Since 1960, agricultural intensification and a corresponding rise in pesticide use has been an environmental concern due to contamination of soil-water-air and movement of chemicals through the trophic chain (Carson, 1962; Krupke et al., 2007; Epstein, 2014; Douglas et al., 2015). Because broad-spectrum pesticides are, by nature, not specific to focal pests, they can affect non-target beneficial organisms (i.e., pollinators, parasitoids, predators) inhabiting crops, as well as unmanaged habitats neighboring agricultural land (Longley and Sotherton, 1997; Aktar et al., 2009). Routes of exposure are varied and challenging to track, but include direct contact with contaminated surfaces or spray droplets, residues remaining on the soil, and consumption via food resources such as leaves, nectar or pollen (Cilgi and Jepson, 1995; Longley and Stark, 1996). In many cases, only a small fraction of active ingredient makes contact with target pests, while the remainder is absorbed by the greater ecosystem. Pesticides applied by aircraft, for example, can reach as little as 50% of the target crop with the remainder moving to surrounding areas as far as 30 km downwind (Pimentel and Levitan, 1986; Pimentel, 1995). As a result, a range of insect pests, from aphids to caterpillars, are estimated to contact <0.1% of insecticides applied for their control (Pimentel and Levitan, 1986). Even newer, more targeted technologies are vulnerable to this pesticide 'loss'; namely, seed treatments that were once touted for their limited off-site drift (Jeschke and Nauen, 2008). New data estimate that only 1.3% of initial seed treatment is recovered from corn plants exposed to the neonicotinoid clothianidin, with the remaining 98–99% of material leached into the environment (Alford and Krupke, 2017).

Off-site exposure to mobile insecticides is particularly a concern for pollinators, many of which inhabit agricultural landscapes and are undergoing long-term population declines. Several studies provide evidence of lower abundance and/or diversity of butterflies in the field margins of insecticide-treated crops compared with unsprayed controls (Rands and Sotherton, 1986; Dover et al., 1990; De Snoo et al., 1998). In most cases, it is unknown whether effects are caused by exposure to adults nectaring on flowering plants or larvae developing on contaminated leaves. However, a field experiment exposing Pieris brassicae caterpillars at different distances downwind to spray drift from the insecticide diflubenzuron, showed higher mortality when developing on leaves of their host-plant up to 16 m from the field edge (Davis et al., 1991). Similarly, several studies illustrate that the nectar and/or pollen of wild flowering plants on crop field edges contain residues of neonicotinoid insecticides among other agrochemicals (Krupke et al., 2012; Botías et al., 2015, 2016; David et al., 2016; Mogren and Lundgren, 2016). Indeed, much of the recent focus of nontarget impacts on pollinators centers on the neonicotinoids, due in large part to their widespread adoption in global agriculture (Douglas and Tooker, 2015). Although this work has primarily targeted bees, increasing evidence suggests that butterflies are also affected. Two recent time-scale analyses of reductions in butterfly diversity over the past several decades link these changes with the introduction and rise of neonicotinoids in the UK (Gilburn et al., 2015) and California (Forister et al., 2016). These correlative analyses were complimented by a few experimental lab studies showing strong negative effects on larval development for butterflies reared at fieldrealistic exposure levels for clothianidin (Pecenka and Lundgren, 2015) and imidacloprid (Whitehorn et al., 2018). Yet, due to the strong research emphasis on bees and pollen/nectar composition, we still lack field data on dietary exposure to pesticides for butterfly larvae developing on leaves of host-plants bordering cropland.

The common milkweed Asclepias syriaca L. is an abundant and opportunistic herbaceous plant growing in disturbed agricultural areas throughout the eastern United States (Woodson, 1954). It is notorious as being the primary larval food plant for the migratory monarch butterfly (Danaus plexippus L.) throughout its summer breeding range (Seiber et al., 1986; Wassenaar and Hobson, 1998). While Asclepias is a relatively diverse genus in North America and monarchs are capable of feeding on most, if not all, of these species, A. syriaca is by far the most widely available and used by monarchs in the Midwestern U.S. (Hartzler and Buhler, 2000; Zaya et al., 2017). Because A. syriaca grows in close proximity to corn and soybean fields and monarchs specialize on milkweed, this system offers a unique opportunity to examine the links between crop management, pesticide leaf concentrations, and butterfly development. Importantly, monarch populations have declined sharply over the last 20 years with censuses in overwintering sites reporting an 82% decrease in population size (Inamine et al., 2016; Semmens et al., 2016; Malcolm, 2018).

TABLE 1 | Area of corn and soybean, expressed as percent of total land use, planted in a 1 km radius around milkweed sites sampled in 2015 and 2016.

TABLE 2 | Local site characteristics and number of plant replicates for milkweeds sampled in 2015 and 2016.

Although pesticides have been considered as a factor underlying the monarch decline (see Oberhauser et al., 2006; Krischik et al., 2015; Pecenka and Lundgren, 2015; Thogmartin et al., 2017a), it is difficult to evaluate this hypothesis because we lack data on field exposure during larval development. Interestingly, monarch declines have temporally coincided with the increase in use of neonicotinoids throughout agricultural regions in their summer breeding habitat, leading some to speculate whether this is a correlative or causal relationship (Stone, 2013). A recent petition by the U.S. Fish & Wildlife Service to protect monarchs under the endangered species act highlights this point: "It is notable that the monarch decline has occurred during the same time period that the use of neonicotinoid insecticides in the key monarch breeding areas has dramatically increased, although, to our knowledge no one has tested the hypothesis that neonicotinoid use is a significant driver of monarch population dynamics."

A lab toxicity assay of monarch larvae exposed to different concentrations of clothianidin—the main neonicotinoid seed treatment applied to corn—showed lethal effects with an LC50 at 15.6 ng/g and sub-lethal effects at as little as 1 ng/g (Pecenka and Lundgren, 2015). Despite the lack of data on realistic field exposure, some have taken proactive measures to protect monarchs against potential harm. In 2014, for instance, the U.S. Fish & Wildlife Service phased out neonicotinoid insecticides on crops grown on National Wildlife Refuge System lands. Further, the U.S. Department of Agriculture developed a wildlife habitat evaluation guide and decision support tool for monarch butterfly restoration in which a 125-foot-wide pesticide-free buffer around restored milkweed habitat is advocated (USDA-NRCS 2016). To our knowledge, these buffers have not been "ground truthed" by quantifying actual pesticide residues on milkweed plants varying in their distance from the edge of agricultural fields. Such data are critical for defining the validity of nearest-distance thresholds used by land managers creating monarch habitat. Given that recent monarch population models estimate that 1.6 billion milkweed stems need to be added to the Midwestern region to achieve future conservation goals, close proximity to agricultural land is unavoidable (Pleasants, 2017; Thogmartin et al., 2017b).

With this in mind, our primary aim in this study was to define and quantify the spectrum of pesticides exposed to potential consumption by monarch caterpillars on their hostplant, A. syriaca, in agricultural landscapes. Secondarily, we assessed how pesticide presence varies with linear distance between focal milkweeds and cropland. This was done to test the degree to which pesticide residues diminish with increasing spatial isolation at a local-scale, which is most relevant to land managers who often have some amount of flexibility over local habitat placement on their property. Pesticide-free buffers assume a proximity threshold beyond which exposure is minimal to non-existent. Last, we compared the effectiveness of nearest-distance buffer models with broader landscapescale analyses of land use to determine which better predicts monarch exposure.

#### METHODS

#### Study Areas

In 2015 and 2016, we sampled A. syriaca at seven sites across two counties—Tippecanoe and Newton—in northwestern Indiana, USA. Each site was separated from the nearest site by at least 2 km with the farthest two sites ca. 100 km apart. A site consisted of a patch of at least 30 milkweed plants growing in an area adjacent to a corn or soybean field. Although all milkweed patches were embedded within agricultural landscapes dominated by corn and soybean production (see **Table 1** for land use data and SI Appendix, **Figures S1** and **S2** for reference GIS land use maps to visualize surrounding habitats for a representative agricultural and natural site, respectively), the local habitat varied widely from unmanaged crop field edges to large prairies used in restoration or conservation. As a result, the degree of isolation separating milkweeds from the nearest crop field varied widely, from 0 to >2 km; however, most

were within a 100 m buffer zone of the field edge. Because we were constrained by the location of existing milkweeds and site configuration, we had little control over min/max distances, as well as other factors that could affect pesticide movement, e.g., soil type, direction of milkweed patch relative to crop field (upwind vs. downwind). Data on number of plants sampled per site/year, distance range separating milkweeds from crop, size of neighboring crop field, and direction of milkweeds compared to crop are provided in **Table 2**. Sites included:

# Field Sampling Milkweed Leaf Samples

During June, July, and August in both years, leaf tissue was collected from milkweed plants for chemical analysis. On average, we sampled 48 plants per site each year, with 524 total milkweed plants sampled over the 2-year period across all sites. Within a given site, sampled plants were semi-randomly chosen to span a distance gradient along a transect extending out from the crop field edge. Each month, two leaves were removed to provide at least one-gram of tissue for analysis. The two leaves were located in the central portion of the plant, avoiding the new growth in the apical meristem and older senescent leaves at the bottom of the stem. This leaf position roughly coincides with where we often observe monarch larvae feeding in the field. Leaves were sealed in plastic bags and kept in a cooler with ice before they were transferred to a −80◦C freezer in the laboratory. Because we collected whole-leaf samples, we do not know whether residues were on plant surfaces or inside of plant tissues. Similarly, due to the large number of pesticides measured and logistical challenges with sampling from multiple field sites over time, we did not attempt to control for variation in other factors that undoubtedly impact pesticide detection, e.g., rainfall, time since application, half-life. However, our sampling design over two years with several samples at different time points within a given year, using multiple sites, and a relatively large number of plant replicates per site, was in part intended to account for this inevitable background "noise" and provide a reasonable estimate for average exposure at a given time and place.

Plants were labeled with colored flagging tape to sample the same individuals in subsequent months and georeferenced to calculate the linear distance between focal milkweeds and the nearest corn/soybean field in the study area. To calculate the distance of each individual plant to the crop fields we used an ArcGIS model for each individual site. The tools used in the model include: "Project" that converts data from one coordinate system (WGS\_1984) to another (NAD\_1983\_UTM\_Zone\_16N); "Near" which calculates the distance between the input feature (milkweeds) and the near feature (crop field); "Add field" that adds a new field to a table, in our case the distance from milkweeds to the crop; and "Calculate field" which calculates the values within the new field in the table (SI Appendix, **Figure S3**).

#### Soil Samples

We collected five soil samples per site during June, July and August 2016, resulting in 15 total samples per site. Soil was collected from random locations in the same approximate area where milkweeds were growing at different distances from the crop (SI Appendix, **Figure S4**). To do so, we used a soil core (2 cm diameter), sampling the top ca. 18 cm, although the sampling depth varied with soil compaction across sites. Because soil type plays an important role in the retention or degradation of pesticides, we identified the types of soils at each site using the USDA Soil Survey Geographic Database (SSURGO) map data, which contains information for 3,200 soil surveys (SI Appendix, **Table S1**).

TABLE 3 | Summary data for pesticides detected in milkweed leaf samples across both years of the study.

*"% detection"* = *percent of leaf samples with measurable levels (i.e., above the limit of detection, LOD) for a given pesticide. n.d.* = *not detected.*

are calculated from samples summed across all study sites. Months with different letters, by pesticide, indicate significant differences (*P* < 0.05).

*"% detection"* = *percent of samples with detectable levels (i.e., above the limit of detection, LOD) for a given pesticide. LOD and % recovery data are approximately the same as those reported in* Table 3 *for leaf values.*

#### Land Use Analysis

Although we measured the linear distance of each plant to a specific crop field within our study areas, distance alone may be a poor predictor of variation in pesticide residues. Thus, we quantified the area of corn and soybean in a 1 km radius buffer around the milkweed sampling sites since most of the pesticide inputs are compounds applied to these two crops, which dominate land use in our region. To do so, we used the ArcGIS buffer geoprocessing tool with a 1 km radius, extracted by mask to obtain the crops just within the buffer and tabulated area to calculate the percent of corn, soybean and other crops as a fraction of total land use. Land use data were obtained from USDA NASS Cropland Data Layer for Indiana (www. indianamap.org).

We also estimated corn and soybean pesticide use at a broader geographical scale (county-level) to assess the relationship between pesticide inputs in those crops and the residues associated with our plants. We used the USGS pesticide database, which estimates pesticide applications per crop per state; we used Epest-low values, which are more conservative and tend to better match other estimates. To quantify the amount of pesticides applied in the two counties (Tippecanoe and Newton) where milkweeds were sampled, we first divided the total amount of each corn or soybean pesticide applied at the state-level (i.e., for Indiana only) by state-wide acreage to provide a per area use rate in each year. This approach assumes that statewide averages are reflected in local grower practices, which may not always be the case. The per-unit rate was then multiplied by the area of corn or soybean planted per county in that year to estimate how much of each pesticide was applied near milkweed sites (SI Appendix, **Table S2**). Because USGS datasets stopped including seed treatments in their pesticide surveys after 2014, we unfortunately could not include neonicotinoids and some fungicides using this approach. However, virtually all corn (>90%) in our area is seed treated with clothianidin and thus total corn acreage is a good proxy for neonicotinoid input (Douglas and Tooker, 2015). In addition to the information provided by USGS, a list of the pesticides applied during our sampling to corn or soybean close to the milkweeds was provided by the staff managers at the different sites (SI Appendix, **Table S3**).

#### Laboratory Analysis Leaf Pesticide Residue Analysis

QuEChERS (Quick-Easy-Cheap-Effective-Rugged-Safe) extraction method was used to identify pesticide residues associated with milkweed samples. We screened 65 commonly used pesticides following the approach by Long and Krupke (2016). Multiple leaves within a sampled plant/date were combined and chopped with scissors to obtain a roughly homogenized 1 g sample. All plant tissues were processed with scissors and forceps, cleaned in a 70% alcohol solution before processing each sample and latex gloves were used to avoid contamination between samples. Each 1 g sample was transferred into a 7 ml homogenizer tube (Bertin-technologies) with 2 g of zirconium oxide beads (2 mm diameter; Bertin-technologies). To homogenize the tissue, 2 ml of double deionized (dd) water was added to each tube, after which tubes were set in a Precellys 24 lysis homogenizer, which processed samples using four cycles at 5,000 rpm. Homogenized samples were transferred to 15 ml tubes, and 2 ml dd water and 4 ml of the extraction solvent acetonitrile were added. The 15 ml tubes contained the 1 g plant tissue, 4 ml dd water and 4 ml acetonitrile. Ten µl of an isotopically labeled internal standard mix containing the pesticides screened was added to the 15 ml tubes. The standards help in the quantification of the pesticides in the samples, because a calibration curve is then created to assign a concentration value to peaks obtained from the processed samples.

The anhydrous salts magnesium sulfate (1.2 g) and sodium acetate (0.3 g) were added to enhance the extraction efficiency and induce phase separation with acetonitrile. Each 15 ml tube was agitated for 1 min with a S8220 Deluxe Mixer Vortex (Scientific Products) and shaken on a VWR W-150 Waver Orbital Shaker at speed 10 for 10 min. The tubes were centrifuged at 4◦C, 2,500 rpm for 10 min, for phase separation. One ml of supernatant was added to 2 ml Agilent dispersive Solid Phase Extraction tubes (part no: 5982-5321), containing 25 mg PSA, 7.5 mg GCB and 150 mg MgSO4, cleaning up the samples before the analysis by liquid chromatography. The dispersive SPE tubes with the 1 ml supernatant were spun in a vortex (Labnet VX100) for 10 min and centrifuged at 15,000 rpm for 5 min in an Eppendorf Centrifuge 5424. The supernatant was then transferred into 2 ml Eppendorf tubes, which evaporated overnight in a speed vacuum (SC250EXP, ThermoFisher Scientific). The dry residue at the bottom of the tubes was mixed with 100 µl of acetonitrile, spun for 10 min in a vortex, centrifuged for 5 min, and the supernatant was transferred to liquid chromatography mass spectrometry (LC-MS) autosampler vials. The identification, quantification and separation of the pesticide residues were carried out in an Agilent 1200 rapid resolution liquid chromatography with a triple quadrupole mass spectrometry (Agilent 6460 series) and an Agilent Zorbax SB-Phenyl 4.6 × 150 mm, 5µm column (Agilent technologies, Santa Clara, CA). Both the QuEChERS method

modification and LC-MS analysis were performed at the Bindley Bioscience Center at Purdue University.

#### Soil Pesticide Residue Analysis

QuEChERS extraction method was modified and used to identify pesticide residues in soil, similar to the above-described protocol. Seven grams of wet soil were weighed on a scale (Mettler Toledo model MS3001S). The samples were dried for 2 days at 105◦C in individual aluminum baking cups. The dry weight of each individual sample was recorded to calculate the pesticide concentration in ng/g per sample; dry weight varied between 5.04 and 6.96 g. Dry soil was sieved and slowly added and mixed to avoid clumping with 5 ml dd water in a 50 ml falcon tube. The 50 ml tubes were agitated for 1 min, then 5 ml of acetonitrile (ACN) at 99% and acetic acid at 1% were added, followed by 10 µl of an isotopically labeled internal standards mix containing the 65 pesticides targeted for screening. The tubes were agitated in a vortex for 7 min and then 4 g of magnesium sulfate (MgSO4) and 1 g of sodium acetate (NaOAC) were added slowly, shaking regularly in a vortex to facilitate the incorporation of the salts with the soil and avoid clumps. Upon adding salts, tubes were agitated in a vortex for another 2 min to dissolve any clumps. The samples were centrifuged for 5 min at 4,000 rpm and 1.4 ml of supernatant was transferred into dispersive Solid Phase Extraction tubes (part no: 5982-5122), containing 50 mg PSA, 50 mg C18EC and 150 mg MgSO4, to clean up the samples before the analysis by liquid chromatography. The dispersive SPE tubes were spun for 5 min and centrifuged at 5,000 rpm for 3 min; 1 ml of supernatant was then transferred to 2 ml Eppendorf tubes and left to dry overnight in a speed vacuum (SC250EXP, ThermoFisher Scientific). The next day, samples were resuspended in 100 µl of acetonitrile, spun for 5 min and centrifuged for 7 min at 13,000 rpm before transferring the supernatant into an LC-MS vial. Pesticide identification and quantification were carried out as described above for leaf samples.

### Statistical Analysis

We only targeted pesticides for statistical analysis and figures if they were detected in >1% of milkweed samples with overall concentrations >1 ng/g. Pesticides that fell below these thresholds were considered either too sporadic or diffuse to cause significant ecological impacts on monarch populations.

The effects of year, month and site on pesticide presence in milkweed tissue were evaluated with a mixed model logistic regression, with binary data (SI Appendix, **Table S4**). When pesticide residues were found in association with milkweed tissue we assigned a value of 1 and when pesticide residues were below the detection limit we gave a value of 0. Site was considered as a random factor, and year and month were fixed factors. For this analysis, we only used 0/1 data, rather than the actual TABLE 5 | The effects of month and distance separating focal plants from the nearest cropland on thiamethoxam detection associated with milkweed leaves at three sites (A–C) sampled in 2016.

*Data from 2015 were not used due to the overall low detection rates of thiamethoxam (see* Figure 1A*). Significant (P* < *0.05) effects bolded for emphasis.*

concentrations due to the large number of samples below the detection limit.

We used a correlation analysis to test the relationship between pesticide concentrations found in soil vs. corresponding values in milkweed leaves. To do so, we created a 5 m buffer around the points where soil samples were collected and selected the plants inside the buffer (SI Appendix, **Figure S4**). These soil-plant samples were paired together as spatially cooccurring to test for a correlative pattern. In cases where multiple plants were within the soil buffer we averaged the plant data to create a single mean value for each pesticide at that location.

To evaluate the effects of land use on pesticide residues associated with milkweed leaves we used a three-tiered approach, starting with local habitat placement and ending with landscapescale crop pesticide use. For local habitat placement, we used a two-part hurdle model with logistic regression using binary data based on detection frequency, followed by a secondary analysis using the continuous concentration data with non-detections removed. For this analysis, we focused on the three insecticides thiamethoxam, clothianidin, deltamethrin—since the impacts of fungicides/herbicides on monarchs at this point are unknown. Because distance to field is confounded with site, we were unable to include both factors in the model. In working with naturally occurring milkweed patches we were constrained by existing plant distributional patterns (see **Table 2**), resulting in some sites with all milkweeds clustered relatively close to the field margin (0–30 m for 2015 TPAC) and other sites that were far further away (2,300–2,400 m for 2015 Kankakee far). Thus, we developed site-specific models that include the factors year (when appropriate; some pesticides were mostly detected one of the 2 years), month, and distance separating milkweed plants from the nearest agricultural field. This allows us to test the effects of spatial isolation, while controlling for temporal variation. We only analyzed sites in which the distance gradient spanned the 125 ft distance threshold proposed for milkweed restoration. Several of our sites (see **Table 2**) included milkweeds that far exceeded this distance threshold, even at the closest proximity, and, consequently, distance from nearest crop field is biologically less relevant in these cases.

Next, simple linear regressions per year and pesticide active ingredient were used to quantify the relationship between percent of corn and soybean planted in a 1 km radius around milkweed habitats and the frequency of milkweed leaves with pesticide residues. For this analysis, we took advantage of natural variation in land use surrounding our sites, which varied widely from no agriculture to ca. 80% cropland (see **Table 1**). Last, we used correlations to determine whether corn or soybean pesticides applied at the county-level reflected the frequency of residues associated with milkweed leaves. This analysis used Tippecanoe as the focal county since this housed the majority of our milkweed sites and has a similar agricultural backdrop to the other county (Newton) surveyed. Also, we focused this county analysis only on fungicides for two reasons: one, given the chemical and application differences across pesticide classes, we wanted to avoid directly comparing, for example, insecticides and herbicides; and two, fungicides had the most active ingredients- −6 compounds—which allowed us to make this comparison (i.e., we were unable to use a correlation with only 2 or 3 data points in the case of insecticides and herbicides).

All statistical analyses were conducted with R software 3.5.1 using the packages car, ggplot2, lmer4, and multicomp, except for local habitat use (i.e., distance from crop), for which we employed the Proc Genmod and Proc GLM functions in SAS, V. 9.4.

### RESULTS

#### Leaf Pesticides

Across both years of the study, 14 pesticides commonly used in crops in Indiana were detected on milkweed leaves (**Table 3**). It is important to note, however, that this is not a comprehensive list. While we screened a relatively large number of pesticides, focusing on ones that we know are ubiquitous components of row crop pest management in our region, some compounds are difficult to detect due to factors such as high volatility

(e.g., dicamba) or require a different, more specialized analytical approach for quantification (e.g., glyphosate).

Clothianidin, the insecticide that to date has received the most attention for potential non-target impacts on monarchs, was only detected in 4–8% of total samples; however, those values are somewhat misleading since it averages across all sites and dates. As a general pattern for both sampling years, we almost exclusively detected clothianidin in June, but not in July or August (**Figures 1A,D**). During these early season samples, clothianidin was detected in ca. 15–25% of plants with nearly 60% of milkweeds at some sites testing positive. Interestingly, both thiamethoxam (neonicotinoid) and deltamethrin (pyrethroid) varied dramatically in their detection rates across years (SI Appendix, **Table S4**), with both compounds occurring at high frequencies in 2016 (75–99%) while being virtually absent from samples in 2015 (**Figures 1A,D**). Imidacloprid (neonicotinoid) was only found in a small number of plants (0.2%) in the first year of this study.

Atrazine was the most commonly detected (80–87% on average, although some months approached 100% of samples) and occurred at the highest mean concentrations (6.84 and 37.0 ng/g) of any pesticide surveyed in either year, followed by s-metolachlor and acetochlor among the herbicides (**Table 3**). Notably, s-metolachlor displayed consistent withinseason patterns in both sampling years whereby detection rates were several-fold higher early in the season before gradually declining in July and August (**Figures 1B,E**; SI Appendix, **Table S4**).

Overall, fungicides were the most omnipresent of pesticides detected on milkweed with 6 compounds consistently occurring on leaves. Several fungicides, most notably propiconazole (98% detection rate in 2016), were somewhat commonly detected, but only at trace (<1 ng/g) amounts. The compounds that combined relatively high concentrations and detection rates included pyraclostrobin (31–55%) and trifloxystrobin (27–40%; **Table 3**). In contrast with the herbicide s-metolachlor, which decreased throughout the season, the two strobilurin fungicides showed the opposite pattern, gradually increasing from June to August in both years (**Figures 1C,F**; SI Appendix, **Table S4**). Propiconazole detection frequency displayed a nearly 3-fold increase between years one and two, from 34 to 98% of samples.

#### Soil Pesticides

We found 7 pesticides in soil across the sites sampled in 2016 (**Table 4**), which were a subset of the 14 pesticides recorded from milkweed leaves. Clothianidin was the only insecticide detected and it was found in all samples consistently throughout the summer (**Figure 2A**), in contrast with leaf presence, which was restricted to only June. Thus, clothianidin was far more ubiquitous in the soil than leaves. Importantly,

soil concentrations of clothianidin were highly correlated with levels in co-occurring milkweed leaves (**Figure 3**; r = 0.763, p < 0.0001). This was the only pesticide showing a soilplant association.

We detected three herbicides—atrazine, s-metolachlor, acetochlor—and three fungicides—azyoxystrobin, pyraclostrobin, metalaxyl. Similar to clothianidin, soil concentrations of these compounds tended to be far more stable over time, i.e., leaf values fluctuated dramatically across months when the same soil values remained relatively constant (compare **Figure 1** vs. **Figure 2**) even though half-life of pesticides vary with soil physical and chemical characteristics and our plants grow under different soil types (SI Appendix, **Table S1**).

#### Land Use

Linear distance separating milkweed plants from agricultural fields was a strong predictor of thiamethoxam detection frequency at all of the sites evaluated (**Table 5**). However, distance frequently interacted with sampling month, resulting in variation in the nature of the relationship over time. In 6 of 9 cases (3 sites × 3 months), detection rates declined with increasing distance separating milkweed from crop field up to 150 m, although the shape of this relationship varied (**Figure 4**). The other two insecticides either showed no spatial patterning (clothianidin; no significant main or interactive effects of distance from crop) or were detected in nearly 100% of samples and thus did not have sufficient variation in detection frequency to statistically evaluate using binary data (deltamethrin in 2016, **Table 3**). When continuous concentration data were used after removing samples below the detection threshold, one of the three sites also showed a distance relationship involving thiamethoxam (**Figure 5**; distance x month, F = 11.35, P < 0.0001). Similar to detection data, concentrations were higher in milkweeds growing closer to field edges. As with binary data, no relationships were observed for clothianidin or deltamethrin.

Although we found substantial site-level variation in pesticide presence on milkweed, landscape composition—namely, amount of corn and soybean—within a 1 km radius surrounding focal sites was a poor predictor of our data. Across both 2015 (**Figure 6**) and 2016 (**Figure 7**), only one pesticide pyraclostrobin in 2015 (**Figure 6D**; F = 8.61, P < 0.05) showed a relationship between land use and detection frequency (SI Appendix, **Table S5**). In this case, percent of plants with measurable amounts of pyraclostrobin increased from ca. 40 to 70% when comparing the least to most agricultural sites.

Finally, at the county-level, which encompasses the broadest spatial scale employed (for reference, Tippecanoe Co. is ca. 1,300 km2 ), the total amount of fungicides applied to soybean had a marginally significant (r = 0.85, P = 0.06) effect on the percent detection frequency of fungicides for milkweed leaves in 2015 (**Figure 8B**). However, other relationships were not significant (corn 2015, r = 0.38, P = 0.28; corn 2016, r = 0.18, P = 0.77).

# DISCUSSION

Our study clearly shows that the foliage of milkweed growing in prairies and unmanaged habitats neighboring cropland contains residues from a wide variety of agricultural pesticides, primarily those applied to corn and soybean. The actual risk of these pesticides, however, depends on how frequently milkweeds contain those levels in the field. Our data reveal strong spatiotemporal variation in pesticide occurrence across sites, months, and years, which means that the threat posed by these chemicals depends on if, when, and where they coincide with monarch colonization and phenology. Below we highlight the implications of these findings for each of the three major pesticide classes and discuss whether pesticide exposure can be avoided based on local and landscape-level habitat placement.

#### Insecticides

Contamination of non-target plants by neonicotinoids used in agriculture is widely reported, but almost exclusively for pollen or nectar samples taken from flowers (Greatti et al., 2003; Krupke et al., 2012; Bonmatin et al., 2014; Botías et al., 2015, 2016; Mogren and Lundgren, 2016). Consistent with this literature, our study found neonicotinoid residues associated with milkweed leaves around farmland, specifically the active ingredients clothianidin and thiamethoxam. Although seed treatment data are no longer reported for U.S. row crops, most corn in our region is seed treated, primarily with clothianidin, and much of the soybean acreage also employs a seed treatment, mainly thiamethoxam (Douglas and Tooker, 2015). Corn and soybean dominate land use in the areas surrounding each of our milkweed sites, and thus it is not surprising that these two insecticides were among the ones most commonly detected.

Importantly, the leaf concentrations we recorded (up to 56.5 and 151.3 ng/g for clothianidin and thiamethoxam, respectively) are within the range previously reported from other studies. For example, a recent analysis of clothianidin on the leaves of plants used in pollinator strips bordering seed-treated corn fields

reported values that were comparable to our milkweed data (Mogren and Lundgren, 2016), including sunflower (max. 81 ng/g), buckwheat (max. 54 ng/g), and phacelia (max. 33 ng/g). Interestingly, some milkweed concentrations were also roughly similar to those reported from the leaves of seed-treated crops such as corn (7–86 ng/g at 20–34 days post planting; Alford and Krupke, 2017) and soybean (105 ng/g in V1 stage and 1.7 ng/g in V4 stage after 17 and 56 days; Magalhaes et al., 2009). Perhaps most relevant to our study, Pecenka and Lundgren (2015) documented clothianidin in 36–64% of milkweed leaves surveyed in South Dakota at mean concentrations of 1.24 and 1.11 ng/g. By comparison, we detected clothianidin at a far lower rate (4.6 and 8.1%, overall, for the 2 years), but with comparable mean values (0.71 and 0.48 ng/g). Pecenka and Lundgren (2015) used dose-response curves for monarch larvae to clothianidin, which revealed the LC50 at 15.63 ng/g and sublethal effects at as little as 1 ng/g. Based on extrapolating these calculations to our field data, sublethal effects should be observed for monarchs on 5–8% of leaves surveyed (averaged across all sites, months, and years; risk varies seasonally), whereas lethal effects (i.e., >LC50) are limited to 1.4% of samples. It is important to note that our assessment is based solely on clothianidin, for which data exist on monarch growth and survival. Our second sampling year revealed that thiamethoxam can be much more prevalent detected in 75% of samples—but its toxicity to monarchs is unknown at present.

Another critical aspect of our neonicotinoid data is that during both years of the study, residues diminished dramatically over the course of the summer. We virtually only detected clothianidin in June, and thiamethoxam detection in year 2 dropped by ∼50% from June-July to August. This within-season decline would be consistent with pesticide degradation from the putative time of exposure (i.e., when seed-treated fields are planted in late spring) to the timing of when milkweeds were sampled. More importantly, the data suggest that early-season monarchs are at greater risk from neonicotinoid exposure than subsequent generations occurring later in the season. Similarly, our data suggest strong annual fluctuations in risk, indicating that monarchs likely encounter a different suite of pesticides each year. Thiamethoxam and deltamethrin, for example, were

more prevalent in the second sample year. This is likely a result of local or regional differences in pest management approaches employed by farms. Active ingredients for foliar sprays such as deltamethrin can vary greatly across years, depending on factors such as price and availability. Thiamethoxam is more likely to be a reflection of seed treatments, which vary with the relative acreage of corn vs. soybean in the landscape. Further, in corn/soy rotations, the insecticides used will change on an alternate year basis. Overlaying temporal variation in pesticide presence with the timing of non-target insect colonization and development is a key component to risk assessment that, to our knowledge, is rarely incorporated into such studies.

While we did not document the mechanism by which neonicotinoids moved from cropland to milkweeds in this study, for clothianidin we found a strong positive relationship between soil and leaf concentrations (**Figure 3**). This could be simply correlative (i.e., areas with high neonicotinoid deposition result in correspondingly higher concentrations both in soil and on plant surfaces), or indicative of systemic uptake from soil into nearby plants. In all cases, we analyzed whole tissue samples so, unfortunately, do not know whether pesticides are on the leaf surface or inside the plant, for systemic compounds. Overall, the clothianidin concentrations in our soil samples (range: 0.88–8.59 ng/g; mean: 1.75 ng/g) were comparable with those reported in other studies of agricultural soils, i.e., 6.57 ng/g (Botías et al., 2015), 7.0 ng/g (Xu et al., 2016), 2.1 and 6.3 ng/g (Krupke et al., 2012).

Last, in 2016 we frequently detected the pyrethroid deltamethrin in milkweed samples. Although pyrethroids are considered highly toxic to lepidopterans in general, nothing is known specifically about the deltamethrin-monarch relationship. A few studies have found negative non-target effects of the related pyrethroids, permethrin, and resmethrin, used in mosquito control on monarch caterpillars (Oberhauser et al., 2006, 2009). Similarly, field applications of deltamethrin in the UK increased mortality of Pieris butterfly larvae developing in hedgerows bordering cereals (Cilgi and Jepson, 1995). Topical application of 20 ng was sufficient to kill 50% of P. brassicae individuals after 2 weeks of exposure (Cilgi and Jepson, 1995); however, host plants influence caterpillar susceptibility to deltamethrin (Tan and Guo, 1996), and thus it is difficult to extrapolate these values for milkweed.

# Herbicides and Fungicides

Monarch decline is often attributed to an indirect effect from glyphosate reducing milkweed abundance (Hartzler, 2010; Pleasants and Oberhauser, 2013; Pleasants, 2017). Yet, the direct effects of herbicides on monarchs (i.e., those not merely caused by a reduction in milkweed availability) are unknown, and likely dismissed since herbicides are considered non-toxic to insects (but see Russell and Schultz, 2009; Stark et al., 2012). Potential non-target pathways could occur via herbicide exposure, either topically or orally, changing some aspect of caterpillar physiology or altering the milkweed-monarch interaction by e.g., interfering with or amplifying the induced defense pathways employed by milkweeds (Boutin et al., 2004, 2014). For instance, the herbicide 2,4D functions as a plant defense elicitor, resulting in resistance to herbivorous insects on plants exposed to low doses (Xin et al., 2012). Also, drift of the herbicide dicamba into field margins reduced pollinator visitation rates (Bohnenblust et al., 2016), impacted the abundance of several arthropods in the community (Egan et al., 2014), and decreased caterpillar development (Bohnenblust et al., 2016). Herbicides such as glyphosate can even act directly on pollinators by disrupting their gut microbiome (Motta et al., 2018).

Of the herbicides sampled, atrazine was the most commonly detected and at the highest concentrations. Much of what is known about atrazine's impacts on invertebrates comes from aquatic food webs where run-off into streams or lakes alters community structure (Dewey, 1986; Gruessner and Watzin, 1996). While these are mostly indirect effects via reductions in the population of algae or related macrophytes, direct effects of atrazine on insects are documented (Miota et al., 2000; Graymore et al., 2001), as well as their role in synergizing insecticides such as organophosphates (Anderson and Lydy, 2002).

For fungicides, the compounds we detected in milkweed leaves largely match those reported from pollen, honey, nectar, wax, and foliage of wildflowers or crops (Krupke et al., 2012; Sanchez-Bayo and Goka, 2014; David et al., 2016; Long and Krupke, 2016). Fungicides inhibiting ergosterol biosynthesis, like propiconazole, act as synergists for neonicotinoid insecticides, increasing their toxicity to bees by inhibiting cytochrome P450s that function in detoxification (Pilling and Jepson, 1993; Pilling et al., 1995; Johnson et al., 2013). There is also the potential for additive toxicity when insects are exposed to mixtures of pesticides. Propiconazole was detected in 98% of milkweed samples in 2016, in many cases co-occurring with insecticides like deltamethrin and thiamethoxam. The high frequency of the fungicides propiconazole, pyraclostrobin, and trifloxystrobin compared with metalaxyl and azoxystrobin could be related to the high use of these fungicides to increase yield in hybrid corn and soybean (Paul et al., 2011; Mahoney et al., 2015).

# Land Use

For the neonicotinoid thiamethoxam, we found that detection frequency and concentrations tended to be higher on milkweeds growing in closer proximity to agricultural land. This suggests that spatially isolating milkweed restoration sites from crop fields could be an effective approach to reduce risk. To our knowledge, the proposed 125 ft buffer distance is a somewhat arbitrary value that is not based on specific criteria; however, our data nevertheless suggest that milkweed habitat restoration abiding by this rule would likely result in fewer plants containing thiamethoxam and at lower concentrations (see values >38.1 m on **Figures 4**, **5**). What remains unclear is the degree to which these reductions result in enhanced survival and/or performance of monarch caterpillars, which is the ultimate goal. This would require experimentally rearing larvae on plants in the field along a distance transect extending from a crop field edge. In fact, bypassing high quality monarch habitat on land that is relatively close to a corn or soybean field could have a net detrimental effect on monarch conservation if the benefits of additional milkweed stems exceed the detrimental impact of higher pesticide load; a scenario that is entirely plausible, depending on the factor(s) most limiting monarch fitness. This is particularly true for pesticides such as clothianidin that already occur at relatively low frequencies. Deltamethrin also occurred at frequencies and concentrations that were independent of distance to crop. This could be due to the fact that this insecticide is likely applied via aerially spraying, which may result in greater propensity for drift beyond the immediate surrounding of crop fields.

At the landscape level, amount of row crop production in the 1 km radius around milkweed sites was generally a poor predictor of pesticide presence on milkweeds. Only one of the pesticides tested—pyraclostrobin—showed a significant relationship whereby prevalence increased on milkweeds with increasing agricultural intensity. That being said, several of the pesticides, including clothianidin in both years, were most prevalent at the most heavily agricultural site while showing the lowest occurrence at the least agricultural site. We suspect that the lack of statistical power due to low site replication (n = 6 and 5 sites in 2015 and 2016, respectively) played a role in these outcomes, especially for a coarse predictor variable like total crop acres that does not account for variation in local site factors. A similar conclusion was drawn from a recent study of pesticide residues on native bees; despite trends, land cover in a 1 km radius around sites was non-significant, likely due to low site replication (Hladik et al., 2018). Our county-level analysis led to an analogous conclusion. Correlations suggested that greater use by farmers at the regional scale increased prevalence of fungicides on milkweeds, but statistical effects were equivocal (i.e., marginal significance) due to low replication (**Figure 8**).

### CONCLUSIONS

Risk assessment evaluating the potential impacts of pesticides on monarchs entails a two-step process; first, documenting the chemicals that larvae and/or adults are exposed to in the environment, and second, experimentally testing those chemicals most commonly encountered to assess lethal and sub-lethal effects. Here, we take the first step in this process, documenting the spectrum of pesticides encountered by monarch larvae on the most critical host-plant in their summer breeding range, A. syriaca. We strongly emphasize, however, that pesticide presence does not necessarily translate into impact. Unlike honeybees, for which LD50 data are widely available on most compounds, at present such information is only available for clothianidin in the monarch system. Clearly, a major emphasis of future research efforts should be to close this knowledge gap by quantifying monarch larval responses to a range of pesticides under controlled lab settings. Based on our field data, obvious starting points for these trials would be insecticides such as thiamethoxam and several of the ubiquitous fungicides that occur on milkweed leaves.

Assuming pesticide presence is undesirable for land managers focused on restoring milkweed for monarch conservation, our data secondarily point to local habitat placement—namely, site isolation—as an effective tool for reducing non-target exposure. Additional work to help refine these recommendations could focus on site-specific factors that contribute to off-site pesticide drift beyond simple linear distance, e.g., wind direction, slope, soil type.

#### AUTHOR CONTRIBUTIONS

PO-A wrote the manuscript, collected, and analyzed the data. IK guided the research, reviewed, and edited the manuscript.

# FUNDING

This research was supported by a dissertation fellowship from the Colombian government-Colciencias, and partial grants from the Nature Conservancy, Xerces Society, Experiment.com, and Purdue AgSeed.

## ACKNOWLEDGMENTS

We are grateful to the many collaborators who made this project possible. Specifically, we thank those students involved in lab/field assistance for sample collection and analysis (Ivy Widick, Ashley Kirtman, Carolina Zamorano, Javier Lenzi, Rosario Uribe, Sylvia Bonilla, Rachel Thomas, Andrés Sandoval, Megan McCarty, Kayleigh Hauri); ArcGIS professional guidance (Mayra Rodriguez and Mariam Valladares); staff members at each field location for their help with site logistics (Pete Illingworth, Jay Young, Nate Linder, Brian Baheler, John Shuey, James Beaty, Jason Getz, Kile Westerman); personnel who assisted with pesticide residue analysis at the Metabolite Profiling Facility of the Bindley BioScience Center at Purdue (Amber Hopf Jannasch, Bruce Cooper, Jackeline Franco, Elizabeth Long, Christian Krupke, Mercedes Laland, Spencer Bockover); Laura Cruz for statistical guidance; and Maggie Douglas for help with interpreting state- and county-level pesticide records. A special thanks to Larry Bledsoe who provided detailed information related to corn and soybean at the beginning of this project and tools necessary for the success of the data collection in the field. Finally, thanks to thesis committee members—Cliff Sadof, Michael Scharf, Jen Zaspel, and Kevin Gibson—and students/postdocs in the Kaplan Lab—Wadih Ghanem, Laura Ingwell, Jacob Pecenka, Christie Shee, John Ternest—for their valuable comments on the manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo. 2019.00223/full#supplementary-material

# REFERENCES

organophosphate insecticide. Bull Environ. Contam. Toxicol. 57, 683–690. doi: 10.1007/s001289900244

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Olaya-Arenas and Kaplan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Expanding the Isotopic Toolbox to Track Monarch Butterfly (Danaus plexippus) Origins and Migration: On the Utility of Stable Oxygen Isotope (δ 18O) Measurements

#### Keith A. Hobson1,2 \*, Kevin J. Kardynal 2 and Geoff Koehler 2

*1 Department of Biology, University of Western Ontario, London, ON, Canada, 2 NHRC Stable Isotope Laboratory, Environment and Climate Change Canada, Saskatoon, SK, Canada*

The measurement of naturally occurring stable hydrogen (δ 2H) and carbon (δ 13C) isotopes in wings of the eastern North American monarch butterflies (*Danaus plexippus*) have proven useful to infer natal origins of individuals overwintering in Mexico. This approach has provided a breakthrough for monarch conservation because it is the only viable means of inferring origins at continental scales. Recently, routine simultaneous analyses of tissue δ 2H and δ 18O of organic materials has emerged leading to questions of whether the dual measurement of these isotopes could be used to more accurately infer spatial origins even though the two isotopes are expected to be coupled due to the meteoric relationship. Such refinement would potentially increase the accuracy of isotopic assignment of wintering monarchs to natal origin. We measured a sample of 150 known natal-origin monarchs from throughout their eastern range simultaneously for both δ 2H and δ 18O wing values. Wing δ 2H and δ 18O values were correlated (*r* 2 = 0.42). We found that wing δ 2H values were more closely correlated with amount-weighted growing season average precipitation δ 2H values predicted for natal sites (*r* 2 = 0.61) compared to the relationship between wing δ 18O values and amount-weighted growing season average precipitation δ 18O values (*r* 2 = 0.30). This suggests that monarch wing δ 2H values will be generally more useful in natal assignments than δ 18O values. Spatial information related to the use of deuterium excess in environmental waters was similarly found to be not useful when applied to monarch wings likely due to the considerable variance in wing δ 18O values. Nonetheless, we recommend further testing of monarch wing δ 2H and δ 18O values from known natal sites with an emphasis on field data across a strong gradient in precipitation deuterium excess.

Keywords: deuterium, stable isotopes, oxygen-18, natal origins, wing chitin, assignment

#### Edited by:

*Jay E. Diffendorfer, United States Geological Survey, United States*

#### Reviewed by:

*David Nelson, University of Maryland Center for Environmental Science (UMCES), United States Sarah Magozzi, The University of Utah, United States*

> \*Correspondence: *Keith A. Hobson<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Conservation, a section of the journal Frontiers in Ecology and Evolution*

> Received: *27 February 2019* Accepted: *29 May 2019* Published: *18 June 2019*

#### Citation:

*Hobson KA, Kardynal KJ and Koehler G (2019) Expanding the Isotopic Toolbox to Track Monarch Butterfly (Danaus plexippus) Origins and Migration: On the Utility of Stable Oxygen Isotope (*δ *18O) Measurements. Front. Ecol. Evol. 7:224. doi: 10.3389/fevo.2019.00224*

# INTRODUCTION

The monarch butterfly (Danaus plexippus) is an iconic migratory insect that navigates over thousands of kilometers between natal sites in the eastern USA and Canada and overwintering sites in the Oyamel fir (Abies religiosa) forests of central Mexico (Urquhart and Urquhart, 1976; Ries et al., 2015; but see Vander Zanden et al., 2018). This outstanding migration involves multiple generations whereby those individuals returning to wellestablished, long-term overwintering sites do so without any previous experience of their locations. Despite being a highprofile conservation issue among Mexico, Canada and the USA, monarch butterflies have declined considerably over the last two decades (Semmens et al., 2016). Potential causes for the decline are many but likely factors include loss of milkweed (Asclepia spp.) on the breeding grounds, declining extent and quality of overwintering sites, climate change and the myriad of challenges faced during the migratory passage (Flockhart et al., 2015; Thogmartin et al., 2017). Key to understanding declines of this and other migratory species is the establishment of migratory connections that link breeding, stopover and wintering sites (Hobson and Wassenaar, 2019). However, this is challenging for small insects. Previous attempts have used an impressive mark recapture effort involving tagging Monarchs on breeding and stopover sites with labels affixed to wings (Monarch Watch; https://www.monarchwatch.org/tagmig/index.htm), an approach requiring recovery en route and at the few publicly accessible wintering sites in Mexico. However, during the mid-1990s Hobson et al. (1999) and Wassenaar and Hobson (1998) developed an approach using ratios of naturally occurring stable isotopes in Monarch wings to infer natal origins. Those studies marked a turning point in the conservation of migratory monarchs since it readily identified the US Midwest as the center of production of Monarchs recruited into the overwintering population in Mexico. Since then, the approach has been used by other researchers interested in Monarch patterns of spring recolonization in North America (Miller et al., 2012; Flockhart et al., 2013), the effects of natal origin on parasite loads (Altizer et al., 2015). Other applications have involved the role of wing coloration in flight distance (Hanley et al., 2013) and general conservation concerns related to where most individuals are being produced (Flockhart et al., 2017).

The stable isotope approach to tracking migrant animals is based on the fact that naturally occurring stable isotopes of several elements in nature can provide information on provenance and/or habitat. Spatial patterns of stable isotopes (i.e., "isoscapes") are ultimately transferred to animal tissues through local foodwebs and so spatial information related to the period of tissue growth can be inferred providing we know the nature of such isoscapes and how stable isotope values change or discriminate from the source to the tissue of interest (Hobson and Wassenaar, 2019). The early studies on stable isotope patterns in eastern monarchs indicated that both stable–carbon (13C/12C expressed as δ 13C) and stable-hydrogen (2H/1H, δ 2H) isotope ratio measurements of wings could provide information on monarch natal origins in Canada and the USA. However, δ 2H measurements provided a more powerful means of inferring origins than δ 13C measurements. Hydrogen in foodwebs is ultimately derived from precipitation. The amount-weighted average precipitation δ 2H (expressed as δ 2Hp) varies across continents according to well-established principles (Clark and Fritz, 1997) and these isoscape patterns are transferred to plants and higher trophic-level organisms (Bowen and West, 2019). Metabolically inert tissues such as animal keratins and chitins are related to local growing season averaged δ 2Hp and can be later measured to infer natal origins once the relationship between δ 2Hp and tissue δ 2H is established (Hobson, 2019). This principle has been used effectively to infer origins of numerous taxa including insects, birds and mammals on several continents (Hobson et al., 2018a; Hobson and Wassenaar, 2019).

Water contains both hydrogen and oxygen but measurement of the stable isotope ratios in oxygen (18O/16O; δ 18O) have not been used extensively to study animal movement (but see Bryant and Froehlich, 1995; Kohn, 1996; Hobson et al., 2004, 2009a, 2012; Bowen et al., 2005; Ehleringer et al., 2008; Chesson et al., 2013; Hobson and Koehler, 2015; Pekarsky et al., 2015). This derives from the fact that oxygen has a compressed isotopic scale compared to hydrogen and that δ 2H and δ 18O values in environmental waters are highly correlated via the global meteoric water relationship (Craig, 1961). Also, previously, measurement of δ 18O in organic materials has been challenging analytically (Hobson and Wassenaar, 2019). Oxygen in animal tissues can be derived from air, drinking water, and diet whereas hydrogen is derived from only diet and drinking water. However there is potential for δ 18O when combined with δ 2H measurements to provide additional information on provenance of animals because environmental waters can record the degree of evapotranspiration through the evaluation of deuterium excess (defined as δ 2H-8∗ δ 18O; Dansgaard, 1964; Rozanski et al., 1993). In highly evaporative systems, we expect departures from the global meteoric water line whereby deuterium excess values are higher than the global average of 10‰, a phenomenon highly associated with low relative humidity. So, again, the relationship between δ 2H and δ 18O values in insect wing chitin (δ 2Hw and δ 18Ow) has the potential to contain important environmental and climate information.

The evaluation of both δ 2Hw and δ 18Ow values of monarch butterflies was first reported by Fourel et al. (1998) as a proof of principle for the measurement of these isotopes via continuous-flow isotope ratio mass spectrometry. That study showed a positive correlation between the two isotopes. However, since then, no studies have further investigated the relationship between these isotopes in monarch wings in particular, and only a few researchers have reported on δ 2H and δ 18O values among higher-order taxa. Hobson and Koehler (2015) reported that while feathers of American Redstart (Setophaga ruticilla) showed a good relationship between δ 2H and mean growing-season precipitation δ 2H (r 2 = 0.77), the same was not true for feather δ 18O (r 2 = 0.32). However, for dragonflies, insects that form wings from nutrients derived from an aquatic larval stage, Hobson et al. (2012) found an excellent relationship between δ 2Hw and δ 18Ow values (r 2 = 0.92). The question remains, then,

TABLE 1 | Mean isotope values (± 1 SD ‰) of wild reared monarch butterflies from eastern North America and mean growing-season δ 18Op (GSO) and δ 2Hp (GSD) at each location plus the 95% CI shown in parentheses from Bowen et al. (2005).

*All data shown in* Table S1*.*

whether δ 2Hw and δ 18Ow values of terrestrial insects are also well-correlated and indeed reflect underlying relationships in environmental waters that could be used to increase the accuracy of assignment to origin compared to the use of δ 2H measurements alone. Here, we report measurements of δ 2Hw and δ 18Ow values of monarch butterflies sampled at known origin natal sites in eastern North America. Our objective was to determine if a dual isotope approach could provide additional environmental information related to regional patterns in evaporative conditions that could ultimately contribute to more accurate delineation of natal origins of monarchs and other terrestrial insects.

#### MATERIALS AND METHODS

#### Monarch Sample

Monarch wing material was obtained from samples reported in Wassenaar and Hobson (1998) and Hobson et al. (1999). Those were monarchs raised on milkweed exposed only to precipitation at 31 known sites throughout the range of the eastern monarch population and which formed the calibration relationship between mean growing season δ 2Hp and δ 2Hw reported by Hobson et al. (1999) (**Table 1**). Additionally, monarchs collected from roadkill mortality (n = 92) at a single site in northeast Mexico during the autumn migration of 2015–16 were used to investigate assignment to natal origins using both δ 2Hw and δ 18Ow values.

#### Stable Isotope Analyses

All monarch wing samples were soaked and rinsed in a 2:1 chloroform:methanol solution and air dried. Subsamples were cut from the same region of the hindwing to reduce intersample variance due to isotopic effects from pigmentation (Hobson et al., 2017) and weighed (0.35 ± 0.02 mg) into silver capsules. All samples were prepared for δ 2H and δ 18O analysis at the Stable Isotope Laboratory of Environment Canada, Saskatoon, Canada. Our approach involved the analysis of both δ 2H and δ 18O on the same analytical run (i.e., both H2 and CO gases were analyzed from the same pyrolysis) from samples and standards. All measurements were performed on a HTC system (Thermo Finnigan, Bremen, Germany) equipped with a Costech Zero-Blank autosampler. The helium carrier gas rate was set to 120 ml/min through a 0.6 m ¼-inch 5- Å molecular sieve (80–100 mesh) GC column. The HTC reactor was operated at a temperature of 1,400◦C and the GC column temperature was set to 90◦C. After separation, the gases were introduced into a Delta V plus isotope-ratio mass spectrometer via a ConFlo IV interface (Thermo Finnigan, Bremen, Germany). The eluted N2 was flushed to waste by

FIGURE 1 | Relationship between δ 2Hw and δ 18Ow for monarchs raised outdoors at 31 known sites (Table 1) throughout their range in 1996. Sample originally reported for δ 2Hw and δ 13Cw by Hobson et al. (1999) but these are new δ 2Hw values measured simultaneously with δ 18Ow.

withdrawing the CF capillary from the ConFlo interface. We used keratin reference standards CBS and KHS to calibrate sample δ 2H (−197 and −54.1‰, respectively) and δ 18O (+2.50 and +21.46‰, respectively; Qi and Coplen, 2011). These standard values were used vs. the newly reported values of Soto et al. (2017) simply to maintain consistency with our earlier published work for monarch assignments and we note that this will not affect the assignment per se (providing appropriate calibration algorithms are used). Based on replicate (n = 5) withinrun measurements of keratin standards, sample measurement error was estimated at ±2‰ for δ 2H and ±0.4‰ for δ 18O. All H results are reported for non-exchangeable H and for both H and O in typical delta notation, in units of per mil (‰), and normalized on the Vienna Standard Mean Ocean Water – Standard Light Antarctic Precipitation (VSMOW-SLAP) standard scale.

#### Isotopic Assignments

We created δ 18Ow and δ 2Hw isoscapes based on derived transfer functions using wing stable isotope values of known origin monarchs found in this study. This was accomplished by calibrating amount-weighted growing-season average precipitation δ 18O and δ 2H (δ 18Op and δ 2Hp) (Terzer et al., 2013; IAEA/WMO, 2015) surfaces into separate δ 2Hw and δ 18Ow isoscapes. We then depicted potential origins of a sample of roadkilled monarchs salvaged during their autumn migration through northeastern Mexico. That depiction was done using techniques described previously (Hobson et al., 2018b). Briefly, we used a likelihood-based assignment method (Hobson et al., 2009b; Wunder, 2010; Van Wilgenburg et al., 2012) to assign individual monarchs using δ 2Hw and δ 18Ow to each calibrated wing isoscape (see results), separately. We used the residual SD error of 9.33‰ for δ 2Hw and 1.50‰ for δ 18Ow from regressions in our assignments. We limited assignments to the current known geographic range for the eastern monarch

population and used this as a spatial mask (i.e., clip) to limit our analysis. We estimated the likelihood that a cell (pixel) within the δ 2Hw and δ 18Ow isoscapes represented a potential origin for a sample by using a normal probability density function (pdf) based on the observed δ 2Hw and δ 18Ow, and thus depicted the likely origins of each monarch by assigning individuals to the δ 2Hw and δ 18Ow isoscapes, separately. We arbitrarily chose a 2:1 odds ratio to include only those pixels (coded 1) with at least a 67% probability of origin vs. all others (coded 0). This resulted in a binary map per assigned individual of presence and absence. We then summed the results of individual assignments by stacking the surfaces for a final depiction. We conducted geographic assignments to origin using functions within the R statistical computing environment (R Core Team., 2016) employing the "raster" (Hijmans, 2016) and "maptools" (Bivand and Lewin-Koh, 2016) packages. Thus, the final assignment surface depicted the number of individuals co-assigned at each pixel based on the odds criteria. We also conducted assignment to origin analysis for individual Monarch samples using a dual-isotope (δ 2Hw, δ 18Ow) approach applied with a multivariate normal probability density function (mvnpdf; see Hobson et al., 2014 for details). Similar to the univariate pdf approach, the mvnpdf method calculates the probability that a particular spatially referenced cell represents a potential origin in calibrated raster isoscape space.

FIGURE 4 | Depiction of (A) the expected monarch δ 18Ow isoscape based on the relationship derived between δ 18Ow and amount-weighted mean growing season average δ 18Op (Bowen et al., 2005) shown in Figure 2 and (B) the expected monarch δ 2Hw isoscape based on the relationship derived between δ 2Hw and amount-weighted mean growing-season average δ 2Hp (Bowen et al., 2005) shown in Figure 3. Legend is the number of individuals assigned to a pixel based on the odds ratio criterion used.

Frontiers in Ecology and Evolution | www.frontiersin.org

ratio criterion used.

## RESULTS

#### Monarch Calibration

We found a positive relationship between monarch δ 2Hw and δ 18Ow values for known-origin, outdoor-raised individuals (δ 2Hw = 5.21 ∗ δ 18Ow – 211, r 2 = 0.42; **Figure 1**). The relationship between δ 18Ow and δ 18Op was positive (δ 18Ow = 0.54 ∗ δ 18Op + 22.5; **Figure 2**) but relatively weak (r 2 = 0.30, p < 0.001). In contrast, the relationship between δ 2Hw and δ 2Hp was positive (δ 2Hw = 0.78 ∗ δ 2Hp – 77.42; **Figure 3**) and stronger (r 2 = 0.61, p < 0.001).

#### Assignment Evaluation

Using the relationships established between δ 18Ow and δ 2Hw values and δ 18Op and δ 2Hp values, respectively, we created predicted monarch wing isoscapes for each isotope (**Figures 4A,B**). These surfaces are superficially similar due to the correlation between isotopes. We then used these surfaces to assign origins of a sample of monarchs from roadkill mortality during their fall migration. That sample included monarchs from across their range and we contrasted depictions of origins of these individuals using only δ 2Hw values, only δ 18Ow values and both δ 18Ow and δ 2Hw values in a multivariate normal assignment. These depictions were similar in that they showed highest probability in the southwestern portion of the range but differences were also apparent (**Figures 5A–C**). The dual isotope approach constrained the assignment compared to the use of δ 2Hw values alone.

#### Deuterium Excess

We contrasted deuterium excess values calculated for individual monarch wings and compared these to the growing-season average deuterium excess calculated for each natal site. No correlation was found between these values (**Figure 6**, r 2 = 0.02). This suggests that little information associated with

precipitation environmental deuterium excess was transferred to monarchs per se.

# DISCUSSION

Our results confirm a strong relationship between wing chitin δ 2H values and precipitation δ 2H values at known natal sites. This result underlines the strong assignment power of using Monarch wing δ 2H values to infer origins and confirms that even though the earlier investigations of Monarch δ 2H values to infer origins used different analytical techniques (i.e., offline zinc reduction and steam equilibration, Wassenaar and Hobson, 1998; Hobson et al., 1999), the power of that isotopic assignment holds. The different analytical approaches resulted in different calibrations between wing chitin δ 2H (δ 2Hw) and predicted amount-weighted growing season δ 2Hp (earlier method: δ 2Hw = 0.62 ∗ δ 2Hp – 79; r 2 = 0.69; recent method: δ 2Hw = 0.78 ∗ δ 2Hp – 77.4; r 2 = 0.61) and we recommend that from now on authors use the recent calibration relationship reported here. The δ 2Hw results contrasts with the much weaker relationship we found between δ 18Ow and δ 18Op. Thus, while the measurement of δ 18Ow possibly confers additional information on origin, the strong relationship between δ 2Hw and δ 18Ow values likely precludes the effective use of a dual isotope approach in most cases. This result has been seen previously in assignment exercises involving several taxa (reviewed by Hobson and Koehler, 2015). Our comparison of deuterium excess values calculated for monarch wings and those of the mean growing season average deuterium excess for natal sites confirms that little environmental information related to evaporative conditions was available from our sample by running δ 18O analyses. Nonetheless, we recognize the potential utility of using both δ 18Ow and δ 2Hw values, especially for areas corresponding to potential origins involving high deuterium excess values that were not included in our study. It may be possible then, to identify migrant individuals raised in xeric vs. humid environments and we encourage further work in this area for migrant insects in general. Clearly, further dual isotope analyses of a large sample of monarchs from known natal origins that spans large isotopic gradients in North America will be useful in testing situations where this more extensive analytical approach might be justified.

It is interesting to speculate why tissue δ 18O values do not seem to add much additional information on origins of Monarch Butterflies and other terrestrial taxa based on δ 2H analyses alone. The typical response to this question is that oxygen enters metabolic pathways from more diverse origins than hydrogen (Hobson and Koehler, 2015). Fundamentally, oxygen is derived metabolically from diet, drinking water and air whereas hydrogen is derived only from diet and drinking water. Atmospheric oxygen is added to the body water pool during metabolism and respiration and has a relatively constant isotopic composition (Luz and Barkan, 2011). Therefore, the oxygen isotopic coupling between tissues and environmental waters is dependent on the relative contribution of respirative processes to the oxygen isotopic composition of body water. Additionally, with insects, atmospheric oxygen for respiration is obtained primarily by diffusion through the spiricules and tracheal pathways (Chown et al., 2006) which may further alter its isotopic composition. It is worth noting that while the hydrogen isotopic compositions of chitin are broadly similar to those of keratins and other tissues of birds or mammals (Ehleringer et al., 2008; Hobson and Koehler, 2015), the δ 18O values of chitin are generally more positive. This, along with the relatively poor fit between oxygen isotopic compositions of wing chitin and precipitation, likely indicates that slightly different sources, processes or pathways are utilized for oxygen in terrestrial insects than in other taxa. This would not necessarily preclude the use of both isotopes in typical assignments to origin but such assignment models require isotopes to be relatively orthogonal. For oxygen isotopes, current analytical methods result in a much higher relative measurement error than for hydrogen isotopes (0.4 vs. 2‰ for hydrogen). To use both isotopes to determine an inferred deuterium excess of environmental waters requires the Meteoric Water Line multiplicative factor of 8, so that currently we can only determine the deuterium excess values to precisions of about 3.2‰. Precipitation varies in deuterium excess from ∼0 to +10‰ which means that such poor precisions will require large numbers of samples depending on which populations are being examined. This is not to say that measurement of both isotopes in monarch tissue will not be useful, but that, in general, the use of wing chitin δ 2H is the most preferred for single isotope assignments.

In addition to the earlier proof of concept papers on the use of both wing δ 18O and δ 2H values to investigate natal origins of monarch butterflies (Fourel et al., 1998), our investigation has underlined the fact that there is much future work required to move the field forward. For example, we stress again that monarchs deriving from natal sites in areas of high deuterium excess (e.g., southern states) might still produce wings which reflect these relationships and so to improve our statistical power more sampling in those areas would be desirable.

#### REFERENCES

# DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and/or the **Supplementary Files**.

# ETHICS STATEMENT

Only dead monarch butterflies were examined in this study. These were derived from collections, natural overwinter mortality and roadkills, so no animal care or ethics approval was necessary.

# AUTHOR CONTRIBUTIONS

KH and GK conceived of the idea and performed stable isotope measurements. KK performed isotope assignments and assisted with figures. All authors contributed to the writing of the manuscript.

# FUNDING

This study was funded by Environment and Climate Change Canada operating funds to KH and GK and by an NSERC Discovery Grant to KH.

# ACKNOWLEDGMENTS

We thank Blanca X. Mora Alvarez for assistance with preparing samples for stable isotope analyses. Two reviewers provided useful comments on an earlier draft of the paper.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo. 2019.00224/full#supplementary-material

of spring re-colonization in eastern North America. PLoS ONE 7:e31891. doi: 10.1371/journal.pone.0031891

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Hobson, Kardynal and Koehler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Does Nature Need Cities? Pollinators Reveal a Role for Cities in Wildlife Conservation

Abigail Derby Lewis 1 \*, Mark J. Bouman1 , Alexis M. Winter 1 , Erika A. Hasle1 , Douglas F. Stotz 1 , Mark K. Johnston1 , Karen R. Klinger 1 , Amy Rosenthal 1 and Craig A. Czarnecki 2

*1 Keller Science Action Center, Field Museum, Chicago, IL, United States, 2 United States Fish and Wildlife Service, Minneapolis, MN, United States*

It is well-established that cities need nature for critical ecosystem services—from storing carbon, to reducing temperatures, to mitigating stormwater—and there is growing momentum to seek out strategies for how these services can intersect with urban design and planning efforts. Social scientists and conservation planners increasingly point to urban residents' need to breathe fresh air, encounter the natural world, and have room to play. It is less obvious, perhaps, whether nature needs cities in order to thrive. The evidence from both urban planning and conservation planning is increasingly "yes." As changes in land use and land cover sweep the planet, cities are becoming important refugia for certain wildlife populations. In recent years, urban planning has embraced the concept of "green infrastructure" as a way to embed green space across metropolitan landscapes to draw on the inherent benefits nature provides to cities, as well as to create habitat for wildlife. We explore this evolving view of cities and nature in the fields of urban and conservation planning. We argue the time is ripe to bring these worlds together, and, using our empirical work, establish that cities matter for monarch butterflies, other pollinators, and at-risk wildlife species.

#### Edited by:

*Jay E. Diffendorfer, United States Geological Survey, United States*

#### Reviewed by:

*Scott Black, Xerces Society for Invertebrate Conservation, United States Mohammad Imam Hasan Reza, Presidency International School, Bangladesh*

\*Correspondence:

*Abigail Derby Lewis<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Conservation, a section of the journal Frontiers in Ecology and Evolution*

Received: *29 December 2018* Accepted: *24 May 2019* Published: *21 June 2019*

#### Citation:

*Derby Lewis A, Bouman MJ, Winter AM, Hasle EA, Stotz DF, Johnston MK, Klinger KR, Rosenthal A and Czarnecki CA (2019) Does Nature Need Cities? Pollinators Reveal a Role for Cities in Wildlife Conservation. Front. Ecol. Evol. 7:220. doi: 10.3389/fevo.2019.00220* Keywords: urban, ecology, wildlife, conservation, culture, monarch, pollinators

# WHY FOCUS CONSERVATION EFFORTS IN CITIES?

More than 80% of Americans live in urban areas1 , as does over half the world's population (UN DESA, 2018). In contrast, in 1960 twice as many people in the world lived in rural areas (2 billion) as urban (1 billion)2 . This trend is expected to continue, with nearly 70% of the world's population living in urban areas by 2050 (UN DESA, 2018). However, a striking 60% of the additional land projected to become urban by 2030 is yet to be built (GFDRR World Bank, 2015). Each day in the US more than 4,000 acres of open space are lost to development, the equivalent of more than three acres per minute (Williams, 1975).

As changes in land use and land cover sweep the planet, converting grasslands, forests, wetlands, and other available habitat into agricultural fields and developed landscapes, cities are becoming increasingly important refugia for an array of wildlife populations, including threatened and endangered species (Aronson et al., 2014; Ives et al., 2016). This pattern reflects, in part,

1U.S. Census Bureau. (2010). Available online at: https://www.census.gov/geo/reference/ua/urban-rural-2010.html 2Our World in Data. Available online at: https://ourworldindata.org/urbanization

the propensity to locate urban development in biologically diverse areas such as coastal and riparian locations (Luck, 2007). Indeed, populations of many species are reappearing in force across urban spaces—from fishers (LaPoint et al., 2015) and coyotes (Morey et al., 2007) to bullfinches (Audet et al., 2016) and peregrines (Caballero et al., 2016). Other urban wildlife dwellers include migratory species of birds, dragonflies and butterflies that rely on habitat patches in cities to move through landscapes dominated by large-scale agriculture (Seewagen et al., 2011; Goertzen and Suhling, 2013; Tam and Bonebrake, 2016). Significantly, several American cities support a higher diversity of native bee species—including the endangered rusty patched bumble bee (Bombus affinis)—than do adjacent rural areas (Hall et al., 2017; U.S. Fish Wildlife Service, 2017). These examples of wildlife species utilizing urban habitat illustrate that developed areas can be important in the conservation of species of high concern.

Given these trends, we have a small but critical window of time to develop and implement strategies that create highly functional urban landscapes with benefits for both people and nature (Intergovernmental Science-Policy Platform on Biodiversity Ecosystem Services (IPBES), 2019). Understanding how habitat can best be embedded in urban landscapes is important to help curb a potential "sixth mass extinction" (Ceballos et al., 2015, 2017). This situation cannot be overstated: recent studies reveal that the number of mammals, birds, fish and reptiles on Earth has been reduced by 60% in <50 years (World Wildlife Fund, 2018). In Germany, flying insect populations have plunged by 75% in the last 25 years (Hallmann et al., 2017), and a similar trend has been observed in Puerto Rico (Lister and Garcia, 2018).

Powerful urbanization trends have understandably been accompanied by a sense that nature has been displaced in urban landscapes and can only be found where cities don't exist (Hartig and Kahn, 2016). On the one hand, urban life has been characterized as "distanced from nature" (Tuan, 1978) accompanied by an "extinction of experience" (Pyle, 1978, 1993) as people move to urban settings (Miller and Hobbs, 2002; Turner et al., 2004; Zhang et al., 2014; Soga and Gaston, 2016). On the other hand, the conservation community has achieved huge victories in places far from the urban world, and a side effect has been to reify the notion of "wilderness" in the American mind (Nash, 1967). Large protected areas have "increasingly become the means by which many people see, understand, experience, and use the parts of the world that are often called nature and the environment" (West et al., 2006, p. 255).

As we'll discuss, our work on monarch conservation surfaces new ways to bring the potential for urban conservation into sharper focus. This new way of "seeing" cities includes: valuing new potential partners for nature, many of them historically excluded from the conservation narrative (Finney, 2014; Taylor, 2016); applying finer scales of analysis, with the aid of new data and geospatial tools; and recognizing other practices now being adopted to create sustainable cities for people. A main takeaway from our research is that the places called "urban" in all their size, density, and heterogeneity (Wirth, 1938) do contain powerful voices, activities, and opportunities for conservation. In spite of the perception that racial minorities and low-income Americans—who are often well-represented in urban regions—are considered to have little concern for nature, a recent study reports their higher concern for nature than white and higher-income respondents (Pearson et al., 2018). Activating that concern for conservation may entail folding "nature" into the broader set of priorities that residents and community-based organizations have.

From backyards to rooftops to parks, urban residents have seen to it that nature has a place in the city, from the ground up. Improvements in technology have made this activity increasingly visible from the sky down. Wherever possible, our monarch research employed high-resolution imagery, enhanced by technology such as LiDAR3 , which enabled precise characterization of land cover at the sub-meter scale. This helps us to visualize what is happening in the urban area with greater precision than the commonly used National Land Cover Database (NLCD), a 30-meter resolution dataset most appropriate to use when studying county-level units or larger (Wickman et al., 2014). When NLCD is applied to highly heterogeneous metropolitan landscapes, large swaths of land are classified as high, medium and low intensity developed land cover classes. While "low intensity developed" and "medium intensity developed" indicate a moderate proportion (20–79%) of the land cover is impervious, higher resolution data are needed to quantify and visualize the remaining land that is permeable and usable as green space. **Figure 1** demonstrates a sample area in Chicago's urban core viewed using NLCD, aerial imagery with LiDAR, and plantable space. Finer grain analysis can support the growing recognition that cities are ripe with opportunity and interest to create spaces where both people and wildlife benefit (Rosenzweig, 2003). This new perspective helps us to appreciate nature abounding in a multitude of contexts that intersect with how people live, work and play in urban areas, including in churchyards and school yards, along boulevards, and amidst corporate campuses, residential yards and community gardens (Beatley, 2011; Van Horn and Aftandilian, 2015; Johnston et al., 2019).

Much of what can be seen, and what exists as an opportunity to enhance urban biodiversity going forward, comes in the context of burgeoning "green infrastructure" efforts (Benedict and McMahon, 2006; Hostetler et al., 2011; Ahern, 2013). The uptick of interest in green infrastructure relates to how well it supports a number of needs in urban areas, including: enforcement efforts to bring municipalities into compliance with the pollution control provisions of the federal Clean Water Act4 ; nature-based solutions to reduce climate impacts such as flooding in urban landscapes (Derby Lewis et al., 2015); public interest in native landscaping (McMahan, 2006); the growing sector of

3LIDAR, or Light Detection and Ranging, is a "remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth." Available online at: https://oceanservice.noaa.gov/facts/lidar.html

4Technical Report: Tools, Strategies and Lessons Learned from EPA Green Infrastructure Technical Assistance Projects. Available online at: https://www. epa.gov/green-infrastructure/tools-strategies-and-lessons-learned-epa-greeninfrastructure-technical

FIGURE 1 | Comparison of land cover data as classified when applying (A) the National Land Cover Dataset derived from 30-m spatial resolution Landsat satellite data, (B) higher spatial resolution land cover data derived using 2-foot spatial resolution multispectral aerial imagery and LiDAR data, and (C) grass-shrub land cover class in isolation (i.e., plantable space). This scale reveals the many opportunities that exist even under highly developed conditions. Sources: Multi-Resolution Land Characteristics Consortium (U.S.). "National Land Cover Dataset (NLCD)" and University of Vermont Spatial Analysis Lab "Chicago Urban Tree Canopy."

urban farming (Jarosz, 2008; Lovell, 2010); and increased access to nature, particularly in marginalized neighborhoods (Wolch et al., 2014).

We propose that pollinator-focused efforts can help to find alignment between conservation goals and concerns important to cities and urban dwellers. Pollinators are small organisms that interact with households and neighborhoods, but operate in the larger landscape scale—and their associated habitats can leverage a variety of design and management activities that are underway in cities and offer pathways to connect nature and cultural heritage in urban communities. In short, monarchs (and other pollinators) point to new ways to "see" the city as a space for conservation, with new partners, new tools, and new practices.

#### HOW CAN THE MONARCH BUTTERFLY HELP US TO UNDERSTAND THE ROLE CITIES CAN PLAY IN CONSERVATION?

While many pollinators in general have wide public appeal, the monarch butterfly (Danaus plexippus) is arguably an ideal ambassador to engage the public on conservation issues.  They also represent a powerful cultural symbol that facilitates people talking about conservation—and to one another (Gustafsson et al., 2015). Monarchs have been referred to as a convener: a species able to connect people across a continent who witness the stunning migration in their own backyard. Currently, there is heightened public awareness that monarchs, like many pollinators locally and globally, are declining rapidly. Over the last two decades, the eastern monarch population has decreased by more than 80% (Semmens et al., 2016), while the western population has declined by a staggering 97% (Schultz et al., 2017). The public interest in monarchs and a growing awareness of their plight create an opportunity to translate attitudes into practices that can help a range of pollinators across the urban landscape. Through our efforts and those of others, we are beginning to discover how monarchs are relevant to the future of conservation, as well as different entry points—from social justice and cultural history to sustainable food initiatives—for engaging people in creating urban habitat (Gustafsson et al., 2015; Derby Lewis et al., 2018).

# A MONARCH'S VIEW OF THE CITY

A combination of efforts such as the creation of a Federal Strategy to Promote the Health of Pollinators5 and an assessment to determine whether monarchs need Endangered Species Act protection6 , along with a variety of current urban monarch initiatives (e.g., Mayors' Monarch Pledge7 and Monarch Watch8 ), led the U.S. Fish and Wildlife Service and the Field Museum in Chicago to collaborate in assessing the role cities could play in helping to increase the amount of habitat available to support eastern monarch butterflies. While this research does not apply to the Western monarch population, whose numbers have declined so dramatically (Schultz et al., 2017) that individuals are not frequently observed in the urban landscape, lessons can be drawn to support broader pollinator habitat efforts in metropolitan landscapes throughout the United States.

The aim of this effort was (1) to evaluate how much the urban sector, which is the second largest land use sector in the eastern monarch's Midwestern breeding range (Thogmartin et al., 2017), could contribute to the national goal of adding 1.8 billion milkweed stems (Semmens et al., 2016) and (2) to identify best practices for engaging a diversity of urban stakeholders in the creation of monarch habitat. Our team of ecological and social scientists then translated this information into a suite of spatial and social planning tools to help decision makers identify where the biggest opportunities exist to increase monarch habitat, and guidance on how to turn potential into reality. Details of the methods used to estimate the number of existing and potential milkweed stems occurring in urban landscapes, and to identify strategies to engage stakeholder groups to create habitat, have been published elsewhere (Johnston et al., 2019). For the purposes of this perspective, we provide a high-level summary of the approach and key findings, and discuss implications of this work in a broader context.

To understand the ecological landscape from a monarch's perspective, we conducted field sampling to estimate how much milkweed is currently on the ground and quantified the potential space for planting additional monarch habitat (i.e., the amount of grass/shrub land cover identified using high-resolution imagery and LiDAR data9 ) in different land use classes within four major metropolitan regions: St. Paul-Minneapolis, Chicago, Kansas City and Austin. For comparison purposes, land use types were consolidated into 16 classes based on land management (e.g., residential-single family, community and cultural, open space conservation, etc.). Milkweed stems were counted in three ways: (1) randomly sampled census blocks located at staggered distances along transects extending through the metropolitan area, (2) targeted sampling of open space conservation and nonconservation areas, and (3) targeted sampling of locations where milkweed was intentionally planted. Based on the density of milkweed present in each of the land use classes, we extrapolated the amount of milkweed that is currently present and the potential to add additional milkweed stems in metropolitan areas across the US eastern range of monarchs. Our findings indicate the collective impact of this potential contribution could provide

5Pollinator Partnership Action Plan. Available online at: https://www.whitehouse. gov/sites/whitehouse.gov/files/images/Blog/PPAP\_2016.pdf

6Assessing the Status of the Monarch Butterfly. Available online at: https://www.fws. gov/savethemonarch/SSA.html

7Mayors' Monarch Pledge is a program designed to support U.S. cities, municipalities, and other communities committing to create habitat for the monarchs and other pollinators. Available online at: https://www.nwf.org/Garden-For-Wildlife/About/National-Initiatives/Mayors-Monarch-Pledge.aspx

8Monarch Watch is "a nonprofit education, conservation, and research program based at the University of Kansas". Available online at: https://monarchwatch.org/ 9Urban Tree Canopy Assessment. Available online at: https://www.nrs.fs.fed.us/ urban/utc/

nearly a third of the additional 1.8 billion milkweed stems needed in the Midwest to stabilize the eastern monarch's population (Johnston et al., 2019).

Additionally, we looked at the potential plantable space across each land use class to provide a more detailed characterization of the urban landscape. For example, in the Chicago region, we found that residential land had one of the highest amounts of potential plantable space. Using a land use lens allowed us to link high-potential areas with the stakeholders that would need to be engaged to increase monarch habitat. We were then able to pair those stakeholders with evidence-based approaches to enhance uptake (**Figure 2**).

To identify appropriate approaches for different stakeholder groups in an urban setting, we conducted social science research to assess the motivations, concerns, interests, challenges, and strategies of those both directly and indirectly involved in making their city's landscape more hospitable to monarchs. We surveyed people engaged in different environmental practices (e.g., planting/managing land, designing landscapes, monitoring the natural environment) and within the different land use classes laid out by the team's geospatial analysts. With people who had extensive knowledge or experience relevant to monarch conservation, we conducted semi-structured interviews, as they make efficient use of the participant's time and are well-suited to the exploratory phase of research (Schensul and LeCompte, 2013). For participants drawn from the "interested public," we used an online survey, which allowed us to reach more people. We collected and analyzed 734 online surveys and 75 semistructured interviews in the four pilot metropolitan areas and found that interest in creating monarch habitat was present to varying degrees across all groups, but it took different forms. For example, while some stakeholder groups are singularly focused on the monarch, others may be more interested in broader habitat creation and/or wary of the regulations that singlespecies conservation can bring. This information was used to highlight best practices for engaging urban stakeholders and to develop approaches that connect to community interests and assets (e.g., social justice initiatives, green infrastructure planning, urban farming efforts, public art) in engaging a wide cross-section of urban residents to take actions aligned with wildlife conservation goals.

#### DOES NATURE NEED CITIES?

Our results add to a growing body of literature showing that metropolitan areas matter for wildlife conservation (Morey et al., 2007; LaPoint et al., 2015; Caballero et al., 2016). Despite being developed, these landscapes have high potential to maintain functional habitat for a variety of species, including migratory and threatened endemic species. Habitat within and between US

10www.fieldmuseum.org/monarchs

cities can help connect the dots for monarchs, other pollinators, and birds along migratory pathways from Mexico to Canada and back.

The importance of cities for maintaining insect pollinators is particularly noticeable, given the relatively small spatial and temporal requirements for functional pollinator habitat that can be satisfied in urban green spaces. Although urban habitats are highly heterogeneous, with habitat often occurring in isolated patches, evidence suggests there is sufficient opportunity for pollinators to use these spaces (Tommasi et al., 2004; Glaum et al., 2017; Hall et al., 2017)—sometimes even greater opportunity than in surrounding rural areas.

As is the case with any land use sector, however, there are considerations that need to be addressed. For example, the widescale use of pesticide and herbicide in urban landscapes (Hladik and Kolpin, 2015) by public entities and private landowners poses a threat to insect population health. To ensure a net gain for pollinator populations utilizing urban habitat, approaches that limit insecticide exposure in urban areas are recommended.

Interdisciplinary methods that bring together the insights of social, natural and spatial sciences can shed light on the conservation approaches with the most ecological and social potential to scale effective solutions. Our work suggests that the collective impact of conservation-related actions by urban stakeholder groups can play a fundamental role in supporting wildlife—including nearly a third of the milkweed needed for the eastern monarch (Johnston et al., 2019). By identifying the ecological potential and understanding the social perspectives and interests of different stakeholder groups, it is possible to enhance the uptake of conservation strategies within urban areas, where these practices are important for threatened species.

Metropolitan areas also offer the opportunity to engage millions of people in conservation efforts. Despite urban areas' representing only 3% of the total landmass in the US, these areas have a disproportionate influence on the landscape, and investments must be made to turn the urban conservation potential into a reality. Expanding the functional habitat within these urban centers and increasing the commitment of urban stakeholder groups to conservation goals could greatly contribute to the achievement of those goals.

This means we must identify the different entry points where conservation goals can include input from urban partners and overlap with community values and concerns. Embracing community values as assets in conservation planning creates more opportunity for habitat and fosters meaningful new partnerships that are essential in highlighting conservation relevance in a rapidly expanding urban world.

A broader vision of what conservation is, what nature looks like beyond protected lands, and who is included in the conservation community is long overdue. Acknowledging that there are different ways that heritage and history shape how people experience the natural world, or see nature as a part of their lives, is an important first step in broadening the conservation community (Campbell, 2015). Our research indicates that cities can play a critical role in species and habitat conservation and that interdisciplinary approaches that engage urban stakeholders can have an outsize impact on wildlife conservation.

### AUTHOR CONTRIBUTIONS

ADL, MB, AW, EH, DS, MJ, and CZ contributed to the conception of the design and the study. ADL wrote the first draft of the manuscript. ADL, MB, and AW wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

# FUNDING

A cooperative agreement between the United States Fish and Wildlife Service, Chicago Wilderness, and the Field Museum. Grant Award number CW FMNH 103115. Publication fees for open access were paid for by the Field Museum.

#### ACKNOWLEDGMENTS

We would like to thank the deeply committed and creative team of individuals whose work is reflected in this study, including Tim Bodeen, Katie Boyer, Brittany Buckles, Wendy Caldwell, Louise Clemency, Jill Erickson, Caitlin Dix, Ryan Drum, Nigel Golden, Adriana Fernandez, Jessica Hellman, Kyle Kasten, Marc Lambruschi, Laura Lukens, Cora Lund-Preston, Patrick Martin, Katie Maxwell, Tom Melius, Kelley Myers, Rosie Nguyen, Karen Oberhauser, John Rogner, Jason Rowader, Glen Salmon, Kristin Shaw, Wayne Thogmartin, Chuck Traxler, Kristin Voorhies, Gwen White, and Barbara Willy. We also thank Debra Moskovits, Ellen Woodward, Iza Redlinski, and Jacob Campbell for their review and comments on this paper.

#### REFERENCES

Audet, J., Ducatez, S., and Lefebvre, L. (2016). The town bird and the country bird: problem solving and immunocompetence vary with urbanization. Behav. Ecol. 27, 637–644. doi: 10.1093/beheco/arv201

Beatley, T. (2011). Biophilic Cities: Integrating Nature into Urban Design and Planning. Washington, DC: Island Press.

Caballero, I. C., Bates, J. M., Hennen, M., and Ashley, M. V. (2016). Sex in the city: breeding behavior of urban peregrine falcons in the Midwestern US. PLoS ONE 11:e0159054. doi: 10.1371/journal.pone.0159054

Benedict, M. E., and McMahon, E. T. (2006). Green Infrastructure: Linking Landscapes and Communities. Washington, DC: Island Press.

Zhang, W., Goodale, E., and Chen, J. (2014). How contact with nature affects children's biophilia, 420 biophobia and conservation attitude in China. Biol. Conserv. 177, 109–116. doi: 10.1016/j.biocon.2014.06.011

**Disclaimer:** The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Derby Lewis, Bouman, Winter, Hasle, Stotz, Johnston, Klinger, Rosenthal and Czarnecki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Estimating Milkweed Abundance in Metropolitan Areas Under Existing and User-Defined Scenarios

Mark K. Johnston\*, Erika M. Hasle, Karen R. Klinger, Marc P. Lambruschi, Abigail Derby Lewis, Douglas F. Stotz, Alexis M. Winter, Mark J. Bouman and Izabella Redlinski

*Keller Science Action Center, Field Museum, Chicago, IL, United States*

#### Edited by:

*Wayne E. Thogmartin, United States Geological Survey, United States*

#### Reviewed by:

*Carl Stenoien, University of Minnesota Twin Cities, United States Karen Tuerk, Monarch Joint Venture, United States*

> \*Correspondence: *Mark K. Johnston<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Conservation, a section of the journal Frontiers in Ecology and Evolution*

> Received: *01 February 2019* Accepted: *21 May 2019* Published: *21 June 2019*

#### Citation:

*Johnston MK, Hasle EM, Klinger KR, Lambruschi MP, Derby Lewis A, Stotz DF, Winter AM, Bouman MJ and Redlinski I (2019) Estimating Milkweed Abundance in Metropolitan Areas Under Existing and User-Defined Scenarios. Front. Ecol. Evol. 7:210. doi: 10.3389/fevo.2019.00210* Metropolitan areas play an undetermined role in supporting migratory monarch butterfly (*Danaus plexippus*) populations despite providing habitat areas rich with milkweed (*Asclepias* spp.), the obligate host plants for monarch larvae. Researchers from the US Geological Survey and collaborating institutions have called for an "all hands on deck" approach to establishing monarch butterfly habitat by focusing on potential contributions from all land use sectors at levels necessary to sustain the eastern migratory monarch butterfly population. To understand the current and potential contribution of milkweed stems in metropolitan areas, our research teams surveyed milkweed densities using a new "metro-transect" protocol and conducted interviews and surveys across a diverse set of stakeholder groups in four major metropolitan areas (Chicago, Minneapolis-St. Paul, Kansas City, and Austin). We developed Geographic Information System (GIS) tools that use these data to model existing milkweed stems in metropolitan areas, and to estimate the potential to add additional milkweed stems with the adoption of milkweed-friendly planting practices across different land use classes (e.g., residential, institutional, and commercial). By extrapolating metropolitan Chicago milkweed densities across US Census urbanized areas in the northern US range of the eastern monarch butterflies, we estimate that approximately 29.8 million stems of milkweed can be added under modest "enhanced" milkweed densities, and up to 271 million stems may be added under "exemplary" milkweed densities. Both estimates are derived from a two percent "adoption rate," or landowner conversion of green spaces. These findings show that metropolitan areas provide important habitat opportunities and should be included prominently in monarch conservation strategies when working toward national goals to increase the amount of milkweed stems and monarch habitat across the Midwest. Municipal decision-makers and planners can estimate their capacity to add stems across the metropolitan landscape by identifying where the biggest opportunities exist with help from our Urban Monarch Conservation Planning Tools.

Keywords: milkweed, monarch butterfly, urban, metropolitan, habitat sampling, GIS tools, conservation

# INTRODUCTION

Monarch butterflies (Danaus plexippus) east of the Rocky Mountains migrate annually between central Mexico and Canada (Flockhart et al., 2013). The overwintering population has decreased more than 80% over two decades (Brower et al., 2012; Semmens et al., 2016). One prevailing theory for this decline is the milkweed-limitation hypothesis (Pleasants and Oberhauser, 2012; Pleasants et al., 2017; but see Dyer and Forister, 2016). This hypothesis posits that increased efficiency in agricultural practices (i.e., universal use of glyphosate and Roundup Ready crops) and widespread conversion of grasslands to other land uses have resulted in a precipitous decline in milkweed stems (Asclepias spp.) that monarch larvae depend on as their obligate host plants (Zaya et al., 2017). The US Fish and Wildlife Service (USFWS) is in the process of assessing whether monarch butterflies should be listed as threatened or endangered1 . This determination will be based in part on a national Species Status Assessment (SSA) underway by the USFWS, which takes into consideration current and pledged habitat conservation and restoration efforts across all sectors. Research at the national scale suggests that a fivefold increase in milkweed stems is needed to address extinction risks associated with the eastern monarch population (Pleasants, 2017; Thogmartin et al., 2017a). To accomplish this increase, Thogmartin et al. (2017b) recommend an "all hands on deck" approach, with participation in milkweed restoration efforts by five land-cover sectors including "perennial herbaceous vegetation on protected lands, land enrolled in Conservation Reserve Program (CRP), lands in rights-of-way status, land associated with agricultural practices, and the urban/suburban sector." The USFWS and Monarch Joint Venture2 have expanded the "all hands on deck" initiative to recommend a collaborative partnership effort by organizations and individuals to increase pollinator habitat.

"Urban/suburban" areas are classified as "developed" areas in the National Land Cover Dataset and Cropland Data Layer. These lands make up the second largest footprint (45,313 km2 ) of the five land-cover sectors in the Midwest, but their importance and potential for breeding monarchs are not well understood. Efforts at the national scale are now underway to improve our understanding of milkweed and monarch butterfly densities. Some examples include the Integrated Monarch Monitoring Program (IMMP) (Cariveau et al., 2019), the Monarch Larva Monitoring Project (Prysby and Oberhauser, 2004; Kountoupes and Oberhauser, 2008), and sampling along roadside rights-ofway (Kasten et al., 2016). Based on our work in urban landscapes, we know milkweed is present in developed areas (**Figure 1**), and some studies have shown increased egg loading in urban area gardens (Cutting and Tallamy, 2015; Stenoien et al., 2015) and high potential in urban rights-of-way (Leston and Koper, 2016). However, published estimates of milkweed densities in urban landscapes are currently limited to the survey results compiled by Thogmartin et al. (2017b), which estimate 0.1 to 1.0 stems/acre in urban areas. Supplemental results from this US Geological Survey (USGS) publication are presently the sole data source used to inform the Monarch Conservation Database (MCD),3 a USFWS database system that tracks the current and anticipated monarch habitat contributions of participating organizations across the US to inform the SSA. If a main limiting factor in rebounding migratory monarch populations is the decreased availability of milkweed plants, then it is imperative to have accurate estimates about the existing density and the habitat potential of milkweed in urban landscapes, and, because of the urgency of the issue, it is necessary to have them soon.

This paper addresses milkweed abundance across the various land use types that characterize US metropolitan areas—from their historic urban cores through suburbia and into the urbanrural fringe typical of federally-defined Metropolitan Areas4 and provides tools for estimating the potential for adding more habitat in these areas. In this study, we centered our analysis on "metropolitan areas" and "urbanized areas" as defined by the US Census, which provide established countybased boundaries for examining developed areas, rather than using more broadly defined terms such as "cities," "urban," and "suburban." Our first goal was to estimate the current amount of milkweed available to monarch butterflies in Chicago, Minneapolis-St. Paul, Kansas City, and Austin as a basis for understanding the relative contribution of major metropolitan areas; our second goal was to understand the capacity of these metropolitan areas for increasing the amount of milkweed available for breeding monarch butterflies; our third goal was to provide geospatial planning tools that clarify opportunities for habitat expansion and support the development of conservation strategies in metropolitan areas; and our fourth goal was to estimate the potential for adding milkweed across other urbanized areas in the northern range of monarch butterflies in the United States. Based on our past work, we hypothesized that existing milkweed densities in metropolitan areas are higher than previously published by Thogmartin et al. (2017b), and that these areas have the potential to contribute a sizeable portion of the milkweed needed to support the eastern population of monarch butterflies.

#### METHODS

Our study was conducted with support from local partners across four major metropolitan areas along the monarch's migratory route: Chicago, Minneapolis-St. Paul, Kansas City, and Austin (**Figure 2**). Selections were based on: their geographic location along the monarch flyway, organizations interested in partnering, geospatial data availability, and variation in environmental conditions (e.g., land use proportions and growing conditions).

1Assessing the status of the monarch butterfly https://www.fws.gov/ savethemonarch/SSA.html

2 2018 Monarch Conservation Implementation Plan https://monarchjoint venture.org/images/uploads/documents/2018\_Monarch\_Conservation \_Implementation\_Plan\_FINAL\_1.pdf

3The Monarch Conservation Database https://www.fws.gov/savethemonarch/ mcd.html

4As deployed by the US Census Bureau using the 2010 OMB definitions found in the June 28, 2010 Federal Register; https://www.census.gov/programs-surveys/ metro-micro.html

FIGURE 2 | Analysis extent of four metropolitan areas: Chicago, Minneapolis-Saint Paul, Kansas City, and Austin, where field research was conducted to study ecological and social factors related to monarch butterfly habitat in urban/suburban areas. Shaded areas represent urbanized areas (US Census) within the metropolitan analysis extent (heavy outlined boundaries). Metro-transect lines shown in blue run from densely populated urban centers to rural sparsely populated areas. Evenly spaced sampling points (red) show where field teams were directed for sampling.

Our research methods are organized into three sections briefly summarized here and further described below (**Figure 3**). The Primary Data Sources and Data Calculations section includes both data sources and density calculations, which are used as inputs for the models and tools. In the Estimation Methods section, we walk through our Baseline Estimation Methods, which describe our geospatial analyses and tools used for estimating existing stems of milkweed on the landscape that are used as input for the User-Defined Scenario Estimation Methods, which describes the methods and tool used for estimating habitat potential. Outputs from the baseline and scenario planning tools were then used in the Extrapolation Methods section for estimating baseline, enhanced, and exemplary milkweed counts across urbanized areas in the eastern US monarch butterfly range.

#### Primary Data Sources and Data Calculations Geospatial Data

In developing our methods for sampling across metropolitan areas, we used precise land use data and high-resolution land cover data, typically provided by Metropolitan Planning Organizations (MPOs)—although data coverage, classification, and resolution can vary substantially across regions.  In Austin, the regional planning agency serves Travis, Williamson, Bastrop, Caldwell, and Hays counties. Since available data only included Travis County, where Austin is located, we reduced our analysis extent to this central county. A list of key data sources used is found in **Supplementary Table 1**.

The National Land Cover Database covers the nation with 30 meter resolution cells sorted into 20 land cover classes, including low, medium, and high density developed land. Increasingly, higher resolution data are available for major urban areas, though typically with fewer land cover classes. For Chicago, Minneapolis-St. Paul, and Austin we had high-resolution land cover data (two-foot in Chicago, one-meter in Minneapolis-St. Paul, and three-foot in Austin), which included seven land cover classes for Chicago and Minneapolis-St. Paul (bare soil, buildings, grass/shrub, other paved surfaces, roads/railroads, tree canopy, and water) and only one land cover class (tree canopy) for Austin, which we combined with a "remaining pervious" GIS layer. Kansas City presented a significant data challenge, as the land cover layer was at a much coarser (eight-foot) resolution.

We created a dataset called "plantable space," to describe both where pollinator habitat exists, and where non-forested green space exists that could potentially be converted to pollinator habitat. This layer was primarily derived from the grass/shrub land cover class. Although many green spaces may have a land use that is not compatible with conversion to pollinator habitat (e.g., recreational sport fields and airports), throughout much of metropolitan areas, these existing non-forested green spaces are where most of the opportunities to plant milkweed exist. While there are some exceptions where built up (hardscape) areas may be converted to habitat areas (e.g., the conversion of a rail line), the vast majority of opportunities are in existing vegetated areas

includes everything from turf grass and shrubs to high quality prairie sites. In many cases only a fraction of plantable space might be reasonably converted into pollinator habitat due to land use restrictions (e.g., sports fields), but by eliminating built up areas (e.g., roads) we can improve our density estimates.

due to lower conversion costs. We excluded the majority of tree canopy, because densely forested areas do not have enough light penetration to support the growth of most milkweed species, although Chaplin and Walker (1982) note that fourleaf milkweed (Asclepias quadrifolia) is commonly found in low-light forest understory. From expert opinion and estimates based on the proportion of our occurrence data located under tree canopy, we estimate that approximately 20% of forested areas provide suitable habitat for milkweed species capable of growing under the partial shade of solitary trees and along the woodland edge. In densely forested areas, plantable space calculations included only grass/shrub land cover, while in all other green spaces, plantable space calculations included grass/shrub as well as 20% of tree canopy. To examine how plantable space is distributed across land use categories, we isolated the grass/shrub land cover class and then reclassified it by the land use class where it occurs. This combined data layer is the basis for our modeling tools (**Figure 4**).

Inventories of existing land use are commonly divided into parent categories such as residential, commercial, and industrial. These datasets are developed by MPOs and—for the four metropolitan areas—originally contained between 22 and 60 land use classes. To facilitate comparisons across our study areas, we consolidated land use classes into 16 standardized categories, bearing in mind as we did so that land use in this case had much to do with how land users might manage their land for monarch conservation (**Table 1**). Both the creation of our 16 land use classes and the consolidation of each city's land use into these classes was done by our multidisciplinary team of ecologists, social scientists, and geospatial analysts. We relied heavily on the metadata provided with each dataset and consulted with local partners as we combined classes together. For more details see **Supplementary Section 1**.

The way metropolitan areas classify open space within their land use is an important factor influencing both our plantable space and our final baseline calculations. In Austin and Chicago, the land use data sets differentiated between recreational open

*For more details on land use category descriptions, see* Supplementary Table 2*.*

space and open space managed for conservation, whereas in Minneapolis-St. Paul and Kansas City, the land use data did not differentiate between these categories. For Minneapolis-St. Paul we migrated their combined "preserves and parks" land use into Open space conservation (OS-C) because it contained a large wildlife refuge, and in Kansas City we migrated their "parks and open space" land use into Open space non-conservation (OS-NC) in consultation with local partners.

Of the 16 defined land use classes, three encompass the land along rights-of-way, which makes up the third largest sector (43,148 km2 ) of the five examined by Thogmartin et al. (2017b). We extracted minor road rights-of-way to incorporate the strip of land or "greenway" between streets and sidewalks or drainage ditches, typically owned by the city or county in more rural areas. Often Minor right-of-way areas are not separated from Major rights-of-way. We isolated Minor right-of-way areas by applying a series of steps including buffering and extracting intersecting major roads, highways and ramps. For definitions of all land use categories see **Supplementary Table 2**.

To assist field teams conducting natural areas sampling in each metropolitan area, we used various geospatial protected area layers from the local landowner authority, or state/national protected land datasets5 to aid in planning field sampling locations. These boundaries were also used in calculating milkweed densities. US census blocks were used as geographic analysis units for aggregating land use and plantable space model inputs and outputs to/from our geospatial tools. Census blocks vary in size based on population density and allow for comparison of land use areas by demographic characteristics.

For extrapolation when estimating baseline and scenario estimates for urban areas at regional scales, we used a USGSdeveloped habitat raster developed for a national milkweed calculator which estimates the amount of milkweed present at the county scale. This tool uses as its basis for extrapolation a combined raster layer comprised of the Cropland Data Layer (CDL), the National Land Cover Database (NLCD), and US Census Bureau right-of-way data6 .

#### Field Sampling and Density Calculation Methods

We used three field sampling methods to acquire information about current milkweed densities across different land use classes including: (1) randomized metro-transects for baseline density estimates, (2) targeted sampling of natural areas, and (3) targeted sampling of other "enhanced sites." Both methods of targeted sampling were used for enhanced and exemplary density estimates. Sampling occurred throughout the 2016 growing season, approximately June through September of 2016 (and 2017 in Chicago). To estimate the baseline density of milkweed in each metropolitan land use area, milkweed stems were quantified using randomly sampled "metro-transects" running from more developed to less developed lands. While this approach allowed us to quickly and efficiently examine existing milkweed densities in most land use classes, this method was not practical for examining natural areas in Open space conservation and Open space non-conservation lands. Because sampling in natural areas required concentrated surveying in appropriate areas, advanced planning, and ample survey time, we conducted targeted rather than randomized sampling of natural areas. Since this approach was biased toward sites with higher milkweed densities, baseline densities for Open space conservation and Open space nonconservation areas were calculated using alternative methods described below. In addition to targeted sampling of natural areas, targeted "enhanced sites" sampling was conducted for all land use classes to get an understanding of the typical density of milkweed at sites where it is intentionally planted. The top ten percent of these enhanced sites were used to define "exemplary sites," which set our upper limit on the amount of greenspace landowners might convert to habitat across different land uses.