DOI: 10.5061/dryad.9ghx3fft0
title: Data from: Native cover crops enhance biodiversity and ecosystem services in hazelnut orchards

description: Agroecological restoration aims to restore biodiversity and ecosystem
 function in agricultural landscapes while sustaining crop production.
 Adopting native plants as cover crops may restore ecological value to
 cropping systems such as nut orchards. We focused on Oregon
 hazelnut orchards and compared how four seed mixes (native annuals, native
 perennials, conventional cover crops, and unseeded controls) performed
 under three levels of orchard floor disturbance (flailing, flailing and
 scraping, and unmanaged/none) across three different orchard ages with
 corresponding differences in canopy shade over a two-year
 period. We evaluated cover crop performance by three criteria:
 the survival criteria (response to disturbance and shading), the
 production criteria (effects on weeds, erosion potential, and soil
 moisture) and the ecological functioning criteria (abundance and diversity
 of native plants and pollinator visitations). We found that native species
 generally outperformed conventional cover crops and bare ground across
 these criteria. Plant survival was not affected by disturbance
 but shading reduced survival of most species. Native annuals had high
 cover in the first year, and native perennials had high cover across both
 years. Native perennial species provided the best weed reduction and
 erosion control while not reducing soil moisture, and hosted the highest
 pollinator abundances and diversity. Synthesis and
 Applications: Our results suggest that agroecological restoration
 of orchards through native cover cropping is a viable strategy for
 improving ecological outcomes without compromising production needs.
description: <em>Data collection</em> To
 evaluate cover crop survival, we visually estimated percent cover of each
 species at peak biomass in May 2020 and 2021. To quantify shading, we
 measured the canopy cover of each subplot in July each year using Canopeo
 (Patrignani & Ochsner 2015), a mobile application that analyzes
 fractional green canopy cover from digital images. Imagery was gathered in
 July 2020 at the subplot-level by taking upward-facing photos at chest
 height. This was repeated in July 2021, following an ice storm in February
 2021 that broke tree limbs and reduced canopy cover, especially in the
 40-year-old orchard. Because shading in a portion of the 15-year-old
 orchard was increased by rows of taller vegetation to the south (Figure
 S1) we estimated the proportion of plots that were in shade from adjacent
 trees at noon during mid-spring monitoring. Dense shade affected
 approximately 60% of three plots, leading us to calculate canopy cover as
 a weighted average within the subplot (40%) and the adjacent vegetation
 (60%). We addressed our production criteria by
 monitoring weed (any non-planted, volunteer vegetation) cover, total
 winter vegetation cover, and soil moisture in each subplot. We visually
 estimated plant cover of each present species each spring. We estimated
 winter vegetation and bare ground in January of the second year, after
 perennial plants had established. Winter vegetation cover was used as a
 proxy for erosion as it affects the extent to which soil is bare and
 vulnerable to winter rains. We measured soil moisture as volumetric water
 content (VWC) to a depth of 15cm over two time periods: weekly from
 April-May 2020 and monthly from March-August 2021 (Figure S4). In this
 critical period rainfall is reduced but cover crops are abundant, and thus
 most likely increase water stress in trees. To minimize spatial
 variability in VWC, we replaced the probes of Campbell Scientific
 HydroSenseII moisture meter with 1 cm threaded bolts which were touched to
 two six-inch steel box nails permanently embedded in the center of each
 subplot (Grinath et al. 2019). To calibrate the nail measurements, we took
 a subset of measurements with both nails and probes at the same location
 over time and fit a linear regression (Figure S2). To
 assess how the timing of species flowering and senescence aligned with
 management practices (such as flailing and pesticide application), which
 is relevant for all three criteria, we evaluated phenology from April
 through August 2021. Each month, we recorded the life stage of each
 species by the following categories: pre-flowering vegetative, first
 flower, peak flowering, last flower, post-flower vegetative, or senesced.
 First and last flower were both defined as approximately 10% of flower
 buds open. At peak flower, we estimated floral abundance as the number of
 inflorescences per target species to the nearest category: 1, 2, 5, 10,
 25, 50, and intervals of 50 thereafter. We asked the farm to
 observationally record whether any combination of species and treatment
 interfered with harvest. Finally, to evaluate our
 ecological functioning criteria we monitored pollinator visitations within
 each cover crop subplot in June and July of 2020, and monthly from April
 through August 2021. We used observational surveys to quantify abundance
 and aerial net collections to quantify diversity. We surveyed during dry
 conditions with partial or no cloud cover between 12pm and 6pm, recording
 each pollinator morphospecies visitation and its host plant for two
 minutes. We also conducted two-minute aerial net collections of
 pollinators in each subplot, excluding honeybees and queen bumblebees.
 Samples were classified to order using a dissecting microscope, except the
 primary contributors to pollination, Anthophila and Syrphidae (Ssymank et
 al. 2008; Youngsteadt 2020), which were identified to genus using Jackson
 (2019) and Miranda et al. (2013). <em>Data
 Analysis</em> All data cleaning and analyses were
 carried out using R version 4.2.3 (R Core Team 2023). We used field data
 to separately evaluate the three cover crop success criteria in this
 study, namely criteria related to survival, agricultural production, and
 ecological functioning. First, to evaluate survival
 criteria, we focused on the aggregate cover of seed mixes (summed percent
 cover of each species included within a seed mix) and its change across
 time. Then, we created a model using the <em>lmer
 </em>function from package <em>lme4</em> (Bates et al.
 2015) using aggregate seed mix cover as a response variable and seed mix,
 management treatment, and orchard age (as a rough proxy for shading), as
 fixed factors, and year and block as random factors. We evaluated the
 model using <em>check_model</em> and other functions from
 package <em>performance</em> (Lüdecke at al. 2021). We
 detected some non-normality of residuals. Consequently, we used package
 g<em>lmmTMB </em>(Brooks et al. 2017) to create models with
 the same set of predictors but with different error distributions (in
 particular, Tweedie and generalized Poisson distributions). These models
 resulted in qualitatively similar results, indicating that the results of
 our original model were robust. We proceeded with the original
 <em>lmer</em> model, because of its ease of interpretation. We
 evaluated differences between groups with post-hoc Tukey tests using
 <em>emmeans</em> from package <em>lsmeans
 </em>(Lenth 2016). We also evaluated survival
 criteria by assessing the survival of individual species within our seed
 mixes across orchard canopy density. We built separate models for each
 species and each year of the study to describe the relationship between
 species cover (response) and orchard canopy cover (predictor), including
 both a linear and quadratic term for orchard canopy (using the
 <em>lm </em>and <em>poly </em>functions from
 package <em>stats</em> (R Core Team 2023)). If the quadratic
 predictor was not significant, we proceeded to use only the linear term;
 otherwise we used both the linear and quadratic predictor. Because of
 species dispersal between year one and year two, our data from the second
 year includes observations made outside of each species’ subplots but
 within their respective main plot. To evaluate
 production criteria, we analyzed cover crop effects on spring weed cover
 and total winter vegetation cover (as a proxy for erosion reduction). For
 both spring weeds and winter ground cover, we created linear mixed models
 using <em>lmer</em>. Models used orchard canopy density and
 seed mix treatment as fixed effects and year as a random effect. For
 spring weeds, the response variable was the difference in spring weed
 cover between each respective seed mix and the control treatment (lower
 values represent greater weed control compared to the control treatment).
 For winter vegetation, the response variable was the difference in bare
 ground cover between each respective seed mix and the control treatment
 (lower values represent increased ground cover relative to the unseeded
 control and therefore reduced potential for soil erosion). We included
 only winter vegetation data from 2021, to evaluate perennial plant
 establishment in the second year of the study. We included orchard canopy
 cover and year in the models, because associated environmental data can
 affect baseline weed pressure (Figure S3). We also checked model
 assumptions and created alternative models, similar to as described above,
 but we again proceeded with our original model as the most parsimonious
 and easiest to interpret. We also assessed production
 criteria by evaluating the effect of cover crops on available soil
 moisture. We used mixed models to compare the effect of seed mix treatment
 on VWC using for each year separately. We used seed mix, time period (week
 in 2020, month in 2021) and their interaction as fixed effects and orchard
 and block as random effects. We highlighted a common reference period of
 April 1 through May 17 for comparison between years (Figure S4, red
 boxes). To contextualize soil moisture with climate, we downloaded daily
 precipitation data from PRISM and calculated weekly mean precipitation
 during each sampling period (PRISM Climate Group 2022). Because shading
 has a strong influence on soil moisture, we could not disentangle the
 affects of shading and seed mix in the inconsistently shaded 15-year-old
 orchard, which we dropped from all moisture analyses.
 To assess how cover crops altered ecological functioning, we
 first evaluated floral abundance and phenology. We aggregated species
 flowering estimates at the subplot level each month of the second year and
 compared overall floral abundance between seed mixes using mixed models
 with orchard age, seed mix and their interaction as fixed effects and
 block nested in month as random effects. To compare floral phenology
 between seed mixes, we used separate mixed models for each orchard age (as
 shade influenced phenology) with floral abundance as the response
 variable, month, seed mix and their interaction as fixed factors and block
 as a random effect. Because flailing and scraping started between our June
 and July monitoring surveys (which removed flower heads), our models use
 data from all plots through June, and only from unmanaged plots
 thereafter. Second, we evaluated pollinator abundance
 and diversity. To characterize pollinator abundance, we summed visitations
 from the observational surveys by host (including weeds) each month. To
 characterize diversity, we calculated pollinator richness (the number of
 unique taxa observed) within host, subplot, and month. For both responses,
 we constructed models with the same predictive factors as our floral
 abundance and phenology models. To compare the relationship between floral
 abundance and pollinator visitations independent of time we ran a linear
 regression of floral abundance and pollinator visitations within each
 orchard.
description: # Data from: Native cover crops enhance biodiversity and ecosystem
 services in hazelnut orchards
 [https://doi.org/10.5061/dryad.9ghx3fft0](https://doi.org/10.5061/dryad.9ghx3fft0) ## Description of the data and file structure We experimentally tested native cover crops as a tool for agroecological restoration in Oregon hazelnut orchards, with an eye toward restoring oak-prairie understory species and associated pollinators. Because of farmland development in the fertile Willamette Valley, oak-prairie communities are limited to marginal sites representing 2% of their historical range (Wright 2020). New orchards, which are rapidly increasing (NASS 2017), have open canopies that mirror those of open prairies, while established orchards share similar canopy structure to oak woodlands. First, we examined which species of native annual and perennial cover crops can survive (Figure 1a; survival criteria), considering how orchard floor management and canopy shade affect survival. Second, we evaluated how cover crops impact agricultural production (Figure 1b; production criteria), considering their ability to exclude weeds, reduce erosion potential, and modify soil moisture. Finally, we assessed cover crop contributions to ecological functioning by hosting native pollinators (Figure 1c; ecological functioning criteria). ### Files and variables #### File: canopy\_cover.csv **Description:** measurement of canopy cover over research plots. ##### Variables * orchard_age: maturity of orchard (15, 40 or 60 years old) * block: replicates within each orchard * management: whether plots have been flailed, scraped, or neither * seedmix: whether the plot was seeded with a native annual mix, native perennial mix, conventional cover crops, or nothing * canopy: canopy cover in percent * year: year that data was collected #### File: community\_cover.csv **Description:** addressing ground level community variation. Some cells have blank values.  This represents plots where the species indicated in the rows were not seeded.  ##### Variables * orchard_age: see above * block:see above * management:see above * seedmix:see above * bare: percent bare ground * weeds: percent weeds * amsinckia: % * plectritis:% * collomia:% * clarkia:% * epilobium:% * gilia:% * sanguisorba:% * lotus:% * achillea:% * geum:% * prunella:% * potentilla:% * eriophyllum:% * agoseris:% * viola:% * lomatium:% * barley:% * oats:% * vetch:% * clover:% * all_annuals: sum of all annnual cover * all_perennials: sum of all perennial cover * notes: #### File: collectedpollinators\_2021.csv **Description:** pollinators collected from plots with netting methods ##### Variables * Day: of sampling * Month: of sampling * Orchard Age: see above * Block:see above * Management:see above * Seed Mix:see above * Host Plant: plant pollinator was gathered from * Order: of pollinator * Genus: of pollinator * Notes: * Count: for multiples #### File: community\_cover21.csv **Description:** raw data fed into community cover dataset, from just 2021. same columns.  Some cells have blank values.  This represents plots where the species indicated in the rows were not seeded.  ##### Variables * orchard_age: * block: * management: * seedmix: * bare: * weeds: * amsinckia: * plectritis: * collomia: * clarkia: * epilobium: * gilia: * sanguisorba: * lotus: * achillea: * geum: * prunella: * potentilla: * eriophyllum: * agoseris: * viola: * lomatium: * barley: * oats: * vetch: * clover: * carex: * festuca: * danthonia: * notes: some values are blank where no notes were made #### File: soil\_moisture\_2020.csv **Description:** measurements of soil moisture in plots in 2020.  ##### Variables * Orchard Age: see above * Block:see above * Management:see above * Seeding Plot:see above * Soil moisture (VWC%/US) 4/7/2020: volumetric water content measurment in plot on 04/07/2020 * Soil moisture 4/24-25/2020: see above, new sampling date (same for next three columns) * Soil Moisture 5/1-2/20: * Soil Moisture 5/10/20: * Soil Moisture 5/17/20: #### File: floral.csv **Description:** estimates of floral resource availability across plots over time. cells with blank values were plots where the indicated species were not found.  ##### Variables * orchardage: see above * block: see above * management: see above * amsmen: estimate number of flowers. column name is a species code (Amsinckia mensesii).  same for the rest of the columns below.  * plecon: * colgra: * clapur: * epiden: * gilcap: * sanann: * lotpur: * achmil: * geumac: * pruvul: * potgra: * erilan: * agogra: * viopra: * lomnud: * barley: * oats: * vetch: * clover: * cartum: * fesroe: * dancal: ## Code/software All analysis conducted in R. Comments provided in scripts to guide analysis.