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New paper: Dealing with "errors due to imperfect detection" = "I don't see everything"

This new paper might look, at first glance, pretty esoteric to most of the ADU's citizen scientists. But there is a key phrase in the abstract "errors due to imperfect detection" which resonates with every participant in ADU projects: "I don't see everything. I miss some species." So this paper, of which ADU postdoc Fitsum Abadi Gebreselassie is first author, develops the statistical approach for dealing with this problem. Fitsum is currently in France, taking up an opportunity there to learn more statistical theory, and returns to continue his postdoc next year, supported by the Claude Leon Foundation.

Here is the full reference to the paper. Abadi F, Gimenezb O, Jakobere H, Stauberf W, Arlettaza R, Schaub M 2012. Estimating the strength of density dependence in the presence of observation errors using integrated population models. Ecological Modelling 242: 1–9.

ABSTRACT: Assessing the strength of density dependence is crucial for understanding population dynamics, but its estimation is difficult. Because estimates of population size and demographic parameters usually include errors due to imperfect detection, estimations of the strength of density dependence will be biased if obtained with conventional methods and lack statistical power to detect density dependence. We propose a Bayesian integrated population model to study density dependence. The model allows assessing the effect of density both on the population growth rate as well as the demographic parameters while accounting for imperfect detection. We studied the performance of this model using simulation and illustrate its use with data on Red-backed Shrikes Lanius collurio. Our simulation results showed that the strength of density dependence is identifiable and it was estimated with higher precision using the integrated population model than the conventional regression model. As expected, the conventional regression model tended to overestimate density dependence at the population level whereas underestimates at the demographic level, but the bias was small. The analysis of the Red-backed Shrike data revealed negative density dependence at the population level most likely mediated by a density-dependent decline in adult survival. This work highlights the potential of integrated population models in assessing density dependence and its practical application in population studies.

This paper is part of a series of papers in Statistical Ecology that Fitsum has co-authored, and have featured in previous ADU news items: Population dynamics of Hoopoes and Wrynecks and Improving farmland biodiversity in vineyards.