Selecting the right statistical model for analysis of insect count data by using information theoretic measures

• Published in: Bulletin of entomological research Vol. 96; no. 5; pp. 479 - 488
• Main Author: Sileshi, G.
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.10.2006
• Subjects: Agroforestry; Animal, plant and microbial ecology; Animals; Bayesian theory; Binomial distribution; Biological and medical sciences; data analysis; Datasets; Ecological effects; Foliage; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Heterogeneity; information criteria; information criterion; information science; Insecta; Insects; Methods and techniques (sampling, tagging, trapping, modelling...); model uncertainty; Models, Statistical; overdispersion; Population Density; Probability distribution; Random variables; Regulatory agencies; statistical analysis; Statistical inference; Statistical models; variance; variance heterogeneity; zero-inflation; model uncertainty; information criterion; overdispersion; zero-inflation; Heterogeneity; Arthropoda; Insecta; Statistical model; Method; Invertebrata; Sampling; Statistical study; Animal population; Optimization; Variance; Enumeration(counting)
• ISSN: 0007-4853; 1475-2670
• DOI: 10.1079/BER2006449

Researchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the negative binomial distribution and zero-inflated negative binomial models were compared using six count datasets on foliage-dwelling insects and five families of soil-dwelling insects. Akaike's and Schwarz Bayesian information criteria were used for comparing the various models. Over 50% of the counts were zeros even in locally abundant species such as Ootheca bennigseni Weise, Mesoplatys ochroptera Stål and Diaecoderus spp. The Poisson model after correction for overdispersion and the standard negative binomial distribution model provided better description of the probability distribution of seven out of the 11 insects than the log-normal, standard Poisson, zero-inflated Poisson or zero-inflated negative binomial models. It is concluded that excess zeros and variance heterogeneity are common data phenomena in insect counts. If not properly modelled, these properties can invalidate the normal distribution assumptions resulting in biased estimation of ecological effects and jeopardizing the integrity of the scientific inferences. Therefore, it is recommended that statistical models appropriate for handling these data properties be selected using objective criteria to ensure efficient statistical inference.