Experimental and Applied Acarology https://doi.org/10.1007/s10493-019-00391-3

Wind speed predicts population dynamics of the eriophyid Floracarus perrepae on invasive Old World climbing fern () in a shade house colony

Aaron S. David1 · Ian M. Jones2 · Ellen C. Lake1

Received: 11 March 2019 / Accepted: 31 May 2019 © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Abstract Lygodium microphyllum is one of the most noxious invasive plants in Florida, USA, smoth- ering native vegetation in cypress swamps, pine fatwoods, and Everglades tree islands and altering fre regimes. The eriophyid mite Floracarus perrepae was introduced from Aus- tralia to help control L. microphyllum infestations. While F. perrepae exhibits high popula- tion growth rates in its native range, its population dynamics in Florida are unknown, par- ticularly the dynamics that occur within the leaf roll galls the mite induces on the margins of leaves. Here, we monitored a shade house colony of F. perrepae in south Florida for 2 years to identify seasonal patterns and potential climate drivers of within-gall mite den- sity. Gall dissections of mite-infested colony plants were performed monthly. Mite density within galls exhibited two cycles per year: a strong cycle that boomed in spring and busted in summer, and a weak cycle that moderately increased mite density in fall and declined in winter. Climate variables, particularly those related to wind speed, were positively associ- ated with higher mite density. Our study sheds light on the within-gall dynamics of F. per- repae and suggests that the highest within-gall mite densities occur in the spring and fall.

Keywords Weed biological control · Population dynamics · Leaf roll galls · Climate factors

Introduction

Lygodium microphyllum (Cav.) R. Br (Polypodiales: Lygodiaceae), Old World climbing fern, is a Category 1 Invasive Plant in Florida, USA (FLEPPC 2019). Originally introduced from its native tropical and subtropical habitat in the Old World (Australia, Asia, Africa), it was frst reported as naturalized in Florida in 1965 (Beckner 1968; Pemberton and Ferriter 1998). The plant invades several habitat types including cypress swamps, pine fatwoods,

* Aaron S. David [email protected]

1 USDA-ARS Invasive Plant Research Laboratory, 3225 College Avenue, Fort Lauderdale, FL 33314, USA 2 Department of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada

Vol.:(0123456789)1 3 Experimental and Applied Acarology and Everglades tree islands where it can smother native vegetation and alter fre regimes (Pemberton and Ferriter 1998; Pemberton et al. 2002). To help manage L. microphyllum infestations in Florida, the mite Floracarus perrepae Knihinicki and Boczek (: ) is currently being reared and released as a biological control agent as part of the Comprehensive Everglades Restoration Plan (U.S. Congress 2000). Floracarus perrepae is the most widely distributed herbivore of L. microphyllum in the plant’s native range, and has been observed in Australia, China, India, and several countries in southeast Asia (Goolsby et al. 2003). It induces marginal leaf roll galls on the subpinnae (subleafets) (Freeman et al. 2005), and, over time, these galls can lead to leaf necrosis and reduce L. microphyllum biomass production (Goolsby et al. 2004). In its native range, F. perrepae populations grow rapidly in the feld (doubling time of 50 days, intrinsic rate of increase of 5.02 per year), in part because of short development times (9–12 days), female-biased populations (10.5:1), and the fact that a single female can initiate galling (Ozman and Goolsby 2005). However, in the years following its approval for release (2006) and initial feld releases in Florida (beginning in 2008), F. perrepae establishment was limited (Boughton and Pemberton 2011; Lake et al. 2014). One potential reason for F. perrepae’s limited establishment in Florida could be unfa- vorable abiotic conditions. Previous work in the native range showed that F. perrepae was active year-round (Ozman and Goolsby 2005). The mite occupied sites with relatively low precipitation, and, within sites, galling was most prevalent when temperatures were below 27 °C (Goolsby et al. 2005; Ozman and Goolsby 2005). Additionally, many species of erio- phyid are known to be wind-dispersed (Smith et al. 2010), thus wind conditions may also be an important factor for dispersal and establishment of F. perrepae. To better establish F. perrepae across Florida, it is important to understand the season- ality of the populations and the abiotic conditions necessary for establishment and popula- tion growth. Yet, we lack an understanding of the abiotic factors underlying population dynamics in its introduced range in Florida, particularly F. perrepae’s activity within galls where they complete their lifecycle. In this study, we monitored a shade house colony of mites in south Florida for 2 years. Our objectives were to identify (1) seasonal patterns of within-gall mite density and (2) potential climate variables associated with within-gall mite density.

Methods

Floracarus perrepae colony

Floracarus perrepae mites were originally collected from the Iron Range, Queensland, Australia (USDA-ARS Australian Biological Control Laboratory, Indooroopilly, Accession Number 2003325) and imported to the USDA-ARS Invasive Plant Research Laboratory, Fort Lauderdale, FL (26°5′6″N, 80°14′24″W) (Boughton and Pemberton 2011). A colony of mites collected from a feld site in Florida was maintained in an outdoor shade house at the laboratory. Lygodium microphyllum plants were propagated from spores collected from local south Florida infestations near Hobe Sound, FL (27°3′29″N, 80°8′13″W). Young plants were repotted to 11.4 L pots as they grew, and fertilized with 12-6-8 slow release (90 day) granulated fertilizer (Diamond-R, Winter Garden, FL, USA) as needed. Plants grew year round, and aboveground portions of plants were trimmed as needed. Plants were watered daily using overhead sprinklers. Floracarus perrepae dispersed among colony

1 3 Experimental and Applied Acarology plants (colony size fuctuated between ~ 150 and 250 plants) and initiated gall formation without researcher assistance. Individual subpinnae often supported multiple galls.

Data collection

We estimated within-gall mite density by conducting monthly gall dissections. Each month, we haphazardly selected fve plants in the colony that exhibited galls. From each plant, we haphazardly selected fve galls throughout the plant to sample across any varia- tion in gall age. We avoided selecting galls that had clearly desiccated. We inspected these galls for mites by carefully unrolling the galls under a dissection microscope and counting the number of live F. perrepae (nymphs and adults). Climate data were derived from 12 variables downloaded from the University of Flori- da’s Florida Automated Weather Network (FAWN) Fort Lauderdale station located 150 m from the shade house colony. We downloaded daily readings for the following: tempera- ture (mean, minimum, maximum), dew point, mean relative humidity, total rainfall, total rainfall during major events (lasting > 15 min), wind speed (mean, minimum, maximum), barometric pressure, and evapotranspiration.

Data analysis

For each sampling period, we quantifed the mean and standard error of the number of mites per gall using the mean mite count of the fve selected plants (N = 5 per sampling period). We similarly estimated the proportion of active galls using the binomial propor- tion of galls containing ≥ 1 mite. Finally, we calculated the mean and standard error of the number of mites in active galls only (i.e., zeros from empty galls were omitted). We used our data to investigate seasonality of mite densities. First, we tested whether the mite density time series data were nonlinear using surrogate tests in the nonlinearT- series R package (Garcia 2019). Then, we identifed peaks and valleys in the data to deter- mine when population boomed and busted. We used principal components analysis (PCA) to summarize climate variables as four major, orthogonal axes. For each sampling date, we averaged daily climate variable meas- urements for the 10 days leading up to and including the sampling date to approximate the development time from egg to adult (Ozman and Goolsby 2005). In preliminary analyses we explored these data using other lengths of time (e.g., 20 days to approximate two gen- erations) and found similar results. We standardized climate variables (mean = 0, standard deviation = 1) for the PCA because variables were measured using diferent units. We investigated associations between climate variables and mite populations using mul- tiple linear regression. We tested for the efects of the predictor variables of year and the frst four principal component axes, on total mites per gall, proportion of active galls, and mites per active gall. We note that we used the principal component axes, and not the cli- mate variables themselves, in these analyses to reduce collinearity and non-independence among the multivariate climate variables. In preliminary analyses, we included the quad- ratic terms of each principal components axis to test for nonlinear trends; because none of these quadratic terms were signifcant, we dropped them from subsequent models. To meet assumptions of normality, mite densities (total and in active galls only) were not trans- formed, while proportions of active galls were logit-transformed. All analyses were con- ducted using R v.3.5.1 (R Development Core Team 2017). 1 3 Experimental and Applied Acarology

Results

Seasonal patterns

Seasonal patterns in within-gall mite density were best characterized as having two cycles during the year—a strong cycle that boomed in spring and busted in summer, and a weak cycle that moderately increased mite density in fall and declined in winter—that were largely consistent between sampling years (Fig. 1). Across all sampling periods, the mean (± S.E.) mite density was 6.96 ± 0.87 individuals per gall (Fig. 1a). Furthermore, 74.2 ± 3.9% of galls contained at least one live mite and were thus considered ‘active’ (Fig. 1b). Within active galls, mean mite density was 8.91 ± 1.00 (Fig. 1c). Mean mite

Fig. 1 Seasonal variation in Floracarus perrepae density in 2017–2018. a Number of mites per gall (mean ± S.E.) across all galls. b Proportion of galls that were active with at least one live mite present (mean ± binomial S.E.). c Number of mites per gall (mean ± S.E.) within active galls only

1 3 Experimental and Applied Acarology density was nonlinear (surrogate test P = 0.006) across the time series, and therefore sug- gestive of seasonality. We observed peaks in mite density in April–May, following by pop- ulation crashes in June-July (Fig. 1a). We also observed moderate increases in mite density in October in both years.

Climate variables

The frst four axes from the PCA explained 91.7% of the variance in climate variables (Table 1). PC1 (46.9% variance explained) was positively associated with dew point (eigenvector loading: 0.41) and temperature metrics (mean temperature = 0.40, minimum temperature = 0.40, maximum temperature = 0.40). PC2 (22.8%) was positively asso- ciated with wind speed metrics (mean wind speed = 0.56, min. wind speed = 0.53, max. wind speed 0.47). PC3 (14.6%) was positively associated with rainfall metrics (total rain- fall = 0.55, total rainfall during major events = 0.47). PC4 (7.0%) was positively associated with mean relative humidity (0.56). We summarized the daily and monthly measurements of four of these metrics (mean wind speed, mean temperature, total rainfall, and mean rela- tive humidity) in Fig. 2. Of the four principal components axes, only PC2 was associated with mean mite densities (Table 2). The PC2 axis was associated with higher mite density (F1,19= 17.0, P < 0.001), proportion of active galls (F1,19 = 5.49, P = 0.03) and mite density in active galls (F1,19 = 12.23, P < 0.001). Because the PC2 axis was highly correlated with wind speed, we conducted additional analyses using mean wind speed as the sole predictor variable, and found that wind speed was positively associated with both mite density (F1,23 = 4.75, 2 2 P = 0.04, ­R = 0.14) and mite density within active galls (F1,23 = 4.75, P = 0.04, ­R = 0.14; Fig. 3), though wind speed had relatively low explanatory power in both analyses. Wind

Table 1 Loadings and variance Climate variable PC1 PC2 PC3 PC4 explained from the frst four axes of a principal components Temperature (mean) 0.40 0.05 − 0.18 0.05 analysis of climate variables Temperature (min.) 0.41 0.06 − 0.12 0.12 Temperature (max.) 0.39 − 0.02 − 0.25 − 0.05 Dew point 0.41 − 0.02 − 0.06 0.20 Relative humidity 0.21 − 0.25 0.37 0.56 Rainfall (total) 0.24 − 0.14 0.55 − 0.20 Rainfall (total during major 0.28 − 0.12 0.47 − 0.32 events lasting > 15 min) Wind speed (mean) − 0.04 0.56 0.14 0.20 Wind speed (min.) 0.00 0.53 0.16 0.38 Wind speed (max.) 0.02 0.47 0.28 − 0.43 Barometric pressure − 0.27 − 0.23 0.01 − 0.05 Evapotranspiration 0.31 0.16 − 0.32 − 0.35

Variance explained (%) 46.9 22.8 14.6 7.0 Climate variables for each month (averaged across the 10 days leading up to and including the sampling date) were standardized (mean = 0 and standard deviation = 1) prior to analysis because variables were measured in diferent units 1 3 Experimental and Applied Acarology

Fig. 2 Daily and monthly averaged climate metrics during the study for a mean wind speed, b mean tem- perature, c total rainfall, and d mean relative humidity. Daily measurements (light colors, thin lines) were downloaded from the University of Florida’s Florida Automated Weather Network (FAWN) Fort Lauderd- ale station located 150 m from the shade house colony. Monthly measurements (dark colors, thick lines) were calculated as the mean of daily measurements for the 10 days leading up to and including the sam- pling date. Note that in panel c the y-axis was truncated to better depict average trends

Table 2 Efects of climate- Term df Mean mites Proportion Mean mites in derived principal components active galls active galls axes on mite density metrics F P F P F P

Year 1 7.70 0.010 0.47 0.50 6.71 0.020 PC1 1 0.03 0.86 1.18 0.29 0.02 0.89 PC2 1 16.97 < 0.001 5.49 0.030 12.23 < 0.001 PC3 1 0.40 0.54 0.36 0.56 0.16 0.69 PC4 1 2.09 0.16 1.50 0.23 1.47 0.24 Residuals 19

Linear models were constructed for each response variable and ana- lyzed using analysis of variance. To meet assumptions of normality, ‘mean mites’ and ‘mean mites in active galls’ data were not trans- formed, and proportion of active gall data were logit-transformed

1 3 Experimental and Applied Acarology

Fig. 3 Association between aver- age wind speed and the number of Floracarus perrepae mites per active gall. Numbers in the plot represent the month galls were sampled. Trendline shows rela- tionship across 2017–2018 data

speed was not associated with the proportion of active galls (F1,23 = 1.33, P = 0.26). None of the other principal component axes (PC1, PC3, or PC4) were signifcantly associ- ated with any of the response variables (P values ranged from 0.16 to 0.89). We found higher overall mite density (F1,19 = 7.70, P = 0.01) and higher mite density in active galls (F1,19 = 6.71, P = 0.02) in 2017 compared to 2018, and the 2 years did not difer in their proportions of active galls (F1,19 = 0.81, P = 0.38).

Discussion

The biological control agent F. perrepae may be an important tool for L. microphyllum management, yet the population biology of the mite in the introduced range is largely unknown, limiting the development of optimal rearing and release strategies. In this study, we showed that mite density within galls of a shade house colony exhibited two cycles per year: a strong cycle that boomed in spring and busted in summer, and a weak cycle that moderately increased mite density in fall and declined in winter. Climate variables related to wind speed were associated with higher mite density. Eriophyid mites such as F. perrepae are increasingly being deployed for weed biologi- cal control (Smith et al. 2010). However, many questions about their use remain, particu- larly their efcacy at controlling their target weeds, their susceptibility to natural enemies, and their ability to disperse (Smith et al. 2010). Studies such as ours that track eriophyid mite densities can begin to illuminate their population dynamics. For example, the mites Aceria pongamiae Keifer and Aceria lantanae Cook (both : Eriophyidae) also exhib- ited strong seasonal dynamics in mite density on their hosts, the tree Pongamia pinnata L. (Fabaceae) in India (Nasareen and Ramani 2014), and the weed Lantana camara L. (Ver- benaceae) in South Africa (Mukwevho et al. 2017), respectively. Understanding how eriophyid mites respond to abiotic conditions has been critical for working with some species during host-range testing (Paynter and Shaw 1997), and inter- preting mite feld establishment, impact, and distribution patterns (Smith et al. 2010; Pratt et al. 2019). Abiotic conditions, particularly temperature and humidity, can infuence the survival of dispersing mites and colonization of new host plants (Paynter and Shaw 1997; Melo et al. 2014). In our study, climate infuenced mite density within galls, albeit in an 1 3 Experimental and Applied Acarology unexpected way. We might have predicted that temperature and rainfall would have had strong efects on mite density based on laboratory assays and feld surveys conducted in the native range (Ozman and Goolsby 2005; Goolsby et al. 2005), but this was not the case (though we caution that our climate analyses were correlative and the climate data them- selves were multivariate and should be interpreted carefully). In previous work, rainfall was negatively correlated with F. perrepae presence across the native range, and it was hypoth- esized that particularly persistent and heavy rainfall could wash of dispersing mites that had not yet developed galls (Goolsby et al. 2005). Our colony experienced daily overhead watering. This frequent soaking of plants may have limited our ability to detect an efect of rainfall or relative humidity on mite density, as experimental plants never experienced extended dry periods as feld plants may have done in the winter months. Nevertheless, our fndings clearly demonstrated that F. perrepae can persist in the presence of frequent precipitation. Furthermore, during feld studies of F. perrepae phenology in Australia, the within-gall mite density was negatively associated with temperature (Ozman and Goolsby 2005), though we found no such efect in our study. Instead, wind speed was strongly, positively associated with within-gall mite density. Floracarus perrepae move by walking and “jumping”, a process by which the mites launch themselves into the air, facilitating wind dispersal (Ozman and Goolsby 2005). Ambulatory movement by eriophyids is limited by these mites having only two pairs of legs and their vulnerability to desiccation (Galvão et al. 2012). Although potted L. microphyllum plants in the shade house were, at times, connected by bridges formed by intertwined rachises, it is likely that most movement between plants was via wind dispersal. Thus, it is plausible that stronger winds helped the mite disperse throughout the shade house plants, and such a process may also occur in the feld. The fndings of the current study might also suggest that dispersal of F. perrepae may be limited in the absence of wind. Some of the most efective biological control agents have been very slow dispersers (Paynter and Bellgard 2011). However, intense release eforts may be required to compensate for slow dispersal (Paynter et al. 2012). Following initial releases at one site, F. perrepae were recovered in a pattern that suggested they had dispersed with the prevailing winds (Lake et al. 2014). Further research is needed to learn more about the dispersal behavior of F. perrepae within and between populations of L. microphyllum to optimize the design of release strategies. Interestingly, approximately one quarter (26%) of galls dissected in this study were unoccupied. Eriophyid mites are known to disperse in response to deteriorating host plant conditions (Galvão et al. 2012), and empty galls could indicate that F. perrepae had vacated a gall due to worsening plant conditions such as leaf desiccation (Ozman and Goolsby 2005). Ozman and Goolsby (2005) noted that desiccation of plant material caused by F. perrepae feeding would prompt adult mites to abandon galls or rachis tips and frequently observed mites exhibiting the characteristic jumping behavior when leaving feeding sites. Alternatively, females of other eriophyid species are known to crawl along the plant and induce gall formation at several locations before returning later to oviposit (Paynter and Shaw 1997). Such behavior, although not documented for F. perrepae, could account for the presence of empty galls. Finally, predators could have reduced mite density and poten- tially contributed to the number of inactive galls. Predatory mites and the mite pathogen, Hirsutella thompsonii Fisher were frequently observed in the native range (Goolsby et al. 2004; Ozman and Goolsby 2005). On rare occasions we observed predatory mites during our data collection, though did not identify them or record their instances; based on their infrequency we believe it is unlikely that predatory mites were a major source of mortality in the present study. 1 3 Experimental and Applied Acarology

An improved understanding of the seasonal dynamics of within-gall F. perrepae density contributes to our growing knowledge of its population biology. We still lack information about the spread of mites between subpinnae and the conditions necessary to induce gall- ing, though ongoing studies are assessing seasonal patterns of F. perrepae gall incidence in the feld in Florida (A.S. David and E.C. Lake unpubl. data). Synthesizing this informa- tion will inform whether the mite population as a whole exhibits these two cycles per year, or whether these cycles are limited to the within-gall dynamics. Ultimately, this improved understanding of F. perrepae population biology informs the timing of releases of this bio- logical control agent in the feld and its integration with other management tools.

Acknowledgements We thank A. Abdel-Kader, A. Carmona, K. Ducassi, C. Martinez, J. Murphy, and A. Pitcher for providing technical assistance. Funding was provided by the U.S. Department of Agriculture (USDA), the Florida Fish and Wildlife Conservation Commission, and the South Florida Water Manage- ment District and U.S. Army Corps of Engineers as part of the Comprehensive Everglades Restoration Plan. We thank Editor J. Bruin and three anonymous reviewers for their constructive feedback on the manu- script. Mention of trade names or commercial products in this publication is solely for the purpose of pro- viding specifc information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity employer and provider.

Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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