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Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context

Citation

Case, Bradley et al. (2020), Data from: The roles of non-production vegetation in agroecosystems: a research framework for filling process knowledge gaps in a social-ecological context, Dryad, Dataset, https://doi.org/10.5061/dryad.8931zcrmn

Abstract

1. An ever-expanding human population, climatic changes, and the spread of intensive farming practices is putting increasing pressure on agroecosystems and their inherent biodiversity. Non-production vegetation elements, such as woody patches, riparian margins, and restoration plantings, are vital for conserving agroecosystem biodiversity. Further, such elements are key building blocks that are manipulated via land management, thereby influencing the biotic and abiotic processes that underpin functioning agroecosystems.

2. Despite this critical role, there has been a lack of synthesis on which types of vegetation elements drive and/or support ecological processes, and the mechanisms by which this occurs. Using a systematic, quantitative literature review of 342 articles, we asked: what are the effects of non-production vegetation on agroecosystem processes and how are these processes measured within global agroecosystems?

3. Woody patches, hedgerows and borders, riparian margins, and shelterbelts were the most studied types of non-production vegetation. The majority (61%) of studies showed positive effects of non-production vegetation on ecological processes, where the presence, level or rate of the studied process was increased or enhanced.

4. However, four key research gaps were revealed: (1) most studies (83%) used proxies for, instead of direct measurements of, ecosystem processes related to non-production vegetation; (2) study designs used to investigate non-production vegetation effects on ecosystem processes directly were largely limited to observational comparisons of non-production vegetation types, farm-scale vegetation configurations, and different proximities to vegetation in terms of the effect on ecological processes; relatively few studies used manipulative experiments (3) the relatively few studies directly measuring ecosystem processes were dominated by four process categories: invertebrate biocontrol, predator and natural enemy spillover, animal movement, and ecosystem cycling, and (4) the methods used to directly measure non-production vegetation effects comprised a surprisingly limited set of approaches.

5. To fill key research gaps that will inform the use of non-production vegetation to enhance agroecosystem processes, we present a framework for future research that emphasises the need to combine an understanding of human decision making with carefully-designed and targeted investigations into the roles of taxa, ecosystem processes, and landscape heterogeneity related to non-production vegetation, at multiple spatial scales within agroecosystems.

Methods

1. We searched the international literature for relevant articles following a modified systematic review protocol (Pickering & Byrne 2014). Relevant articles were defined as those that described primary research conducted on farmland that addressed questions regarding any ecological function(s) associated with non-production vegetation.

2. Five distinct search strings, comprised of various keywords (Table S1) were designed to find relevant papers addressing the following broad topics related to non-production vegetation in agroecosystems; faunal diversity and use of vegetation; spatial arrangement of vegetation in the landscape; pests and disease in vegetation; weeds in vegetation; abiotic functional responses to vegetation.

3. The proportion of relevant articles returned by 30 scholarly databases was examined using all five search strings, and nine databases were selected based on the proportion of these relevant returns: BASE, BioOne, Google Scholar, JSTOR, Jurn, ProQuest, Science Direct, Scopus, and Web of Science.

4. Each search string was entered into each of the nine databases in turn, and all relevant articles were downloaded (where relevance was determined by reading the abstract). If no relevant articles were found for the first 100 returns, we moved on to the next search string.

5. For every relevant article found, we also checked all citing articles using the “Cited by” function in Google Scholar. After the initial two databases were searched, we extracted all keywords from papers and ranked them by the number of occurrences. We updated the search strings to include any commonly-used keywords that were missing from the strings. The faunal and spatial searches that were carried out first yielded significant overlap relative to our article selection criteria, and were ultimately combined into one faunal database.

6. Once all searches were complete, we extracted and cross-referenced all reference lists from the downloaded articles using ParsCit (Kan et al. 2011) to compile a final list of articles across all searches.

7. A total of 704 articles were read and 342 were included using the criteria that they were: (1) empirical (not modelling or meta-analysis) studies within agroecosystems; and (2) they at least discussed the effects of non-production vegetation on processes, not just biodiversity, within agroecosystems.

Usage Notes

The review was based on a series of searches focussed on the role of non-production vegetation in terms of its effects on soil and water, weed, disease, and faunal properties and processes.  As such, the compiled review data are in an excel workbook, with worksheets for each of these four aspects.  Each of the four worksheets also has a corresponding metadata worksheet that describes the columns and the data collected.

Funding

Ministry of Business, Innovation and Employment, Award: C09X1501