Observing shifts in phenology of tropical flowering plants

Skylar Graves; Erin A Manzitto-Tripp

doi:10.1371/journal.pone.0342105

. 2026 Feb 25;21(2):e0342105. doi: 10.1371/journal.pone.0342105

Observing shifts in phenology of tropical flowering plants

Skylar Graves ^1,^*, Erin A Manzitto-Tripp ^1,²

Editor: Hong Qin³

PMCID: PMC12935240 PMID: 41739735

Abstract

Changes in flowering can cause misalignment with pollinators and seed dispersers, thus causing changes in fitness of both the plant species and their mutualists. Phenological shifts in tropical flowering plants are poorly documented despite the widely understood importance of measuring flowering phenology. It has been hypothesized that changes in tropical flowering have been less severe than those in non-tropical latitudes due to minimal change in temperature annually. Furthermore, many tropical species flower continuously throughout the year, as they are not restricted by a cold induced dormancy period. To test this hypothesis, we used museum specimens to examine shifts in phenology of flowering plant species from across the global tropics. We identified species that flower once a year, every year, for four consecutive months or less, as these species can best be compared to temperate studies. This selection criteria resulted in 33 species across the tropics. Between 1794 and 2024, we documented an average absolute shift in flowering of 2.04 days per decade across all species, with a range of 0.037 days per decade to 14.10 days per decade. This shift is comparable to changes seen elsewhere around the globe, including those in temperate, boreal and alpine desert plants. This change has been shown to be severe enough to cause interspecific misalignment. Our work shows that changes in tropical flowering phenology are not insulated from the impacts of climate change, as previously assumed.

Introduction

Climate change has driven shifts in the behavior and life cycles of animals, plants, and many other organisms [1–3]. For plants, numerous studies in non-tropical habitats have documented the growing season lengthening, causing spring and autumn flowering to occur earlier and later, respectively [1–3]. Responses of plants to climate change can have cascading impacts across ecosystems [1,4–8]. For example, climate change has induced changes in insect emergence [9–14], which, coupled with changes in flowering timing can cause pollinator misalignment. Furthermore, climate change can impact migration timing [15], which in turn can disrupt seed dispersal. These changes and more in turn fracture communities and food chains [5,6,16,17]. Plant phenology is especially sensitive to a changing climate, with changes in reproductive cycles serving as immediate and visible indicators of broader change and predictors of more drastic ecological responses [7,18,19]. Because plants are the basis of the vast majority of terrestrial ecosystems, understanding changes in plant phenology informs us of broader impacts of climate change.

Despite the universally recognized importance of our documentation of shifts in flowering phenology [1–20], tropical latitudes have been largely overlooked. Global tropical latitudes house approximately 57% of global vascular plant biodiversity [20] and 62% of global vertebrate biodiversity [21]. In addition, nearly 180 species of plants (and ~1,100 non-plant species) from the tropics are described as new to science each year [20]. Given the vast biodiversity seen in the tropics this is a large blind spot regarding understanding the global impacts of climate change. Furthermore, biodiversity increases the complexity of the ecosystem dynamics, and thus the effect misalignment in interspecific interactions can cause [20–21]. Due to the lack of cold-induced dormancy period and consistent photoperiod throughout the year [22], flowering patterns of many tropical plant species are drastically different from those of non-tropical species [22]. Many species have continuous flowering patterns, meaning they flower consistently throughout most or all of the year. However, other species have discrete flowering patterns that more closely mirror those in non-tropical and boreal regions [22]. Species that have discrete flowering periods are more tractable from a perspective of understanding phenological change, as these flowering patterns mirror those seen in non-tropical latitudes. Such species therefore enable comparison to phenological changes documented from non-tropical latitudes. From an ecological perspective, species with discrete flowering periods have a higher likelihood of misaligned interspecific interactions as a result of changes in reproductive period [22,23]. Measuring species with discrete flowering times is therefore likely to target the species most vulnerable to change.

Ecological changes that affect tropical latitudes have cascade impacts across the planet, such as changes in nutrient cycling which can cause changes in atmospheric CO2 uptake globally and alter nutrient runoff which effects global marine ecosystems [22]. As a result, documentation of phenological shifts in and amongst tropical floras is a crucial piece of information towards more complete understanding of the global impacts of anthropogenic climate change [24]. Here, we identified 33 species of flowering plants from tropical latitudes that have discrete flowering times, spanning 15 families and 8 locations, then asked whether (1) these species have undergone changes in flowering phenology through time, and (2) how these changes compared to those seen in plants from non-tropical latitudes.

Methods

We selected tropical locations that were proximal to biological research stations and/or preserves, increasing the likelihood of steady collection efforts through time. These locations (Table 1; Fig 1) were chosen specifically due to their large number of herbarium collections as well as the consistency of the collections throughout time [25–31]. The temporal regularity of collections minimizes collector bias inherent in the usage of museum specimens. These locations were subsequently pared down to fewer candidate sites after criteria for inclusion were applied (see below, Table 2). Rates of collections were observed. Across all locations there was a decrease in collections from 1914 to 1945, likely due to global events, but the consistency in collections before and after yielded a robust dataset.

Table 1. Approximate centroid latitude and longitude coordinates of locations included in this study.

Location	Latitude	Longitude
INPA Reserves, Brazil	−3.119	−60.021
Jatun Sacha, Ecuador	−1.066	−77.616
Las Cruces, Costa Rica	8.954	−83.070
Tropenbos International, Bolivia	−17.846	−60.738
Cocha Cashu, Peru	−11.888	−71.407
Catimbau National Park, Brazil	−8.592	−37.247
Isthmus of Kra, Myanmar	10.333	99.000
Bia National Park, Ghana	6.504	−3.077
Southern Guinea Savanna Research Station, Guinea	9.296	5.063

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Fig 1 — Map created using the R package “maps”. INPA Reserves Amazon Basin, Brazil. Jatun Sacha, Ecuador. Tropenbos International, Bolivia. Catimbau National Park, Brazil. Cocha Cashu, Peru. Bia National Park, Ghana. Southern Guinea Savanna Research Station, Guinea. Isthmus of Kra, Thailand/ Myanmar.

Table 2. Table includes location, species name and taxonomic authority, IUCN Red List Status, known pollinators, known seed dispersal agents and habit.

Location	Species	IUCN	Pollinator	Seed dispersal	Habit
INPA Reserves, Brazil	Ceiba erianthos (Cav.) K.Schum.	LC	Bats	Wind/ water	Tree
INPA Reserves, Brazil	Ceiba jasminodora (A.St.-Hil.) K.Schum.	VU	Moths	Wind/ water	Tree
INPA Reserves, Brazil	Ceiba schottii Britten & Baker f.	LC	Butterflies	Wind/ water	Tree
INPA Reserves, Brazil	Ceiba trischistandra (A.Gray) Bakh.	N/A	Bats, bees, hummingbirds	Wind	Tree
INPA Reserves, Brazil	Peltogyne pauciflora Benth.	LC	Bees	Wind	Tree
INPA Reserves, Brazil	Peltogyne recifensis Ducke	NT	Bees, bats, birds	Wind	Tree
INPA Reserves, Brazil	Porcelia ponderosa Rusby	LC	Beetles	Animal	Tree
Tropenbos International, Bolivia	Bougainvillea modesta Heimerl	LC	Bees, butterflies	Wind/ water	Tree
Tropenbos International, Bolivia	Bougainvillea stipitata Griseb.	N/A	Butterfly, moth	Wind/ water	Tree
Catimbau National Park, Brazil	Aeschynomene martii Benth.	N/A	Bee	Water	Shrub
Catimbau National Park, Brazil	Barnebya harleyi W.R.Anderson & Gates	LC	Bee (Frieseomelitta meadewaldoi)	Wind	Climbing tree
Catimbau National Park, Brazil	Caesalpinia pluviosa DC.	N/A	Butterflies, bees, hummingbirds	Wind/ water	Tree
Catimbau National Park, Brazil	Mimosa acutistipula (Mart.) Benth.	N/A	Wind, bees	Wind/ water	Tree/ shrub
Catimbau National Park, Brazil	Pseudobombax parvifolium Carv.-Sobr. & L.P.Queiroz	LC – one paper argues it should be NT	Bats, moths	Wind/ gravity	Tree
Catimbau National Park, Brazil	Terminalia fagifolia Mart.	LC	Bees, butterflies	Cassowaries	Tree
Cocha Cashu, Peru	Dioscorea bulbifera L.	N/A	Insects	Wind – bulbils	climbing herbaceous
Bia National Park, Ghana	Aeschynomene indica L.	LC	True bugs, wasp, wind, Ceratina japonica	Water	Herb
Bia National Park, Ghana	Alafia barteri Oliv.	N/A	Bees, butterflies	Mammals	Shrub
Bia National Park, Ghana	Annona glauca Schumach. & Thonn.	N/A	Beetles	Fish	Tree
Bia National Park, Ghana	Combretum acutum M.A.Lawson	N/A	Unknown	Wind	Tree
Bia National Park, Ghana	Crotalaria mortonii Hepper	LC	Bees	Wind/ water	Subshrub
Bia National Park, Ghana	Dracaena phrynioides Hook.	N/A	Hawk moth	Primates, birds, rodents	Subshrub
Bia National Park, Ghana	Landolphia micrantha (A.Chev.) Pichon	N/A	Bees, ants, butterflies	Primates	Vine
Bia National Park, Ghana	Marantochloa leucantha (K. Schum.)	N/A	Bees	Primates	Herb
Bia National Park, Ghana	Stylosanthes erecta P.Beauv.	N/A	Bees	Water, small animals	subshrub
Bia National Park, Ghana	Terminalia laxiflora Engl.	LC	Bees	Wind	Tree
Bia National Park, Ghana	Tetrorchidium didymostemon (Baill.) Pax & K.Hoffm.	LC	Insect	Unknown	Tree
Bia National Park, Ghana	Tricalysia pallens Hiern.	LC	Insect	Birds	Shrub
Bia National Park, Ghana	Triclisia subcordata Oliv.	N/A	Insect	Primates	Climbing
Bia National Park, Ghana	Vangueriella nigerica (Robyns) Verdc.	LC	Bees, butterflies, beetles, wind	Wind/ water	Tree
Southern Guinea Savanna Research Station, Guinea	Diospyros lotus L.	LC	Bees	Birds, mammals	Tree
Jatun Sacha, Ecuador	Rudgea crassipetiolata Zappi & E.Lucas	N/A	Bees	Wind, water, birds, mammals	Tree
Isthmus of Kra, Thailand/ Myanmar	Nymphoides aurantiaca (Dalzell) Kuntze	LC	Insect	Water	Aquatic

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Herbarium data retrieval and dataset compilation

The use of herbarium specimens introduces the concern of bias in datasets. However, it has been shown that the unwanted impacts of collector bias can be ameliorated with large datasets and utilization of mean flowering, as opposed to peak or first flowering [25–30,32,33]. First flowering refers to the time when the earliest flowers open, whereas peak flowering refers to the time when the largest number of flowers are in bloom. Due to the nature of museum specimens, the measure of peak or first flowering cannot be guaranteed, thus we have decided to use mean flowering, as mean flowering or average flowering has been shown to be accurately represented by museum specimens as well as be biologically informative [25–30,32,33]. Additionally, it has been shown that phenology studies with field observations yielded similar results to those utilizing only herbarium specimens [25–30,32–34].

Construction of datasets

Flowering data were collected from GBIF, a publicly available repository of natural history collections and observations; for purposes of this study, we retained data that derived only from museum specimens, specifically excluding other types of data (e.g., observations). Using the map polygon feature on GBIF, a polygon was drawn around the boarders of each preserve. A list of all angiosperm species on GBIF within the bounds of polygons, which approximated the bounds of each biological research stations/ preserve, was compiled (GBIF.org, January 23^rd- April 17^th 2025). Selecting only preserved specimens; flowering specimens were downloaded in complete Darwin Core format. From this initial list, we manually reviewed photos of digitized specimens to determine presence or absence of flowering. It is generally considered best practice for Herbarium specimens to be prepared with reproductive material, namely flower, fruit or ideally both. All flowering phenophases were included in this study, i.e., flowering was considered “present” if reproductive whorls were visible. Using the filter by month feature on GBIF, we recorded every month in which flowering was occurring, for each species, in each year, across all collections. A total of 8225 specimens were utilized in this study, with an average of 274 specimens per species (S1 Table). Of the species included in this study, 21 are trees, seven are shrubs or subshrubs, four are climbing, three are herbs and one is aquatic (Table 2). All species included in this study utilize animal pollinators, with most using a variety of insects and some using a vertebrates such as bats and birds. Two species also utilize wind pollination. Of the species included in this study, 22 species use abiotic seed dispersal, such as wind and water, and 11 use animal seed dispersal. According to the IUCN red list, 17 species are categorized as Least Concern, One species is categorized as Near Threatened (Peltogyne recifensis), and One species is categorized as Vulnerable (Ceiba jasminodora) (IUCN 2015). Of the species included in this study, the IUCN did not have a report on 15 species. The IUCN reports the population is stable for 12 species and decreasing for one species (Espeletia brachyaxiantha)(IUCN 2025). All other population changes are Unknown (Table 2).

Data subsets

We constructed six datasets considered final for purposes of downstream exploration, following implementation of four criteria (Table 3) for inclusion of a given species in each matrix: (1) maximum number of months flowering, (2) minimum number of herbarium specimens, (3) minimum span of years collected, and (4) maximum number of specimens collected in a single day (Table 3).

Table 3. Six datasets were constructed by implementing four different criteria that span four different parameters: (1) maximum number of months flowering, (2) minimum number of herbarium specimens per species, (3) minimum span of years collected in the flowering phenophase, and (4) maximum number of specimens collected in a single day.

Dataset	Months flowering	# Specimens	Year span	Specimens per day	# Species meeting criteria
1	≤4	≥20	≥29	Not Applicable	33
2	≤3	≥20	≥29	Not Applicable	16
3	≤4	>50	≥29	Not Applicable	26
4	≤4	≥20	On or after 1960	Not Applicable	32
5	≤4	≥20	Before 1960	Not Applicable	11
6	≤4	≥20	≥29	Max 1 per day	33

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Statistical analyses

To facilitate the analysis of phenological shifts through time, we converted flowering date into Julian date and year. In this study, Julian date refers to the date of collection, as the date of first flowering or peak flowering cannot be determined from specimen records [25]. Utilizing collection dates has been shown to represent a reliable estimate of flowering period [28,35].

Determining changes in flowering date.

To test changes in flowering date in all species in each of the six datasets, we conducted a series of analyses regressing Julian date of flowering onto year. These data have a circular (Von Mises) distribution owing to some species flowering periods spanning the new year [36–43]. We employed circular GLM statistics following Mulder and Klugkist (2017). Leap years were identified, and dates were scaled appropriately as a year consisting of 366 days. Julian dates were converted to radians using the formula (2 * pi * day-of-year/ 365 – pi). A Bayesian generalized linear model with a Von Mises distribution was run utilizing the circglmbayes package in R [44,45]. For a circular response y on -pi:pi and a single continuous predictor x, the model we used was a: y = \beta_0 + inv_tan_half (\beta_1 * x). The training algorithm was a mix of Gibbs sampling, fast rejection sampling, and Metropolis-Hastings. Using two training algorithms is the most efficient and stable way to fit a Bayesian model where no single sampling regime works for all parameters. Four chains were used, with a burnin of 200 and 2500 iterations. The coefficients were saved to later create summary statistics. Separate regressions were run for each species, individually.

For each species, circular slope values were derived along with the associated standard deviation of the posterior samples for each slope parameter and date range. Uncertainty was assessed using standard deviations of the posterior samples for each slope parameter associated with each species. Utilizing flowering date values from the regression and span of years, changes in flowering date per decade (ΔDOY/decade) were calculated. Absolut value of the slopes were calculated to assess and compare magnitude of the change in flowering date irrespective of direction. Averages of the absolute value of the (circular) slope and ΔDOY/decade were calculated.

Affiliation and directionality of shifts.

We summarized the counts of how many species had a positive slope (indicating flowering is now occurring later in the year vs. earlier time periods) compared to the number of species that had a negative slope (indicating flowering is now occurring earlier in the year, vs. earlier time periods).

Phylogenetic signal

We assembled a molecular phylogeny for 33 tropical plant species using publicly available internal transcribed spacer (ITS) sequence data from GenBank. Species names were matched to NCBI taxonomy using the rentrez R package, and the five most relevant ITS sequences per species were retrieved. Sequences were downloaded and written to a FASTA file for alignment.

Sequence alignment was performed using the ClustalW algorithm via the msa R package. The resulting multiple sequence alignment was exported in FASTA format and converted to DNAbin format for downstream analysis.

A maximum likelihood (ML) phylogeny was inferred using the phangorn package in R. First, a neighbor-joining (NJ) tree was constructed from a pairwise distance matrix and used as a starting tree. A General Time Reversible (GTR) model with estimates of invariant sites and among-site rate variation (G + I model) was applied. Model parameters and tree topology were optimized with stochastic rearrangement. Bootstrap support was calculated from 100 replicates and mapped onto the ML topology.

To assess phylogenetic signal in flowering phenology, we compiled species-level estimates of the change in flowering date per decade derived from regression slopes of flowering Julian day over time. Species names were matched to phylogenetic tip labels, and the tree was pruned to retain only taxa with available trait data.

We calculated Blomberg’s K, a measure of phylogenetic signal using the phytools package, with 1,000 random permutations to assess statistical significance. Because the ML tree lacked branch lengths for some tips, we ensured all edges had positive length either by using compute.brlen() with the Grafen method or assigning a small constant value to near-zero branches.

Results

ΔDOY/decade

The average absolute ΔDOY/decade across all species was 2.04 days per decade. The species with the largest ΔDOY/decade was Peltogyne recifensis in the INPA Reserves Brazil, with a ΔDOY/decade of 14.1 days later per decade. This represents a total change in flowering date of 80.47 days later over 57 years, from 1951–2008. The largest ΔDOY/decade is an order of magnitude greater than the second largest ΔDOY/decade, which is more representative of the overall results, and therefore included here: The species with the second largest ΔDOY/decade is Barnebya harleyi in Catimbau National Park in Caatinga Brazil with a ΔDOY/decade of 5.84 days later per decade. This represents a total change in flowering date of 29.82 days later over 51 years, from 1971–2022. The smallest change in flowering later in the year was Dioscorea bulbifera in Cocha Cashu Peru with a ΔDOY/decade of 0.17 days later per decade. This represents a total change in flowering date of 3.98 days later over 229 years, from 1794–2023.

The species with the largest ΔDOY/decade earlier in the year was Crotalaria mortonii in Bia National Park Ghana with a ΔDOY/decade of 4.08 days earlier per decade. This represents a total change in flowering date of 17.15 days earlier over 42 years, from 1953–1995.The species with the smallest ΔDOY/decade was Rudgea crassipetiolata in Jatun Sacha Ecuador with a ΔDOY/decade of 0.0369 days earlier per decade. This represents a total change in flowering date of 0.17 days earlier over 46 years, from 1974–2020 (S1 Table; Fig 2). Of the 33 species, 10 had negative slopes, indicating a change in flowering to earlier in the year, whereas 23 species had a positive slope, indicating a change in flowering to later in the year (S1 Table; Fig 3). The impacts of each of the six data subsets can be seen in Table 4.

Table 4. Averages pulled from each of the six datasets. Dataset number, average absolute slope, SD, number of species with positive slope (indicating flowering occurring later in the year than historic records), number of species with negative slope (indicating flowering occurring earlier in the year than historic records), average change in flowering date per decade and number of species that met the dataset criteria (Table 3).

Dataset	Slope	SD	Positive slope	Negative slope	Days per decade	# species
1	0.028	0.04	23	10	2.03	33
2	0.033	0.015	11	5	1.63	16
3	0.026	0.016	17	7	1.47	26
4	0.036	0.02	20	12	2.35	32
5	0.043	0.028	6	4	2.14	10
6	0.021	0.032	23	9	1.85	33

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Phylogenetic signal

We found a significant phylogenetic signal in the magnitude of change in flowering time across species (Blomberg’s K = 0.266, P = 0.001) (Fig 4). Although the value of K was substantially lower than 1, indicating that closely related species do not resemble each other as strongly as expected under a Brownian motion model of trait evolution, the signal was still stronger than would be expected by chance. This suggests that while flowering phenological shifts exhibit a weak overall tendency to be phylogenetically conserved, evolutionary history still plays a statistically significant role in explaining interspecific variation in flowering responses over time (Table 3).

Discussion

Our research builds upon a growing body of evidence documenting impacts on the life cycles of plant species by bringing new information to light from tropical latitudes. Our work utilizing thousands of museum specimens not only demonstrated changes in flowering phenology of tropical plants but showed that the magnitude of change was comparable to those previously documented in non-tropical latitudes, therefore rejecting our hypothesis that tropical shifts are smaller than those seen in non-tropical latitudes. Our work furthermore showed a lack of uniformity in direction of shifts, consistent with our prediction that some species will flower earlier compared to historic records and others will flower later than historic records. These changes reflect crucial knowledge in our efforts to more completely understand the impacts of climate change by incorporating new data from tropical latitudes.

Across all 33 species in this study, we documented an average absolute change in flowering times of 2.04 ΔDOY/decade. This number reflects values previously derived from non-tropical studies. In a study that considered diverse biomes from across Earth, i.e., temperate, boreal, and tropical ecosystems, it has been shown that there has been an average phenological shift (ΔDOY/decade) of 2.51 days [46]. Other studies to have emphasized a regional perspective have shown greater or smaller shifts. For example, in British plants, Fitter & Fitter (2002) found an average shift in average flowering date of 4.5 days between 1991 and 2000. Auffret (2021) found that flowering start dates among plants in the Swedish flora shifted an average of days per decade. Similarly, Speed et al., (2022) found that Norwegian plant flowering shifted start date an average of 0.9 days per decade. Across Northern Canada, Bjorkman et al., (2015) found an average lengthening of flowering period of 1.2 days per decade. In alpine Tibet, Chen et al., (2023) found that flowering date advanced an average of 10.3 and 7.5 days respectively across two years of the study. In the southwestern United states, Rafferty et al., (2020) found shifts in average flowering duration ranged from 3.6 days per decade to 25 days per decade, from high to low elevation respectively. In Massachusetts, United States, Bertin (2015) found an average change in flowering onset of 2.9 days per decade. Across central North America, Austin et al. (2024) showed that species increased their flowering duration of 1.15 days per decade. In the southern hemisphere, in Sydney Australia, Everingham, et al., (2023) found that changes in mean flowering date ranged from 2.8 days per decade to 17.8 days per decade. Despite differences in drivers of flowering time found in tropical climates compared to non-tropical climates, our results show clearly that tropical latitudes have been comparably affected by climate change, and in some instances (e.g., northern boreal regions), shifts in tropical plants have been more severe. We emphasize that our results are based on species with discrete flowering periods, therefore removing artificial inflation that can result from consideration of species with longer “continuous” flowering periods [28,47–52].

Directionality of change in flowering date

We found that roughly 1/3 of the species in our study (i.e., 10 of 33; Table 2, Fig 2) are now flowering earlier in the year compared to historical flowering date, and approximately 2/3 of the species (i.e., 23 of 33; Table 2, Fig 2) are flowering later in the year compared to historical flowering date. We expected that tropical species flowering at comparable times in a given year may not all change their flowering in the same direction, and this pattern was supported [1–3]. Although there is not strict uniformity in the direction of change in flowering date in non-tropical locations, there is a greater consistency, due in large part to the drivers of flowering, such as increase or decrease in temperature and photoperiod [28,47–52]. The triggers of flowering in the tropics include myriad factors such as irradiance and solar availability [53–57], temperature [57–59], precipitation [57,60,61], and pollinator availability [57,62], thus some taxa may have separate flowering triggers than others occupying the same habitat. Excluding Peltogyne recifensis, we recovered comparable shifts in flowering days across the remainder of species (i.e., 32 of 33 species), with a total range of 0.369 to 5.84 days per decade (i.e., 5.81 days per decade) (S1 Table). In contrast, the number of days shifted in Peltogyne recifensis was order of magnitude greater than flowering changes in the remaining species. The reason for this difference remains unknown. Further studies focusing on the genus Peltogyne could provide further insights and yield possible conservation strategies.

Biological impact

All the species included in this study had mutualistic interactions with at least one animal species. All species utilized animal pollinators. This reliance on animal pollination gives rise to the risk of misalignment with said pollinators. This misalignment can lead to changes in fitness for the species [5,6,9–14,16,17]. Of the species included in this study, 11 species utilize animals for seed dispersal. The change in reproductive timing of the plants can affect the abundance of the fruit available for consumption by the animals who rely on them [5,6,16,17]. Many of the animals who rely on these plants are primates, who are already an at-risk taxa (IUCN). The greater ecological implications of the changes in flowering time observed here cannot fully be determined, as we are looking at a small subset of the overall biodiversity found in the tropics, and the change in behavior of mutualistic partners cannot be determined here. Further exploration into the greater ecological changes seen should be a point of priority for the scientific community.

Impacts of implementation of dataset criteria

In this study, we explored whether and how four different criteria involved in the construction of (six) different datasets impacted resultant patterns in tropical plant phenological shifts. Specifically, we considered the maximum number of total months flowering, the minimum number of herbarium specimens per species, the minimum span of years a species was collected in the flowering phenophase, and the maximum number of specimens (of a given species) collected from a single site in a single day (Tables 3 and 4). We found minimal variation in resultant change in flowering date across the (six) datasets. The largest variance from the overall dataset was in collections made after 1960, which had a marginally greater change in flowering date (2.35 days per decade) compared to the overall average (2.04 days per decade). This change is likely due to the increasing change in climate seen in recent decades [1–20].

We considered whether flowering duration had an impact on resultant days shifted [6,52] (Tables 3 and 4). Under the assumption that measures in flowering phenology are only valid for organisms that (1) only flower once per year and (2) have relatively short, cyclical flowering events, we aimed to provide an analysis of how flowering period length affected estimated flowering shift magnitude. Our analyses showed that shifts in number of days were comparable between analyses that considered whether flowering duration was less than 4 months vs. flowering duration less than 3 months) (Table 4). Future work should continue to explore this complex topic, particularly in portions of the planet where longer flowering periods are common, to assess whether our results are likely to be widespread, as others have found.

Conclusion

We documented changes in flowering dates across 33 species of tropical flowering plants. We show that phenological change in tropical plants with discrete flowering times has been comparable to those seen in non-tropical environments. Furthermore, we have shown shifts have not been seasonally uniform across taxa, perhaps as a function of how triggers of flowering differentially impact tropical plants compared to how they impact plants in non-tropical latitudes. What remains to be explored in tropical ecosystems is the degree to which abiotic factors such as temperature and precipitation, as well as biotic factors such as pollination and herbivory, impact flowering (Graves et al., in prep).

Supporting information

S1 File. Suplimentary file contains additional information on data subsets used in this study.

(DOCX)

pone.0342105.s001.docx^{(23.6KB, docx)}

S1 Table. Location, plant species, plant family, slope of regression, regressing flowering date on year with Von Mises distribution, standard deviation, total change in flowering date scaled to change in days per decade, duration of flowering (in months), years said specimens spanned, and number of specimens.

(DOCX)

pone.0342105.s002.docx^{(16.1KB, docx)}

S1 Fig. Circular Slope and circular standard deviation of species that flower for 3 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years (Dataset 2).

Species arranged by location.

(PDF)

pone.0342105.s003.pdf^{(79.9KB, pdf)}

S2 Fig. ΔDOY/year of species that flower for 3 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years (Dataset 2).

Species arranged by location.

(PDF)

pone.0342105.s004.pdf^{(71.2KB, pdf)}

S3 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 50 specimens, spanning a minimum of 29 years (Dataset 3).

Species arranged by location.

(PDF)

pone.0342105.s005.pdf^{(103.8KB, pdf)}

S4 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 50 specimens, spanning a minimum of 29 years (Dataset 3).

Species arranged by location.

(PDF)

pone.0342105.s006.pdf^{(95.3KB, pdf)}

S5 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, only including specimens from 1960 or later (Dataset 4).

Species arranged by location.

(PDF)

pone.0342105.s007.pdf^{(130.6KB, pdf)}

S6 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, only including specimens from 1960 or later (Dataset 4).

Species arranged by location.

(PDF)

pone.0342105.s008.pdf^{(107.2KB, pdf)}

S7 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, Only including specimens from before 1960 (Dataset 5).

Species arranged by location.

(PDF)

pone.0342105.s009.pdf^{(61.3KB, pdf)}

S8 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, Only including specimens from before 1960 (Dataset 5).

Species arranged by location.

(PDF)

pone.0342105.s010.pdf^{(55.1KB, pdf)}

S9 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years – Only including one specimen per species per day (Dataset 6).

Species arranged by location.

(PDF)

pone.0342105.s011.pdf^{(126.1KB, pdf)}

S10 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years – Only including one specimen per species per day (Dataset 6).

Species arranged by location.

(PDF)

pone.0342105.s012.pdf^{(114KB, pdf)}

Acknowledgments

The authors thank Ryan Hollingsworth for editorial support, Dani Bosse, Kendall Origer, Thummim Pradhan, Aurora Health, Anika Shoemaker, and Gerano Morales for help with scoring GBIF images to determine flowering periods, Seth Raynor for editorial support, Brett Melbourne for aid in statistical analyses and debugging, Beelzebub The Cat for contributions and support, and Gladiana Spitz for aid in code design and structure, debugging and design of figures.

Data Availability

All data and code used in this study are available at: https://github.com/SkylarMGraves/Observing-shifts-in-phenology-of-tropical-flowering-plants.

Funding Statement

The author(s) received no specific funding for this work.

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Reviewers' comments:

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #2: Yes

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Reviewer #1: Details about responses to the questions above: Some of the data do not support the conclusions, particularly in the comparisons of the findings regarding shifts in flowering phenology in tropical latitudes to findings from other locations. Specifically, more attention needs to be given to the direction of shifts, the uniformity of shift directions among species, and the 'severity' of responses. Given that collection time is proxying for flowering time, attempts to tease out any effects of collection time on the flowering phenological results could be made or explanations could be give for why this may not be possible. The fit of the regression lines used to determine the responses of flowering to time should be analyzed. The criteria for inclusion of specimens in the study should be better justified.

Additional comments:

Abstract

More justification for the potential ecological significance of the study focus rather than justification based solely on the lack of previous studies with this focus would strengthen the overall presentation of the work.

The second bullet point cites ‘this hypothesis’. To emphasize the reference, I would suggest changing ‘postulated’ to ‘hypothesized’ in the previous bullet point. Also in the second bullet point, an ‘of’ is missing between ‘phenology flowering’.

Use consistency in the presentation of significant digits in the ‘Results’.

More ecological context in the ‘Conclusion’ section would strengthen the Abstract.

Introduction

Is ‘anthropogenic-mediated’ relevant in the first sentence? Such shifts would apply to climate change more broadly. Also, this sentence needs citation.

The ‘cascade effects’ (line 34) could be extrapolated. Cascading impacts on what? I would rewrite this and the following sentence as one: ‘Responses of plants to climate change can have cascading impacts across ecosystems.’

The paragraph of lines 40-44 is very short. Could this be further described on combined with the previous paragraph?

The ‘universally recognized importance’ cited in line 45 needs support.

Some link between the biodiversity information in lines 46-49 and the study focus on phenology should be made. Does this biodiversity suggest the potential for strong cascading effects, for example?

What do flowers from plants with discrete flowering periods in the tropics generally respond to if not temperature- or photoperiod-dependent? If not temperature, why would flowering phenology shifts be hypothesized? What proportion of plant species in the tropics have discrete flowering periods? Is this common?

The sentence in lines 54-55 is unnecessary.

The paragraph in lines 60-63 is very short. Either elaborate or combine this with the previous paragraph as the topic seems the same. Also, the sentence in lines 60-61 seems overly obvious.

What are examples of global impacts that changes in the flowering phenology of tropical plants could have (lines 65-67)?

Were the 33 plants selected at random? Or were these the only ones that fit the discrete-flowering criterion for which specimens were available? If the latter, I would use the word ‘identified’ instead.

The paragraph in lines 71-77 is unnecessary. Limitations of the study should be described in the Discussion section.

Methods

Remove the first part of the first sentence unless it is made less general.

Was any analysis of collection efforts through time made to tease out any influence of collection effort changes on flowering phenology shifts? For example, was there no clear pattern of changes in collection efforts?

Herbarium collections ‘prior’ to when? (line 82) Also, I would change ‘regularity’ to ‘consistency’.

What ‘criteria for inclusion’ are being referenced in line 85?

‘Mean’ flowering vs. ‘peak’ flowering could be explained in lines 95-96. What was measured in the present study?

The statement in lines 96-98 is attributable to a single study and this should be described. This is not necessarily a broader generalization.

Is the first ‘species’ that appears in line 111 meant to be ‘specimens’?

Why was 4 or less months/year of flowering considered the cut-off for including species in this study? Why not 5 months or less? Would this have affected the results potentially? Similarly, please justify other inclusion criteria (i.e., 20 specimens per site, 29 years of data, etc.).

Was there any possibility of making distinctions between flowering stages that could have further informed the timing of flowering phenology?

Can some explanation of how collection and flowering generally align be added to strengthen the use of collection time as a proxy for flowering time? Flowering specimens are encouraged in herbarium collections.

Were line regressions used to derive the absolute slopes analyzed for significance (fit of the yearly points along the best-fit line)?

Results

Changes in flowering date should be described in terms of earlier vs. later? A change in and of itself is not as informative. I would present results for the negative sloped species distinctly from the positively sloped species as these could represent distinct ecological responses that could have distinct ecological relevance.

The figure captions should be more ‘stand-alone’ by explaining abbreviations used, study focus on the tropics, etc.

In the figures, could species be presented by family vs. location?

Suggest moving Table 2 to the Supplement. Some of the information in this table is double depicted in the figures.

Discussion

The study brings new ‘information’ to light, not new ‘data’.

In non-tropical latitudes, are shifts more likely to be in one direction (i.e., earlier in the spring, later in the fall)? Or do you similar see changes in both directions depending on the species and location?

The assertion that tropical shifts are not ‘less severe’ than those documented at other latitudes needs to be substantiated?

How ‘severe’ were those shifts? The existence of shifts and their severity are distinct concepts (lines 173-177).

Why was your prediction that the species would show a lack of uniformity in the direction of their flowering phenology shifts (line 177, lines 196-198)?

Lines 182-192: Again, the direction of the shifts seems ecological relevant. Direction should be discussed.

If precipitation is suspected to be a major driver of phenological shifts in the tropics, could that have been investigated here?

What major changes in precipitation have occurred through time in the study locations? Could that explain phenological shifts in flowering?

A major limitation of the Discussion is the lack of attention given to the ecological relevance of the findings. What could the detected shifts (which were not uniform in direction) mean for other species, communities, ecosystem? How pollinator-specific are these species (i.e., are they generalists or specialists in this regard)? Are they all primary animal-pollinated? Have other systems at other latitudes also showed such non-uniformity in the direction of phenological shifts, and if so, what has that meant for other species, communities, ecosystems?

The ’Human Impacts’ subsection seems extraneous. More important would be discussion of the potential ecological relevance of the findings.

Reviewer #2: In PONE-D-25-54215 entitled “Observing shifts in phenology of tropical flowering plants”, the authors test for phenological shifts in tropical flowering plants. They were particluarlly interested in comparing their results with temperate species and thus restricted their analyses to species with distinct seasonal patterns (i.e., blooming for 3-4 months). The manuscript was very easy to read and addressed a timely topic that would be of interest to a broad audience. Many of the species observed exhibited phenological shifts to those documented for temperate species, which has interesting ecological implications. I commend the authors for their significant efforts to ensure robust results and to discuss them without hyperbole. However, there are several parts of the manuscript that would benefit from increased clarity and depth (see my comments below for details). Upon revision, I think this manuscript will make an important contribution to the field.

While easy to read, the manuscript appeared to lack sufficient support for the claims upon first reading. However, this was because much of the pertinent information is in the supplementary materials and never referenced in the main text. The authors conducted several analyses to test potential biases or weaknesses in their analyses. These additional analyses indicate that their results are robust to those biases, but they were not described or referenced in the main text as they should be (indeed it would strengthen the paper to include them).

Moreover, two results deserve additional discussion. The first is the similar pacing of phenological change before and after 1960. Given that climate change is accelerating, seeing similar changes in both time periods could indicate a slower rate of response or constraints on phenological shifts. While the authors do not directly test that idea, it warrants further discussion in the main text. The presence of a phylogenetic signal, while not particularly strong, also deserves discussion in the main text.

As is common in phenology studies, species varied in the degree of change through time. However, the authors provide little rationale for why we might see such differences in this data set. What are the documented climate change patterns for these locations? What habitats are represented? In which habitats are the species found and do they specialize on specific habitats? Are these long-lived or short-lived species? What kinds of growth habits do they exhibit (e.g., forbs, woody vines or shrubs, trees)? All of these factors could influence species' response to climate changes, and it is unclear what subset of tropical habits were studied here. I am not suggesting that the authors have to change their analyses to test whether any of these factors predict phenological changes. However, these traits do warrant description in the methods and, if there are indicators that any of these factors might influence species responses, they should be addressed in the discussion.

Finally, the methods need more detail for them to be interpretable, particularly for those less familiar with circular linear regression (e.g., description of the purpose of the training algorithm and the various sampling regimes).

**********

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PLoS One. 2026 Feb 25;21(2):e0342105. doi: 10.1371/journal.pone.0342105.r002

Author response to Decision Letter 1

15 Dec 2025

Below is the same as the "Response to Reviewers" document:

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Reviewers' comments:

Reviewer's Responses to Questions

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Reviewer #1: Partly

Reviewer #2: Yes

________________________________________

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Reviewer #1: No

Reviewer #2: Yes

________________________________________

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Thank you for your feedback. The inclusion of the tests conducted (previously in the supplemental materials, now in the main text) should address the above concerns. Additional exposition of comparison to non-tropical latitudes added in discussion section. Direction of shifts specified in results and discussion. It has been shown that the unwanted impacts of collector bias can be ameliorated with large datasets and utilization of mean flowering, as opposed to peak or first flowering (25-30 32, 33-34). Additionally, it has been shown that phenology studies with field observations yielded similar results to those utilizing only herbarium specimens (25-30 32, 33-34). Due to the nature of a Bayesian analysis, testing the fit of the regression is built into the process. The training algorithm was a mix of Gibbs sampling, fast rejection sampling, and Metropolis-Hastings. Using two training algorithms is the most efficient and stable way to fit a Bayesian model where no single sampling regime works for all parameters. Four chains were used, with a burnin of 200 and 2500 iterations. The convergence of the chains indicates the best model fit. Furthermore, uncertainty was assessed using standard deviations of the posterior samples for each slope parameter associated with each species. The inclusion of the six data subsets should address the concern regarding the justification for the inclusion of the species.

Abstract

Understood. This change has been made.

Use consistency in the presentation of significant digits in the ‘Results’.

Understood. This change has been made.

More ecological context in the ‘Conclusion’ section would strengthen the Abstract.

Understood. This change has been made.

Introduction

Is ‘anthropogenic-mediated’ relevant in the first sentence? Such shifts would apply to climate change more broadly. Also, this sentence needs citation.

Term ‘anthropogenic-mediated’ has been removed and citation has been added.

Extrapolation on the cascade effects has been done. Sentences have been re-written as one, as suggested.

The paragraph of lines 40-44 is very short. Could this be further described on combined with the previous paragraph?

Lines have been combined with previous paragraph.

The ‘universally recognized importance’ cited in line 45 needs support.

Citation added.

Some link between the biodiversity information in lines 46-49 and the study focus on phenology should be made. Does this biodiversity suggest the potential for strong cascading effects, for example?

Connecting information added.

It is not that plants in the tropics don’t respond to temperature, it is that there is not the same distinct seasonality as a cold/dark induced dormancy period that cause either spring or autumn flowering to be most common across taxa. There is more complexity to the triggers of flowering in tropical latitudes. We address this topic in depth in a paper currently in review at PNAS.

The proportion of tropical plants with discrete flowering periods is relatively small, however, species that have discrete flowering periods are more tractable from a perspective of understanding phenological change, as these flowering patterns mirror those seen in non-tropical latitudes. Such species therefore enable comparison to phenological changes documented from non-tropical latitudes. From an ecological perspective, species with discrete flowering periods have a higher likelihood of misaligned interspecific interactions as a result of changes in reproductive period (22, 23). Measuring species with discrete flowering times is therefore likely to target the species most vulnerable to change.

The sentence in lines 54-55 is unnecessary.

Understood. This change has been made.

The paragraph in lines 60-63 is very short. Either elaborate or combine this with the previous paragraph as the topic seems the same. Also, the sentence in lines 60-61 seems overly obvious.

Understood. This change has been made.

What are examples of global impacts that changes in the flowering phenology of tropical plants could have (lines 65-67)?

Examples have been added.

These were the only ones that fit the discrete-flowering criterion for which specimens were available. Term changed to ‘identified’

The paragraph in lines 71-77 is unnecessary. Limitations of the study should be described in the Discussion section.

Paragraph deleted.

Methods

Remove the first part of the first sentence unless it is made less general.

First part of sentence removed.

Rates of collections were analyzed in selection of locations. With the exception of a reduction of collections during the World Wars, there was a steady rate of collections across time, hence the selection of these locations over ones with less frequent/regular collections.

Herbarium collections ‘prior’ to when? (line 82) Also, I would change ‘regularity’ to ‘consistency’.

Terminology changed as requested.

What ‘criteria for inclusion’ are being referenced in line 85?

Please see table 2 and all information moved from supplemental to main text.

‘Mean’ flowering vs. ‘peak’ flowering could be explained in lines 95-96. What was measured in the present study?

Explanation on difference between mean flowering, peak flowering and first flowering added. Mean flowering was used in this study.

The statement in lines 96-98 is attributable to a single study and this should be described. This is not necessarily a broader generalization.

I cited the first study to establish this idea, however I have now since added more studies that support this idea.

Is the first ‘species’ that appears in line 111 meant to be ‘specimens’?

Yes. Apologies for the typo.

All of this was addressed in the supplemental, which has since been moved to the main text. We hope this explains the rationality effectively.

Was there any possibility of making distinctions between flowering stages that could have further informed the timing of flowering phenology?

It has been shown that the unwanted impacts of collector bias can be ameliorated with large datasets and utilization of mean flowering, as opposed to peak or first flowering (25-30 32, 33-34). For this reason, we chose to use Mean flowering. Although we could theoretically look at various phenophases, we would have less confidence in the results, and it would take additional mon

Attachment

Submitted filename: Response to reviewers.docx

pone.0342105.s014.docx^{(33.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342105.r003

Decision Letter 1

Hong Qin

11 Jan 2026

Dear Dr. Graves,

Please submit your revised manuscript by Feb 25 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Hong Qin, PhD

Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: (No Response)

Reviewer #2: The author thoroughly responded to my recommendations (at times may have even over-corrected). I appreciate all of the changes the authors made to the text and tables but suggest that some of the added figures are unnecessary. Presenting the results of the competing models in table 3 was sufficient to address my concerns regarding providing evidence to support their claims in the text. In my opinion, Figures 4-8 could remain in the supplementary materials.

I would be interested in seeing the phylogeny with the change in flowering mapped on (color-coded by slope or similar would be sufficient). At present, the reader must cross reference multiple figures in order to connect the phylogeny in the figure with the stated results. If the response was mapped onto the phylogeny, the phylogenetic patterns would be much more interpretable.

Because the figure panels were each uploaded separately, the scaling of the text will be off once the panels are combined into a single figure, making the labels challenging to read. The authors will likely need to modify the figures to make them more legible.

The “Outlier Species” section should be included in the “Directionality of change...” section. In my opinion, it doesn’t warrant its own section.

Discussing how these analyses were generally robust to potential biases would be informative for the field. However, the length of the section “Impacts of implementation of dataset criteria” could be shortened to include only the first 2 paragraphs which synthesize the patterns rather than going through each criterion in detail.

Overall, I commend the authors for their thorough changes and look forward to seeing the final product in print.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2026 Feb 25;21(2):e0342105. doi: 10.1371/journal.pone.0342105.r004

Author response to Decision Letter 2

15 Jan 2026

Below are the reviewers comments in red and my responses in black.

The author thoroughly responded to my recommendations (at times may have even over-corrected). I appreciate all of the changes the authors made to the text and tables but suggest that some of the added figures are unnecessary. Presenting the results of the competing models in table 3 was sufficient to address my concerns regarding providing evidence to support their claims in the text. In my opinion, Figures 4-8 could remain in the supplementary materials.

I am glad you found the corrections thorough! Figures 4-8 have been returned to the supplementary materials.

I made a phylogeny color coded by the change in flowering date in days.

I believe this is referring to the 2 paneled figures of figures 4-8. Because these have been moved to the supplement, I simplified by making each figure independent instead of paneled. I split the figure captions to match.

The “Outlier Species” section should be included in the “Directionality of change...” section. In my opinion, it doesn’t warrant its own section.

I moved the “Outlier Species” section to the “Directionality of change” section

I shortened the section to only include the first two paragraphs as suggested.

Overall, I commend the authors for their thorough changes and look forward to seeing the final product in print.

Thank you for all your comments and suggestions. I really appreciate the attention to detail you put in to make this paper the best it can be!

Attachment

Submitted filename: Response to reviewers_2026.docx

pone.0342105.s015.docx^{(15KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342105.r005

Decision Letter 2

Hong Qin

19 Jan 2026

Observing shifts in phenology of tropical flowering plants

PONE-D-25-54215R2

Dear Dr. Graves,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Hong Qin, PhD

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: The authors addressed all of my recommendations, and I believe the article is ready for acceptance. I look forward to seeing it in print!

**********

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Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0342105.r006

Acceptance letter

Hong Qin

PONE-D-25-54215R2

PLOS One

Dear Dr. Graves,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Suplimentary file contains additional information on data subsets used in this study.

(DOCX)

pone.0342105.s001.docx^{(23.6KB, docx)}

(DOCX)

pone.0342105.s002.docx^{(16.1KB, docx)}

S1 Fig. Circular Slope and circular standard deviation of species that flower for 3 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years (Dataset 2).

Species arranged by location.

(PDF)

pone.0342105.s003.pdf^{(79.9KB, pdf)}

S2 Fig. ΔDOY/year of species that flower for 3 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years (Dataset 2).

Species arranged by location.

(PDF)

pone.0342105.s004.pdf^{(71.2KB, pdf)}

S3 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 50 specimens, spanning a minimum of 29 years (Dataset 3).

Species arranged by location.

(PDF)

pone.0342105.s005.pdf^{(103.8KB, pdf)}

S4 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 50 specimens, spanning a minimum of 29 years (Dataset 3).

Species arranged by location.

(PDF)

pone.0342105.s006.pdf^{(95.3KB, pdf)}

S5 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, only including specimens from 1960 or later (Dataset 4).

Species arranged by location.

(PDF)

pone.0342105.s007.pdf^{(130.6KB, pdf)}

S6 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, only including specimens from 1960 or later (Dataset 4).

Species arranged by location.

(PDF)

pone.0342105.s008.pdf^{(107.2KB, pdf)}

S7 Fig. Circular Slope and circular standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, Only including specimens from before 1960 (Dataset 5).

Species arranged by location.

(PDF)

pone.0342105.s009.pdf^{(61.3KB, pdf)}

S8 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, Only including specimens from before 1960 (Dataset 5).

Species arranged by location.

(PDF)

pone.0342105.s010.pdf^{(55.1KB, pdf)}

Species arranged by location.

(PDF)

pone.0342105.s011.pdf^{(126.1KB, pdf)}

S10 Fig. ΔDOY/year of species that flower for 4 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years – Only including one specimen per species per day (Dataset 6).

Species arranged by location.

(PDF)

pone.0342105.s012.pdf^{(114KB, pdf)}

Attachment

Submitted filename: Response to reviewers.docx

pone.0342105.s014.docx^{(33.2KB, docx)}

Attachment

Submitted filename: Response to reviewers_2026.docx

pone.0342105.s015.docx^{(15KB, docx)}

Data Availability Statement

All data and code used in this study are available at: https://github.com/SkylarMGraves/Observing-shifts-in-phenology-of-tropical-flowering-plants.

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PERMALINK

Observing shifts in phenology of tropical flowering plants

Skylar Graves

Erin A Manzitto-Tripp

Roles

Abstract

Introduction

Methods

Table 1. Approximate centroid latitude and longitude coordinates of locations included in this study.

Fig 1. Map of locations for the 33 species utilized in this study.

Table 2. Table includes location, species name and taxonomic authority, IUCN Red List Status, known pollinators, known seed dispersal agents and habit.

Herbarium data retrieval and dataset compilation

Construction of datasets

Data subsets

Statistical analyses

Determining changes in flowering date.

Affiliation and directionality of shifts.

Phylogenetic signal

Results

ΔDOY/decade

Fig 2. Change in flowering date per decade of species that flower for 4 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years.

Fig 3. Circular Slope and standard deviation of species that flower for 4 months or less, with a minimum of 20 specimens, spanning a minimum of 29 years.

Phylogenetic signal

Fig 4. Unrooted Maximum Likelihood phylogeny (inferred using the phangorn package) of 33 species included in this study, constructed from multiple representative sequences per each tip species.

Discussion

Directionality of change in flowering date

Biological impact

Impacts of implementation of dataset criteria

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

Refrences

Decision Letter 0

Hong Qin

Roles

Author response to Decision Letter 1

Decision Letter 1

Hong Qin

Roles

Author response to Decision Letter 2

Decision Letter 2

Hong Qin

Roles

Acceptance letter

Hong Qin

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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