Wildflower habitats in turfgrass systems have variable effects on arthropod functional group abundance over multiple years

doi:10.21203/rs.3.rs-2468291/v1

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Wildflower habitats in turfgrass systems have variable effects on arthropod functional group abundance over multiple years

https://doi.org/10.21203/rs.3.rs-2468291/v1

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Urbanization is rapidly influencing the abundance and diversity of arthropods. Within urban systems, managed turfgrass is a prominent cultivated crop which can support certain arthropod populations, but is inadequate for supporting many others. In an effort to mitigate the negative effects of turfgrass on arthropod biodiversity, increasing numbers of land managers are choosing to partially convert turf habitat to wildflower habitat using commercially-available seed mixes. However, the population dynamics of diverse arthropod groups in these systems is poorly known, with consequentially little information on best long-term practices for managing wildflower habitats in turfgrass systems. To address this gap, we sampled arthropods in turfgrass systems pre- and post-implementation of wildflower habitats and examined the change in abundance of multiple taxa and functional groups. Arthropod groups had diverse responses to wildflower habitat implementation, with bees having no significant changes in abundance, butterflies being most abundant one-year post-implementation, and predatory wasps and flies being relatively more abundant one- and two-year post-implementation. These results point to the need for more research on the long-term effects of wildflower habitats on beneficial insects in turfgrass habitats, and how to optimize management of both wildflower habitats and nearby turfgrass to support arthropod biodiversity.

Turfgrass

arthropod conservation

habitat management

urban ecology

pollinators

natural enemies

Urbanization in the US has been widely documented, with approximately 2.2 million acres of land converted to urban area each year (McFrederick and LeBuhn 2006). Urbanization has been implicated in lower arthropod abundance and species richness in many regions globally (Bates et al. 2011, Fenoglio et al. 2020). Turfgrass is a prevalent plant system in urban areas, particularly in residential, commercial, and recreational spaces. Although turfgrass supports relatively high arthropod abundance and diversity compared to other urban ground cover types (Potter and Braman 1991, Reng-Moss et al. 1998, Norton et al. 2014), managed turfgrass tends to support lower arthropod biodiversity than more diverse and structurally complex plant communities (Francoeur et al. 2021). Some highly managed turfgrass systems also experience frequent disturbance and chemical inputs in the form of mowing and pesticides, further limiting their ability to support diverse arthropod communities (Lyman et al. 2007, Dobbs and Potter 2015, Norton et al. 2019). To offset this trend, it is essential to investigate the potential methods by which green spaces can support and augment arthropod communities.

One increasingly popular method for supporting arthropods in urban areas is planting wildflower habitat. Wildflower habitats provide pollen and nectar resources, alternative prey, and refuge from frequent disturbance (Frank and Shrewsbury 2004, Baldock et al. 2020, Ganser et al. 2021). Adding wildflower habitat to turfgrass areas such as golf courses can support elevated pollinator and natural enemy abundance and diversity (Frank and Shrewsbury 2004, Dobbs and Potter 2016). This has the added benefit of reducing input and labor demands required by turfgrass management, and can potentially augment biological control of turfgrass pests (Dale et al. 2020, Frank and Shrewsbury 2004). However, despite the demonstrated benefits to many taxa, wildflower habitats do not necessarily boost the abundance of all beneficial arthropod groups (Braman et al. 2002, Frank et al. 2007). For example, Braman et al. (2002) did not observe an increase abundance of ground-dwelling spiders and ants in wildflower plots adjacent to turfgrass, representing two major natural enemy groups that are potentially overlooked by current wildflower habitat management practices. In order to fully understand how wildflower habitats support arthropod diversity, it is necessary to quantify how a broad array of taxa and functional groups respond to wildflower habitat implementation.

Although turfgrass is highly prevalent in urban areas, studies of wildflower habitat in turfgrass are relatively few compared to that in agricultural systems (Billeisen et al. 2022, Dale et al. 2020, Frank and Shrewsbury 2004, Braman et al. 2002). Of these studies, even fewer examine changes in arthropod taxa more than one year following habitat establishment (Frank and Shrewsbury 2004, Braman et al. 2002). Influential factors like vegetation complexity, flower abundance, and plant diversity can change with successional age in wildflower habitats (Albrecht et al. 2021, Steffan-Dewenter and Tscharntke 2001). Habitat age can consequently significantly affect the abundance and richness of several arthropod groups, with variable changes occurring over time depending on trophic level and taxon (Campbell et al. 2020, Frank et al. 2012, Noordijk et al. 2010). For example, Albrecht et al. (2021) found that wild bee and hoverfly abundance declined two years after the establishment of wildflower strips. Predator-prey ratios have also been observed to increase in older habitats compared to newly established habitats (Denys and Tscharntke 2002). Wildflower habits are intended to persist for extended periods of time, which requires long-term management plans. It is therefore important to understand the multi-year effects of wildflower habitat on arthropod communities in turfgrass systems.

This study examines changes in the abundance of common insect taxa collected in turfgrass habitats before and for two years after establishment of nearby wildflower habitats. We also explore whether the abundance of insect functional groups changes post-wildflower establishment. We expect all examined taxa and functional groups to show increases in abundance post-wildflower establishment. Ultimately, these preliminary questions will point to further research needs in a long-term wildflower habitat-establishment study.

Insect sampling

Insects were collected from six managed turfgrass habitats in the Piedmont and Sandhill ecoregions of North Carolina, comprising four golf course sites and two home lawns. In 2018, insect sampling occurred on sunny days at all sites every 2–4 weeks from July through October. During each sampling bout, surveyors walked the entire site at a moderate pace for 20 minutes and conducted modified sweep net sampling. All collected insects were sacrificed in a collection jar with ethyl acetate and stored in Whirl-Pak bags (NASCO Sampling/Whirl-Pak, Madison, WI) with 70% ethanol solution until further processing.

In addition to sweep net samples, 15 pan traps were placed along a 68.5 m transect within the turf directly parallel to each future wildflower site, with 4.572 m between each pan trap. Pan traps were painted either blue, white, or yellow (Rust-Oleum, Vernon Hills, IL), with traps alternating in color. Each pan trap was filled with approximately 120 mL of a solution of 5 mL dish detergent (Dawn, Procter & Gamble, Cincinatti, OH) diluted with 3.8 L water. Pan traps were deployed between 0800–1000 hrs and retrieved after approximately six hrs. Collected arthropods were then rinsed and stored in a Whirl-Pak bag. Specimens were either washed and pinned or placed in vials with ethanol.

In October 2018, wildflower plots were prepped by clearing existing vegetation and bare soil was seeded with American Meadows Southeast Pollinator Wildflower seed mix (American Meadows, Shelburne, VT, plant species listed in Supp. Table 1) at a density of 22.68 kg seed/acre. In 2019, surveyors sampled for insects at all sites every 2–4 weeks from May to October using the methods described above. In October 2019, sites were mowed and re-seeded. In early 2020, large plant species (largely trees and shrubs) were removed from the sites. Surveyors resumed sampling for insects at all sites every 2–4 weeks from July to October 2020. Pesticide inputs were minimal at golf course sites in early 2020 due to reduced management as a result of the COVID-19 pandemic.

Identification

All insects were identified to family, superfamily (for Cynipoidea, Chalcidoidea, and Proctotrupoidea) or order (Thysanoptera and a single damaged Zygopteran specimen) (henceforth “families”) using Marshall 2020 and other sources. All relevant families were assigned to four functional groups of conservation concern: bees, butterflies, predatory flies, and predatory wasps. We examined the response of these taxa as collective functional groups or feeding guilds (henceforth “guilds”), due to their high abundance in our sampling and their relevance as beneficial taxa in turfgrass systems (Supp. Tables 2). Bees and butterflies were assigned to separate guilds due to differences in feeding strategy through development, which may affect their responses to wildflower plantings. Similarly, flies and wasps were examined as separate predatory groups to determine whether these guilds have different responses to these habitats. “Predatory” in this instance refers to families with both predatory and parasitoid feeding strategies. Though we recognize that these four guilds contain families with diverse life history strategies, we elected to focus on the dominant reported feeding strategy in an effort to represent broad responses across guilds. For bees specifically, our collections was overwhelmingly comprised of non-brood parasites, which further supported treating bees as a distinct guild.

Data analysis

In order to standardize the sampling period across years, we excluded samples that were collected in May and June from analyses, since these months were not sampled in 2018 and 2020.

For every unique combination of site and date, we pooled family and guild abundance values across trapping methods. Abundance data were square root-transformed to improve normality. To determine whether the abundance of each insect family or guild captured per sampling event differed within and across years, we constructed separate linear mixed-effects models for each family and guild. In each model, year, Julian day, and their interaction were included in the model as explanatory variables. Year was treated as a factor and Julian day was treated as a continuous variable. Site identity was included in the models as a random effect. Families with at least 30 total individuals collected across all three years were included in these analyses.

Statistical analyses were conducted using R software (v.4.2.1, R Core team 2022). All linear mixed-effects models were constructed using the package ‘lme4’ (Bates et al. 2017) and tested using the ‘Anova’ function within the package ‘car’ (Fox and Weisberg 2011). Where interactions were significant, Type III sums of squares were calculated. For families and guilds with a significant main effect of year and no interaction effect, we compared pairwise year means using the ‘tukey’ method within the package ‘emmeans’ (Lenth et al. 2022). Sampling abundance curves were generated using the package ‘BiodiversityR’ (Kindt and Coe 2005).

In total, we collected 8532 insects across three years of sampling, comprising 7 orders and 71 families. Of this total number, 3762 (44.1%) were in Dolichopodidae, 2202 (25.8%) were in Sarcophagidae, and 555 (6.5%) were in Halictidae. We collected 34 unique taxa in 2018, 55 in 2019, and 41 in 2020. A sampling abundance accumulation curve of family richness indicates that species richness in 2019 and 2020 was similar. The overall family diversity in 2018 does not approach an asymptote, suggesting that sampling may have inadequately captured family diversity during that year (Supp. Figure 1). Abundance of insects per sampling date varied widely, with a range of 1-289 individuals captured on a given sampling date pooled across all sites (Supp. Figure 2.).

Family abundance

Of the 18 insect families included in our analyses, nine exhibited a significant interaction effect between year and Julian day on abundance per sampling event (model results summarized in Table 1). This included Aphididae (F_2,92 = 3.35, p = 0.04, Fig. 1a), Apidae (F_2,92 = 8.86, p < 0.001, Fig. 1b), Dolichopodidae (F_2,91 = 5.80, p < 0.01, Fig. 1c), Formicidae (F_2,93 = 7.60, p < 0.001, Fig. 1d), Hesperiidae (F_2,93 = 4.46, p = 0.01, Fig. 1e), Miridae (F_2,93 = 4.53, p = 0.01, Fig. 1f), Scoliidae (F_2,91 = 3.45, p < 0.05, Fig. 1g), Sphecidae (F_2,91 = 3.45, p = 0.04, Fig. 1h), and Vespidae (F_2,92 = 4.73, p = 0.01, Fig. 1i). In general, there were more apids, scoliids, sphecids, and vespids in late 2020 compared to late 2018 and late 2019 (Figs. 1b, 1g, 1h, 1i, respectively). In both late 2019 and late 2020, there were more dolichopodids than in late 2018 (Fig. 1c). Several groups showed an apparent drop in abundance between equivalent periods in subsequent years, with more aphids and formicids in late 2019 compared to late 2020 (Figs. 1a and 1d, respectively), as well as more hesperiids in late 2018 and late 2019 compared to late 2020 (Fig. 1e).

We observed a significant year effect for five families; Calliphoridae (F_2,94 = 4.87, p = 0.01, Fig. 1j), Cicadellidae (F_2,94 = 6.77, p < 0.01, Fig. 1k), Crabronidae (F_2,93 = 4.95, p = 0.01, Fig. 1l), Halictidae (F_2,92 = 10.16, p < 0.001, Fig. 1m), Muscidae (F_2,94 = 7.02, p < 0.01, Fig. 1n) and Sarcophagidae (F_2,92 = 10.62, p < 0.001, Fig. 1o). There were significantly more calliphorids (t_95.7 = 2.86, p = 0.04), cicadellids (t_95.7 = 3.64, p < 0.01), halictids (t_92.7 = 4.38, p < 0.001), and muscids (t_95.7 = 2.87, p = 0.01) in 2019 than in 2020. There were significantly more crabronids (t_91.2 = 2.78, p = 0.01) and sarcophagids (t_91.1 = 2.33, p < 0.001) in 2020 than 2018. Finally, there were significantly more muscids (t_93.8 = 3.11, p = 0.01) and sarcophagids (t_92.0 = 4.31, p < 0.001) 2019 compared to 2018.

There was a slight but significant effect of Julian day on abundance per sampling bout for Muscidae (F_1,93 = 11.47, p < 0.001) and Syrphidae (F_1,93 = 40.55, p < 0.001, Fig. 1p), with a small increase in abundance over a given year for both families (0.00004 and 0.0003 individuals for each successive Julian day, respectively).

There was no significant effect of either year, Julian day, or their interaction on the abundance of Chloropidae or Ichneumonidae (0.05 < p < 0.81, Fig. 1q and 1r, respectively). However, there was a marginally non-significant interaction effect between year and Julian day on ichneumonid abundance, such that ichneumonids were more abundant in late 2019 and late 2020 compared to late 2018 (F_2,91 = 3.08, p = 0.05, Fig. 1r).

Table 1

Results of linear mixed-effects models examining abundance of each family per sampling event (ANOVA (Type II or III Wald F-tests with Kenward-Roger degrees of freedom).
	Year				Julian day				Year*Julian day
Family	F statistic	df	df residual	p-value	F statistic	df	df residual	p-value	F statistic	df	df residual	p-value
Aphididae	1.98	2	91.54	0.14	0.09	1	92.39	0.77	3.35	2	91.54	0.04*
Apidae	6.84	2	91.50	< 0.01*	1.41	1	92.31	0.24	8.86	2	91.50	< 0.001*
Calliphoridae	4.87	2	94.46	< 0.01*	0.68	1	93.41	0.41	1.49	2	93.47	0.23
Chloropidae	1.49	2	92.12	0.23	0.10	1	93.83	0.76	2.45	2	92.15	0.09
Cicadellidae	6.77	2	94.29	< 0.01*	0.09	1	92.99	0.76	1.13	2	93.11	0.33
Crabronidae	4.95	2	92.90	0.01*	1.19	1	91.56	0.28	0.97	2	91.68	0.38
Dolichopodidae	2.90	2	91.30	0.06	2.06	1	91.78	0.15	5.80	2	91.30	< 0.01*
Formicidae	6.36	2	92.54	< 0.01*	6.34	1	94.73	0.01*	7.60	2	92.61	< 0.001*
Halictidae	10.16	2	91.95	< 0.001*	3.25	1	91.22	0.07	0.55	2	91.28	0.58
Hesperiidae	13.03	2	94.25	< 0.001*	17.27	1	92.90	< 0.001*	4.46	2	93.03	0.01*
Ichneumonidae	1.91	2	91.33	0.15	0.06	1	91.86	0.81	3.08	2	91.33	0.05
Miridae	4.24	2	92.68	0.02*	4.07	1	94.99	< 0.05*	4.53	2	92.75	0.01*
Muscidae	7.02	2	94.37	< 0.01*	11.47	1	93.17	< 0.01*	0.26	2	93.26	0.78
Sarcophagidae	10.62	2	92.39	< 0.001*	1.76	1	91.36	0.19	0.02	2	91.44	0.98
Scoliidae	12.44	2	92.71	< 0.001*	2.44	1	91.48	0.12	3.10	2	91.58	< 0.05*
Sphecidae	2.28	2	91.48	0.11	0.24	1	92.24	0.62	3.45	2	91.48	0.04*
Syrphidae	1.19	2	94.07	0.31	40.55	1	92.57	< 0.001*	2.22	2	92.72	0.11
Vespidae	2.23	2	92.37	0.11	0.00	1	94.37	0.97	4.73	2	92.42	0.01*
*Significance levels: p < 0.05, p < 0.01, p < 0.001*

Guild abundance

Model results for guilds are summarized in Table 2. There was no significant effect of either year (F_2,91 = 2.53, p = 0.09), Julian day (F_1,91 = 4.44, p = 0.04), or their interaction (F_1,91 = 2.34, p = 0.10, Fig. 2a) on overall bee abundance.

There was a significant effect of year on butterfly (F_2,94 = 13.01, p < 0.001, Fig. 2b) abundance. There were significantly more butterflies in 2019 than both 2018 (t_93.3 = 2.53, p = 0.03) and 2020 (t_95.4 = 5.28, p < 0.001). In addition to the main effect of year, there was also a significant effect of Julian day on abundance per bout in butterflies. Butterflies demonstrated a slight but significant positive correlation between abundance per bout and Julian day (F_1,92 = 19.99, p < 0.001), with an average increase of 0.0002 individuals for each subsequent Julian day.

Predatory wasps exhibited a significant interaction effect between year and Julian day on abundance per sampling event (F_2,91 = 5.70, p = 0.01, Fig. 2c), with a general higher abundance of wasps in late 2019 and 2020 compared to late 2018. There was also a significant interaction day between year and Julian day on predatory fly abundance (F_2,91 = 3.97, p = 0.02, Fig. 2d), again with a general higher abundance of predatory wasps in late 2019 and 2020 compared to late 2018.

Table 2

Results of linear mixed-effects models examining abundance of each guild per sampling event (ANOVA (Type II or III Wald F-tests with Kenward-Roger degrees of freedom). Starred values indicate significance (α = 0.05).
	Year				Julian day				Year*Julian day
Family	F statistic	df	df residuals	p-value	F statistic	df	df residuals	p-value	F statistic	df	df residuals	p-value
bees	2.53	2	91.36	0.09	4.44	1	91.93	0.04*	2.34	2	91.35	0.10
butterflies	13.01	2	93.79	< 0.001*	19.99	1	92.19	< 0.001*	3.06	2	92.35	0.05
predatory flies	1.74	2	91.30	0.18	0.22	1	91.78	0.64	3.97	2	91.29	0.02
predatory wasps	2.80	2	91.25	0.07	0.94	1	91.66	0.33	5.25	2	91.25	0.01*
*Significance levels: p < 0.05, p < 0.01, p < 0.001*

As predicted, none of the examined groups decreased in abundance in 2019. However, bees and several families, including chloropids, ichneumonids, and syrphids, showed no change in abundance between years, indicating no significant effect of wildflower habitat. This trend was particularly unexpected concerning bees, syrphids, and ichneumonids, since these groups typically depend on floral resources for at least part of their life cycle. In a 2022 study, Billeisen et al. examined differences in bee abundance between 2018 and 2019 alone using the same system, and observed a significant increase between the two years. Indeed, when 2020 data is omitted from the present analyses, we observe a significant jump in bee abundance from 2018 to 2019. However, we observed no differences in bee abundance when comparing 2018 and 2020 alone. This distinction is noteworthy because, even if bee abundance may show an apparent peak one year post-wildflower establishment, bee abundance without continued management may ultimately drop to pre-implementation levels. Bee abundance in 2019 and 2020 may have also been somewhat impacted by the increased abundance of predatory flies and wasps, leading to an overall lack of a significant effect on bee abundance. When examining factors that may influence syrphid abundance, attractiveness of flowers to syrphids can vary between flower colors and inflorescence shapes (Colley and Luna 2000). Our seed mix contained Lobularia maritima, which is known to be an especially effective attractant of syrphids (Hogg et al. 2011). However, L. maritima did not flower successfully in our plots. In addition, bee visitation to other flowers may competitively exclude syrphids (Hogg et al. 2011). Therefore, our wildflower habitats may have been effectively neutral in terms of supporting syrphids. Similar trends may also partially explain the lack of an effect on ichneumonids, which, like syrphids, preferentially feed on flowers with short corollas (Jervis et al. 1993, Campbell et al. 2012). Chloropids have a wide array of lifestyles, but include many grass feeders, including species that may be considered turf pests (Tolley and Niemczyk 1988). Consequently, it is possible that wildflower habitat plays a minimal role in affecting chloropid abundance in turfgrass habitats.

Some families (Calliphoridae, Muscidae, Halictidae, and Cicadellidae) and one guild (butterflies) showed significant declines in abundance in 2020 compared to 2019. Several families (Aphididae, Formicidae and Hesperiidae) were also less abundant in late 2020 compared to a similar time period in previous years. The reasons for these declines are not immediately clear, but may be due to a number of interacting factors. First, we observed a decline in flower abundance at our sites in 2020. Out of the 16 plant species included in the American Meadows Southeast Pollinator Wildflower seed mix, nine are annual, and the self-seeding success of these species is not known. In agricultural systems, wildflower strips tend to have more perennial plant species and lower plant diversity as succession progresses (Frank and Künzle 2006). Although the sites were re-seeded in 2019, the seeds were not placed on bare soil as they were in 2018, which may have reduced germination success. In addition, although trees and large shrubs were removed in early 2020, other small plants were not removed except for mowing. This may have contributed to an overall decline in floral abundance in our plots. Reduced floral resources may play a role in the reduced number of halictids and butterflies in 2020, as abundance of these groups can respond strongly to variation in floral resource abundance (Potts et al. 2003, Myers et al. 2012, Stein et al. 2020). These results point to the necessity of continued active management of wildflower habitats, as well as maintaining mixed successional-stage wildflower habitats to support a large diversity of arthropod taxa (Eccard 2022).

Interguild interactions are another factor in this system that may affect abundance between years. The effects and repercussions of interguild predation are variable and often difficult to predict (Tscharntke et al. 2016, Martin et al. 2013). For example, overall predatory wasp and fly abundance was high in both late 2019 and late 2020. This surge in predators and parasitoids may have potentially depressed prey groups. Aphids and ants were both more abundant in late 2019 compared to late 2020, which may reflect interdependency between aphids and attending ants (Sanchez et al. 2020). Apids were also more abundant in late 2020, and interspecific competition for floral resources may have also depressed other pollinating individuals (Fründ et al. 2013).

Another factor that may have contributed to declines is staggered pesticide inputs in 2020. Early in the 2020 growing season, land managers at golf courses were unable to make regular pesticide applications due to the COVID-19 pandemic. Therefore, individuals that were released from pesticide pressure early in the year may have been subsequently hit during a vulnerable life history stage when management practices resumed later in the year. Both butterflies and muscids showed slight but significant increases in abundance as the year progressed, and thus were potentially especially affected by latent inputs in 2020. The most common butterfly species from our samples were Hylephila phyleus and Lerema accius, both of which depend on grasses as larval host plants (Tashiro and Mitchell 1985, Ainslie 1922), and would therefore be affected directly by turf pesticides. Reduced chemical inputs in 2020 may have also caused an increase in arthropod prey that were otherwise not well detected in our pan traps, thereby boosting the populations of predator groups. For example, crabronids were more abundant in 2020 than in 2018. Anecdotally, captured cabronid species included Cerceris bicornuta, a known predator of the turfgrass pests Sphenophorus spp. (Evans and Rubink 1978). Scoliids, sphecids, and vespids were also abundant in late 2020, and these taxa include predators of green June beetles (Cotinis nitida) (Alcock 2016), fall armyworm (Spodoptera frugiperda) (Held et al. 2008) and other turfgrass pests. However, it remains unclear to what degree chemical inputs played a role in these declines. To disentangle these effects, it may be necessary to monitor arthropod populations in turfgrass systems with variable pesticide inputs.

It should be noted that our trapping methods may be inadequate for estimating the abundance of certain taxa. Sticky traps are known to be an effective method for sampling parasitoid wasps (Campbell et al. 2012). Therefore, overall parasitoid wasp abundance may be poorly reflected by our study. In addition we did not employ pitfall trips, which are a common method for trapping ground-crawling predators such as carabids and spiders (Reng-Moss et al. 1998, Saarikivi et al. 2010). Other studies have observed changes in ground crawling predator abundance between years in turfgrass systems while using pitfall traps (Braman and Pendley 1993, Braman et al. 2002). Ground-crawling predators therefore represent another major component of the natural enemy community which is likely overlooked by the present study.

This study points to a number of research priorities for turfgrass systems. Long-term arthropod monitoring beyond the scope of this study (i.e. > 2 years post-wildflower establishment) is essential. Using a long-term wildflower plot, researchers can track diverse arthropod taxa over multiple years, tease out intraguild interactions, and monitor how turfgrass management practices may affect arthropod diversity and abundance. Ultimately, the goal of a long-term monitoring project would be to identify best practices for maintaining the benefits of wildflower habitat over many years. Between 2005 and 2017, approximately 46% of golf courses expanded the amount natural habitat present on the course (Gelernter et al. 2017), pointing to the increasing need for research-based management. This rise also points to widespread trends within the industry towards reducing areas of intensively-managed turfgrass, and the opportunities for creating more impactful conservation areas are substantial. In the southeastern US, urban and suburban areas are expected to increase 200-300% by 2060 (Terando et al. 2014). Given the increased predominance of urban systems, it is essential to tailor management of wildflower habitats in turfgrass systems in a way that mitigates the negative effects of urbanization on arthropods.

Statements and Declarations

Funding

This work was supported by a grant from the Center for Turfgrass Environmental Research and Education.

Competing Interests

The authors declare no competing financial or non-financial interests.

Author Contributions

Data analysis was performed by L.E.H. The first draft of the present manuscript was written by L.E.H. and reviewed and edited by T.L.B. and L.D.K. All authors approved the final manuscript.

Acknowledgements

We thank all land managers and homeowners, including Keith Roberson, Joe York, Bob Farren, Brooke Barker, Dr. Danesha Seth Carley, and Justin and Laura Fox for their time and cooperation in this study. Sam Stoyko and Andrew Clarke collected and pinned specimens. Grant Goodman assisted with specimen labelling and data management.

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Wildflower habitats in turfgrass systems have variable effects on arthropod functional group abundance over multiple years

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Abstract

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Introduction

Methods

Insect sampling

Identification

Data analysis

Results

Family abundance

Guild abundance

Discussion

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References

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