DOI: 10.5061/dryad.j9kd51cjx
title: Data from: People’s appreciation of colorful field margins in intensively used arable landscapes and the conservation of plants and invertebrates

description: Sown field margins can improve the conservation of biodiversity in rural
 areas and can contribute to the aesthetics of rural landscapes, thereby
 potentially increasing public support for agri-environmental measures.
 However, these two functions do not necessarily coincide. This raises the
 question whether field margins that are appreciated for their contribution
 to landscape aesthetics also deliver on the conservation of biodiversity.
 We conducted choice experiments with different groups of citizens and
 collected biodiversity data in the Netherlands, to investigate if the
 number of colors and vegetation cover in field margins increased
 respondents’ appreciation for them, and how these visual cues correlated
 with taxonomic diversity and abundance of plants and invertebrates in
 those field margins. Using manipulated photos, we also assessed whether
 the presence of colorful field margins in a range of different rural
 landscapes increased respondents’ appreciation of those landscapes.
 Respondents preferred colorful margins with high vegetation cover and
 showed a preference for green rural landscapes with colorful field
 margins. The presence of colorful field margins increased landscape
 aesthetics most in the least appreciated landscapes. The number of colors
 correlated positively with the diversity of sown and spontaneous plant
 species, and overall invertebrate abundance and abundance of
 predatory invertebrates, but was not related to invertebrate diversity.
 Our results show for the first time that colorful field margins support
 both public appreciation and diversity of plants and abundance of
 ground-dwelling invertebrates, with potential advantages to farmers in
 terms of natural pest control, at least in intensively used agricultural
 landscapes. However, management practices to maintain a high number of
 colors over time may be detrimental for invertebrate diversity. To
 optimize the different functions, we recommend that field margin layouts
 should consist of a perennial part that is allowed to develop over time,
 in combination with a part that is managed for its colorfulness.
description: <em>1.1.      </em><em>General set up
 </em> Field work was carried out in Zeeland in
 2006, a province in the southwest of the Netherlands, which is dominated
 by intensive arable agriculture on marine clay soils. The province is made
 up by five areas of open, flat landscapes in the marine clay district
 separated by strands of the Scheldt River estuary. By selecting farms only
 in this province, the influence of differences in soil or landscape
 context were minimized (Noordijk et al. 2010). Main crops in the rotation
 are winter wheat, potatoes, onions and sugar beet (Lokhorst et al. 2009).
 An arable farm had on average an area of 0.32 km<sup>2</sup>
 crop land in 2006 (CBS 2022). Parcels have no natural boundaries, but are
 typically bounded by ditches, roads, hedges, or dykes.
 In the Netherlands, subsidised schemes for promoting
 agri-environmental management have been in place since 1975 (De Snoo et
 al. 2016). Non-crop field margins became part of those schemes later
 (around 2000) and were a popular option in Zeeland. The field margins in
 our research were targeted at fauna conservation, specifically focusing on
 birds and insects. Subsidy prescriptions require that these margins are
 6-12 m wide, at least 50 long, and border cropland (LNV 2006). All margins
 were sown with either grasses, wildflowers, or a mixture of these when
 they were created, but sowing was not annually. The use of synthetic
 pesticides and fertilizer is not allowed, and mowing is permitted once a
 year between July 15th and September 14th. Exceptions are made for locally
 combating very persistent weeds. All farmers involved in field margin
 management were member of a local agri-environmental farmer collective (De
 Snoo et al. 2016). For our research we randomly selected 36 arable farms
 with field margins throughout the whole province. On these farms, 54 field
 margins were visited in July 2006 to collect data about plants and
 insects. During these visits, digital pictures of the field margins were
 taken. One with a landscape perspective and one close-up, showing a
 representative part of the margin vegetation from above. Pictures were
 standardized as much as possible in terms of distance and angle and were
 taken at least 10 m away from field corners and disturbances such as field
 access or machinery tracks. To standardize the number of colors, all
 colors observed on the 54 pictures couples were assigned to the closest
 matching color in the 40 colors standard MS Office 2003 color chart. To
 avoid variation in interpretation of colors by different people, this was
 done by the same person (WV). A complete overview of
 all the variables that were available on the field margins in the study is
 given in the Supplementary Material, Table S1. All plants species and
 groups of invertebrates found are in Table S1 and S2 . None of the farms
 were organic. The amount of semi-natural area on the farm did not
 correlate to the number of colors, cover, or the biodiversity variables
 (results not shown). Distance of margins to semi-natural area was not
 assessed.   <em>1.2.     
 </em><em>Aesthetic appreciation of field
 margins</em> We tested the effect of two visual
 indicators on the aesthetic perception of field margins by the
 respondents: the number of colors and the vegetation cover. To assess the
 influence of the two visual indicators on the aesthetic appreciation of
 respondents, we assigned all close-up pictures of the field margins to one
 of three color classes (high: ≥ 7 colors, medium: 4-6 colors and low:
 between 2-3 colors) and one of two vegetation cover classes (high:
 &gt;90% cover and low: 40%-90% cover) based on the actual distribution
 of the number of colors and vegetation cover (Fig. S1a and b in
 Supplementary Material). Field margin photos were then combined in a
 series of six photos that provided a full factorial representation of
 color and vegetation cover. To account for potential effects of
 variability between photos within classes, we created two such ‘margin
 photo series’ to present to the respondents (Fig. S2).
 To assess which type of field margin added the most to landscape
 aesthetics as perceived by the respondents, artificial landscape pictures
 were created with Adobe Photoshop by combining a picture of one of our
 field margins with three types of rural landscape contexts, the ‘landscape
 series’: a landscape with only green elements present, a landscape with
 both green elements and buildings (houses and farmhouses) present, and a
 landscape with green elements and the presence of a larger road with
 traffic. These landscape contexts were all photographed in the same
 province as where the field margin study was conducted. To limit the
 number of pictures the respondents had to classify, we only used two color
 classes (high or low) and only used pictures of margins with full
 vegetation cover. The photo of the crop next to the field margin that was
 used to create the images was kept constant in each picture. Again, we
 created full factorial combinations of these three types of landscape
 context and the two-color classes of the field margin, resulting in a
 series of six pictures. Again, to account for potential effects of
 variability between pictures within classes, we created two photo series
 to present to the respondents (Fig. S3). To assess the
 aesthetic appreciation of the field margins, a total of 108 respondents
 ranked the six pictures in each series from the highest to the lowest
 attractiveness. We asked three citizen groups of respondents, i.e., urban
 inhabitants, inhabitants of rural villages, and farmers, to rank the same
 4 series of pictures to assess differences in appreciation between the
 three groups. We conducted the study in different provinces (the Dutch
 provinces of <em>Noord-Holland</em> and
 <em>Zuid-Holland</em>) than where the pictures were taken to
 avoid local bias. Urban inhabitants were interviewed in the main shopping
 street in the city of <em>Leiden (Zuid-Holland)</em>, rural
 inhabitants were interviewed outside a mall in the rural village of
 <em>Nieuw-Vennep (Noord-Holland)</em>. Farmers were
 interviewed in the <em>Haarlemmermeerpolder
 (Noord-Holland)</em>, an area with arable farms that are generally
 comparable with those in the province of Zeeland in terms of soil type,
 size, farming intensity and crops grown. Farmers were first contacted by
 phone and later visited on their farms to do the ranking.
 The 108 respondents were distributed over the three citizen
 groups as follows: 40 urban inhabitants, 38 rural inhabitants and 30
 farmers. For each respondent, we also registered gender, age (in years),
 highest level of education (primary school, preparatory vocational
 secondary education, senior general secondary and university preparatory
 education, vocational education and training, and higher education).
 Interviews took place in May and July 2007. 
 <em>1.3.      </em><em>Biodiversity assessment
 of field margins </em> In the 54 field margins
 with seeded species, we measured richness and abundance of plant species
 and invertebrate groups. All biodiversity assessments were made at the
 locations in the field margin where the pictures were taken.
 Vegetation composition, relative cover of each plant species, and
 total cover were recorded in 25 m long and 1 m wide transects in the
 middle of each margin in June and July 2006, using an adapted
 Braun-Blanquet method (Barkman et al. 1964). For assessing the number of
 plant species, native species were identified using the local flora of Van
 der Meijden (1990). Plants that could not be identified in the field were
 collected and compared with herbarium material or identified by experts
 from the Dutch Foundation for Floristic Research (FLORON). Sown cultivars
 were identified using Brickell (1999). Vegetation data were processed
 using Turboveg (Hennekens and Schaminée 2001). All plant species that were
 found, but of which no seeds were sown during the establishment of the
 field margins, are indicated as ‘spontaneous plant species’ in the rest of
 this paper. For studying the relationship between number of colors and the
 abundance of plants, we added the cover of each individual plant species
 to a total sum per margin as a proxy for the total abundance of plants in
 the margins. This was also done with the spontaneous plant
 species. As mentioned above, the field margins in our
 research were targeted at fauna conservation. Their agricultural function
 was the stimulation of natural pest control. Pollination was not of
 interest for farmers given their cropping systems. For this reason, we
 only studied the taxonomic richness and abundance of soil dwelling
 invertebrates. These were sampled at the end of June and the beginning of
 July 2006 (weeks 26–27) using 4 pitfalls traps in each field margin
 (fixation liquid: 50% ethylene glycol) placed 10 m apart. The traps had a
 diameter of 11 cm, were 7 cm deep, had an elevated plastic cover to keep
 out rainwater, and were open for 7 days. Catches of the 4 traps were
 pooled to represent one sample for each field margin. Invertebrates were
 classified to family level if possible and otherwise to order level by J.
 Noordijk (Noordijk et al. 2010). Invertebrates were classified into four
 functional groups based on their main food source. Chilopoda, Araneae,
 Coccinellidae (including their larvae), carnivorous Carabidae, and
 Staphylinidae were considered to be predators. Isopoda, Diplopoda, and
 Collembola were considered as detritivores. Gastropoda, Curculionidae,
 Orthoptera, Cicadellidae, Heteroptera, and Aphidoidea were considered to
 be herbivores. And all other species groups were classified as
 omnivores.   <em>1.4.     
 </em><em>Data analysis</em> All
 statistical analyses were performed in R software version 4.0.3 (R Core
 Team 2020). We used conjoint analysis (Green et al.
 2001) to assess the preference of respondents for the pictures in each
 series. The two series of pictures of margins, as well as the two series
 of landscapes, represent two observations within a single test and,
 therefore, cannot be considered independent observations. Rankings of
 pictures with the same combination of color class and vegetation cover
 class of the margin series were averaged for each respondent before
 analysis, as was done with the rankings of the landscape series. The full
 factorial setup of each photo series allowed us to assess the contribution
 of each variable (the color and vegetation cover classes in the margin
 series and colorfulness of the margin and landscape context classes in
 landscape series) to the appreciation of the respondents of each
 photograph. All these analyses were performed using the
 <em>conjoint</em> package in R (Bak and Bartlomowicz
 2012). For testing the differences between the three
 groups of citizens (urban inhabitants, rural inhabitants, farmers) and the
 effect of gender, age, level of education on their preferences, we applied
 the method of Lee and Yu (2013) for testing the difference between
 marginal matrices, but used Fisher exact probability, the function
 <em>fisher.test()</em> of R, instead of the Chi-square
 test. Spatial autocorrelation in the number of colors
 in the margins and the main biodiversity response variables was checked by
 calculation Moran’s I with the function <em>moranI()</em> of
 the package <em>lctools</em> of R, using a weight that selects
 the 5 nearest margins, which usually includes the second margin of the
 same farm, as well as the margins of two neighboring farms (Kalogirou
 2020). In the results, we have given the P-values of the randomized
 z-score, which in all cases were almost equal to the P-values of the
 resampling. We used Linear Mixed Models (LMM) to test
 whether the various measures of biodiversity of the field margins were
 related to the color and cover classes, as well as to age of the field
 margin, i.e., the time since sowing. These analyses were performed using
 the <em>lmer()</em> function of the <em>lme4</em>
 package in R (Bates et al. 2015). The different measures of biodiversity
 were the response variables, with either color or cover as fixed effect
 variable and farm as random effect variable. For testing the confounding
 effect of age on the relationship between the measure of biodiversity and
 color, we used the LMMs for color, but extended them by including age, as
 well as the interaction between age and color as fixed effect variables.
 Residuals were checked in all cases. Graphs were made with the
 <em>scatterplot()</em> function of the
 <em>car</em> package in R (Fox and Weisberg 2019).
description: # Data from: People’s appreciation of colorful field margins in
 intensively used arable landscapes and the conservation of plants and
 invertebrates
 [https://doi.org/10.5061/dryad.j9kd51cjx](https://doi.org/10.5061/dryad.j9kd51cjx) ## Description of the data and file structure Journal: Agronomy for Sustainable Development. DOI: 10.1007/s13593-023-00933-5. Title: People’s appreciation of colorful field margins in intensively used arable landscapes and the conservation of plants and invertebrates. To ensure the protection of participants' privacy, several steps were undertaken to de-identify the data. Firstly, we ensured that the data does not contain any information that could directly reveal the identity of the participants such as names, addresses, phone numbers, etc. Additionally, the data was modified by limiting the number of indirect identifiers to three and aggregating age to age range. By so doing, the risk of re-identification was significantly reduced. Participants were informed that the data were collected for use in research only and that participants would be made unidentifiable in the results. The excel data file contains all data used for the analyses in De Snoo et al. except those used for the calculation of the Moran’s I, because of privacy concerns. It is divided into two datasets that are fit for direct use in R (R Core Team (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. [https://www.r-project.org/](https://www.r-project.org/): ‘Picture scores R’ and ‘Biodiversity data R’. See for more details on the applied statistical analyses the description of the methods in the article. For ‘Picture scores R’ a legend is added in the dataset in which the codes of the variable values can be found. The variable names of the ‘Biodiversity data R’ contain all information needed for the interpretation of the variable values, knowing that .cm stands for ‘in centimeter’, .m for ‘in meter’, .y for ‘year’, N.µg.per.g.dry.matter for ‘nitrogen in µg per g dry matter’,  and .perc for ‘percentage’. NA means ‘not available’ in both datasets.