 1965 to 1970 were set at zero to account for the progressive recruitment of chicks marked at the start of the study (Jenouvrier et al 2008a). We examined the effect of ECEs by using covariates which define extremely poor years (ECE t ¼À1), extremely good years (ECE t ¼ 1), and ordinary years (ECE t ¼ 0) on all vital rates.…”

“…This skipping behavior is common in marine predators experiencing varying environmental conditions (Cam et al 1998, Jenouvrier et al 2005a, 2008a, Chambert et al 2012. For an environment characterized by extremely unfavorable conditions, most of the individuals skip breeding, and non-breeding is the most abundant adult stage in the stable stage population structure of Southern Fulmar.…”

Forecasting ocean warming impacts on seabird demography: a case study on the European storm petrel

“…Seabird species have evolved in oceanic systems characterized by cyclic climatic events, but are negatively affected by the rapid increase of environmental stochasticity resulting from global warming, which can induce a temporal mismatch between life history strategies and increasingly erratic environmental oscillations (Jenouvrier et al 2008, Ainley et al 2010, Barbraud et al 2011, Sydeman et al 2012. However, the effects of extreme climatic events are far from being understood; some cases remain unexplained and results of studies are contradictory (Croxall et al 2002, Forcada & Trathan 2009, Bailey & van de Pol 2016.…”

Floater strategies and dynamics in birds, and their importance in conservation biology: towards an understanding of nonbreeders in avian populations

“…when prospecting available breeding territories and mates) to solitary birds (e.g. Anderson et al, 2002Anderson et al, , 2003Imber et al, 2005;Dittmann, Ezard & Becker, 2007;Jenouvrier et al, 2008), which will determine the application of various conservation measures; (3) we need better empirical data (including biological models involving more diversified life histories) to improve our knowledge of and ability to predict population stability, persistence and extinction risks.…”