Demographic data are based on the Norwegian ortolan bunting population monitored during the breeding period (May and June of each year) in central Hedmark County (60°29′ to 60°53′N and 11°40′ to 12°18′E) and Akershus County between 1996 and 2005 [e.g., (15, 16)]. This is the last breeding population in Norway, which decreased from approximately 250 singing males in the 1990s to only 14 males in 2016. Juvenile and adult male birds were captured and fitted with a metal ring and colored rings, allowing individual identification of adult males (15). In each year, field observers recorded recaptures (mainly visual) of already marked birds, including information on their breeding status (paired or unpaired). We did not estimate survival for females because the sample size was too small.

We analyzed 419 adult male capture-recapture histories using multievent capture-recapture models, with the program E-SURGE 1.9.0 (34). We estimated three basic types of parameters: the initial state probabilities (from the Π-vector, following E-SURGE’s notations), the between-state transition probabilities (probabilities of transition between states and survival from the Ψ and Φ matrices respectively), and the event probabilities P (from the B matrix). We subdivided the B matrix into two matrices, the P matrix for recapture probabilities and the A matrix for probabilities to confirm the uncertain breeding states when detected. We modeled the between-state transitions (Ψ) conditional on survival (Φ). We estimated parameters simultaneously by standard maximum likelihood procedures, and models were ranked using Akaike’s information criterion (AIC) adjusted for small sample size AICc (35).

Using this framework, we examined the effects of reproductive status (paired versus unpaired males) on male ortolan bunting survival Φ, breeding transition Ψ, and recapture P and state assignment A probabilities. On these parameters, we assessed the effect of time (t), as well as the effects of the breeding state of the individual before the current recapture (f) and during the current recapture (to). Parameter constancy is denoted with “i.” The combination of these effects in a given parameter could be either additive (+) or nonadditive (.). We achieved the model selection by starting from model Φ(f.t) Ψ( P(f.t) A(f) and using a sequential approach to model simplification. We assumed in all cases that the probabilities of assignment A of recaptured individuals to the various states were state dependent [i.e., A(f)]. We began the model selection by testing temporal and state effects and their interactions on P; then, we used the most parsimonious P model to model Ψ and, finally, Φ.

Because there is currently no general procedure for goodness-of-fit (GOF) tests of multievent models, we estimated the GOF of a simplified dataset with known breeding states. We considered the Jolly-Move (JM) model as the starting general model (36). We used the program U-CARE 2.3.2 (37) to perform GOF tests and assess data conformity to JM.

GOF results indicated that data conformed to the JM model (global test, P value = 0.565 and c-hat = 0.96). On the basis of the model selection procedure described in methods, the most parsimonious model was Φ(i) Ψ (f) P(f + t) A(f), in which survival was constant over time and similar among breeding statuses (table S3). The survival probability averaged 0.64 (SE = 0.02) for adult males regardless of their breeding status.

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