Fecundity without nectar is insufficient for persistence of a blue butterfly

Fecundity without nectar is insufficient for persistence of a blue butterfly | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fecundity without nectar is insufficient for persistence of a blue butterfly Kelsey C King, Cheryl Schultz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3967556/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Oecologia → Version 1 posted 4 You are reading this latest preprint version Abstract Organisms with complex life cycles undergo ecological transitions between life stages, often resulting in stage-specific resource use. The relative contribution of each stage-specific resource to vital rates influences population dynamics and subsequently whether habitats can support viable populations. In Lepidopterans, survival to reproduction requires sufficient resources for immature life stages, but the extent to which resources for adults are critical to population persistence is variable. We studied Boisduval's blue butterflies ( Icaricia icarioides ), in a greenhouse experiment, to quantify the effect of the adult diet, nectar, on vital rates. Butterflies fed ad libitum produced 3.4 times more eggs, on average, over their lifetime and lived 6 more days relative to those which only had access to water. We used these experimental data to parameterize a population model to test if vital rates with and without nectar result in viable population growth rates. We found that Boisduval’s blue butterfly populations will not persist without nectar resources (λ < 1). In this species, although host plant resources contributed to reproduction and allowed the butterfly to produce ~50 eggs without nectar, these resources consumed as larva did not compensate for adult malnutrition beyond a critical threshold. The relative abundance and quality of each stage-specific resource can therefore determine at what threshold other resource(s) are limiting the population. This study highlights the value of incorporating effects on vital rates across the life cycle to evaluate the effect on populations. Population Viability Nutrition Fecundity Lycaenidae Resources Figures Figure 1 Figure 2 Figure 3 Introduction Complex life cycles, in which organisms undergo a distinct ecological transition between life stages (Wilbur 1980; Kingsolver et al. 2011). Ecological transitions such as migratory birds and fish moving to new biomes, or insects and amphibians undergoing metamorphosis result in new suites of abiotic conditions and biotic interactions for the organisms separating resources in space and/or time (Wilbur 1980; Werner 1988). Despite decades of work to understand the relative importance of resources throughout complex life cycles (e.g. Mittelbach and Osenberg 1993; Taborsky 2006; Richardson and Smiseth 2019), questions remain about the extent to which stage-specific resources promote population viability (Sánchez‑Hernández et al. 2019; Turlure et al. 2019). The limitation on population growth rate from a reduction in a stage-specific resource depends on the resources available in other life stages and their influence on vital rates. For example, resources that contribute to adult survival may be compensated for by resources that contribute to fecundity such that declines in one and increases in another lead to no net change to the population (Bieber and Ruf 2005; van de Wolfshaar et al. 2011; Reichstein et al. 2015). Therefore, whether habitats can support viable populations of organisms with complex life cycles depends on the relative contribution of each resource to vital rates, rather than total abundance of each resource. Butterflies are an ideal model system to investigate the relative role of stage-specific resources in population dynamics. Insects have discrete life stages, associated with specific resources, that often do not overlap temporally and/or spatially. In butterflies, sufficient host plant tissues for larvae are essential for survival to later stages, but the relative importance of host tissues and the adult diet, nectar, for reproduction varies by species (O’Brien et al. 2004; Jervis et al. 2005). However, the threshold whereby nectar abundance becomes limiting on populations is generally unknown. For example, many field studies document positive correlations between nectar and butterfly population growth rate or size (e.g. Murphy et al. 1983; Schultz and Dlugosch 1999; Boggs and Inouye 2012). In contrast, other authors have found conditions where nectar may not be required for viable butterfly populations (Jervis and Boggs 2005; Matter et al. 2009). However, field studies are likely only able to capture when nectar is one of the strongest influences on vital rates, and not when factors such as environment or host plant resources are stronger influences on population growth rates. The relative importance of nectar, and likelihood that nectar limits populations, is dependent upon reproductive and resource allocation strategies that vary across species. Butterflies allocate the sugars, amino acids, and salts of which nectar is comprised of towards reproduction and/or adult longevity. Nearly all laboratory studies on how nectar affects Lepidopteran fecundity have reported positive correlations between more nectar and higher lifetime fecundity (ESM 1; >30 studies, e.g. Beach et al. 1985; Mason et al. 1989; Boggs and Ross 1993; Stevens et al. 2002; Niitepõld et al. 2014). Higher fecundity is attributed to higher daily egg production (e.g. Mason et al. 1989; Romeis and Wackers 2002; Woods et al. 2010; Liu et al. 2017) and/or prolonged adult longevity (e.g. Hill and Pierce 1989; Boggs and Ross 1993; Saastamoinen et al. 2010), varying by taxa and nutritional components of nectar. Sugar is the primary nutritional component of nectar, but other components of nectar such as amino acids, are positively associated with increased lifetime fecundity (Romeis and Wackers 2002; Grill et al. 2013), possibly through compensation for larval malnutrition (Mevi‑Schütz and Erhardt 2003). Nectar improving fecundity is most evident in species such as Mormon fritillary ( Argynnis [= Speyeria ] mormonia ) which would deposit few to no eggs without nectar (Boggs and Ross 1993). However, not all species have shown such reliance on nectar for reproduction; the common imperial blue butterfly ( Jalmenus evagorus ) can produce ~ 200 eggs in the absence of nectar (Hill and Pierce 1989). Differences such as those described between the Mormon fritillary and common imperial blue are tied to different reproduction strategies (Jervis et al. 2005), but it is unclear how these reproduction strategies relate to the relative importance of nectar for population persistence. Insects allocate resources from each stage to reproduction in a ratio described by ovigeny strategy. Pro-ovigenic insects use resources consumed as a larvae to develop eggs during the pupal stage and emerge as adults with a majority of eggs developed (Rosenheim et al. 2000; Jervis et al. 2005). Synovigenic insects rely on resources consumed as an adult to develop most of their eggs, and emerge from the pupae with few eggs developed, hence are reliant on nectar for population persistence. Highly mobile, host plant specialists that lay eggs singly are more likely to be synovigenic, and sedentary host plant generalists that lay eggs in clusters more likely to be pro-ovigenic (Jervis et al. 2005). For example, the life history traits and previous research with some fritillaries ( Argynnis spp.) suggest they are highly synovigenic, and some checkerspots ( Euphydryas spp.) are more pro-ovigenic (Boggs and Ross 1993; O’Brien et al. 2004; Jervis et al. 2005). Therefore, previous results indicate those fritillaries would be highly reliant on nectar resources to maintain viable populations, and those checkerspots minimally reliant, though this is dependent on other vital rates such as immature survival. Reliable determination of where a species is on the spectrum of ovigeny, is limited by a lack of empirical testing and the prevalence of intermediate strategies (Rosenheim et al. 2000; Davis et al. 2016). Further understanding of reproductive strategies could be found by evaluating intermediate strategies and linking resource allocation to population dynamics. We use Boisduval’s blue butterflies ( Icaricia icarioides ) as a model species to understand the importance of nectar nutrition for population persistence. Prior studies on species with similar life history traits, and predictions based on the life history traits of Boisduval’s blue we expect this species is intermediate in egg maturation strategy (e.g. sedentary, host plant specialist, laying eggs singly Jervis et al. 2005; Molleman et al. 2011; Swanson et al. 2016), and therefore nectar may or may not be required for persistence. Additionally, this species is unlikely to experience significant larval diet restriction (e.g., host plant is either present or not, larvae are sedentary). We used this species to determine how nectar nutrition influences vital rates, and if resulting vital rates without nectar can produce viable populations. We conducted a greenhouse experiment to estimate vital rates on diets of varying sugar and amino acid concentrations to parameterize a population model and quantify the effects of nectar on Boisduval’s blue butterfly population growth rate. We hypothesized that this species would not reach sustainable population growth rates without nectar (based on field studies on this species: e.g. Schultz and Dlugosch 1999; Thomas and Schultz 2016), and that amino acids would be important for fecundity but not longevity (based on studies in other butterflies: e.g. Mevi‑Schutz and Erhardt 2005; O’Brien et al. 2005; Cahenzli and Erhardt 2012). Methods We performed two experiments to assess how nectar influences the longevity and fecundity of adult female Boisduval’s blue butterflies ( Icaricia [= Plebejus ] icarioides ) and how diets affect the population growth rate. Sugar and amino acids are the two major nutritional components of nectar (Corbet 2003; McDade and Weeks 2004; Arnold 2016) and are present in varying ratios in flowers visited by Boisduval’s blues (unpublished data). In our first experiment, we modified available nectar sugar; in our second experiment, we varied sugar and amino acids. Study species and sites Boisduval’s blues butterflies are a complex of ~ 25 recognized subspecies (Pelham 2008; Warren et al. 2017). The complex includes the federally threatened Fender's blue ( I. i. fenderi ) and endangered Mission blue ( I. i. missionensis ), the extinct pheres blue ( I. i. pheres ), the Washington state candidate for endangered species listing Puget blue ( I. i. blackmorei )(WDFW 2022), and the Alberta/Saskatchewan imperiled pembina blue ( I. i. pembina ) (Alberta Environment and Parks 2017; Government of Saskatchewan 2021). Boisduval’s blues are non-migratory and univoltine, lay eggs singly, are nectar generalists, and overwinter as second instar larvae in the soil at the base of a host lupine ( Lupinus spp. )(Schultz and Dlugosch 1999; James and Nunnallee 2011; Schultz et al. 2012). The species occurs in meadows and prairies, where lupine and nectar are present during their flight period. We use Puget blues in 2019 and pembina blues in 2020 due to COVID-19 shutdowns during the Puget blue flight period in 2020. Puget blue butterflies are a candidate for listing as a Washington State endangered species that inhabits the Puget Sound area, Olympic Peninsula of Washington, and southern British Columbia, Canada. They use Lupinus albicaulis as a host at the collection site, Joint Base Lewis-McChord (46.92, -122.73; Rainier, Washington, U.S.A.), which is owned by the U.S. Department of Defense and managed jointly by the U.S. Army and U.S. Air Force. Pembina blues are common throughout the Cascade Mountains in Oregon and Washington and their range is the largest of all subspecies, present throughout most of the interior of Canada and the United States. We collected pembina blues within the Gifford-Pinchot National Forest (45.78, -122.17; Yacolt, Washington, U.S.A.) and Mt. Hood National Forest and Wilderness Area (45.34, -121.67; Government Camp, Oregon, U.S.A.). They typically use Lupinus latifolius (varying ssp.) as their host plant at these sites. Experimental Procedures In the first experiment, testing sugar levels, we worked with Puget blue in 2019, and for the second experiment, testing sugar and amino acids, we worked with pembina blue in 2020. All butterflies in the experiment were collected as newly eclosed adult females indicated by minimal wing wear from wild populations. Butterflies were chilled after capture in the field and then placed in individual housing before transport to the Washington State University Vancouver greenhouse. Butterflies were randomly assigned to a treatment and provided with nectar or water. Females that died within 48 hours of capture are excluded from the trial (n = 2) because death is more likely due to conditions the butterfly was experiencing before capture. We also excluded infertile females or females damaged during captivity (n = 2). Butterflies were housed in the Washington State University Vancouver greenhouse with a lupine stem (see ESM 2 for husbandry). Fresh sponges with the diet were placed daily, and eggs were removed from the lupine daily. To assess response to experimental treatments, we collected the following data: daily number of eggs laid, the butterfly’s weight every three days, longevity and unlaid eggs, and weight at death. Longevity was measured as the number of days from the date of collection to the date of death. We measured unlaid eggs by dissecting the abdomen of females within 48 hours of their death, following O’Brien et al. (2004). Eggs are classified either as developed eggs (> 0.5 mm in diameter) or partially absorbed eggs ( 2%) were likely eggs that never developed; these eggs were small (~ 0.1 mm), clear, and hard to detect. Nearly all eggs classified as partially absorbed eggs were green or cloudy white indicating development had occurred. If Boisduval’s blues are pro-ovigenic we would expect that a majority of eggs are developed at the time of eclosion (Hill and Pierce 1989; O’Brien et al. 2004; Miller 2005; Jervis et al. 2005), and undersized colored eggs at death are more likely to be those that are being reabsorbed than never developed. Sugar Nutrition Experiment Our experiment included three treatments to investigate how sugar affected butterfly longevity and fecundity. We made one batch of sucrose solution at 300 mg per mL, which is thought to be an ideal viscosity for proboscis feeding (following results of Kim et al. 2011), and froze it in aliquots and used it as needed; we also froze aliquots of water and used those as required. Common composite flowers in the habitats where Boisduval’s blue butterflies reside are commonly ~ 65 mg sucrose/flower (R. Bonoan personal communication). We used three nectar treatments: ad libitum , restricted, and water. The females in the ad libitum (A) treatment were fed twice daily, 2mL of the sucrose solution (1200 mg sucrose/day, 18x composite flower), in the morning and the afternoon. The females in the restricted nectar treatment (R) received 1 mL of nectar (300 mg sucrose/day, 4.6x composite flower) in the afternoon for 1 hour, with water available throughout the rest of the day. The water treatment (W) received only water on their sponges. Each treatment had 10 individually housed females, except one ad libitum female had a cystic mass in the abdomen that prevented dissection and was excluded (n = 9). Sugar and Amino Acid Experiment This experiment was conducted in 2020 with pembina blues. After our first experiment, we saw that the sugar levels appear to exceed the daily requirement for Puget blues and reduced the amount of sucrose provided. We used four treatments, water (W), lupine (L), flower (F), and flower plus lupine (F + L) with eight females per treatment. The water treatment (W) received fresh sponges daily with water. Nectar treatments were simulated after two nectar species, sickle-keeled lupine, lupine (L) and composite flowers, flower (F), each contained 65 mg of sucrose per day, but varied the quantity of amino acids. In our final treatment, flower plus lupine (F + L), butterflies had access to both nectar treatments, where each treatment (F or L) was given daily on separate sponges (total availability is 130 mg of sucrose and 23 mg of amino acid per day). Amino acid levels were based on field sampling of histidine, as an indicator of amino acids (R. Bonoan personal communication), with the lupine treatment having higher amino acids, and flower treatment is lower (17 and 6 mg/day respectively of histidine, and the quantity of other amino acids are dependent upon the blend used: see ESM 2 Table 1 ). The amino acid blend included all nine essential amino acids plus cystine and tyrosine. Each treatment, including water, had the addition of sea salt (2 mM of sodium), as an estimate for nectar salt (McDade and Weeks 2004). We prepared the two nectars and the water in one batch and froze the solution into aliquots to be used as needed. Each treatment had 8 individually housed females. Table 1 Mean and 95% bootstrap prediction intervals by treatment across all butterflies ( Icaricia icarioides ) in both the Sugar and the Sugar and Amino Acids experiments. Total fecundity is eggs laid from collection day to death, and longevity is the number of days lived from collection day to death and unlaid eggs are those inside an individual at time of death determined via dissection. Superscript letters refer to the significant differences between pairs via estimated marginal means with Tukey’s post-hoc correction. Experiment Treatment Toal Fecundity (95% CI) Longevity (95% CI) Unlaid Eggs (95% CI) Sugar (n = 10 females per treatment) Ad Libitum 228 (127, 342) a 11 (8, 14) a 46 (31, 62) a Restricted 280 (164, 396) b 12 (8, 16) b 46 (29, 62) b Water Only 68 (43, 95) ab 5 (4, 6) ab 89 (68, 111) ab Sugar and Amino Acids (n = 8 females per treatment) Flower & Lupine 175 (100, 274) a 20 (14, 25) a 26 (5, 51) a Flower 101 (53, 164) 13 (7, 18) b 43 (16, 80) Lupine 136 (42, 252) 13 (10, 17) c 19 (10, 28) b Water Only 34 (7, 70) a 4 (3, 4) abc 89 (70, 103) ab Analysis We performed the same types of analyses for the two experiments. We used R for statistical analyses (R Core Team 2024), and we used ANOVA from the car library created generalized linear and generalized linear mixed models, visually assessing if models met the appropriate model assumptions. We performed Wald χ 2 tests to detect evaluate treatment effects using ANOVA from the car package (Fox and Weisberg 2019). We performed pairwise comparisons of treatments with the Tukey’s contrast of the estimated marginal means using emmeans (Lenth 2024). We created 95% bootstrapped prediction intervals using glm.predict (Schlegel 2024) and bootpredictlme4 for mixed models (Duursma 2023). Data was manipulated and figures were made using tidyverse (Wickham et al. 2019) and ggpubr (Kassambara 2023). We tested the effects of diet on weight using a Gaussian generalized linear mixed model with the individual butterfly as a random effect on intercept (weight ~ treatment *day in trial) using the lme4 library (Bates et al. 2015). We modeled daily fecundity as an effect of treatment and day on each diet (daily eggs laid ~ treatment * day in trial) with individual butterfly as a random effect on intercept using a negative binomial generalized linear mixed model with a log link. We used quasi-Poisson generalized linear models to evaluate longevity (longevity ~ treatment), total fecundity (total fecundity ~ treatment), and unlaid eggs (unlaid eggs ~ treatment) after finding overdispersion in Poisson models. We evaluated all model distributions by comparing residuals and Q-Q plots, evaluating dispersion parameters and selected the best fitting distribution using AIC or qAIC from MASS (Venables and Ripley 2002). Population model We used experimental data to estimate parameters in a population model and to estimate relative population growth rates. We estimate two parameters from our data, butterfly longevity, L , (Eq. 1) and fecundity, F (Eq. 2). \(L \sim QPois\left({\mu }_{x}\right)\) (Eq. 1) The average longevity of females, L , is estimated assuming Poisson distribution adjusted for overdispersion (Quasi-Poisson) using the mean of the given treatment, x . \(F=\sum _{D=1}^{L}{f}_{D} \sim Negbinom\left({\mu }_{xD}\right)\) (Eq. 2) We use daily fecundity, f D , across the lifespan, L , summed to estimate the average total fecundity per female, F (Eq. 2). f D , is the fecundity on day, D , from a negative binomial distribution using the mean fecundity on that day, D , for the given treatment, x , for each day from 1 until the longevity, L , of the butterfly. The daily fecundity, f D , is summed to estimate total fecundity, F , and then we estimate population growth rate, λ (Eq. 3), where we assume a 50:50 sex ratio, and therefore the number of eggs per capita is ½ F . \({\lambda }=\frac{1}{2}\times F \times \text{s}\) (Eq. 3) Estimates of immature survival, s , set at 2.7%, based on the mean of estimates from prior studies of Boisduval’s blue survival from egg in summer to post-diapause survival the following spring (n = 46 site-years) (Schultz and Crone 1998; Warchola et al. 2017; Schultz and Ferguson 2020). Post-diapause to eclosion survival is not easily measured in situ (pupae are in the soil or cryptic), individual larvae are free to move out of survey areas, and some proportion of individuals observed are near pupation (variable by site-year); therefore, 2.7% survival, on average, represents the minimum survival of eggs to mid-way to pupation after the breaking of diapause. In the laboratory, Puget blue egg to post-diapause survival was 37% and post-diapause to adult survival was 20% across two rearing environments, resulting in 7.5% of eggs surviving to become adult butterflies, which is thought to be only a moderate improvement in survival over what occurs in nature (Schultz et al. 2009). Therefore, given the limits of the existing data, we assume that post-diapause to eclosion survival is 100%. Additionally, we assume that immature survival is not affected by nectar treatments, because nectar quality has shown no effect (Woods et al. 2010; Niitepõld 2019) or has improved immature survival (e.g. Jensen et al. 1974; Song et al. 2007; Marchioro and Foerster 2012), and therefore our model conservatively represents the effect of nectar on the population growth rates. The population growth rate was calculated 10,000 times per diet, with randomly selected longevity ( L ) and fecundity ( f D ) for each calculation to sum for total fecundity ( F ); therefore, variation in λ represents variation from individual daily fecundity and longevity within the experiment. When comparing resulting population growth rates to determine if treatments provide sufficient resources across treatments, we consider λ = 1 to be the minimum indication of stable population, and λ = 1.55 as the threshold for long-term stable populations given previous research on the species documenting high stochasticity and possibility of density dependence (Schultz and Hammond 2003). We evaluated the sensitivity of the population model (Eq. 4) to the survival parameter by solving the equation for fecundity, F i , that results from a given immature survival, s i , when λ = 1.55. \({F}_{i}=1.55 ÷({s}_{i}\times \frac{1}{2})\) (Eq. 4) Results Sugar Experiment Total fecundity (χ² = 16.472, p < 0.001) and daily fecundity (χ² = 8.75, p = 0.013) differed among treatments. Butterflies in sugar treatments, on average, had more than twice the fecundity of the water treatment butterflies (Table 1 ). Total fecundity differed between treatments with and without nectar but between nectar treatments (A - R z-ratio= -0.786 p = 0.7119, A - W z-ratio: 2.904 p = 0.01, R – W z-ratio = 3.487 p = 0.001). Additionally, there was a difference in daily fecundity of nectar treatment butterflies beginning on day five of the diets (A-R: z ratio = 1.08, p = 0.053). The difference between the daily fecundity of sugar treatments was not dependent on day of the trial, but the day of trial did significantly affect the daily eggs laid (χ² = 2.01, p = 0.37 and χ² = 66.5, p < 0.001 respectively; Fig. 1 A). The number of unlaid eggs (unlaid eggs at death) were affected by treatment (χ² = 13.88, p < 0.001) and were highest in the females from the water treatment (SI 3). The females in the two sugar treatments differed from the water treatment (R-W: z ratio = -2.99, p = 0.008; A-W: z ratio = -3.123, p 0.99). However, the number of partially absorbed eggs was less variable in ad libitum females compared to the females in the restricted treatment (Table 1 ). Female Puget blues fed sugar lived longer than those without sugar (Table 1 ; χ² = 18.04, p < 0.001). The longevity did not differ between the ad libitum and restricted butterflies (z ratio = -0.64, p = 0.80), but both differed from the water fed females (A-W: z ratio = 3.26, p = 0.003; R-W: z ratio = 3.81, p < 0.001 respectively). The sugar treatments affected the weight of the butterflies throughout the experiment (χ² = 8.94, p = 0.01). Overall, there was no difference in weight by treatment alone, but there was a difference in weight between the treatments over time where, differences in weight increased throughout the experiment (χ² = 22.94, p < 0.001). Females from the ad libitum treatment maintained higher weights than the restricted treatment females, with the difference between the butterflies occurring after the fifth day on the diets (estimated marginal means test on fifth day, z ratio = 1.08, p = 0.053). Only the females in the ad libitum treatment gained large amounts of visible fat, where their abdomens swelled with fat stores beyond wild butterflies (ESM 2 Fig. 3 ). Sugar and Amino Acid Experiment Total fecundity differed among treatments (χ² = 8.70, p = 0.03; Table 1 ), with a marginal difference only between the highest nectar, lupine and flower with the no nectar treatment (F + L-W: z ratio = 2.435 p = 0.07). Daily fecundity differed among treatments (Fig. 1 B; χ² = 9.04, p = 0.029) and by day in the trial (χ² = 165.2, < 0.001) with an interaction between treatment and day (χ² =13.13, p = 0.004). Beginning on day one, flower plus lupine females differed from the water treatment (z ratio = 5.22, p = 0.02) and throughout the lifespan of the water females, but no other differences were observed. The number of unlaid eggs differed among treatments (χ² = 17.13, p < 0.001, n = 8 per treatment; Table 1 ), with the higher amino acid treatments being different from the water, and no other differences between treatments (F + L-W: z ratio = -2.92, p = 0.019; L-W: z ratio = -3.21, p = 0.007; F-W: z ratio = -2.01, p = 0.18; F + L-F: z ratio = -1.13, p = 0.67; F + L-L: z ratio = 0.51, p = 0.96; F-L: z ratio = 1.60, p = 0.38). Longevity of pembina blues differed among treatments (Table 1 , χ² = 35.42, p < 0.001), where females from nectar treatments differed from females fed water (F + L-W: z ratio = 5.05, p < 0.001; F-W: z ratio = 3.60, p < 0.001, L-W: z ratio = 3.72, p = 0.001). We did not observe main treatment effects on the weight of butterflies (χ² = 5.47, p = 0.140), though weight did vary by treatment over time and by day in the trial(χ² = 26.70, p < 0.001; χ² = 122.5, p < 0.001 respectively). Population Model Using parameters from the sugar experiment, both restricted butterflies (mean λ = 3.07: 95% CI: 1.78, 4.36) and ad libitum butterflies (mean λ = 2.40: 1.31, 3.48) had an average population growth rate above 1.55 and water had λ 3.0% for λ > 1 and > 4.6% survival for λ > 1.55. Using parameters from the sugar and amino acid experiment, only the flower plus lupine butterflies had a population growth rate above λ = 1.55 (Fig. 2 B). The water fed butterfly’s population growth rate was the lowest (mean λ = 0.16: 0, 0.33). The lupine treatment and the flower treatment fed butterflies had similar population growth rates (mean λ = 1.14: 0.48, 1.81; mean λ = 1.29: 0.70, 1.87), but the flower plus lupine butterflies outperformed these treatments (mean λ = 2.17: 1.58, 2.77). Given the average fecundity of pembina blue females on the water diet, 33 eggs per female, egg to pupae survival would need to be > 9.1% for λ > 1.55 and > 6% for λ > 1. We calculated the fecundity required to reach the target population growth rate λ = 1.55 (Fig. 3 ), using the range of published immature survival values using our population model. When immature survival is > 5%, small changes to fecundity have small impacts on the population growth rate, whereas 74 eggs/female for λ > 1 or > 114 eggs/female for λ > 1.55. Discussion The persistence of Boisduval’s blue butterflies is contingent upon nectar availability. Boisduval’s blue butterflies laid eggs without nectar (~ 50 eggs), but the resulting fecundity is inadequate to achieve an increasing population growth rate (resulting in λ 1). Host resources are required for immature survival, and nectar is required for sufficient fecundity to sustain the population, which is the mechanism by which both host plant and nectar abundance can limit Boisduval’s blue population size (e.g. Schultz and Dlugosch 1999). Limitation by both stage-specific resources can be observed for Boisduval’s blue because some threshold exists whereby depletion of one resource cannot be compensated for by the other. Studies on non-insect taxa have similarly found that declining reproductive period resources can be compensated for by resources that support adult survival, until a threshold is reached; beyond which population growth rates are in decline (Bieber and Ruf 2005; Reynolds‑Hogland et al. 2007; Nater et al. 2021). Our findings illustrate the mechanisms by which nectar is crucial to maintain Boisduval’s blue butterfly populations and allow us to infer when nectar may be more or less crucial. Integrating the results of experiments that capture vital rates into population models allows us to contextualize the results and infer mechanisms of population dynamics. The availability of nectar improved the fecundity of Boisduval’s blues, which we found exhibits an intermediate ovigeny strategy. The butterflies laid ~ 50 eggs without nectar (20% of the highest observed fecundity), which are eggs largely developed with host plant resources, much fewer than the common imperial blue, which is the closest relative for which data is available (host resources developed ~50% of eggs; Hill and Pierce 1989). Daily fecundity of Boisduval’s blues improved with the high amino acid treatment compared to water but did not differ between water and other nectar treatments, an indication of a critical threshold where sufficient levels of amino acids may support egg production. Nectar availability can improve daily fecundity through increased egg production or decreased egg resorption. Increased daily egg production with nectar is synovigeny (Rosenheim et al. 2000), but daily fecundity will also increase as egg resorption ceases as nutritional needs are met (Stjernholm et al. 2005; Moore and Attisano 2011). Our findings indicate that Boisduval’s blue butterflies are likely using egg resorption to meet nutritional requirements, more so when no nectar is available. However, we cannot determine if direct allocation of nectar to egg production occurs without further study. Therefore, these findings suggest that Boisduval’s blues are synovigenic to intermediate in ovigeny strategy (host resources developed ~ 20% of eggs), which could be dependent upon host quality and or quantity. Further research into how variation in host resources alters the allocation of those resources to reproduction could provide further insight into these intermediate ovigeny strategies, and when nectar limits populations. The nutritional quality of nectar, specifically the presence of amino acids, can be an important factor in influencing butterfly reproduction but not the population growth rate of Boisduval’s blues. We found that butterflies fed more amino acids (e.g., lupine treatment), had less unlaid eggs and higher lifetime fecundity compared to the butterflies fed less amino acid, (e.g., the flower treatment). However, the higher fecundity of females fed more amino acids, and not additional sugar, did not translate to an improvement in the population growth rate compared to females fed less amino acids. Amino acids may compensate for previous nutrition deficits (O’Brien et al. 2005; Cahenzli and Erhardt 2013), especially essential amino acids, which can be limiting to reproduction (Romeis and Wackers 2002), and without sufficient amino acids egg resorption may increase. We did not observe any differences in longevity due to amino acids, similar to previous studies (e.g. Mevi‑Schütz and Erhardt 2003; Molleman et al. 2008; Cahenzli and Erhardt 2012; Grill et al. 2013). Therefore, cumulatively amino acids had no effect on population growth rate in this study, but amino acids in nectar could be crucial when prior resources were insufficient, most importantly, if amino acids can mitigate declines in fecundity associated with poor host quality. The longevity of Boisduval’s blue butterfly doubled when provided with nectar. This is similar to findings in common imperial blues (Hill and Pierce 1989), and aligns with the survival-reproduction tradeoff observed in butterfly resource allocation (O’Brien et al. 2004; Jervis et al. 2005; Niitepõld et al. 2014). According to predictions by Jervis et al. (2005), species that live much longer with nectar than without, typically invested host plant resources heavily into reproduction, as seen in variable checkerspot, large white butterfly ( Pieris brassicae ), squinting bush brown ( Bicyclus anynana ), and wheat armyworm ( Mythimna sequax ) (Murphy et al. 1983; Romeis and Wackers 2002; Saastamoinen et al. 2010; Marchioro and Foerster 2012). Conversely species that did not live longer with nectar than without nectar are likely investing host plant resources more into adult longevity (soma), such as observed in the painted lady butterfly ( Vanessa cardui ), Mormon fritillary, orange sulfur ( Colias eurytheme ), and Glanville fritillary ( Melitaea cinxia ) (Woods et al. 2010; Niitepõld et al. 2014; Niitepõld 2019). Species which lay eggs without nectar are less reliant on nectar for maintaining populations than those that cannot. However, for Boisduval’s blue butterflies it is the improvement in adult longevity, rather than changes in daily fecundity, that results in higher lifetime fecundity and is crucial in reaching stable population growth rates. Translating the effects of resources on vital rates to the population provide clarity on how nectar can limit populations, therefore, it will be useful to evaluate other intermediate and pro-ovigenic species for understanding how nectar influences butterfly persistence generally. Our population models allowed us to capture the critical threshold whereby nectar can become limiting to Boisduval’s blue populations. When immature survival is > 7.5%, the fecundity of butterflies without nectar (~ 50 eggs) can produce viable population growth rates. However, as immature survival falls below 7.5% fecundity must increase rapidly to meet viable population growth rates, and therefore it is increasingly likely that nectar can limit the population through insufficient reproduction. The mean immature survival (Schultz and Crone 1998; 2.6%; Warchola et al. 2017; Schultz and Ferguson 2020) for Boisduval’s blue falls below the threshold, and therefore the increased total fecundity of butterflies fed nectar (~ 130 mg of sucrose per day) is required to meet viable population growth rates. In contrast, common imperial blue butterflies had larval survival of ~ 14% (obligate ant tended sp. Pierce et al. 1987), and laid ~ 200 eggs without nectar (Hill and Pierce 1989), therefore in our population model, this species could persist without nectar, depending on the viable population growth rate (Morris and Doak 1984; Gerber and Demaster 1999; Schultz and Hammond 2003). Further research linking stage-specific resources to population effects across species with different reproductive strategies will inform the conditions under which species with a given life history strategies are likely to persist. The crucial role of nectar described here illustrates the interplay of stage-specific resources on population dynamics of organisms with complex life cycles. For example, if host plant quality or abundance were to decline, our results indicate that more nectar would be required to sustain populations. In addition to numerous anthropogenic threats to insect habitats and host plant abundance and quality is likely to decline in nutritional quality as carbon dioxide concentrations increase from greenhouse gas emissions (Ebi and Ziska 2018; Decker et al. 2018; Johnson et al. 2020). Future research could evaluate if declining host quality can be compensated for by improvements in nectar abundance and/or quality as suggested is possible by this research. Unfortunately for nectivorous organisms broadly, declines in nectar resources have been documented across large scales and are suggested to be one factor driving declines in many pollinator species across the globe (Biesmeijer et al. 2006; Wallisdevries et al. 2012; Baude et al. 2016; Wepprich et al. 2019; Crossley et al. 2021). The interplay of stage-specific resources provides an opportunity to compensate for broad declines in other resources, but more research is needed to understand the margin where compensation is possible. Declarations Acknowledgments We thank the U.S. Department of Defense and Washington State University for supporting our research. We appreciate Joint Base Lewis-McChord, Gifford-Pinchot National Forest, and Mount Hood National Forest for allowing us to conduct research and for maintaining habitat. This work would not have been completed with the help of many collaborators, including Elizabeth E. Crone, Cassandra F. Doll, Samantha Bussan, Christopher Jason, Chelsea Thomas, and Alyxandra James. We also thank Sun Gro Horticulture and Portland Hydroponics for soil donations to complete this research. Funding This research was funded by the Department of Defense Strategic Environmental Research and Development Program (award # RC-2700) and Washington State University. Conflict of interest We declare no conflicts of interest. Ethics approval All applicable institutional and/or national guidelines for the care and use of animals were followed. Activities are exempt from Institutional Animal Care and Use Committee review, as invertebrates are not considered animals under local legislation. Research was conducted under Washington State Department of Fish and Wildlife scientific collection permits Schultz 19-121 and Schultz 20-175 and additional permits to access collection sites. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets and code generated during the current study are available in the GitHub repository, https://github.com/prairie-rex/FecWOutNectar. Code availability See previous statement. Authors’ contributions KCK and CBS conceived, designed, and executed the experiments. KCK analyzed the data. KCK and CBS wrote the manuscript. References Alberta Environment and Parks (2017) Element Occurence Data. In: Alberta Conservation Information Management System. https://albertaparks.ca/albertaparksca/management-land-use/alberta-conservation-information-management-system-acims/download-data/. Accessed 12 Nov 2021 Arnold P (2016) Variation in nectar composition: the influence of nectar quality on monarch success. Master thesis Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01 Baude M, Kunin WE, Boatman ND, et al (2016) Historical nectar assessment reveals the fall and rise of floral resources in Britain. Nature 530:85–88. https://doi.org/10.1038/nature16532 Bieber C, Ruf T (2005) Population dynamics in wild boar Sus scrofa : ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. J Appl Ecol 42:1203–1213. https://doi.org/10.1111/j.1365-2664.2005.01094.x Biesmeijer JC, Roberts SPM, Reemer M, et al (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:351–354. https://doi.org/10.1126/science.1127863 Boggs CL, Inouye DW (2012) A single climate driver has direct and indirect effects on insect population dynamics. Ecol Lett 15:502–508. https://doi.org/10.1111/j.1461-0248.2012.01766.x Boggs CL, Ross CL (1993) The effect of adult food limitation on life history traits in Speyeria mormonia (Lepidoptera: Nymphalidae). Ecology 74:433–441. https://doi.org/10.2307/1939305 Cahenzli F, Erhardt A (2012) Enhancing offspring quality or quantity? Different ways for using nectar amino acids in female butterflies. Oecologia 169:1005–1014. https://doi.org/10.1007/s00442-012-2254-7 Cahenzli F, Erhardt A (2013) Nectar amino acids enhance reproduction in male butterflies. Oecologia 171:197–205. https://doi.org/10.1007/s00442-012-2395-8 Corbet SA (2003) Nectar sugar content: estimating standing crop and secretion rate in the field. Apidologie 34:1–10. https://doi.org/10.1051/apido:2002049 Crossley MS, Smith OM, Berry LL, et al (2021) Recent climate change is creating hotspots of butterfly increase and decline across North America. Glob Chang Biol 27:2702–2714. https://doi.org/10.1111/gcb.15582 Davis RB, Javoiš J, Kaasik A, et al (2016) An ordination of life histories using morphological proxies: capital vs. income breeding in insects. Ecology 97:2112–2124. https://doi.org/10.1002/ecy.1435 Decker LE, de Roode JC, Hunter MD (2018) Elevated atmospheric concentrations of carbon dioxide reduce monarch tolerance and increase parasite virulence by altering the medicinal properties of milkweeds. Ecol Lett 21:1353–1363. https://doi.org/10.1111/ele.13101 Duursma R (2023) bootpredictlme4: Predict Method For lme4 With Bootstrap. Version 0.1. rdrr. URL https://rdrr.io/github/RemkoDuursma/bootpredictlme4/ Ebi KL, Ziska LH (2018) Increases in atmospheric carbon dioxide: Anticipated negative effects on food quality. PLoS Med 15:e1002600. https://doi.org/10.1371/journal.pmed.1002600 Fox J, Weisberg S (2019) An R Companion to Applied Regression. Version Third edition. Sage, Thousand Oaks, CA Gerber LR, Demaster DP (1999) A quantitative approach to endangered species act classification of long‐lived vertebrates: application to the north pacif ic humpback whale. Conserv Biol 13:1203–1214. https://doi.org/10.1046/j.1523-1739.1999.98277.x Government of Saskatchewan (2021) Tracked Taxa List: Invertebrates. In: Saskatchewan Conservation Data Centre. http://biodiversity.sk.ca/SppList.htm. Accessed 14 Nov 2021 Grill A, Cerny A, Fiedler K (2013) Hot summers, long life: egg laying strategies of Maniola butterflies are affected by geographic provenance rather than adult diet. CTOZ 82:27–36. https://doi.org/10.1163/18759866-08201002 Hill CJ, Pierce NE (1989) The effect of adult diet on the biology of butterflies : 1. The common imperial blue, Jalmenus evagoras. Oecologia 81:249–257. https://doi.org/10.1007/BF00379812 James DG, Nunnallee D (2011) Life histories of Cascadia butterflies. Oregon State University Press, Corvallis, Oregon Jensen RL, Newsom LD, Gibbens J (1974) The soybean looper: effects of adult nutrition on oviposition, mating frequency, and longevity. J Econ Entomol 67:467–470. https://doi.org/10.1093/jee/67.4.467 Jervis MA, Boggs CL, Ferns PN (2005) Egg maturation strategy and its associated trade-offs: a synthesis focusing on Lepidoptera. Ecol Entomol 30:359–375. https://doi.org/10.1111/j.0307-6946.2005.00712.x Jervis MA, Boggs CL (2005) Linking nectar amino acids to fitness in female butterflies. Trends Ecol Evol 20:585–587. https://doi.org/10.1016/j.tree.2005.08.015 Johnson SN, Waterman JM, Hall CR (2020) Increased insect herbivore performance under elevated CO2 is associated with lower plant defence signalling and minimal declines in nutritional quality. Sci Rep 10:14553. https://doi.org/10.1038/s41598-020-70823-3 Kassambara A (2023) ggpubr: “ggplot2” Based Publication Ready Plots. Version 0.6.0. R package Kim W, Gilet T, Bush JWM (2011) Optimal concentrations in nectar feeding. Proc Natl Acad Sci USA 108:16618–16621. https://doi.org/10.1073/pnas.1108642108 Kingsolver JG, Woods HA, Buckley LB, et al (2011) Complex life cycles and the responses of insects to climate change. Integr Comp Biol 51:719–732. https://doi.org/10.1093/icb/icr015 Lenth RV (2024) emmeans: Estimated Marginal Means, aka Least-Squares Means. Version 1.10.0. R package Liu K, Zhu P, Lü Z, et al (2017) Effects of sesame nectar on longevity and fecundity of seven Lepidoptera and survival of four parasitoid species commonly found in agricultural ecosystems. J Integr Agric 16:2534–2546. https://doi.org/10.1016/S2095-3119(17)61665-4 Marchioro CA, Foerster LA (2012) Importance of carbohydrate sources to the reproductive output of the wheat armyworm Pseudaletia sequax . Agric For Entomol 14:29–35. https://doi.org/10.1111/j.1461-9563.2011.00548.x Mason LJ, Johnson SJ, Woodring JP (1989) Influence of carbohydrate deprivation and tethered flight on stored lipid, fecundity, and survivorship of the soybean looper (Lepidoptera: Noctuidae). Environ Entomol 18:1090–1094. https://doi.org/10.1093/ee/18.6.1090 Matter SF, Ezzeddine M, Duermit E, et al (2009) Interactions between habitat quality and connectivity affect immigration but not abundance or population growth of the butterfly, Parnassius smintheus . Oikos 118:1461–1470. https://doi.org/10.1111/j.1600-0706.2009.17438.x McDade LA, Weeks JA (2004) Nectar in Hummingbird-pollinated Neotropical Plants II: Interactions with Flower Visitors. Biotropica 36:216–230. https://doi.org/10.1111/j.1744-7429.2004.tb00313.x Mevi-Schutz J, Erhardt A (2005) Amino acids in nectar enhance butterfly fecundity: a long-awaited link. Am Nat 165:411–419. https://doi.org/10.1086/429150 Mevi-Schütz J, Erhardt A (2003) Effects of nectar amino acids on fecundity of the wall brown butterfly ( Lasiommata megera L.). Basic and Applied Ecology 4:413–421. https://doi.org/10.1078/1439-1791-00183 Miller WE (2005) Extrinsic effects on fecundity-maternal weight relations in capital breeding lepidoptera. Journal of the Lepidopterists’ Society 59:143–160 Mittelbach GG, Osenberg CW (1993) Stage-structured interactions in bluegill: consequences of adult resource variation. Ecology 74:2381–2394. https://doi.org/10.2307/1939589 Molleman F, Ding J, Wang J-L, et al (2008) Amino acid sources in the adult diet do not affect life span and fecundity in the fruit-feeding butterfly Bicyclus anynana . Ecol Entomol 33:429–438. https://doi.org/10.1111/j.1365-2311.2008.00986.x Molleman F, Javoiš J, Esperk T, et al (2011) Sexual differences in weight loss upon eclosion are related to life history strategy in Lepidoptera. J Insect Physiol 57:712–722. https://doi.org/10.1016/j.jinsphys.2011.02.009 Moore PJ, Attisano A (2011) Oosorption in response to poor food: complexity in the trade-off between reproduction and survival. Ecol Evol 1:37–45. https://doi.org/10.1002/ece3.4 Morris WF, Doak DF (1984) Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis, 1st edn. CRC Press, Sunderland, Mass Murphy DD, Launer AE, Ehrlich PR (1983) The role of adult feeding in egg production and population dynamics of the checkerspot butterfly Euphydryas editha . Oecologia 56:257–263. https://doi.org/10.1007/BF00379699 Nater CR, Eide NE, Pedersen ÅØ, et al (2021) Contributions from terrestrial and marine resources stabilize predator populations in a rapidly changing climate. Ecosphere 12:. https://doi.org/10.1002/ecs2.3546 Niitepõld K, Perez A, Boggs CL (2014) Aging, life span, and energetics under adult dietary restriction in Lepidoptera. Physiol Biochem Zool 87:684–694. https://doi.org/10.1086/677570 Niitepõld K (2019) Effects of flight and food stress on energetics, reproduction, and lifespan in the butterfly Melitaea cinxia . Oecologia 191:271–283. https://doi.org/10.1007/s00442-019-04489-8 O’Brien DM, Boggs CL, Fogel ML (2005) The amino acids used in reproduction by butterflies: a comparative study of dietary sources using compound-specific stable isotope analysis. Physiol Biochem Zool 78:819–827. https://doi.org/10.1086/431191 O’Brien DM, Boggs CL, Fogel ML (2004) Making eggs from nectar: the role of life history and dietary carbon turnover in butterfly reproductive resource allocation. Oikos 105:279–291. https://doi.org/10.1111/j.0030-1299.2004.13012.x Pelham JF (2008) Catalogue of the butterflies of the United States and Canada. Journal of Research on the Lepidoptera Pierce NE, Kitching RL, Buckley RC, et al (1987) The costs and benefits of cooperation between the Australian Lycaenid butterfly, Jalmenus evagoras , and its attendant ants. Behav Ecol Sociobiol 21:237–248. https://doi.org/10.1007/BF00292505 Reichstein B, Persson L, De Roos AM (2015) Ontogenetic asymmetry modulates population biomass production and response to harvest. Nat Commun 6:6441. https://doi.org/10.1038/ncomms7441 Reynolds-Hogland MJ, Pacifici LB, Mitchell MS (2007) Linking resources with demography to understand resource limitation for bears. J Appl Ecol 44:1166–1175. https://doi.org/10.1111/j.1365-2664.2007.01317.x Richardson J, Smiseth PT (2019) Effects of variation in resource acquisition during different stages of the life cycle on life-history traits and trade-offs in a burying beetle. J Evol Biol 32:19–30. https://doi.org/10.1111/jeb.13388 Romeis J, Wackers FL (2002) Nutritional suitability of individual carbohydrates and amino acids for adult Pieris brassicae . Physiol Entomol 27:148–156. https://doi.org/10.1046/j.1365-3032.2002.00281.x Rosenheim JA, Heimpel GE, Mangel M (2000) Egg maturation, egg resorption and the costliness of transient egg limitation in insects. Proc Biol Sci 267:1565–1573. https://doi.org/10.1098/rspb.2000.1179 R Core Team (2024) R: A language and environment for statistical computing. Version 4.3.2. R Foundation for Statistical Computing, Vienna, Austria Saastamoinen M, van der Sterren D, Vastenhout N, et al (2010) Predictive adaptive responses: Condition-dependent impact of adult nutrition and flight in the tropical butterfly Bicyclus anynana . Am Nat 176:686–698. https://doi.org/10.1086/657038 Sánchez-Hernández J, Nunn AD, Adams CE, Amundsen P-A (2019) Causes and consequences of ontogenetic dietary shifts: a global synthesis using fish models. Biol Rev Camb Philos Soc 94:539–554. https://doi.org/10.1111/brv.12468 Schlegel B (2024) glm.predict: Predicted Values and Discrete Changes for Regression Models. Version 4.3-0. R package, Institut of Political Science, Zurich, Switzerland Schultz C, Thomas R, Cummings K (2012) Assessing nectar resources for Fender’s blue butterfly within upland prairies in Willamette Valley, Oregon. Washington State University, Vancouver, WA Schultz CB, Crone EE (1998) Burning prairie to restore butterfly habitat: a modeling approach to management tradeoffs for the Fender’s blue. Restoration Ecology 6:244–252 Schultz CB, Dlugosch KM (1999) Nectar and hostplant scarcity limit populations of an endangered Oregon butterfly. Oecologia 119:231–238. https://doi.org/10.1007/s004420050781 Schultz CB, Dzurisin JD, Russell C (2009) Captive rearing of Puget blue butterflies ( Icaricia icarioides blackmorei ) and implications for conservation. J Insect Conserv 13:309–315. https://doi.org/10.1007/s10841-008-9174-1 Schultz CB, Ferguson JA (2020) Demographic costs and benefits of herbicide‐based restoration to enhance habitat for an endangered butterfly and a threatened plant. Restor Ecol 28:564–572. https://doi.org/10.1111/rec.13102 Schultz CB, Hammond PC (2003) Using population viability analysis to develop recovery criteria for endangered insects: case study of the Fender’s blue butterfly. Conserv Biol 17:1372–1385. https://doi.org/10.1046/j.1523-1739.2003.02141.x Song Z-M, Li Z, Li D-M, et al (2007) Adult feeding increases fecundity in female Helicoverpa armigera (Lepidoptera: Noctuidae). Eur J Entomol 104:721–724. https://doi.org/10.14411/eje.2007.091 Stjernholm F, Karlsson B, Boggs CL (2005) Age-related changes in thoracic mass: possible reallocation of resources to reproduction in butterflies. Biological Journal of the Linnean Society 86:363–380. https://doi.org/10.1111/j.1095-8312.2005.00542.x Swanson EM, Espeset A, Mikati I, et al (2016) Nutrition shapes life-history evolution across species. Proc Biol Sci 283:. https://doi.org/10.1098/rspb.2015.2764 Taborsky B (2006) The influence of juvenile and adult environments on life-history trajectories. Proc Biol Sci 273:741–750. https://doi.org/10.1098/rspb.2005.3347 Thomas RC, Schultz CB (2016) Resource selection in an endangered butterfly: Females select native nectar species. J Wildl Manage 80:171–180. https://doi.org/10.1002/jwmg.987 Turlure C, Schtickzelle N, Dubois Q, et al (2019) Suitability and transferability of the resource-based habitat concept: a test with an assemblage of butterflies. Front Ecol Evol 7:. https://doi.org/10.3389/fevo.2019.00127 van de Wolfshaar KE, HilleRisLambers R, Gårdmark A (2011) Effect of habitat productivity and exploitation on populations with complex life cycles. Mar Ecol Prog Ser 438:175–184. https://doi.org/10.3354/meps09304 Venables WN, Ripley BD (2002) Modern Applied Statistics with S (Statistics and Computing), 4th edn. Springer, New York Wallisdevries MF, Van Swaay CAM, Plate CL (2012) Changes in nectar supply: A possible cause of widespread butterfly decline. Curr Zool 58:384–391. https://doi.org/10.1093/czoolo/58.3.384 Warchola N, Crone EE, Schultz CB (2017) Balancing ecological costs and benefits of fire for population viability of disturbance-dependent butterflies. J Appl Ecol 55:800–809. https://doi.org/10.1111/1365-2664.12983 Warren AD, Davis KJ, Strangeland EM, et al (2017) Illustrated lists of American butterflies. In: Butterflies of America. http://butterfliesofamerica.com/. Accessed 13 Jul 2021 WDFW (2022) State Listed Species. Washington Department of Fish and Wildlife, Olympia, WA Wepprich T, Adrion JR, Ries L, et al (2019) Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14:e0216270. https://doi.org/10.1371/journal.pone.0216270 Werner EE (1988) Size, scaling, and the evolution of complex life cycles. In: Ebenman B, Persson L (eds) Size-Structured Populations. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 60–81 Wickham H, Averick M, Bryan J, et al (2019) Welcome to the tidyverse. JOSS 4:1686. https://doi.org/10.21105/joss.01686 Wilbur HM (1980) Complex Life Cycles. Annu Rev Ecol Syst 11:67–93. https://doi.org/10.1146/annurev.es.11.110180.000435 Woods WA, Wood CAL, Ebersole J, Stevenson RD (2010) Metabolic rate variation over adult lifetime in the butterfly Vanessa cardui (Nymphalidae: Nymphalinae): aging, feeding, and repeatability. Physiol Biochem Zool 83:858–868. https://doi.org/10.1086/656216 Supplementary Files ESM1.xlsx ESM2.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Oecologia → Version 1 posted Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 25 Feb, 2024 Editor assigned by journal 20 Feb, 2024 First submitted to journal 17 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3967556","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274847282,"identity":"f573d995-6052-48a0-9244-58b6a8fd7621","order_by":0,"name":"Kelsey C King","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6340-3649","institution":"Washington State University - Vancouver","correspondingAuthor":true,"prefix":"","firstName":"Kelsey","middleName":"C","lastName":"King","suffix":""},{"id":274847283,"identity":"e5f2b0a4-7c9a-4b0c-b737-9c609efe1898","order_by":1,"name":"Cheryl Schultz","email":"","orcid":"","institution":"Washington State University Vancouver College of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Cheryl","middleName":"","lastName":"Schultz","suffix":""}],"badges":[],"createdAt":"2024-02-18 17:13:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3967556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3967556/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00442-024-05609-9","type":"published","date":"2024-09-28T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51748016,"identity":"60daf1dd-90e7-4da3-a42c-c53806e151b2","added_by":"auto","created_at":"2024-02-28 11:27:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178081,"visible":true,"origin":"","legend":"\u003cp\u003eMean daily fecundity of butterflies by treatment from day of capture (day 0) to death with 95% prediction interval as estimated by bootstrapped generalized linear mixed models. (a) Sugar nutrition experiment (b) Sugar and amino acid experiment.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/da6ec617a57a6711486d0484.jpeg"},{"id":51748013,"identity":"76547d70-6f71-4ae3-a7f7-b2660720addd","added_by":"auto","created_at":"2024-02-28 11:27:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80866,"visible":true,"origin":"","legend":"\u003cp\u003eMean population growth rate from experimental result parameters by treatment and 95% confidence interval. Variation incorporates the variation in daily fecundity and longevity of female Boisduval’s blue butterflies oberserved within each treatment. Dotted line reflects the estimated population growth rate needed for viable long-term populations for Boisduval’s blue (λ = 1.55; ). (a) Sugar nutrition experiment (n=10). (b) Sugar and amino acid experiment (n=8). Solid line shows a stable growth rate producing the same number of individuals as previous year (λ = 1).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/9d5ea20c9f275a8dc4844538.jpeg"},{"id":51748014,"identity":"74364f71-ae23-4ef0-aa33-2f8526fb9982","added_by":"auto","created_at":"2024-02-28 11:27:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116515,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation model sensitivity to immature survival. We calculate for each immature survival what average fecundity is required to achieve the population growth rate needed for persistence (λ = 1.55). The dashed line indicates the immature survival (2.7%) value used in the population model.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/5701f841b91855c67307b8ab.jpeg"},{"id":65628316,"identity":"10e7c783-5598-4192-bb9e-6cfc5076b4bc","added_by":"auto","created_at":"2024-09-30 16:18:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/235b7347-a08b-4084-9552-12f4bf08c63a.pdf"},{"id":51748015,"identity":"f793aa1f-fdf3-4cd9-8077-3545d4e3aa3b","added_by":"auto","created_at":"2024-02-28 11:27:25","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":26216,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/1a507d41ecae63f7c89117bf.xlsx"},{"id":51748017,"identity":"51bac685-701a-456e-a1e3-3647527f20bf","added_by":"auto","created_at":"2024-02-28 11:27:25","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":166728,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967556/v1/9e0f9082a636dcfccfab9019.pdf"}],"financialInterests":"","formattedTitle":"Fecundity without nectar is insufficient for persistence of a blue butterfly","fulltext":[{"header":"Introduction","content":"\u003cp\u003eComplex life cycles, in which organisms undergo a distinct ecological transition between life stages (Wilbur 1980; Kingsolver et al. 2011). Ecological transitions such as migratory birds and fish moving to new biomes, or insects and amphibians undergoing metamorphosis result in new suites of abiotic conditions and biotic interactions for the organisms separating resources in space and/or time (Wilbur 1980; Werner 1988). Despite decades of work to understand the relative importance of resources throughout complex life cycles (e.g. Mittelbach and Osenberg 1993; Taborsky 2006; Richardson and Smiseth 2019), questions remain about the extent to which stage-specific resources promote population viability (Sánchez‑Hernández et al. 2019; Turlure et al. 2019). The limitation on population growth rate from a reduction in a stage-specific resource depends on the resources available in other life stages and their influence on vital rates. For example, resources that contribute to adult survival may be compensated for by resources that contribute to fecundity such that declines in one and increases in another lead to no net change to the population (Bieber and Ruf 2005; van de Wolfshaar et al. 2011; Reichstein et al. 2015). Therefore, whether habitats can support viable populations of organisms with complex life cycles depends on the relative contribution of each resource to vital rates, rather than total abundance of each resource.\u003c/p\u003e \u003cp\u003eButterflies are an ideal model system to investigate the relative role of stage-specific resources in population dynamics. Insects have discrete life stages, associated with specific resources, that often do not overlap temporally and/or spatially. In butterflies, sufficient host plant tissues for larvae are essential for survival to later stages, but the relative importance of host tissues and the adult diet, nectar, for reproduction varies by species (O’Brien et al. 2004; Jervis et al. 2005). However, the threshold whereby nectar abundance becomes limiting on populations is generally unknown. For example, many field studies document positive correlations between nectar and butterfly population growth rate or size (e.g. Murphy et al. 1983; Schultz and Dlugosch 1999; Boggs and Inouye 2012). In contrast, other authors have found conditions where nectar may not be required for viable butterfly populations (Jervis and Boggs 2005; Matter et al. 2009). However, field studies are likely only able to capture when nectar is one of the strongest influences on vital rates, and not when factors such as environment or host plant resources are stronger influences on population growth rates. The relative importance of nectar, and likelihood that nectar limits populations, is dependent upon reproductive and resource allocation strategies that vary across species.\u003c/p\u003e \u003cp\u003eButterflies allocate the sugars, amino acids, and salts of which nectar is comprised of towards reproduction and/or adult longevity. Nearly all laboratory studies on how nectar affects Lepidopteran fecundity have reported positive correlations between more nectar and higher lifetime fecundity (ESM 1; \u0026gt;30 studies, e.g. Beach et al. 1985; Mason et al. 1989; Boggs and Ross 1993; Stevens et al. 2002; Niitepõld et al. 2014). Higher fecundity is attributed to higher daily egg production (e.g. Mason et al. 1989; Romeis and Wackers 2002; Woods et al. 2010; Liu et al. 2017) and/or prolonged adult longevity (e.g. Hill and Pierce 1989; Boggs and Ross 1993; Saastamoinen et al. 2010), varying by taxa and nutritional components of nectar. Sugar is the primary nutritional component of nectar, but other components of nectar such as amino acids, are positively associated with increased lifetime fecundity (Romeis and Wackers 2002; Grill et al. 2013), possibly through compensation for larval malnutrition (Mevi‑Schütz and Erhardt 2003). Nectar improving fecundity is most evident in species such as Mormon fritillary (\u003cem\u003eArgynnis\u003c/em\u003e [= \u003cem\u003eSpeyeria\u003c/em\u003e] \u003cem\u003emormonia\u003c/em\u003e) which would deposit few to no eggs without nectar (Boggs and Ross 1993). However, not all species have shown such reliance on nectar for reproduction; the common imperial blue butterfly (\u003cem\u003eJalmenus evagorus\u003c/em\u003e) can produce ~ 200 eggs in the absence of nectar (Hill and Pierce 1989). Differences such as those described between the Mormon fritillary and common imperial blue are tied to different reproduction strategies (Jervis et al. 2005), but it is unclear how these reproduction strategies relate to the relative importance of nectar for population persistence.\u003c/p\u003e \u003cp\u003eInsects allocate resources from each stage to reproduction in a ratio described by ovigeny strategy. Pro-ovigenic insects use resources consumed as a larvae to develop eggs during the pupal stage and emerge as adults with a majority of eggs developed (Rosenheim et al. 2000; Jervis et al. 2005). Synovigenic insects rely on resources consumed as an adult to develop most of their eggs, and emerge from the pupae with few eggs developed, hence are reliant on nectar for population persistence. Highly mobile, host plant specialists that lay eggs singly are more likely to be synovigenic, and sedentary host plant generalists that lay eggs in clusters more likely to be pro-ovigenic (Jervis et al. 2005). For example, the life history traits and previous research with some fritillaries (\u003cem\u003eArgynnis\u003c/em\u003e spp.) suggest they are highly synovigenic, and some checkerspots (\u003cem\u003eEuphydryas\u003c/em\u003e spp.) are more pro-ovigenic (Boggs and Ross 1993; O’Brien et al. 2004; Jervis et al. 2005). Therefore, previous results indicate those fritillaries would be highly reliant on nectar resources to maintain viable populations, and those checkerspots minimally reliant, though this is dependent on other vital rates such as immature survival. Reliable determination of where a species is on the spectrum of ovigeny, is limited by a lack of empirical testing and the prevalence of intermediate strategies (Rosenheim et al. 2000; Davis et al. 2016). Further understanding of reproductive strategies could be found by evaluating intermediate strategies and linking resource allocation to population dynamics.\u003c/p\u003e \u003cp\u003eWe use Boisduval’s blue butterflies (\u003cem\u003eIcaricia icarioides\u003c/em\u003e) as a model species to understand the importance of nectar nutrition for population persistence. Prior studies on species with similar life history traits, and predictions based on the life history traits of Boisduval’s blue we expect this species is intermediate in egg maturation strategy (e.g. sedentary, host plant specialist, laying eggs singly Jervis et al. 2005; Molleman et al. 2011; Swanson et al. 2016), and therefore nectar may or may not be required for persistence. Additionally, this species is unlikely to experience significant larval diet restriction (e.g., host plant is either present or not, larvae are sedentary). We used this species to determine how nectar nutrition influences vital rates, and if resulting vital rates without nectar can produce viable populations. We conducted a greenhouse experiment to estimate vital rates on diets of varying sugar and amino acid concentrations to parameterize a population model and quantify the effects of nectar on Boisduval’s blue butterfly population growth rate. We hypothesized that this species would not reach sustainable population growth rates without nectar (based on field studies on this species: e.g. Schultz and Dlugosch 1999; Thomas and Schultz 2016), and that amino acids would be important for fecundity but not longevity (based on studies in other butterflies: e.g. Mevi‑Schutz and Erhardt 2005; O’Brien et al. 2005; Cahenzli and Erhardt 2012).\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eWe performed two experiments to assess how nectar influences the longevity and fecundity of adult female Boisduval’s blue butterflies (\u003cem\u003eIcaricia\u003c/em\u003e [= \u003cem\u003ePlebejus\u003c/em\u003e] \u003cem\u003eicarioides\u003c/em\u003e) and how diets affect the population growth rate. Sugar and amino acids are the two major nutritional components of nectar (Corbet 2003; McDade and Weeks 2004; Arnold 2016) and are present in varying ratios in flowers visited by Boisduval’s blues (unpublished data). In our first experiment, we modified available nectar sugar; in our second experiment, we varied sugar and amino acids.\u003c/p\u003e\u003cp\u003eStudy species and sites\u003c/p\u003e\u003cp\u003eBoisduval’s blues butterflies are a complex of ~ 25 recognized subspecies (Pelham 2008; Warren et al. 2017). The complex includes the federally threatened Fender's blue (\u003cem\u003eI. i. fenderi\u003c/em\u003e) and endangered Mission blue (\u003cem\u003eI. i. missionensis\u003c/em\u003e), the extinct pheres blue (\u003cem\u003eI. i. pheres\u003c/em\u003e), the Washington state candidate for endangered species listing Puget blue (\u003cem\u003eI. i. blackmorei\u003c/em\u003e)(WDFW 2022), and the Alberta/Saskatchewan imperiled pembina blue (\u003cem\u003eI. i. pembina\u003c/em\u003e) (Alberta Environment and Parks 2017; Government of Saskatchewan 2021). Boisduval’s blues are non-migratory and univoltine, lay eggs singly, are nectar generalists, and overwinter as second instar larvae in the soil at the base of a host lupine (\u003cem\u003eLupinus spp.\u003c/em\u003e)(Schultz and Dlugosch 1999; James and Nunnallee 2011; Schultz et al. 2012). The species occurs in meadows and prairies, where lupine and nectar are present during their flight period. We use Puget blues in 2019 and pembina blues in 2020 due to COVID-19 shutdowns during the Puget blue flight period in 2020.\u003c/p\u003e\u003cp\u003ePuget blue butterflies are a candidate for listing as a Washington State endangered species that inhabits the Puget Sound area, Olympic Peninsula of Washington, and southern British Columbia, Canada. They use \u003cem\u003eLupinus albicaulis\u003c/em\u003e as a host at the collection site, Joint Base Lewis-McChord (46.92, -122.73; Rainier, Washington, U.S.A.), which is owned by the U.S. Department of Defense and managed jointly by the U.S. Army and U.S. Air Force. Pembina blues are common throughout the Cascade Mountains in Oregon and Washington and their range is the largest of all subspecies, present throughout most of the interior of Canada and the United States. We collected pembina blues within the Gifford-Pinchot National Forest (45.78, -122.17; Yacolt, Washington, U.S.A.) and Mt. Hood National Forest and Wilderness Area (45.34, -121.67; Government Camp, Oregon, U.S.A.). They typically use \u003cem\u003eLupinus latifolius\u003c/em\u003e (varying ssp.) as their host plant at these sites.\u003c/p\u003e\u003cp\u003eExperimental Procedures\u003c/p\u003e\u003cp\u003eIn the first experiment, testing sugar levels, we worked with Puget blue in 2019, and for the second experiment, testing sugar and amino acids, we worked with pembina blue in 2020. All butterflies in the experiment were collected as newly eclosed adult females indicated by minimal wing wear from wild populations. Butterflies were chilled after capture in the field and then placed in individual housing before transport to the Washington State University Vancouver greenhouse. Butterflies were randomly assigned to a treatment and provided with nectar or water. Females that died within 48 hours of capture are excluded from the trial (n = 2) because death is more likely due to conditions the butterfly was experiencing before capture. We also excluded infertile females or females damaged during captivity (n = 2). Butterflies were housed in the Washington State University Vancouver greenhouse with a lupine stem (see ESM 2 for husbandry). Fresh sponges with the diet were placed daily, and eggs were removed from the lupine daily.\u003c/p\u003e\u003cp\u003eTo assess response to experimental treatments, we collected the following data: daily number of eggs laid, the butterfly’s weight every three days, longevity and unlaid eggs, and weight at death. Longevity was measured as the number of days from the date of collection to the date of death. We measured unlaid eggs by dissecting the abdomen of females within 48 hours of their death, following O’Brien et al. (2004). Eggs are classified either as developed eggs (\u0026gt; 0.5 mm in diameter) or partially absorbed eggs (\u0026lt; 0.5 mm in diameter; partially absorbed eggs). A small percentage of partially absorbed eggs (\u0026gt; 2%) were likely eggs that never developed; these eggs were small (~ 0.1 mm), clear, and hard to detect. Nearly all eggs classified as partially absorbed eggs were green or cloudy white indicating development had occurred. If Boisduval’s blues are pro-ovigenic we would expect that a majority of eggs are developed at the time of eclosion (Hill and Pierce 1989; O’Brien et al. 2004; Miller 2005; Jervis et al. 2005), and undersized colored eggs at death are more likely to be those that are being reabsorbed than never developed.\u003c/p\u003e\u003cp\u003eSugar Nutrition Experiment\u003c/p\u003e\u003cp\u003eOur experiment included three treatments to investigate how sugar affected butterfly longevity and fecundity. We made one batch of sucrose solution at 300 mg per mL, which is thought to be an ideal viscosity for proboscis feeding (following results of Kim et al. 2011), and froze it in aliquots and used it as needed; we also froze aliquots of water and used those as required. Common composite flowers in the habitats where Boisduval’s blue butterflies reside are commonly ~ 65 mg sucrose/flower (R. Bonoan personal communication). We used three nectar treatments: \u003cem\u003ead libitum\u003c/em\u003e, restricted, and water. The females in the \u003cem\u003ead libitum\u003c/em\u003e (A) treatment were fed twice daily, 2mL of the sucrose solution (1200 mg sucrose/day, 18x composite flower), in the morning and the afternoon. The females in the restricted nectar treatment (R) received 1 mL of nectar (300 mg sucrose/day, 4.6x composite flower) in the afternoon for 1 hour, with water available throughout the rest of the day. The water treatment (W) received only water on their sponges. Each treatment had 10 individually housed females, except one \u003cem\u003ead libitum\u003c/em\u003e female had a cystic mass in the abdomen that prevented dissection and was excluded (n = 9).\u003c/p\u003e\u003cp\u003eSugar and Amino Acid Experiment\u003c/p\u003e\u003cp\u003eThis experiment was conducted in 2020 with pembina blues. After our first experiment, we saw that the sugar levels appear to exceed the daily requirement for Puget blues and reduced the amount of sucrose provided. We used four treatments, water (W), lupine (L), flower (F), and flower plus lupine (F + L) with eight females per treatment. The water treatment (W) received fresh sponges daily with water. Nectar treatments were simulated after two nectar species, sickle-keeled lupine, lupine (L) and composite flowers, flower (F), each contained 65 mg of sucrose per day, but varied the quantity of amino acids. In our final treatment, flower plus lupine (F + L), butterflies had access to both nectar treatments, where each treatment (F or L) was given daily on separate sponges (total availability is 130 mg of sucrose and 23 mg of amino acid per day). Amino acid levels were based on field sampling of histidine, as an indicator of amino acids (R. Bonoan personal communication), with the lupine treatment having higher amino acids, and flower treatment is lower (17 and 6 mg/day respectively of histidine, and the quantity of other amino acids are dependent upon the blend used: see ESM 2 Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The amino acid blend included all nine essential amino acids plus cystine and tyrosine. Each treatment, including water, had the addition of sea salt (2 mM of sodium), as an estimate for nectar salt (McDade and Weeks 2004). We prepared the two nectars and the water in one batch and froze the solution into aliquots to be used as needed. Each treatment had 8 individually housed females.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean and 95% bootstrap prediction intervals by treatment across all butterflies (\u003cem\u003eIcaricia icarioides\u003c/em\u003e) in both the Sugar and the Sugar and Amino Acids experiments. Total fecundity is eggs laid from collection day to death, and longevity is the number of days lived from collection day to death and unlaid eggs are those inside an individual at time of death determined via dissection. Superscript letters refer to the significant differences between pairs via estimated marginal means with Tukey’s post-hoc correction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eToal Fecundity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongevity\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnlaid Eggs (95% CI)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003cp\u003e(n = 10 females per treatment)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAd Libitum\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (127, 342)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (8, 14)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (31, 62)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRestricted\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (164, 396)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (8, 16)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (29, 62)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (43, 95)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4, 6)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (68, 111)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSugar and Amino Acids\u003c/p\u003e \u003cp\u003e(n = 8 females per treatment)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlower \u0026amp; Lupine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (100, 274)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (14, 25)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (5, 51)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlower\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (53, 164)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (7, 18)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (16, 80)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (42, 252)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (10, 17)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (10, 28)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (7, 70)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3, 4)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (70, 103)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003cp\u003eWe performed the same types of analyses for the two experiments. We used R for statistical analyses (R Core Team 2024), and we used ANOVA from the car library created generalized linear and generalized linear mixed models, visually assessing if models met the appropriate model assumptions. We performed Wald χ\u003csup\u003e2\u003c/sup\u003e tests to detect evaluate treatment effects using ANOVA from the car package (Fox and Weisberg 2019). We performed pairwise comparisons of treatments with the Tukey’s contrast of the estimated marginal means using emmeans (Lenth 2024). We created 95% bootstrapped prediction intervals using glm.predict (Schlegel 2024) and bootpredictlme4 for mixed models (Duursma 2023). Data was manipulated and figures were made using tidyverse (Wickham et al. 2019) and ggpubr (Kassambara 2023).\u003c/p\u003e\u003cp\u003eWe tested the effects of diet on weight using a Gaussian generalized linear mixed model with the individual butterfly as a random effect on intercept (weight ~ treatment *day in trial) using the lme4 library (Bates et al. 2015). We modeled daily fecundity as an effect of treatment and day on each diet (daily eggs laid ~ treatment * day in trial) with individual butterfly as a random effect on intercept using a negative binomial generalized linear mixed model with a log link. We used quasi-Poisson generalized linear models to evaluate longevity (longevity ~ treatment), total fecundity (total fecundity ~ treatment), and unlaid eggs (unlaid eggs ~ treatment) after finding overdispersion in Poisson models. We evaluated all model distributions by comparing residuals and Q-Q plots, evaluating dispersion parameters and selected the best fitting distribution using AIC or qAIC from MASS (Venables and Ripley 2002).\u003c/p\u003e\u003cp\u003ePopulation model\u003c/p\u003e\u003cp\u003eWe used experimental data to estimate parameters in a population model and to estimate relative population growth rates. We estimate two parameters from our data, butterfly longevity, \u003cem\u003eL\u003c/em\u003e, (Eq.\u0026nbsp;1) and fecundity, \u003cem\u003eF\u003c/em\u003e (Eq.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(L \\sim QPois\\left({\\mu }_{x}\\right)\\)\u003c/span\u003e \u003c/span\u003e(Eq.\u0026nbsp;1)\u003c/p\u003e\u003cp\u003eThe average longevity of females, \u003cem\u003eL\u003c/em\u003e, is estimated assuming Poisson distribution adjusted for overdispersion (Quasi-Poisson) using the mean of the given treatment, \u003cem\u003ex\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(F=\\sum _{D=1}^{L}{f}_{D} \\sim Negbinom\\left({\\mu }_{xD}\\right)\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2)\u003c/p\u003e\u003cp\u003eWe use daily fecundity, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e, across the lifespan, \u003cem\u003eL\u003c/em\u003e, summed to estimate the average total fecundity per female, \u003cem\u003eF\u003c/em\u003e (Eq.\u0026nbsp;2). \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e, is the fecundity on day, \u003cem\u003eD\u003c/em\u003e, from a negative binomial distribution using the mean fecundity on that day, \u003cem\u003eD\u003c/em\u003e, for the given treatment, \u003cem\u003ex\u003c/em\u003e, for each day from 1 until the longevity, \u003cem\u003eL\u003c/em\u003e, of the butterfly. The daily fecundity, \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e, is summed to estimate total fecundity, \u003cem\u003eF\u003c/em\u003e, and then we estimate population growth rate, λ (Eq.\u0026nbsp;3), where we assume a 50:50 sex ratio, and therefore the number of eggs per capita is ½ \u003cem\u003eF\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\lambda }=\\frac{1}{2}\\times F \\times \\text{s}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;3)\u003c/p\u003e\u003cp\u003eEstimates of immature survival, \u003cem\u003es\u003c/em\u003e, set at 2.7%, based on the mean of estimates from prior studies of Boisduval’s blue survival from egg in summer to post-diapause survival the following spring (n = 46 site-years) (Schultz and Crone 1998; Warchola et al. 2017; Schultz and Ferguson 2020). Post-diapause to eclosion survival is not easily measured \u003cem\u003ein situ\u003c/em\u003e (pupae are in the soil or cryptic), individual larvae are free to move out of survey areas, and some proportion of individuals observed are near pupation (variable by site-year); therefore, 2.7% survival, on average, represents the minimum survival of eggs to mid-way to pupation after the breaking of diapause. In the laboratory, Puget blue egg to post-diapause survival was 37% and post-diapause to adult survival was 20% across two rearing environments, resulting in 7.5% of eggs surviving to become adult butterflies, which is thought to be only a moderate improvement in survival over what occurs in nature (Schultz et al. 2009). Therefore, given the limits of the existing data, we assume that post-diapause to eclosion survival is 100%. Additionally, we assume that immature survival is not affected by nectar treatments, because nectar quality has shown no effect (Woods et al. 2010; Niitepõld 2019) or has improved immature survival (e.g. Jensen et al. 1974; Song et al. 2007; Marchioro and Foerster 2012), and therefore our model conservatively represents the effect of nectar on the population growth rates.\u003c/p\u003e\u003cp\u003eThe population growth rate was calculated 10,000 times per diet, with randomly selected longevity (\u003cem\u003eL\u003c/em\u003e) and fecundity (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e) for each calculation to sum for total fecundity (\u003cem\u003eF\u003c/em\u003e); therefore, variation in λ represents variation from individual daily fecundity and longevity within the experiment. When comparing resulting population growth rates to determine if treatments provide sufficient resources across treatments, we consider λ = 1 to be the minimum indication of stable population, and λ = 1.55 as the threshold for long-term stable populations given previous research on the species documenting high stochasticity and possibility of density dependence (Schultz and Hammond 2003). We evaluated the sensitivity of the population model (Eq.\u0026nbsp;4) to the survival parameter by solving the equation for fecundity, F\u003csub\u003ei\u003c/sub\u003e, that results from a given immature survival, \u003cem\u003es\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, when λ = 1.55.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({F}_{i}=1.55 ÷({s}_{i}\\times \\frac{1}{2})\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;4)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSugar Experiment\u003c/p\u003e \u003cp\u003eTotal fecundity (χ\u0026sup2; = 16.472, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and daily fecundity (χ\u0026sup2; = 8.75, p\u0026thinsp;=\u0026thinsp;0.013) differed among treatments. Butterflies in sugar treatments, on average, had more than twice the fecundity of the water treatment butterflies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Total fecundity differed between treatments with and without nectar but between nectar treatments (A - R z-ratio= -0.786 p\u0026thinsp;=\u0026thinsp;0.7119, A - W z-ratio: 2.904 p\u0026thinsp;=\u0026thinsp;0.01, R \u0026ndash; W z-ratio\u0026thinsp;=\u0026thinsp;3.487 p\u0026thinsp;=\u0026thinsp;0.001). Additionally, there was a difference in daily fecundity of nectar treatment butterflies beginning on day five of the diets (A-R: z ratio\u0026thinsp;=\u0026thinsp;1.08, p\u0026thinsp;=\u0026thinsp;0.053). The difference between the daily fecundity of sugar treatments was not dependent on day of the trial, but the day of trial did significantly affect the daily eggs laid (χ\u0026sup2; = 2.01, p\u0026thinsp;=\u0026thinsp;0.37 and χ\u0026sup2; = 66.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe number of unlaid eggs (unlaid eggs at death) were affected by treatment (χ\u0026sup2; = 13.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were highest in the females from the water treatment (SI 3). The females in the two sugar treatments differed from the water treatment (R-W: z ratio = -2.99, p\u0026thinsp;=\u0026thinsp;0.008; A-W: z ratio = -3.123, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) but not each other (z ratio\u0026thinsp;=\u0026thinsp;0.025, p\u0026thinsp;\u0026gt;\u0026thinsp;0.99). However, the number of partially absorbed eggs was less variable in \u003cem\u003ead libitum\u003c/em\u003e females compared to the females in the restricted treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFemale Puget blues fed sugar lived longer than those without sugar (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; χ\u0026sup2; = 18.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The longevity did not differ between the \u003cem\u003ead libitum\u003c/em\u003e and restricted butterflies (z ratio = -0.64, p\u0026thinsp;=\u0026thinsp;0.80), but both differed from the water fed females (A-W: z ratio\u0026thinsp;=\u0026thinsp;3.26, p\u0026thinsp;=\u0026thinsp;0.003; R-W: z ratio\u0026thinsp;=\u0026thinsp;3.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively).\u003c/p\u003e \u003cp\u003eThe sugar treatments affected the weight of the butterflies throughout the experiment (χ\u0026sup2; = 8.94, p\u0026thinsp;=\u0026thinsp;0.01). Overall, there was no difference in weight by treatment alone, but there was a difference in weight between the treatments over time where, differences in weight increased throughout the experiment (χ\u0026sup2; = 22.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Females from the \u003cem\u003ead libitum\u003c/em\u003e treatment maintained higher weights than the restricted treatment females, with the difference between the butterflies occurring after the fifth day on the diets (estimated marginal means test on fifth day, z ratio\u0026thinsp;=\u0026thinsp;1.08, p\u0026thinsp;=\u0026thinsp;0.053). Only the females in the \u003cem\u003ead libitum\u003c/em\u003e treatment gained large amounts of visible fat, where their abdomens swelled with fat stores beyond wild butterflies (ESM 2 Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSugar and Amino Acid Experiment\u003c/p\u003e \u003cp\u003eTotal fecundity differed among treatments (χ\u0026sup2; = 8.70, p\u0026thinsp;=\u0026thinsp;0.03; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a marginal difference only between the highest nectar, lupine and flower with the no nectar treatment (F\u0026thinsp;+\u0026thinsp;L-W: z ratio\u0026thinsp;=\u0026thinsp;2.435 p\u0026thinsp;=\u0026thinsp;0.07). Daily fecundity differed among treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; χ\u0026sup2; = 9.04, p\u0026thinsp;=\u0026thinsp;0.029) and by day in the trial (χ\u0026sup2; = 165.2, \u0026lt; 0.001) with an interaction between treatment and day (χ\u0026sup2; =13.13, p\u0026thinsp;=\u0026thinsp;0.004). Beginning on day one, flower plus lupine females differed from the water treatment (z ratio\u0026thinsp;=\u0026thinsp;5.22, p\u0026thinsp;=\u0026thinsp;0.02) and throughout the lifespan of the water females, but no other differences were observed.\u003c/p\u003e \u003cp\u003eThe number of unlaid eggs differed among treatments (χ\u0026sup2; = 17.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;8 per treatment; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the higher amino acid treatments being different from the water, and no other differences between treatments (F\u0026thinsp;+\u0026thinsp;L-W: z ratio = -2.92, p\u0026thinsp;=\u0026thinsp;0.019; L-W: z ratio = -3.21, p\u0026thinsp;=\u0026thinsp;0.007; F-W: z ratio = -2.01, p\u0026thinsp;=\u0026thinsp;0.18; F\u0026thinsp;+\u0026thinsp;L-F: z ratio = -1.13, p\u0026thinsp;=\u0026thinsp;0.67; F\u0026thinsp;+\u0026thinsp;L-L: z ratio\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.96; F-L: z ratio\u0026thinsp;=\u0026thinsp;1.60, p\u0026thinsp;=\u0026thinsp;0.38).\u003c/p\u003e \u003cp\u003eLongevity of pembina blues differed among treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, χ\u0026sup2; = 35.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), where females from nectar treatments differed from females fed water (F\u0026thinsp;+\u0026thinsp;L-W: z ratio\u0026thinsp;=\u0026thinsp;5.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; F-W: z ratio\u0026thinsp;=\u0026thinsp;3.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, L-W: z ratio\u0026thinsp;=\u0026thinsp;3.72, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWe did not observe main treatment effects on the weight of butterflies (χ\u0026sup2; = 5.47, p\u0026thinsp;=\u0026thinsp;0.140), though weight did vary by treatment over time and by day in the trial(χ\u0026sup2; = 26.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; χ\u0026sup2; = 122.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively).\u003c/p\u003e \u003cp\u003ePopulation Model\u003c/p\u003e \u003cp\u003eUsing parameters from the sugar experiment, both restricted butterflies (mean λ\u0026thinsp;=\u0026thinsp;3.07: 95% CI: 1.78, 4.36) and \u003cem\u003ead libitum\u003c/em\u003e butterflies (mean λ\u0026thinsp;=\u0026thinsp;2.40: 1.31, 3.48) had an average population growth rate above 1.55 and water had λ\u0026thinsp;\u0026lt;\u0026thinsp;1 (mean λ\u0026thinsp;=\u0026thinsp;0.65: 0.19, 1.12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Given the average fecundity of Puget blue females on the water diet (68 eggs per female) immature survival would need to \u0026gt;\u0026thinsp;3.0% for λ\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u0026gt;\u0026thinsp;4.6% survival for λ\u0026thinsp;\u0026gt;\u0026thinsp;1.55.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing parameters from the sugar and amino acid experiment, only the flower plus lupine butterflies had a population growth rate above λ\u0026thinsp;=\u0026thinsp;1.55 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The water fed butterfly\u0026rsquo;s population growth rate was the lowest (mean λ\u0026thinsp;=\u0026thinsp;0.16: 0, 0.33). The lupine treatment and the flower treatment fed butterflies had similar population growth rates (mean λ\u0026thinsp;=\u0026thinsp;1.14: 0.48, 1.81; mean λ\u0026thinsp;=\u0026thinsp;1.29: 0.70, 1.87), but the flower plus lupine butterflies outperformed these treatments (mean λ\u0026thinsp;=\u0026thinsp;2.17: 1.58, 2.77). Given the average fecundity of pembina blue females on the water diet, 33 eggs per female, egg to pupae survival would need to be \u0026gt;\u0026thinsp;9.1% for λ\u0026thinsp;\u0026gt;\u0026thinsp;1.55 and \u0026gt;\u0026thinsp;6% for λ\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e \u003cp\u003eWe calculated the fecundity required to reach the target population growth rate λ\u0026thinsp;=\u0026thinsp;1.55 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), using the range of published immature survival values using our population model. When immature survival is \u0026gt;\u0026thinsp;5%, small changes to fecundity have small impacts on the population growth rate, whereas \u0026lt;\u0026thinsp;5%, or mean immature survival, the fecundity required to reach these growth rate increases rapidly. In the deterministic population model at mean survival, fecundity must be \u0026gt;\u0026thinsp;74 eggs/female for λ\u0026thinsp;\u0026gt;\u0026thinsp;1 or \u0026gt;\u0026thinsp;114 eggs/female for λ\u0026thinsp;\u0026gt;\u0026thinsp;1.55.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe persistence of Boisduval\u0026rsquo;s blue butterflies is contingent upon nectar availability. Boisduval\u0026rsquo;s blue butterflies laid eggs without nectar (~\u0026thinsp;50 eggs), but the resulting fecundity is inadequate to achieve an increasing population growth rate (resulting in λ\u0026thinsp;\u0026lt;\u0026thinsp;1). Conversely the provision of nectar leads to an increasing population growth rate (resulting in λ\u0026thinsp;\u0026gt;\u0026thinsp;1). Host resources are required for immature survival, and nectar is required for sufficient fecundity to sustain the population, which is the mechanism by which both host plant and nectar abundance can limit Boisduval\u0026rsquo;s blue population size (e.g. Schultz and Dlugosch 1999). Limitation by both stage-specific resources can be observed for Boisduval\u0026rsquo;s blue because some threshold exists whereby depletion of one resource cannot be compensated for by the other. Studies on non-insect taxa have similarly found that declining reproductive period resources can be compensated for by resources that support adult survival, until a threshold is reached; beyond which population growth rates are in decline (Bieber and Ruf 2005; Reynolds‑Hogland et al. 2007; Nater et al. 2021). Our findings illustrate the mechanisms by which nectar is crucial to maintain Boisduval\u0026rsquo;s blue butterfly populations and allow us to infer when nectar may be more or less crucial. Integrating the results of experiments that capture vital rates into population models allows us to contextualize the results and infer mechanisms of population dynamics.\u003c/p\u003e \u003cp\u003eThe availability of nectar improved the fecundity of Boisduval\u0026rsquo;s blues, which we found exhibits an intermediate ovigeny strategy. The butterflies laid\u0026thinsp;~\u0026thinsp;50 eggs without nectar (20% of the highest observed fecundity), which are eggs largely developed with host plant resources, much fewer than the common imperial blue, which is the closest relative for which data is available (host resources developed ~50% of eggs; Hill and Pierce 1989). Daily fecundity of Boisduval\u0026rsquo;s blues improved with the high amino acid treatment compared to water but did not differ between water and other nectar treatments, an indication of a critical threshold where sufficient levels of amino acids may support egg production. Nectar availability can improve daily fecundity through increased egg production or decreased egg resorption. Increased daily egg production with nectar is synovigeny (Rosenheim et al. 2000), but daily fecundity will also increase as egg resorption ceases as nutritional needs are met (Stjernholm et al. 2005; Moore and Attisano 2011). Our findings indicate that Boisduval\u0026rsquo;s blue butterflies are likely using egg resorption to meet nutritional requirements, more so when no nectar is available. However, we cannot determine if direct allocation of nectar to egg production occurs without further study. Therefore, these findings suggest that Boisduval\u0026rsquo;s blues are synovigenic to intermediate in ovigeny strategy (host resources developed\u0026thinsp;~\u0026thinsp;20% of eggs), which could be dependent upon host quality and or quantity. Further research into how variation in host resources alters the allocation of those resources to reproduction could provide further insight into these intermediate ovigeny strategies, and when nectar limits populations.\u003c/p\u003e \u003cp\u003eThe nutritional quality of nectar, specifically the presence of amino acids, can be an important factor in influencing butterfly reproduction but not the population growth rate of Boisduval\u0026rsquo;s blues. We found that butterflies fed more amino acids (e.g., lupine treatment), had less unlaid eggs and higher lifetime fecundity compared to the butterflies fed less amino acid, (e.g., the flower treatment). However, the higher fecundity of females fed more amino acids, and not additional sugar, did not translate to an improvement in the population growth rate compared to females fed less amino acids. Amino acids may compensate for previous nutrition deficits (O\u0026rsquo;Brien et al. 2005; Cahenzli and Erhardt 2013), especially essential amino acids, which can be limiting to reproduction (Romeis and Wackers 2002), and without sufficient amino acids egg resorption may increase. We did not observe any differences in longevity due to amino acids, similar to previous studies (e.g. Mevi‑Sch\u0026uuml;tz and Erhardt 2003; Molleman et al. 2008; Cahenzli and Erhardt 2012; Grill et al. 2013). Therefore, cumulatively amino acids had no effect on population growth rate in this study, but amino acids in nectar could be crucial when prior resources were insufficient, most importantly, if amino acids can mitigate declines in fecundity associated with poor host quality.\u003c/p\u003e \u003cp\u003eThe longevity of Boisduval\u0026rsquo;s blue butterfly doubled when provided with nectar. This is similar to findings in common imperial blues (Hill and Pierce 1989), and aligns with the survival-reproduction tradeoff observed in butterfly resource allocation (O\u0026rsquo;Brien et al. 2004; Jervis et al. 2005; Niitep\u0026otilde;ld et al. 2014). According to predictions by Jervis et al. (2005), species that live much longer with nectar than without, typically invested host plant resources heavily into reproduction, as seen in variable checkerspot, large white butterfly (\u003cem\u003ePieris brassicae\u003c/em\u003e), squinting bush brown (\u003cem\u003eBicyclus anynana\u003c/em\u003e), and wheat armyworm (\u003cem\u003eMythimna sequax\u003c/em\u003e) (Murphy et al. 1983; Romeis and Wackers 2002; Saastamoinen et al. 2010; Marchioro and Foerster 2012). Conversely species that did not live longer with nectar than without nectar are likely investing host plant resources more into adult longevity (soma), such as observed in the painted lady butterfly (\u003cem\u003eVanessa cardui\u003c/em\u003e), Mormon fritillary, orange sulfur (\u003cem\u003eColias eurytheme\u003c/em\u003e), and Glanville fritillary (\u003cem\u003eMelitaea cinxia\u003c/em\u003e) (Woods et al. 2010; Niitep\u0026otilde;ld et al. 2014; Niitep\u0026otilde;ld 2019). Species which lay eggs without nectar are less reliant on nectar for maintaining populations than those that cannot. However, for Boisduval\u0026rsquo;s blue butterflies it is the improvement in adult longevity, rather than changes in daily fecundity, that results in higher lifetime fecundity and is crucial in reaching stable population growth rates.\u003c/p\u003e \u003cp\u003eTranslating the effects of resources on vital rates to the population provide clarity on how nectar can limit populations, therefore, it will be useful to evaluate other intermediate and pro-ovigenic species for understanding how nectar influences butterfly persistence generally. Our population models allowed us to capture the critical threshold whereby nectar can become limiting to Boisduval\u0026rsquo;s blue populations. When immature survival is \u0026gt;\u0026thinsp;7.5%, the fecundity of butterflies without nectar (~\u0026thinsp;50 eggs) can produce viable population growth rates. However, as immature survival falls below 7.5% fecundity must increase rapidly to meet viable population growth rates, and therefore it is increasingly likely that nectar can limit the population through insufficient reproduction. The mean immature survival (Schultz and Crone 1998; 2.6%; Warchola et al. 2017; Schultz and Ferguson 2020) for Boisduval\u0026rsquo;s blue falls below the threshold, and therefore the increased total fecundity of butterflies fed nectar (~\u0026thinsp;130 mg of sucrose per day) is required to meet viable population growth rates. In contrast, common imperial blue butterflies had larval survival of ~\u0026thinsp;14% (obligate ant tended sp. Pierce et al. 1987), and laid\u0026thinsp;~\u0026thinsp;200 eggs without nectar (Hill and Pierce 1989), therefore in our population model, this species could persist without nectar, depending on the viable population growth rate (Morris and Doak 1984; Gerber and Demaster 1999; Schultz and Hammond 2003). Further research linking stage-specific resources to population effects across species with different reproductive strategies will inform the conditions under which species with a given life history strategies are likely to persist.\u003c/p\u003e \u003cp\u003eThe crucial role of nectar described here illustrates the interplay of stage-specific resources on population dynamics of organisms with complex life cycles. For example, if host plant quality or abundance were to decline, our results indicate that more nectar would be required to sustain populations. In addition to numerous anthropogenic threats to insect habitats and host plant abundance and quality is likely to decline in nutritional quality as carbon dioxide concentrations increase from greenhouse gas emissions (Ebi and Ziska 2018; Decker et al. 2018; Johnson et al. 2020). Future research could evaluate if declining host quality can be compensated for by improvements in nectar abundance and/or quality as suggested is possible by this research. Unfortunately for nectivorous organisms broadly, declines in nectar resources have been documented across large scales and are suggested to be one factor driving declines in many pollinator species across the globe (Biesmeijer et al. 2006; Wallisdevries et al. 2012; Baude et al. 2016; Wepprich et al. 2019; Crossley et al. 2021). The interplay of stage-specific resources provides an opportunity to compensate for broad declines in other resources, but more research is needed to understand the margin where compensation is possible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the U.S. Department of Defense and Washington State University for supporting our research. We appreciate Joint Base Lewis-McChord, Gifford-Pinchot National Forest, and Mount Hood National Forest for allowing us to conduct research and for maintaining habitat. This work would not have been completed with the help of many collaborators, including Elizabeth E. Crone, Cassandra F. Doll, Samantha Bussan, Christopher Jason, Chelsea Thomas, and Alyxandra James. We also thank Sun Gro Horticulture and Portland Hydroponics for soil donations to complete this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research was funded by the Department of Defense Strategic Environmental Research and Development Program (award # RC-2700) and Washington State University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eWe declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eAll applicable institutional and/or national guidelines for the care and use of animals were followed. Activities are exempt from Institutional Animal Care and Use Committee review, as invertebrates are not considered animals under local legislation. Research was conducted under Washington State Department of Fish and Wildlife scientific collection permits Schultz 19-121 and Schultz 20-175 and additional permits to access collection sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The datasets and code generated during the current study are available in the GitHub repository, https://github.com/prairie-rex/FecWOutNectar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e See previous statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e KCK and CBS conceived, designed, and executed the experiments. KCK analyzed the data. KCK and CBS wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlberta Environment and Parks (2017) Element Occurence Data. In: Alberta Conservation Information Management System. https://albertaparks.ca/albertaparksca/management-land-use/alberta-conservation-information-management-system-acims/download-data/. Accessed 12 Nov 2021\u003c/li\u003e\n\u003cli\u003eArnold P (2016) Variation in nectar composition: the influence of nectar quality on monarch success. Master thesis\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1\u0026ndash;48. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBaude M, Kunin WE, Boatman ND, et al (2016) Historical nectar assessment reveals the fall and rise of floral resources in Britain. Nature 530:85\u0026ndash;88. https://doi.org/10.1038/nature16532\u003c/li\u003e\n\u003cli\u003eBieber C, Ruf T (2005) Population dynamics in wild boar \u003cem\u003eSus scrofa\u003c/em\u003e : ecology, elasticity of growth rate and implications for the management of pulsed resource consumers. J Appl Ecol 42:1203\u0026ndash;1213. https://doi.org/10.1111/j.1365-2664.2005.01094.x\u003c/li\u003e\n\u003cli\u003eBiesmeijer JC, Roberts SPM, Reemer M, et al (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:351\u0026ndash;354. https://doi.org/10.1126/science.1127863\u003c/li\u003e\n\u003cli\u003eBoggs CL, Inouye DW (2012) A single climate driver has direct and indirect effects on insect population dynamics. Ecol Lett 15:502\u0026ndash;508. https://doi.org/10.1111/j.1461-0248.2012.01766.x\u003c/li\u003e\n\u003cli\u003eBoggs CL, Ross CL (1993) The effect of adult food limitation on life history traits in \u003cem\u003eSpeyeria mormonia\u003c/em\u003e (Lepidoptera: Nymphalidae). Ecology 74:433\u0026ndash;441. https://doi.org/10.2307/1939305\u003c/li\u003e\n\u003cli\u003eCahenzli F, Erhardt A (2012) Enhancing offspring quality or quantity? Different ways for using nectar amino acids in female butterflies. Oecologia 169:1005\u0026ndash;1014. https://doi.org/10.1007/s00442-012-2254-7\u003c/li\u003e\n\u003cli\u003eCahenzli F, Erhardt A (2013) Nectar amino acids enhance reproduction in male butterflies. Oecologia 171:197\u0026ndash;205. https://doi.org/10.1007/s00442-012-2395-8\u003c/li\u003e\n\u003cli\u003eCorbet SA (2003) Nectar sugar content: estimating standing crop and secretion rate in the field. Apidologie 34:1\u0026ndash;10. https://doi.org/10.1051/apido:2002049\u003c/li\u003e\n\u003cli\u003eCrossley MS, Smith OM, Berry LL, et al (2021) Recent climate change is creating hotspots of butterfly increase and decline across North America. Glob Chang Biol 27:2702\u0026ndash;2714. https://doi.org/10.1111/gcb.15582\u003c/li\u003e\n\u003cli\u003eDavis RB, Javoi\u0026scaron; J, Kaasik A, et al (2016) An ordination of life histories using morphological proxies: capital vs. income breeding in insects. Ecology 97:2112\u0026ndash;2124. https://doi.org/10.1002/ecy.1435\u003c/li\u003e\n\u003cli\u003eDecker LE, de Roode JC, Hunter MD (2018) Elevated atmospheric concentrations of carbon dioxide reduce monarch tolerance and increase parasite virulence by altering the medicinal properties of milkweeds. Ecol Lett 21:1353\u0026ndash;1363. https://doi.org/10.1111/ele.13101\u003c/li\u003e\n\u003cli\u003eDuursma R (2023) bootpredictlme4: Predict Method For lme4 With Bootstrap. Version 0.1. rdrr. URL https://rdrr.io/github/RemkoDuursma/bootpredictlme4/\u003c/li\u003e\n\u003cli\u003eEbi KL, Ziska LH (2018) Increases in atmospheric carbon dioxide: Anticipated negative effects on food quality. PLoS Med 15:e1002600. https://doi.org/10.1371/journal.pmed.1002600\u003c/li\u003e\n\u003cli\u003eFox J, Weisberg S (2019) An R Companion to Applied Regression. Version Third edition. Sage, Thousand Oaks, CA\u003c/li\u003e\n\u003cli\u003eGerber LR, Demaster DP (1999) A quantitative approach to endangered species act classification of long‐lived vertebrates: application to the north pacif\u0026emsp;ic humpback whale. Conserv Biol 13:1203\u0026ndash;1214. https://doi.org/10.1046/j.1523-1739.1999.98277.x\u003c/li\u003e\n\u003cli\u003eGovernment of Saskatchewan (2021) Tracked Taxa List: Invertebrates. In: Saskatchewan Conservation Data Centre. http://biodiversity.sk.ca/SppList.htm. Accessed 14 Nov 2021\u003c/li\u003e\n\u003cli\u003eGrill A, Cerny A, Fiedler K (2013) Hot summers, long life: egg laying strategies of Maniola butterflies are affected by geographic provenance rather than adult diet. CTOZ 82:27\u0026ndash;36. https://doi.org/10.1163/18759866-08201002\u003c/li\u003e\n\u003cli\u003eHill CJ, Pierce NE (1989) The effect of adult diet on the biology of butterflies : 1. The common imperial blue, \u003cem\u003eJalmenus evagoras.\u003c/em\u003e Oecologia 81:249\u0026ndash;257. https://doi.org/10.1007/BF00379812\u003c/li\u003e\n\u003cli\u003eJames DG, Nunnallee D (2011) Life histories of Cascadia butterflies. Oregon State University Press, Corvallis, Oregon\u003c/li\u003e\n\u003cli\u003eJensen RL, Newsom LD, Gibbens J (1974) The soybean looper: effects of adult nutrition on oviposition, mating frequency, and longevity. J Econ Entomol 67:467\u0026ndash;470. https://doi.org/10.1093/jee/67.4.467\u003c/li\u003e\n\u003cli\u003eJervis MA, Boggs CL, Ferns PN (2005) Egg maturation strategy and its associated trade-offs: a synthesis focusing on Lepidoptera. Ecol Entomol 30:359\u0026ndash;375. https://doi.org/10.1111/j.0307-6946.2005.00712.x\u003c/li\u003e\n\u003cli\u003eJervis MA, Boggs CL (2005) Linking nectar amino acids to fitness in female butterflies. Trends Ecol Evol 20:585\u0026ndash;587. https://doi.org/10.1016/j.tree.2005.08.015\u003c/li\u003e\n\u003cli\u003eJohnson SN, Waterman JM, Hall CR (2020) Increased insect herbivore performance under elevated CO2 is associated with lower plant defence signalling and minimal declines in nutritional quality. Sci Rep 10:14553. https://doi.org/10.1038/s41598-020-70823-3\u003c/li\u003e\n\u003cli\u003eKassambara A (2023) ggpubr: \u0026ldquo;ggplot2\u0026rdquo; Based Publication Ready Plots. Version 0.6.0. R package\u003c/li\u003e\n\u003cli\u003eKim W, Gilet T, Bush JWM (2011) Optimal concentrations in nectar feeding. Proc Natl Acad Sci USA 108:16618\u0026ndash;16621. https://doi.org/10.1073/pnas.1108642108\u003c/li\u003e\n\u003cli\u003eKingsolver JG, Woods HA, Buckley LB, et al (2011) Complex life cycles and the responses of insects to climate change. Integr Comp Biol 51:719\u0026ndash;732. https://doi.org/10.1093/icb/icr015\u003c/li\u003e\n\u003cli\u003eLenth RV (2024) emmeans: Estimated Marginal Means, aka Least-Squares Means. Version 1.10.0. R package\u003c/li\u003e\n\u003cli\u003eLiu K, Zhu P, L\u0026uuml; Z, et al (2017) Effects of sesame nectar on longevity and fecundity of seven Lepidoptera and survival of four parasitoid species commonly found in agricultural ecosystems. J Integr Agric 16:2534\u0026ndash;2546. https://doi.org/10.1016/S2095-3119(17)61665-4\u003c/li\u003e\n\u003cli\u003eMarchioro CA, Foerster LA (2012) Importance of carbohydrate sources to the reproductive output of the wheat armyworm \u003cem\u003ePseudaletia sequax\u003c/em\u003e. Agric For Entomol 14:29\u0026ndash;35. https://doi.org/10.1111/j.1461-9563.2011.00548.x\u003c/li\u003e\n\u003cli\u003eMason LJ, Johnson SJ, Woodring JP (1989) Influence of carbohydrate deprivation and tethered flight on stored lipid, fecundity, and survivorship of the soybean looper (Lepidoptera: Noctuidae). Environ Entomol 18:1090\u0026ndash;1094. https://doi.org/10.1093/ee/18.6.1090\u003c/li\u003e\n\u003cli\u003eMatter SF, Ezzeddine M, Duermit E, et al (2009) Interactions between habitat quality and connectivity affect immigration but not abundance or population growth of the butterfly, \u003cem\u003eParnassius smintheus\u003c/em\u003e. Oikos 118:1461\u0026ndash;1470. https://doi.org/10.1111/j.1600-0706.2009.17438.x\u003c/li\u003e\n\u003cli\u003eMcDade LA, Weeks JA (2004) Nectar in Hummingbird-pollinated Neotropical Plants II: Interactions with Flower Visitors. Biotropica 36:216\u0026ndash;230. https://doi.org/10.1111/j.1744-7429.2004.tb00313.x\u003c/li\u003e\n\u003cli\u003eMevi-Schutz J, Erhardt A (2005) Amino acids in nectar enhance butterfly fecundity: a long-awaited link. Am Nat 165:411\u0026ndash;419. https://doi.org/10.1086/429150\u003c/li\u003e\n\u003cli\u003eMevi-Sch\u0026uuml;tz J, Erhardt A (2003) Effects of nectar amino acids on fecundity of the wall brown butterfly (\u003cem\u003eLasiommata megera\u003c/em\u003e L.). Basic and Applied Ecology 4:413\u0026ndash;421. https://doi.org/10.1078/1439-1791-00183\u003c/li\u003e\n\u003cli\u003eMiller WE (2005) Extrinsic effects on fecundity-maternal weight relations in capital breeding lepidoptera. Journal of the Lepidopterists\u0026rsquo; Society 59:143\u0026ndash;160\u003c/li\u003e\n\u003cli\u003eMittelbach GG, Osenberg CW (1993) Stage-structured interactions in bluegill: consequences of adult resource variation. Ecology 74:2381\u0026ndash;2394. https://doi.org/10.2307/1939589\u003c/li\u003e\n\u003cli\u003eMolleman F, Ding J, Wang J-L, et al (2008) Amino acid sources in the adult diet do not affect life span and fecundity in the fruit-feeding butterfly \u003cem\u003eBicyclus anynana\u003c/em\u003e. Ecol Entomol 33:429\u0026ndash;438. https://doi.org/10.1111/j.1365-2311.2008.00986.x\u003c/li\u003e\n\u003cli\u003eMolleman F, Javoi\u0026scaron; J, Esperk T, et al (2011) Sexual differences in weight loss upon eclosion are related to life history strategy in Lepidoptera. J Insect Physiol 57:712\u0026ndash;722. https://doi.org/10.1016/j.jinsphys.2011.02.009\u003c/li\u003e\n\u003cli\u003eMoore PJ, Attisano A (2011) Oosorption in response to poor food: complexity in the trade-off between reproduction and survival. Ecol Evol 1:37\u0026ndash;45. https://doi.org/10.1002/ece3.4\u003c/li\u003e\n\u003cli\u003eMorris WF, Doak DF (1984) Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis, 1st edn. CRC Press, Sunderland, Mass\u003c/li\u003e\n\u003cli\u003eMurphy DD, Launer AE, Ehrlich PR (1983) The role of adult feeding in egg production and population dynamics of the checkerspot butterfly \u003cem\u003eEuphydryas editha\u003c/em\u003e. Oecologia 56:257\u0026ndash;263. https://doi.org/10.1007/BF00379699\u003c/li\u003e\n\u003cli\u003eNater CR, Eide NE, Pedersen \u0026Aring;\u0026Oslash;, et al (2021) Contributions from terrestrial and marine resources stabilize predator populations in a rapidly changing climate. Ecosphere 12:. https://doi.org/10.1002/ecs2.3546\u003c/li\u003e\n\u003cli\u003eNiitep\u0026otilde;ld K, Perez A, Boggs CL (2014) Aging, life span, and energetics under adult dietary restriction in Lepidoptera. Physiol Biochem Zool 87:684\u0026ndash;694. https://doi.org/10.1086/677570\u003c/li\u003e\n\u003cli\u003eNiitep\u0026otilde;ld K (2019) Effects of flight and food stress on energetics, reproduction, and lifespan in the butterfly \u003cem\u003eMelitaea cinxia\u003c/em\u003e. Oecologia 191:271\u0026ndash;283. https://doi.org/10.1007/s00442-019-04489-8\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien DM, Boggs CL, Fogel ML (2005) The amino acids used in reproduction by butterflies: a comparative study of dietary sources using compound-specific stable isotope analysis. Physiol Biochem Zool 78:819\u0026ndash;827. https://doi.org/10.1086/431191\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien DM, Boggs CL, Fogel ML (2004) Making eggs from nectar: the role of life history and dietary carbon turnover in butterfly reproductive resource allocation. Oikos 105:279\u0026ndash;291. https://doi.org/10.1111/j.0030-1299.2004.13012.x\u003c/li\u003e\n\u003cli\u003ePelham JF (2008) Catalogue of the butterflies of the United States and Canada. Journal of Research on the Lepidoptera\u003c/li\u003e\n\u003cli\u003ePierce NE, Kitching RL, Buckley RC, et al (1987) The costs and benefits of cooperation between the Australian Lycaenid butterfly, \u003cem\u003eJalmenus evagoras\u003c/em\u003e, and its attendant ants. Behav Ecol Sociobiol 21:237\u0026ndash;248. https://doi.org/10.1007/BF00292505\u003c/li\u003e\n\u003cli\u003eReichstein B, Persson L, De Roos AM (2015) Ontogenetic asymmetry modulates population biomass production and response to harvest. Nat Commun 6:6441. https://doi.org/10.1038/ncomms7441\u003c/li\u003e\n\u003cli\u003eReynolds-Hogland MJ, Pacifici LB, Mitchell MS (2007) Linking resources with demography to understand resource limitation for bears. J Appl Ecol 44:1166\u0026ndash;1175. https://doi.org/10.1111/j.1365-2664.2007.01317.x\u003c/li\u003e\n\u003cli\u003eRichardson J, Smiseth PT (2019) Effects of variation in resource acquisition during different stages of the life cycle on life-history traits and trade-offs in a burying beetle. J Evol Biol 32:19\u0026ndash;30. https://doi.org/10.1111/jeb.13388\u003c/li\u003e\n\u003cli\u003eRomeis J, Wackers FL (2002) Nutritional suitability of individual carbohydrates and amino acids for adult \u003cem\u003ePieris brassicae\u003c/em\u003e. Physiol Entomol 27:148\u0026ndash;156. https://doi.org/10.1046/j.1365-3032.2002.00281.x\u003c/li\u003e\n\u003cli\u003eRosenheim JA, Heimpel GE, Mangel M (2000) Egg maturation, egg resorption and the costliness of transient egg limitation in insects. Proc Biol Sci 267:1565\u0026ndash;1573. https://doi.org/10.1098/rspb.2000.1179\u003c/li\u003e\n\u003cli\u003eR Core Team (2024) R: A language and environment for statistical computing. Version 4.3.2. R Foundation for Statistical Computing, Vienna, Austria\u003c/li\u003e\n\u003cli\u003eSaastamoinen M, van der Sterren D, Vastenhout N, et al (2010) Predictive adaptive responses: Condition-dependent impact of adult nutrition and flight in the tropical butterfly \u003cem\u003eBicyclus anynana\u003c/em\u003e. Am Nat 176:686\u0026ndash;698. https://doi.org/10.1086/657038\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Hern\u0026aacute;ndez J, Nunn AD, Adams CE, Amundsen P-A (2019) Causes and consequences of ontogenetic dietary shifts: a global synthesis using fish models. Biol Rev Camb Philos Soc 94:539\u0026ndash;554. https://doi.org/10.1111/brv.12468\u003c/li\u003e\n\u003cli\u003eSchlegel B (2024) glm.predict: Predicted Values and Discrete Changes for Regression Models. Version 4.3-0. R package, Institut of Political Science, Zurich, Switzerland\u003c/li\u003e\n\u003cli\u003eSchultz C, Thomas R, Cummings K (2012) Assessing nectar resources for Fender\u0026rsquo;s blue butterfly within upland prairies in Willamette Valley, Oregon. Washington State University, Vancouver, WA\u003c/li\u003e\n\u003cli\u003eSchultz CB, Crone EE (1998) Burning prairie to restore butterfly habitat: a modeling approach to management tradeoffs for the Fender\u0026rsquo;s blue. Restoration Ecology 6:244\u0026ndash;252\u003c/li\u003e\n\u003cli\u003eSchultz CB, Dlugosch KM (1999) Nectar and hostplant scarcity limit populations of an endangered Oregon butterfly. Oecologia 119:231\u0026ndash;238. https://doi.org/10.1007/s004420050781\u003c/li\u003e\n\u003cli\u003eSchultz CB, Dzurisin JD, Russell C (2009) Captive rearing of Puget blue butterflies (\u003cem\u003eIcaricia icarioides blackmorei\u003c/em\u003e) and implications for conservation. J Insect Conserv 13:309\u0026ndash;315. https://doi.org/10.1007/s10841-008-9174-1\u003c/li\u003e\n\u003cli\u003eSchultz CB, Ferguson JA (2020) Demographic costs and benefits of herbicide‐based restoration to enhance habitat for an endangered butterfly and a threatened plant. Restor Ecol 28:564\u0026ndash;572. https://doi.org/10.1111/rec.13102\u003c/li\u003e\n\u003cli\u003eSchultz CB, Hammond PC (2003) Using population viability analysis to develop recovery criteria for endangered insects: case study of the Fender\u0026rsquo;s blue butterfly. Conserv Biol 17:1372\u0026ndash;1385. https://doi.org/10.1046/j.1523-1739.2003.02141.x\u003c/li\u003e\n\u003cli\u003eSong Z-M, Li Z, Li D-M, et al (2007) Adult feeding increases fecundity in female \u003cem\u003eHelicoverpa armigera\u003c/em\u003e (Lepidoptera: Noctuidae). Eur J Entomol 104:721\u0026ndash;724. https://doi.org/10.14411/eje.2007.091\u003c/li\u003e\n\u003cli\u003eStjernholm F, Karlsson B, Boggs CL (2005) Age-related changes in thoracic mass: possible reallocation of resources to reproduction in butterflies. Biological Journal of the Linnean Society 86:363\u0026ndash;380. https://doi.org/10.1111/j.1095-8312.2005.00542.x\u003c/li\u003e\n\u003cli\u003eSwanson EM, Espeset A, Mikati I, et al (2016) Nutrition shapes life-history evolution across species. Proc Biol Sci 283:. https://doi.org/10.1098/rspb.2015.2764\u003c/li\u003e\n\u003cli\u003eTaborsky B (2006) The influence of juvenile and adult environments on life-history trajectories. Proc Biol Sci 273:741\u0026ndash;750. https://doi.org/10.1098/rspb.2005.3347\u003c/li\u003e\n\u003cli\u003eThomas RC, Schultz CB (2016) Resource selection in an endangered butterfly: Females select native nectar species. J Wildl Manage 80:171\u0026ndash;180. https://doi.org/10.1002/jwmg.987\u003c/li\u003e\n\u003cli\u003eTurlure C, Schtickzelle N, Dubois Q, et al (2019) Suitability and transferability of the resource-based habitat concept: a test with an assemblage of butterflies. Front Ecol Evol 7:. https://doi.org/10.3389/fevo.2019.00127\u003c/li\u003e\n\u003cli\u003evan de Wolfshaar KE, HilleRisLambers R, G\u0026aring;rdmark A (2011) Effect of habitat productivity and exploitation on populations with complex life cycles. Mar Ecol Prog Ser 438:175\u0026ndash;184. https://doi.org/10.3354/meps09304\u003c/li\u003e\n\u003cli\u003eVenables WN, Ripley BD (2002) Modern Applied Statistics with S (Statistics and Computing), 4th edn. Springer, New York\u003c/li\u003e\n\u003cli\u003eWallisdevries MF, Van Swaay CAM, Plate CL (2012) Changes in nectar supply: A possible cause of widespread butterfly decline. Curr Zool 58:384\u0026ndash;391. https://doi.org/10.1093/czoolo/58.3.384\u003c/li\u003e\n\u003cli\u003eWarchola N, Crone EE, Schultz CB (2017) Balancing ecological costs and benefits of fire for population viability of disturbance-dependent butterflies. J Appl Ecol 55:800\u0026ndash;809. https://doi.org/10.1111/1365-2664.12983\u003c/li\u003e\n\u003cli\u003eWarren AD, Davis KJ, Strangeland EM, et al (2017) Illustrated lists of American butterflies. In: Butterflies of America. http://butterfliesofamerica.com/. Accessed 13 Jul 2021\u003c/li\u003e\n\u003cli\u003eWDFW (2022) State Listed Species. Washington Department of Fish and Wildlife, Olympia, WA\u003c/li\u003e\n\u003cli\u003eWepprich T, Adrion JR, Ries L, et al (2019) Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14:e0216270. https://doi.org/10.1371/journal.pone.0216270\u003c/li\u003e\n\u003cli\u003eWerner EE (1988) Size, scaling, and the evolution of complex life cycles. In: Ebenman B, Persson L (eds) Size-Structured Populations. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 60\u0026ndash;81\u003c/li\u003e\n\u003cli\u003eWickham H, Averick M, Bryan J, et al (2019) Welcome to the tidyverse. JOSS 4:1686. https://doi.org/10.21105/joss.01686\u003c/li\u003e\n\u003cli\u003eWilbur HM (1980) Complex Life Cycles. Annu Rev Ecol Syst 11:67\u0026ndash;93. https://doi.org/10.1146/annurev.es.11.110180.000435\u003c/li\u003e\n\u003cli\u003eWoods WA, Wood CAL, Ebersole J, Stevenson RD (2010) Metabolic rate variation over adult lifetime in the butterfly \u003cem\u003eVanessa cardui\u003c/em\u003e (Nymphalidae: Nymphalinae): aging, feeding, and repeatability. Physiol Biochem Zool 83:858\u0026ndash;868. https://doi.org/10.1086/656216 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Population Viability, Nutrition, Fecundity, Lycaenidae, Resources","lastPublishedDoi":"10.21203/rs.3.rs-3967556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3967556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOrganisms with complex life cycles undergo ecological transitions between life stages, often resulting in stage-specific resource use. The relative contribution of each stage-specific resource to vital rates influences population dynamics and subsequently whether habitats can support viable populations. In Lepidopterans, survival to reproduction requires sufficient resources for immature life stages, but the extent to which resources for adults are critical to population persistence is variable. We studied Boisduval's blue butterflies (\u003cem\u003eIcaricia icarioides\u003c/em\u003e), in a greenhouse experiment, to quantify the effect of the adult diet, nectar, on vital rates. Butterflies fed \u003cem\u003ead libitum \u003c/em\u003eproduced 3.4 times more eggs, on average, over their lifetime and lived 6 more days relative to those which only had access to water. We used these experimental data to parameterize a population model to test if vital rates with and without nectar result in viable population growth rates. We found that Boisduval’s blue butterfly populations will not persist without nectar resources (λ \u0026lt; 1). In this species, although host plant resources contributed to reproduction and allowed the butterfly to produce ~50 eggs without nectar, these resources consumed as larva did not compensate for adult malnutrition beyond a critical threshold. The relative abundance and quality of each stage-specific resource can therefore determine at what threshold other resource(s) are limiting the population. This study highlights the value of incorporating effects on vital rates across the life cycle to evaluate the effect on populations.\u003c/p\u003e","manuscriptTitle":"Fecundity without nectar is insufficient for persistence of a blue butterfly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 11:27:20","doi":"10.21203/rs.3.rs-3967556/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-05T02:51:25+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-25T16:50:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-20T07:05:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oecologia","date":"2024-02-17T13:28:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4237b6de-a538-499f-ac45-4a05fc0f0091","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:12:32+00:00","versionOfRecord":{"articleIdentity":"rs-3967556","link":"https://doi.org/10.1007/s00442-024-05609-9","journal":{"identity":"oecologia","isVorOnly":false,"title":"Oecologia"},"publishedOn":"2024-09-28 15:57:18","publishedOnDateReadable":"September 28th, 2024"},"versionCreatedAt":"2024-02-28 11:27:20","video":"","vorDoi":"10.1007/s00442-024-05609-9","vorDoiUrl":"https://doi.org/10.1007/s00442-024-05609-9","workflowStages":[]},"version":"v1","identity":"rs-3967556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3967556","identity":"rs-3967556","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}