{"paper_id":"0a778eec-b92e-4f42-bf06-e7202db05ac2","body_text":"1 \nUrban Environments Reshape Reproductive Phenology in Plants Across the Tropics 1 \n 2 \nROHIT RAJ JHA1, *, ANITA SIMHA1, RICHARD EKENG ITA1, RACHANA RAO1, DAIJIANG LI2, GAURAV 3 \nKANDLIKAR1 4 \n1 Department of Biological Sciences, Louisiana State University, Baton Rouge – 70803, 5 \nLouisiana, USA 6 \n 2 Department of Botany, College of Letters and Science, University of Wisconsin-Madison, 7 \nWisconsin, USA 8 \n  9 \n*Correspondence  10 \nRohit Raj Jha, Department of Biological Sciences, Louisiana State University, Baton Rouge – 11 \n70803, LA, USA 12 \nEmail ID: rohitrajjh@gmail.com 13 \nORCID ID: 0000-0001-9797-6031 14 \n  15 \n  16 \n 17 \n  18 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 2 \nAbstract 19 \nPlant phenological responses to global change phenomena like urbanization remain understudied 20 \nin the tropics, hindering predictions regarding the dynamics of tropical ecosystems amid rapid 21 \nland use changes. Studies of tropical phenology are limited by complexities, like the limited 22 \navailability of phenological data, especially in urbanized landscapes. Observations recorded on 23 \ncitizen science platforms can overcome this limitation by providing vast, spatially distributed 24 \ndata. In this study, we utilize iNaturalist data to evaluate plant reproductive phenology in tropical 25 \nurban vs. rural habitats. We first compare iNaturalist data (111533 records) to herbarium 26 \ncollections (217991 records) in order to validate their use, and we then investigate urban-rural 27 \nphenology differences within 25-km spatial grids for 238 species. Data from iNaturalist and 28 \nherbaria yield complementary insights, with the former being uniformly distributed between 29 \nurban and rural settings, and the latter biased towards rural observations. On average, we found 30 \nspecies to have significantly longer reproductive duration (β = 11.79 ± 2.83 SE, t = 4.16, p < 31 \n10^4), and correspondingly weaker strength of seasonality in urban settings than in nearby rural 32 \nlocalities. We also find trait-mediated variation, with seasonal, annual, and herbaceous plants 33 \nshowing more pronounced differences in reproductive duration and seasonality strength. These 34 \nresults suggest that urbanization in tropical landscapes might have important implications for 35 \nplant demography, with potential consequences for community and ecosystem dynamics. Our 36 \nwork also points to the value of integrating insights from natural history collections with data 37 \nfrom citizen science platforms for enabling broad-scale insights into ecological dynamics in 38 \ntropical urban landscapes. 39 \n 40 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 3 \n 41 \nKey words: 42 \nPlant phenology, urbanization, tropical ecosystems, reproductive seasonality, citizen science, 43 \niNaturalist, herbarium records  44 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 4 \n1. INTRODUCTION 45 \nPhenology, the study of the timing of recurring biological events, links organisms to their abiotic 46 \nenvironment, including temperature, precipitation, and photoperiod, and underpins ecosystem 47 \nfunctioning (Caparros-Santiago et al., 2021; Johansson et al., 2015). In terrestrial systems, the 48 \ntiming of events such as leaf-out, flowering, and fruiting in plants can influence primary 49 \nproductivity, nutrient cycling, and energy flow within ecosystems (Forrest & Miller-Rushing, 50 \n2010; Gallinat et al., 2021; Moore et al., 2016; Tang et al., 2016), as well as ecological 51 \ninteractions like pollination, herbivory, predation, and competition (Forrest & Miller-Rushing, 52 \n2010; Johansson et al., 2015; Kharouba et al., 2018). Shifts in plant phenology are among the 53 \nearliest and most consistent biological responses to global environmental change, serving as key 54 \nindicators of species’ adaptive capacity and ecosystem resilience (Cleland et al., 2007; Parmesan 55 \n& Yohe, 2003). Most current understanding of these impacts comes from temperate regions, 56 \nwhere seasonal variations in important cues like climate and daylength are well-defined across 57 \nthe year. For example, Menzel et al., (2006)’s landmark study linking phenology to climate 58 \nwarming relied on phenological data from a systematic network across Europe to show that a 59 \nvast majority of plant species show signals of advanced leafout, flowering, and fruiting, 60 \nconsistent with warmer winter temperatures and earlier spring conditions. Climate-driven shifts 61 \nin plant phenology have now been identified across temperate regions (Stuble et al., 2021), 62 \nalthough the dynamics and drivers of these shifts can vary across continents (Zohner et al., 63 \n2017). Conversely, much less is known about phenological responses to global environmental 64 \nchange in tropical ecosystems (Piao et al., 2019), where rainfall patterns and uniform 65 \ntemperatures give rise to complex, multi-modal seasonal patterns (Abernethy et al., 2018; 66 \nFitchett et al., 2015). 67 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 5 \n 68 \nAmong the most prominent forces for environmental change in the tropics is 69 \nurbanization, which is associated with changes to local temperature patterns, water availability, 70 \nand biological diversity (Kabano et al., 2021; McDonald et al., 2020; Ogunbode et al., 2025; 71 \nRibeiro et al., 2024). Studies of urban phenological shifts are useful for identifying urban heat 72 \nislands and evaluating impacts of warming on phenology (Jochner et al., 2013). In temperate 73 \ncities, urban heat islands and irrigation systems often reduce climatic constraints on plant 74 \ndevelopment, leading to earlier or extended reproductive periods in cities (Li et al., 2019; Neil & 75 \nWu, 2006; D. S. Park et al., 2023; Wohlfahrt et al., 2019). Although urbanization has been rapid 76 \nand widespread throughout the tropics and is of particular concern for tropical biodiversity 77 \nconservation (McDonald et al., 2020; Simkin et al., 2022), tropical cities are surprisingly 78 \nunderstudied in phenological research (Bonebrake et al., 2025), which constrains predictions 79 \nregarding the resilience of tropical plant dynamics amid rapid climatic and land use changes. 80 \nUnderstanding how urbanization influences reproductive phenology in the tropics is therefore a 81 \npressing research gap (Kabano et al., 2021; Marcacci et al., 2023). 82 \nSeveral challenges complicate studies of tropical phenology. First, unlike in temperate 83 \nregions, where strong seasonal temperature and photoperiod cues trigger synchronized 84 \nphenological events, tropical phenology is influenced by a broader suite of environmental cues 85 \nsuch as rainfall, irradiance, and local microclimate (Borchert, 1996; Borchert et al., 2002; 86 \nJochner et al., 2013; Numata et al., 2022). These cues may operate asynchronously, and their 87 \ntiming, strength, and biological relevance can vary across space, years, and species phenology 88 \n(Abernethy et al., 2018; J. Y. Park et al., 2019; Sakai, 2001; Singh & Kushwaha, 2005). Second, 89 \ntropical ecosystems are generally more species-rich than temperate regions, which results in 90 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 6 \ntropical plant communities encompassing a wider variety of phenological strategies (Abernethy 91 \net al., 2018; J. Y. Park et al., 2019; Singh & Kushwaha, 2005). Along with this, plants’ life 92 \nhistory and growth form also play a definite role in phenological responses, as annual and 93 \nperennial species and contrasting growth forms vary in resource allocation strategies and 94 \ndependency on environmental cues (Borchert, 1996; Sakai, 2001). Finally, the study of tropical 95 \nphenology has historically been constrained by the limited availability of long-term phenological 96 \nmonitoring plots and field stations (Abernethy et al., 2018; Bush et al., 2017, 2018). Although 97 \nremote sensing techniques can capture seasonal changes in canopy greenness, they fall short in 98 \ncapturing species- and population-level phenology (Fisher et al., 2006; Zhang et al., 2006) 99 \n(Fisher et al., 2006; Zhang et al., 2006).  100 \nOne potential approach to overcome these limitations is through using data derived from 101 \ncitizen science platforms, which have the potential to transform phenology research by providing 102 \nvast, globally distributed datasets of species occurrences. These data are timestamped, 103 \ngeoreferenced, and often include photographs that enable the inference of phenological events 104 \nacross unprecedented spatial and temporal scales (Barve et al., 2020; Callaghan et al., 2020; 105 \nWolf et al., 2022). For instance, iNaturalist, a widely used citizen science platform, currently 106 \nhosts over 69 million research grade plant observations. 4.6 million of these observations come 107 \nfrom tropical Asia, a traditionally data-scarce region, and this number is increasing rapidly (Di 108 \nCecco et al., 2021). In temperate ecosystems, iNaturalist records have been proven to yield 109 \nvaluable insights into plant phenology. For example, Li et al. (2019) used 22 million images to 110 \nextract phenology records from the USA and Europe and found that urbanization advances 111 \nflowering and leaf-out in colder regions. Similarly, (Iwanycki Ahlstrand et al., 2022) compared 112 \ndirected citizen science, herbarium, and iNaturalist records for three spring flowering species in 113 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 7 \nDenmark, and found that iNaturalist provided the broadest spatial coverage and captured peak 114 \nflowering well. Moreover, combining citizen science dataset with traditional monitoring 115 \nnetworks and herbarium records can help fill spatial and temporal gaps, enhancing the detection 116 \nof climate-driven phenological changes (Davis et al., 2015; D. S. Park et al., 2023; Willis et al., 117 \n2017). In sum, despite important limitations such as observer bias, variable sampling effort, and 118 \nuneven taxonomic coverage, opportunistically collected observation records in citizen science 119 \nplatforms can yield meaningful phenological insights, especially when analyzed with modern 120 \ncircular statistics and hierarchical modeling techniques (Capinha et al., 2024; Lai, 2025; Pabon-121 \nMoreno et al., 2019; Willig et al., 2024). 122 \nIn this study, we utilize iNaturalist data to evaluate plant reproductive phenology in urban 123 \nvs. rural habitats across the tropical latitudes. We first evaluated the value of iNaturalist 124 \nobservations for tropical phenological research by comparing phenological estimates derived 125 \nfrom these observations to those derived from herbarium specimens collected in tropical 126 \nlatitudes. Next, we asked whether plant reproductive phenology in urban centers differs from 127 \nreproductive phenology in adjacent rural areas. Finally, we evaluated whether plant growth and 128 \nreproductive strategies impact phenological responses to urbanization.  Our study offers one of 129 \nthe first large-scale, trait-specific evaluations of how urbanization influences reproductive 130 \nphenology in tropical plants. 131 \n  132 \n2. METHODS 133 \n2.1. Data acquisition 134 \n  135 \n2.1.1 Plant observations from iNaturalist 136 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 8 \nAs a source of our citizen science data, we used plant data collected through iNaturalist (Fig. 1, 137 \nleft panel). To retrieve observations along with their phenology status (presence of flowers, 138 \nfruits, or both), we accessed data through PhenoBase (Dinnage et al., 2025). PhenoBase, through 139 \nits machine learning system known as PhenoVision, has processed millions of field photos from 140 \niNaturalist and provides them with phenology status and geographic and temporal metadata. 141 \nPhenoVison detects flowers and fruits using a Vision Transformer (ViT) model fine-tuned with a 142 \nmasked autoencoder pretraining method. This machine learning program was trained on over 1.5 143 \nmillion human-annotated iNaturalist images, achieving high validation accuracy (98.5% for 144 \nflowers, 95% for fruits). Additional calibration steps refined detection thresholds and reduced 145 \nfalse positives, ensuring high-confidence phenological observations. As annotations on 146 \nPhenoBase are limited to observations between 2010 and 2023, we limited our search to those 147 \nyears. We downloaded 217,991 observations representing 296 species that had a minimum of 148 \n100 observations within the tropics (23.50 N – 23.50 S) during the defined time period.  149 \n  150 \n2.1.2.  Plant observations from herbarium records 151 \nWe retrieved data from the Global Biodiversity Information Facility (GBIF) to compare 152 \nphenological estimates derived from observations on iNaturalist to those derived from tropical 153 \nplant specimens deposited into herbaria worldwide (Fig. 1, left panel). We queried GBIF for all 154 \nplant specimen records from tropical latitudes collected between 2010-2023, with the additional 155 \nconstraints that the record was not marked as having a geospatial issue and had collection 156 \ncoordinates with uncertainty of less than 100m. To expand the geographic and taxonomic scope 157 \nof this herbarium-derived data, we also retrieved tropical herbarium records from Tropicos, a 158 \ncurated botanical database maintained by the Missouri Botanical Garden; this dataset had not 159 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 9 \nbeen retrieved with our previous query as it has unspecified coordinate uncertainties. Together, 160 \nthese queries yielded 111533 observations from 3853 species with a minimum of 10 records 161 \ncollected globally within 2010-2023. Subsequent analyses assume that the presence of a 162 \nherbarium record indicates that the plant in question was reproductive (i.e., flowering or fruiting) 163 \nat the time of collection, based on common collection practices that promote collecting plants 164 \nwith reproductive features present (Goëau et al., 2020; Heberling et al., 2019; Willis et al., 2017).  165 \n 166 \n2.1.3 Urbanization 167 \nTo assign urbanization status for each observation, we used the 2020 version of the Global 168 \nHuman Settlement-Degree of Urbanization (GHS-SMOD) layer, developed by the European 169 \nCommission’s Joint Research Centre (Fig. 1, left panel) (Florczyk et al., 2019). This layer offers 170 \nconsistent global data on human settlement intensity at a 1 km resolution, based on population 171 \ndensity and built-up areas. Each grid cell is classified into one of eight urbanization categories, 172 \nranging from dense city centers to largely uninhabited, very low-density zones, allowing a 173 \ncontinuous representation of settlement intensity. We simplified these categories into two 174 \ncategories: urban (Grid Codes 21, 22, 23, 30: representing peri-urban to urban cores) and rural 175 \n(Grid Codes 11–13: covering very low-density rural to rural clusters), and assigned an 176 \nurbanization class to each plant observation (Melchiorri et al., 2018, 2019; Santillan & Heipke, 177 \n2023). 178 \n 179 \n2.2. Data analysis 180 \n  181 \n2.2.1 Comparison between phenology estimation 182 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 10 \nTo determine if species-level reproductive seasonality from citizen-science data aligns with 183 \nherbarium-based inferences, we compared observations from iNaturalist and herbaria. For each 184 \nspecies in each dataset (iNaturalist and herbarium), day-of-year (DOY) values were converted to 185 \nradians and summarized using circular statistics in the circular package in R (Lund et al., 2025). 186 \nWe estimated the mean vector length (r), a concentration parameter from 0 to 1 that measures the 187 \nstrength of seasonality (Fig. 1, middle panel) (Morellato et al., 2009) and used this to classify the 188 \nstrength of seasonality for each species (Weak (r < 0.35), Moderate (0.35 ≤ r < 0.70), and Strong 189 \n(r ≥ 0.70)). We selected this r -based classification because r directly measures effect size, 190 \nindicating how closely observations cluster around the mean flowering date, regardless of sample 191 \nsize (Alsammani et al., 2023; Staggemeier et al., 2020; Willig et al., 2024). Moreover, when 192 \ncomparing the same species across two large, uneven datasets (GBIF vs. iNaturalist), p-values 193 \nfrom circular significance tests can be misleading, as the Rayleigh p-value is highly sample-size-194 \ndependent (Alsammani et al., 2023; Willig et al., 2024). We then used a confusion matrix to 195 \ncompare the agreement in flowering strength between two datasets. 196 \n 197 \n2.2.2 Using iNaturalist observations to compare rural vs. urban phenology  198 \n 199 \nExclusion of multimodal species 200 \n 201 \nIn the tropics, some species can express multimodal reproductive phenology, with multiple 202 \nflowering peaks per year (Wright et al., 2019), which complicate comparisons of phenology 203 \nacross urban and rural contexts. To account for this, we first used the Hermans-Rasson (HR) test, 204 \na robust, nonparametric method for detecting nonuniformity that does not depend on a specific 205 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 11 \ndistribution (Hermans & Rasson, 1985; Landler et al., 2019) and excluded species that express 206 \nmultimodal reproductive phenology from subsequent analyses. First, we used Rayleigh’s test to 207 \ndetermine whether observations are significantly clustered around a mean direction (Morellato et 208 \nal., 2009).   Next, we used the Rayleigh test to evaluate the strength of seasonality (aseasonal, 209 \nwhen Rayleigh p > 0.05, or seasonal, Rayleigh p < 0.05). Both seasonal and aseasonal species 210 \nwere included in the analysis because the seasonality-strength measure r is relevant for unimodal 211 \ndistributions and interpretable as “weak seasonality” in uniform cases. However, species with 212 \nmultimodal reproductive phenology were excluded, as these violate the assumptions behind the 213 \ncircular statistics used in our analysis (Datta et al., 2025; Morellato et al., 2009; Staggemeier et 214 \nal., 2020).  215 \nModeling reproductive seasonality strength and duration 216 \nReproductive seasonality strength (r) was estimated from circular statistics (Fig. 1, right panel). 217 \nWe estimated total reproductive duration by calculating the percentiles along the circular 218 \nordering of flowering and fruiting angles and taking the angular distance between the 5th and 219 \n95th percentiles, and finally converting this angular distance back into days of year.   220 \n 221 \nNext, to test whether seasonality and duration differ between urban and rural 222 \nenvironments, we fitted the following linear mixed-effects models: 223 \n 224 \n          225 \n  226 \nHere, phenology ijk was either a species i’s estimated $r$ or its estimated reproductive duration 227 \nin grid cell j and in urban class k. β₁ captures the fixed effect of urbanization as rural and urban 228 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 12 \nclass, and species and grid were treated as random intercepts to account for taxonomic and 229 \nspatial non-independence.  230 \n 231 \nFinally, as the circular statistics in our modeling approach can be sensitive to the number 232 \nof observations being used to generate the statistic (e.g. longer flowering durations or reduced 233 \nseasonality strength could be estimated simply due to more extensive sampling), we additionally 234 \nconducted a supplemental analysis in which we subsampled the data to have equal representation 235 \nwithin each species-grid-urban class category (Fig. S5).. Briefly, we “rarefied” the data 1000 236 \ntimes to ensure equal representation in each urban class within a species-grid cell combination, 237 \nestimated phenology using the circular statistics above, and fitted the global linear mixed effect 238 \nmodel. As the results from this approach were consistent with the results of our “global” model 239 \nthat included all observations (Fig. S5), we focus our main text on results from the global model.  240 \n 241 \nModeling with species’ traits 242 \nTo assess how different plant growth and reproductive strategies impact phenological responses 243 \nto urbanization, we also modeled: (1) seasonality class, including seasonal and aseasonal species, 244 \n(2) life duration categories, such as annual and perennial species, and (3) life forms, namely 245 \nherbs, shrubs, and vines separately for smaller subsets of the data. Trait-specific information for 246 \nall the species was compiled through manual online searches. We only consulted the 247 \nauthoritative botanical sources like the USDA and botanical gardens' websites.   We fitted 248 \nmodels with interactive effects of urbanization class and plant traits (seasonality, life duration, or 249 \nlife forms). All the models were fitted using lmerTest with Satterthwaite-adjusted t-tests, 250 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 13 \nweighting observations by sample size per grid (Kuznetsova et al., 2017). All analyses were 251 \nperformed using R version 4.5.1 (R Core Team, 2025).  252 \n 253 \n 254 \n3. RESULTS 255 \n3.1 Data overview 256 \nWe had 217,991 unique observations with flowers or fruits or both from iNaturalist and 111,533 257 \nfrom herbaria, spanning 2010-2023. Our iNaturalist and herbaria datasets had 296 and 3,835 258 \nunique species, respectively. They had 229 species in common. 259 \nOur iNaturalist dataset (Fig. 2, top panel) showed broad tropical coverage across 260 \ncontinents, with dense clusters of observations in South and Southeast Asia, East Africa, and 261 \nCentral America, with roughly equal distributions between rural and urban regions (Fig 2a).  In 262 \ncontrast, the herbarium data obtained from GBIF (Fig. 2, bottom panel) were spatially sparser, 263 \nconcentrated mainly in Latin America, Northern Australia, West Africa, and parts of Southeast 264 \nAsia. Very few herbarium records (~3.3%) were collected in urban locations (Fig. 2b), consistent 265 \nwith the traditional botanical survey bias toward less disturbed ecosystems. However, herbarium 266 \nrecords spanned 3,835 species in contrast to the 229 species represented in iNaturalist, indicating 267 \nthat iNaturalist offers broader spatial coverage with comparable number of observations in urban 268 \nand rural areas, while herbaria provide taxonomically rich, rural-biased observations. 269 \nSimilarly, we found iNaturalist observations peaking in April–May (late dry to early wet 270 \nseason in many tropical regions), reflecting heightened flowering visibility and user activity. 271 \nSimilarly, we observed secondary peaks in October–November, suggesting a bimodal 272 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 14 \nphenological rhythm consistent with intertropical seasonal transitions. Both urban and rural 273 \nclasses show similar seasonal timing, but urban peaks were slightly higher in April, May, and 274 \nNovember, reflecting more observer activity and maybe prolonged reproductive activity in cities 275 \ndue to heat islands (Supplementary file, Fig. 1, top panel). Whereas the herbaria dataset with a 276 \nconsistent rural-dominated signal showed a peak collection timing between March and June, and 277 \nmoderate activity throughout the year. The absence of strong urban representation mainly 278 \nreflects sampling bias rather than biological inactivity (Supplementary file, Fig. 1, bottom panel).  279 \n 280 \n3.2 Phenological insights from iNaturalist and herbarium-derived  281 \nClassifications of reproductive seasonality were broadly consistent across the herbarium-derived 282 \nand iNaturalist datasets. Of the 229 species in this comparison, 118 were identified as weakly 283 \nseasonal with both iNaturalist and herbarium data, and only 7 were identified as strongly 284 \nseasonal with both datasets (Table 1, top panel). The most common discrepancy occurred due to 285 \nspecies being assigned as having “moderate” seasonality through herbarium data and only 286 \n“weak” seasonality when phenology was assessed with iNaturalist records. Values of the 287 \nseasonality metric (r) from herbarium and iNaturalist records were significantly correlated with 288 \none another (Spearman’s ρ = 0.31, p = 1.31 × 10⁻⁵).  289 \n 290 \n3.3 Comparison of reproductive phase duration and reproductive seasonality strength  291 \n3.3.1. Global comparison 292 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 15 \nAcross 3104 grid × species × urban class combinations, representing 285 unique grids and 238 293 \nspecies, reproductive duration ranged from 3 to 364 days. On average across species and grid 294 \ncells, plant reproductive periods were significantly longer in urban than in rural contexts (Fig. 295 \n4A; rural flowering period = 195.04 ± 5.06; urban flowering period = 206.83 ± 5.06; p = 3.26e-296 \n05, Table S1). The strength of seasonality (r) was correspondingly lower in urban than in rural 297 \nsettings (Fig. 4B; rural r = 0.61 ± 0.014; urban r = 0.59 ± 0.014; p = 3.1e-04, Table S2).  298 \n3.3.2. Comparison across seasonal and aseasonal species 299 \nFor species with seasonal reproduction (i.e., those with Raleigh p < 0.05, Datta et al., 2025), 300 \nreproductive periods were three weeks longer in urban than in rural settings (rural reproductive 301 \nduration = 177.57 days ± 5.56; urban reproductive duration = 198.78 ± 5.35; p = 5.12 e-15; Fig. 302 \n5A and Table S1). Species with aseasonal reproduction had on average longer reproductive 303 \nperiods; these species also experienced extended reproductive periods in urban than rural 304 \nsettings, although this effect was weaker than for seasonal species (rural reproductive duration = 305 \n213.65 days ± 5.42; urban reproductive duration = 222.57 ± 5.35; p = .03; Fig. 5B and Table S1). 306 \nSimilar results were also detected when measuring seasonality based on r rather than flowering 307 \nduration (Table S2 and Fig. S2).  308 \n3.3.3. Comparison across annual and perennial species 309 \nFor annual species, reproductive periods were nearly a month longer in urban than in rural 310 \nlocalities (rural reproductive duration = 171.29 days ± 8.4; urban reproductive duration = 201.12 311 \n± 8.15; p = 1.4 × 10^ (-8); Fig. 6A and Table S1). In contrast, reproductive periods were about a 312 \nweek longer in urban than in rural settings for perennial species (rural reproductive duration = 313 \n205.33 days ± 5.5; urban reproductive duration = 213.5 ± 5.46; p = 1.4 × 10^-8; Fig. 6A and 314 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 16 \nTable S1). Similarly, annual plant species had considerably lower seasonality (r) in urban than in 315 \nrural settings, but urbanization was associated with only a modest reduction in seasonality for 316 \nperennial species (Fig. S3 and Table S2). 317 \n3.3.4. Comparison across herbs, shrubs, and vines 318 \nReproductive periods were significantly longer in urban than in rural localities for herbaceous 319 \nplants and for vines (Herbaceous: rural reproductive period = 187.51 ± 6.61, urban reproductive 320 \nperiod = 211.9 ± 6.47, p = 5.07e-10; Vines: rural reproductive period = 196.79±10.52, urban 321 \nreproductive period = 212.25 ± 10.23, p = 3.49e-4, see Figs. 7A and 7C). However, no such 322 \neffect of urbanization on reproductive period was found in shrubs (rural reproductive period = 323 \n209.43±6.89, urban reproductive period = 207.85 ± 6.76, Fig. 7B, n.s.)). Similar patterns were 324 \ndetected with the strength of seasonality r, which was lower in urban than in rural localities for 325 \nherbs and vines but not for shrubs (Fig. S4 and Table S2).  326 \n 327 \n4. DISCUSSION 328 \n 329 \nUrbanization is among the most important drivers of environmental change across the tropics, 330 \nyet its influence on the timing of plant reproductive phenology remains poorly studied at broad 331 \nspatial and taxonomic scales. By integrating multi-continental citizen science observations with 332 \ncurated herbarium records, our study reveals two central findings. First, despite fundamental 333 \ndifferences in sampling design, citizen science and herbarium datasets converge on consistent 334 \nestimates of species-level reproductive seasonality strength at local grid scales, supporting the 335 \nuse of citizen science-based observations for phenology-related studies in the tropics. Second, 336 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 17 \nacross thousands of species-grid combinations, urban plant populations exhibit longer 337 \nreproductive duration and reduced seasonality strength relative to rural populations, although the 338 \nmagnitude and direction of this effect vary across species and functional groups. Together, these 339 \nresults demonstrate that urbanization leaves a detectable and biologically meaningful imprint on 340 \ntropical reproductive phenology, while also highlighting the value of combining opportunistic 341 \nand curated data streams to understand phenological responses to global change in the tropics. 342 \n4.1. Using citizen science data for studying tropical urban phenology 343 \nA vital contribution of our study lies in demonstrating the efficiency of citizen science 344 \nobservations in answering phenological questions that would be difficult to address using 345 \nherbarium data alone. Although herbarium collections are taxonomically rich and provide vital 346 \nphenological insights (Karthikeyan et al., 2025; Ordoñez et al., 2025), these observations tend to 347 \nbe from less developed (or rural) locations (Fig. 2b), which limits their utility for studying urban 348 \necology. In contrast, iNaturalist observations are distributed over dense urban centers, peri-urban 349 \nmosaics, and rural inland habitats, facilitating urban phenology studies. As a result, the use of 350 \niNaturalist data in peer-reviewed studies has increased rapidly, with the data coming from 128 351 \ncountries representing 638 taxonomic families (Mason et al., 2025).  This extensive coverage by 352 \niNaturalist illustrates the expanding reach of citizen science, even in biodiversity-rich but 353 \ntraditionally under-sampled tropical regions (Barve et al., 2020; Iwanycki Ahlstrand et al., 2022). 354 \nAt the same time, the dataset from herbaria, though biased toward rural observations, 355 \noffer taxonomically rich, high-quality records and remain equally important in phenology and 356 \nclimate change studies. This result (3835 unique species from herbarium records compared to 357 \n296 from iNaturalist) aligns with previous demonstrations that herbarium records capture high 358 \ntaxonomic and functional diversity (Eckert et al., 2024), which could make them reliable data 359 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 18 \nsources for studying phenological signatures of diverse plant taxa (Iwanycki Ahlstrand et al., 360 \n2022; Willis et al., 2017). Our comparison of reproductive seasonality between data sources 361 \nfurther reveals that many species classified as moderately and strongly seasonal based on 362 \nherbaria records are referred as weakly seasonal in iNaturalist observations. This discrepancy in 363 \nseasonality classification exists likely due to differences in sampling strategies. Herbarium 364 \nspecimens are often collected during peak phenophases via directed short field campaigns, 365 \nwhereas citizen science platforms like iNaturalist observations are results of opportunistic, 366 \nrepeated encounters across seasons. These contrasts between the herbaria and citizen science 367 \ndatasets affirm their complimentary strength, as combining these datasets bridges spatial and 368 \necological gaps in tropical monitoring by integrating real-time, human-centered observations 369 \nwith long-term scientific records (Ramirez-Parada et al., 2024; Williamson et al., 2025). It also 370 \nsupports previous claims regarding the potential value of dedicated citizen science efforts in 371 \ntropical areas, and particularly in tropical cities, to fill crucial data gaps in support of long term 372 \nresearch and monitoring (e.g., SeasonWatch, Ramaswami et al., 2021). 373 \n 374 \n4.2. Urbanization impacts on plant reproductive phenology 375 \nAnalyzing nearly 3100 combinations of grid cells, species, and urban classes, we 376 \nobserved that urban populations had longer reproductive duration and lower seasonality strength 377 \nthan rural observations. Our observed patterns across the tropics mirror findings from temperate 378 \ncities, where urban heat islands, increased atmospheric CO2, regulated irrigation, and altered 379 \nlight regimes prolong resource availability. These signatures of urbanization enable plants to 380 \nflower earlier, flower longer, or produce multiple flowering cycles within a year (Fujiwara et al., 381 \n2025; Neil & Wu, 2006; D. S. Park et al., 2023; Sexton et al., 2023; Wohlfahrt et al., 2019). The 382 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 19 \nsimilarity in phenological signal between temperate and tropic responses to urbanization may 383 \nseem surprising, given the widely documented complexity and diversity of tropical phenology 384 \nstrategy. Here, the convergence between tropical and temperate urban phenology responses may 385 \nreflect common urban mechanisms overriding otherwise distinct climate drivers. Indeed, our 386 \ndetection of a consistent signal of urbanization underscores its potentially powerful role as a 387 \ndriver of phenological shifts among diverse taxa. 388 \nDespite the clear urbanization effect in our global model (Table S1, S2), we also saw 389 \npronounced interspecific variation in both the direction and magnitude of phenological 390 \nresponses. Trait-level analyses (Figs.5-7) reveal varied phenological responses between life 391 \ncycles and plant functional types. Seasonal and annual species, particularly with herbs and vines, 392 \nexhibited stronger urban-associated increases in reproductive duration and reduction in 393 \nseasonality strength. Longer reproductive periods for annual plants in urban settings might be 394 \ndue to their being able to exploit prolonged favorable conditions (Fujiwara et al., 2025), in 395 \ncontrast to perennial species, which are more likely to retain conservative phenological schedules 396 \n(Marcacci et al., 2023; Sexton et al., 2023; Stanley & Ashman, 2025). Although relatively few 397 \nstudies have examined variation in phenological responses across functional groups (herbs, 398 \nshrubs, vines) to urbanization, recent results from a review paper suggest that the composition of 399 \nplant species and their functional group can lead to diverse phenology due to differences in 400 \ngrowth characteristics and adaptability (Sun et al., 2026). As temperature is found to be a 401 \nprimary driver of flowering (Sun et al., 2026; Zhou et al., 2023), and temperature tends to vary 402 \nbetween urban-rural settings  (Ogunbode et al., 2025; Ribeiro et al., 2024), these variation in 403 \ntemperature and distinctiveness of plant species across functional group could explain variation 404 \nin phenological responses across functional groups between urban-rural environments.     405 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 20 \nTogether, the results suggest a clear effect of urbanization on tropical reproductive 406 \nphenology through an extended reproducing duration in urban settings. Prolonged reproductive 407 \nphases could increase overlap among co-flowering species, modify plant-pollinator interactions, 408 \nalter mutualistic networks, and intensify interspecific pollen transfer, with consequences for 409 \nreproductive success and competitive dynamics (Dzul-Cauich & Munguía-Rosas, 2025; 410 \nMarcacci et al., 2023; Sexton et al., 2023). At the broader scale, lengthened reproductive seasons 411 \nmay have ecosystem-level consequences by altering carbon uptake, surface energy balance, and 412 \nfeedbacks between vegetation and urban microclimates (Williamson et al., 2025; Wohlfahrt et 413 \nal., 2019). In rapidly urbanizing tropical regions, where biodiversity is high and ecological 414 \ninteractions are tightly linked to phenological timing, such shifts could propagate through 415 \nmutualistic networks and ecosystem processes, meriting further study.   416 \n 417 \n4.3. Limitations 418 \nOur work provides important insights into the implications of urbanization for plant phenology 419 \nacross the tropics, but a few important caveats should shape the interpretation of our results. Our 420 \nanalyses are implemented with urbanization classifications at a 1x1 km resolution within 25x25 421 \nkm grid cells, assuming limited impacts of fine-scale environmental heterogeneity on the 422 \nphenology within urban-rural landscapes. Our urban classification likewise simplifies a 423 \nmultidimensional gradient that includes variation in land use, management intensity, and 424 \nmicroclimate. On-the-ground studies of phenology and demography across urban-rural gradients 425 \ncan help disentangle these multidimensional signatures of urbanization. Additionally, both 426 \ncitizen-science and herbarium datasets remain influenced by observer behavior and collection 427 \npractices, particularly in urban settings where observations are more frequent and temporally 428 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 21 \nbiased. Although we control for potential biases towards higher density of sampling in urban 429 \nsettings (Fig. S5), it remains possible that more extensive temporal coverage of data contribute to 430 \na broader estimate of flowering duration or weaker seasonality strength in urban environments. 431 \nFinally, our comparison of phenology estimates from iNaturalist, and herbarium specimens 432 \nassumed that all herbaria specimens were reproductive at the time of collection. Although this 433 \nassumption is consistent with most collection practices (Goëau et al., 2020; Heberling & Isaac, 434 \n2017), it may have led to our overestimating reproductive periods. Making herbarium specimens 435 \nmore valuable for phenological research will require continued advancements that draw on 436 \nhuman and machine-based annotations of herbaria observations (Grady et al., 2025).     437 \n 438 \n4.4. Conclusions 439 \nThis study represents one of the first large-scale applications of iNaturalist to investigate plant 440 \nphenology across urban contexts in tropical ecosystems. By coupling these records with 441 \nherbarium observations, we demonstrate that opportunistic, crowd-sourced imagery can yield 442 \necologically consistent and complementary insights into reproductive periodicity. The approach 443 \nestablishes a scalable framework for monitoring phenological resilience to urbanization and 444 \nclimate change, particularly in data-poor tropical regions where systematic observations are 445 \nlimited. Our findings suggest that abiotic features associated with urbanization result in extended 446 \nreproductive periods than in nearby rural localities, with potential downstream impacts on 447 \npopulation, community, and ecosystem dynamics. As digital archives and machine-learning-448 \nassisted phenophase annotation advance, integrating citizen-science, herbarium, and remote-449 \nsensing data will enable dynamic, high-resolution phenological mapping across space and time. 450 \nSuch efforts can transform tropical ecology from a historically data-scarce discipline into one 451 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 22 \ndriven by continuous, community-powered observation networks, improving forecasts of 452 \nbiodiversity responses to global change. 453 \n 454 \n5. Author Contributions 455 \nRohit Raj Jha, Anita Simha, Richard Ekeng Ita, Rachana Rao, and Gaurav Kandlikar conceived 456 \nthe idea. All authors contributed to data acquisition. Data curation was done by Rohit Raj Jha 457 \nand Gaurav Kandlikar. Rohit Raj Jha completed the formal analysis with Gaurav Kandlikar. 458 \nRohit Raj Jha wrote the paper with inputs from Gaurav Kandlikar, Daijiang Li, and Anita Simha, 459 \nand all authors reviewed the paper. 460 \n 461 \nAcknowledgements 462 \nWe thank the contributors to iNaturalist and Herbarium observations and the authors of various 463 \nopen-source packages that make this computational work possible. We acknowledge the 464 \nLouisiana State University startup funds to Gaurav Kandlikar and the National Science 465 \nFoundation grant DBI2223508 to Daijiang Li. We thank Karthik Thrikkaderri for his insights 466 \nduring data acquisitions and comments on the manuscript.   467 \n 468 \nEthics statement 469 \nNone 470 \nConflicts of Interest 471 \nThe authors declare no conflicts of interest. 472 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 23 \n  473 \nData Availability Statement 474 \nThe data and codes associated with this manuscript are archived in Zenodo 475 \n(https://doi.org/10.5281/zenodo.18252047).  476 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 34 \n 723 \n 724 \n 725 \n726 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 35 \n 727 \nFigure 1. Overview of data source, phenological metrics, and analytical framework used to 728 \nassess urban-rural differences in tropical plant reproductive phenology  729 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 36 \n 730 \n 731 \n 732 \nFigure 2. Spatial distribution of observations included from iNaturalist (top row) and herbaria 733 \n(bottom row) across tropical regions. Colored points represent urban (red) and rural (blue) 734 \nobservations. iNaturalist data exhibit broad spatial coverage with strong urban clustering, 735 \nwhereas herbaria data show a rural-dominated sampling pattern. Inset bars represent total counts 736 \nof rural and urban observations and are scaled consistently across both figure panels.  737 \n 738 \n 739 \n 740 \n  741 \n 742 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 37 \n 743 \n Figure 3. Circular flowering phenology for Cyanthillium cinereum (Bottom right) developed 744 \nusing iNaturalist observations from 2010 to 2023, covering a single grid (Bottom left). Rural 745 \niNaturalist records show a more concentrated, seasonal flowering peak, whereas urban 746 \nobservations display a broader, less seasonal distribution. r indicates the corresponding values for 747 \nreproductive seasonality strength, and n is the number of observations in each urban class. 748 \nSpecimen credit: Lava Chen, 2026  749 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 38 \n 750 \nFigure 4. Urban–rural differences in reproductive duration (A) and seasonality strength (B) 751 \nacross all species x grids x urban class combinations. Each colored point represents one species 752 \nin a grid cell, in either rural (blue) or urban (red) localities; thin grey lines connect urban and 753 \nrural estimates. The white points and lines represent model-estimated marginal means for rural 754 \nand urban localities. This result of longer reproductive periods in urban than in rural conditions 755 \nwas supported in a supplemental analysis that controlled for potential effects of uneven urban vs. 756 \nrural sampling intensities within grids.  757 \n  758 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 39 \n 759 \nFigure 5. Paired urban–rural reproductive durations for aseasonal and seasonal species found in 760 \nboth rural and urban grid cells. A: Species categorized as aseasonal (with no peak) showed a 761 \nlonger flowering duration in urban settings. B: Species with seasonality (single-peak) in 762 \nflowering also displayed a similar pattern with longer duration of flowering in urban regions.  763 \nThin gray lines denote raw paired data; black points and lines represent model-estimated 764 \nmarginal means.  765 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 40 \n766 \nFigure 6. Paired urban–rural durations for annual and perennial species found in both rural and 767 \nurban grid cells. A: Annual species displaying a significant longer reproductive phase in urban 768 \nregions. B: Perennial species showing similar length of reproductive phase for rural and urban 769 \nobservations. Thin gray lines denote raw paired data; black points and lines represent model-770 \nestimated marginal means.  771 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 41 \n 772 \nFigure 7. Paired urban–rural reproductive durations for herbs, shrubs, and vines found in both 773 \nrural and urban grid cells. A: Herbs displaying a significant longer reproductive phase in urban 774 \nregions. B: Shrubs showing no difference in reproductive duration between rural and urban 775 \nobservations. C: Vines with a significant difference in reproductive duration between rural and 776 \nurban observations. Thin gray lines denote raw paired data; black points and lines represent 777 \nmodel-estimated marginal means.  778 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint \n\n 42 \nTable 1. Confusion matrix comparing reproductive seasonality classifications based on data from 779 \niNaturalist (rows) and herbaria (columns). The top half shows species-level comparisons, 780 \nincluding only taxa present in both datasets. The bottom half shows grid-level data based on 781 \nshared species × grid combinations across iNaturalist and herbaria. The diagonal cells' value, 782 \ndenoted in bold shows agreement between datasets. 783 \n  784 \niNat / Herbaria Weak Moderate Strong \nSpecies-level seasonality classification \nWeak 118 53 3 \nModerate 17 24 2 \nStrong 0 3 6 \n \n    \n    \n    \n  785 \n  786 \n 787 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 30, 2026. ; https://doi.org/10.64898/2026.01.28.702306doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}