Treating gaps and biases in biodiversity data as a missing data problem

Author:

Bowler Diana E.1ORCID,Boyd Robin J.1ORCID,Callaghan Corey T.2ORCID,Robinson Robert A.3ORCID,Isaac Nick J. B.1ORCID,Pocock Michael J. O.1ORCID

Affiliation:

1. UK Centre for Ecology & Hydrology Maclean Building Benson Lane Wallingford OX10 8BB UK

2. Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center University of Florida 3205 College Avenue Davie Florida 33314‐7719 USA

3. British Trust for Ornithology The Nunnery Thetford Norfolk IP24 2PU UK

Abstract

ABSTRACTBig biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large‐scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long‐term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow.

Funder

Natural Environment Research Council

Publisher

Wiley

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