Pruning of Small Fruit Crops Can Affect Habitat Suitability for Drosophila Suzukii

Agriculture, Ecosystems and Environment 294 (2020) 106860

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Agriculture, Ecosystems and Environment

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Pruning of small fruit crops can affect habitat suitability for Drosophila T suzukii Torsten Schöneberga,1, Arielle Arsenault-Benoita,1, Christopher M. Taylora, Bryan R. Butlerb, Daniel T. Daltonc, Vaughn M. Waltonc, Andrew Petrand, Mary A. Rogersd, Lauren M. Diepenbrocke, Hannah J. Burrackf, Heather Leachg, Steven Van Timmereng, Philip D. Fanningg, Rufus Isaacsg, Brian E. Gressh, Mark P. Boldai, Frank G. Zalomh, Craig R. Roubosj, Richard K. Evansj, Ashfaq A. Sialj, Kelly A. Hambya,* a Department of Entomology, University of Maryland, College Park, MD, 20742, USA b Department of Plant Science and Landscape Architecture, College Park, MD, 20742, USA c Department of Horticulture, Oregon State University, Corvallis, OR, 97331, USA d Department of Horticultural Science, University of Minnesota, Saint Paul, MN, 55108, USA e Entomology and Nematology Department, University of Florida, Lake Alfred, FL, 33850, USA f Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA g Department of Entomology, Michigan State University, East Lansing, MI, 48824, USA h Department of Entomology and Nematology, University of California, Davis, CA, 95616, USA i University of California Cooperative Extension, Santa Cruz County, Watsonville, CA, 95076, USA j Department of Entomology, University of Georgia, Athens, GA 30602, USA

ARTICLE INFO ABSTRACT

Keywords: Insect activity, survival, and development are affected by climatic conditions that elicit effects at multiple scales. Spotted-wing drosophila Pruning small fruit crop canopies alters the microclimate, which in turn may influence insect pest activity. We Blackberry investigated the effect of three canopy density treatments (high, medium, low)on Drosophila suzukii Blueberry (Matsumura) (Diptera: Drosophilidae) fruit infestation in blueberries and caneberries using a two-year, multi- Raspberry state experiment. We quantified the effect of canopy density on canopy microclimate, fruit quality (total soluble Canopy microclimate solids, fruit firmness), and yield. To better understand heterogeneity in canopy microclimate, parameters were Cultural management further separated by canopy location (exterior vs. interior) in Maryland. In both crops, meta-analyses reveal small magnitude effects of the plant canopy on microclimate, whereas analysis of variance did not separate these effects, with mean canopy differences of 0.1–0.7 °C and 0.5–1.3 % relative humidity (RH) between caneberry canopy densities and locations. In caneberry multi-state trials, 0.14 fewer D. suzukii larvae (g fruit)−1 occurred on average in the low canopy density treatment, and 0.2 fewer D. suzukii larvae (g fruit)−1 occurred in exterior raspberries in Maryland compared with the canopy interior. Artificially infested blueberry fruit indicated immature D. suzukii survival within fruit can vary across canopy densities and locations. Although lower total yield was produced in low density canopies, canopy density did not influence berry quality or marketable yield. Microhabitats provide important shelter from extreme environmental conditions; the availability of shelter and ability to locate it affects insect pest populations and distributions. Understanding how crop canopy microclimate affects D. suzukii infestation can inform efforts to develop habitat manipulation tactics and improve the efficiency of fruit production.

1. Introduction Rinehart et al., 2000; Henderson and Roitberg, 2006). Temperature and relative humidity (RH) influence development rate, phenology, fitness, As poikilothermic organisms, insects are particularly sensitive to population size, distribution, activity, and habitat selection (Bach, fluctuations and extremes in climatic conditions (Ferro et al., 1979; 1993; Drake, 1994; Henderson and Roitberg, 2006; Cui et al., 2008; Li

⁎ Corresponding author at: Department of Entomology, 4112 Plant Sciences, College Park, MD, 20742, USA. E-mail address: [email protected] (K.A. Hamby). 1 Torsten Schöneberg and Arielle Arsenault-Benoit contributed equally to this work. https://doi.org/10.1016/j.agee.2020.106860 Received 1 September 2019; Received in revised form 2 February 2020; Accepted 7 February 2020 Available online 02 March 2020 0167-8809/ © 2020 Elsevier B.V. All rights reserved. T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860 et al., 2011; Zhang et al., 2013). To avoid climate stress and thermo- range expansion into new geographic regions has disrupted integrated regulate, insects seek optimal microhabitats (Larmuth, 1979; Leimar pest management (IPM) programs, increasing insecticide sprays in et al., 2003; Henderson and Roitberg, 2006; Adar et al., 2016) in their susceptible crops (Beers et al., 2011; Asplen et al., 2015; Diepenbrock mobile life stages. The availability and accessibility of heterogenous et al., 2017). Insecticides are currently the primary and most efficacious microhabitats underlies ectotherms’ ability to behaviorally buffer management approach in both conventional (Haye et al., 2016) and against climate extremes (Roslin et al., 2009; Scheffers et al., 2014; organic (Sial et al., 2019) systems, and alternative strategies that are Sears et al., 2016). Microclimate heterogeneity occurs at the scale of a economically and environmentally sustainable are needed. In organic single leaf (Ferro et al., 1979; Pincebourde and Casas, 2019), within the production systems where chemical control options are limited, it is plant canopy (Pincebourde et al., 2007), and within a habitat (Suggitt particularly important to implement cultural controls, such as pruning, et al., 2011; Ulyshen, 2011). While scale and time can reduce the to minimize D. suzukii populations. magnitude of climate differences (Faye et al., 2014; Pincebourde and Drosophila suzukii is sensitive to abiotic conditions, and adult ac- Casas, 2019), a wide range of microclimates may occur at distances less tivity within crops correlates with time of day, temperature, and re- than 40 cm (Faye et al., 2017). In agroecosystems, crop microclimate lative humidity. In hot, dry climates D. suzukii captures peak in spring contributes to insect pest distributions and abundances (Saxena and and fall, with less activity during the summer (Wang et al., 2016). Even Saxena, 1975; Ferro et al., 1979; Willmer et al., 1996; Henderson and during the summer when unfavorably hot weather occurs, cooler per- Roitberg, 2006), and affects the success of biological control agents iods of the day (morning and evening hours) are often favorable for (Suh et al., 2002; Shipp et al., 2003). Plant size, architecture, growth activity (Wang et al., 2016; Swoboda-Bhattarai and Burrack, 2020). pattern, and canopy porosity determine canopy microclimate char- Drosophila suzukii adult activity usually occurs in the early morning and acteristics including light penetration, aeration, temperature, humidity, around twilight, during mild temperature and high humidity conditions leaf wetness, and capacity for buffering climate fluctuations (Huber and (Evans et al., 2017; Van Timmeren et al., 2017b; Jaffe and Guédot, Gillespie, 1992; Willaume et al., 2004; Pincebourde et al., 2007; 2019; Tait et al., 2019; Swoboda-Bhattarai and Burrack, 2020). Droso- McDonald et al., 2013); these plant characteristics can be manipulated phila suzukii exhibits optimal development between 22–28 °C, with the through breeding and cultural practices, and can be exploited for pest highest reproductive rate at approximately 23 °C (Ryan et al., 2016). In management (Altieri, 1983; Simon et al., 2006, 2007). a laboratory experiment, Ryan et al. (2016) showed that D. suzukii adult Cultural modification of crop architecture can directly remove in- life span is lowered, no oviposition occurs, and no adults emerge from sect pests as well as reduce their recruitment and survival. For example, eggs exposed to constant temperatures above 30 °C, with no pupation leaf removal in grape (Vitis vinifera L.) vineyards can simultaneously occurring above 31 °C. Simulated “heat waves” with fluctuating tem- remove leafhopper nymphs (Stapleton et al., 1990), and thinning cuts perature and relative humidity also reduce D. suzukii survival and dis- of fruiting spurs may remove aphids in apples (Malus x domestica Borkh) rupt egg production (Eben et al., 2018). Relative humidity in- (Simon et al., 2006). Summer pruning and growth regulators reduce dependently influences D. suzukii survival and habitat preferences. In Lobesia botrana (Denis and Schiffermüller) (Lepidoptera: Tortricidae) the laboratory, female D. suzukii could survive more than 20 days at infestation relative to untreated grapevines (V. vinifera)(Vartholomaiou 71–94 %RH, whereas survival was less than three days between 20–33 et al., 2008), and looser clusters are less favorable to larvae (Fermaud, %RH (Tochen et al., 2016). Drosophila suzukii adults sense humidity, 1998; Vartholomaiou et al., 2008). Canopy complexity and number of favoring high humidity over low humidity in laboratory bioassays branching points also influence dispersal of pests (Simon et al., 2012) as (Fanning et al., 2019). In the field, adults appear to seek areas of high well as host finding by biological control agents (Gingras and Boivin, humidity and low temperature for refuge from unfavorable climatic 2002; Riihimäki et al., 2006). Canopy porosity, size, and structure af- conditions. For example, D. suzukii adults are more active in the lower fect the movement of pesticides and pheromones through plant ca- and interior parts of raspberry (Rubus idaeus L.) canopies (Rice et al., nopies (Simon et al., 2007), with smaller and more open canopies im- 2017), which are likely shadier and more humid. Additionally, higher proving spray coverage (VanEe et al., 2000; Cross et al., 2001). D. suzukii infestation occurs in interior blackberry (Rubus subgenus Although pruning to adjust canopy architecture is common practice Rubus Watson) fruit (Diepenbrock and Burrack, 2017), which may re- in fruit crops and influences pest pressure and management activities, sult from differences in adult egg-laying activity, larval success, ora pest management is rarely the primary reason for pruning. Pruning is combination of both factors. Therefore, it may be possible to manip- instead conducted with horticultural goals such as plant health and ulate crop architecture and microclimate to reduce favorability for D. vigor, improved fruit quality and yield, and production efficiency. In suzukii, while maintaining the productivity of fruit crops. caneberries (Rubus L. spp.), canopies are manipulated via spacing, In this study, we explored canopy manipulation as a potential training, and trellising, which can increase light penetration and air management strategy to reduce D. suzukii infestation and worked to circulation throughout the plant and reduce humidity (Prange and understand how changes in microclimate might affect damage caused DeEll, 1997; Strik and Cahn, 1999). Further, an open, manageable ca- by D. suzukii. We predicted that pruning host crop canopies to low, nopy allows for more efficient harvest with less overripe fruit remaining medium, or high densities would significantly alter the microclimate (Prange and DeEll, 1997; Strik and Cahn, 1999). Commercial blue- within the canopy, and that low density canopies would be less hos- berries (Vaccinium L. spp.) are typically pruned moderately to balance pitable to D. suzukii. We examined the impact of pruning on tempera- yield, fruit size, plant health, and harvest efficiency (Hanson et al., ture and RH, D. suzukii fruit infestation, yield and fruit quality in or- 2000; Strik et al., 2003). Pruning of old wood as well as dead, damaged, ganic berry systems. or shaded branches low to the ground reduces crowding; increasing light and air circulation (Prange and DeEll, 1997; Hanson et al., 2000). 2. Materials and methods Additionally, flowering and fruiting phenology can be altered bythe timing of pruning, as flowering is initiated by temperature, light, and 2.1. Sites and experimental design plant vigor (Oliveira et al., 2004). Thus, canopy architecture affects fruit quality and yield in addition to microclimate parameters such as From 2015 to 2017, experiments with small fruit crops susceptible light penetration, temperature, and humidity. to D. suzukii were conducted concurrently at grower cooperator farms An invasive pest of soft-skinned fruits, Drosophila suzukii and university research facilities in seven U.S. states (California, (Matsumura) (Diptera: Drosophilidae) oviposits in ripening, otherwise Georgia, Maryland, Michigan, Minnesota, North Carolina, and Oregon) marketable fruit prior to harvest and causes severe economic damage to examine the effect of pruning on D. suzukii infestation (Table 1). All due to the zero tolerance for larvae in fresh market fruit (Goodhue experiments were conducted in organic or unsprayed production sys- et al., 2011; Asplen et al., 2015; Haye et al., 2016). Its introduction and tems with a minimum of three canopy density treatment replicates per

2 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860

Table 1 Overview of multi-state experiment replication, harvest weeks1 and area sampled in 2015–2017.

Crop group Year State Sites Treatment Replicates Harvest Weeks Crop Area sampled

Caneberry 2016 North Carolina 1 4 5 Rubus spp. 6.1 m 2016 Michigan 1 3 4–7 Rubus idaeus 6.0 m 2016 Maryland 2 3/site 12 Rubus idaeus 1.5 m

2017 California 1 4 3 Rubus idaeus 2.3 m 2017 North Carolina 1 4 6–8 Rubus spp. 6.1 m 2017 Michigan 1 4 7 Rubus idaeus 6.0 m 2017 Maryland 2 2–3/site 12 Rubus idaeus 1.5 m

Blueberry 2015 Oregon 1 3 1 Vaccinium corymbosum 1 bush

2016 Oregon 1 3 1 Vaccinium corymbosum 1 bush 2016 Georgia 1 3 3 Vaccinium virgatum 1 bush 2016 Michigan 1 4 2 Vaccinium corymbosum 2 bushes 2016 Minnesota 1 3 2–4 Vaccinium corymbosum 1 bush 2016 Maryland 2 3/site 3–6 Vaccinium corymbosum 1 bush

2017 Oregon 1 3 1 Vaccinium corymbosum 1 bush 2017 Georgia 1 3 3–4 Vaccinium virgatum 1 bush 2017 Michigan 1 4 3–4 Vaccinium corymbosum 4 bushes 2017 Minnesota 1 3 5 Vaccinium corymbosum 1 bush 2017 Maryland 2 3/site 3–8 Vaccinium corymbosum 1 bush

1 Consecutively numbered sampling week. site. Horticultural practices and management of the crops in our study virgatum Aiton) plants were qualitatively pruned to high (25 % less than varied considerably by crop group, cultivar, and region. growers’ standard), medium (growers’ standard), and low density (25 % Caneberry canopies were qualitatively (MD, CA) or quantitatively more than growers’ standard). Site-specific information and further (MI, NC) pruned to high, medium, or low density, which was de- details can be found in supplementary material section 1.2 (blue- termined in each state using the standard practice and growth habit of berries). the cultivar. In Maryland and California primocane raspberries (R. The duration of harvest and response variables assessed varied by idaeus), experts qualitatively pruned to high (growers’ standard: no site (Table 2) and included fruit temperature, firmness, total soluble pruning), medium (∼25 % of canopy removed), and low (∼50 % ca- solids (TSS), D. suzukii infestation, and yield. Except where otherwise nopy removed) density canopies. Michigan floricane raspberries (R. noted, statistical analysis was conducted with ‘R Studio’ (R Studio idaeus) were quantitatively pruned by retaining 0 floricanes in the low Team, 2016) version 1.1.463. Due to variability in the amount of density treatment, 30 floricanes in the medium density treatment sampling between sites and years, a ‘siteyear’ random factor that (growers’ standard) and 60 floricanes in the high density treatment. combined site and year (e.g., MD-Site1-2017), was created to reduce North Carolina floricane blackberries (Rubus subgenus Rubus) were missing data in the model. Any remaining missing data were removed pruned quantitatively by retaining four floricanes in the low density case-wise. Data were averaged across sampling weeks, except where treatment (33 % less than growers’ standard), six floricanes in the otherwise noted. A linear mixed effects model was created for each crop medium density treatment (growers’ standard) and eight floricanes in [R-package ‘lme4’ (Bates et al., 2015)] that included canopy density the high density treatment (33 % more than growers’ standard). For (high, medium, low) as a fixed factor, with ‘siteyear’ as a random factor. caneberries, pruning treatments were applied to lengths of row, and Homogeneity of variance and normality of residuals were checked data were collected from both sides of the row, except for Michigan using Levene’s and Shapiro-Wilk tests, respectively. Data transforma- raspberries in 2017 where only one side of the row was sampled. Site- tion was performed when necessary based on an analysis of transfor- specific horticultural information and further details can be found in mation by Box and Cox (1964) [R-package ‘boxcoxmix’ (Almohaimeed supplementary material section 1.1 (caneberries). and Einbeck, 2018)] to meet model assumptions. If no effective trans- For blueberries, treatments were applied at the bush level, and a formation could be found, non-parametric approaches were used. center data collection plant was flanked on each side by at least one buffer plant of the same pruning regime, except for one site in Maryland 2.2. Effect of canopy density on fruit temperature with insufficient bushes for buffer plants. Seasonal timing, standard practice, and crop management varied by site, crop, and region. In To determine how pruning regime affected fruit temperature, the Maryland, highbush blueberry (Vaccinium corymbosum L.) plants were internal temperature was measured for a random subsample (3–10 pruned qualitatively to high (growers’ standard: no canopy removed), caneberries; 5 blueberries) of harvested fruit in the field. In 2016, medium (∼25 % canopy removed), and low (∼50 % canopy removed) measurements were only taken in Maryland raspberries and blue- density canopies. In Michigan, blueberry (V. corymbosum) plants were berries, while in 2017 all states except Minnesota and Oregon partici- pruned to high (no pruning), medium (growers’ standard: all small pated (Table 2). Measurements were taken a minimum of three times shoot growth, any drooping canes, and a few large canes removed), and per season, with weekly measurements at many sites. In 2016, fruit low (all small shoot growth, any drooping canes, and the majority of were removed from the plant, split in half and immediately measured large canes removed). In Minnesota, blueberry (V. corymbosum) plants using an infrared thermometer (Southwire 700 F, Southwire Tools, were pruned to high (25 % less than growers’ standard), medium Carrollton, GA). Because the infrared thermometer could overheat in (growers’ standard), and low (25 % more than growers’ standard). In the field and required destructive sampling of the fruit, in 2017a Oregon, blueberry (V. corymbosum) plants were qualitatively pruned to thermocouple probe (Digi-sense Handheld Thermometer 86460-06, high (only dead branches removed and slight thinning), medium Cole-Parmer Instrument Co., Vernon Hills, IL) was used instead. Probes (growers’ standard), and low (dead branches and major canes were were inserted approximately 3 mm into the center of the fruit while it removed) canopy density. In Georgia, rabbiteye blueberry (Vaccinium was still on the plant. State-specific information can be found in

3 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860

Table 2 Horticultural and D. suzukii parameters measured and collection intervals in each state and crop in 2016 and 2017.

Year State* Crop Fruit temperature Fruit firmness Total soluble solids (°Brix) D. suzukii infestation Artificial infestation Total yield Marketable yield

2016 NC Blackberry — 4 times/season 4 times/season Twice/week — Twice/week Twice/week 2016 MI Raspberry — — — 1 time/season — — — 2016 MD Raspberry Weekly 4 times/season Biweekly Weekly — Weekly Weekly 2017 CA Raspberry 3 times/season — — Weekly — 3 times/week 3 times/week 2017 NC Blackberry 4 times/season — 4 times/season Twice/week — Twice/week Twice/week 2017 MI Raspberry 2 times/season — 1 time/season 2 times/season — — — 2017 MD Raspberry Weekly Weekly Weekly Weekly — Weekly Weekly 2015 OR Blueberry — — — — 1 time/season — — 2016 GA Blueberry — — — — 3 times/season — — 2016 MI Blueberry — — — Weekly — — — 2016 MN Blueberry — — — — — — — 2016 MD Blueberry Weekly Weekly Weekly Weekly — Weekly Weekly 2016 OR Blueberry — — — — 1 time/season — — 2017 GA Blueberry Weekly — Weekly Weekly 3 times/season Weekly Weekly 2017 MI Blueberry Weekly — Weekly Weekly — — — 2017 MN Blueberry — — Weekly — — Weekly Weekly 2017 MD Blueberry Weekly Weekly Weekly Weekly — Weekly Weekly 2017 OR Blueberry — — — — 1 time/season — —

* CA: California, GA: Georgia, MD: Maryland, MI: Michigan, MN: Minnesota, NC: North Carolina, OR: Oregon; — = not measured or excluded due to lack of corresponding natural infestation data. supplementary material section 1.3. The effect of canopy density on density (high, medium, low) as a fixed factor with ‘siteyear’ asa fruit temperature was analyzed using a linear mixed effects model for random factor. For fruit firmness, caneberry and blueberry data were each crop [R-package ‘lme4’ (Bates et al., 2015)] that included canopy both 1/(y) transformed to meet model assumptions. Analyses were density (high, medium, low) as a fixed factor with ‘siteyear’ asa performed on untransformed data for TSS in both crops. Because random factor. Subsamples were averaged for each treatment replicate treatment did not affect TSS or firmness, no post-hoc tests were per- and data were averaged across sampling weeks prior to analysis. No formed. transformations were necessary. Because no significant effects of canopy density on fruit temperature were observed, no post-hoc tests 2.4. Effect of canopy density on D. suzukii infestation were performed. Naturally occurring infestation was measured on a minimum of 10 2.3. Effect of canopy density on fruit firmness and total soluble solids randomly chosen harvested fruit (additional details in supplementary material Section 1.6) from each treatment replicate using larval ex- The effect of canopy density on fruit firmness (cN) was measured for traction methods similar to Van Timmeren et al. (2017a). Fruit were weighed before larval extraction to calculate the number of D. suzukii subsamples of harvested fruit in Maryland blueberries and raspberries −1 (2016 and 2017) and in 2016 North Carolina blackberries (Table 2). larvae per gram of fruit [D. suzukii (g fruit) ]. Infestation levels were Generally, the firmness of 3–25 fruit per plant and location wasmea- evaluated at all caneberry sites (2016, 2017), as well as in Georgia sured after each harvest, dependent upon availability of ripe fruit. For (2017), Michigan (2016, 2017), and Maryland (2016, 2017) blueberries blueberries and raspberries, one measurement per berry was recorded. (additional details in Table 2 and supplementary material section 1.6). In blackberries, the mean firmness of multiple drupes was recorded to The effect of canopy density on D. suzukii infestation was analyzed account for the variation in individual drupes. A flat tipped tension using a linear mixed effects model for each crop [R-package ‘lme4’ gauge modified with an insect pin was depressed (blunt end) onto the (Bates et al., 2015)] that included canopy density (high, medium, low) fruit surface at a 90° angle and gentle pressure was applied until the as a fixed factor with ‘siteyear’ as a random factor. Data were averaged across sampling weeks prior to analysis. Caneberry data were 1/(y+1) skin was pierced (Burrack et al., 2013, additional details in supple- 3 mentary material section 1.4). transformed and blueberry data were 1/(y+1) transformed to meet Total soluble solids (TSS), measured as °Brix, indicates the amount model assumptions, and Tukey-Kramer post-hoc tests were performed. of sugar and other organic compounds in fruit juice, and higher values correspond to increased sugar content. TSS was measured on pooled 2.5. Effect of canopy density on yield samples of 3–5 berries randomly selected from the marketable harvest in each treatment replicate, as available. The berries from each sample Yield measurements were collected weekly from a subsection of the were crushed together in a WhirlPak® bag (Nasco, Wisconsin, USA) or treatment replicates in California (2017), Maryland (2016, 2017), and other sterile container with a filter to remove particulate matter from North Carolina (2016, 2017) caneberries, as well as in Maryland (2016, the juice. The filtered liquid was transferred to a handheld re- 2017), Minnesota (2017), and Georgia (2017) blueberries (Table 2). For fractometer to determine °Brix. Each juice sample was measured in caneberries, the entire pruned area on both sides of the row was har- triplicate. Between measurements, the sensor was rinsed with deionized vested, and yield was calculated as grams per row meter (total length of water and dried. Occasionally, berries were frozen until analyzed (ad- both sides of the row). For blueberries, all fruit on the central data ditional details in supplementary material section 1.5). In 2016, TSS collection plant(s) were harvested and yield was calculated as grams was measured in Maryland blueberries and raspberries as well as in per bush (additional details in supplementary material section 1.7). North Carolina blackberries (Table 2). In 2017, all states except Cali- Harvested fruit was chilled during transportation from the farm to the fornia (raspberries) and Oregon (blueberries) measured TSS (Table 2). laboratory. Afterwards, harvested berries were assessed for damage, Subsamples were averaged by replicate and then averaged across sorted into marketable fruit and unmarketable fruit, and weighed. Fruit sampling weeks prior to analysis. The effect of canopy density on fruit were considered unmarketable due to D. suzukii damage (visible larvae firmness and TSS was analyzed using a linear mixed effects modelfor or extremely soft fruit), other insect feeding, evidence of pathogens, or each crop [R-package ‘lme4’ (Bates et al., 2015)] that included canopy other visible physical damage. The effect of canopy density on yield was

4 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860 analyzed using a linear mixed effects model for each crop [R-package separately for blueberry and raspberry data. ‘lme4’ (Bates et al., 2015)] that included canopy density (high, medium, In the first stage of the meta-analysis, a random effects model was low) as a fixed factor with ‘siteyear’ as a random factor. Data were applied on the site- and year-specific correlation matrices to create a averaged across sampling weeks, and total yield and marketable yield pooled correlation matrix (Cheung and Chan, 2009). Due to the small were 1/sqrt(y+1) transformed for caneberries to meet model assump- number of sites, the variance component was estimated with a diagonal tions. No data transformations were necessary for blueberries. Tukey- matrix. In the second stage, the pooled correlation matrix was used as Kramer post-hoc tests were performed. the observed covariance matrix to fit the causal model (McArdle and McDonald, 1984). The R-package ‘metaSEM’ (Cheung, 2015) was used 2.6. Effect of canopy density on canopy microclimate to conduct the weighted least square estimation.

HOBOware sensors (HOBO Pro V2, U23-001, Onset Computer 2.8. Effect of canopy density on microclimate differences between canopy Corporation, Bourne, MA, USA) were installed mid-height in the in- locations terior (center of row or bush) and exterior (edge of row or bush) part of the canopy in the center of each treatment block to determine pruning In Maryland, data collection for each parameter was additionally impacts on canopy microclimate at all sites. The sensors recorded separated by canopy location to examine the heterogeneity of responses temperature and RH at regular intervals (10–30 min) throughout the to canopy density treatments. Fruit collected within the outer 15 cm of fruiting season. To standardize climate parameters and determine the the plant canopy were separated as ‘exterior’ and all other canopy lo- predictive power of biologically relevant thresholds for D. suzukii sur- cations were combined for ‘interior’, otherwise fruit measurements vival, the number of hours per week (six days prior to sampling plus the were the same as described in sections 2.1–2.6 and Table 2. Due to day fruit infestation was sampled at each site) where temperatures were patchiness in the location of ripe fruit in each replicate each week, ≥ 30.9 °C [upper development threshold (Ryan et al., 2016)] or ≥ 32.9 missing data occurred. Therefore, separate linear mixed effects models °C [32 °C near critical thermal maximum for D. suzukii (Enriquez and for the various response variables were created, with missing data re- Colinet, 2017)] were calculated. For humidity, thresholds of < 70 %RH moved case-wise. The model included canopy location (interior, ex- and < 80 %RH were used. Because most sites did not separate data terior), canopy density (high, medium, low), and the interaction term as collection by canopy location, climate data were averaged across lo- fixed factors; with site, year, and harvest week (sampling weeksub- cation for each treatment replicate for each harvest week in analyses, sample numbered consecutively for the duration of harvest) included as except where specified. Data were subsequently averaged across sam- nested random factors. These were nested as follows: harvest week pling weeks prior to analysis. Only sites with corresponding natural nested within year nested within site. If no significant interaction oc- infestation data were included in analyses. curred, it was removed from the model. Homogeneity of variance and Because no data transformation met the model assumptions, non- normality of residuals were checked using Levene’s test and Shapiro- parametric Kruskal-Wallis tests on untransformed data were performed. Wilk test, respectively. If model assumptions could not be met, non- One exception was in caneberries where hours ≥ 32.9 °C was analyzed parametric tests were performed. In caneberries, berry temperature, using a linear mixed effects model [R-package ‘lme4’ (Bates et al., TSS, penetration force and D. suzukii infestation were analyzed using 2015)] that included canopy density (high, medium, low) as a fixed linear mixed effects models as described above followed by Tukey- factor with ‘siteyear’ as a random factor. Because no significant treat- Kramer post-hoc tests. Berry temperature was (y)2 transformed, pene- ment effects were observed, no post-hoc tests were performed. tration force was sqrt(y) transformed and D. suzukii infestation was 1/ sqrt(y+1) transformed. No data transformation for TSS was needed. 2.7. Direct and mediated associations between canopy density and Total yield and marketable yield were analyzed using Kruskal-Wallis infestation tests followed by Dunn’s tests. Blueberry data were analyzed using linear mixed effects models as described above followed by Tukey- A two-stage structural equation modeling approach (Cheung and Kramer post-hoc tests. Berry temperature and penetration force were Chan, 2009) was used to evaluate the direct and mediated associations untransformed, with TSS data 1/y transformed. For D. suzukii in- between canopy density and infestation across sites and years in a meta- cidence, total yield and marketable yield, Kruskal-Wallis tests were analysis. Categorical variables were coded such that they numerically performed. increased with intensity; for example, canopy density (low = 1, To quantify the magnitude of the differences in microclimate by medium = 2, high = 3). Canopy temperature, canopy humidity, har- canopy location, mean berry temperatures of interior fruit were sub- vest week (sampling week subsample numbered consecutively for the tracted from the mean berry temperature of exterior fruit for each re- duration of harvest), berry temperature, canopy density, and D. suzukii plicate in each sampling week. In raspberries, the magnitude of the infestation variables were included in the analyses. Box-Cox transfor- difference in berry temperature was analyzed using a Kruskal-Wallis mations were then performed as necessary to stabilize variance (sup- test followed by Dunn’s test. In blueberries data were analyzed using plementary material Table S1). A polyserial correlation was used to the linear mixed effects model described above followed by a Tukey- measure the association between the transformed continuous variables Kramer post-hoc test. and the treatment (Drasgow, 2004), and heterogeneous correlation Canopy climate data (temperature and RH) recorded by data loggers matrices were calculated for each site and year [R-package ‘polycor’ during the sampling period were also analyzed by canopy location for (Fox, 2016)]. Missing data were removed case-wise before each ana- Maryland sites. Response variables included mean temperature and lysis. Sites and years without observed infestation were removed from mean RH as well as the number of hours ≥ 30.9 °C, ≥ 32.9 °C, < 80% the meta-analysis because the polyserial correlation was not estimable. RH and ≤70 %RH. For raspberries and blueberries, all climate factors We specified a causal network to link treatment, microclimate were analyzed using Kruskal-Wallis tests followed by Dunn’s post-hoc parameters, and infestation, including canopy density treatment, har- tests. vest week, berry temperature, canopy temperature, and canopy RH in the model. To better understand the microclimate parameters, means as 2.9. Effect of canopy density on artificial D. suzukii infestation well as the two threshold-based calculations were analyzed for temperature and RH variables. Model selection based on the Akaike In Georgia in 2016 and 2017 and Oregon from 2015 to 2017 (Tables Information Criterion and Bayesian Information Criterion determined 1 and 2), blueberry fruit were artificially infested in the laboratory and which microclimate parameters best explained infestation results deployed for one week in the field to evaluate immature D. suzukii (supplementary material Table S2). The same causal network was fit survival in each pruning treatment. Adult D. suzukii were allowed to

5 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860 mate and oviposit on blueberries in the laboratory. Subsequently, the numbers of eggs laid into each berry were counted under a microscope. Berries were divided into organza mesh bags so that each bag contained an equal or nearly equal number of eggs and berries. In Georgia in 2016 Hours < 70RH % 46.4 ± 1.0 (n = 24) 50.0 ± 2.3 (n = 24) 47.3 ± 1.1 (n = 24) 49.4 ± 1.3 (n = 21) 52.0 ± 1.7 (n = 21) and 2017, on average 66 and 60 eggs per bag per treatment replicate 50.3 ± 1.4 (n = 21) were deployed, respectively. In Oregon, 203, 76 and 73 eggs per bag per treatment replicate were deployed in 2015, 2016 and 2017, respectively. The bags were then positioned within the canopies of each treatment plant for one week (same experimental design as described in Hours ≥ 32.9 °C 5.9 ± 1.0 (n = 24) 6.3 ± 0.8 (n = 24) 6.2 ± 1.0 (n = 24) 6.3 ± 1.5 (n = 21) 10.6 ± 2.9 (n = 21) 2.1). Laboratory control bags were held at room temperature during the 7.1 ± 1.2 (n = 21) deployment period to confirm egg viability, except for Oregon in 2017. Drosophila suzukii survival to adult emergence under treatment and laboratory conditions was quantified. [%] 81.8 ± 0.4 (n = 24) 80.5 ± 1.0 (n = 24) 81.8 ± 0.5 (n = 24) 81.8 ± 0.52 (n = 21) 81.3 ± 0.46 (n = 21) 81.9 ± 0.38 (n = 21) 2.9.1. Georgia After field deployment, fruit were held in the organza bags under controlled laboratory conditions for three weeks. All emerged adults from each bag were counted. Bags were deployed three times during the season, and replicate bags from all dates were pooled for analysis. For the linear mixed effects model, the canopy density (high, medium, low) 21.1 ± 0.5 (n = 24) 21.2 ± 0.5 (n = 24) 21.6 ± 0.7 (n = 24) 23.2 ± 0.41 (n = 21) 23.5 ± 0.39 (n = 21) was set as fixed factor with experiment date as a random factor. 23.2 ± 0.39 (n = 21) Homogeneity of variance and normality of residuals were checked using Levene’s test and Shapiro-Wilk test, respectively. The two years were analyzed separately due to variability between years. For 2016, a Kruskal-Wallis test followed by Dunn’s test was performed, because data could not meet the assumption of homogeneity of residual variance despite transformation. For 2017, untransformed data were ana- Marketable yield [g*] Mean temperature [°C] Mean RH 48.2 ± 10.1 (n = 23) 51.6 ± 11.7 (n = 23) 41.2 ± 9.4 (n = 23) 159.8 ± 32.9 (n = 21) 149.1 ± 34.0 (n = 21) lyzed using a linear mixed effects model, followed by a Tukey-Kramer 115.9 ± 20.6 (n = 21) post-hoc test.

2.9.2. Oregon To examine the effect of canopy location, bags were placed at dif- Total yield [g*] 115.7 ± 20.8 a (n = 26) 113.3 ± 21.0 ab (n = 26) 96.9 ± 18.8 b (n = 26) 180.0 ± 33.6 (n = 21) 172.2 ± 35.7 (n = 21) 135.3 ± 22.4 (n = 21) ferent heights [base (on the ground below the bush), middle of the canopy, and top of the canopy] within each treatment bush one time

per season. Following deployment, berries were transferred to venti- (g for blueberries; RH = relative humidity, n = number of samples. lated plastic containers and a capped 1 oz plastic deli cup of deionized −1 water with a sponge was fitted inside to avoid desiccation. Containers −1 1.06 ± 0.23 a (n = 30) 0.94 ± 0.22 ab (n = 30) D. suzukii fruit) 0.92 ± 0.22 b (n = 30) 0.23 ± 0.05 (n = 21) 0.19 ± 0.05 (n = 22) were held for two weeks in the laboratory, and flies that emerged were 0.21 ± 0.05 (n = 22) removed and counted three days per week for five weeks. After that time, it was assumed that emerging flies could potentially be from a second generation, so the experiment concluded. Due to high temperatures in the field 2016, almost all larvae died during deployment (only 1 bag contained 2 emergent D. suzukii). Because the data set was comprised of zeros, no analysis was performed for this year. For 2015 9.2 ± 0.26 (n = 23) 9.1 ± 0.27 (n = 23) Total soluble solids (°Brix) 8.8 ± 0.31 (n = 23) 13.4 ± 0.52 (n = 25) 13.5 ± 0.37 (n = 24) and 2017, our linear mixed effects model included canopy density, 13.4 ± 0.55 (n = 24) for caneberries and as g bush canopy location and the respective interaction as fixed factors, and year −1 was included as a random factor. No significant interaction between canopy density and canopy location was found so this term was removed from the model. Homogeneity of variance and normality of re- parameters measured in caneberries and blueberries, averaged across harvest weeks prior to summary across all site years. siduals were evaluated using Levene’s test and Shapiro-Wilk test, re- 8.2 ± 0.92 (n = 15) 8.1 ± 0.97 (n = 15) [cN] 8.4 ± 1.18 (n = 15) 28.9 ± 0.34 (n = 12) 28.8 ± 0.58 (n = 11) 28.3 ± 0.71 (n = 11) spectively. Survivorship data were log (y+1) transformed in order to meet assumptions, and a Tukey-Kramer post-hoc test was performed. D. suzukii

3. Results

Summary data for each individual state are presented in supple- (n = 23) (n = 23) (n = 23) (n = 19) (n = 19) mentary material Table S3. (n = 18)

3.1. Effect of canopy density on fruit temperature Caneberry 26.7 ± 0.77 Caneberry 27.2 ± 0.80 Blueberry 30.0 ± 0.66 Canopy density did not affect temperature in the interior of the fruit Blueberry 30.4 ± 0.75 for caneberries (F(2, 62) = 1.7, P = 0.187) or blueberries (F(2, 50) = 2.3, P = 0.111). In caneberries, fruit temperature minimally increased (0.2–0.5 °C on average) with reduced canopy density, with a similar temperature shift in blueberries (0.3–0.4 °C on average) (Table 3). Fruit High Medium Caneberry 26.9 ± 0.77 Canopy density Crop Fruit temperature [°C] Fruit firmness Low High Medium Blueberry 30.3 ± 0.70 Low temperatures in caneberries were 3 °C lower on average compared with Table 3 Mean ± standard error of horticultural and Separate analyses were performed forKramer each crop test and at parameter. α Means = separations were 0.05, only * performed yield for was parameters with calculated significant as treatment g effects. row-meter Means followed by the same letter do no separate with a Tukey-

6 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860 blueberries. fruiting season for the caneberry cultivars used in this study). Pruning treatment slightly affected internal berry temperature (standardized 3.2. Effect of canopy density on fruit firmness and total soluble solids regression coefficient: -0.01) as well as hours < 70 %RH (standardized regression coefficient: –0.04) (Fig. 1). This suggests that higher density Canopy density did not affect fruit firmness in caneberries (F(2, 40) canopies had slightly cooler fruit and were slightly more humid. = 1.1, P = 0.335) or blueberries (F(2, 30) = 0.7, P = 0.529). TSS (°Brix) For blueberries, pruning treatment exhibited direct connections to was not altered by canopy density treatments in caneberries (F(2, 62) = and effects on internal berry temperature (standardized regression 1.0, P = 0.371) or blueberries (F(2, 65) = 0.8, P = 0.461). Generally, coefficient: –0.10), mean RH (standardized regression coefficient: caneberries were softer and exhibited lower TSS compared with blue- –0.03) and mean temperature (standardized regression coefficient: berries (Table 3). 0.02) (Fig. 2). Similar to caneberries, as canopy density increased, blueberry fruit were slightly cooler. In contrast to caneberries, canopies 3.3. Effect of canopy density on D. suzukii infestation were slightly less humid and warmer with increasing canopy density. None of the parameters in the network had a significant effect on D. Canopy density influenced D. suzukii infestation in caneberries (F(2, suzukii infestation (Fig. 2). 81) = 4.8, P = 0.010), but not in blueberries (F(2, 58) = 1.4, P = 0.243). In caneberries, D. suzukii infestation decreased with decreasing canopy 3.7. Effect of canopy density on microclimate differences between canopy density, with greater infestation in the high canopy density (1.06 D. locations suzukii (g fruit)−1) compared with the low canopy density (0.92 D. suzukii (g fruit)−1)(Table 3). Greater D. suzukii infestation was ob- In raspberries, no interaction occurred between canopy location and served in caneberries compared with blueberries. State specific in- density treatments; thus, the interaction term was removed from the model. festation levels varied for both crops (supplementary material Table Canopy location affected berry temperature(1, (F 607) = 59.2, P < 0.001), S3). with warmer exterior fruit. TSS (F(1, 226) = 0.1, P = 0.709) and penetration force (F(1, 384) = 0.2, P = 0.664) were not influenced by canopy location. 3.4. Effect of canopy density on yield Slightly higher D. suzukii infestation occurred in interior fruit (F(1, 576) = 5.5, P = 0.020), with 2.3 ± 0.2 D. suzukii (g fruit)−1 in exterior fruit −1 In caneberries, canopy density altered the total yield (F(2, 70) = 3.8, compared to 2.5 ± 0.1 D. suzukii (g fruit) in interior fruit (Table 4). 2 2 P = 0.027) but not marketable yield (F(2, 62) = 2.9, P = 0.062). Total Marketable yield (X(1, 792) = 0.2, P = 0.634) and total yield (X(1, 792) = 1.3, yield decreased with decreasing canopy density, with the high canopy P = 0.247) were similar across canopy locations (Table 4). Decreasing −1 density treatment yielding more (116 g row-meter ) compared with canopy density reduced penetration force (F(2, 384) = 3.4, P = 0.034), D. -1 2 the low canopy density (97 g row-meter )(Table 3). In blueberries, no suzukii incidence (F(2, 576) = 6.1, P = 0.002), total yield (X(2, 792) = 14.8, 2 differences were detected for total yield(2, (F 56) = 1.8, P = 0.173) or P < 0.001) and marketable yield (X(2, 792) = 8.9, P = 0.012). No effect of marketable yield (F(2, 56) = 2.1, P = 0.127). However, total yield and canopy density was observed for berry temperature (F(2, 607) = 0.6, P = marketable yield tended to decrease with decreasing canopy density 0.554) or TSS (F(2, 226) = 3.0, P = 0.050, no post-hoc separation). (Table 3, supplementary material Table S3). In blueberries, there was no interaction between canopy location and density, and this term was removed from the model. Fruit was

3.5. Effect of canopy density on canopy microclimate warmer (F(1, 333) = 5.0, P = 0.026) and firmer (F(1, 309) = 4.4, P = 0.036) in the exterior of the bush. Drosophila suzukii infestation was not 2 Because the more extreme temperature and humidity thresholds best affected (1,(X 317) = 3.6, P = 0.058), with 1.2 ± 0.2 D. suzukii (g fit data in the meta-analysis and results were similar for other thresholds, fruit)−1 in exterior fruit compared to 2.5 ± 0.4 D. suzukii (g fruit)−1 in results are presented for hours ≥ 32.9 °C and hours < 70 %RH. In ca- interior fruit on average (Table 4). Canopy location did not impact 2 2 neberries, canopy density treatments did not affect mean temperature marketable yield (X(1, 425) = 1.0, P = 0.324), total yield (X(1, 432) = 2 (X(2, 72) = 0.3, P = 0.848) or hours ≥ 32.9 °C (F(2, 63) = 0.4, P = 1.5, P = 0.223), or TSS (F(1, 279) = 0.3, P = 0.611) (Table 4). Canopy 0.657); though very small magnitude increases of mean temperature (0.5 density did not change berry temperature (F(2, 333) = 0.1, P = 0.865), °C) and hours ≥ 32.9 °C (∼0.3 h) occurred with decreasing canopy TSS (F(2, 279) = 0.2, P = 0.812), penetration force (F(2, 309) = 1.9, P = 2 density (Table 3). No effects of canopy density were observed for mean 0.150), D. suzukii incidence (X(2, 317) = 0.1, P = 0.968), marketable 2 2 2 2 RH (X(2, 72) = 0.6, P = 0.740) or hours < 70 %RH (X(2, 72) = 0.3, P = yield (X(2, 425) = 1.3, P = 0.510), or total yield (X(2, 432) = 3.3, P = 0.860). Similarly, in blueberries, treatments did not affect mean tem- 0.196) (Table 4). 2 2 perature (X(2, 63) = 0.5, P = 0.799) or hours ≥ 32.9 °C (X(2, 63) = 1.3, P In raspberries, the magnitude of the difference between berry tem- 2 2 = 0.518), mean RH (X(2, 63) = 0.8, P = 0.668), or hours < 70 %RH (X(2, peratures from the interior relative to the exterior of the canopy did not 2 63) = 1.1, P = 0.574). No patterns emerged for blueberry climate vary by canopy density (X(2, 304) = 1.4, P = 0.506) (Table 4). In parameters (Table 3). State specific canopy microclimate responses are blueberries, the magnitude of the difference between interior and ex- presented in supplementary material Table S3. terior fruit temperatures varied by canopy density (F(2, 138) = 4.4, P = 0.014), with the largest differences in the high canopy density, while 3.6. Direct and mediated associations between canopy density and the medium and low canopy densities were nearly equal (Table 4). infestation Except for one temperature threshold in blueberries, canopy location did not influence canopy microclimate parameters in either crop. 2 For caneberries, hours < 70 %RH was negatively correlated (stan- In raspberries, no effect was observed for mean temperature (X(1, 780) = 2 dardized regression coefficient: –0.36) with D. suzukii incidence (D. 0.05, P = 0.819), hours ≥ 30.9 °C (X(1, 780) = 0.001, P = 0.973), hours −1 2 2 suzukii (g fruit) )(Fig. 1); thus, a more humid environment corre- ≥ 32.9 °C (X(1, 780) = 0.002, P = 0.962), mean RH (X(1, 758) = 0.8, P = 2 sponded with higher D. suzukii infestation. Canopy density also directly 0.358), hours < 80 %RH (X(1, 758) = 1.3, P = 0.250) or hours < 70 % 2 affected D. suzukii infestation, with higher density canopies exhibiting RH (X(1, 758) = 1.1, P = 0.305) (Table 5). In blueberries, canopy lo- 2 slightly greater D. suzukii infestation, though the effect size was much cation did not affect mean temperature (X(1, 424) = 1.0, P = 0.327), 2 2 smaller (standardized regression coefficient: 0.04). The largest effect hours ≥ 30.9 °C (X(1, 424) = 2.7, P = 0.101), mean RH (X(1, 424) = 1.9, 2 was observed for the connection between berry temperature and har- P = 0.165), hours < 80 %RH (X(1, 424) = 1.9, P = 0.168) or 2 vest week (standardized regression coefficient: –0.67), indicating that hours < 70 %RH (X(1, 424) = 2.5, P = 0.117); but did impact hours ≥ 2 berry temperature decreased during the season (summer into fall 32.9 °C (X(1, 424) = 4.9, P = 0.028), with more hours above the

7 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860

Fig. 1. Structural equation models exploring the effects of canopy density on Drosophila suzukii infestation in caneberries. Boxes represent measured variables and their data transformations: 1. Canopy Density denotes the categorical canopy density (Low = 1, Medium = 2, High = 3); 2. Ln (Canopy Hrs≥32.9 °C + 1) denotes natural log transformed +1 h with canopy temperature equal to or above 32.9 °C per week; 3. Canopy Hrs < 70 %RH denotes hours with relative humidity below 70 % per week; 4. Harvest Week denotes consecutively numbered sampling week; 5. °C Berry denotes mean fruit temperature measured in the fruit interior; 6. Ln[D. suzukii (g fruit)−1+1] denotes natural log transformed +1 D. suzukii larvae per gram of fruit. Arrows represent unidirectional relationships among variables. Arrows for non-significant paths (P ≥ 0.05) are grey, significant paths (P < 0.05) are black. The thickness of the paths has been scaled based on the magnitude of the standardized regression coefficients (presented next to the path), and positive coefficients indicate positive associations.

Fig. 2. Structural equation models exploring the effects of canopy density on Drosophila suzukii infestation in blueberries. Boxes represent measured variables and their data transformations: 1. Canopy Density denotes categorical canopy density (Low = 1, Medium = 2, High = 3); 2. (Mean Canopy °C)2 denotes squared mean canopy temperature; 3. Mean Canopy % RH denotes mean relative humidity; 4. Harvest Week denotes consecutively numbered sampling week; 5. °C Berry denotes mean fruit temperature measured in the fruit interior; 6. [D. suzukii (g fruit)−1]−2 denotes negative quadratic transformed D. suzukii larvae per gram of fruit. Arrows for non-significant paths (P ≥ 0.05) are grey, significant paths (P < 0.05) are black. The thickness of the paths has been scaled based on the magnitude of the standardized regression coefficients (presented next to the path), and positive coefficients indicate positive associations, with the exception of the blueberry infestation data due to its negative square transformation. threshold in the exterior canopy (Table 5). base (0.9 ± 0.4 D. suzukii bag−1)(Table 7). In laboratory control bags, 95.0 ± 19.7 insects survived to adulthood (203 eggs bag−1) in 2015 −1 3.8. Effect of canopy density on artificial infestation and 24.2 ± 2.8 (76 eggs bag ) adults emerged in 2016.

3.8.1. Georgia 2 4. Discussion Canopy density (X(2, 54) = 8.7, P = 0.013) affected D. suzukii survival in 2016, with greater survival in the high canopy density treat- We conducted a multi-site experiment across small fruit production ment compared with low canopy density (Table 6). In 2017, canopy regions in the U.S. Management practices, seasonal phenology, harvest density did not impact survival (F(2, 24) = 1.0, P = 0.371). Greater D. duration, and climate varied across experimental sites; however, our suzukii survival was observed in 2017 compared with 2016 (Table 6). multi-site approach provided a robust data set to evaluate trends across The average survival in laboratory controls was 42.1 ± 5.3 and production systems and climates, and we accounted for site-to-site 37.8 ± 2.7 D. suzukii bag−1 in 2016 (66 eggs bag−1) and 2017 (60 eggs −1 variability in our analyses. Although canopy density did not affect cli- bag ), respectively. mate parameters in most analyses, the meta-analyses exhibited small- magnitude standardized regression coefficients [0.02 to 0.04, sign of 3.8.2. Oregon value (direction of effect) varies] for connections between canopy No interaction occurred between canopy location and density density and canopy climate parameters, with a strong direct connection treatments, and the interaction term was removed from the model. (standardized regression coefficient: −0.36) between hours < 70%RH

Canopy density did not affect D. suzukii survival (F(2, 70) = 0.3, P = and D. suzukii infestation in caneberries. Indeed, despite small differ- 0.771) (Table 7). Canopy location significantly altered D. suzukii sur- ences (on average 0.14 fewer D. suzukii (g fruit)−1 occurred in the ca- vival (F(2, 70) = 68.6, P < 0.001), with more (31.1 ± 6.1 D. suzukii neberry low canopy density treatment), low canopy density reduced bag−1) surviving in the middle of the blueberry plant compared with naturally occurring D. suzukii infestation in caneberry in a manner de- the top and base (Table 7). The top of the plant (16.7 ± 3.7 D. suzukii tectable in both the meta-analysis and by ANOVA. However, blueberry bag−1) was more suitable for D. suzukii survival compared with the canopy density did not affect naturally occurring infestation in either

8 .Shnbr,e al. et Schöneberg, T.

Table 4 Mean ± standard error of horticultural and D. suzukii parameters measured in Maryland raspberries and blueberries, summarized across site years (2016 and 2017 at two sites) and harvest weeks (12 in caneberries, 3–6 in blueberries).

Crop Canopy Location Canopy density Fruit temperature [°C] Difference in berry temperature by Total soluble solids Fruit firmness [cN] D. suzukii (g Total Yield Marketable Yield [g*] location (interior-exterior) [°C] (°Brix) fruit)−1 [g*]

Raspberry Exterior 27.7 ± 0.3 9.3 ± 0.1 5.5 ± 0.1 2.3 ± 0.2 21.8 ± 1.1 6.8 ± 0.5 (n = 325) a (n = 116) (n = 208) (n = 307) b (n = 396) (n = 396) Raspberry Interior 27.0 ± 0.3 9.2 ± 0.1 5.4 ± 0.1 2.5 ± 0.1 26.1 ± 1.4 8.5 ± 0.6 (n = 333) b (n = 142) (n = 210) (n = 318) a (n = 396) (n = 396) Blueberry Exterior 30.4 ± 0.2 14.0 ± 0.2 28.9 ± 0.3 1.2 ± 0.2 58.8 ± 7.9 49.2 ± 6.9 (n = 168) a (n = 136) (n = 173) a (n = 155) (n = 216) (n = 211) Blueberry Interior 30.0 ± 0.2 13.9 ± 0.2 27.9 ± 0.3 2.5 ± 0.4 102.6 ± 15.4 86.9 ± 13.2 (n = 192) b (n = 167) (n = 162) b (n = 162) (n = 216) (n = 214) 9 Raspberry High 27.1 ± 0.4 0.78 ± 0.1 9.5 ± 0.1 5.6 ± 0.1 2.6 ± 0.1 27.6 ± 1.7 8.5 ± 0.7 (n = 230) (n = 107) (n = 92) (n = 148) a (n = 220) a (n = 264) a (n = 264) a Raspberry Medium 27.3 ± 0.4 0.57 ± 0.1 9.0 ± 0.1 5.5 ± 0.1 2.3 ± 0.2 24.7 ± 1.5 8.2 ± 0.7 (n = 222) (n = 102) (n = 90) (n = 138) ab (n = 212) b (n = 264) a (n = 264) ab Raspberry Low 27.7 ± 0.4 0.80 ± 0.2 9.1 ± 0.1 5.3 ± 0.2 2.3 ± 0.2 19.6 ± 1.4 6.3 ± 0.7 (n = 206) (n = 95) (n = 76) (n = 132) b (n = 193) b (n = 264) b (n = 264) b Blueberry High 30.1 ± 0.3 −0.79 ± 0.3 14.1 ± 0.3 28.9 ± 0.5 1.8 ± 0.3 110.1 ± 21.0 93.9 ± 17.9 (n = 122) (n = 55) b (n = 98) (n = 112) (n = 104) (n = 144) (n = 143) Blueberry Medium 30.3 ± 0.3 0.05 ± 0.3 13.8 ± 0.2 28.4 ± 0.4 1.8 ± 0.4 74.3 ± 11.5 61.4 ± 10.0 (n = 128) (n = 59) a (n = 110) (n = 115) (n = 109) (n = 144) (n = 143)

Blueberry Low 30.1 ± 0.3 −0.07 ± 0.3 13.9 ± 0.3 27.9 ± 0.4 1.9 ± 0.4 57.7 ± 9.9 48.7 ± 8.8 Agriculture, EcosystemsandEnvironment294(2020)106860 (n = 110) (n = 49) ab (n = 95) (n = 108) (n = 104) (n = 144) (n = 139)

Separate analyses were performed for each crop and parameter. Means separations were performed for parameters with significant treatment effects. Means followed by the same letter do no separate with aDunn’sor Tukey-Kramer test at α = 0.05, separate analyses were performed for each crop; n = number of samples; * yield was calculated as g row-meter−1 for caneberries and as g bush−1 for blueberries. T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860

Table 5 Mean ± standard error of climate parameters in Maryland raspberries and blueberries, summarized across site years (2016 and 2017 at two sites) and harvest weeks (12 in caneberries, 3–6 in blueberries).

Crop Canopy Location Mean Temperature Hours ≥ 32.9 °C Mean %RH Hours ≤ 70 %RH

Raspberry Exterior 20.9 ± 0.2 6.1 ± 0.5 82.7 ± 0.3 46.4 ± 0.9 (n = 384) (n = 384) (n = 384) (n = 384) Raspberry Interior 21.0 ± 0.2 6.0 ± 0.5 82.2 ± 0.4 48.5 ± 1.1 (n = 396) (n = 396) (n = 374) (n = 374) Blueberry Exterior 24.3 ± 0.1 9.4 ± 0.8 a 81.3 ± 0.3 51.5 ± 1.1 (n = 208) (n = 208) a (n = 208) (n = 208) Blueberry Interior 24.1 ± 0.1 7.4 ± 0.7 b 81.8 ± 0.4 49.2 ± 1.1 (n = 216) (n = 216) b (n = 216) (n = 216)

Table 6 indicate that canopy management should not be overlooked when Mean ± standard error D. suzukiisurvival per bag in laboratory infested Georgia considering best management production practices. In some locations, −1 −1 blueberries in 2016 (66 eggs bag ) and 2017 (60 eggs bag ). Laboratory depending on canopy size, benefits of pruning are more pronounced, −1 controls emerged 42.1 ± 5.3 D. suzukiibag in 2016 and 37.8 ± 2.7 D. suzu- resulting in clear improvement of production efficiency. Such condi- −1 kiibag in 2017. tions are highlighted below, in addition to potential mechanisms by Year n Canopy density D. suzukii bag−1 which canopy density treatments could have influenced D. suzukii behavior, fruit resource quality, canopy microclimate, plant productivity, 2016 18 High 9.8 ± 2.7 a and crop management. 2016 18 Medium 2.2 ± 1.0 ab 2016 18 Low 1.3 ± 1.0 b We used natural D. suzukii infestation as our primary metric to quantify D. suzukii responses to canopy density and location. Natural 2017 9 High 19.7 ± 4.4 a infestation is mediated by foraging, mating, and oviposition activity in 2017 9 Medium 23.0 ± 4.3 a addition to survival of eggs and larvae. Adult D. suzukii can use a wide 2017 9 Low 18.2 ± 3.3 a range of habitats, moving over the course of the day (Evans et al., 2017; Treatments with the same letters do not separate, according to Dunn’s test Van Timmeren et al., 2017b; Jaffe and Guédot, 2019; Swoboda- (2016) or Tukey-Kramer test (2017) both at α = 0.05; n = number of samples. Bhattarai and Burrack, 2020) and season (Tait et al., 2018; Santoiemma et al., 2019) to find resources and optimal microclimates. Canopy ma- Table 7 nipulations may have altered adult D. suzukii within-crop foraging and Mean ± standard error D. suzukiisurvival per bag in Oregon blueberries in 2015 oviposition activity by changing habitat conditions. Canopy manip- (203 eggs bag−1) and 2017 (73 eggs bag−1). Data from both years was com- ulations may have additionally altered the location and availability of bined for statistical analysis. No statistical analysis was performed for 2016 (76 preferred oviposition sites within the crop canopy. In contrast, eggs eggs bag−1) data, because almost all larvae died due to high temperatures. and, to a lesser extent, larvae are confined within fruit. Although the −1 Laboratory controls emerged 95.0 ± 19.7 D. suzukiibag in 2015 and fruit provides moisture and buffers fluctuations in ambient tempera- −1 24.2 ± 2.8 D. suzukiibag in 2016. Laboratory controls were not performed in ture, immature D. suzukii are less able to escape unfavorable fruit 2017. temperatures and may die. Our artificial infestation experiments Year Canopy n D. suzukii Canopy n D. suzukii quantified immature D. suzukii survival under various blueberry canopy −1 −1 location bag density bag conditions. In this setting, both canopy density and location within the canopy affected immature D. suzukii survival. In Oregon artificial in- 2015 + 2017 Top 27 16.7 ± 3.7 High 25 20.2 ± 6.3 b festation experiments, canopy location influenced D. suzukii survival 2015 + 2017 Middle 27 31.1 ± 6.1 Medium 23 17.9 ± 4.9 more strongly than canopy density, with the greatest number of flies a surviving in the middle of the plant. In Georgia, canopy density im- 2015 + 2017 Base 18 0.9 ± 0.4 c Low 24 16.3 ± 4.2 pacted immature survival in one year but not the other. Differences in

2016 Top 18 0.0 ± 0.0 High 18 0.0 ± 0.0 results were likely due to ambient weather during the week of exposure. 2016 Middle 18 0.0 ± 0.0 Medium 18 0.0 ± 0.0 Although variation in weather also influenced the natural infestation 2016 Base 18 0.1 ± 0.1 Low 18 0.1 ± 0.1 data, in comparison to the artificial infestation trials the impact of week-to-week variation in weather was reduced because data were Means with the same letters in each column do not separate with a Tukey- collected over multiple weeks and averaged. Both natural and artificial Kramer test at α = 0.05; n = number of samples. infestation metrics indicate that canopy density and location play a role in D. suzukii infestation patterns. type of analysis. Blueberries experienced lower levels of field infesta- Crop canopy microclimate, specifically light penetration, tempera- tion relative to caneberries, which is likely due to D. suzukii preference ture, and humidity, can alter fruit quality (Prange and DeEll, 1997; and pressure (Burrack et al., 2015). Raspberries and blackberries tend Haselgrove et al., 2000; Edgley et al., 2019). In this study, we quanti- to be the most preferred and highest quality fruit hosts for D. suzukii fied the effect of canopy density on fruit firmness and TSS becauseboth (Bellamy et al., 2013; Burrack et al., 2013; Little et al., 2017). In ad- can influence D. suzukii infestation in small fruit crops. Drosophila su- dition, caneberry crop phenology, especially in primocane-fruiting zukii prefer softer fruit as demonstrated by choice tests with various cultivars, overlaps with higher D. suzukii pressure because they ripen blueberry varieties (Lee et al., 2011; Kinjo et al., 2013) and different later in the season than most blueberry cultivars. The lower infestation berry fruits (Little et al., 2017). In our multi-state studies, fruit firmness rates observed in blueberries reduced our statistical power; sites and was not affected by canopy density; however, low density Maryland years without observed infestation were removed from the meta-ana- raspberry canopies exhibited slightly (0.3 cN) softer fruit. In caneberry lysis because the polyserial correlation was not estimable. Although and blueberry fruit, higher TSS was previously correlated with greater differences were statistically difficult to detect and subtle, ourdata

10 T. Schöneberg, et al. Agriculture, Ecosystems and Environment 294 (2020) 106860

D. suzukii oviposition (Lee et al., 2011, 2016). In contrast, Little et al. total and marketable yield in caneberries, with the lowest yield ob- (2017) reported that fruit with lower TSS and pH were preferred in served in low density treatments. The potential for long-term effects of oviposition choice tests. Similar to field studies in blackberries (Takeda, the pruning treatments on yield over time were not addressed in the 2002) and grapes (Haselgrove et al., 2000), TSS was not affected by current study, but should be considered to fully understand the eco- canopy density or canopy location in this study. Neither fruit firmness nomic implications of pruning to reduce infestation by D. suzukii. nor TSS explain the D. suzukii infestation patterns observed in our Although heavy pruning can reduce yield, lower density canopies study. also have production benefits. Open canopies can improve harvest ef- Increasing canopy density slightly reduced berry temperatures in ficiency and decrease the likelihood of overripe fruit remaining inthe both crops [small negative correlation coefficients in both meta-ana- field (Prange and DeEll, 1997), which is particularly important for pick- lyses, 0.2–0.4 °C difference between overall treatment means], likely your-own operations because consumers prefer to harvest canopy ex- because surrounding leaves increased shade. Additionally, exterior fruit teriors, leaving berries in the interior. Improved harvest efficiency were 0.4–0.7 °C warmer than interior fruit in both crops in Maryland. In could also help with sanitation control measures by decreasing D. su- laboratory choice oviposition studies, most of the total oviposition (90 zukii oviposition sites. Moreover, less dense canopies can improve spray %) occurred on fruit that were between 16.0–28.6 °C, with 50 % of total coverage (VanEe et al., 2000; Cross et al., 2001), especially in the ca- oviposition on fruit between 19.7–24.8 °C (Zerulla et al., 2017). Ovi- nopy interior (Yeary et al., 2018). Some of our study sites used organic position activity decreased at fruit temperatures above 28 °C (Zerulla insecticides, and improved coverage may have contributed to reduc- et al., 2017). Although mean fruit temperatures exceeded the most tions in D. suzukii infestation; however, similar trends were also ob- preferred thermal conditions in both crops, only blueberry tempera- served in unsprayed sites and artificial infestation experiments. The tures exceeded 28 °C. Ambient temperatures were warmer at blueberry potential for synergism between the effects of pruning on canopy mi- and blackberry sites (supplementary material Table S3) and their dark croclimate, harvest efficiency, and spray coverage makes canopy ma- color likely absorbs more solar radiation (Wheelwright and Janson, nipulation attractive as part of an IPM approach for D. suzukii. 1985). In Maryland, higher D. suzukii incidence occurred in interior In other systems, crop habitat has been successfully manipulated for Maryland raspberries, which were on average 0.7 °C cooler (27.0 ± 0.3 larger changes in microclimate that negatively affect development and °C) than the exterior fruit. Berry temperature did not affect D. suzukii survival of pests. Similar to our observations, shade decreased tem- infestation in the meta-analysis and did not correspond with infestation perature (up to 5 °C) and increased humidity (up to 10 %) in coffee in other analyses. plants, increasing Costa Rican weevil [Exophthalmus jekelianus White Meta-analyses revealed that Drosophila suzukii infestation was more (Coleoptera: Curculionidae)] visual damage ratings (0–5 scale) directly connected to very small canopy density-mediated changes in from < 0.2 to > 1 (Henderson and Roitberg, 2006). Comparably, in canopy microclimate. Studies in grapes (Kraus et al., 2018) and annual crops, altering microclimate through mowing increases average blackberries (Diepenbrock and Burrack, 2017) report higher humidity hay sward temperature (2−3 °C), reducing grasshopper abundance in dense crop canopies, in comparison to field edges or more open ca- [Orthoptera: Acrididae] by more than half (Gardiner and Hassall, nopy architectures. Drosophila suzukii prefer high humidity environ- 2009). Additionally, the importance of refuge microhabitats is pre- ments (when RH differential is ≥ 25 %)(Fanning et al., 2019; Tait dicted to increase as climate changes. Using non-mobile pests as a et al., 2019), exhibit greater fecundity and longevity at ≥ 82 %RH model for inaccessible refuges, apple leafminer [Phyllonorycter blan- (Tochen et al., 2016), are more active within crops during periods of cardella Fabricius (Lepidoptera: Gracillariidae)] development rates vary high humidity (Evans et al., 2017; Van Timmeren et al., 2017b; Jaffe significantly with 1.5 °C differences in temperature (Pincebourde et al., and Guédot, 2019), and are affected by crop irrigation in dry climates 2007). Climate change model simulations indicate that pruning in- (Rendon and Walton, 2018). In addition to RH, temperatures above 30 creases apple leafminer mortality across predicted climate change sce- °C negatively influence D. suzukii development, reproduction rate, and narios, with overall mortality increasing as temperatures warm survivorship (Tochen et al., 2014; Ryan et al., 2016; Evans et al., 2018; (Saudreau et al., 2013). Thus, microhabitat refuges will be increasingly Green et al., 2019). Thus, the microclimate in high canopy density critical for buffering extreme conditions (Scheffers et al., 2014), and caneberry treatments likely provided a better habitat for D. suzukii. potentially provide further opportunity to manage insect pests through Beyond temperature and RH, canopy density may alter light penetra- manipulations of crop microclimate. tion. Drosophila suzukii are most active at dawn and dusk (Evans et al., 2017; Van Timmeren et al., 2017b; Jaffe and Guédot, 2019; Swoboda- 5. Conclusions Bhattarai and Burrack, 2020), as well as in shadier interior canopy locations (Rice et al., 2017). Therefore, low light intensity habitats may Although we observed relatively small temperature (0.1–0.7 °C) and be preferred, as exhibited in the closely related Drosophila melanogaster relative humidity (0.5–1.3 %RH) differences between caneberry canopy Meigen (Rieger et al., 2007). densities and locations, small reductions (0.14–0.2 D. suzukii (g Pruning of berry crops is a complex process with many interacting fruit)−1) in infestation occurred in exterior fruit and low canopy den- variables (Gundersheim and Pritts, 1991). Canopy manipulations can sity caneberries. In addition, canopy density and canopy location af- stimulate plant growth (Gundersheim and Pritts, 1991; Strik et al., fected immature D. suzukii survival in artificially infested blueberries. 2003), and high lateral cane density can reduce yield due to crowding, These subtle differences may become more pronounced over longer shading, and abiotic resource limitations (Takeda, 2002; Oliveira et al., periods of time. Larger effects on microclimate might be achieved by 2004). However, cane density and yield are positively correlated at combining cultural strategies, such as drip irrigation to reduce humidity most cane densities (Gundersheim and Pritts, 1991; Strik and Cahn, and trellising to create an open canopy. Canopy manipulation can in- 1999). Canopy responses to our pruning treatments varied (supple- fluence biological control efficacy in addition to improving spraycov- mentary material Table S4), and pruning in the dormant season often erage and potentially increasing insecticide efficacy. While further op- did not generate quantifiable differences in canopy LAI, PAR, andLUX timization and integration with other management strategies will be at the time when measurements were taken, during the harvest season. necessary, canopy manipulation has potential as part of IPM programs Regrowth affected the intended high, medium, and low pattern, espe- for D. suzukii and is an important horticultural practice to improve cially for the medium density treatment, and this may have inhibited production efficiency. treatment separation in this study. Our canopy density treatments reduced total yield but not marketable yield in caneberries and did not Declaration of Competing Interest affect yield in blueberries across study sites. However, yield varied dramatically across the study sites. In Maryland, canopy density altered The authors declare that they have no known competing financial

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