Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset.

ABSTRACT: To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed

SUBMITTER: Goldstein BR 

PROVIDER: S-EPMC9296480 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature