DOI: 10.7291/d1q085
title: Gardener demographics, experience, and motivations drive differences in plant species richness and composition in urban gardens

description: Urban agriculture has received considerable attention for its role in
 supporting biodiversity and ecosystem services, and health and well-being
 for growing urban populations. Urban gardens managed with agroecological
 practices and higher plant diversity support more biodiversity and may
 support higher crop production. Plant selection in gardens is a function
 of temperature and environmental conditions and also depends on gardener
 socio-demographic characteristics, motivations for gardening, and
 gardening experience. In this study, we examined how plant richness and
 composition vary with gardener socio-demographic factors, gardening
 experience and garden use, and gardener motivations. We focused on the
 socio-demographic factors of age, gender, education, and region of
 national origin, used information on years spent gardening and hours spent
 in gardens as a proxy for gardening experience, and collected information
 on motivations, as well as crop and ornamental plants grown by individual
 gardeners. We found that gender, region of origin, time spent gardening,
 and gardener motivations all influenced plant richness or composition.
 Specifically, women plant more plant species than men, especially of
 ornamental plants, and individual gardeners motivated by nature connection
 tend to plant strongly different plant compositions in their gardens. We
 also found that region of national origin strongly influences crop
 composition. In contrast to previous studies, we did not find that
 gardeners more motivated by food grow a higher proportion of or more crop
 species compared with ornamentals. Thus we show that multiple
 socio-demographic characteristics and motivations influence garden plant
 communities, and thus assuring access to gardens for all groups may boost
 plant richness and support ecosystem services in gardens.
description: We conducted this research in 20 urban, community, allotment
 gardens in Monterey (36.2400° N, 121.3100° W), Santa Clara (37.3600° N,
 121.9700° W), and Santa Cruz (37.0300° N, 122.0100° W) counties, in
 California, U.S.A between June-October 2017. The gardens range in size
 from 444 to 15,525 m<sup>2</sup> and serve between 5 - 92
 different gardeners (or gardener families).  We
 designed survey questionnaires to assess gardener socio-demographic
 backgrounds, experience with gardening, motivations for gardening, and the
 plant species that each gardener grows. To learn about gardener
 socio-demographic characteristics, we asked gardeners to provide
 information on age, gender, highest level of completed education,
 household income, and national origin of gardeners or their parents as a
 potential indicator of differences in ethno-cultural variables or
 foodways. To learn about gardener experience, we asked about years of
 gardening experience, and number of hours per week spent in the garden. To
 learn about gardener motivations, we asked gardeners to list the top
 reason why they garden (open-ended). We collected continuous data on age,
 years gardening, and hours per week spent gardening, and categorical data
 on gender, education level, and household income, as well as open-ended
 answers about motivations for gardening and national origin. To learn
 about the plant species gardeners are growing, we prompted gardeners with
 a list of commonly cultivated crop and ornamental species (based on four
 years of vegetation surveys from the study sites), and then asked
 gardeners to list any crops or ornamental plants that we had not
 mentioned. Crops included food crops, herbs, spices, species used for tea,
 as well as one species used for dye. Ornamentals included flowers and
 other decorative plants.  We surveyed 185 gardeners,
 but not all gardeners answered all questions. A substantial fraction of
 gardeners (~20%) did not provide information about household income, thus
 we chose not to include income as a factor in our analysis. We included
 only those surveys where gardeners provided information on all other
 variables, and where there were at least five individuals in each category
 for the categorical variables. This resulted in a total of 166 surveys
 from 19 gardens in our analysis. Surveys were given in English (n=126),
 Spanish (n=39), and Korean (n=1) and either read out loud by the
 researcher in person (n=140) or by phone (n=3), filled out by the gardener
 on their own (n=22), or read out loud to a gardener by another gardener
 (n=1). We used a qualitative inductive approach to
 search for common answers and themes in gardener responses, and then to
 code or summarize the responses to open ended questions.
 We used generalized linear mixed models (GLMM) to test the
 differences in total plant, crop, and ornamental species richness, and the
 proportion of all plants that were crops based on socio-demographic
 features (age, gender, education, region), experience (years gardening,
 hours spent in garden), and motivation. We focused our analysis at the
 individual garden plot scale to understand how gardener backgrounds shape
 the agrobiodiversity of plants within their plots, as this is the scale at
 which plant management decisions are most often made within urban,
 community, allotment gardens. We created four global models with either 1)
 plant species richness, 2) crop species richness, 3) ornamental plant
 species richness, or 4) the proportion of cultivated plants that were
 crops as the dependent variables, and age, gender, region, education
 level, years gardening, hours spent gardening, and motivation as
 independent variables. We also included garden as a random factor. All
 statistical analysis was conducted in R version 1.1.456 (R Development
 Core Team 2018). We checked the variable inflation factor with the ‘vif’
 function in the ‘car’ package version 3.0-2 (Fox and Weisberg 2011), and
 for all global models, all VIF scores were below 2.8. We then used the
 ‘dredge’ function in the ‘MuMIn’ package version 1.42.1 (Barton 2012) to
 run all iterations of predictor variables, and ran model selection with
 the AIC scores to select the best models. If any models were within 2 AIC
 scores of the best model, we use the ‘model.avg’ function to average these
 top models. We used natural log transformed numbers of total plant
 species, ornamental plant species, and hours per week spent in the garden
 to conform to the normal distribution. For the proportion of cultivated
 plants that were crops we used the ‘cbind’ function create a variable that
 included both number of crop species and number of ornamental species. We
 used a gaussian distribution for all models.  We
 assessed differences in plant composition in three ways. First, we created
 non-metric multidimensional scaling (NMDS) plots to visualize differences
 in plant, crop, and ornamental composition based on socio-demographic
 features (age, gender, education, region), experience (years gardening,
 hours spent in garden), and motivation. We used a permutational
 multivariate analysis of variance (PERMANOVA) using the ‘adonis’ function
 in the ‘vegan’ package version 2.5-4 (Oksanen et al. 2018) to assess
 dissimilarity among socio-demographic features by comparing the centroid
 and dispersion of different socio-demographic groups. We used Bray-Curtis
 distances, included each socio-demographic feature as a predictor
 variable, included garden as ‘strata’, conducted 999 permutations, and
 made pairwise comparisons with the ‘pairwiseAdonis” function (Martinez
 Arbizu 2018). Then, we used an analysis of similarity (ANOSIM) with
 function ‘anosim’ in the ‘vegan’ package to compare within and between
 group differences in age, gender, education, region of national origin,
 and motivations. We used Bray-Curtis distances, ran 999 permutations, and
 made pairwise comparisons with the pairwise.adonis function using a false
 discovery rate correction for multiple comparisons.