Missing data in population time series: any imputation outperforms deletion

Missing data in population time series: any imputation outperforms deletion | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 12 May 2026 V1 Latest version Share on Missing data in population time series: any imputation outperforms deletion Authors : Hashini Vihanga Goluwa Makkala Gunadasa 0000-0002-4390-8495 [email protected] , Aaron Greenville [email protected] , and Glenda Wardle [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003043/v1 27 views 27 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Long term population monitoring is essential for detecting ecological change and guiding conservation decisions, yet these datasets are incomplete. Missing observations arise from funding constraints, harsh field conditions, or logistical challenges, affecting species abundance and environmental covariates. In temporally structured datasets, the timing, length, and clustering of these gaps can distort ecological inference, reduce model performance, and obscure true population trends. Despite these challenges, robust statistical approaches for handling missing data remain underused in ecological time-series analyzes. To evaluate the performance of different imputation strategies under realistic conditions, we used a case study to simulate time-series of four lengths and introduced missing data under scenarios that varied the amount, clustering, and temporal position of gaps. We then applied four commonly used imputation methods—Mean substitution, Moving Average, Kalman Filtering, and Multivariate Imputation by Chained Equations (MICE). Performance was assessed by comparing imputed values to true values and evaluating effects on population predictions from state space models, relative to deleting missing records. Our results show that, for abundance time-series, the choice of imputation method had a limited influence on different missingness patterns and prediction accuracy. However, all imputation approaches improved population predictions compared to excluding missing observations. Although no single method consistently outperformed others, MICE yielded the lowest error when missing data occurred early in the series, whereas the Kalman filter performed better when later portions contained more gaps. Notably, when a large proportion of data was missing in the final three-quarters of the series, Mean substitution and MICE resulted in lower prediction errors. These results highlight the importance of addressing missing data using methods tailored to the temporal structure of ecological time-series rather than ignoring gaps. They support wider adoption of multiple imputation in ecological research and offer practical guidance for improving the reliability of long term population assessments and forecasting. Supplementary Material File (1. functions_simulation_imputation.r) 1. functions_simulation_imputation Download 9.90 KB File (2. job_simulation_imputation.r) 2. job_simulation_imputation Download 3.12 KB File (3. sequential_imputation_prediction.r) 3. sequential_imputation_prediction Download 4.87 KB File (4. plots.r) 4. plots Download 8.86 KB File (qrymammalspvegrainyear.csv) qrymammalspvegrainyear Download 17.71 KB Information & Authors Information Version history V1 Version 1 12 May 2026 Collection Ecology and Evolution Authors Affiliations Hashini Vihanga Goluwa Makkala Gunadasa 0000-0002-4390-8495 [email protected] View all articles by this author Aaron Greenville [email protected] View all articles by this author Glenda Wardle [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 27 views 27 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hashini Vihanga Goluwa Makkala Gunadasa, Aaron Greenville, Glenda Wardle. Missing data in population time series: any imputation outperforms deletion. Authorea . 12 May 2026. DOI: https://doi.org/10.22541/authorea.15003043/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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