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Incorporating field behaviors into monarch surveys to promote informed conservation actions
Journal for Nature Conservation 53 (2020) 125761
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Journal for Nature Conservation journal homepage: www.elsevier.com/locate/jnc
Incorporating field behaviors into monarch surveys to promote informed conservation actions
T
Katherine C. Kral-O’Briena,*, Torre J. Hovickb, Ryan F. Limbb, Jason P. Harmona, Erin H. Gillamc a
Department of Entomology, North Dakota State University, Fargo, ND, USA Range Science, North Dakota State University, Fargo, ND, USA c Biological Sciences, North Dakota State University, Fargo, ND, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Asclepias Conservation management Danaus plexippus Host plant abundance
Monarch butterflies (Danaus plexippus) are an iconic species across North America, but ongoing winter population declines have made them a species of conservation concern. Incorporating behaviors into monitoring could connect landscape and behavioral ecology to benefit monarch conservation. Yet, behavioral field observations for breeding adult monarchs are seldom the focus of research, despite their potential benefits to targeted conservation. Our objectives were to quantify monarch activity budgets and compare behaviors of individuals at different sites with varying local vegetation and landscape characteristics in the Northern Great Plains. We opportunistically made 15-minute field observation surveys and recorded behaviors including basking, flying, mating, nectaring, ovipositing, and resting of wild monarchs. We collected plant community data including flowering ramet density and plant species composition. Over two years, we made behavioral observations on 51 monarchs. Collectively, monarchs spent most of their time flying and nectaring during observations. Contrary to our hypothesis, ovipositing and nectaring did not peak at sites with the highest milkweed (Asclepias spp.) cover. Instead, we observed more nectaring as milkweed decreased and plant species diversity increased. Individually, the proportion of time spent ovipositing and the abundance of adults increased at sites with both milkweed cover and higher plant species diversity. Our findings suggest we can improve conservation efforts for monarchs by promoting nectar sources and milkweed across landscapes, providing resources for all life stages. Moreover, continuing behavioral observations to understand how monarchs utilize sites with different vegetation and landscape characteristics can provide complementary information to abundance surveys to better inform conservation actions.
1. Introduction Eastern North American monarchs (Danaus plexippus Linnaeus, 1758) are facing declines within the winter population (Davis & Dyer, 2015; Pleasants et al., 2017). The population decreases over the past two decades have increased research interest (Badgett & Davis, 2015) and subsequently garnered substantial support from the public to increase the amount of research and applied conservation efforts (Davis & Dyer, 2015; Diffendorfer et al., 2014). Researchers and volunteers collect egg, larvae, and adult data each year in the US through various citizen science programs (Ries & Oberhauser, 2015). This wealth of knowledge allows researchers to monitor monarch population trends across their entire range (e.g., Howard & Davis, 2015; Pleasants et al., 2017) and has been extremely valuable for conservation. Despite this breadth of research and support from the public, questions remain on
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how to best address and improve conservation for this valued species (Davis & Dyer, 2015). One way to develop meaningful conservation is to use multiple forms of data (e.g., abundance and behavior) to help better understand species of concern and direct conservation priorities and goals. Survey efforts typically focus on abundance estimates, but researchers and citizen science programs could enhance the inferential power of their surveys by also including behavioral observations in monitoring protocols (Knowlton & Graham, 2010; Pickens & Root, 2009). Behaviors provide important details about how butterflies utilize specific sites that is difficult to infer with abundance data alone. Without behavior data, one could misidentify sites with lower habitat quality as preferred habitat or vice versa (Greggor et al., 2016). Moreover, behavioral data also can help determine how butterflies are influenced by local vegetation variables (e.g., Pickens & Root, 2009). At
Corresponding author at: NDSU Dept. 7650, PO Box 6050, Fargo, ND, 58108, USA. E-mail address: [email protected] (K.C. Kral-O’Brien).
https://doi.org/10.1016/j.jnc.2019.125761 Received 16 May 2019; Received in revised form 1 November 2019; Accepted 6 November 2019 1617-1381/ © 2019 Elsevier GmbH. All rights reserved.
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the monarch during each observation. If applicable, we also noted the plant species used for basking, nectaring, ovipositing, or resting.
larger scales, incorporating such behaviors into landscape-level monitoring protocols connects landscape and behavioral ecology to determine how individuals utilize areas, select resources, or move through landscapes (Knowlton & Graham, 2010; Lima & Zollner, 1996). Land managers can then use this information to guide conservation and management decisions that increase resources for crucial functions including food acquisition and reproduction (Berger-Tal et al., 2011; Pickens & Root, 2009). Monarch conservation could benefit from such behavioral information focusing on summer breeding adults. This is particularly true as we try to evaluate or augment different sites that monarchs need and use in their summer grounds (Pleasants, 2017). Some monarch behaviors have been included in conservation efforts, including navigation (e.g., Reppert, Gegear, & Merlin, 2010), oviposition (e.g., Tschenn, Losey, Jesse, Obrycki, & Hufbauer, 2001), larval survival (e.g., Nail, Stenoien, & Oberhauser, 2015), migration patterns (e.g., McCord & Davis, 2010), or overwintering (e.g., Brower et al., 2011). While all this data has been invaluable for understanding monarch physiology and specific aspects of their behavior, it gives less information about how monarch adults use different sites. One study did investigate how behaviors of adult monarchs during the summer breeding season responded to vegetation characteristics (James, 2016), however it focused on an individual site as opposed to comparisons across sites. Collecting behavioral data at larger scales will allow researchers to create important connections between landscapes and behaviors, an invaluable detail for migratory species like the monarch. Additionally, by including behaviors in monarch surveys, we can determine how site characteristics influence behavior and habitat use. For example, we expect behaviors such as nectaring and ovipositing to increase as sites become increasingly suitable and useful for monarchs (Pleasants et al., 2017). Consequently, our main objective was to observe behaviors of breeding monarchs across a range of sites in the Northern Great Plains to evaluate the influence of local and landscape features on monarch activity. We then compared how local and landscape factors at a site correlated with behavioral observations and abundance data to understand how such data can help identify important site characteristics and inform conservation practices.
2.2. Local vegetation and landscape variables We collected local vegetation variables to test whether the plant community influenced monarch behavior. We counted the number of flowering ramets along eight, 100-m transects in 5-m belts established for butterfly surveys to obtain average nectar resource density at each site (Moranz, Debinski, McGranahan, Engle, & Miller, 2012). We also estimated average plant species canopy cover during peak plant production in late July using 30, 1 m2 quadrat frames. We collected canopy cover for all plant species (Kral-O’Brien, Limb, Hovick, & Harmon, 2019), but we were particularly interested in milkweed (Asclepias spp.) cover as it is the host plant for monarchs and a target for conservation efforts (Pleasants, 2017). Although common to collect milkweed stem density (Pleasants & Oberhauser, 2013), our surveys were not exclusive to monarchs, so we were interested in the entire plant community. In addition to vegetation variables, we also created 1,000-m buffers around survey sites in ArcGIS (v. 10.2) to quantify the percentage of major cover types including perennial grassland, wetland, hay ground, open water, cropland, and development (roads, buildings, etc.) within each buffer (see Kral et al., 2018). 2.3. Analysis We used individual ethograms to quantify activity budgets for all individuals with observations recorded for at least 10 min (Richer, Coulson, & Heath, 1997; Peixoto and Benson, 2009). Then we compared the average proportion of time monarchs from the same site spent in each behavior in relation to local vegetation and landscape variables using multivariate ordination in the R statistical environment (R Development Core Team, 2015). This allowed us to evaluate how variables influence monarch behavior at the site level, in other words, how individuals collectively tended to behave at a given site. We combined all individuals from both years (similar to James, 2016), because we found no significant difference in activity budgets when comparing males and females (F1, 48 = 0.79, p > 0.05), month of observation (F2, 48 = 1.37, p > 0.05), or time of observation (F2, 48 = 1.73, p > 0.05). We conducted all multivariate ordination procedures using the vegan package in R (Oksanen, 2015). We used non-metric multidimensional scaling (NMDS) with the “capscale” function and Bray-Curtis distance measures to create ordination biplots for average site activity budgets (Stafford et al., 2011). We considered three-dimensional ordinations meaningful (i.e., they could inform overarching patterns observed in behaviors across sites) if they had stress values under 0.20 (Clark, 1993). In ordinations, sites are plotted as points (site scores) and differ based on activity budgets—the average proportion of time spent in each behavior. Behaviors are plotted as text (species scores) and denote the dominate behavior among sites. When sites are located closer to a behavior, that behavior makes up the majority of the observation. We removed mating from potential behaviors because it was rare and only occurred at two sites. Using the function “envfit”, we tested how monarch activity budgets at the site level related to local vegetation variables that included ramet density, forb species richness and diversity, plant species richness and diversity, invasive grass cover (Poa pratensis L. and Bromus inermis Leyss.), invasive forb canopy cover, and milkweed canopy cover, along with landscape variables that included percent cover of perennial grassland, crop land, hay ground, open water, and wetland. We tested for correlations among variables to eliminate any redundancies in our analysis (r ≥ 0.60) (Kral et al., 2018). Remaining variables that are significantly correlated with behaviors at a site will be plotted as a vector. Moving towards the end of the arrow and vector label signifies that variable increases, while moving in the opposite direction of the arrow signifies the variable decreases.
2. Materials and methods 2.1. Monarch behavioral observations We opportunistically made behavioral observation surveys (hereafter monarch observations) on wild monarchs encountered during field surveys (hereafter butterfly surveys; see Kral, Hovick, Limb, & Harmon, 2018) for grassland butterfly species from 2016 to 2017. We conducted butterfly surveys from June 1st until mid-August each year. We had 29 potential survey sites within perennial grasslands located in North Dakota, South Dakota, and Minnesota, USA (Supplementary Fig. 1). Most sites were managed by the US Fish and Wildlife Service, but we also conducted surveys on privately-owned lands. Most sites were managed with different combinations of cattle grazing and prescribed fire, but several sites had idle management—no disturbance in > 4 years (Supplementary Table 1). We conducted scheduled butterfly surveys for approximately two hours on three separate occasions each year (see Kral et al., 2018). When we observed a monarch, we suspended butterfly surveys to collect monarch observations and created ethograms—a list of pre-determined behaviors—for individual monarchs (McCord & Davis, 2010). We conducted 15-minute observations (Pickens & Root, 2009) and categorized behaviors in 20-second intervals using seven a priori behaviors identified from previous monarch research (McCord & Davis, 2010; Pliske, 1975): basking, courtship, flying, mating, nectaring, ovipositing, and resting (Table 1). If an individual transitioned to another behavior within a 20-second timeframe, we recorded the dominant behavior. We recorded the time of day, weather conditions, and sex of 2
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Table 1 Description for each a priori behavior used for wild monarch field observations from 2016 to 2017 in North Dakota, South Dakota, and Minnesota, USA. Behaviors and descriptions modified from Pliske (1975) and McCord and Davis (2010). The average percent of time ( ± standard error) spent in each behavior during surveys is included along with GLM results determining the significant variables that influenced behavior at the local or landscape scale (listed parenthetically) and the statistical model. In addition to behavior, we included a description of abundance as a response variable, average abundance on sites where monarch behavior was collected, and GLM model results. Response
Description
Average
Significant variable(s)
Statistical model
Nectaring
Adult on flowering forb with proboscis extended Female resting on vegetation with abdomen flexed laying eggs
25.6 ± 0.05
Plant diversity (local)
6.73 ± 0.02
Plant diversity × milkweed (local)
Y = −2.30 + 3.02 * plant diversity (F3,12 = 5.6, p = 0.01) Y = −0.20 + 0.25 * plant diversity + 0.10 * milkweed + 1.45 * (plant diversity – 0.85) * (milkweed cover – 0.50) (F3,12 = 2.5, p = 0.05) Y = 0.89 – 1.03 * plant diversity + 0.06 * milkweed - 2.43 (plant diversity – 0.85) * (milkweed – 0.50) (F3,12 = 25, p < 0.001) –
Ovipositing
Basking
Adult is slowly moving wings back-andforth while resting on a substrate
4.98 ± 0.02
Plant diversity × milkweed (local)
Courtship
Two individuals displaying courtship behaviors (male pursuing female) Individual moving across landscape
0.00 ± 0.00
–
35.6 ± 0.04
Male and female joined at the abdomen Stationary adult neither nectaring nor basking Raw count of monarchs observed at each site
7.84 ± 0.04 19.2 ± 0.04
Perennial grassland cover (landscape) – Plant diversity (local)
11.9 ± 2.37
Plant diversity × milkweed (local)
Flying Mating Resting Abundance
Y = −0.07 + 0.57 * perennial grassland (F1,14 = 4.9, p = 0.04) – Y = 1.87 – 1.94 * plant diversity (F1,14 = 4.6, p = 0.05) Y = −53.2 + 68.7 * plant diversity + 15.5 * milkweed + 163 * (plant diversity – 0.85) * (milkweed – 0.50) (F3,12 = 4.7, p = 0.02)
We used the results of our multivariate analysis to guide comparisons between behavior and abundance (Hovick, Elmore, Fuhlendorf, Engle, & Hamilton, 2015). We determined that plant species diversity and milkweed cover were influential at the site level on behavior (see Results). Therefore, we used generalized linear modeling (GLM) to determine how these variables influenced certain behaviors. We focused on ovipositing, as it was the only behavior related to reproduction that we consistently observed, and nectaring. Additionally, we used GLM to evaluate the influence of local vegetation and landscape variables on raw abundance of monarchs observed at each site during butterfly surveys to provide a comparison with the behavioral information. 3. Results We recorded behaviors of 51 wild monarchs over two summers totaling 13 h of observation time. Of the 51 individuals, we observed 35 females, 15 males, and 1 undetermined sex. We made monarch behavioral observations at 16 of the 29 sites, with most observations occurring in northeast South Dakota (Supplementary Fig. 1).
Fig. 1. Multidimensional scaling ordination using Bray-Curtis distance for collective monarch behaviors observed during the breeding season from 2016 to 2017 in the Northern Great Plains, USA. Site scores are represented with pie charts showing the activity budgets—the average proportion of time spent in each recorded behavior. Activity budgets at sites differed based on vegetation characteristics measured at the site. The nearest behavior label to a particular site represents which behavior made up the largest proportion of time at that given site. If a site is located between two behaviors, both behaviors were prominent in the activity budgets of the individuals at that site. The environmental vectors show the strength and direction of each variable based on the site scores. As milkweed cover (Asclepias cover) increased at sites, monarchs collectively spent more time resting and basking and less time nectaring. As plant diversity increased at sites, monarchs collectively spent more time nectaring.
3.1. Collective behavior Our ordination allows us to look at how monarch behavior varied across sites and compare how that variation correlated with environmental variables. The data can inform overarching patterns across sites as its stress score was 0.04 (stress values under 0.20 are meaningful, Clark, 1993). We use a standard graphical representation of our ordination analysis (Fig. 1) where axes do not directly correspond to any single variable. However, they help orientate sites such that sites closer to each other on the graph are more similar to each other in terms of how much time monarchs spent performing different behaviors at that site. Besides their relative location to each other, Fig. 1 reveals even more information about what behaviors were performed at each site (monarch activity budgets). First, rather than just showing a simple dot to mark the location of each site, we include a pie chart that gives the overall activity budget for monarchs at that site (e.g., monarchs at a particular site spent an average of 55 % of their time nectaring, 35 % resting, and 10 % basking). Thus, sites that are relatively closer to each other in Fig. 1 have more similar pie charts than sites that are farther
away. Second, our ordination identifies the graph location with the highest amount of a given behavior. For example, sites located more towards the top right had more basking behavior (shown by the location of the word “basking”; these sites had more yellow in the pie chart), and sites towards the left had more nectaring behavior (more purple at sites near the word “nectaring”). In addition, our ordination analysis tested whether the behaviors at a site correlated with changes in different environmental variables or vegetation characteristics across sites. We determined that activity 3
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4. Discussion
budgets at sites were significantly influenced by milkweed cover at those sites (R2 = 0.46, p = 0.02). As milkweed cover increased at a site (indicated with the arrow going towards the right side of the figure), the proportion of time spent basking and resting at that site increased compared to flying, ovipositing, and nectaring (see site pie charts set within Fig. 1). The arrow indicates that sites toward the right side of the figure had more milkweed cover, more basking, and more resting. Moreover, sites where monarchs spent more time nectaring were correlated with increasing plant species diversity (indicated with the arrow going towards the left side of the figure, R2 = 0.41, p = 0.04). This means that sites with higher plant diversity also had monarchs that spent more time nectaring. Despite the significant effects of milkweed cover and plant species diversity, sites where monarchs spent relatively more time flying and ovipositing (i.e., our primary behavior associated with reproduction) were not significantly correlated with either vegetation variables. Instead, those sites are ordinated in the middle of the plot where sites had a moderate cover of both milkweed and plant diversity (Fig. 1). Additionally, we did not find any significant relationships between landscape variables and activity budgets at a site (p > 0.05).
Behavioral studies for wild butterflies are infrequently conducted (Peixoto & Benson, 2009), even though they can improve conservation efforts beyond abundance estimates alone (James, 2016; Knowlton & Graham, 2010). By connecting behaviors to local and landscape variables, we can determine if and how butterflies utilize different sites (e.g., nectaring, reproduction). For example, we hypothesized that sites with the highest milkweed cover would have the highest abundance and be used the most for nectaring and reproduction. In actuality, sites with more observations of oviposition had relatively moderate amounts of milkweed cover, and our analyses suggest that milkweed cover was not sufficient to explain patterns in oviposition observations. Instead, oviposition was maximized as a combined function of both milkweed cover and nectar resource availability (as measured by plant species diversity). Additionally, we determined that sites with high monarch abundance had similar relationships with vegetation variables as sites with more oviposition observations. This suggests, monarch abundance may be useful for predicting valuable oviposition sites. However, if we only used abundance to indicate high value sites, we would not identify sites that provide valuable nectar sources for monarchs. Sites with more nectaring observations had higher plant diversity, but those were not the sites with higher monarch abundance. Therefore, increasing the cover of milkweed will not universally improve monarch habitat. While milkweed cover is obviously essential to monarchs (Pleasants, 2017), multiple reasons may explain why sites with the most milkweed cover did not have the highest monarch abundance, oviposition observations, and nectaring observations. First, this pattern may be related to previous work that has demonstrated that females often lay eggs in low density milkweed patches (Zalucki & Lammers, 2010). Females may select lower density patches for ovipositing because milkweed quality is improved, and they can better avoid natural enemies (Nail et al., 2015; Pitman, Flockhart, & Norris, 2018). Second, milkweed cover and plant diversity were not evenly distributed across our sites, including a lack of sites that were relatively high in both. Many of the sites with relatively higher plant diversity had relatively less milkweed cover. We attribute this pattern to invasive plant species including smooth brome which was correlated with milkweed cover. This invasive grass reduces nectar sources and overall plant species diversity (Hendrickson & Lund, 2010). Given the importance of both milkweed coverage (Pleasants, 2017) and nectar sources (James, 2016), monarchs may oviposit and visit sites with both resources compared to sites with more milkweed but few to no additional floral resources. If that is broadly true, conservation efforts should consider the availability of both milkweed and additional nectar sources. One of our goals was to assess if behaviors provide new information for conservation practices compared to abundance estimates alone. Monarch abundance was related to the interaction of milkweed and plant diversity. However, the behaviors we were interested in (nectaring and ovipositing) did not correlate to the same site characteristics. Using only abundance estimates, conservation practices would fail to detect the large importance of plant diversity for nectaring. This furthers the idea that without behaviors, uncertainty exists whether butterflies use a site for reproductive purposes (e.g., ovipositing) or are just temporarily present (e.g., resting/transient) (Pickens & Root, 2009). For example, we previously modeled monarch density across a wider range of sites and found monarch density was influenced by landscape variables, not local vegetation (Kral et al., 2018). We now expect this was likely driven by the large number of monarchs detected while flying, which are influenced by the cover of perennial grasslands at the landscape level (Table 1). We suggest the differences among these papers reflect the conclusions that come from using monarch abundance alone compared to adding behavioral information. Both metrics can provide complementary insights towards how and why monarchs respond to different sites across a landscape. Monarch behavior varied in relation to vegetation characteristics.
3.2. Individual behaviors and abundance The ordination identified the overarching patterns between all monarch behaviors at a site and plant diversity at that site, as well as overall monarch behaviors and milkweed cover. However, understanding the exact quantitative nature of these patterns requires complementary, secondary analyses investigating what variables were significantly related to each behavior by itself. Our secondary analyses revealed a significant interaction between plant species diversity and milkweed cover for monarch abundance, proportion of time spent ovipositing, and the proportion of time spent basking (Table 1). Since each of these factors was affected by the interaction between two independent variables, it can be difficult to easily understand the exact nature of their relationship. One way around this difficulty is to use a type of three-dimensional graph that shows both independent variables and the dependent variable at the same time (e.g., Fig. 2). While more complicated than simple linear regressions, the three-dimensional approach avoids the potential misunderstandings that can occur when looking at each factor individually when the interaction has already explained that you need to look at both factors at the same time. In our case, the full equation for the statistical model (Table 1) and the three-dimensional graph (Fig. 2), demonstrate how plant species diversity and milkweed cover interact to influence our response variables. For both abundance (Fig. 2A) and proportion ovipositing (Fig. 2B), the highest values (darkest areas of graph) are not with the highest milkweed cover (farthest top) or the highest plant species diversity (farthest right). Instead, the highest values for each response variable are at a relatively high plant species diversity and an intermediate milkweed cover. Another benefit of the graph is to demonstrate the nature of our different sites, in this case, that we did not have any points that were very high in both milkweed cover and plant species diversity (farthest top right corner has no dots). Besides the interaction found above, we also found that plant species diversity was a significant variable in explaining proportion of time resting (Table 1) and the proportion of time nectaring at a site (Table 1, Fig. 2C). We used the same three-dimensional graph to show the relationship with nectaring (Fig. 2C) to keep it consistent with the other graphs. The important thing to notice for nectaring is that the highest amount of time nectaring was found at sites with the highest plant species diversity (Fig. 2C, site towards the right side of the graph). Finally, we also found a positive, univariate relationship between time spent flying and the amount of perennial grassland cover in the surrounding landscape (Table 1). This means that we had more observations of monarchs flying at sites with more grasslands around them. 4
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Fig. 2. Average monarch abundance (A) and proportion of time spent in individual behaviors of nectaring (B) or ovipositing (C) in relation to average milkweed (Asclepias spp) cover (y-axis) and Simpson’s plant species diversity (x-axis) for monarchs observed in the Northern Great Plains, USA, from 2016 to 2017. Black circles represent sites. For abundance and nectaring (A and B), the highest numbers are located in the upper right corner, representing an interaction between milkweed cover and plant species diversity. However, the highest numbers for nectaring (C) were located on the right side of the graph, representing a relationship with plant species diversity alone.
2016; Baum & Sharber, 2012). Consequently, we speculate that management focused on restoring natural disturbances could also be used, with the correct temporal and spatial scale, to increase nectar resources (Towne & Kemp, 2008) and increase native plant diversity (Hendrickson & Lund, 2010). These management actions would likely provide nectar sources for adults and milkweed for larvae at the same site. Protocols used here can easily be included in other monitoring, but we recommend several changes that may better inform conservation. Flying should be separated into multiple categories to potentially detect differences between sexes, as male patrolling was the most common
Therefore, disturbance management (fire, grazing, haying) can be used to manipulate vegetation and support important behaviors (nectaring, ovipositing). Natural and non-agricultural areas are vital for monarch conservation (Nail et al., 2015; Pitman et al., 2018), but planting more milkweed in areas where milkweed is already available may not be sufficient for meeting all of monarchs needs, particularly for nectaring. Since both nectaring and ovipositing are crucial behaviors for successful monarch populations, our results reinforce the need for monarch management within and between sites that promotes both milkweed and additional nectar sources. Prescribed fire and haying are already used to increase milkweed availability (Alcock, Browner, & Williams, 5
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behavior of wild monarchs in Washington State (James, 2016). Since our surveys were opportunistic, we did not include factors that may also help discern behavioral differences such as parasite load and wing condition (Bradley & Altizer, 2005; Goehring & Oberhauser, 2002; Hirota & Obara, 2000). More observations of individuals—at each site and between years—would also help to distinguish this difference, as we observed 19 individuals in 2016 and 32 individuals in 2017. Additionally, future research should connect activity budgets and site characteristics with individual fitness to increase the inferential strength of shorter behavioral observations (like those conducted in this study). We determined how site variables correlated with activity budgets, but we do not know how either correlates with reproductive or adult success at a given site. Repeating site visits to observe the same monarchs throughout their life and assessing survival at each life stage could elucidate the connection between behaviors, success, and ecological variables (Berger, Olofasson, Gotthard, Wiklund, & Friberg, 2012; Lind & Cresswell, 2005; Sih, Bell, Johnson, & Ziemba, 2004).
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Biological Conservation, 217, 54–65. Pleasants, J. (2017). Milkweed restoration in the Midwest for monarch butterfly recovery: Estimates of milkweeds lost, milkweeds remaining and milkweeds that must be added to increase the monarch population. Insect Conservation and Diversity, 10, 42–53. Pleasants, J. M., & Oberhauser, K. S. (2013). Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conservation and Diversity, 6, 135–144. Pleasants, J. M., Zalucki, M. P., Oberhauser, K. S., Brower, L. P., Taylor, O. R., & Thogmartin, W. E. (2017). Interpreting surveys to estimate the size of the monarch butterfly population: Pitfalls and prospects. PloS One, 12, e0181245. Pliske, T. E. (1975). Courtship behavior of the monarch butterfly, Danaus plexippus L. Annals of the Entomological Society of America, 68, 143–151. R Development Core Team (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Reppert, S. M., Gegear, R. J., & Merlin, C. (2010). Navigational mechanisms of migrating monarch butterflies. Trends in Neurosciences, 33, 399–406. Richer, R. A., Coulson, I. M., & Heath, M. E. (1997). Foraging behaviour and ecology of the Cape pangolin (Manis temminckii) in north‐western Zimbabwe. African Journal of Ecology, 35, 361–369.
5. Conclusions Even though monarchs are proposed for listing under the Endangered Species Act (USFWS United States Fish & Wildlife Service, 2017), behavioral studies for summer-breeding monarchs to connect behaviors to vegetation variables are rare (but see James, 2016). After observing monarchs in the field for two years, we determined that activity budgets were influenced by local and landscape variables, including milkweed cover, plant species diversity, and perennial grassland cover. Interestingly, we found 1) milkweed cover was not a predictor for all behaviors; 2) monarchs appear to optimize milkweed cover and plant species diversity when ovipositing but place a special emphasis on plant species diversity when nectaring; and 3) behavior data can inform site utilization beyond what is provided by abundance data. However, more research is necessary to discern how activity budgets relate to lifetime fitness. Instead of just planting more milkweed in semi-natural areas, we suggest conservation efforts should consider utilizing disturbance and other management options to increase availability of both milkweed plants and other nectar sources. Funding This work was supported by the USFWS (grant number F15AC01207), North Dakota Game and Fish (grant number F15AP00605), and North Dakota Agricultural Experiment Station. Funders had no role in study design, data collection, analysis, or manuscript submission. Declarations of Competing Interest None. Acknowledgements Special thanks to our field technicians, Adrienne Antonsen and Dani Ethington, and two anonymous reviewers for their helpful suggestions. References Alcock, J., Browner, L. P., & Williams, E. H., Jr. (2016). Monarch butterflies use regenerating milkweeds for reproduction in mowed hayfields in northern Virginia. Journal of the Lepidopterists’ Society, 70, 177–181. Badgett, G., & Davis, A. K. (2015). Population trends of monarchs at a northern monitoring site: Analyses of 19 years of fall migration counts at Peninsula Point, MI. Annals of the Entomological Society of America, 108, 700–706. Baum, K. A., & Sharber, W. V. (2012). Fire creates host plant patches for monarch butterflies. Biology Letters, 8, 968–971. Berger, D., Olofasson, M., Gotthard, K., Wiklund, C., & Friberg, M. (2012). Ecological constraints on female fitness in a phytophagous insect. The American Naturalist, 180, 464–480.
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frequent summer burning. Rangeland Ecology & Management, 61, 509–520. Tschenn, J., Losey, J. E., Jesse, L. H., Obrycki, J. J., & Hufbauer, R. (2001). Effects of corn plants and corn pollen on monarch butterfly (Lepidoptera: Danaidae) oviposition behavior. Environmental Entomology, 30, 495–500. USFWS United States Fish and Wildlife Service (2017). Assessing the status of the monarch butterfly. https://www.fws.gov/savethemonarch/SSA.html. Zalucki, M. P., & Lammers, J. H. (2010). Dispersal and egg shortfall in Monarch butterflies: what happens when the matrix is cleaned up? Ecological Entomology, 35, 84–91.
Ries, L., & Oberhauser, K. (2015). A citizen army for science: Quantifying the contributions of citizen scientists to our understanding of monarch butterfly biology. BioScience, 65, 419–430. Sih, A., Bell, A. M., Johnson, J. C., & Ziemba, R. E. (2004). Behavioral syndromes: An integrative overview. The Quarterly Review of Biology, 79, 241–277. Stafford, R., Stafford, R., Goodenough, A. E., Slater, K., Carpenter, W., Collins, L., et al. (2011). Inferential and visual analysis of ethogram data using multivariate techniques. Animal Behaviour, 83, 563–569. Towne, E. G., & Kemp, K. E. (2008). Long-term response patterns of tallgrass prairie to
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Contents lists available at ScienceDirect
Journal for Nature Conservation journal homepage: www.elsevier.com/locate/jnc
Incorporating field behaviors into monarch surveys to promote informed conservation actions
T
Katherine C. Kral-O’Briena,*, Torre J. Hovickb, Ryan F. Limbb, Jason P. Harmona, Erin H. Gillamc a
Department of Entomology, North Dakota State University, Fargo, ND, USA Range Science, North Dakota State University, Fargo, ND, USA c Biological Sciences, North Dakota State University, Fargo, ND, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Asclepias Conservation management Danaus plexippus Host plant abundance
Monarch butterflies (Danaus plexippus) are an iconic species across North America, but ongoing winter population declines have made them a species of conservation concern. Incorporating behaviors into monitoring could connect landscape and behavioral ecology to benefit monarch conservation. Yet, behavioral field observations for breeding adult monarchs are seldom the focus of research, despite their potential benefits to targeted conservation. Our objectives were to quantify monarch activity budgets and compare behaviors of individuals at different sites with varying local vegetation and landscape characteristics in the Northern Great Plains. We opportunistically made 15-minute field observation surveys and recorded behaviors including basking, flying, mating, nectaring, ovipositing, and resting of wild monarchs. We collected plant community data including flowering ramet density and plant species composition. Over two years, we made behavioral observations on 51 monarchs. Collectively, monarchs spent most of their time flying and nectaring during observations. Contrary to our hypothesis, ovipositing and nectaring did not peak at sites with the highest milkweed (Asclepias spp.) cover. Instead, we observed more nectaring as milkweed decreased and plant species diversity increased. Individually, the proportion of time spent ovipositing and the abundance of adults increased at sites with both milkweed cover and higher plant species diversity. Our findings suggest we can improve conservation efforts for monarchs by promoting nectar sources and milkweed across landscapes, providing resources for all life stages. Moreover, continuing behavioral observations to understand how monarchs utilize sites with different vegetation and landscape characteristics can provide complementary information to abundance surveys to better inform conservation actions.
1. Introduction Eastern North American monarchs (Danaus plexippus Linnaeus, 1758) are facing declines within the winter population (Davis & Dyer, 2015; Pleasants et al., 2017). The population decreases over the past two decades have increased research interest (Badgett & Davis, 2015) and subsequently garnered substantial support from the public to increase the amount of research and applied conservation efforts (Davis & Dyer, 2015; Diffendorfer et al., 2014). Researchers and volunteers collect egg, larvae, and adult data each year in the US through various citizen science programs (Ries & Oberhauser, 2015). This wealth of knowledge allows researchers to monitor monarch population trends across their entire range (e.g., Howard & Davis, 2015; Pleasants et al., 2017) and has been extremely valuable for conservation. Despite this breadth of research and support from the public, questions remain on
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how to best address and improve conservation for this valued species (Davis & Dyer, 2015). One way to develop meaningful conservation is to use multiple forms of data (e.g., abundance and behavior) to help better understand species of concern and direct conservation priorities and goals. Survey efforts typically focus on abundance estimates, but researchers and citizen science programs could enhance the inferential power of their surveys by also including behavioral observations in monitoring protocols (Knowlton & Graham, 2010; Pickens & Root, 2009). Behaviors provide important details about how butterflies utilize specific sites that is difficult to infer with abundance data alone. Without behavior data, one could misidentify sites with lower habitat quality as preferred habitat or vice versa (Greggor et al., 2016). Moreover, behavioral data also can help determine how butterflies are influenced by local vegetation variables (e.g., Pickens & Root, 2009). At
Corresponding author at: NDSU Dept. 7650, PO Box 6050, Fargo, ND, 58108, USA. E-mail address: [email protected] (K.C. Kral-O’Brien).
https://doi.org/10.1016/j.jnc.2019.125761 Received 16 May 2019; Received in revised form 1 November 2019; Accepted 6 November 2019 1617-1381/ © 2019 Elsevier GmbH. All rights reserved.
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the monarch during each observation. If applicable, we also noted the plant species used for basking, nectaring, ovipositing, or resting.
larger scales, incorporating such behaviors into landscape-level monitoring protocols connects landscape and behavioral ecology to determine how individuals utilize areas, select resources, or move through landscapes (Knowlton & Graham, 2010; Lima & Zollner, 1996). Land managers can then use this information to guide conservation and management decisions that increase resources for crucial functions including food acquisition and reproduction (Berger-Tal et al., 2011; Pickens & Root, 2009). Monarch conservation could benefit from such behavioral information focusing on summer breeding adults. This is particularly true as we try to evaluate or augment different sites that monarchs need and use in their summer grounds (Pleasants, 2017). Some monarch behaviors have been included in conservation efforts, including navigation (e.g., Reppert, Gegear, & Merlin, 2010), oviposition (e.g., Tschenn, Losey, Jesse, Obrycki, & Hufbauer, 2001), larval survival (e.g., Nail, Stenoien, & Oberhauser, 2015), migration patterns (e.g., McCord & Davis, 2010), or overwintering (e.g., Brower et al., 2011). While all this data has been invaluable for understanding monarch physiology and specific aspects of their behavior, it gives less information about how monarch adults use different sites. One study did investigate how behaviors of adult monarchs during the summer breeding season responded to vegetation characteristics (James, 2016), however it focused on an individual site as opposed to comparisons across sites. Collecting behavioral data at larger scales will allow researchers to create important connections between landscapes and behaviors, an invaluable detail for migratory species like the monarch. Additionally, by including behaviors in monarch surveys, we can determine how site characteristics influence behavior and habitat use. For example, we expect behaviors such as nectaring and ovipositing to increase as sites become increasingly suitable and useful for monarchs (Pleasants et al., 2017). Consequently, our main objective was to observe behaviors of breeding monarchs across a range of sites in the Northern Great Plains to evaluate the influence of local and landscape features on monarch activity. We then compared how local and landscape factors at a site correlated with behavioral observations and abundance data to understand how such data can help identify important site characteristics and inform conservation practices.
2.2. Local vegetation and landscape variables We collected local vegetation variables to test whether the plant community influenced monarch behavior. We counted the number of flowering ramets along eight, 100-m transects in 5-m belts established for butterfly surveys to obtain average nectar resource density at each site (Moranz, Debinski, McGranahan, Engle, & Miller, 2012). We also estimated average plant species canopy cover during peak plant production in late July using 30, 1 m2 quadrat frames. We collected canopy cover for all plant species (Kral-O’Brien, Limb, Hovick, & Harmon, 2019), but we were particularly interested in milkweed (Asclepias spp.) cover as it is the host plant for monarchs and a target for conservation efforts (Pleasants, 2017). Although common to collect milkweed stem density (Pleasants & Oberhauser, 2013), our surveys were not exclusive to monarchs, so we were interested in the entire plant community. In addition to vegetation variables, we also created 1,000-m buffers around survey sites in ArcGIS (v. 10.2) to quantify the percentage of major cover types including perennial grassland, wetland, hay ground, open water, cropland, and development (roads, buildings, etc.) within each buffer (see Kral et al., 2018). 2.3. Analysis We used individual ethograms to quantify activity budgets for all individuals with observations recorded for at least 10 min (Richer, Coulson, & Heath, 1997; Peixoto and Benson, 2009). Then we compared the average proportion of time monarchs from the same site spent in each behavior in relation to local vegetation and landscape variables using multivariate ordination in the R statistical environment (R Development Core Team, 2015). This allowed us to evaluate how variables influence monarch behavior at the site level, in other words, how individuals collectively tended to behave at a given site. We combined all individuals from both years (similar to James, 2016), because we found no significant difference in activity budgets when comparing males and females (F1, 48 = 0.79, p > 0.05), month of observation (F2, 48 = 1.37, p > 0.05), or time of observation (F2, 48 = 1.73, p > 0.05). We conducted all multivariate ordination procedures using the vegan package in R (Oksanen, 2015). We used non-metric multidimensional scaling (NMDS) with the “capscale” function and Bray-Curtis distance measures to create ordination biplots for average site activity budgets (Stafford et al., 2011). We considered three-dimensional ordinations meaningful (i.e., they could inform overarching patterns observed in behaviors across sites) if they had stress values under 0.20 (Clark, 1993). In ordinations, sites are plotted as points (site scores) and differ based on activity budgets—the average proportion of time spent in each behavior. Behaviors are plotted as text (species scores) and denote the dominate behavior among sites. When sites are located closer to a behavior, that behavior makes up the majority of the observation. We removed mating from potential behaviors because it was rare and only occurred at two sites. Using the function “envfit”, we tested how monarch activity budgets at the site level related to local vegetation variables that included ramet density, forb species richness and diversity, plant species richness and diversity, invasive grass cover (Poa pratensis L. and Bromus inermis Leyss.), invasive forb canopy cover, and milkweed canopy cover, along with landscape variables that included percent cover of perennial grassland, crop land, hay ground, open water, and wetland. We tested for correlations among variables to eliminate any redundancies in our analysis (r ≥ 0.60) (Kral et al., 2018). Remaining variables that are significantly correlated with behaviors at a site will be plotted as a vector. Moving towards the end of the arrow and vector label signifies that variable increases, while moving in the opposite direction of the arrow signifies the variable decreases.
2. Materials and methods 2.1. Monarch behavioral observations We opportunistically made behavioral observation surveys (hereafter monarch observations) on wild monarchs encountered during field surveys (hereafter butterfly surveys; see Kral, Hovick, Limb, & Harmon, 2018) for grassland butterfly species from 2016 to 2017. We conducted butterfly surveys from June 1st until mid-August each year. We had 29 potential survey sites within perennial grasslands located in North Dakota, South Dakota, and Minnesota, USA (Supplementary Fig. 1). Most sites were managed by the US Fish and Wildlife Service, but we also conducted surveys on privately-owned lands. Most sites were managed with different combinations of cattle grazing and prescribed fire, but several sites had idle management—no disturbance in > 4 years (Supplementary Table 1). We conducted scheduled butterfly surveys for approximately two hours on three separate occasions each year (see Kral et al., 2018). When we observed a monarch, we suspended butterfly surveys to collect monarch observations and created ethograms—a list of pre-determined behaviors—for individual monarchs (McCord & Davis, 2010). We conducted 15-minute observations (Pickens & Root, 2009) and categorized behaviors in 20-second intervals using seven a priori behaviors identified from previous monarch research (McCord & Davis, 2010; Pliske, 1975): basking, courtship, flying, mating, nectaring, ovipositing, and resting (Table 1). If an individual transitioned to another behavior within a 20-second timeframe, we recorded the dominant behavior. We recorded the time of day, weather conditions, and sex of 2
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Table 1 Description for each a priori behavior used for wild monarch field observations from 2016 to 2017 in North Dakota, South Dakota, and Minnesota, USA. Behaviors and descriptions modified from Pliske (1975) and McCord and Davis (2010). The average percent of time ( ± standard error) spent in each behavior during surveys is included along with GLM results determining the significant variables that influenced behavior at the local or landscape scale (listed parenthetically) and the statistical model. In addition to behavior, we included a description of abundance as a response variable, average abundance on sites where monarch behavior was collected, and GLM model results. Response
Description
Average
Significant variable(s)
Statistical model
Nectaring
Adult on flowering forb with proboscis extended Female resting on vegetation with abdomen flexed laying eggs
25.6 ± 0.05
Plant diversity (local)
6.73 ± 0.02
Plant diversity × milkweed (local)
Y = −2.30 + 3.02 * plant diversity (F3,12 = 5.6, p = 0.01) Y = −0.20 + 0.25 * plant diversity + 0.10 * milkweed + 1.45 * (plant diversity – 0.85) * (milkweed cover – 0.50) (F3,12 = 2.5, p = 0.05) Y = 0.89 – 1.03 * plant diversity + 0.06 * milkweed - 2.43 (plant diversity – 0.85) * (milkweed – 0.50) (F3,12 = 25, p < 0.001) –
Ovipositing
Basking
Adult is slowly moving wings back-andforth while resting on a substrate
4.98 ± 0.02
Plant diversity × milkweed (local)
Courtship
Two individuals displaying courtship behaviors (male pursuing female) Individual moving across landscape
0.00 ± 0.00
–
35.6 ± 0.04
Male and female joined at the abdomen Stationary adult neither nectaring nor basking Raw count of monarchs observed at each site
7.84 ± 0.04 19.2 ± 0.04
Perennial grassland cover (landscape) – Plant diversity (local)
11.9 ± 2.37
Plant diversity × milkweed (local)
Flying Mating Resting Abundance
Y = −0.07 + 0.57 * perennial grassland (F1,14 = 4.9, p = 0.04) – Y = 1.87 – 1.94 * plant diversity (F1,14 = 4.6, p = 0.05) Y = −53.2 + 68.7 * plant diversity + 15.5 * milkweed + 163 * (plant diversity – 0.85) * (milkweed – 0.50) (F3,12 = 4.7, p = 0.02)
We used the results of our multivariate analysis to guide comparisons between behavior and abundance (Hovick, Elmore, Fuhlendorf, Engle, & Hamilton, 2015). We determined that plant species diversity and milkweed cover were influential at the site level on behavior (see Results). Therefore, we used generalized linear modeling (GLM) to determine how these variables influenced certain behaviors. We focused on ovipositing, as it was the only behavior related to reproduction that we consistently observed, and nectaring. Additionally, we used GLM to evaluate the influence of local vegetation and landscape variables on raw abundance of monarchs observed at each site during butterfly surveys to provide a comparison with the behavioral information. 3. Results We recorded behaviors of 51 wild monarchs over two summers totaling 13 h of observation time. Of the 51 individuals, we observed 35 females, 15 males, and 1 undetermined sex. We made monarch behavioral observations at 16 of the 29 sites, with most observations occurring in northeast South Dakota (Supplementary Fig. 1).
Fig. 1. Multidimensional scaling ordination using Bray-Curtis distance for collective monarch behaviors observed during the breeding season from 2016 to 2017 in the Northern Great Plains, USA. Site scores are represented with pie charts showing the activity budgets—the average proportion of time spent in each recorded behavior. Activity budgets at sites differed based on vegetation characteristics measured at the site. The nearest behavior label to a particular site represents which behavior made up the largest proportion of time at that given site. If a site is located between two behaviors, both behaviors were prominent in the activity budgets of the individuals at that site. The environmental vectors show the strength and direction of each variable based on the site scores. As milkweed cover (Asclepias cover) increased at sites, monarchs collectively spent more time resting and basking and less time nectaring. As plant diversity increased at sites, monarchs collectively spent more time nectaring.
3.1. Collective behavior Our ordination allows us to look at how monarch behavior varied across sites and compare how that variation correlated with environmental variables. The data can inform overarching patterns across sites as its stress score was 0.04 (stress values under 0.20 are meaningful, Clark, 1993). We use a standard graphical representation of our ordination analysis (Fig. 1) where axes do not directly correspond to any single variable. However, they help orientate sites such that sites closer to each other on the graph are more similar to each other in terms of how much time monarchs spent performing different behaviors at that site. Besides their relative location to each other, Fig. 1 reveals even more information about what behaviors were performed at each site (monarch activity budgets). First, rather than just showing a simple dot to mark the location of each site, we include a pie chart that gives the overall activity budget for monarchs at that site (e.g., monarchs at a particular site spent an average of 55 % of their time nectaring, 35 % resting, and 10 % basking). Thus, sites that are relatively closer to each other in Fig. 1 have more similar pie charts than sites that are farther
away. Second, our ordination identifies the graph location with the highest amount of a given behavior. For example, sites located more towards the top right had more basking behavior (shown by the location of the word “basking”; these sites had more yellow in the pie chart), and sites towards the left had more nectaring behavior (more purple at sites near the word “nectaring”). In addition, our ordination analysis tested whether the behaviors at a site correlated with changes in different environmental variables or vegetation characteristics across sites. We determined that activity 3
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4. Discussion
budgets at sites were significantly influenced by milkweed cover at those sites (R2 = 0.46, p = 0.02). As milkweed cover increased at a site (indicated with the arrow going towards the right side of the figure), the proportion of time spent basking and resting at that site increased compared to flying, ovipositing, and nectaring (see site pie charts set within Fig. 1). The arrow indicates that sites toward the right side of the figure had more milkweed cover, more basking, and more resting. Moreover, sites where monarchs spent more time nectaring were correlated with increasing plant species diversity (indicated with the arrow going towards the left side of the figure, R2 = 0.41, p = 0.04). This means that sites with higher plant diversity also had monarchs that spent more time nectaring. Despite the significant effects of milkweed cover and plant species diversity, sites where monarchs spent relatively more time flying and ovipositing (i.e., our primary behavior associated with reproduction) were not significantly correlated with either vegetation variables. Instead, those sites are ordinated in the middle of the plot where sites had a moderate cover of both milkweed and plant diversity (Fig. 1). Additionally, we did not find any significant relationships between landscape variables and activity budgets at a site (p > 0.05).
Behavioral studies for wild butterflies are infrequently conducted (Peixoto & Benson, 2009), even though they can improve conservation efforts beyond abundance estimates alone (James, 2016; Knowlton & Graham, 2010). By connecting behaviors to local and landscape variables, we can determine if and how butterflies utilize different sites (e.g., nectaring, reproduction). For example, we hypothesized that sites with the highest milkweed cover would have the highest abundance and be used the most for nectaring and reproduction. In actuality, sites with more observations of oviposition had relatively moderate amounts of milkweed cover, and our analyses suggest that milkweed cover was not sufficient to explain patterns in oviposition observations. Instead, oviposition was maximized as a combined function of both milkweed cover and nectar resource availability (as measured by plant species diversity). Additionally, we determined that sites with high monarch abundance had similar relationships with vegetation variables as sites with more oviposition observations. This suggests, monarch abundance may be useful for predicting valuable oviposition sites. However, if we only used abundance to indicate high value sites, we would not identify sites that provide valuable nectar sources for monarchs. Sites with more nectaring observations had higher plant diversity, but those were not the sites with higher monarch abundance. Therefore, increasing the cover of milkweed will not universally improve monarch habitat. While milkweed cover is obviously essential to monarchs (Pleasants, 2017), multiple reasons may explain why sites with the most milkweed cover did not have the highest monarch abundance, oviposition observations, and nectaring observations. First, this pattern may be related to previous work that has demonstrated that females often lay eggs in low density milkweed patches (Zalucki & Lammers, 2010). Females may select lower density patches for ovipositing because milkweed quality is improved, and they can better avoid natural enemies (Nail et al., 2015; Pitman, Flockhart, & Norris, 2018). Second, milkweed cover and plant diversity were not evenly distributed across our sites, including a lack of sites that were relatively high in both. Many of the sites with relatively higher plant diversity had relatively less milkweed cover. We attribute this pattern to invasive plant species including smooth brome which was correlated with milkweed cover. This invasive grass reduces nectar sources and overall plant species diversity (Hendrickson & Lund, 2010). Given the importance of both milkweed coverage (Pleasants, 2017) and nectar sources (James, 2016), monarchs may oviposit and visit sites with both resources compared to sites with more milkweed but few to no additional floral resources. If that is broadly true, conservation efforts should consider the availability of both milkweed and additional nectar sources. One of our goals was to assess if behaviors provide new information for conservation practices compared to abundance estimates alone. Monarch abundance was related to the interaction of milkweed and plant diversity. However, the behaviors we were interested in (nectaring and ovipositing) did not correlate to the same site characteristics. Using only abundance estimates, conservation practices would fail to detect the large importance of plant diversity for nectaring. This furthers the idea that without behaviors, uncertainty exists whether butterflies use a site for reproductive purposes (e.g., ovipositing) or are just temporarily present (e.g., resting/transient) (Pickens & Root, 2009). For example, we previously modeled monarch density across a wider range of sites and found monarch density was influenced by landscape variables, not local vegetation (Kral et al., 2018). We now expect this was likely driven by the large number of monarchs detected while flying, which are influenced by the cover of perennial grasslands at the landscape level (Table 1). We suggest the differences among these papers reflect the conclusions that come from using monarch abundance alone compared to adding behavioral information. Both metrics can provide complementary insights towards how and why monarchs respond to different sites across a landscape. Monarch behavior varied in relation to vegetation characteristics.
3.2. Individual behaviors and abundance The ordination identified the overarching patterns between all monarch behaviors at a site and plant diversity at that site, as well as overall monarch behaviors and milkweed cover. However, understanding the exact quantitative nature of these patterns requires complementary, secondary analyses investigating what variables were significantly related to each behavior by itself. Our secondary analyses revealed a significant interaction between plant species diversity and milkweed cover for monarch abundance, proportion of time spent ovipositing, and the proportion of time spent basking (Table 1). Since each of these factors was affected by the interaction between two independent variables, it can be difficult to easily understand the exact nature of their relationship. One way around this difficulty is to use a type of three-dimensional graph that shows both independent variables and the dependent variable at the same time (e.g., Fig. 2). While more complicated than simple linear regressions, the three-dimensional approach avoids the potential misunderstandings that can occur when looking at each factor individually when the interaction has already explained that you need to look at both factors at the same time. In our case, the full equation for the statistical model (Table 1) and the three-dimensional graph (Fig. 2), demonstrate how plant species diversity and milkweed cover interact to influence our response variables. For both abundance (Fig. 2A) and proportion ovipositing (Fig. 2B), the highest values (darkest areas of graph) are not with the highest milkweed cover (farthest top) or the highest plant species diversity (farthest right). Instead, the highest values for each response variable are at a relatively high plant species diversity and an intermediate milkweed cover. Another benefit of the graph is to demonstrate the nature of our different sites, in this case, that we did not have any points that were very high in both milkweed cover and plant species diversity (farthest top right corner has no dots). Besides the interaction found above, we also found that plant species diversity was a significant variable in explaining proportion of time resting (Table 1) and the proportion of time nectaring at a site (Table 1, Fig. 2C). We used the same three-dimensional graph to show the relationship with nectaring (Fig. 2C) to keep it consistent with the other graphs. The important thing to notice for nectaring is that the highest amount of time nectaring was found at sites with the highest plant species diversity (Fig. 2C, site towards the right side of the graph). Finally, we also found a positive, univariate relationship between time spent flying and the amount of perennial grassland cover in the surrounding landscape (Table 1). This means that we had more observations of monarchs flying at sites with more grasslands around them. 4
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Fig. 2. Average monarch abundance (A) and proportion of time spent in individual behaviors of nectaring (B) or ovipositing (C) in relation to average milkweed (Asclepias spp) cover (y-axis) and Simpson’s plant species diversity (x-axis) for monarchs observed in the Northern Great Plains, USA, from 2016 to 2017. Black circles represent sites. For abundance and nectaring (A and B), the highest numbers are located in the upper right corner, representing an interaction between milkweed cover and plant species diversity. However, the highest numbers for nectaring (C) were located on the right side of the graph, representing a relationship with plant species diversity alone.
2016; Baum & Sharber, 2012). Consequently, we speculate that management focused on restoring natural disturbances could also be used, with the correct temporal and spatial scale, to increase nectar resources (Towne & Kemp, 2008) and increase native plant diversity (Hendrickson & Lund, 2010). These management actions would likely provide nectar sources for adults and milkweed for larvae at the same site. Protocols used here can easily be included in other monitoring, but we recommend several changes that may better inform conservation. Flying should be separated into multiple categories to potentially detect differences between sexes, as male patrolling was the most common
Therefore, disturbance management (fire, grazing, haying) can be used to manipulate vegetation and support important behaviors (nectaring, ovipositing). Natural and non-agricultural areas are vital for monarch conservation (Nail et al., 2015; Pitman et al., 2018), but planting more milkweed in areas where milkweed is already available may not be sufficient for meeting all of monarchs needs, particularly for nectaring. Since both nectaring and ovipositing are crucial behaviors for successful monarch populations, our results reinforce the need for monarch management within and between sites that promotes both milkweed and additional nectar sources. Prescribed fire and haying are already used to increase milkweed availability (Alcock, Browner, & Williams, 5
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behavior of wild monarchs in Washington State (James, 2016). Since our surveys were opportunistic, we did not include factors that may also help discern behavioral differences such as parasite load and wing condition (Bradley & Altizer, 2005; Goehring & Oberhauser, 2002; Hirota & Obara, 2000). More observations of individuals—at each site and between years—would also help to distinguish this difference, as we observed 19 individuals in 2016 and 32 individuals in 2017. Additionally, future research should connect activity budgets and site characteristics with individual fitness to increase the inferential strength of shorter behavioral observations (like those conducted in this study). We determined how site variables correlated with activity budgets, but we do not know how either correlates with reproductive or adult success at a given site. Repeating site visits to observe the same monarchs throughout their life and assessing survival at each life stage could elucidate the connection between behaviors, success, and ecological variables (Berger, Olofasson, Gotthard, Wiklund, & Friberg, 2012; Lind & Cresswell, 2005; Sih, Bell, Johnson, & Ziemba, 2004).
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M., & Miller, J. R. (2012). Untangling the effects of fire, grazing, and land-use legacies on grassland butterfly communities. Biodiversity and Conservation, 21, 2719–2746. Nail, K. R., Stenoien, C., & Oberhauser, K. S. (2015). Immature monarch survival: Effects of site characteristics, density, and time. Annals of the Entomological Society of America, 108, 680–690. Oksanen, J. (2015). Multivariate analysis of ecological communities in R: Vegan tutorial. http://cc.oulu.fi/-jarioksa/opetus/metodi/vegantutor.pdf. Peixoto, P. E. C., & Benson, W. W. (2009). Daily activity patterns of two co-occurring tropical satyrine butterflies. Journal of Insect Science, 9, 1–14. Pickens, B. A., & Root, K. V. (2009). Behavior as a tool for assessing a managed landscape: a case study of the karner blue butterfly. Landscape Ecology, 24, 243–251. Pitman, G. M., Flockhart, D. T. T., & Norris, D. R. (2018). Patterns and causes of oviposition in monarch butterflies: Implications for milkweed restoration. 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5. Conclusions Even though monarchs are proposed for listing under the Endangered Species Act (USFWS United States Fish & Wildlife Service, 2017), behavioral studies for summer-breeding monarchs to connect behaviors to vegetation variables are rare (but see James, 2016). After observing monarchs in the field for two years, we determined that activity budgets were influenced by local and landscape variables, including milkweed cover, plant species diversity, and perennial grassland cover. Interestingly, we found 1) milkweed cover was not a predictor for all behaviors; 2) monarchs appear to optimize milkweed cover and plant species diversity when ovipositing but place a special emphasis on plant species diversity when nectaring; and 3) behavior data can inform site utilization beyond what is provided by abundance data. However, more research is necessary to discern how activity budgets relate to lifetime fitness. Instead of just planting more milkweed in semi-natural areas, we suggest conservation efforts should consider utilizing disturbance and other management options to increase availability of both milkweed plants and other nectar sources. Funding This work was supported by the USFWS (grant number F15AC01207), North Dakota Game and Fish (grant number F15AP00605), and North Dakota Agricultural Experiment Station. Funders had no role in study design, data collection, analysis, or manuscript submission. Declarations of Competing Interest None. Acknowledgements Special thanks to our field technicians, Adrienne Antonsen and Dani Ethington, and two anonymous reviewers for their helpful suggestions. References Alcock, J., Browner, L. P., & Williams, E. H., Jr. (2016). Monarch butterflies use regenerating milkweeds for reproduction in mowed hayfields in northern Virginia. Journal of the Lepidopterists’ Society, 70, 177–181. Badgett, G., & Davis, A. K. (2015). Population trends of monarchs at a northern monitoring site: Analyses of 19 years of fall migration counts at Peninsula Point, MI. Annals of the Entomological Society of America, 108, 700–706. Baum, K. A., & Sharber, W. V. (2012). Fire creates host plant patches for monarch butterflies. Biology Letters, 8, 968–971. Berger, D., Olofasson, M., Gotthard, K., Wiklund, C., & Friberg, M. (2012). Ecological constraints on female fitness in a phytophagous insect. The American Naturalist, 180, 464–480.
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Ries, L., & Oberhauser, K. (2015). A citizen army for science: Quantifying the contributions of citizen scientists to our understanding of monarch butterfly biology. BioScience, 65, 419–430. Sih, A., Bell, A. M., Johnson, J. C., & Ziemba, R. E. (2004). Behavioral syndromes: An integrative overview. The Quarterly Review of Biology, 79, 241–277. Stafford, R., Stafford, R., Goodenough, A. E., Slater, K., Carpenter, W., Collins, L., et al. (2011). Inferential and visual analysis of ethogram data using multivariate techniques. Animal Behaviour, 83, 563–569. Towne, E. G., & Kemp, K. E. (2008). Long-term response patterns of tallgrass prairie to
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