Climate variability reshapes the cues governing phenology

Climate variability reshapes the cues governing phenology | 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 Biological Sciences - Article Climate variability reshapes the cues governing phenology Christie Bahlai, Karolina Polakowski, Douglas Landis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9451861/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The timing of life history events govern how organisms interact with their environment, shaping reproduction, population dynamics, and ecological interactions 1 . In insects, this timing often tracks environmental conditions such as temperature or moisture, yet increasing climate variability may challenge the reliability of these cues 2–4 . Fireflies provide a unique and sensitive system for testing how seasonal timing is regulated, because adult activity and reproduction are extremely sensitive to environmental cues, with potential implications for population dynamics and fitness 5–7 . Using a long‑term, structured dataset, we find that environmental cues shaping seasonal activity have changed, alongside population decline and habitat use change that were not apparent in shorter studies. Here we show that fireflies have shifted from temperature‑driven to photoperiod‑constrained phenology, coinciding with an emerging population decline and a reorganization of habitat use. These results demonstrate that climate change can alter not only the timing of biological events but the environmental signals organisms rely on to track seasonal change 8,9 . By showing that phenological mechanisms themselves can reorganize under sustained climate variability, this work challenges assumptions underlying many phenological models and highlights the importance of long‑term, high‑quality data for understanding ecological responses to environmental change 10,11 . Biological sciences/Ecology/Population dynamics Biological sciences/Ecology/Climate-change ecology/Phenology Biological sciences/Ecology/Ecological modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Main Phenology—the seasonal timing of biological events—is a fundamental axis through which organisms interact with their environment 1 . In insects, phenological timing is commonly a response to integrated environmental cues rather than fixed photoperiod cues, with accumulated temperature often serving as a primary driver of development, emergence, and reproduction 2,4,12 . Degree‑day metrics, which integrate time and temperature to quantify heat accumulation, have therefore become a cornerstone of insect phenology models 13 . This framework carries an implicit assumption: that the environmental cues governing seasonal timing, and their relative importance, remain stable through time 14 . Accelerating climate variability increasingly challenges this assumption 15,16 . Short‑term weather anomalies, warmer springs, altered precipitation regimes, and more frequent extremes can decouple long‑standing relationships between environmental conditions and biological timing 17 . In insects, these anomalies often exert stronger effects on phenology than long‑term seasonal means, introducing noise into traditionally reliable cues such as temperature accumulation 18 . Under increasingly variable conditions, the informational value of climatic cues may degrade, raising questions about whether organisms can continue to rely on them to accurately time seasonal activity 19–21 . When historically informative cues become unreliable, organisms may shift toward alternative signals that provide greater consistency or synchronization with biotic needs 8,9 . Photoperiod (measured by calendar date) represents one such cue: unlike temperature or precipitation, day length varies predictably across years at consistent latitudes and is unaffected by interannual climatic variability. Photoperiod is often considered the most reliable driver in the phenology of plants e.g. 22 . A transition toward photoperiod‑constrained phenology may represent a stabilizing response to environmental uncertainty in insects, allowing organisms to anchor seasonal timing to invariant signals e.g. 23 . However, such shifts may also carry ecological costs by reducing phenological flexibility under variable conditions. Fireflies (Coleoptera: Lampyridae) provide an ideal system in which to examine these dynamics because adult activity is highly seasonal and constrained to brief windows during which mating and reproduction must occur 24 . Successful reproduction depends on precise temporal overlap between males and females, making fireflies particularly sensitive to changes in activity timing 25 . Phenological changes in this taxon have been linked to declining populations 26 . Previous work in this system 27 examined 12 years of adult firefly captures at the Kellogg Biological Station and demonstrated that degree‑day accumulation was a stronger predictor of peak activity than calendar‑based measures. Peak abundance occurred near 800 degree‑days, though substantial interannual variation remained, largely associated with precipitation extremes. Habitat type strongly structured firefly abundance, indicating persistent habitat associations. While weather variables governed phenological timing in this study, this reliance on specific habitats suggests an additional axis of vulnerability if habitat conditions fail to remain stable as phenological cues reorganize. A long‑term test of cue reliability Although earlier work established temperature as the primary phenological driver among this population, it remains unknown whether this cue reliance has remained stable under increasingly variable climate regimes 20 . Short‑term datasets may obscure slow reorganizations in cue use or fail to capture rare but influential climatic extremes 18,28–30 . By extending analyses across longer time frames, it becomes possible to directly test whether previously identified phenological relationships persist, erode, or reorganize when confronted with increasing contemporary variability 20,31 . Long‑term datasets expand the temporal space sampled, encompassing anomalously warm, cool, wet, and dry seasons, and therefore provide a crucial baseline for evaluating whether established cue–phenology relationships remain robust or whether additional mechanisms have emerged as drivers of seasonal timing 9,28,32 . Here, we revisit the Hermann et al 27 . system using a 22‑year continuous dataset of adult firefly captures spanning 2004–2025, representing a unique long‑term phenological record for an insect system. Data were collected under a consistent, structured sampling design with standardized effort across years, providing a rare opportunity to evaluate temporal patterns independently of variation in observer effort. By comparing the original study period (“Baseline”; 2004–2015) with a subsequent decade (“New”; 2016–2025), and by analyzing the full time series, we test whether the relative importance of phenological cues has remained stable through time. Specifically, we assess whether degree‑day accumulation continues to outperform calendar‑based measures, whether precipitation and habitat associations have changed, and whether long‑term population patterns emerge that are not detectable in shorter records. This temporal depth allows us to evaluate whether phenological mechanisms have reorganized under contemporary climate variability and to assess the ecological consequences of any such shifts for population dynamics and habitat‑associated activity. Changing dominance of environmental predictors Using data from 2004-2025 compiled through the long term insect survey conducted as part of the Kellogg Biological Station Long‑Term Ecological Research site 33 , we modelled the relationships between firefly activity and temperature-based, precipitation-based, and photoperiod-based environmental cues. A total of 30,978 fireflies were captured; 17,083 (average 0.57 per trap) were recorded during the Baseline period, while 13,695 (average 0.45 per trap) were captured in the New period. We used hierarchical generalized additive models with group‑specific smooth terms to evaluate how the relationships between environmental predictors and firefly captures varied across the two study periods 34 . Relationships between environmental predictors and adult firefly abundance differed markedly between the study periods. Deviance patterns revealed a pronounced reorganization of cue importance between periods (Fig. 1). Consistent with strong seasonal collinearity among predictors, these shifts were not readily apparent from raw capture data alone (Fig. S1). During the Baseline period, adult abundance was dominated by degree‑day accumulation, which exhibited the strongest evidence of association with activity (F = 60.2; Fig. 1). Week of year contributed little to model fit (F = 7.1), indicating that seasonal timing was governed primarily by accumulated thermal conditions rather than photoperiod. Other environmental variables, including minimum and maximum temperatures and weekly precipitation, showed comparatively weaker relationships (all F < 26.2). In contrast, predictor importance reorganized in the New study period (2016–2025). Here, week of year emerged as the dominant predictor (F = 37.7), while the influence of degree‑day accumulation declined substantially (F = 22.1). Remaining environmental variables were statistically significant but reduced effects (all F < 17.8), reflected an overall contraction in the influence of these cues. Interannual variation was the second most influential smoothed term in both periods, though it captured more variation in the New period (Baseline: F = 27.7; New: F = 35.2). Among the remaining environmental variables, all exhibited greater evidence of association in the Baseline period than in the New period (F Baseline > F New for all), and their relative ranking remained consistent (minimum temperature > maximum temperature > weekly precipitation). The parametric habitat term also showed a notable decline in explanatory strength, exhibiting far greater importance during the Baseline period (F = 54.3) than in the New period (F = 13.4), suggesting a reorganization of habitat‑associated activity patterns over time. Functional relationships reorganize The partial effect curves revealed a clear reorganization of functional relationships governing firefly activity across the two study periods (Fig. 2). During the Baseline period, the degree‑day smooth exhibited a pronounced, nonlinear rise in abundance with increasing thermal accumulation, with peak activity occurring at 747 degree‑days (Fig. 2A). In the New period, this relationship became substantially more variable across the growing season, displaying a local inflection near 731 degree‑days followed by an extended plateau and increasing uncertainty late in the season. Together, these patterns indicate a weakened or diminished role for accumulated heat in determining adult activity. In contrast, the smooth for week of year strengthened markedly in the New period, shifting from a modest seasonal pattern into a sharply defined, photoperiod‑aligned peak at 27.8 weeks (near 3 July; Fig. 2B). The Baseline period displayed a far more irregular relationship with week, with multiple shallow peaks dispersed across the growing season. This divergence strongly suggests a transition from flexible, thermally responsive timing to a more rigid, photoperiod‑constrained phenological structure. The remaining environmental predictors also differed between periods, but in every case the Baseline smooths showed greater curvature within biologically relevant ranges (Fig. 2C–E), consistent with a stronger and more coherent influence of weather‑based cues earlier in the time series. Year‑to‑year variation exhibited cyclical structure across both periods (Fig. 2F), though the New period showed a higher‑amplitude signal, suggesting that less variation was captured by environmental predictors in contemporary years. Habitat‑associated estimates likewise displayed greater variability in the Baseline period (Fig. 2G), pointing to a reorganization of habitat‑linked activity patterns consistent with broader system‑level shifts. Importantly, these partial effects corroborate the comparative‑evidence patterns derived from F‑statistics and deviance partitioning, demonstrating that the shift in cue importance reflects genuine changes in underlying functional relationships rather than artifacts of model structure or predictor collinearity. System structure shifts: decline emerges Long‑term abundance patterns indicate that this phenological reorganization occurred concurrently with a shift in population trajectory (Fig. 3). Within each study period, short‑interval linear trends were not statistically significant (all p > 0.05), reflecting cyclical interannual variability rather than directional change. However, when the full 22‑year time series was considered, a significant negative trend in captures emerged (t = 2.75, p = 0.006), corresponding to an average loss of 0.015 ± 0.005 adults per trap per year. This rate of decline equates to a 46.5% reduction in captures since 2004, or an average decline of 2.1% per year, revealing a previously unrecognized long‑term loss that was masked when analyses were restricted to shorter temporal windows 27 . Multivariate analyses of habitat‑associated activity patterns revealed additional system‑level reorganization across the two study periods (Fig. 4). Habitat use patterns exhibited substantial overlap between periods, yet with different dispersion patterns: the Baseline period exhibited a slightly greater spread in ordination space, whereas the New period formed a more constrained cluster. This contrast indicates higher habitat‑associated variability in the earlier period (i.e. increased specificity or preference for habitats) and reduced heterogeneity in more recent years. Despite the overlapping ellipses, the positions of points suggested directional differences in habitat associations. Baseline observations were more frequently associated with forested treatments, whereas New observations tended to align more strongly with the organic‑management treatment. PERMANOVA confirmed significant differences in multivariate community structure between periods (F = 3.1, p = 0.028), indicating that habitat‑linked activity patterns were reorganized even as overall ordination space remained partially shared. Discussion Our results reveal a fundamental shift in the environmental cues governing firefly phenology. Whereas earlier work in this system demonstrated that adult activity was primarily temperature‑driven, and modulated by precipitation extremes 27 , we find that the relative importance of temperature has declined over time. Instead, firefly phenology has become increasingly constrained by calendar timing, consistent with photoperiodic control. This transition reflects a qualitative change in phenological mechanism, not simply a directional advance or delay in seasonal activity and indicates that the rules governing firefly seasonal timing have reorganized under contemporary environmental conditions. This shift is consistent with theoretical expectations that organisms should favor more reliable cues when historically informative signals degrade (Ettinger et al. 2021, Gotthard et al. 2026, Tarascio et al. 2026). Temperature accumulation is an inherently variable cue whose reliability depends on relatively stable seasonal progression but may be advantageous when extreme events are rare. Under increasingly erratic climate regimes characterized by anomalous warming, late frosts, and precipitation extremes, the informational value of thermal cues may diminish 8,9 . Photoperiod, by contrast, remains invariant across years and provide a stable reference for seasonal timing. Increasing reliance on invariant cues therefore likely reflects a response to declining cue reliability rather than a loss of thermal sensitivity per se , representing a recalibration of the environmental information used to initiate seasonal activity. A key consequence of this transition is reduced phenological flexibility. Such rigidity may increase the risk of mismatch between life history events and other environmental conditions, resulting in fitness consequences 35 . Notably, the emergence of a sustained population decline coincides with this shift in cue dominance. Although short‑term (decade long) analyses failed to detect directional trends due to high interannual variability, the full 22‑year record reveals a significant long‑term decline at the rate of ~2% per year. These results suggest that demographic consequences of phenological reorganization may unfold gradually and remain obscured in shorter datasets 17,36 . System‑level reorganization of activity patterns Many firefly species are habitat specialists 37 , but the dominant species at our study site is not: Photinus pyralis occupies a broad geographic range and exhibits flexible habitat use 38 . Ecological theory often predicts that such habitat generalists should be better able to accommodate environmental change, particularly under increasing climatic variability 39,40 . Despite this apparent generalism, habitat‑associated activity patterns changed across study periods. Earlier years were characterized by substantial variation in capture across habitat types, with apparent preferences for treatments with low soil disturbance and moderate vegetation height 27 . In contrast, the New period showed reduced differentiation among habitat types, suggesting a decline in habitat specialization even within an already generalist species. These patterns indicate that fireflies are not only responding differently to temporal cues but are also engaging with their surrounding habitats in less differentiated ways. Together, the concurrent shifts in phenology and habitat‑associated activity suggest a restructuring of ecological relationships that extends beyond seasonal timing alone, consistent with broader changes in system organization. Dynamic phenological drivers Phenological traits are often highly heritable, yet many phenological models assume that the identity and relative importance of environmental drivers remain fixed through time 10,11 . Our results demonstrate that beyond shifts in sensitivity to individual drivers, cue dominance itself may change under sustained climatic variability. If such cue switching is widespread, phenological forecasts based on static driver relationships may mischaracterize organismal responses and underestimate the potential for phenological mismatch and population decline 17,35,36 . Incorporating dynamic cue use into phenological frameworks may therefore be essential for improving predictive capacity under climate change. Fireflies may serve as particularly informative sentinel taxa for detecting interacting effects of climate, habitat, and environmental change. Their reliance on short, synchronous breeding windows, sensitivity to temperature, moisture and habitat quality, and strong habitat associations make them especially vulnerable to phenological constraint and ecological reorganization 6,37,41 . While larvae of this taxon primarily dwell on soil and in leaf litter, moderating conditions from ambient air, and other studies have shown their development may be responsive to both photoperiod and temperature 42,43 . Additionally, as organisms sensitive to environmental change, they may move to more favorable areas, making concurrent mismatch of phenological cues inevitable 44 . The coincident shifts in cue use, habitat‑associated activity, and population trajectory observed here indicate that changes in firefly phenology may signal broader ecological disruption in temperate systems, with implications for conservation strategies that account for both habitat stability and increasing environmental variability 5,45 . Several considerations shape the scope of inference in this study. Trap captures provide indices of abundance rather than absolute population sizes 46 , week of year serves as a proxy rather than a direct measure of photoperiod 47 , and all relationships described remain correlative 48 . At the same time, interpretation of long‑term phenological and population change depends critically on sampling structure 49 . Firefly studies increasingly draw on diverse data sources that have expanded spatial and temporal coverage 12 , but datasets collected under consistent, ‘denominator‑controlled’ sampling designs offer a distinct advantage for evaluating temporal change 50 . The structured, effort‑standardized framework used here enabled detection of phenological reorganization and population decline across decades despite pronounced interannual variability, highlighting the importance of long‑term, high‑information datasets for resolving slow or subtle ecological change 51 . Climate change may alter not only the timing of biological events, but the rules organisms use to track their environment. Our results show that phenological systems can reorganize fundamentally under sustained climatic variability, with cascading consequences for population dynamics and ecological structure that only long‑term data can reveal. Methods This study was conducted at the W.K. Kellogg Biological Station Long‑Term Ecological Research (KBS LTER) site in southwestern Michigan, USA 52 . We used long‑term insect monitoring data from the Main Cropping System Experiment (MCSE), a large‑scale agronomic experiment established in 1989. Reference forest plots were incorporated into the monitoring program in 1993. The MCSE includes seven plant community treatments spanning a gradient from annually managed cropping systems to perennial vegetation. Annual systems consist of a three‑year rotation of maize, soybean, and wheat managed under four intensities (conventional, no‑till, reduced‑input, and biologically based), while perennial treatments include forage crops (alfalfa until 2017, thereafter switchgrass), poplar plantations, and early‑successional vegetation maintained by annual burning. Each treatment is replicated six times in 1‑ha plots. Three additional forest site types: conifer plantations, late‑successional deciduous forest, and successional forest on abandoned agricultural land, were established within 3 km of the MCSE in 1993; forest plots are also 1 ha and replicated three times per treatment. Data for this study were drawn from two publicly available KBS datasets: a weather dataset including daily maximum and minimum temperature and precipitation, and a dataset primarily focused on lady beetle observations, which also recorded other insect captures 27,53 . Firefly abundance data were collected alongside the ladybeetle experiment beginning in 2004. Fireflies were recorded to family, though spot checks indicate most captures belonged to the genus Photinus , primarily Photinus pyralis , the common big dipper firefly, while the presence of other species cannot be excluded (Appendix S1). Observations were conducted weekly throughout the sampling season at five stations per replicate (MCSE and forest sites). Insects were sampled using unbaited, two-sided yellow sticky cards mounted 1.2 m above the ground and exposed for one week per deployment 54 . Seasonal sampling duration varied slightly with crop planting dates but averaged 14 ± 1 weeks per year. Insect abundance data are publicly accessible (http://lter.kbs.msu.edu/datatables/67). To characterize weather conditions at a scale relevant to insect sampling, we used data from the KBS on-site weather station (http://lter.kbs.msu.edu/datatables/7). Small gaps in the record were filled via linear interpolation using adjacent daily values 53 . Temperature and precipitation were summarized at both weekly and seasonal scales. Weekly summaries included cumulative growing degree days (base 10 °C), total precipitation, and minimum and maximum temperatures for each sampling period. Time structure To capture temporal dynamics, we analyzed two distinct study periods: a Baseline study conducted in Hermann et al. 27 (2004-2015) and a contemporary study which we termed New (2016-2025). Sampling methodology was consistent across the two time periods, with a change in manufacturer of traps in 2021 when the previous traps became unavailable 54 . Analyses considered within-season patterns separately for each study, while interannual variation was accounted for in the GAMs as described below. Analytical approach We modeled adult firefly abundance using generalized additive models (GAMs) fit with a quasi-Poisson error distribution and log link to account for overdispersed count data 53 . To account for variation in sampling effort, we included the log of trap number as an offset. Predictor effects were modeled using smooth functions, with separate smooths estimated for each study via factor-by-smooth interactions. Interannual variation was included as a penalized smooth of year, with an increased smoothing penalty to constrain the magnitude of year-to-year variation and prevent temporal trends from dominating within-season dynamics. Environmental covariates, including degree day accumulation, week of year (as a proxy for photoperiod), minimum temperature, maximum temperature, and weekly precipitation, were modeled using shrinkage smooths to allow non-informative terms to be penalized toward zero. A parametric interaction between habitat and study was also included. Models were fit using restricted maximum likelihood (REML), and all analyses were conducted in R using the mgcv package 55,56 . To quantify the relative importance of each predictor in explaining variation in adult firefly abundance, we summarized smooth and parametric terms using their associated F‑statistics and changes in explained deviance. In mgcv, F‑statistics assess the strength of evidence that a term explains non‑zero variation in the response, accounting for its estimated degrees of freedom; however, they do not represent direct estimates of variance explained 55 . We therefore interpret F‑values as indicators of the relative contribution of each smooth or parametric term, while relying on explained deviance and model comparison to assess each predictor’s contribution to overall model fit. This combined approach enables consistent comparison of environmental and habitat predictors within and across study periods. Plots of adult captures per trap over time were generated, with linear fits overlaid to illustrate trends across years and treatments, providing context for the modeled effects. Broad temporal patterns in adult firefly abundance were then explored using simple linear regression on raw capture data for each study period. This provided a straightforward assessment of population trends over time, complementing the GAM analyses by highlighting overall increases or decreases in abundance across years and treatments. Firefly habitat use patterns were evaluated using non-metric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarities 57 . Ellipses representing standard deviation around study centroids were drawn around each study group to visualize between-study variation. Differences in habitat use patterns between studies were formally tested using permutational multivariate analysis of variance (PERMANOVA). All analysis code and data is available in a public repository https://github.com/BahlaiLab/Lampy_2025 Declarations ACKNOWLEDGEMENTS The long-term insect monitoring experiment at KBS-LTER was initiated in 1989 by Stuart Gage and Manuel Colunga-Garcia. We sincerely thank the ongoing efforts of the research staff supporting the long-term study, in particular Stacey Van Der Wulp, Sven Bohm, Julia Perrone, Elizabeth D'Auria, and many undergraduate assistants over the project's history. FUNDING Support for this research was provided by National Science Foundation Grants (DBI 2045721 and DEB 2225092) to Christie A. Bahlai, NSF Long-term Ecological Research Program (DEB 2224712) at the Kellogg Biological Station, and Michigan State University AgBioResearch. Douglas A. Landis acknowledges support from the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research Award DE-SC0018409. AUTHOR CONTRIBUTIONS C.B. conceived the study, developed the methodology, and supervised the project. C.B. and D.L. curated the data, acquired funding, and administered the project. C.B. and K.P. performed the formal analyses. Visualization was carried out by C.B. and K.P. 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Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Global Change Biology 13 , 1860–1872 (2007). Boyd, R. J., Powney, G. D. & Pescott, O. L. We need to talk about nonprobability samples. Trends in Ecology & Evolution https://doi.org/10.1016/j.tree.2023.01.001 (2023) doi:10.1016/j.tree.2023.01.001. Franklin, J. F. Importance and Justification of Long-Term Studies in Ecology. in Long-Term Studies in Ecology (ed. Likens, G. E.) 3–19 (Springer New York, New York, NY, 1989). doi:10.1007/978-1-4615-7358-6_1. Robertson, G. P. & Hamilton, S. K. Long-term ecological research at the Kellogg Biological Station LTER site. The ecology of agricultural landscapes: Long-term research on the path to sustainability 1 , 32 (2015). Arnold, M. B. et al. Coexistence between similar invaders: The case of two cosmopolitan exotic insects. Ecology 104 , e3979 (2023). McNamara Manning, K., Perrone, J., Petrycki, S., Landis, D. A. & Bahlai, C. A. Adapting to changing methodology in a long‐term experiment. Ecosphere 15 , e4951 (2024). Wood, S. N. Generalized Additive Models: An Introduction with R (2nd Ed.) . (Chapman and Hall/CRC, 2017). Wood, S. N. Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models. Journal of the Royal Statistical Society Series B: Statistical Methodology 73 , 3–36 (2011). Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.8-0. 2.7-3 https://doi.org/10.32614/CRAN.package.vegan (2026). iNaturalist community. Observations of Lampyridae from southwestern Michigan, USA observed between 2010-2025. https://www.inaturalist.org (2026). Additional Declarations There is NO Competing Interest. 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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-9451861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":625550453,"identity":"e9d20313-6ca7-4b21-960d-957ef78dce14","order_by":0,"name":"Christie Bahlai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACCQY2hgcMEnISEC4zkVoSGCSMgVoYG0jRwpA4g2gt/LObnz1IqLFIn9nee/wBQ4V1YgNBS+4cMzdIOCaRO5vnHFD1mXTCWgwkEswkEtgkcudJ5Bg2MLYdJkZL+jeJhH8S6XLyb4Ba/hGlJcdMIrFNIkFaggeopYEILRJ3zpRJJPZJGM7syTGckXAs3ZigFv7Z7dskPnyrk5c4fsbgw4caa1mCWlBBAmnKR8EoGAWjYBTgAgCCbju54dT1WQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8937-8709","institution":"Kent State University","correspondingAuthor":true,"prefix":"","firstName":"Christie","middleName":"","lastName":"Bahlai","suffix":""},{"id":625550454,"identity":"0ef1022b-903e-4f50-8282-fae6a237e67a","order_by":1,"name":"Karolina Polakowski","email":"","orcid":"","institution":"Kent State University","correspondingAuthor":false,"prefix":"","firstName":"Karolina","middleName":"","lastName":"Polakowski","suffix":""},{"id":625550455,"identity":"e7d278d0-57d7-4180-af3b-0f95536690c2","order_by":2,"name":"Douglas Landis","email":"","orcid":"https://orcid.org/0000-0003-4943-6000","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Landis","suffix":""}],"badges":[],"createdAt":"2026-04-17 17:55:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9451861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9451861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107305612,"identity":"842e71e3-89b3-4612-9cfc-bc26f1030148","added_by":"auto","created_at":"2026-04-20 08:20:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDeviance attributed to environmental predictors in a hierarchical generalized additive model of adult firefly abundance.\u003c/em\u003e Bars show the F‑statistic associated with each predictor, with separate values for the Baseline and New study periods. Higher F‑statistics indicate stronger evidence that a given predictor contributes to explaining variation in abundance. The comparison illustrates a reorganization of environmental cue importance between study periods, including a shift from degree‑day dominance in the Baseline period to greater influence of week of year in the New period.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/ba971b0db4e206047ed548d3.png"},{"id":107305613,"identity":"7add7e26-b130-4890-b227-f7dd4c3cd59f","added_by":"auto","created_at":"2026-04-20 08:20:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModeled partial effects of environmental conditions and habitat on adult firefly abundance. \u003c/em\u003eEach curve shows the estimated response to a single predictor while all other variables are held constant, with separate smooths for the Baseline (salmon) and New (blue) study periods. Degree‑day accumulation (A) and week of year (B) illustrate shifts in dominant phenological cues between periods, while minimum temperature, maximum temperature, and precipitation (C–E) show changes in secondary environmental relationships. Year‑to‑year variation (F) and habitat effects (G; shown as parametric contrasts) highlight broader system‑level differences between study periods.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/6e4c84a8f0b44a729634add9.png"},{"id":107305614,"identity":"33f38d83-fb42-444f-ac43-d282fb9238e5","added_by":"auto","created_at":"2026-04-20 08:20:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual firefly captures from 2004–2025 across ten plant‑community treatments at the Kellogg Biological Station\u003c/em\u003e. Points show annual mean adults per trap, with salmon points indicating data from the Baseline study period (2004–2015) and blue points representing data collected in the New period (2016–2025). The dashed grey line depicts a nonparametric GAM smooth highlighting interannual variability, while the solid black line shows the overall linear trend across the full time series. This long‑term trend reveals a contemporary decline that is not detectable when examining individual study periods in isolation.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/e89f1a7f2e8879d03e249383.png"},{"id":107305615,"identity":"40a607a3-db3b-4eb9-9d13-604e1ad90220","added_by":"auto","created_at":"2026-04-20 08:20:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNon‑metric multidimensional scaling (NMDS) of firefly captures across plant‑community treatments, grouped by study period. \u003c/em\u003eEach point represents a treatment–replicate combination, colored by study period. Ellipses indicate one standard deviation around study centroids, illustrating greater dispersion in the Baseline period and tighter clustering in the New period. Although ellipses overlap, ordination positions suggest that Baseline observations are more frequently associated with forest treatments, whereas New observations tend to align with the organic‑management treatment. NMDS stress = 0.16.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/a1bc26ab22d7664044c45e51.png"},{"id":108803433,"identity":"3fefcf55-2d9e-47fe-93b1-1093cd9b2305","added_by":"auto","created_at":"2026-05-08 14:53:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":534902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/fa8beb6e-1c36-42ca-bb52-59e4e92b8de5.pdf"},{"id":107305611,"identity":"9d06f095-a667-42ac-b359-8b86ad455ede","added_by":"auto","created_at":"2026-04-20 08:20:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170759,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9451861/v1/421accecd6c328a3ebf815f2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Climate variability reshapes the cues governing phenology","fulltext":[{"header":"Main","content":"\u003cp\u003ePhenology\u0026mdash;the seasonal timing of biological events\u0026mdash;is a fundamental axis through which organisms interact with their environment\u003csup\u003e1\u003c/sup\u003e. In insects, phenological timing is commonly a response to integrated environmental cues rather than fixed photoperiod cues, with accumulated temperature often serving as a primary driver of development, emergence, and reproduction\u003csup\u003e2,4,12\u003c/sup\u003e. Degree‑day metrics, which integrate time and temperature to quantify heat accumulation, have therefore become a cornerstone of insect phenology models\u003csup\u003e13\u003c/sup\u003e. This framework carries an implicit assumption: that the environmental cues governing seasonal timing, and their relative importance, remain stable through time\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003eAccelerating climate variability increasingly challenges this assumption\u003csup\u003e15,16\u003c/sup\u003e. Short‑term weather anomalies, warmer springs, altered precipitation regimes, and more frequent extremes can decouple long‑standing relationships between environmental conditions and biological timing\u003csup\u003e17\u003c/sup\u003e. In insects, these anomalies often exert stronger effects on phenology than long‑term seasonal means, introducing noise into traditionally reliable cues such as temperature accumulation\u003csup\u003e18\u003c/sup\u003e. Under increasingly variable conditions, the informational value of climatic cues may degrade, raising questions about whether organisms can continue to rely on them to accurately time seasonal activity\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003eWhen historically informative cues become unreliable, organisms may shift toward alternative signals that provide greater consistency or synchronization with biotic needs\u003csup\u003e8,9\u003c/sup\u003e. Photoperiod (measured by calendar date) represents one such cue: unlike temperature or precipitation, day length varies predictably across years at consistent latitudes and is unaffected by interannual climatic variability. Photoperiod is often considered the most reliable driver in the phenology of plants \u003csup\u003ee.g. 22\u003c/sup\u003e. A transition toward photoperiod‑constrained phenology may represent a stabilizing response to environmental uncertainty in insects, allowing organisms to anchor seasonal timing to invariant signals \u003csup\u003ee.g. 23\u003c/sup\u003e. However, such shifts may also carry ecological costs by reducing phenological flexibility under variable conditions.\u003c/p\u003e\n\n\u003cp\u003eFireflies (Coleoptera: Lampyridae) provide an ideal system in which to examine these dynamics because adult activity is highly seasonal and constrained to brief windows during which mating and reproduction must occur\u003csup\u003e24\u003c/sup\u003e. Successful reproduction depends on precise temporal overlap between males and females, making fireflies particularly sensitive to changes in activity timing\u003csup\u003e25\u003c/sup\u003e. Phenological changes in this taxon have been linked to declining populations\u003csup\u003e26\u003c/sup\u003e. Previous work in this system\u003csup\u003e27\u003c/sup\u003e examined 12 years of adult firefly captures at the Kellogg Biological Station and demonstrated that degree‑day accumulation was a stronger predictor of peak activity than calendar‑based measures. Peak abundance occurred near 800 degree‑days, though substantial interannual variation remained, largely associated with precipitation extremes. Habitat type strongly structured firefly abundance, indicating persistent habitat associations. While weather variables governed phenological timing in this study, this reliance on specific habitats suggests an additional axis of vulnerability if habitat conditions fail to remain stable as phenological cues reorganize.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eA long‑term test of cue reliability\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAlthough earlier work established temperature as the primary phenological driver among this population, it remains unknown whether this cue reliance has remained stable under increasingly variable climate regimes\u003csup\u003e20\u003c/sup\u003e. Short‑term datasets may obscure slow reorganizations in cue use or fail to capture rare but influential climatic extremes \u003csup\u003e18,28\u0026ndash;30\u003c/sup\u003e. By extending analyses across longer time frames, it becomes possible to directly test whether previously identified phenological relationships persist, erode, or reorganize when confronted with increasing contemporary variability\u003csup\u003e20,31\u003c/sup\u003e. Long‑term datasets expand the temporal space sampled, encompassing anomalously warm, cool, wet, and dry seasons, and therefore provide a crucial baseline for evaluating whether established cue\u0026ndash;phenology relationships remain robust or whether additional mechanisms have emerged as drivers of seasonal timing\u003csup\u003e9,28,32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere, we revisit the Hermann et al\u003csup\u003e27\u003c/sup\u003e. system using a 22‑year continuous dataset of adult firefly captures spanning 2004\u0026ndash;2025, representing a unique long‑term phenological record for an insect system. Data were collected under a consistent, structured sampling design with standardized effort across years, providing a rare opportunity to evaluate temporal patterns independently of variation in observer effort. By comparing the original study period (\u0026ldquo;Baseline\u0026rdquo;; 2004\u0026ndash;2015) with a subsequent decade (\u0026ldquo;New\u0026rdquo;; 2016\u0026ndash;2025), and by analyzing the full time series, we test whether the relative importance of phenological cues has remained stable through time. Specifically, we assess whether degree‑day accumulation continues to outperform calendar‑based measures, whether precipitation and habitat associations have changed, and whether long‑term population patterns emerge that are not detectable in shorter records. This temporal depth allows us to evaluate whether phenological mechanisms have reorganized under contemporary climate variability and to assess the ecological consequences of any such shifts for population dynamics and habitat‑associated activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanging dominance of environmental predictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from 2004-2025 compiled through the long term insect survey conducted as part of the Kellogg Biological Station Long‑Term Ecological Research site\u003csup\u003e33\u003c/sup\u003e, we modelled the relationships between firefly activity and temperature-based, precipitation-based, and photoperiod-based environmental cues. A total of 30,978 fireflies were captured; 17,083 (average 0.57 per trap) were recorded during the Baseline period, while 13,695 (average 0.45 per trap) were captured in the New period. \u003c/p\u003e\n\u003cp\u003eWe used hierarchical generalized additive models with group‑specific smooth terms to evaluate how the relationships between environmental predictors and firefly captures varied across the two study periods\u003csup\u003e34\u003c/sup\u003e. Relationships between environmental predictors and adult firefly abundance differed markedly between the study periods. Deviance patterns revealed a pronounced reorganization of cue importance between periods (Fig. 1). Consistent with strong seasonal collinearity among predictors, these shifts were not readily apparent from raw capture data alone (Fig. S1).\u003c/p\u003e\n\u003cp\u003eDuring the Baseline period, adult abundance was dominated by degree‑day accumulation, which exhibited the strongest evidence of association with activity (F = 60.2; Fig. 1). Week of year contributed little to model fit (F = 7.1), indicating that seasonal timing was governed primarily by accumulated thermal conditions rather than photoperiod. Other environmental variables, including minimum and maximum temperatures and weekly precipitation, showed comparatively weaker relationships (all F \u0026lt; 26.2). In contrast, predictor importance reorganized in the New study period (2016\u0026ndash;2025). Here, week of year emerged as the dominant predictor (F = 37.7), while the influence of degree‑day accumulation declined substantially (F = 22.1). Remaining environmental variables were statistically significant but reduced effects (all F \u0026lt; 17.8), reflected an overall contraction in the influence of these cues. Interannual variation was the second most influential smoothed term in both periods, though it captured more variation in the New period (Baseline: F = 27.7; New: F = 35.2). Among the remaining environmental variables, all exhibited greater evidence of association in the Baseline period than in the New period (F\u003csub\u003eBaseline\u003c/sub\u003e \u0026gt; F\u003csub\u003eNew\u003c/sub\u003e for all), and their relative ranking remained consistent (minimum temperature \u0026gt; maximum temperature \u0026gt; weekly precipitation). The parametric habitat term also showed a notable decline in explanatory strength, exhibiting far greater importance during the Baseline period (F = 54.3) than in the New period (F = 13.4), suggesting a reorganization of habitat‑associated activity patterns over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional relationships reorganize\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe partial effect curves revealed a clear reorganization of functional relationships governing firefly activity across the two study periods (Fig. 2). During the Baseline period, the degree‑day smooth exhibited a pronounced, nonlinear rise in abundance with increasing thermal accumulation, with peak activity occurring at 747 degree‑days (Fig. 2A). In the New period, this relationship became substantially more variable across the growing season, displaying a local inflection near 731 degree‑days followed by an extended plateau and increasing uncertainty late in the season. Together, these patterns indicate a weakened or diminished role for accumulated heat in determining adult activity. In contrast, the smooth for week of year strengthened markedly in the New period, shifting from a modest seasonal pattern into a sharply defined, photoperiod‑aligned peak at 27.8 weeks (near 3 July; Fig. 2B). The Baseline period displayed a far more irregular relationship with week, with multiple shallow peaks dispersed across the growing season. This divergence strongly suggests a transition from flexible, thermally responsive timing to a more rigid, photoperiod‑constrained phenological structure.\u003c/p\u003e\n\u003cp\u003eThe remaining environmental predictors also differed between periods, but in every case the Baseline smooths showed greater curvature within biologically relevant ranges (Fig. 2C\u0026ndash;E), consistent with a stronger and more coherent influence of weather‑based cues earlier in the time series. Year‑to‑year variation exhibited cyclical structure across both periods (Fig. 2F), though the New period showed a higher‑amplitude signal, suggesting that less variation was captured by environmental predictors in contemporary years. Habitat‑associated estimates likewise displayed greater variability in the Baseline period (Fig. 2G), pointing to a reorganization of habitat‑linked activity patterns consistent with broader system‑level shifts.\u003c/p\u003e\n\u003cp\u003eImportantly, these partial effects corroborate the comparative‑evidence patterns derived from F‑statistics and deviance partitioning, demonstrating that the shift in cue importance reflects genuine changes in underlying functional relationships rather than artifacts of model structure or predictor collinearity.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSystem structure shifts: decline emerges\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eLong‑term abundance patterns indicate that this phenological reorganization occurred concurrently with a shift in population trajectory (Fig. 3). Within each study period, short‑interval linear trends were not statistically significant (all p \u0026gt; 0.05), reflecting cyclical interannual variability rather than directional change. However, when the full 22‑year time series was considered, a significant negative trend in captures emerged (t = 2.75, p = 0.006), corresponding to an average loss of 0.015 \u0026plusmn; 0.005 adults per trap per year. This rate of decline equates to a 46.5% reduction in captures since 2004, or an average decline of 2.1% per year, revealing a previously unrecognized long‑term loss that was masked when analyses were restricted to shorter temporal windows\u003csup\u003e27\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eMultivariate analyses of habitat‑associated activity patterns revealed additional system‑level reorganization across the two study periods (Fig. 4). Habitat use patterns exhibited substantial overlap between periods, yet with different dispersion patterns: the Baseline period exhibited a slightly greater spread in ordination space, whereas the New period formed a more constrained cluster. This contrast indicates higher habitat‑associated variability in the earlier period (i.e. increased specificity or preference for habitats) and reduced heterogeneity in more recent years. Despite the overlapping ellipses, the positions of points suggested directional differences in habitat associations. Baseline observations were more frequently associated with forested treatments, whereas New observations tended to align more strongly with the organic‑management treatment. PERMANOVA confirmed significant differences in multivariate community structure between periods (F = 3.1, p = 0.028), indicating that habitat‑linked activity patterns were reorganized even as overall ordination space remained partially shared.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results reveal a fundamental shift in the environmental cues governing firefly phenology. Whereas earlier work in this system demonstrated that adult activity was primarily temperature‑driven, and modulated by precipitation extremes\u003csup\u003e27\u003c/sup\u003e, we find that the relative importance of temperature has declined over time. Instead, firefly phenology has become increasingly constrained by calendar timing, consistent with photoperiodic control. This transition reflects a qualitative change in phenological mechanism, not simply a directional advance or delay in seasonal activity and indicates that the rules governing firefly seasonal timing have reorganized under contemporary environmental conditions.\u003c/p\u003e\n\u003cp\u003eThis shift is consistent with theoretical expectations that organisms should favor more reliable cues when historically informative signals degrade (Ettinger et al. 2021, Gotthard et al. 2026, Tarascio et al. 2026). Temperature accumulation is an inherently variable cue whose reliability depends on relatively stable seasonal progression but may be advantageous when extreme events are rare. Under increasingly erratic climate regimes characterized by anomalous warming, late frosts, and precipitation extremes, the informational value of thermal cues may diminish\u003csup\u003e8,9\u003c/sup\u003e. Photoperiod, by contrast, remains invariant across years and provide a stable reference for seasonal timing. Increasing reliance on invariant cues therefore likely reflects a response to declining cue reliability rather than a loss of thermal sensitivity \u003cem\u003eper se\u003c/em\u003e, representing a recalibration of the environmental information used to initiate seasonal activity.\u003c/p\u003e\n\u003cp\u003eA key consequence of this transition is reduced phenological flexibility. Such rigidity may increase the risk of mismatch between life history events and other environmental conditions, resulting in fitness consequences\u003csup\u003e35\u003c/sup\u003e. Notably, the emergence of a sustained population decline coincides with this shift in cue dominance. Although short‑term (decade long) analyses failed to detect directional trends due to high interannual variability, the full 22‑year record reveals a significant long‑term decline at the rate of ~2% per year. These results suggest that demographic consequences of phenological reorganization may unfold gradually and remain obscured in shorter datasets\u003csup\u003e17,36\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSystem‑level reorganization of activity patterns\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eMany firefly species are habitat specialists\u003csup\u003e37\u003c/sup\u003e, but the dominant species at our study site is not: \u003cem\u003ePhotinus pyralis\u003c/em\u003e occupies a broad geographic range and exhibits flexible habitat use\u003csup\u003e38\u003c/sup\u003e. Ecological theory often predicts that such habitat generalists should be better able to accommodate environmental change, particularly under increasing climatic variability\u003csup\u003e39,40\u003c/sup\u003e. Despite this apparent generalism, habitat‑associated activity patterns changed across study periods. Earlier years were characterized by substantial variation in capture across habitat types, with apparent preferences for treatments with low soil disturbance and moderate vegetation height\u003csup\u003e27\u003c/sup\u003e. In contrast, the New period showed reduced differentiation among habitat types, suggesting a decline in habitat specialization even within an already generalist species. These patterns indicate that fireflies are not only responding differently to temporal cues but are also engaging with their surrounding habitats in less differentiated ways. Together, the concurrent shifts in phenology and habitat‑associated activity suggest a restructuring of ecological relationships that extends beyond seasonal timing alone, consistent with broader changes in system organization.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDynamic phenological drivers\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePhenological traits are often highly heritable, yet many phenological models assume that the identity and relative importance of environmental drivers remain fixed through time\u003csup\u003e10,11\u003c/sup\u003e. Our results demonstrate that beyond shifts in sensitivity to individual drivers, cue dominance itself may change under sustained climatic variability. If such cue switching is widespread, phenological forecasts based on static driver relationships may mischaracterize organismal responses and underestimate the potential for phenological mismatch and population decline\u003csup\u003e17,35,36\u003c/sup\u003e. Incorporating dynamic cue use into phenological frameworks may therefore be essential for improving predictive capacity under climate change.\u003c/p\u003e\n\u003cp\u003eFireflies may serve as particularly informative sentinel taxa for detecting interacting effects of climate, habitat, and environmental change. Their reliance on short, synchronous breeding windows, sensitivity to temperature, moisture and habitat quality, and strong habitat associations make them especially vulnerable to phenological constraint and ecological reorganization\u003csup\u003e6,37,41\u003c/sup\u003e. While larvae of this taxon primarily dwell on soil and in leaf litter, moderating conditions from ambient air, and other studies have shown their development may be responsive to both photoperiod and temperature\u003csup\u003e42,43\u003c/sup\u003e. Additionally, as organisms sensitive to environmental change, they may move to more favorable areas, making concurrent mismatch of phenological cues inevitable\u003csup\u003e44\u003c/sup\u003e. The coincident shifts in cue use, habitat‑associated activity, and population trajectory observed here indicate that changes in firefly phenology may signal broader ecological disruption in temperate systems, with implications for conservation strategies that account for both habitat stability and increasing environmental variability\u003csup\u003e5,45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSeveral considerations shape the scope of inference in this study. Trap captures provide indices of abundance rather than absolute population sizes\u003csup\u003e46\u003c/sup\u003e, week of year serves as a proxy rather than a direct measure of photoperiod\u003csup\u003e47\u003c/sup\u003e, and all relationships described remain correlative\u003csup\u003e48\u003c/sup\u003e. At the same time, interpretation of long‑term phenological and population change depends critically on sampling structure\u003csup\u003e49\u003c/sup\u003e. Firefly studies increasingly draw on diverse data sources that have expanded spatial and temporal coverage\u003csup\u003e12\u003c/sup\u003e, but datasets collected under consistent, \u0026lsquo;denominator‑controlled\u0026rsquo; sampling designs offer a distinct advantage for evaluating temporal change\u003csup\u003e50\u003c/sup\u003e. The structured, effort‑standardized framework used here enabled detection of phenological reorganization and population decline across decades despite pronounced interannual variability, highlighting the importance of long‑term, high‑information datasets for resolving slow or subtle ecological change\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eClimate change may alter not only the timing of biological events, but the rules organisms use to track their environment. Our results show that phenological systems can reorganize fundamentally under sustained climatic variability, with cascading consequences for population dynamics and ecological structure that only long‑term data can reveal.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was conducted at the W.K. Kellogg Biological Station Long‑Term Ecological Research (KBS LTER) site in southwestern Michigan, USA\u003csup\u003e52\u003c/sup\u003e. We used long‑term insect monitoring data from the Main Cropping System Experiment (MCSE), a large‑scale agronomic experiment established in 1989. Reference forest plots were incorporated into the monitoring program in 1993.\u003c/p\u003e\n\u003cp\u003eThe MCSE includes seven plant community treatments spanning a gradient from annually managed cropping systems to perennial vegetation. Annual systems consist of a three‑year rotation of maize, soybean, and wheat managed under four intensities (conventional, no‑till, reduced‑input, and biologically based), while perennial treatments include forage crops (alfalfa until 2017, thereafter switchgrass), poplar plantations, and early‑successional vegetation maintained by annual burning. Each treatment is replicated six times in 1‑ha plots. Three additional forest site types: conifer plantations, late‑successional deciduous forest, and successional forest on abandoned agricultural land, were established within 3 km of the MCSE in 1993; forest plots are also 1 ha and replicated three times per treatment.\u003c/p\u003e\n\u003cp\u003eData for this study were drawn from two publicly available KBS datasets: a weather dataset including daily maximum and minimum temperature and precipitation, and a dataset primarily focused on lady beetle observations, which also recorded other insect captures\u003csup\u003e27,53\u003c/sup\u003e. Firefly abundance data were collected alongside the ladybeetle experiment beginning in 2004. Fireflies were recorded to family, though spot checks indicate most captures belonged to the genus \u003cem\u003ePhotinus\u003c/em\u003e, primarily \u003cem\u003ePhotinus pyralis\u003c/em\u003e, the common big dipper firefly, while the presence of other species cannot be excluded (Appendix S1).\u003c/p\u003e\n\u003cp\u003eObservations were conducted weekly throughout the sampling season at five stations per replicate (MCSE and forest sites). Insects were sampled using unbaited, two-sided yellow sticky cards mounted 1.2 m above the ground and exposed for one week per deployment\u003csup\u003e54\u003c/sup\u003e. Seasonal sampling duration varied slightly with crop planting dates but averaged 14\u0026thinsp;\u0026plusmn;\u0026thinsp;1 weeks per year. Insect abundance data are publicly accessible (http://lter.kbs.msu.edu/datatables/67).\u003c/p\u003e\n\u003cp\u003eTo characterize weather conditions at a scale relevant to insect sampling, we used data from the KBS on-site weather station (http://lter.kbs.msu.edu/datatables/7). Small gaps in the record were filled via linear interpolation using adjacent daily values\u003csup\u003e53\u003c/sup\u003e. Temperature and precipitation were summarized at both weekly and seasonal scales. Weekly summaries included cumulative growing degree days (base 10 \u0026deg;C), total precipitation, and minimum and maximum temperatures for each sampling period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo capture temporal dynamics, we analyzed two distinct study periods: a Baseline study conducted in Hermann et al.\u003csup\u003e27\u003c/sup\u003e (2004-2015) and a contemporary study which we termed New (2016-2025). Sampling methodology was consistent across the two time periods, with a change in manufacturer of traps in 2021 when the previous traps became unavailable\u003csup\u003e54\u003c/sup\u003e. Analyses considered within-season patterns separately for each study, while interannual variation was accounted for in the GAMs as described below.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAnalytical approach\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe modeled adult firefly abundance using generalized additive models (GAMs) fit with a quasi-Poisson error distribution and log link to account for overdispersed count data\u003csup\u003e53\u003c/sup\u003e. To account for variation in sampling effort, we included the log of trap number as an offset. Predictor effects were modeled using smooth functions, with separate smooths estimated for each study via factor-by-smooth interactions. Interannual variation was included as a penalized smooth of year, with an increased smoothing penalty to constrain the magnitude of year-to-year variation and prevent temporal trends from dominating within-season dynamics. Environmental covariates, including degree day accumulation, week of year (as a proxy for photoperiod), minimum temperature, maximum temperature, and weekly precipitation, were modeled using shrinkage smooths to allow non-informative terms to be penalized toward zero. A parametric interaction between habitat and study was also included. Models were fit using restricted maximum likelihood (REML), and all analyses were conducted in R using the mgcv package\u003csup\u003e55,56\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo quantify the relative importance of each predictor in explaining variation in adult firefly abundance, we summarized smooth and parametric terms using their associated F‑statistics and changes in explained deviance. In mgcv, F‑statistics assess the strength of evidence that a term explains non‑zero variation in the response, accounting for its estimated degrees of freedom; however, they do not represent direct estimates of variance explained\u003csup\u003e55\u003c/sup\u003e. We therefore interpret F‑values as indicators of the relative contribution of each smooth or parametric term, while relying on explained deviance and model comparison to assess each predictor\u0026rsquo;s contribution to overall model fit. This combined approach enables consistent comparison of environmental and habitat predictors within and across study periods.\u003c/p\u003e\n\u003cp\u003ePlots of adult captures per trap over time were generated, with linear fits overlaid to illustrate trends across years and treatments, providing context for the modeled effects. Broad temporal patterns in adult firefly abundance were then explored using simple linear regression on raw capture data for each study period. This provided a straightforward assessment of population trends over time, complementing the GAM analyses by highlighting overall increases or decreases in abundance across years and treatments.\u003c/p\u003e\n\u003cp\u003eFirefly habitat use patterns were evaluated using non-metric multidimensional scaling (NMDS) based on Bray\u0026ndash;Curtis dissimilarities\u003csup\u003e57\u003c/sup\u003e. Ellipses representing standard deviation around study centroids were drawn around each study group to visualize between-study variation. Differences in habitat use patterns between studies were formally tested using permutational multivariate analysis of variance (PERMANOVA).\u003c/p\u003e\n\u003cp\u003eAll analysis code and data is available in a public repository https://github.com/BahlaiLab/Lampy_2025 \u0026nbsp;\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe long-term insect monitoring experiment at KBS-LTER was initiated in 1989 by Stuart Gage and Manuel Colunga-Garcia. We sincerely thank the ongoing efforts of the research staff supporting the long-term study, in particular Stacey Van Der Wulp, Sven Bohm, Julia Perrone, Elizabeth D\u0026apos;Auria, and many undergraduate assistants over the project\u0026apos;s history.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFUNDING \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport for this research was provided by National Science Foundation Grants (DBI 2045721 and DEB 2225092) to Christie A. Bahlai, NSF Long-term Ecological Research Program (DEB 2224712) at the Kellogg Biological Station, and Michigan State University AgBioResearch. Douglas A. Landis acknowledges support from the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research Award DE-SC0018409.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.B. conceived the study, developed the methodology, and supervised the project. C.B. and D.L. curated the data, acquired funding, and administered the project. C.B. and K.P. performed the formal analyses. Visualization was carried out by C.B. and K.P. C.B. and K.P. wrote the original draft of the manuscript, and all authors (C.B., K.P., and D.L.) contributed to reviewing and editing the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Christie Bahlai, [email protected] \u003c/p\u003e\n\u003cp\u003eReprints and permissions information is available at www.nature.com/reprints\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInouye, D. W. Climate change and phenology. \u003cem\u003eWIREs Climate Change\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e764 (2022).\u003c/li\u003e\n\u003cli\u003eBuckley, L. B. 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Observations of Lampyridae from southwestern Michigan, USA observed between 2010-2025. https://www.inaturalist.org (2026).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9451861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9451861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe timing of life history events govern how organisms interact with their environment, shaping reproduction, population dynamics, and ecological interactions \u003csup\u003e1\u003c/sup\u003e. In insects, this timing often tracks environmental conditions such as temperature or moisture, yet increasing climate variability may challenge the reliability of these cues \u003csup\u003e2–4\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFireflies provide a unique and sensitive system for testing how seasonal timing is regulated, because adult activity and reproduction are extremely sensitive to environmental cues, with potential implications for population dynamics and fitness\u003csup\u003e5–7\u003c/sup\u003e. Using a long‑term, structured dataset, we find that environmental cues shaping seasonal activity have changed, alongside population decline and habitat use change that were not apparent in shorter studies.\u003c/p\u003e\n\u003cp\u003eHere we show that fireflies have shifted from temperature‑driven to photoperiod‑constrained phenology, coinciding with an emerging population decline and a reorganization of habitat use. These results demonstrate that climate change can alter not only the timing of biological events but the environmental signals organisms rely on to track seasonal change \u003csup\u003e8,9\u003c/sup\u003e. By showing that phenological mechanisms themselves can reorganize under sustained climate variability, this work challenges assumptions underlying many phenological models and highlights the importance of long‑term, high‑quality data for understanding ecological responses to environmental change\u003csup\u003e10,11\u003c/sup\u003e.\u003c/p\u003e","manuscriptTitle":"Climate variability reshapes the cues governing phenology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 08:20:24","doi":"10.21203/rs.3.rs-9451861/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"adcc0bef-a5e0-4501-b15f-24fb2e56a49f","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66584191,"name":"Biological sciences/Ecology/Population dynamics"},{"id":66584192,"name":"Biological sciences/Ecology/Climate-change ecology/Phenology"},{"id":66584193,"name":"Biological sciences/Ecology/Ecological modelling"}],"tags":[],"updatedAt":"2026-04-28T17:40:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 08:20:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9451861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9451861","identity":"rs-9451861","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}