DOI: 10.5061/dryad.47d7wm3gm
title: Rare species biodiversity, socio-demographics and local and landscape characteristics in Northern California community urban gardens

description: Cities are sometimes characterized as homogenous with species assemblages
 composed of abundant, generalist species having similar ecological
 functions. Under this assumption, rare species, or species observed
 infrequently, would have especially high conservation value in cities for
 their potential to increase functional diversity. Management to increase
 the number of rare species in cities could be an important conservation
 strategy in a rapidly urbanizing world. However, most studies of species
 rarity define rarity in relatively pristine environments where human
 management and disturbance is minimized. We know little about what species
 are rare, how many species are rare, and what management practices promote
 rare species in urban environments. Here, we identified which plants and
 species of birds and bees that control pests and pollinate crops are rare
 in urban gardens and assessed how social, biophysical factors, and
 cross-taxonomic comparisons influence rare species richness. We found
 overwhelming numbers of rare species, with over 50% of plant cultivars
 observed classified as rare. Our results highlight the importance
 of women, older individuals, and gardeners who live closer to
 garden sites in increasing the number of rare plants within urban
 areas. Fewer rare plants were found in older gardens and gardens
 with more bare soil. There were more rare bird species in larger gardens
 and more rare bee species where canopy cover was higher. We also found
 that in some cases, rarity begets rarity, with positive correlations found
 between the number of rare plants and bee species and between bee and bird
 species. Overall, our results suggest that urban gardens include
 a high number of species existing at low frequency and that
 social and biophysical factors promoting rare, planned biodiversity can
 cascade down to promote rare, associated biodiversity.
description: <strong>Study Region </strong> We
 worked in 18 urban community gardens in three counties (Santa Clara, Santa
 Cruz, and Monterey) in the central coast region of California, USA. The
 gardens differ in local habitat (structural and compositional diversity of
 both crop and non-crop species) and landscape context (amount of natural,
 agricultural, and urban land cover in the surrounding area). All gardens
 have been cultivated for five to 47 years and range from 444 to 15,525
 m<sup>2</sup> in size. All of the gardens use organic
 management practices and prohibit the use of chemical pesticides and
 insecticides. Gardens were chosen because they represent sites across a
 gradient of urban, natural, and agricultural landscapes and were separated
 from each other by &gt;2 km, the farthest distance between gardens was
 90 km and the closest was 2 km (Cohen et al., 2020; Egerer et al., 2017;
 Philpott and Bichier, 2017). Gardener demographic data indicates that
 gardeners are diverse in their make-up, covering a range of family sizes,
 education, salary, and food insecurity levels (Egerer et al., 2017;
 Philpott et al., 2020). <strong>Data
 Collection</strong> We provide the following
 framework (Fig. 1) to help visualize the specific set of questions posed
 in this study and the data and analyses used to address them. First, we
 ask which gardener characteristics (Q1), and which local and landscape
 garden features affect the number of rare plant cultivars (Q2a) and rare
 bird and bee species (Q2b) in urban community gardens. We include
 cultivars as distinct types per (Reiss and Drinkwater 2018). Subsequently,
 we ask if there is an association between the number of rare plant
 cultivars and the number of rare bird and bee species (Q3), and if the
 number of rare bird and bee species are also related to one another
 (Q4).   The data analyzed for this research was
 collected in two summer field seasons (2015, 2017), from May to September,
 which is the peak urban garden growing season for the region. Gardener
 characteristics data (defined below) and gardener self-reported plant data
 were collected in summer 2017 to address Q1 (Fig. 1). Direct sampling of
 biodiversity (plants, bees, birds) and garden characteristics was done in
 summer 2015 to address Q2-4 (Fig. 1). Though structural equation modeling
 (SEMs) was considered, there is no direct way to compare data from 2017
 and 2015 because of the methodological differences outlined below. Thus,
 separate statistical analyses are conducted for 2017 and 2015 data. We can
 test the relationship between gardener characteristics and number of rare
 plant cultivars because gardeners reported what plants they grew in our
 surveys. We cannot directly test how gardener characteristics influenced
 the number of rare bird and bee species because gardeners were not asked
 about these species. Instead, we infer effects of gardener characteristics
 on bees and birds indirectly via the overall research framework in Figure
 1. We explain the specific methods for each type of data collection and
 the analysis below. Gardener characteristics
 data We surveyed gardeners from 18 urban
 community gardens during the 2017 summer field season. Survey
 questionnaires collected information on gardener demographic information
 as well as gardening experience and use data (Table 1). Specifically, we
 surveyed 185 gardeners in total, or six to 14 gardeners per garden
 (9.5-65% of the gardener population in a site). We only included surveys
 in our analysis if plant information on the survey was completed (n=162).
 We administered surveys in English (n=123), Spanish (n=38), and Bosnian
 (n=1) and either read the survey out loud in person (n=138) or via phone
 (n=1), and either had the gardener fill out the survey themselves (n=21)
 or had a gardener read the survey to another gardener (n=1). Two of the
 surveys did not have information on the method of survey administration.
 We also note that despite best efforts to surveys gardens equally, uneven
 gardener availability resulted in unequal gardener sampling across the 18
 community gardens, requiring us to calculate the number of rare plant
 cultivars in gardener-reported data (2017) by gardener surveys rather than
 by garden as was done in direct field-based data (2015) described
 below. Gardener-reported plant
 data Gardeners were asked to identify and
 list the plant species and cultivars that they planted in their plots. We
 then classified gardener-reported plants into either crop or ornamental
 species. Crop species included fruits, vegetables, herbs, and other
 consumable plants. Ornamental species included plants grown for decorative
 purposes, such as flowers and non-food providing crops. Though we included
 plant cultivars as distinct types, gardeners varied in the level of
 cultivar specificity provided, which we acknowledge is a limitation to our
 study. We looked up scientific names for common names provided and
 supplemented these results with direct field-based plant data where
 researchers identified species and cultivars in the field using methods
 described in detail below. Garden characteristics
 data <em>Landscape-level garden
 data</em> For each garden, we measured the
 surrounding landscape composition within buffers surrounding gardens at
 the 0.5, 1, and 3 km scale. We used the 2011 National Land Cover Database
 (NLCD) (Jin et al. 2015) to calculate the percentage of urban NLCD land
 cover class using ArcGIS (v. 10.1) (ESRI 2011). Urban land cover was
 calculated by combining developed low, medium, and high intensity
 developed land. Urban land cover is correlated with many other land use
 categories (e.g., natural land), thus we chose to focus on only urban land
 cover in our models because we were most interested in the effects of
 urbanization on biodiversity; further, urban land cover has been a
 significant predictor of biodiversity in previous analyses of these
 gardens (Quistberg et al. 2016, Egerer et al. 2017). Urban cover at the 1
 km scale best predicted pooled species rarity across taxa, exhibiting the
 lowest AIC of all the scale models (Appendix S1: Table S1), thus the 1 km
 spatial scale was used for all subsequent analyses.
 <em>Local-level garden data</em>
 To collect local-scale garden characteristics, we established a
 20 x 20 m plot in the center of each garden. In this plot, we measured
 canopy cover using a spherical densiometer at the center and N, S, E, and
 W edges of the plot, counted the number and species of trees and shrubs,
 and counted the number of trees or shrubs in flower within the plot. We
 determined age and size of each garden by examining historic Google Earth
 images and noting the first appearance of the gardens, and then we used
 ground-truthed GPS points taken from each garden to calculate size. For a
 few of the gardens older than 35 years, we used historical information
 gained through community resources or discussions with farm management to
 determine age. We measured ground characteristics using
 four 1 x 1 m sub-plots within the 20 x 20 m plots. The 1 x 1 m sub-plots
 were randomly placed anywhere (including pathways) within the 20 x 20 m
 plots. Within each 1 x 1 m sub-plot, we measured the height of the tallest
 herbaceous vegetation and estimated ground cover composition (percent bare
 soil, rocks, leaf litter, grass, mulch). We repeated
 sampling once per month between May and September 2015 and calculated the
 mean value for each environmental variable for each garden at each time
 point. Field-based biodiversity
 data <em>Field-based plant
 data</em> We measured plant biodiversity using
 the same four 1 x 1 m sub-plots within the 20 x 20 m plots. Within each
 sub-plot, we identified the species and cultivars of all herbaceous plants
 and measured the percent cover for each species and cultivar. This was
 measured once per month for five sampling periods, separated by roughly 21
 days. As with gardener-reported plant data, researchers classified
 field-based plant data into either crop or ornamental species and
 cultivars. Plants that did not fit crop or ornamental categories were
 designated weeds. Gardeners were not asked to report any weeds, thus not
 classified in gardener-reported plant data.
 <em>Bird data</em> All bird
 surveys were conducted by one observer (PB) at each sampling period (see
 Mayorga et al., 2020). In each garden, this person performed a 10-minute
 point count. Due to small sizes and irregular shape of some gardens,
 fixed-radius point counts were not used. Instead, the observer stood
 approximately at the center of each garden and recorded all birds seen or
 heard within the garden.  We assumed that birds within 30 m that were
 heard but not seen, were in the plot unless visually observed to be
 outside of the plot. Each site was visited during different times during
 daylight hours (i.e. morning, afternoon, evening) across sample periods to
 reduce bias in the survey. All birds that were seen or heard inside the
 garden were identified, and totals were calculated for each
 round. <em>Bee data </em>
 We sampled bees with both elevated pan traps and hand
 netting (Grundel et al. 2011), using 400 ml plastic bowls (yellow, white,
 and blue) painted with Clear Neon Brand and Clear UV spray paint for pan
 traps (see Quistberg et al. 2016). We placed pan traps from approximately
 8-11 AM and collected traps between 4-7 PM on the same day, and sampling
 was repeated 5 times across the summer. We placed three 1 m tall PVC pipes
 in the ground in a triangle formation, 5 m apart within each of the 20 x
 20 m plots and placed one bowl of each color on top of PVC tubes (Tuell
 and Isaacs 2009). We filled bowls with 300 ml of water and 4 ml of
 unscented Dawn dish soap. In addition, we sampled bees using aerial nets
 at each site for a total of 30 min per site, not including handling time.
 We netted bees that were observed on flowers, within 20 m of and inside
 the 20 x 20 m plots in each site. We stored all captured bees for later
 identification. We performed bee identifications with reference to online
 resources, image databases, books, and dichotomous keys (Ascher and
 Pickering, 2015; Frankie et al. 2014; Gibbs, 2010; Michener, 2007). We identified all specimens to the
 highest taxonomic level possible or designated morphospecies. We compared
 our specimens to those held in the Kenneth S. Norris Center for Natural
 History on the University of California, Santa Cruz campus. All voucher
 specimens are housed in the Philpott Lab at the University of California,
 Santa Cruz.   <strong>Defining
 rarity </strong> We considered a species or
 cultivar as rare if it occupied less than or equal to 1% of all samples
 (n=18 garden samples for field-based data for each of 5 sampling rounds
 and n=185 gardener survey samples for gardener-reported data) (as
 per (Lyons et al., 2005)). In our study, a rare species or cultivar was
 found in only one of all 18 sites sampled (1/18 is less than or equal to
 5%, which is the lowest occurrence rate possible for our sample size) or
 was reported in only 2 of all 185 gardener surveys (2/185 rounds down to
 1%) for gardener-reported plant data. To assess whether we adequately
 sampled the biodiversity of each taxon and sampling scheme, species
 accumulation curves were produced for the gardener-reported plant data,
 and field-based plant, bird, and bee biodiversity data (Figures S1-4). We
 tallied the number of rare species and cultivars and compared this to
 total numbers of species or cultivars documented for each taxonomic group
 and sampling protocol to determine the extent of rarity we observed in
 urban gardens. Full lists of rare and common cultivars and species and
 their frequencies are available in Appendix S1: Tables S2-9.
 <strong>Analysis </strong> We
 constructed four generalized linear mixed models (GLMM) that together
 address our questions in Figure 1 (Bolker et al., 2009). We used these
 four models to predict the number of rare gardener-reported plant,
 field-based plant, bird, and bee species or cultivars as a function of the
 following fixed and random effects:
 <em>r<sub>plant-reported
 2017</sub></em> ~ gardener characteristics +
 (1|garden)+ <em style="font-family:'Times New Roman' , serif;font-size:medium;">x</em>
                                       (1)
 <em>r<sub>plant-field 2015</sub></em> ~
 garden characteristics +
 <em>r<sub>bee</sub> </em>+
 <em>r<sub>bird</sub> </em>+ (1|round) + <em style="font-family:'Times New Roman' , serif;font-size:medium;">x</em>
                           (2)
 <em>r<sub>bee</sub> </em>~ garden
 characteristics +<em> r<sub>plant-field
 2015</sub> </em>+
 <em>r<sub>bird</sub> </em>+ (1|round) +
 <em>X</em>                          (3)
 <em>r<sub>bird</sub> </em>~ garden
 characteristics + <em>r<sub>bee</sub></em> +
 <em>r<sub>plant-field 2015</sub></em> + (1|round)
 + <em>X</em>                          (4)
 Where r is the number of rare plant cultivars reported by
 gardeners or rare plant cultivars, bee, and bird species observed in
 field-based surveys. We used garden as a random effect for
 gardener-reported data (Eq. 1) and sampling round as a random effect for
 field-based data (Eqs. 2-4) because the number of rare species or
 cultivars is calculated by garden for field-based data and by survey for
 gardener-reported data. We assumed Poisson error distributions, <em style="font-family:'Times New Roman' , serif;font-size:medium;">x </em>, for
 plant count data and transformed our rare bird and bee observations into a
 binomial presence/absence variable as most counts (97.6% of rare birds and
 98.9% of rare bees) were 1 or 0. Thus, we assumed binomial error
 distributions, <em>X</em>, for the bird and bee count
 data.  Our fixed effects include gardener, garden
 characteristics, and cross-taxonomic effects. Gardener and garden
 characteristics are composed of several variables detailed below. For all
 fixed effects, we utilized a VIF cut-off of three to remove any collinear
 variables (Zuur et al. 2009). Categorical variables were coded in an
 ordinal format when appropriate (see Table 1) as per (Hildebrand et al.
 1977). All analyses and figures were run and generated using the R
 environment using packages tidyverse, lme4, ggpubr, car, corrplot, and
 vegan (Bates et al., 2015, R Core Team, 2016, Wei and Simko, 2017, Fox and
 Weisberg, 2019, Wickham et al., 2019, Alboukadel 2020, and Oksanen et al.,
 2020). Gardener
 characteristics We used the
 sociodemographic variables described in Table 1 as fixed effects to
 predict the number of gardener-reported rare plant cultivars (Eq. 1).
 After removing collinear variables, the final fixed effects for gardener
 traits included age, number of people in the family, gender, number of
 languages other than English, distance of home to the garden, income,
 education, number of years gardening, number of hours gardening, and food
 insecurity.  Garden
 characteristics  Several local and
 landscape-level garden characteristics were measured and broadly divided
 into three groups: permanent garden and landscape variables, woody
 vegetation variables, and ground cover variables. The full list of
 measured variables is available above in methods. In each group, we tested
 for collinearity between variables, and then among collinear sets of
 variables, we retained variables for final models based on perceived
 importance for the taxa in this system  (Quistberg et al. 2016, Burks and
 Philpott 2017). For example, in the permanent garden and landscape group,
 size and age of gardens are anticipated to influence the biogeography and
 microclimates experienced by taxa at garden sites (Smith et al. 2005,
 Potter and LeBuhn 2015). We are also interested in urbanization, thus
 selected the variable % urban cover over the variable % agriculture cover,
 which were correlated. We chose % canopy cover for vegetation
 characteristics because this is a widely used metric in other studies, it
 is positively correlated with the number of trees and shrubs, and we did
 not want to use the number of trees and shrubs since this variable is also
 a component of our dependent variable, the number of rare plant species.
 In the category of ground cover, we chose percent bare soil because there
 is evidence from the literature that many bee species are strongly
 influenced by this metric and it is negatively correlated with percent
 mulch and straw (Quistberg et al. 2016). We excluded percent herbaceous
 plants in this category because of the potential conflict with our
 dependent variable, the number of rare plant species and cultivars. The
 same garden characteristic variables were used to predict all rare taxa
 using (Eqs. 2-4). From here on, we only discuss variables that were
 retained in our final models. These include garden age, garden size, %
 urban at 1km, % canopy cover, and % bare soil. Testing whether rarity
 begets rarity Since we are interested in
 whether the number of rare species and cultivars of plants, birds, and
 bees are associated, our GLMMs also include the number of rare plant,
 bird, and bee species or cultivars as explanatory variables where
 appropriate (Eqs. 2-4). In addition to these models, which test for
 potential causal relationships, we ran Pearson’s r tests to assess
 correlations between the numbers of rare species or cultivars across taxa
 (Fig. S1). When correlating each pair of taxa, we constrained our analysis
 to include only data that were collected during the same sampling rounds
 and gardens.