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5 results for “multinomial model”
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</p>
Field evaluation of abundance estimates under binomial and multinomial N‐mixture models
<p>Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories, (binomial N‐mixture models, or <i>binmix</i>) and on detection histories based on territory mapping data (multinomial N‐mixture models or <i>multimix</i>). However, calibration studies which evaluate these two N‐mixture model approaches are needed. We analysed conventional territory mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity‐nesting forest species (Great Tit <i>Parus major</i> and Eurasian Blue Tit <i>Cyanistes caeruleus</i>). In the same study area, we used two benchmarks: (i) occupancy data from a dense nest box scheme; (ii) total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nest box occupancy yields a minimum number of territories, since some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates under the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the "truth". We conclude that N‐mixture‐models estimated abundance reliably, even for our very small sample sizes. Territory‐mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a "territory editor" factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.</p>
A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures
Open the record for dataset details and reuse information.
Field evaluation of abundance estimates under binomial and multinomial N‐mixture models
Open the record for dataset details and reuse information.
A Multinomial Process Model of Moral Judgment
ClinicalTrials.gov study NCT02799277. IPD Sharing: NO. Countries: 1. Publications: 0.
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