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15 April 2025

Filling in the Gaps: Working in Un-Surveyed Areas to Create Baseline Data, Habitat Use and Abundance Estimates for Butterflies in Eastern Wyoming, USA

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Wyoming Natural Diversity Database, and Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071, USA
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Environments2025, 12(4), 122;https://doi.org/10.3390/environments12040122
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Abstract

Butterflies are charismatic insects that are critical pollinators for plants that humans and wildlife depend on; however, butterflies have some of the largest measured declines. Two iconic butterflies in the USA, the Regal Fritillary (Argynnis idalia) and the Monarch (Danaus plexippus), are proposed for protection due to decreasing numbers from overexploitation, agricultural activities, disease and development. We surveyed 145 sites and walked 557 km from mid-June to August in 2019 and 2021 to create baseline information for butterflies and fill in knowledge gaps for these species. We observed 16,986 individuals and identified 52 species of butterflies. Generally, butterflies were more abundant at lower wind speeds and in areas with higher forb cover and less bare ground. We identified more species in locations with taller grass, higher bloom densities, higher cover of forbs, and less bare ground. We observed 11 Regal Fritillaries and 31 Monarchs extending from northeastern to southeastern Wyoming in a variety of habitats. The predicted suitable habitat of Regal Fritillaries and Monarchs was highly influenced by sagebrush cover and mean temperature of the wettest quarter according to species distribution models. Our results provide information to managers about the status, distribution, and habitat use of butterflies in a previously un-surveyed area. Historical butterfly surveys were limited to mountain ecosystems, and we provide baseline information on the habitat characteristics that support the highest abundances and most species in prairie ecosystems that can be compared to future efforts.

1. Introduction

Insects in Europe and North America have declined over the past few decades [1,2,3]. These animals continue to decline faster than birds and plants [4], conveying the need for monitoring and conservation efforts. Pollinating insects (e.g., butterflies, bees, flies, and beetles) are of particular concern because of the ecological services they provide in natural environments and to food crops [5]. Butterflies are one of the insect orders reported with the highest rates of decline [3], potentially due to overexploitation, agricultural activities, urban development, disease, and altered temperature and precipitation patterns [3,6,7]. The drastic decline in butterfly diversity was first reported in Belgium in 2001 [8] and has since been observed in other European countries, the USA, Asia, the Arctic, and the Neotropics [3,7]. Currently, eight butterflies are protected under the US Endangered Species Act (ESA) in the United States and five are under review (explorer.natureserve.org). In Wyoming, the Regal Fritillary (Argynnis idalia) and Monarch (Danaus plexippus) are proposed threatened under the US Endangered Species Act (ESA).
Species distribution models (SDM) are useful tools for establishing baseline information, estimating where rare species may occur [9,10,11], calculating changes over time [12,13], and guiding sampling efforts [14,15]. SDM relate observations to landscape and climate to predict suitable habitat of a species where surveys have not occurred. Combining historical and current observations can result in better predictions of suitable habitat and insight into future changes [12]. Maximum entropy (MaxEnt) is one model that typically uses detections that results in high performing models [10] even for rare or infrequently sampled species to predict suitable habitat [11]. Species distribution models only predict areas of suitable habitat but not the range of the species due to over-predicting suitable habitat [16,17]. Species distribution models use coarse-scale habitat information to predict suitability and combining these data with finer scales measurement (e.g., floral surveys, vegetation characteristics) can result in a better understanding of habitat selection.
Some butterfly species are declining, including the Regal Fritillary and Monarch. The Regal Fritillaries live in the eastern USA [18] and their numbers declined by 75–95% since 1990 [19] due in part to the rapid loss and fragmentation of tallgrass prairie ecosystems by agriculture (farming and grazing) and grassland fires [20,21]. Regal Fritillaries largely disappeared from the eastern portion of their range [22] and the western distribution of these butterflies is unknown with most maps excluding Wyoming as part of their range. Monarch occur across the USA with individuals in the eastern USA decreasing by 80–97% [23,24] and individuals in the western USA decreasing by 98% since 1997 [24]. The decline of Monarchs may be largely driven by fewer Milkweed (Ascelpias spp.), their larval host plant (e.g., [25,26]), disease, agricultural intensification and pesticide use (e.g., [27,28,29]). Migrating Monarchs east of the Rocky Mountains are better understood than western populations [30]. Monarch and Milkweed conservation gained considerable momentum in recent years [26,31]; the current distribution of Monarchs in Wyoming is largely unknown despite the surge of interest.
Our goal was to establish baseline data on butterfly abundance, richness, and habitat use, while also estimating the status of the Regal Fritillary and Monarch in a previously un-surveyed area. Historically, four observations of the Regal Fritillary and 24 Monarch sightings were recorded in Wyoming before 2005 [32]. Due to more recent Monarch sampling efforts, 168 Monarchs have been observed in Wyoming since 2010 [33]. Our specific questions were as follows. (1) How does butterfly abundance and richness relate to habitat conditions? (2) How abundant and what was the distribution of the Regal Fritillary and the Monarch? (3) What habitats did we observe the Regal Fritillary and the Monarch in? (4) What variables predicted the most suitable habitat for Regal Fritillary and Monarch using SDM? We surveyed butterflies at 145 sites in eastern Wyoming, which is an ideal place to measure habitat use due to the abundance of rangeland and low human population. Our results provide information on butterflies of management concern to resource managers in areas where little is known about them.

2. Materials and Methods

2.1. Study Area

We surveyed adult butterflies in eastern Wyoming, USA, to assess the abundance and richness of these insects in areas that have not been previously surveyed. We visited 145 sites in 9 counties from mid-June to late August in 2019 and 2021 (Figure 1). The sites were selected throughout eastern Wyoming to sample a wide range of habitat types mainly on public lands. We considered areas with wet meadows, depressions, the presence of streams, gullies, and other locations with high densities of nectar-rich plants the best habitat for Regal Fritillaries and Monarchs. However, we searched wooded areas, mixed-grass prairies, sagebrush steppes, and other available habitats in eastern Wyoming to account for the nomadic nature of female Regal Fritillaries in late summer and encompass the full diversity of butterflies there. Regal Fritillaries are considered tall-grass prairie specialist; however, this habitat does not occur in Wyoming. Eastern Wyoming typically has 81–140 days that are frost-free annually [34]. Mean annual precipitation ranges between 15 and 64 cm, with most precipitation falling in May and June. Elevation of basin habitats in eastern Wyoming range between 952 and 2200 m.
Figure 1. We surveyed 145 sites, from mid-June to late August in 2019 and 2021, where two people walked independent transects. The locations of the 11 Regal Fritillaries at 9 sites and 31 Monarch butterflies at 22 sites are shown on the map. The inset map shows the location of Wyoming in the United States of America and the area we survey in the red frame.

2.2. Target Species

Male Regal Fritillaries emerge first in early summer and later mate with females who lay eggs in the autumn. Interestingly, adult females do not rely on the presence of their larval host plants, violets (Viola spp.), to lay eggs. Instead, Regal Fritillaries have high fecundity and lay > 2000 eggs per individual on the underside of leaves [35] increasing the chance that some emergent caterpillars will locate violets. Adult Regal Fritillaries prefer high densities of forbs and a diversity of flowering plants [36], gravitating towards areas with recent fires due to increased floral resources [37]. These butterflies generally avoid areas with bare ground; instead, they prefer land with more litter and lower grazing intensities [36]. Regal Fritillaries do not migrate (directional movement with distinct timing), but females are nomadic in late summer. Larvae hibernate in prairies with stable resources [19] from September to June [38].
Monarchs are well-known for migrating ≤ 3600 km from their summer breeding habitat to overwintering areas in southern Mexico, taking ~75 days to make the journey [39]. In fact, 3–5 generations of butterflies laying eggs along the route are needed to complete their annual migration [39]. In general, eastern inland Monarchs converge in Mexico each fall, and Monarchs on the East Coast migrate to Florida to overwinter [40]. On the other hand, most western populations move to southern California or congregate in Mexico to overwinter. The overwintering sites are much smaller than their summer habitat, but the overwintering sites must have sufficient nectar for initiating and completing migration when air temperatures begin to rise in the spring [41]. During the Monarch’s breeding and migration season, they require trees for roosting and Milkweed to lay eggs on.

2.3. Field Sampling

We walked transects to estimate the abundance and richness of butterflies among sites. Two observers walked independent transects to survey for all butterflies with an emphasis on Regal Fritillaries and Monarchs. The transect length was generally >1 km and >30 min. In 2019, we walked transects to record butterfly presence, and paused at the beginning, middle, and end of each transect to record floral data. In 2021, we divided transects into 2 parts; we counted butterflies during the first half and we measured floral data during the second half. Additionally, we watched for Monarchs and Regal Fritillaries during the second half of the surveys, but we did not count the butterfly assemblage during this time. We recorded the distance and time of each transect on a GPS unit. Butterflies visible within ~5 m of the observer were recorded at the family, subfamily, genus or species-level depending on the group. We target-netted a few individuals of each species for later identification, except for Monarchs and Regal Fritillaries, which were not collected. The collected specimens were spread and pinned for identification and used to estimate the richness of butterflies in eastern Wyoming [42].
We collected vegetation and environmental data to assess how site conditions influenced butterfly abundance and richness. In 2019, three vegetation quadrats (929 cm2) were recorded at three locations along one transect at each site (n = 9). The maximum height of grass and shrubs within each quadrat were measured, and we counted the number of blooms by species to estimate flower availability to pollinators. In 2021, one quadrat (1 m2) was recorded at the beginning, middle, and end of one transect at each site (n = 3). We visually estimated the percent cover of grass, shrubs, forbs, and bare ground in addition to grass and shrub height, and flower density. We also measured bloom diameter by species to estimate the surface area of flowers available to butterflies. We recorded land use as resource extraction (e.g., oil or timber), grazing (e.g., cow, sheep or horse), agriculture (i.e., farming), recreation, and roadsides. Air temperature and average wind speed were recorded using a shielded Kestrel 2000 at the beginning of each transect, and we visually estimated percent cloud cover during the transects.

2.4. Statistical Analysis

We estimated how habitat characteristics and atmospheric conditions altered the abundance and richness of butterflies using generalized linear models (glm). We analyzed butterfly counts and richness along each transect as response variables with an offset of track distance to account for different effort. We included the mean values for the number of blooms in 1 m2 quadrats, grass height, shrub height, wind speed, air temperature, cloud cover, the categorical variables current grazing, and land use as predictor variables. We divided the number of butterflies or richness of species encountered during a transect by the distance walked to standardize butterfly abundance (butterflies/km) and richness (species/km) for plots. We used a glm with a negative binomial distribution (glm.nb; [43]), because our data most closely matched this distribution. We used estimated marginal means (emmeans) to calculate differences among ≥3 levels of a categorical variable [44]. Our predictor variables were not correlated, and other model assumptions were met. We estimated the effects of percent cover classes and bloom surface area on butterfly abundance and richness from data recorded in 2021 only because we did not record these variables in 2019. Our data contained outliers; therefore, we used median-based linear regression mblm; [45] that is not influenced by outliers. Median-based linear regression only allows one predictor to be analyzed at a time and does not allow for offsets; therefore, we used butterflies/km or species/km as our response variables. We plotted butterfly abundance and richness against cover classes using trend lines and error based on means because calculations with medians are not available; thus, the relationship shown in plots is not what was calculated in the non-parametric regression analysis. We did not analyze the habitat characteristics of locations where Regal Fritillaries and Monarchs were observed because too few observations limited our analysis. We used the plyr [46] and tidyverse packages [47] to prepare our data for analyses.

2.5. Species Distribution Models

We used statewide historical and current observations of Regal Fritillary and Monarch to create SDM using Maximum Entropy (Maxent). Maxent models use locations where a species has been observed and are useful to predict suitable habitat for rare species [10]. We used 16 unique points to model the habitat of the Regal Fritillary and 70 unique points to model Monarch habitat. Historically, four observations of Regal Fritillary and 24 Monarch sightings were recorded in Wyoming before 2005 [32]. Due to recent Monarch sampling, 168 Monarchs have been observed in Wyoming since 2010 [33,48] in addition to the observations in our study. Ten thousand randomly selected locations across Wyoming were used as background sites. We created two ten-fold cross-validation models with 32 potential climatic and landscape predictors that we chose a priori based on our knowledge of the species and their life cycles (Table A3). Predictors for final models were chosen on overall contribution, jackknife contribution, and low correlation (R2 < 0.8). We binned the probability levels (p) into three equally interval and selection section with low (0 < p ≤ 0.31), medium (0.31 < p ≤ 0.69), or high (0.69 < p ≤ 1.0).

3. Results

3.1. Butterfly Surveys

In 2019, we conducted 166 surveys at 90 sites (Figure 1) that averaged 2.5 km (0.42 to 6.72 km; total of 408 km walked) in length and took 95 min on average (25 to 223 min; total of 647 h walked). In 2021, we performed 92 butterfly surveys at 55 sites that averaged 1.2 km (0.91 to 2.90 km; total of 149 km walked) in length and 30 min (23 to 78 min; total of 76 h walked). We recorded 14,760 butterflies, and an average of 90 butterflies/km (0 to 282) and six species (1 to 19) per survey in 2019. We observed 2226 butterflies, and an average of 30 butterflies/km (0 to 229) and three species (ranged from 1 to 7) per survey in 2021. Mean air temperature was 25 °C (5.1 to 39.2 °C) and mean wind speed was 5.5 km/h (0 to 22.5 km/h) during surveys.
Pieridae composed 50% of the butterflies (36% Sulphurs and 13% Whites) we encountered along transects, followed by Nymphalidae (21%), Lycaenidae (12%), Hesperiidae (4%), and Papilionidae (2%; Table A1 and Table A2). We identified 52 butterfly species and Nymphalidae (13 genera; 26 species) were the most specious, followed by Lycaenidae (4 genera; 9 species), Pieridae (4 genera; 7 species), Hesperiidae (5 genera; 6 species), and Papilionidae (2 genera; 4 species). The most commonly collected species were the Clouded Sulphur (Colias philodice), the Checkered White (Pontia protodice), and the Melissa Blue (Plebejus melissa). Olympia marble (Euchloe melissa), Mormon Fritillary (Speyeria mormonia), Hoary Comma (Polygonia gracilis), Sagebrush Checkerspot (Chlosyne acastrus), Acmon Blue (Plebejus acmon), Greenish Blue (Plebejus saepiolus), Silvery Blue (Glaucopsyche lygdamus), and Purplish Copper (Lycaena helloides) were collected once during the two years of surveys.
Habitat characteristics and land use varied among sites. Grass was present at all sites (Table 1), but 30% of sites lacked shrubs and 21% of sites lacked blooming flowers. Most sites were used for grazing (83%), followed by recreation (10%), farming (4%), roadsides (2%), and resource extraction (1%). In 2021, sagebrush steppe was the most surveyed habitat (68%), followed by grassland (26%) and riparian (4%).
Table 1. The range of habitat characteristics measured among all sampled sites where Regal Fritillaries were observed and where we encountered Monarch butterflies.
Butterfly abundance varied by some habitat characteristics and site conditions. We observed marginally more butterflies in areas with higher densities of blooming flowers (glm; df = 153, z = 1.9, p = 0.057; Figure 2a), but we did not observed differences in butterfly abundance with grazing status (glm; z = 1.5, p = 0.14). We counted fewer individuals on land used for recreation compared to agriculture and grazing (glm, z = −1.4 to −2.4, p = 0.01 to 0.15; emmeans, p = 0.01; Figure 2b). Butterfly abundance did not respond to grass height (glm; z = 0.87, p = 0.38) or shrub height (glm; z = 1.3, p = 0.18). We counted more butterflies at lower wind speeds (glm; z = −3.1, p = 0.002; Figure 2c), but air temperature (glm; z = −1.1, p = 0.27) and cloud cover (glm; z = −1.3, p = 0.20) did not alter their abundance. Bloom surface area explained little variance in butterfly abundance (mblm; df = 51, p = 0.17), but we observed more butterflies at sites with higher forb cover (mblm; V = 566,911, p < 0.001; Figure 2d), lower cover of shrubs (mblm; V = 310,372, p = 0.002; Figure 2e), lower cover of grass (mblm; V = 418,630, p = 0.02; Figure 2f), and less bare ground (mblm; V = 392,719, p = 0.0004; Figure 2g).
Figure 2. (a) Butterfly abundance was positively related to bloom density, and (b) sites used for recreation had lower abundance than land used for agriculture and grazing. (c) We observed more butterflies at lower wind speeds. (d) The cover of forbs was positively related to butterfly abundance, and the cover of (e) shrubs, (f) grass, and (g) bare ground were negatively related to butterfly abundance. Blue lines were calculated from least squares regression and grey areas around lines are 95% confidence intervals. Note that the relationships between cover classes and butterfly abundance were plotted as means, but the statistics were based on medians.
Butterfly richness also varied by some habitat characteristics and site conditions. We did not observe a difference in species richness along a gradient of flower density (glm; df = 116, z = −1.0, p = 0.32); however, we observed more species in areas with taller grass (glm; z = 2.9, p = 0.005; Figure 3a) and fewer species in area with taller shrubs (glm; z = −4.1, p < 0.001; Figure 3b). We did not observe differences in butterfly abundance with grazing status (glm; z = 0.7, p = 0.50), but we counted more individuals on land used for agriculture compared to land used for grazing and recreation (glm, z = −1.0 to −2.9, p = 0.02 to 0.30; emmeans, p < 0.03; Figure 3c). We counted more butterflies at lower wind speeds (glm; z = −4.0, p < 0.001; Figure 3d), but air temperature (glm; z = 1.1, p = 0.27) and cloud cover (glm; z = −0.9, p = 0.37) did not alter their richness. We observed more butterfly species in areas with a larger surface area of blooms (mblm; df = 29, V = 356 p = 0.03; Figure 3e). Higher forb cover (mblm; V = 67,901, p < 0.001; Figure 3f) and lower cover of bare ground (mblm; V = 42,686, p = 0.007; Figure 3g) were related to more butterfly species, but shrub (mblm; V = 42,455, p = 0.82) and grass cover (mblm; V = 48,506, p = 0.47) were not related to butterfly richness.
Figure 3. The richness of butterflies was positively associated with (a) grass and (b) shrub height. (c) We observed more butterflies in agricultural lands compared to land used for grazing and recreation. (d) Higher wind speeds reduced the number of species we observed. (e) Higher surface area of blooms was related to higher richness of butterflies. (f) We observed more butterfly species in areas with higher forb cover and (g) a lower proportion of bare ground. Blue lines were calculated from least squares regression and grey areas around lines are 95% confidence intervals. Note that the relationships between cover classes and butterfly richness were plotted as means, but the statistics were based on medians.
We observed seven Regal Fritillaries at six sites in 2019, and four individuals at three sites in 2021 (Table 1). The butterfly was present in five counties, spanning from the northern to southern boundaries of eastern Wyoming and ~180 km west of the South Dakota and Nebraska borders (Figure 1). Twenty-five percent of the sites we observed with Regal Fritillaries lacked blooming flowers. In general, we observed Regal Fritillaries at sites with low to moderate grass and shrub height and bloom density. We observed 26 Monarchs at 17 sites in 2019, and 5 Monarchs at 5 sites in 2021 in 9 counties (Figure 1). We observed Monarchs at sites with moderate grass height and low to moderate shrub height and flower density (Table 1). Regal Fritillaries and Monarchs were primarily observed on land used for grazing which is a dominate land use in eastern Wyoming.

3.2. Predicting Suitable Habitat for Regal Fritillary and Monarchs

The Regal Fritillary SDM fit well (area under the curve = 0.91; Figure 4a) and predicted that suitable habitat was most affected by mean temperature of the wettest quarter (64.6% contribution), precipitation of the warmest quarter (15.4%), sagebrush cover (7.3%), herbaceous cover (4.8%), forest cover (3.6%), mean temperature of the coldest quarter (1.8%), precipitation of the coldest quarter (1.2%), precipitation seasonality (0.9%), and minimum temperature of the coldest month (0.4%; Figure 4a). Specifically, the Regal Fritillary habitat coincided with warmer and wetter weather throughout the year. Regal Fritillary is predicted to occur in areas with high herbaceous cover, and low forest and sagebrush cover. Areas in which the model predicted the most suitable habitat occurred in sagebrush steppe and grasslands.
Figure 4. The habitat suitability for (a) Regal Fritillaries and (b) Monarchs, generated by a species distribution modeling created using Maximum Entropy. The blue triangles represent historical observations (before 2005) and the yellow squares visualize current sightings (2005–2021).
The Monarch SDM fit satisfactory (area under the curve = 0.71; Figure 4b) and predicted that suitable habitat was most affected by sagebrush cover (29.3% contribution), precipitation seasonality (13.2%), mean temperature of the wettest quarter (11.9%), forest cover (9.8%), mean temperature (7.4%), precipitation of the wettest month (5%), number of frost days (5%), herbaceous cover (4.9%), habitat heterogeneity (2.7%), topographic position index (31-cell focal window; 2.5%), organic soil matter (2.3%), mean temperature of the driest quarter (2%), annual precipitation (1.7%), distance to water (1.6%), isothermy (0.4%), and mean temperature of the coldest quarter (0.4%; Figure 4b). Generally, our models predicted that habitat with low sagebrush cover, shorter distance to water, and high forest and herbaceous cover increased the suitability of habitat for Monarchs. Warmer and wetter areas coincided with higher predicted habitat suitability for Monarchs. Much of Wyoming is predicted to be suitable habitat, and the habitats include sagebrush steppe, prairie, forest, and riparian areas.

4. Discussion

We discovered Regal Fritillaries using eastern Wyoming during both years of sampling; however, this area is typically not considered part of their range. Sampling species at the edge of their range is critical because that is where species persist after range contractions [49]. A study tested the paradigm that 245 species contracted to the core of their range where habitat is assumed to be most suitable. However, the results showed that these species persisted at the edge of their range where their abundance is lower, habitat is poorer and more fragmented, and immigration is low. We investigated Regal Fritillaries at the western edge of their range (e.g., [42]) and discovered a low abundance of individuals. Regal Fritillaries use eastern Wyoming despite the lack of tallgrass prairie ecosystems and likely poorer habitat for them; however, current abundances are higher east of the Wyoming boarder in the Midwest. On the other hand, Monarchs occur across most of the US, but eastern and western populations are likely split by the Rocky Mountains. We do not know the degree to which individuals intermix between populations in this region. The Rocky Mountain states may act as periphery habitat, as Monarchs have much lower abundances and parasite loads in this area compared to their core range [29]. Overall, Regal Fritillary and Monarch populations overlapped in eastern Wyoming, and Monarchs were nearly 3 times more abundant than Regal Fritillaries. Monitoring declining species at the periphery of their range is critical to their persistence.
Species distribution models predict suitable habitat from a limited number of observations which can be informative for infrequently observed species [50]. Such models should not be interpreted as the species range; SDM predict areas with similar habitat to where the species was observed. The SDM for Regal Fritillary coincided with the lack of forest and sagebrush cover, and the presence of herbaceous cover. Other studies in the midwestern US reported that Regal Fritillary abundance was positively associated with forbs [21,36] and negatively related to shrub cover [36,51], similar to the predictions of our SDM. Our SDM predicted that Monarch occurrences coincided with decreased habitat heterogeneity and distance to water, and increased organic soil matter, and forest, herbaceous, and sagebrush cover. The model fit was poorer for the Monarch SDM, and we suspect that this was due to observations in a variety of habitats because they migrate. Habitat suitability did not vary greatly across the state, and streams were highlighted perhaps due to more floral resources in the semi-arid landscape of Wyoming.
Species distribution models are frequently created across a species range using climatic variables only; however, SDM produced at smaller spatial scales are also useful. The habitat occupied by a species can vary across their range [49] allowing SDM constructed at local or regional scales to identify unique habitat selection [52,53]. Such models can be the first to detect species declines in a portion of their range [54,55,56,57]. Political boarders are not usually critical to the species, but they can be vital to conservation efforts. Climatic variables are commonly used to model suitable habitat across the range of species, probably because these variables are available across larger areas [55,58]. However, landscape variables can contribute substantially to models. Climatic variables contributed more to the Regal Fritillary model than landscape variables in our study primarily due to the high contribution of mean temperature of the wettest quarter. Conversely, climatic and landscape variables contributed nearly equally to the Monarch SDM. Landscape variables are much easier to incorporate into local and regional models due to the scale of the project and the availability of such data within smaller areas (e.g., states).
Reports of Regal Fritillary associations with their host plant vary from not dependent [19,35,38] to completely reliant [36]. Regal Fritillaries were reported to be more reliant on large tracts of grasslands and topographic diversity, e.g., dry mesic grassland and wet mesic grassland [19] than their host plant, but the top predictors of Regal Fritillary habitat in a random forest model were the presence of violets, percent forb cover, climatic variables, and habitat isolation (distance/permeability; [36,59]). The presence of Regal Fritillaries has also been related to flower density and the cover of grass [60]. Characteristics of locations where Regal Fritillaries were observed did not differ between low and high quality habitat [61]. Our model also predicted that higher cover of herbaceous plants increased habitat suitability. In Wyoming, we observed this butterfly near late- and long-blooming plants, such as sweet clover (Melilotus officinalis), thistle (Cirsium, Onopordum, and Carduus sp.), and alfalfa (Medicago sativa). Regal Fritillaries can feed on nectar from a variety of plants, and thistles and milkweed were the mostly commonly used nectar sources [18]. Larval host plants can often predict the presence of butterfly species with diet restrictions, low mobility or habitat specialization [62]; however, Regal Fritillaries do not lay eggs on violets and we never observed them together. This may in part be due to Wyoming’s extensive rangelands that are classified as short-grass prairie. All our sightings of Regal Fritillaries were in August, when fewer forbs were flowering and nectar was likely scarce. While most Regal Fritillary populations peak in July [63], we observed more Regal Fritillaries in August, when females are highly nomadic and travel through habitats with limited resources [38]. Additionally, Wyoming has the second-highest mean elevation of any state in the United States (2040 m), which may alter the Regal Fritillary’s life cycle [64].
Monarch habitat is primarily explained by the presence of milkweeds (e.g., [25,26,65]); however, we only observed Monarchs with milkweeds at 10% of the sites. Milkweeds grow in many habitats, including rangeland, agricultural areas, wetlands, prairies, meadows, open-forests, woodlands, and along roadsides [31,66]. Thirteen species and >1000 occurrences of milkweed have been documented in eastern Wyoming (Rocky Mountain [67]), offering opportunities for Monarchs to lay eggs and travel across the state. Our infrequent sightings of Monarchs with milkweeds may be due to migrating. Our SDM predicted that increased herbaceous cover indicated suitable habitat, perhaps because these areas have more nectar resources which they require for migration [41]. We observed Monarchs near sweet clover, alfalfa, gumweed (Grindelia sp.), and sunflowers (Helianthus spp. and Helianthella spp.), which were likely nectar resources. Butterflies and nectar resources are positively related [62], as we also observed. Higher cover of forbs was associated with higher abundance and richness of butterflies probably because this variable indicates the presence of flowering plants. Consequently, increases in the cover of shrub, grass and bare ground limited the cover of forbs. Conversely, Monarchs are specialists because they only lay eggs on milkweeds and primarily feed on milkweed as caterpillars. Therefore, the presence of Monarchs is usually associated with these plants [68]; however, adults can use nectar from a variety of blooming plants. The abundance and richness of butterflies in Wyoming may be heavily dependent on the presence of adequate floral resources, and Wyoming’s abundant grasslands may provide many nectar resources.
In our study, ~70% of Regal Fritillary and Monarch observations were on lands used for livestock grazing. Previous studies reported positive [69], neutral [51], and negative [21,70] effects of grazing on Regal Fritillary and Monarch abundance. The various effects of grazing on grasslands are well-studied (e.g., [71,72,73]) and depend on many variables, such as productivity (e.g., [74]), livestock species (e.g., [75]), timing (e.g., [76]), and intensity (e.g., [71,77,78]). In general, butterflies are often more abundant and diverse in areas with more flowers (e.g., [79,80]), and grazing can increase forb abundance in some situations [81]. Most sites were on public lands with some degree of grazing, but very few were grazed intensely (M. Mazur, personal observation). Well-managed rangelands may offer suitable habitat for Regal Fritillary and Monarch butterflies.
Butterflies prefer mild weather conditions [82] and are known to be sensitive to weather and climate conditions [83]. We sampled during a variety of weather conditions and observed fewer butterfly species at higher wind speeds, as has been reported in other areas [83]. Wyoming is the fourth windiest state in the US (mean 9.3 m/s), so surveying at low wind speeds can be difficult. Larger butterflies, such as Fritillaries, are probably most affected by the wind due to their larger wingspan. Cloud coverage can reduce butterfly flight [84], but we did not observe a relationship between cloud cover and butterfly abundance and richness. Butterflies are typically more active and fly longer distances at warmer temperatures [84,85]; however, butterfly abundance and temperature were not related within the range of temperatures we measured.
Butterflies have infrequently been surveyed in eastern Wyoming, and historical data are lacking. A literature review revealed eight journal articles that reported information on butterflies in Wyoming (Web of Science, searched Wyoming and butterfl* or Lepidoptera; accessed on 28 February 2025). Of these articles, four focused on genetics, and all examined either one or two species in mountain habitats [86,87,88,89,90,91,92,93]. More baseline information on the butterfly communities of prairie habitats is needed to assess future change.

5. Conclusions

Some previously widespread and abundant pollinators are declining [94,95]. Species are assumed to have a higher extinction risk when they are specialists, occupy a small area, are infrequently encountered, or live in restricted habitats [96,97]; therefore, the loss of endemic or rare species is predicted based on their biology. Conversely, the loss of widespread species is concerning and suggests that large-scale factors are affecting them, such as the loss of habitat, floral resources, disease, and an altered climate [3,6,7,98,99]. The loss of pollinators will affect other trophic levels due to connections within food webs. For example, pollinators and the plants they depend on are declining synchronously in Europe [100]. We expect that the loss of pollinators will alter the vertebrate animals that depend on the forbs and shrubs they maintain through pollination. Managing declining species requires considering the entire ecosystem from soil, plants, insects and vertebrate animals. Resilient populations of pollinators need robust ecosystems to thrive in. Recent studies have stressed managing pollinators and plant communities in unison [96,101]. We suggest studying ecosystems as a whole to understand connections and better manage declining species that were once widespread and common.

Author Contributions

Conceptualization, M.M. and L.M.T.; methodology, M.M. and L.M.T.; formal analysis, M.M. and C.B.; investigation, M.M. and Z.S.; resources, L.M.T.; data curation, M.M.; writing—original draft preparation, M.M. and L.M.T.; writing—review and editing, M.M., Z.S., C.B. and L.M.T.; visualization, M.M. and C.B.; supervision, L.M.T.; project administration, L.M.T.; funding acquisition, L.M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Wyoming Governor’s Office.

Data Availability Statement

Some data are available in Appendix A. Due to data sensitivity, the data are available upon request through the Wyoming Natural Diversity Database, University of Wyoming. https://www.uwyo.edu/wyndd/index.html. accessed on 27 February 2025.

Acknowledgments

We would like to thank Kamaile DeLong, Ryan Burns, Bryan Tronstad, Tresize Tronstad, Joy Handley, Tighe Jones, and Katrina Cook for performing butterfly surveys and pinning specimens. Carlos Martinez Del Rio, Ken Gerow, Kamaile DeLong, and Katrina Cook provided comments that improved this work. Finally, we thank private landowners for allowing us to survey on their lands making this study possible.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
SDMSpecies Distribution Model
USUnited States of America
MaxEntMaximum Entropy

Appendix A

Table A1. Number of butterflies collected in eastern Wyoming from mid-June through August 2019 and 2021. We surveyed 145 sites and walked 557 km of transects.
Table A1. Number of butterflies collected in eastern Wyoming from mid-June through August 2019 and 2021. We surveyed 145 sites and walked 557 km of transects.
TaxaTotal
Nymphalidae478
Argynnis idalia11 (not collected)
Aglais milberti2
Cercyonis oetus15
Cercyonis pegala74
Chlosyne acastus1
Chlosyne gorgone25
Coenonympha tullia18
Erebia epipsodea5
Euphydryas anicia13
Euphydryas chalcedona4
Euptoieta claudia60
Danaus plexippus31 (not collected)
Neominois ridingsii4
Oeneis uhleri2
Phyciodes cocyta16
Phyciodes pulchella3
Phyciodes tharos94
Polygonia gracilis1
Speyeria aphrodite1
Speyeria coronis1
Speyeria cybele3
Speyeria hesperis1
Speyeria aphrodite24
Speyeria callippe10
Speyeria coronis6
Speyeria cybele2
Speyeria edwardsii18
Speyeria hesperis24
Speyeria mormonia1
Vanessa atalanta2
Vanessa cardui42
Vanessa virginiensis6
Pieridae547
Colias alexandra11
Colias eurytheme81
Colias philodice286
Euchloe olympia1
Pieris rapae10
Pontia protodice11
Pontia occidentalis5
Pontia protodice142
Lycaenidae188
Glaucopsyche lygdamus1
Lycaena dione5
Lycaena helloides2
Lycaena hyllus7
Plebejus acmon5
Plebejus icarioides21
Plebejus melissa140
Plebejus saepiolus1
Strymon melinus6
Hesperidae100
Atalopedes campestris5
Epargyreus clarus2
Hesperia juba3
Hesperia uncas4
Pholisora catullus12
Pyrgus communis74
Papilionidae26
Papilio eurymedon3
Papilio polyxenes2
Papilio rutulus4
Parnassius smintheus17
TOTAL1339
Table A2. Number of butterflies observed at each site in 2019 and 2021. Butterfly groupings include Nymphalidae, Regal Fritilaries (Argynnis idalia), Monarchs (Danaus plexippus), Pieridae, Lycaenidae, Hesperiidae, Papilionidae and total count.
Table A2. Number of butterflies observed at each site in 2019 and 2021. Butterfly groupings include Nymphalidae, Regal Fritilaries (Argynnis idalia), Monarchs (Danaus plexippus), Pieridae, Lycaenidae, Hesperiidae, Papilionidae and total count.
Site IDDateNymph.Regal.Mon.Pier.Lyc.Hesp.Pap.Total
21927-August-20191004634054
21927-August-20191006764078
450EofBeaverCreek15-August-20191001320110144
450EofBeaverCreek15-August-2019100289100291
BH124-July-20198100781907185
BH124-July-201913100331200176
BH224-July-20191500320020
BH224-July-2019800500013
BH325-July-2019670053604130
BH325-July-2019850075404168
BH425-July-2019380027100378
BH425-July-201979003112120134
BH526-July-20192170177003297
BH526-July-20191540252700213
BH628-July-20191380047100186
BHBLM127-July-20195201430059
BHBLM127-July-20191550115130174
BorderBLM16-August-20195009220099
BorderBLM16-August-20197012671041
CastleCreek15-August-201900000000
CastleCreek15-August-201900000101
CGSP0019-July-20198000241280124
CGSP0019-July-2019200145105282
CGW128-August-20199001098130139
CGW128-August-201922005915610157
CGW1029-August-20190004300043
CGW1029-August-20193001520020
CGW1129-August-2019000175060181
CGW1129-August-20194702136080191
CGW1230-August-2019100700330104
CGW1230-August-20191001509025
CGW1330-August-20190002500025
CGW1330-August-20194001701022
CGW1429-August-20199006902080
CGW1429-August-201923006302088
CGW1630-August-20191103602039
CGW1630-August-20191002604031
CGW228-August-201916001308037
CGW228-August-201923005228058
CGW328-August-201917005803078
CGW328-August-201970033010050
Chey_0124-June-2021200230613
Chey_0327-August-202181019103040
Chey_0327-August-2021101673017
Chey_0423-August-20216002510023
Chey_0423-August-202100003003
Chey_066-July-2021400700011
Chey_066-July-202130030107
Chey_06a23-August-20211006917033
Chey_06a23-August-2021300426015
Chey_076-July-202130000003
Chey_076-July-202100000000
Chey_081-July-20213001401018
Chey_081-July-20216000011320
Chug_0217-August-20210101000010
Chug_0217-August-2021010972018
Chug_0327-August-202120000002
Chug_0327-August-202110000001
CW11-July-20191700592050101
CW102-July-201919001555246
CW145-July-20193001303019
CW42-July-201921002518055
DT128-July-20197300500078
DT128-July-201981001602099
DT229-July-2019108007100116
DT229-July-20191170318160142
DurhamRanch122-August-20199111743530221
DurhamRanch122-August-2019702261920279
DurhamRanch222-August-2019400957420175
DurhamRanch222-August-20190023350038
Fairview14-August-2019000981340133
Fairview14-August-2019300112720124
FL00126-June-20190002210023
FL00126-June-20198003020040
FortLaramie22-August-20190001301014
FortLaramie22-August-20190011201013
FortLaramie223-August-20195001807030
FortLaramie223-August-20190014700047
FosterRoad27-August-201900010001
FosterRoad27-August-201900000101
Gill_0227-July-20215001430022
Gill_059-August-202100030003
Gill_059-August-2021100670014
Gill_069-August-20210001020012
Gill_069-August-202100050005
Glen_0122-July-202100000000
Glen_0122-July-202100000000
Glenr-0115-July-202125006470096
Glenr-0115-July-202121005510178
Glenr-0215-July-202120030005
Glenr-0215-July-202110171009
Glenr-0615-July-20213400130001165
Glenr-0615-July-202128004430075
GLSP00111-July-20191000530018
GLSP00111-July-20191300400017
GLSP00211-July-201927009220058
GLSP00211-July-20193400540043
Grieves14-August-201915002531044
Grieves14-August-20192003520039
GSP00126-June-20190002139125
GSP00126-June-201913009182042
HJ0018-July-20192500251813283
HJ0018-July-2019350082015785
HJ0028-July-201970042154270
HJ0028-July-201934003520150104
HJ0039-July-201930042203270
HJ0039-July-2019750040653138
HJ0049-July-201910001051000125
HJ0049-July-20192400423115148
Howard15-August-2019100152700160
Howard15-August-20190002847039
Hul_0328-July-20211000000010
Hul_0428-July-20216900650080
Hul_0528-July-202120002004
Hul_0611-August-202100001001
Hul_0611-August-20210001710018
Hul_0711-August-20210001910020
Hul_0711-August-202100000000
Hul_0811-August-20210001050015
Hul_0811-August-20212102810031
HWY22327-August-201910026172046
HWY22327-August-201932021553082
Key_0127-July-202100020002
KeyholeWest12-August-20191002760034
KeyholeWest12-August-20191003450040
La_0117-August-202100030003
La_0117-August-202100081009
Lar_0123-June-202100000101
Lar_0223-June-202100010001
Lar_0523-June-202118003501743
LCW17-August-201928002870100136
LCW17-August-20194900607830190
LCW37-August-201914100374430225
LCW37-August-2019150039220112
LCW75-August-201933002092064
LCW75-August-20198110265670170
LCW8A6-August-201922004654077
LCW8A6-August-20193400849150142
LCW8B6-August-2019290094120126
LCW8B6-August-20195004618060
LCW96-August-20198005711067
LCW96-August-201923004930075
LCWBLM16-August-2019470017180082
LCWBLM16-August-201949006140069
Lin_037-July-2021162000020164
Lin_037-July-20217900200081
Lusk_0216-July-2021200607015
Lusk_0216-July-20217001200019
Lusk_0314-July-20212002330028
Lusk_0314-July-20219002510035
Lusk_0416-July-202140010005
Lusk_0416-July-202140010005
Lusk_0512-August-20210008400084
Lusk_0512-August-20211008811091
LW18-August-20199500441710157
LW18-August-2019540022110087
LW48-August-20198100133310218
LW48-August-201910600293110167
MBR00110-July-20191000350018
MBR00110-July-20192600031030
Mule_0221-July-202100030003
Mule_0221-July-202100000000
Mule_0321-July-202113005100028
Mule_0321-July-20211100050016
Mule_0421-July-20214016931077
MushCreek14-August-20192004260050
MushCreek14-August-2019200601110083
Newc_0226-July-202140021007
Newc_0626-July-202147001662071
Newc_0726-July-202111002910041
Ngil_0110-August-20212007674089
Ngil_0110-August-20210003040034
Ngil_0210-August-20210004611048
Ngil_0210-August-20210013200032
Ngil_0310-August-20210001020012
Ngil_0310-August-2021000560011
Orin_0116-July-2021900400013
Orin_0116-July-20211600401021
Pine_026-July-2021500500010
Pine_026-July-2021300600110
ReedLandLLC121-August-201922002081150246
ReedLandLLC121-August-201914001587391219
ReedLandLLC221-August-2019300121070131
ReedLandLLC221-August-201918006205085
Rock_0329-June-20213000001013
Rock_0329-June-20214000001418
Rock_0429-June-202120000002
Rock_0429-June-202120000114
Short_0117-July-20214002820034
SodaGleason13-August-20191600832330125
SodaGleason13-August-20190007680084
Solar_012-July-202110001024
SpringerWHMA123-August-20192202237010260
SpringerWHMA123-August-20192900143190182
STB1016-July-201900000000
STB1016-July-201900000000
STB1115-July-201920010003
STB1115-July-201940010005
STB1116-July-20190001800018
STB1116-July-20191001900020
STB1218-July-2019120028160056
STB1819-July-20194102118300162
STB1819-July-201934021501400198
STB1917-July-2019590068000127
STB1917-July-2019480049480109
STB416-July-201915002811045
STB416-July-20195001208025
STB618-July-20198002924043
STB618-July-20196006415076
STBBLM19-July-201913001700030
STBBLM19-July-201925004550075
Syb_018-July-20211200310016
Syb_018-July-20211200500017
TableMountain120-August-20192002420028
TableMountain120-August-20194003270043
TableMountain220-August-20190003100031
TableMountain220-August-20193004210046
TB00117-June-20197001241024
TB00217-June-201900000000
TB00217-June-201900011002
TB00318-June-20195007450057
TB00318-June-201911003606080
TB00418-June-2019200015450080
TB00418-June-20193500156020112
TB00519-June-201980000151096
TB00519-June-201920000628750354
TB00619-June-20191200454500102
TB00619-June-2019620022150170251
TBSE317-July-201913002230038
TBSE317-July-201915002700042
Thun_0122-July-20213003170023
Thun_0122-July-20210009121022
TTBW00124-June-201911005150031
TTBW00124-June-20193500750151108
TTBW00224-June-20194005270743
TTBW00224-June-201931003501017111
TW1427-June-201911002245042
TW1427-June-20194001541024
TW74-July-20199001531028
TW827-June-20191001800019
TW827-June-20191002625034
TW94-July-201930000003
TWBLM128-June-201914000192320184
WH00125-June-2019800520015
WH00125-June-20196001703026
Wheat_018-July-202150010028
Wheat_018-July-20213501000035
Wheat_037-July-202150000005
Wheat_037-July-202150000005
Wheat_048-July-202110000001
Wheat_048-July-202120000002
Wri_0418-August-20210001310014
Wri_0518-August-202100010001
Wri_0518-August-202100000000
York13-August-201926001512520204
York13-August-20192811991210140
Table A3. The 32 potential climatic and landscape predictors evaluated to create the species distribution models for Monarchs and Regal Fritillary butterflies.
Table A3. The 32 potential climatic and landscape predictors evaluated to create the species distribution models for Monarchs and Regal Fritillary butterflies.
Climate PredictorsLandscape Predictors
Bioclim1% Bare ground
Bioclim2Contagion (aggregation of patches)
Bioclim3Distance to water
Bioclim4Elevation
Bioclim5Forest cover
Bioclim6Frost days
Bioclim7Growing degree days
Bioclim8Herbaceous cover
Bioclim9Surface organic matter cover
Bioclim10Sagebrush cover
Bioclim11Shrub cover
Bioclim12Topographic position index (11)
Bioclim13Bare ground cover
Bioclim14
Bioclim15
Bioclim16
Bioclim17
Bioclim18
Bioclim19

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