Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

• Published in: Ecological research Vol. 31; no. 3; pp. 289 - 305
• Main Authors: Yamaura, Yuichi, Kéry, Marc, Andrew Royle, J
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.05.2016; Blackwell Publishing Ltd
• Subjects: Behavioral Sciences; Biomedical and Life Sciences; Community ecology; Count data; Data augmentation; Ecology; Estimating techniques; Evolutionary Biology; False negative; Forestry; Life Sciences; Mathematical models; Miyadi Award; Performance assessment; Plant Sciences; Population density; probability; Species diversity; Species richness; Zoology; β (beta) diversity; Data augmentation; Count data; β (beta) diversity; False negative; Species richness
• ISSN: 0912-3814; 1440-1703
• DOI: 10.1007/s11284-016-1340-4

Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance [Formula: see text]: 0.1, 0.5, 1, 5), detection probability [Formula: see text]: 0.1, 0.2, 0.5), and number of sampling sites (n ₛᵢₜₑ : 10, 20, 40) and visits (n ᵥᵢₛᵢₜ : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables [Formula: see text], [Formula: see text], n ₛᵢₜₑ , n ᵥᵢₛᵢₜ) increased. Detection probability [Formula: see text] was most important for the estimates of mean abundance, while [Formula: see text] was most influential for covariate effect and species richness estimates. For all parameters, increasing n ₛᵢₜₑ was more beneficial than increasing n ᵥᵢₛᵢₜ . Minimal conditions for obtaining adequate performance of community abundance models were n ₛᵢₜₑ ≥ 20, [Formula: see text] ≥ 0.2, and [Formula: see text] ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.