View in EDS

Time Lags in Biodiversity Data Processing Create the Illusion of an Invasion Slowdown.

Saved in:
Bibliographic Details
Title: Time Lags in Biodiversity Data Processing Create the Illusion of an Invasion Slowdown.
Authors: Brock, Kelsey C.1,2 (AUTHOR) kbrock5@uwyo.edu, Daehler, Curtis C.2 (AUTHOR)
Source: Global Ecology & Biogeography. Dec2025, Vol. 34 Issue 12, p1-16. 16p.
Subjects: Biodiversity, Statistical bias, Introduced species, Simulation methods & models, Acquisition of data
Geographic Terms: Hawaii Island (Hawaii)
Abstract: Aim: Understanding invasion trends is essential for mitigating the impacts of invasive species, yet their quantification is easily affected by temporal biases. Previous research has identified biases introduced by inadequate field surveying (delayed detection) but has overlooked the time between a species' detection and the communication of that detection in status reports, databases, or checklists –time delays we refer to as data processing lags. To address this gap, we conducted simulation studies and compared two real‐world data sets to evaluate the effects of data processing lags on perceived invasion trends. Location: General (simulations) and the Hawaiian Islands (case study). Time Period: 100 years. Major Taxa Studied: General (simulations) and vascular plants (case study). Methods: We manipulated the duration of data processing lags, varied where records were collected along the data pipeline (e.g., from identified specimens vs. published checklists), and examined how missing records affect perceived invasion trends. We also compared two real‐world datasets of Hawaiian plant invasions to assess the effect of different data collection practices. Results: Data processing lags can significantly distort invasion trends, creating the illusion of recent slowdowns in species invasions. Moreover, retrieving data at later stages in the pipeline can exacerbate this illusion. Our comparative analysis of real‐world datasets confirms our simulated trends and highlights how different data collection methodologies can change the perceived shape and trajectory of invasion trends. Shortening data processing lags and improving the completeness of record retrieval can help "rescue" trendlines from perceived slowdowns. Main Conclusions: Data processing lags are an under‐recognised source of temporal bias that, like detection lags, can mislead invasion trend analyses. Future research should aim to further characterise data processing lags so that they can be accounted for. Shortening data processing lags through improved taxonomic expertise and data infrastructure can help avoid misleading conclusions about invasion dynamics. [ABSTRACT FROM AUTHOR]
Copyright of Global Ecology & Biogeography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Aim: Understanding invasion trends is essential for mitigating the impacts of invasive species, yet their quantification is easily affected by temporal biases. Previous research has identified biases introduced by inadequate field surveying (delayed detection) but has overlooked the time between a species' detection and the communication of that detection in status reports, databases, or checklists –time delays we refer to as data processing lags. To address this gap, we conducted simulation studies and compared two real‐world data sets to evaluate the effects of data processing lags on perceived invasion trends. Location: General (simulations) and the Hawaiian Islands (case study). Time Period: 100 years. Major Taxa Studied: General (simulations) and vascular plants (case study). Methods: We manipulated the duration of data processing lags, varied where records were collected along the data pipeline (e.g., from identified specimens vs. published checklists), and examined how missing records affect perceived invasion trends. We also compared two real‐world datasets of Hawaiian plant invasions to assess the effect of different data collection practices. Results: Data processing lags can significantly distort invasion trends, creating the illusion of recent slowdowns in species invasions. Moreover, retrieving data at later stages in the pipeline can exacerbate this illusion. Our comparative analysis of real‐world datasets confirms our simulated trends and highlights how different data collection methodologies can change the perceived shape and trajectory of invasion trends. Shortening data processing lags and improving the completeness of record retrieval can help "rescue" trendlines from perceived slowdowns. Main Conclusions: Data processing lags are an under‐recognised source of temporal bias that, like detection lags, can mislead invasion trend analyses. Future research should aim to further characterise data processing lags so that they can be accounted for. Shortening data processing lags through improved taxonomic expertise and data infrastructure can help avoid misleading conclusions about invasion dynamics. [ABSTRACT FROM AUTHOR]
ISSN:1466822X
DOI:10.1111/geb.70168