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Article

Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens

by
Katy V. Rondinel-Mendoza
1,*,
Juan Lorite
1,2,
Macarena Marín-Rodulfo
1 and
Eva M. Cañadas
1
1
Departamento de Botánica, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
2
Interuniversity Institute for Earth System Research, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Plants 2024, 13(4), 522; https://doi.org/10.3390/plants13040522
Submission received: 19 January 2024 / Revised: 7 February 2024 / Accepted: 12 February 2024 / Published: 14 February 2024

Abstract

:
Phenological studies have a crucial role in the global change context. The Mediterranean basin constitutes a key study site since strong climate change impacts are expected, particularly in mountain areas such as Sierra Nevada, where we focus. Specifically, we delve into phenological changes in endemic vascular plants over time by analysing data at three scales: entire massif, altitudinal ranges, and particular species, seeking to contribute to stopping biodiversity loss. For this, we analysed 5262 samples of 2129 herbarium sheets from Sierra Nevada, dated from 1837 to 2019, including reproductive structure, complete collection date, and precise location. We found a generalized advancement in phenology at all scales, and particularly in flowering onset and flowering peak. Thus, plants flower on average 11 days earlier now than before the 1970s. Although similar trends have been confirmed for many territories and species, we address plants that have been studied little in the past regarding biotypes and distribution, and which are relevant for conservation. Thus, we analysed phenological changes in endemic plants, mostly threatened, from a crucial hotspot within the Mediterranean hotspot, which is particularly vulnerable to global warming. Our results highlight the urgency of phenological studies by species and of including ecological interactions and effects on their life cycles.

1. Introduction

Phenology (i.e., the study of the timing of recurring biological events in the animal and plant world, the causes of their timing (biotic and abiotic forces), and the interrelations among phases of the same or different species [1]) is an integrative science that has achieved a crucial role in the current context of global change [2]. In fact, changes in the timing of phenological events are among the most important indicators of global warming [3,4,5]. Thus, many studies have confirmed that plants are modifying the timing of the development and the shape of their vegetative and reproductive structures in response to global warming [6,7,8]. This could explain the shift in the distribution range of certain species reported as new at the regional or country level [7], or promote speciation [9]. In particular, phenological events at temperate latitudes have advanced by between 1.5 and 2.5 days per decade since the 1970s [7,10].
Phenological changes in plants have consequences not only on the reproductive success of the species, but also on cascades at different levels and across functional groups within communities, including decomposers, detritivores, herbivores, predators, pollinators, and seed dispersers [11,12,13]. In this sense, phenological changes may influence the synchronization between flowering and pollinator activity or between fruiting and seed disperser activity, and, thus, the connectivity and gene flow through pollen and seed movements across landscapes [2]. Therefore, plant phenology is extremely relevant for ecological processes and for biodiversity conservation over time, and ultimately for the maintenance of essential ecosystem services [14,15]. Consequently, phenological changes will have significant impacts on agriculture, forestry, human health, and the global economy [16].
The Mediterranean basin constitutes a key area for the study of phenological changes, since it is considered one of the regional foci where climate change will exert particularly strong effects [17,18]. Although there have been few previous studies in this region on long-term phenological changes, the limited precedents suggest that global warming and increasing drought frequency have led to major shifts in the timing of phenophases in Mediterranean ecosystems [19]. In particular, [20] found that an increase of 1.4 °C from 1952 to 2000 led to a generalized phenological advancement in recent decades (i.e., on average, leaves unfold 16 days earlier, leaves fall 13 days later, and flowering occurs 6 days earlier).
Within the Mediterranean, mountain areas are especially vulnerable to climate change [21]. This is particularly concerning in summit areas, as they often constitute biodiversity nano-hotspots rich in endemic species [22,23]. Studies of recent changes in vascular plant richness across Europe’s major mountain ranges found that, on average, species moved upslope, and the loss of endemic species was particularly severe in the Mediterranean mountains. However, mountaintop endemic species are unable to adopt vertical migration strategies [24], which is crucial to coping with climate change [25]. Yet, to our knowledge, phenology and phenological changes in plants endemic to the Mediterranean mountains have been poorly addressed and encompass few species (e.g., [26,27]).
One of the main difficulties in studying phenological changes is the limited availability of long-term series data. In this regard, herbarium sheets are a powerful source of spatially and temporally extensive data on plant functional traits, and therefore are very valuable for the study of phenological changes over time [28,29]. Thus, many studies based on herbarium data have revealed changes in reproductive phenology in response to global warming and altered precipitation patterns [30,31,32]. In line with other phenological studies, analyses of herbarium samples have confirmed an advancement in phenology in recent decades [33,34,35].
This study presents a comprehensive analysis of the phenological shifts in endemic plants of Sierra Nevada, a Mediterranean mountain massif in southwestern Europe, using herbarium samples from the last 183 years. Sierra Nevada stands out as one of the main biodiversity hotspots within the larger Mediterranean hotspot [22,36], yet is severely threatened by the impacts of climate change [23,37,38]. Remarkably, Sierra Nevada houses the highest peak (Mulhacén, 3482 m asl) in the Iberian Peninsula and supports a high level of plant biodiversity [39]. Its unique ecological setting has attracted numerous renowned botanists since the 19th century [40], resulting in the preservation of a substantial number of herbarium sheets coming from their field expeditions. Consequently, Sierra Nevada serves as an invaluable natural laboratory for investigating phenological changes based on historical herbarium specimens.
Specifically, our analysis focuses on discerning phenological trends in endemic vascular plants over time, examining whether these trends exhibit consistency across different altitudinal ranges (non-alpine vs. alpine zones) and specific species. The ultimate aim is to discern trends that provide valuable insights into how to address the critical challenge of halting biodiversity loss.

2. Methods

2.1. Study Area

Sierra Nevada is a small (ca. 2100 km2) and isolated mountain range in southeastern Spain (from 36°50′24″ to 37°15′0″ N latitude and 3°44′24″ to 2°35′24″ W longitude) exhibiting a diverse topography and an extensive altitudinal gradient from 200 to 3479 m asl. This geographical uniqueness makes it resemble a sort of continental island, also being the only true alpine region located between the North African mountains (High and Middle Atlas) and the Pyrenees, both several hundreds of kilometres away.
The climate is a typically Mediterranean mountain type, characterized by cold winters and hot summers with pronounced droughts (July–August). Precipitation, mainly in the winter, ranges from 350 to 1200 mm per year, depending mostly on altitude, and 75% occurs in the form of snow above 2000 m asl. The average annual temperature is 12 °C, with strong day–night and winter–summer fluctuations. In the winter, temperatures can drop to −35 °C, and snow can remain for up to 8 (occasionally up to 10) months in the highest areas [38].
Regarding geology, the massif is made up of siliceous rock (i.e., micaschists, phyllites, and quartzites) from the Permo-Triassic surrounded by carbonates (limestones and dolomites) from the Middle-Upper Triassic [41]. The different combinations of climatic conditions and rock types favour the presence of a high level of diversity of habitats and species [42]. In relation to the plant diversity, Sierra Nevada represents one of the most relevant hotspots in the western Mediterranean [43,44], with more than 2348 taxa of vascular plants, including 79 endemic and 16 sub-endemic to Sierra Nevada. It also has 362 taxa inhabiting the alpine zone (about 242 km2), representing 79% of the endemism of the whole area [39,45].

2.2. Phenological Data

Phenological data were obtained by reviewing the herbarium sheets, which encompassed 89 vascular plant species, including 62 endemic and 16 sub-endemic taxa from Sierra Nevada, plus 11 additional taxa which are also relevant for conservation (see Appendix A). We included all endemic and sub-endemic taxa from Sierra Nevada, except those belonging to the Poaceae family due to the inherent difficulty of discerning their phenological stage. A total of 5262 sample “observations” from 2129 herbarium sheets were examined from April 2019 to December 2021. These data came from the main herbaria housing material from Sierra Nevada (herbaria acronyms according to [46]): GDA-GDAC (1954 samples), MA (2002 samples), SEV (646 samples), MGC (346 samples), JAEN (130 samples), and HUAL (61 samples). We also included digital samples from
G (CJBG source: https://www.ville-ge.ch/musinfo/bd/cjb/chg/index.php?lang=en (accessed on 1 March 2020); 13 samples) and RECOLNAT (source: https://www.recolnat.org/en/, accessed on 1 March 2020; 110 samples). Notably, the time period for the dataset ranged from 1837 to 2019 (see Appendix B).
The herbarium sheets finally selected for this study met the following three criteria, which were applied before obtaining the total number of records: (1) At least 50% of the reproductive structures exhibited good preservation; (2) had a complete collection date, including day, month, and year; and (3) had precise geographical information, either in the form of exact coordinates or sufficiently detailed locality descriptions, enabling us to assign precise coordinates (error < 1 km).
Thus, in this first part of this study, phenological, spatial, and temporal information for each individual sample of herbarium sheet was recorded as follows: (I) Number of reproductive structures (no. of flower buds “NB”, no. of flowers “FL”, and no. of fruits “FR”) was recorded. (II) Phenological phase, based on the highest quantitative representativeness and state of development of reproductive structures: We established 6 categories: (1) flowering onset, “FL_O” (state of flower bud); (2) flowering peak, “FL_P” (anthesis of the flower ready for pollination); (3) flowering late, “FL_L”(beginning of adult flower wilting); (4) fruiting onset, “FR_O” (beginning of embryo formation or immature fruits); (5) fruiting peak, “FR_P” (ripe fruits and seeds production in ripe fruits); and (6) fruiting late, “FR_L” (very ripe fruits, close to dehiscence). (III) Complete dates of sheet collection (day/month/year) were recorded, taking into account the leap years. These dates were converted into days of the year, (i.e., 30 July 1954 corresponds to the 211th day of the year). We named this variable “Julian date (JD)”. (IV) Geographical position was noted (with coordinates and/or precise localities, allowing coordinates to be assigned in a subsequent step). (V) Altitude data were obtained from coordinates of a digital elevation model (https://www.ign.es/wms-inspire/mapa-raster, accessed on 8 March 2022) using QGIS Desktop 3.24.1 (http://www.qgis.org, accessed on 8 March 2022).

2.3. Statistical Analyses

In order to explore temporal shifts in phenology from 1837 to 2019 across the 89 species assessed, we fitted generalized linear models (GLMs with family Poisson and link = log) using the Julian date (JD) as the response variable and the collection year of the herbarium sheet and the phenological phase as independent variables (Julian date ~ year * Phenological phase). In addition, we performed lineal models (LMs) for each phase (JD ~ Year) using the complete dataset. Next, we assessed the consistency of phenological trends across different altitudinal ranges. Thus, we divided the dataset into two groups: (1) samples from herbarium sheets collected above 2400 m asl (alpine zone) and (2) samples from sheets collected below 2400 m asl (non-alpine zone). GLMs were fitted for each altitudinal range dataset and LMs were used to explore trends according to phenological phase, as described above. To evaluate the model’s performance, we computed p-values and pseudo-R-squared values for all fitted models compared to the null models. For this purpose, the “nagelkerke” function from the “rcompanion” library was used. In the results section, we present the Nagelkerke pseudo R2 [47] for GLMs and adjusted R2 for LMs.
In order to assess changes by species, we focused on flowering peaks and fruiting peaks, because we had a greater number of herbarium sheets for these phases. In particular, we used data from those species with at least 5 samples per period (≤1969 vs. ≥1970) for each phase. These conditions were met by 18 taxa for the flowering peak and 12 taxa for the fruiting peak phases. Subsequently, to highlight the number of days of advancement or delay in phenology, we divided the dataset into the two aforementioned periods (≤1969 and ≥1970) at all scales studied (complete dataset, by altitudinal zones, and by species), because there has been an inflection point in climate data since the early 1970s [48]. Subsequently, we compared the average Julian dates by fitting different models through permutational ANOVAs using the “lmPerm” R package [49], a flexible and very robust analysis that can cope with heteroscedasticity and a wide variety of statistical distributions.

3. Results

3.1. Phenological Trends at Massif Scale

We found an evident advancement in phenology across the Sierra Nevada massif, as indicated by a significant negative relation between the collection year and the Julian Date of collection applied to entire dataset, regardless of the species (pseudo-R2= 0.06572040; p-value < 0.001). Further analysis, accounting for the different phenological phases, revealed a consistent trend of advancement in all phases, except for the fruiting peak (non-significant) and fruiting late (marginally significant; Table 1 and Figure 1) phases. Flowering onset exhibited the most pronounced advancement, followed by the flowering peak. Comparing the two periods considered (≤1969 vs. ≥1970), on average, the day of flowering onset shifted from 199 for the 1837–1969 period (n = 132) to 188 for the 1970–2019 (n = 412) period, indicating an advancement of approximately 11 days. Meanwhile, for the flowering peak phase, an advancement of 13 days was identified for the same period (Table 2).

3.2. Phenological Trends by Altitudinal Range

The fitted models showed a consistent pattern of phenological advancements in the two altitudinal zones considered (alpine vs. non-alpine; pseudo-R2 = 0.2782160) for the whole dataset. Remarkably, the phenological advancement (Figure 2) was sharper in the non-alpine zone (pseudo-R2 = 0.105) compared to the alpine zone (pseudo-R2 = 0.044).
When considering the phenological phases (excluding fruiting peak and fruiting late, where the sample sizes were too small), in both zones, the most significant phenological changes between the two periods considered (pre-1969 and post-1970) were observed in the flowering onset phase (Table 3). Specifically, in the non-alpine zone, the flowering onset shifted from day 194 on average during the period of 1837–1969 (n = 30) to day 172 on average for the period of 1970–2019 (n = 170), which represents an advancement of approximately 22 days. In the alpine zone, flowering onset occurred, on average, on day 202 during the period of 1837–1969 (n = 99) and on day 199 during the period of 1970–2019 (n = 237), representing an advancement of 3 days. As for the flowering peak phase in the non-alpine zone and the alpine zone, advancements of 18 and 5 days, respectively, were recorded (Appendix C).

3.3. Phenological Trends by Species

The analysis of phenological changes by species when contrasting the two defined periods (pre-1969 and post-1970) showed a significant (or marginally significant) advancement in the flowering peak for eight taxa (Table 4), with the average number of days of advancement varying between 12 (Lepidium stylatum Lag. and Rodr.) and 27 (Ranunculus angustifolius subsp. alismoides (Bory) Malag.). Only in one taxon, i.e., Scorzoneroides microcephala (Boiss.) Holub, was the flowering peak significantly delayed, specifically by 20 days, in the post-1970 period.
Regarding the fruiting peak phase (Table 5), the signal was weaker, and only 4 taxa out of the 12 evaluated showed significant changes when comparing the pre-1969 and post-1970 periods. On the contrary, for two taxa, the fruiting peak was advanced (Biscutella glacialis (Boiss. and Reut.) Jord. and Ranunculus acetosellifolius Boiss.), and for two others, it was delayed (Scorzoneroides microcephala (Boiss.) Holub and Ranunculus angustifolius subsp. alismoides (Bory) Malag.)

4. Discussion

Our study revealed a generalized advancement in the flowering periods of endemic Sierra Nevada plants, and this trend was consistent both throughout the entire massif and for the two altitudinal ranges analysed. Furthermore, at the species level, the trend pointed in the same direction, although this advance was not significant in all cases. Thus, for Sierra Nevada as a whole, we found that flowering begins, on average, 11 days earlier in the current decade than before the 1970s, which represents an average advancement of 2.2 days/decade. This phenological trend agrees with previous evidence obtained for temperate areas [7,50]. The results were also in line with those identified in the Mediterranean area [20,51], although in some of the studies, the changes were less pronounced [52,53].
One of the novelties of our study is that it focuses on endemic plants, mainly herbs (both annual and perennials) and small shrubs [45], whereas most of the previous phenological studies have analysed mainly trees or large shrubs with wide distribution ranges [20,51,52]. Although the timing of phenological events is driven by complex interactions between living organisms and environmental factors [54,55], climatic variables are particular determinants. Numerous studies have evidenced that consistent phenological advancements in recent decades, not only for plants, but also for other groups of organisms, have been driven primarily by increasing temperatures (e.g., [8,53,56]). Therefore, it is expected that these phenological advancement trends will continue to occur as a consequence of global warming.
Certainly, climatic variables such as temperature change significantly with altitude. In this sense, it was expected that there would be differences between the phenological results obtained in the alpine and non-alpine zones. In line with these expectations, we found an earlier onset of flowering and fruiting in recent decades compared to the decades before 1970 at both altitudinal ranges, but the phenology of lowland endemic plants (non-alpine area) advanced more than that of plants in the alpine area. This does not mean that, in alpine zones, the impact of changes on phenology is low, since as altitude increases, the optimal phenological period shortens, and any minor alteration leads to more noticeable effects. Furthermore, it has long been known that phenology is delayed with altitude (e.g., [57]), but climate warming may further reduce altitude-induced phenological change, as highlighted by [58] over the last six decades. This would have serious consequences in terms of the structure and function of mountain ecosystems.
For endemic plants of Sierra Nevada, we identified that the earliest phases, i.e., flowering onset and peak flowering, showed the most marked advancements. It has also been previously highlighted that global warming particularly affects early phenophases, as the influence on late phases is less pronounced or even not significant [59,60]. In fact, we identified this pattern at the three scales studied (entire massif, by altitudinal ranges, and by particular species). An earlier flowering period can generate serious ecological consequences, such as a mismatch between plant phenology pollinators. In this sense, there is a lack of studies that jointly analyse phenological changes across several organisms, but some of them (e.g., [48]) have proven that, in recent decades, insect phenology has experienced a steeper advancement than that for plants, suggesting a progressive decoupling of some plant–insect interactions, such as pollination, herbivory, or seed predation.
Additionally, our study demonstrates the usefulness of herbarium sheets for long-term phenological monitoring in plants, as has already been proven [30,31]. Therefore, it is crucial to continue to supply herbarium collections with recently collected specimens, and to reverse the current sharply declining trend of the collection rate [30]. However, collecting endemic and threatened plants must be limited for obvious legal and conservation reasons; thus, this type of data could be supplemented with data obtained from direct phenological monitoring in the field.
In conclusion, our study provides valuable insights into the plant phenological changes that have been taking place in recent decades. In particular, we confirmed a strong advancement in plant flowering in the context of a Mediterranean mountain, where this topic had barely been addressed previously. Our results were consistent across scales, and they stand out for the long time period (183 years) and the high number of taxa (83) analysed. In addition, most previous studies have focused on phenological changes in widely distributed trees, but our research deals with poorly studied groups: endemic small shrubs and herbs. Therefore, our results are novel and crucial for biodiversity conservation, since our target species were narrow endemic plants, most of them also being threatened. Moreover, these studies are especially relevant when they affect a diversity hotspot such as Sierra Nevada, which stands out within the Mediterranean hotspot [22], and where the consequences of climate warming are expected to be especially severe [37]. Finally, given that the trend toward phenological advancement in recent decades has been confirmed throughout many territories and scales, it is urgent to address phenological changes at the species level, especially in the case of priority species for conservation. Phenological studies by species would become particularly relevant if interactions with pollinators, dispersers, and other ecosystem groups, as well as the consequences on the different stages of the life cycle of plants, were analysed.

Author Contributions

Conceptualization, K.V.R.-M., J.L. and E.M.C.; methodology, K.V.R.-M., J.L. and E.M.C.; software, K.V.R.-M., J.L. and E.M.C.; validation, J.L. and E.M.C.; formal analysis, K.V.R.-M., J.L. and E.M.C.; investigation, K.V.R.-M., J.L. and E.M.C.; resources, J.L. and E.M.C.; data curation, K.V.R.-M., J.L. and E.M.C.; writing—original draft preparation, K.V.R.-M.; writing—review and editing, K.V.R.-M., J.L., E.M.C. and M.M.-R.; visualization, K.V.R.-M., J.L., E.M.C. and M.M.-R.; supervision, J.L. and E.M.C.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financed by the project “Thematic Center on Mountain Ecosystem and Remote sensing, Deep learning-AI e-Services University of Granada–Sierra Nevada” (LifeWatch-2019-10-UGR-01), which was co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line.

Data Availability Statement

Please request authors.

Acknowledgments

We thank the heads and the staff of the herbaria, GDA (University of Granada), JAEN (University of Jaén), HUAL (University of Almería), MGC (University of Málaga), SEV (University of Seville), and MA (Royal Botanical Garden of Madrid), for their help and for providing access to the specimens used in this study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

List of taxa selected for this study. Taxa: Accepted scientific name (including authors). n: number of samples included for the analysis. Distribution range: according Lorite, et al. (2020) [45]. Conservation status: according Lorite, et al. (2020) [45]: CR= Critically endangered. EN = Endangered. VU = Vulnerable. DD = Data deficient. NT = Near threatened. No_th = not threatened, including Least Concern (LC) and No Evaluated (NE).
TaxanDistribution RangeConservation Status
1Ranunculus acetosellifolius Boiss.321endemicNT
2Scorzoneroides microcephala (Boiss.) Holub294endemicVU
3Ranunculus angustifolius subsp. alismoides (Bory) Malag.284endemicNT
4Erigeron frigidus Boiss.268endemicNo_th
5Plantago nivalis Boiss.261endemicNo_th
6Lepidium stylatum Lag. & Rodr.220endemicNo_th
7Leontodon boryi DC.212Iberian Peninsula endemicNT
8Armeria splendens (Lag. & Rodr.) Webb163endemicVU
9Biscutella glacialis (Boiss. & Reut.) Jord.162Iberian Peninsula endemicNT
10Sagina saginoides subsp. nevadensis (Boiss. & Reut.) Greuter & Burdet160endemicNo_th
11Sideritis glacialis Boiss. subsp. glacialis128Iberian Peninsula endemicNo_th
12Viola crassiuscula Bory128endemicNT
13Potentilla nevadensis Boiss.121subendemicNo_th
14Jasione amethystina Lag. & Rodr.112endemicNo_th
15Eryngium glaciale Boiss.109Iberian_North AfricanNo_th
16Nevadensia purpurea (Lag. & Rodr.) Rivas Mart.97endemicVU
17Arenaria pungens subsp. pungens Clemente ex Lag.88Iberian Peninsula endemicNo_th
18Erodium boissieri Coss.81endemicVU
19Draba hispanica subsp. laderoi Rivas Mart., M.E.García & Penas80endemicNo_th
20Pinguicula nevadensis (H. Lindb.) Casper80endemicVU
21Reseda complicata Bory78endemicVU
22Lomelosia pulsatilloides (Boiss.) Greuter & Burdet70endemicNo_th
23Coincya monensis subsp. nevadensis (Willk.) Leadlay66subendemicNT
24Rothmaleria granatensis (DC.) Font Quer65Iberian Peninsula endemicVU
25Centranthus nevadensis Boiss.64Iberian_North AfricanVU
26Erysimum baeticum (Heywood) Polatschek63endemicNo_th
27Genista versicolor Boiss.63subendemicNo_th
28Leucanthemopsis pectinata (L.) G.López & C.E.Jarvis62endemicNo_th
29Thlaspi nevadense Boiss. & Reut.61endemicVU
30Scorzoneroides carpetana subsp. nevadensis (Lange) Izuzq.60subendemicNo_th
31Sarcocapnos speciosa Boiss.56endemicVU
32Androsace vitaliana subsp. nevadensis (Chiarugi) Luceño54endemicVU
33Carex camposii Boiss. & Reut.54subendemicNT
34Linaria glacialis Boiss.54endemicVU
35Thymus serpylloides Bory subsp. serpylloides54endemicNo_th
36Primula elatior (L.) L. subsp. lofthousei (Hesl.-Harr.) W.W. Sm. & H.R. Fletcher53Iberian Peninsula endemicVU
37Sempervivum minutum (Willk.) Pau53subendemicNo_th
38Arenaria tetraquetra subsp. amabilis (Bory) H.Lindb.45endemicNo_th
39Linaria aeruginea subsp. nevadensis (Boiss.) Malag.44endemicNo_th
40Helianthemum pannosum Boiss.41endemicVU
41Pimpinella procumbens (Boiss.) H.Wolff.40endemicVU
42Centaurea pulvinata (Blanca) Blanca39subendemicVU
43Arenaria nevadensis Boiss. & Reut.37endemicCR
44Carduus carlinoides subsp. hispanicus (Kazmi) Franco35endemicNT
45Gentiana pneumonanthe subsp. depressa (Boiss.) Malag.35endemicVU
46Gentiana sierrae Briq.34subendemicVU
47Nepeta × boissieri Willk. (= N. nepetella subsp. laciniata × N. granatensis)32endemicNo_th
48Erodium astragaloides Boiss. & Reut.30endemicCR
49Erodium rupicola Boiss.28subendemicVU
50Erysimum nevadense Reut.25endemicNo_th
51Senecio nevadensis Boiss. & Reut.23endemicVU
52Armeria filicaulis subsp. nevadensis Nieto Fel., Rosselló & Fuertes22endemicVU
53Herniaria boissieri J.Gay22subendemicNT
54Verbascum nevadense Boiss.21subendemicNT
55Alyssum nevadense P.W.Ball & T.R Dudley19endemicVU
56Arabis margaritae Talavera19endemicCR
57Artemisia granatensis Boiss.18endemicNo_th
58Iberis carnosa subsp. embergeri (Serve) Moreno17endemicEN
59Thymus granatensis subsp. granatensis Boiss.17Iberian Peninsula endemicNo_th
60Campanula rotundifolia subsp. willkommii (Witasek.) Blanca15endemicNo_th
61Cerastium alpinum subsp. nevadense (Pau) Mart.Parras & Molero Mesa14endemicNo_th
62Chamaespartium undulatum (Ern) Talavera & L.Sáez14subendemicVU
63Pedicularis verticillata subsp. caespitosa (Webb) I.Soriano14endemicVU
64Chaenorhinum glareosum (Boiss.) Willk.13endemicNo_th
65Laserpitium latifolium subsp. nevadense Mart.-Lirola, Molero Mesa & Blanca12endemicNo_th
66Artemisia alba Turra subsp. nevadensis (Willk.) Blanca & Morales10Iberian Peninsula endemicVU
67Vaccinium uliginosum subsp. nanum (Boiss.) Rivas Mart., Asensi, Molero Mesa & F.Valle10endemicNo_th
68Odontites viscosus subsp. granatensis (Boiss.) Bolliger9endemicCR
69Taraxacum nevadense H.Lindb.9subendemicNo_th
70Alchemilla fontqueri Rothm.8endemicCR
71Cytisus galianoi Talavera & Gibbs8subendemicNT
72Helianthemum appeninum subsp. estevei (Peinado & Mart.Parras) G.López8endemicVU
73Moehringia fontqueri Pau8endemicEN
74Nepeta nepetella subsp. laciniata (Willk.) Aedo8endemicNo_th
75Ranunculus cherubicus subsp. girelae. Fern.-Prieto et al.8endemicDD
76Thymus × pseudogranatensis Vizoso, F.B.Navarro & Lorite (= Th. granatensis subsp. granatensis × Th. zygis subsp. Gracilis7endemicNo_th
77Laserpitium longiradium Boiss.6endemicNo_th
78Hippocrepis nevadensis (Hrabetová) Talavera & E.Domínguez5endemicVU
79Cerastium alpinum subsp. aquaticum (Boiss.) Mart.Parras & Molero Mesa4endemicNo_th
80Narcissus nevadensis Pugsley subsp. nevadensis4subendemicCR
81Pedicularis comosa subsp. nevadensis (Pau) A.M.Romo4endemicVU
82Centaurea bombycina subsp. xeranthemoides (Lange) Blanca, Cueto & M.C.Quesada3endemicVU
83Cirsium x nevadense Willk.3Iberian Peninsula endemic (hybrid)No_th
84Hippocrepis prostrata Boiss.3endemicCR
85Linaria saturejoides subsp. angustealata (Willmott) Malag.3subendemicNo_th
86Salix hastata subsp. sierrae-nevadae Rech.f.3endemicCR
87Tephroseris elodes (Boiss.) Holub subsp. elodes3endemicEN
88Armeria filicaulis subsp. trevenqueana Nieto Fel.2endemicEN
89Artemisia × fragosoana Font Quer (= A. granatensis × A. umbelliformis)2endemicNo_th

Appendix B

Number of observations per year. Year: Years with at least one record. Frequency: Number of observations per given year.
YearFrequency
18375
1851125
185227
18532
18581
18711
18791
189111
18952
18985
19011
19021
19061
19079
190826
192121
1923366
19251
19281
19298
1930174
19311
19331
193411
193599
19414
19421
19431
194439
194621
194759
19503
195141
195379
195426
195510
19573
195814
19597
19608
19633
196434
19654
196613
1967122
1968100
196964
1970137
1971127
197293
1973133
197485
197575
1976259
197759
1978304
1979104
1980296
1981198
1982141
1983223
1984231
198583
198672
1987125
1988205
198962
1990125
199117
199220
199328
199416
199529
1996118
199744
199851
199920
200013
20013
200212
200321
20044
20061
20077
20083
20096
201054
201124
20129
201313
201430
20152
20161
201718
20194

Appendix C

Number of days in advance or delay for each phenological phase by altitudinal zone according to periods. Period (years): detailed period range that differentiates those species data found before 1969 and after 1970. n: number of observations for all phenological phases. Mean_day: average day between periods; n_days: number of days advanced (−) or delayed (+) and (±SE) standard deviation of advanced and delay days. Trends: “advance” if is negative the number of days and “delayed” if is positive the number of days.
Altitudinal ZonePhenological PhasePeriod (Years)(n)Mean_Dayn_DaysTrends
No Alpine
(<2400 m)
Flowering early≤196930194−22 ± 2.39Advanced
≥1970170172
Flowering peak≤196948185−18 ± 1.98Advanced
≥1970283167
Flowering late≤196970182−1 ± 1.81Advanced
≥1970223181
Fruiting early≤196965187−1 ± 1.82Advanced
≥1970236186
Fruiting peak≤1969501914 ± 1.91Delayed
≥1970213195
Fruiting late≤196914195−1 ± 2.36Advanced
≥1970140194
Alpine
(>2400 m)
Flowering early≤196999202−3 ± 1.51Advanced
≥1970237199
Flowering peak≤1969236202−5 ± 0.85Advanced
≥1970469197
Flowering late≤1969412204−1 ± 0.78Advanced
≥1970562203
Fruiting early≤1969250206−3 ± 0.84Advanced
≥1970513203
Fruiting peak≤19691782062 ± 0.99Delayed
≥1970387208
Fruiting late≤1969592127 ± 1.75Delayed
≥1970212219

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Figure 1. Regression plot showing phenological trends of flowering and fruiting phases over time for different phenophases (from flowering onset to late fruiting) for the assessed period (p-value: <2 × 10−16 ***; Pseudo-R2: 0.06572040). Lines show negative linear trends for most of the phenophases during the assessed period (1837–2019). The shaded area shows the standard error of the mean.
Figure 1. Regression plot showing phenological trends of flowering and fruiting phases over time for different phenophases (from flowering onset to late fruiting) for the assessed period (p-value: <2 × 10−16 ***; Pseudo-R2: 0.06572040). Lines show negative linear trends for most of the phenophases during the assessed period (1837–2019). The shaded area shows the standard error of the mean.
Plants 13 00522 g001
Figure 2. Regression plot showing the linear relationship between Julian date and collection year in each altitudinal zone (alpine vs. non alpine). Note that the trends for both altitudinal zones were negative, yet more pronounced for the non-alpine zone (pseudo-R2 = 0.105) than for the alpine zone (pseudo-R2 = 0.044).
Figure 2. Regression plot showing the linear relationship between Julian date and collection year in each altitudinal zone (alpine vs. non alpine). Note that the trends for both altitudinal zones were negative, yet more pronounced for the non-alpine zone (pseudo-R2 = 0.105) than for the alpine zone (pseudo-R2 = 0.044).
Plants 13 00522 g002
Table 1. Summary of the general linear model (model = Julian date ~ year * Phenological phase) for dates (from flowering onset to the late fruiting) during the assessed period (1837–2019) according to the different phenological phases, using the whole dataset of species. Significance levels: *** p < 0.001.
Table 1. Summary of the general linear model (model = Julian date ~ year * Phenological phase) for dates (from flowering onset to the late fruiting) during the assessed period (1837–2019) according to the different phenological phases, using the whole dataset of species. Significance levels: *** p < 0.001.
Phenological PhaseInterceptEstimated Coefficient (Year)±SEp-ValueR-sq.
adj
FloweringOnset822.14222−0.31971±0.052251.81 × 10−9 ***0.06461
Peak581.28464−0.1987±0.030298.42 × 10−11 ***0.03901
Late450.2716−0.12795±0.024913.24 × 10−7 ***0.02007
FruitingOnset452.16529−0.12836±0.026952.17 × 10−6 ***0.02059
Peak285.75619−0.04201±0.027040.130.002705
Late394.65227−0.09379±0.055840.0937800.006428
Table 2. Summary of the generalized linear model comparing pre-1969 and post-1970 data for each phenological phase, applied to the whole species dataset. n = number of observations for each phenophase and period. Mean day = mean day for each phenophase and period. Change = days of advancement (mean ± SE) when comparing the two periods. Significance levels: *** p < 0.001.
Table 2. Summary of the generalized linear model comparing pre-1969 and post-1970 data for each phenological phase, applied to the whole species dataset. n = number of observations for each phenophase and period. Mean day = mean day for each phenophase and period. Change = days of advancement (mean ± SE) when comparing the two periods. Significance levels: *** p < 0.001.
Phenological PhasePeriod nMean DayChangep-ValueR2 adj
Flowering onset≤1969132199−11 ± 1.391.81 × 10−9 ***0.06461
≥1970412188
Flowering peak≤1969295199−13 ± 0.938.42 × 10−11 ***0.03901
≥1970767186
Flowering late≤1969491202−5 ± 0.763.24 × 10−7 ***0.02007
≥1970799197
Fruiting onset≤1969323202−4 ± 0.812.17 × 10−6 ***0.02059
≥1970758198
Fruiting peak≤1969236204−1 ± 0.940.130.002705
≥1970611203
Fruiting late≤196980210−1 ± 1.480.0937800.006428
≥1970358209
Table 3. Summary of the generalized linear model comparing data from alpine vs. non-alpine areas for each phenological phase according to altitudinal zone. Last column shows the number of days of advancement or delay (mean ± SE) according to phenological phase, considering the periods of 1837–1969 and 1970–2019 for each phenological phase. See complete table in (Appendix C). Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 3. Summary of the generalized linear model comparing data from alpine vs. non-alpine areas for each phenological phase according to altitudinal zone. Last column shows the number of days of advancement or delay (mean ± SE) according to phenological phase, considering the periods of 1837–1969 and 1970–2019 for each phenological phase. See complete table in (Appendix C). Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
Altitudinal ZonePhenological PhasesInterceptEstimated Coefficient (Year)±Std. Errorp-ValueR-sq.
adj
Days
Alpine Zone (>2400 m)FloweringOnset527.92939−0.16624±0.056840.00368 **0.02497−3 ± 1.51
Peak307.82898−0.05525±0.025580.0312 *0.00659−5 ± 0.85
Late312.36614−0.05523±0.025930.0334 *0.00464−1 ± 0.78
FruitingOnset387.65284−0.09317±0.030250.00214 **0.01228−3 ± 0.84
Peak204.574630.00135±0.029610.8543275.993 × 10−5+2 ± 0.99
Late170.566050.02419±0.066760.3620.0004877+7 ± 1.75
Non-Alpine Zone
(<2400 m)
FloweringOnset985.66581−0.40963±0.094992.55 × 10−5 ***0.08586−22 ± 2.39
Peak940.34585−0.38911±0.088661.54 × 10−5 ***0.0553−18 ± 1.98
Late495.47307−0.15866±0.059310.0079 **0.02416−1 ± 1.81
FruitingOnset459.91797−0.13807±0.050640.00678 **0.02442−1 ± 1.82
Peak288.22068−0.04785±0.053800.374590.003022+4 ± 1.91
Late354.59352−0.08099±0.090890.37430.005197−1 ± 2.36
Table 4. Summary of the permutational ANOVAs comparing the phenological differences in flowering peak phases between the two assessed periods (≤1969 vs. ≥1970). Mean_day = average day of flowering peak per period (≤1969 vs. ≥1970). Days (adv-del) = Days (mean ± SE) of advancement (negative) or delay (positive), comparing the two periods. Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 4. Summary of the permutational ANOVAs comparing the phenological differences in flowering peak phases between the two assessed periods (≤1969 vs. ≥1970). Mean_day = average day of flowering peak per period (≤1969 vs. ≥1970). Days (adv-del) = Days (mean ± SE) of advancement (negative) or delay (positive), comparing the two periods. Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
TaxaPeriod nMean_DayDays
(adv-del)
p-Value
1Armeria splendens (Lag. and Rodr.) Webb≤19697215−13 ± 3.820.08729
≥197021202
2Biscutella glacialis (Boiss. and Reut.) Jord.≤19695202−13 ± 2.230.05053
≥197029189
3Centranthus nevadensis Boiss.≤19696208−6 ± 3.690.6863
≥19706202
4Erigeron frigidus Boiss.≤1969241974 ± 2.080.2319
≥197029201
5Jasione amethystine Lag. and Rodr.≤19696204−11 ± 5.20.1343
≥19707193
6Lepidium stylatum Lag. and Rodr.≤196937209−12 ± 5.00.0076 **
≥197020197
7Leontodon boryi DC.≤196910207−9 ± 3.510.1221
≥197013198
8Lomelosia pulsatilloides (Boiss.) Greuter & Burdet≤19699216−19 ± 3.55<2.2 × 10−16 ***
≥197010197
9Nevadensia purpurea (Lag. and Rodr.) Rivas Mart.≤196951901 ± 2.610.2859
≥197017191
10Pinguicula nevadensis (H. Lindb.) Casper≤19695214−25 ± 1.33< 2.2 × 10−16 ***
≥197037189
11Potentilla nevadensis Boiss.≤196981998 ± 4.30.1287
≥19708207
12Ranunculus acetosellifolius Boiss.≤196925184 −20 ± 4.590.02086 *
≥197041164
13Ranunculus angustifolius subsp. alismoides (Bory) Malag.≤196910218−27 ± 1.84<2.2 × 10−16 ***
≥197030191
14Reseda complicata (Bory) ≤19697196−2 ± 9.180.9804
≥197021194
15Scorzoneroides microcephala (Boiss.) Holub≤19692319420 ± 3.936 × 10−4 ***
≥197040214
16Sideritis glacialis Boiss. subsp. glacialis ≤1969182016 ± 4.20.5811
≥197027207
17Viola crassiuscula Bory≤196914199−4 ± 2.910.7843
≥197035195
Table 5. Summary of the permutational ANOVAs comparing the phenological differences in fruiting peaks between the two assessed periods (≤1969 vs. ≥1970). Mean_day = average day of flowering peak per period (≤1969 vs. ≥1970). Days (adv-del) = Days (mean ± SE) of advancement (negative) or delay (positive), comparing the two periods. Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 5. Summary of the permutational ANOVAs comparing the phenological differences in fruiting peaks between the two assessed periods (≤1969 vs. ≥1970). Mean_day = average day of flowering peak per period (≤1969 vs. ≥1970). Days (adv-del) = Days (mean ± SE) of advancement (negative) or delay (positive), comparing the two periods. Significance levels: * p < 0.05; ** p < 0.01; *** p < 0.001.
TaxaPeriod nMean_DayDays
(adv-del)
p-Value
1Arenaria pungens subsp.
Pungens Clemente ex Lag.
≤19691620711 ± 6.40.3043
≥197019218
2Biscutella glacialis (Boiss. and Reut.) Jord.≤19698220−30 ± 3.33<2.2 × 10−16 ***
≥197033190
3Erigeron frigidus Boiss.≤1969152046 ± 3.130.4082
≥197019210
4Erodium boissieri Coss.≤19695204−21 ± 9.760.1797
≥197019183
5Eryngium glaciale Boiss.≤19697218−10 ± 16.730.3706
≥19707208
6Leontodon boryi DC.≤1969221994 ± 3.800.5402
≥197024203
7Lepidium stylatum Lag. and Rodr.≤19699212−13 ± 5.930.1747
≥197018199
8Plantago nivalis Boiss.≤196930204−1 ± 2.930.623
≥197053203
9Ranunculus angustifolius subsp. alismoides (Bory) Malag.≤1969719912 ± 1.30<2.2 × 10−16 ***
≥197021187
10Reseda complicata (Bory)≤1969521917 ± 9.50.1628
≥19707236
11Ranunculus acetosellifolius Boiss.≤196916170−14 ± 2.640.0056 **
≥197043184
12Scorzoneroides microcephala (Boiss.) Holub≤19691721321 ± 5.910.0184 *
≥197021234
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Rondinel-Mendoza, K.V.; Lorite, J.; Marín-Rodulfo, M.; Cañadas, E.M. Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens. Plants 2024, 13, 522. https://doi.org/10.3390/plants13040522

AMA Style

Rondinel-Mendoza KV, Lorite J, Marín-Rodulfo M, Cañadas EM. Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens. Plants. 2024; 13(4):522. https://doi.org/10.3390/plants13040522

Chicago/Turabian Style

Rondinel-Mendoza, Katy V., Juan Lorite, Macarena Marín-Rodulfo, and Eva M. Cañadas. 2024. "Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens" Plants 13, no. 4: 522. https://doi.org/10.3390/plants13040522

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