Estimating demographic parameters using a combination of known‐fate and open N‐mixture models

Abstract

Abstract. Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from markresight data sets. We provide implementations in both the BUGS language and an R package.

Integrating distance sampling with minimum counts to improve monitoring

“…The resulting integrated population model could improve precision and assessment of other demographic parameters would become possible. Although parameters representing population dynamics are estimable from repeated survey data alone (Sollmann et al , Schmidt and Rattenbury ), auxiliary data allows additional parameters (e.g., survival, recruitment) to be estimated (Schmidt et al ), potentially leading to an understanding of population drivers (Schaub et al , Abadi et al ). For these reasons we expect the basic structure of our model to be useful in extracting more information from disparate datasets, thereby improving wildlife monitoring and management outcomes.…”

“…Recently, there has been extensive development of integrated population models whereby multiple datasets are jointly analyzed to provide efficient estimation of demographic processes (Schaub et al , Schaub and Abadi , Chandler and Clark , Schmidt et al , Zipkin and Saunders ). We expected that the concept of data integration could be used to link more rigorous survey designs with minimum count survey datastreams.…”

Integrated population model to improve knowledge and management of Idaho wolves

“…We originally modeled our observed pack counts ( n i,t ) as in Schmidt et al () by assuming that that the number of wolves counted during a survey was the result of a binomial process where each wolf in a pack of size N i,t was counted with probability of observation : …”

“…When fit to our data we found that was likely being underestimated, resulting in unrealistically high estimates of pack sizes (e.g., packs with counts consistently in the mid‐teens were estimated to be ∼30 individuals). As with Schmidt et al (), we were able to mitigate this perceived bias by including some perfect counts for which we assumed all pack members were seen and counted during a particular survey (i.e., we set the probability of detection equal to 1 for these counts). However, designating particular counts as perfect was quite subjective and the final estimate was sensitive to the number of perfect counts included.…”

“…In Equation , is the dispersal rate, is the harvest rate, is the non‐harvest mortality rate (see Equation ), and R is the number of wolves recruited into pack. As with the IPM in Schmidt et al () for wolves in Alaska, we assumed that immigration rate was low (Bassing ) and any immigration that does occur could be accommodated by the recruitment parameter. Thus, our recruitment parameter incorporates pup recruitment and immigration, but because we assume low immigration rates, it can be interpreted as predominately pup recruitment.…”