The utility of the 'Arable Weeds and Management in Europe' database: Challenges and opportunities of combining weed survey data at a European scale

Weed Res. 2023 Feb;63(1):1-11. doi: 10.1111/wre.12562. Epub 2022 Dec 8.

Abstract

Over the last 30 years, many studies have surveyed weed vegetation on arable land. The 'Arable Weeds and Management in Europe' (AWME) database is a collection of 36 of these surveys and the associated management data. Here, we review the challenges associated with combining disparate datasets and explore some of the opportunities for future research that present themselves thanks to the AWME database. We present three case studies repeating previously published national scale analyses with data from a larger spatial extent. The case studies, originally done in France, Germany and the UK, explore various aspects of weed ecology (community composition, management and environmental effects and within-field distributions) and use a range of statistical techniques (canonical correspondence analysis, redundancy analysis and generalised linear mixed models) to demonstrate the utility and versatility of the AWME database. We demonstrate that (i) the standardisation of abundance data to a common measure, before the analysis of the combined dataset, has little impact on the outcome of the analyses, (ii) the increased extent of environmental or management gradients allows for greater confidence in conclusions and (iii) the main conclusions of analyses done at different spatial scales remain consistent. These case studies demonstrate the utility of a Europe-wide weed survey database, for clarifying or extending results obtained from studies at smaller scales. This Europe-wide data collection offers many more opportunities for analysis that could not be addressed in smaller datasets; including questions about the effects of climate change, macro-ecological and biogeographical issues related to weed diversity as well as the dominance or rarity of specific weeds in Europe.

Keywords: abundance measures; arable plants; cover estimates; data collection; management; nomenclature; plot size; sampling bias; weed community; weeds.