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Authors
Abstract(s)
Abundance estimation of wildlife populations is a central task in ecology. Camera trapping is a
non-invasive technique that can be used to estimate animal abundance using three classes of statistical
models: mark-recapture models, distance sampling and occupancy models. N-mixture models are a type
of occupancy based models that divide the study area into independent sites and estimate abundance
across these sites using count data of observations. This work uses camera trap data collected from 2016
to 2022 on 10 mammal species in northern Spain, ranging in size from 300 grams to 100 kg, to evaluate
differences in the performance of two N-mixture models fitted using maximum likelihood estimation
(MLE), one assuming a Poisson distribution and one assuming a negative binomial distribution, and
two N-mixture models assuming a Poisson distribution, one fitted using ML and one using Bayesian
inference. It also relates the differences between models with sample size (number of observations per
species and number of sites were a species was detected) and species characteristics (body mass and home
range). The negative binomial model differed in abundance estimates, their precision, and bias compared
to the two models assuming a Poisson distribution, whereas these latter models were relatively similar
in their performance across most species. Neither sample size nor species characteristics appeared to be
strong predictors of performance difference between models. These results suggest that while negative
binomial models may capture overdispersion better than simpler Poisson models, the latter may offer more
robust estimates even in mildly over-dispersed datasets. It highlighted that the selection of an appropriate
distribution may be more influential than the choice of parameter estimation for this particular type of
model.
Description
Trabalho de projeto de mestrado, BioestatĆstica, 2025, Universidade de Lisboa, Faculdade de CiĆŖncias
Keywords
Estimativa de abundĆ¢ncia Modelo āN-mixtureā Armadilhas fotogrĆ”ficas ComparaĆ§Ć£o de modelos Trabalhos de projeto de mestrado - 2025