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Gardener demographics, experience, and motivations drive differences in plant species richness and composition in urban gardens

Citation

Philpott, Stacy et al. (2020), Gardener demographics, experience, and motivations drive differences in plant species richness and composition in urban gardens, Dryad, Dataset, https://doi.org/10.7291/D1Q085

Abstract

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.

Methods

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 m2 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. 

Funding

USDA National Institute of Food and Agriculture, Award: #2016-67019-25185