# Shifting ecological niches of agricultural Prunus species under climate change

Chiara Vanalli, Marino Gatto, Renato Casagrandi, Daniele Bevacqua

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Chiara Vanalli, Marino Gatto, Renato Casagrandi, Daniele Bevacqua. Shifting ecological niches of agricultural Prunus species under climate change. Climatic Change, 2025, 179 (1), pp.9. 10.1007/s10584-025-04103-2. hal-05449287

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# Shifting ecological niches of agricultural *Prunus* species under climate change

Chiara Vanalli¹ID · Marino Gatto<fnref n="2" /> · Renato Casagrandi<fnref n="2" /> · Daniele Bevacqua<fnref n="3" />

Received: 11 June 2025 / Accepted: 19 December 2025
© The Author(s) 2025

## Abstract

Climate change is strongly impacting agriculture, reducing crop production and shifting the geographic distribution of suitable areas for crop cultivation. To safeguard future global yield and feed a growing world population, the migration of crop production areas to new suitable sites represents a way to adapt to a changing climate. Here, we aim to identify the ecological niche of agricultural *Prunus* species, namely peach, plum, almond, apricot and sweet cherry, and examine their expected future shifts under climate change. For each of the five species, we selected from the literature processed-based phenological models for dormancy break, blooming and fruit ripening, whose fulfillment determines whether an area is suitable for crop cultivation. We simulated the current pheno-suitability across Europe and validated the estimated niches with occurrence data from the Global Biodiversity Information Facility. We then implemented the phenological models to predict potential shifts in the suitability niches under future climate change scenarios. Historically, the ecological niche of *Prunus* species spans mid-low European latitudes, while higher latitudes fail to satisfy the forcing requirements for blooming and fruit ripening. In the future, this constraint is expected to become less restrictive with a northwards expansion of the suitable areas. However, this will be contrasted by a contraction of the niche at low latitudes due to dormancy break failures. While bridging established mechanistic knowledge on the climatic effects of plant phenological traits with citizen science observations, our work brings new insights into how fruit crops will respond to global warming.

**Keywords** Plant phenology · Mechanistic modeling · Citizen science · Adaptation

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<footnote>Chiara Vanalli
chiara.vanalli@epfl.ch</footnote>

<footnote n="1">¹ Environmental Computational Science and Earth Observation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Sion 1950, Switzerland</footnote>

<footnote n="2">² Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy</footnote>

<footnote n="3">³ INRAE, UR1115, Plantes et Systèmes de culture Horticoles (PSH), Site Agroparc 84914, France</footnote>
# 1 Introduction

Climate change is modifying the global weather patterns, altering the frequency and intensity of extreme weather events and raising the average temperature across the globe (Lee et al. 2023). Such changes are impacting the agricultural sector, with a reduced productivity growth and an increased vulnerability in global crop yields (Ortiz-Bobea et al. 2021; Lesk et al. 2016), together with a shift of the geographic distribution of suitable areas for crop cultivation (Ramankutty et al. 2002; Rosenzweig et al. 2014; Zabel et al. 2014; Jones and Thornton 2003; Orozco et al. 2024).

Estimates indicate an expected reduction of the global crop yield from 3% to 7% for each degree-Celsius of temperature increase (Zhao et al. 2017; Liu et al. 2016; Rezaei et al. 2023) and a reduced cropland suitability at lower latitudes (Rosenzweig et al. 2014; Zabel et al. 2014). Yet, the world population is growing and is expected to demand 70% more food by the middle of the XXI century (Gomez-Zavaglia et al. 2020). In light of the described disparity between possible reduction in food production and population growth, the achievement of the United Nations Zero Hunger Sustainable Development Goal by 2030 is a challenging target (2024) and asks for well-designed adaptation strategies that can move agriculture towards a more productive and sustainable system.

Examining whether and how the suitable areas for crop cultivation will be shifted due to a changing environment can safeguard future global yield, guiding climate change adaptation through migration of crop production areas to new suitable sites (Sloat et al. 2020; Leng and Huang 2017; Gu et al. 2024). This is particularly relevant for the cultivation of perennial crop whose lifespan covers multiple years and require an adequate agro-economic planning. The study of suitable geographic range for the cultivation of a specific crop dives into the theory of the ecological niche, i.e., the environmental conditions that a species requires to survive and maintain its populations (Hutchinson 1957). Ecological niche modeling (or species distribution modeling) studies where a species can live and thrive in relation to environmental conditions. This is done either by examining the environmental characteristics of areas where the species is already known to exist or by incorporating detailed knowledge of how environmental factors influence the species' traits, thereby affecting its survival (Kearney and Porter 2009; Peterson et al. 2015). The former approach is used to identify the realized species niche, which corresponds to the actual set of environmental conditions, including inter-species ecological interactions, where the species has been found present (Soberón and Nakamura 2009). Recently, the study of the realized species niche have experienced a rapid growth thanks to the great amount of observational data gathered through citizen science platforms (Brun et al. 2024; Isaac et al. 2014). However, approaches based on these large datasets often focus on empirical evidence, overlooking the underlying processes that drive the observed patterns (Ponti and Sannolo 2023). Alternatively, the latter approach, based on the mechanistic understanding of the link between species and environment, is commonly adopted to identify the fundamental species niche, i.e. the potential area of distribution for a species within its range of tolerance to environmental factors in the absence of biotic interactions (Hutchinson 1957). Therefore, the realized niche is embedded in the wider potential areas of suitability identified by the fundamental niche (Hutchinson 1957).

Plant phenology, impacting key events in the plant annual cycle like dormancy, blooming and fruit ripening (Visser et al. 2010; Lieth 2013), is recognized as one of the most impor-
tant climate-dependent determinants that shape the ecological niche of a species (Ponti and Sannolo 2023; Chuine 2010; Chuine and Beaubien 2001). Shifts in plant phenology are among the most significant documented responses to climate change (Fatima et al. 2020; Chmielewski et al. 2004; Sadras and Monzon 2006; Menzel et al. 2006; Liu et al. 2017; Hassan et al. 2024; Piao et al. 2019). Two prominent trends include earlier blooming times (Chmielewski et al. 2004; Menzel et al. 2006; Estrella et al. 2007) and shorter fruit development periods (Pathak and Stoddard 2018). However, in the future, additional trends could emerge due to increasingly mild winter temperatures. Such conditions may prevent flowing in certain plant species if their chilling requirements are not met (Funes et al. 2016; Hänninen and Tanino 2011; Yu et al. 2010; Laube et al. 2014; Vanalli et al. 2021). Failure in the fulfillment of the chilling requirements for dormancy break can disrupt the phenological phases necessary for growth and reproduction, compromising plant blooming, fruit development and indeed the crop yield (Cook and Jacobs 1999; Pagter et al. 2015; Luedeling et al. 2011). Thus, suitable areas for crop cultivation require an environment characterized by favorable climatic conditions for the fulfillment of the different phenological requirements of the plant annual cycle: low temperatures are necessary to break bud dormancy, while high temperatures are necessary to promote bud growth for blooming and fruit ripening. Conversely, locations where the phenological demands are failed to be met are excluded from the ecological niche of the considered crop. Therefore, an improved knowledge of the climatic requirements for the achievement of the different phases of the plant phenological cycle provides essential understanding to study the climate-driven shifts of the fundamental ecological niche and the agricultural suitability of crops.

Process-based phenological models that explicitly consider the different phases of the plant annual cycle are mainly split into one-phase models, which considered only the heat requirements (Chuine et al. 2016; Basler 2016), and two-phase models, which instead assume a required amount of cold temperatures experienced before the heat requirement for blooming is achieved (Hänninen 1990; Chuine 2000; Chmielewski and Götz 2016). While the one-phase modeling framework could perform well in estimating plant phenology in past conditions, when the fulfillment of winter chilling needs was taken for granted, the two-phase models are preferred to project phenology for future climatic conditions (Chuine et al. 2016; Basler 2016), given the limit that dormancy break failures could impose to crop cultivation (Piao et al. 2019). After blooming, fruit growth and ripening are also influenced by climatic conditions. This process is commonly modeled through a Growing Degree Day (GDD) requirement, where enough GDD units need to be cumulated in order to achieve full ripening (Vanalli et al. 2021; Parker and Abatzoglou 2018).

In the present work, we aim to identify the fundamental ecological niches of agricultural *Prunus* species, namely peach (*P. persica*), plum (*P. domestica*), almond (*P. amygdalus*), apricot (*P. armeniaca*) and sweet cherry (*P. avium*) and examine whether and how climate change is expected to reshape them. To do so: i) we performed a literature analysis on calibrated processed-based phenological models for dormancy break, blooming and fruit ripening; ii) we selected the most robust and comprehensive modeling frameworks and we implemented them to assess the historical ecological niche for the considered five species across the European territory; iii) we validated model predictions using presence observations in Europe provided by the Global Biodiversity Information Facility (GBIF) (2024a), testing whether the actual species presences, which belong to the realized niche, are contained in the fundamental niche; iv) we projected future shifts of the niche for each *Prunus*
species across the XXI century under two different climate change scenarios, a more moderate scenario of sustainability, SSP1-2.6, and a more extreme climatic scenario of fossil-fuel-centered development, SSP5-8.5 (Lee et al. 2023). By integrating established mechanistic knowledge of the climatic effects on plant phenological traits with novel data sources, such as citizen science observations, our work offers new insights into how key species in the European agroeconomy, particularly fruit crops, will respond to global warming.

# 2 Methods

## 2.1 Phenological models of agricultural *Prunus* species

We performed a literature analysis following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) criteria to identify mechanistic phenological models of *Prunus* species available in the literature.

Search words: (("Prunus") AND ("persica" OR "domestica" OR "armeniaca" OR "dulcis" OR "avium")) OR ("peach" OR "apricot" OR "plum" OR "almond" OR "cherry") AND ("Phenology" OR "Blooming" OR "Flowering" OR "Chilling" OR "Ripening" OR "Fruit growth") AND ("Temperature" OR "Climate" OR "Thermal" OR "Heat") AND ("Model*" OR "Simulat*" OR "Predict*")). Done on July 5th 2024.

From more than 500 records identified, 95 were selected for full-text analysis (Peach (18), Plum (6), Almond (18), Apricot (24), Cherry (29), see Supplementary Table S1). For plum (*P. domestica*), we also considered phenological models of the Japanese plum (*Prunus salicina*), which is commonly cultivated in Europe. For each species, we scored each model according to the following criteria:

- if model considers both dormancy and forcing = +1

- if data on dormancy break time are available in addition to blooming = +1

- if blooming and ripening time are available from the same dataset = +1

- if phenological model is a result from literature review = +1

- if phenological data location used for model calibration is within Europe = +1

- if phenological data used for model calibration cover a time period of more than 15 years = +1

- if number of considered cultivars is more than 5 = +1

We normalized the blooming and ripening score to 1 and we selected the phenological models with the highest score for each species. If multiple models present the same score, models that compare different phenological modeling frameworks and/or include in their datasets multiple cultivars are preferred (Supplementary Tables S2–S6). The selected phenological models with the relative temperature inputs are reported in Table 1, while equations and parameters are reported in the Appendix (Supplementary Table S7). Every year, a spatial cell is considered suitable for the cultivation of a *Prunus* species, if the phenological requirements for endodormancy break, blooming and ripening are met before September 21st (Vanalli et al. 2021), except for almonds whose limit date, due to their later harvest time, has been set at the end of October (Parker and Abatzoglou 2018, 2017).
<table><caption>**Table 1** Selected phenological models for dormancy break, blooming and fruit ripening for the different *Prunus* species. CP=Chilling portions, GDH= Growing degree hours, GDD= growing degree days; in italics the model inputs are reported (Tmean=mean temperature, Tmin=minimum temperature and Tmax=maximum temperature). For full model equations and parameters, see additional Information Table S7</caption><thead><tr><th>Species</th><th>Dormancy break</th><th>Blooming</th><th>Ripening</th></tr></thead><tbody><tr><td>Peach</td><td>Chuine model (Vanalli et al. 2021)</td><td>Sigmoid model (Vanalli et al. 2021)</td><td>GDD (Vanalli et al. 2021)</td></tr><tr><td rowspan="2">Plum</td><td>Daily Tmean</td><td>Daily Tmean</td><td>Daily Tmean</td></tr><tr><td>CP (Guerrero et al. 2024)</td><td>GDH (Guerrero et al. 2024)</td><td>GDH (Orjuela-Angulo et al. 2022)</td></tr><tr><td rowspan="2">Almond</td><td>Hourly Tmean (Daily Tmin, Tmax)</td><td>Hourly Tmean (Daily Tmin, Tmax)</td><td>Daily Tmean</td></tr><tr><td>CP (Parker and Abatzoglou 2018, 2017)</td><td>GDD (Parker and Abatzoglou 2018, 2017)</td><td>GDD (Parker and Abatzoglou 2018, 2017)</td></tr><tr><td rowspan="2">Apricot</td><td>Hourly Tmean (Daily Tmin, Tmax)</td><td>Daily Tmean</td><td>Daily Tmean</td></tr><tr><td>Chuine model (Chuine et al. 2016)</td><td>Sigmoid model (Chuine et al. 2016)</td><td>GDD (Rumel et al. 2010)</td></tr><tr><td rowspan="2">Cherry</td><td>Daily Tmean</td><td>Daily Tmean</td><td>Daily Tmean</td></tr><tr><td>CP (Fadón et al. 2021)</td><td>GDH (Fadón et al. 2021)</td><td>GDH (Chozinski et al. 2005)</td></tr><tr><td></td><td>Hourly Tmean (Daily Tmin, Tmax)</td><td>Hourly Tmean (Daily Tmin, Tmax)</td><td>Hourly Tmean (Daily Tmin, Tmax)</td></tr></tbody></table>

## 2.2 Occurrences of agricultural *Prunus* species for validation

We used presence-only data of *Prunus* species recorded from 1995 to 2024 from the Global Biodiversity Information Facility (GBIF) (2024g, 2024b, 2024c, 2024d, 2024e, 2024f) to validate the simulations of the selected phenological model across the European territory. For each species, as global metric of model performance we computed the model recall (Sokolova and Lapalme 2009):

$$Recall = \frac{\# \text{ occurrences that belong to suitable cells}}{\text{Total occurrences}} \% \qquad (1)$$

A spatial cell is considered suitable if the phenological requirements for dormancy break, blooming and ripening are satisfied for at least the 90% of the considered time period. Among the unsuitable cells, we defined a chilling failure when the cold requirement for dormancy break are not satisfied, while a forcing failure when the heat requirements for blooming and/or for ripening are not met. To strengthen our validation results, we have also qualitatively compared our pheno-suitability estimates with country production data (2025), specifically the harvested area averaged over the 1995–2024 from FAO statistics (Supplementary Figure S1). To this end, we considered FAO data regarding ‘Peaches and nectarines’ for *P. persica*, ‘Plums and sloes’ for *P. domestica*, ‘Almonds, in shell’ for *P. amygdalus*, ‘Apricots’ for *P. armeniaca*, and ‘Cherries’ for *P. avium*. Although crop production for a
specific country depends on many other socio-economic factors that impede the direct comparison between potential suitable areas and true harvested areas, this information can still bring valuable insights into identifying potential areas of over- and under-estimation of the estimated crop suitability.

## 2.3 Historical and future climatic data

We retrieved historical daily average, maximum and minimum air temperature from the Coupled Model Intercomparison Project Phase 6 (CMIP6, CNRM-CM6-1-HR model) at a spatial resolution of ~0.5 × 0.5 degrees for the European territory (3,872 total cells) (Cds 2021). We analyzed a more sustainable Shared Socioeconomic Pathway (SSP), SSP1-2.6, and a fossil-fuel oriented scenario, SSP5-8.5, with the same temporal and spatial resolution (Lee et al. 2023). We evaluated the climatic data in the historical period, from 1994 to 2014, and in the future, under the two considered scenarios, at the middle of the century, from 2041 to 2060, and at the end of the century, from 2080 to 2100. We used the methods described in López-Bernal et al. (2018) to compute the hourly temperatures from daily values, which are the climatic inputs for the phenological models that run with hourly time step.

## 3 Results

Findings from the performed literature review of available climate-driven process-based phenological models for *Prunus* species highlight the important role that temperature plays for the different phases of the plant phenological cycle and the variety of existing modeling frameworks (Table 1 and Additional Information). For the plant dormancy phase, cold requirements calculated through the Weinberger model (Chilling Hours) (Weinberger 1950) and the Utah model (Chilling Units) (Richardson et al. 1974) are usually outperformed by the Dynamic model (Chilling Portions) (Fishman et al. 1987; Luedeling et al. 2009; Ruiz et al. 2007), which considers a more complex chilling accumulation in a two-step process requiring both cold and mild temperatures. The dynamic model was used to examine the climatic dependence of dormancy break for plum, almond and cherry, while the Chuine model (Chuine et al. 2016; Chuine 2000) was selected for peach and apricot (Table 1). For the remaining phenological phases of blooming and fruit ripening, there is an agreement across the examined frameworks that an accumulation of forcing units, either from a Growing Degree Day/Hour models (GDD, GDH) or from a sigmoidal function of temperature is required. Therefore, by bridging process-based modeling frameworks already available in literature, we were able to reconstruct the constrasting climatic dependence of the multiple phenological phases and to map the phenological suitability of the European territory for the cultivation of peach (*P. persica*), plum (*P. domestica*), almond (*P. amygdalus*), apricot (*P. armeniaca*) and sweet cherry (*P. avium*).

The overlap between the presence data from GBIF of each *Prunus* species (Fig. 1a-j) and the simulated historical ecological niche (Fig. 1k-o) shows a good global recall metrics (Sokolova and Lapalme 2009), namely recall equals 88.36% for peach, 87.03% for plum, 73.14% for almond, 95.76% for apricot, 91.85% for cherry. Specifically, the ecological niche of most species spans low and mid European latitudes with the exception of mountainous areas, such as Alps, Carpathians and Pyrenees. The almond niche is the one that
<figure><img src="image_3.png" /><figcaption>**Fig. 1 (a–e)** Presence data and (f–j) simulated historical phenological suitability for peach (Prunus persica) in orange, plum (Prunus domestica) in purple, almond (Prunus amygdalus) in brown, apricot (Prunus armeniaca) in yellow and sweet cherry (Prunus avium) in magenta (suitability is rescaled from 0 to 1 according to the number of years over the 20 year-period for which the plant fulfills the phenological requirements) ; (k-o) spatial performance of model validation with the number of observed presences within a simulated suitable spatial cell (i.e. true positives) represented in green color scale, while the number of observed presences within a simulated unsuitable spatial cell (i.e. false negatives) is plotted using a red color scale</figcaption></figure>

differentiates most from the others, with the simulated suitability for almonds located only in southern Europe and also confirmed by presence data. This peculiarity is explained by the phenological model with a higher requirement of forcing units, needed for almond growth and hull split (Parker and Abatzoglou 2018) compared to the other crops, which are stone fruits and present a lower forcing requirement. The selected models reproduce well the main production areas of central Europe, but fail to include some locations where presence data are instead reported (k-o). These regions of poor model performance mainly involve plums and cherries and are mainly located in the Alps and in center-northern UK and can be due to specific cultivars that are selected for those areas which are not well represented by the considered phenological models and/or to microclimatic conditions that are playing an important role in these locations. The comparison between our pheno-suitability estimates with country-level FAO production data (Supplementary Figure S1) further reinforces our findings: i) southern Eastern Europe is the main production area for *Prunus* fruits; ii) eastern Europe, despite the low number of GBIF records, is generally quite suitable for *Prunus* production, with the exception for almonds; iii) UK and Ireland, together with Baltic countries, such as Estonia and Latvia, have not registered harvested areas regarding most of the analyzed crop species, suggesting a potential niche overestimation in these territories from the selected phenological models.

We projected the expected future changes in the ecological niche of the considered *Prunus* species at the end of the century under two different climate change scenarios, a sustainabile pathway (SSP1-2.6) and an unconstrained energy use pathway (SSP5-8.5) (Lee et al. 2023). Under both scenarios, we expect an expansion of the suitable areas for *Prunus* cultivation at northern latitudes and in the mountain regions (Fig. 2). This shift is explained
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Future shifts of the ecological niche at the end of the xxi century (2081–2100), compared to the historical period (1995–2014) under a sustainability scenario of climate change SSP1-2.6 (a-e) and a fossil-fuel development scenario SSP5-8.5 (f-j). Red areas represent a future niche contraction, green areas a future expansion, light gray areas are unchanged unsuitable areas and dark gray areas represent no niche change compared to the historical period. (k-o) composition of the European territory between areas of contraction, expansion and unchanged in the future under the SSP1-2.6 scenario (inner donuts) and under the SSP5-8.5 scenario (outer donuts), compared to the historical period</figcaption></figure>

by warmer temperatures that could allow for the fulfillment of forcing requirements for blooming and ripening. Conversely, in the Mediterranean basin a contraction of the suitable areas for *Prunus* cultivation is expected because of too mild winter temperatures that could impede the fulfillment of the chilling requirements for dormancy break (Fig. 2). These changes are projected to be more consistent in the most extreme climate pathway (SSP5-8.5) compared to the more sustainable one (SSP1-2.6). Among the analyzed *Prunus* species, the almond niche is expected to expand the most under future climate change due to its high threshold of GDD requirements for ripening that, in the historical period, was fulfilled only in few Mediterranean areas. The suitability changes at the middle of the century are in line with those described for the end of the century, but they are less significant (Supplementary Figures S2, S3).

Examining the latitudinal gradient of the suitable areas for *Prunus* cultivation, the historical niche for most species is located between 40 and 55 degrees of latitude except for almonds, whose niche is located at a lower latitudinal gradient, between 35 and 45 degrees (Fig. 3a-e). According to the phenological requirements, at the end of the century our findings indicate an average latitudinal shift of +1.31,+4.02 degrees, +0.832,+3.07 for the peach, +1.34,+4.18 for the plum, +2.18,+5.68 for the almond, +0.818,+2.76 for the apricot and +1.36, +4.43 for the cherry, respectively under SSP1-2.6 and SSP5-8.5 scenario. European historical unsuitable areas for *Prunus* cultivation are mainly characterized by a too strict climate that fails to satisfy the forcing requirements (Fig. 3f-j, black lines). Those unsuitable locations are distributed not only at northern latitudes, but also where European mountain chains are located. On the one hand, warmer future climate could drastically decrease forcing failures at high latitudes, on the other hand, growing forcing failures are expected at lower latitudes in particular for the SSP 5–8.5 scenario. This opposite southern trend is probably caused by a very late fulfillment of the chilling requirement for dormancy
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Latitudinal gradient of *Prunus* (a–e) ecological niche and (f–j) phenological failures (in gray chilling failures and in black forcing failures) in the historical period (1995–2014, solid line), at the end of the century under SSP1-2.6 scenario (2081–2100, dashed line), and at the end of the century under SSP5-8.5 scenario (2081–2100, dotted line). (k) Barplot represents the composition of the European territory between suitable areas (colored in orange for peach, purple for plum, brown for almond, yellow for apricot and magenta for cherry) in gray chilling failures and in black forcing failures, during the historical period (H, solid edge) and at the end of the century under the two scenarios (SSP1-2.6 scenario dashed bar edge, SSP5-8.5 scenario dotted bar edge)</figcaption></figure>

break that does not give enough thermal time to the plant to achieve blooming and then to
the fruit to develop and ripe. Failure areas for chilling requirements represent a less impor-
tant constraint for the *Prunus* suitability in the historical period. However, too mild future
winters are expected to increase chilling failures, with a contraction of the ecological niche
at low latitudes (Fig. 3f-j, gray lines). These latitudinal trends, albeit of a smaller magnitude,
are similar to those projected for the middle of the century, when chilling failures at low lati-
tudes are already showing to undermine the *Prunus* phenological suitability (Supplementary
Figure S2, S3). Overall, the decreased forcing failures in northern Europe will outbalance
the increase in chilling failures in southern Europe with a net expansion of the suitable areas
of *Prunus*. By the end of the century, our predictions indicate, for each SSP, an average
increase of +14,+12% for the peach, +13,+15% for the plum, +58,+174% for the almond,
+12,+20% for the apricot, and +6,+3% for the cherry (Fig. 3k). Yet, to benefit from these
new suitable areas, proper planning of migration of cultivation areas is required that needs
to take into consideration not only the climatic suitability for plant phenology, but also agro-economic and land use characteristics.

# 4 Discussion

To face the challenges that climate change poses to agriculture, the examination of whether and how the suitability of crop cultivation is impacted by global warming is required to adequately adapt and guarantee a sufficient future yield. In this study, we focused on five *Prunus* species and evaluated their current and future niches in Europe based on species-specific phenological requirements for dormancy break, blooming, and fruit ripening, recovered from a literature analysis. Historically, the *Prunus* niche estimated from phenological suitability overlaps quite well with the presence data retrieved from GBIF (2024a, 2024b, 2024c, 2024d, 2024e, 2024f). The main historical limitation for *Prunus* cultivation for the European territory is represented by the forcing requirements for blooming and fruit ripening that cannot be satisfied in cold environments at central-high latitudes. Conversely, at the end of the XXI century, this constraint is expected to become less restrictive with a northwards expansion of the suitability niche of +1.31,+4.02 latitudinal degrees on average. This positive expansion will be contrasted by the increase in dormancy break failures in the Mediterranean basin due to too mild winters that would not allow the fulfillment of the chilling requirements.

The described general trends of crop suitability and phenology that we highlighted in the current study for the *Prunus* genus in response to the expected increase in temperature have been predicted for other economically important fruit crops, such as grapes (Bindi et al. 1996) and olives (Tanasijevic et al. 2014). Altogether, this represents an alarming trend for the Mediterranean basin, where most of the production areas are currently located. These latitudinal contrasts, where low latitudes would experience delayed phenology due to chilling requirements and high latitudes anticipated phenology, have been also highlighted in other areas of the world (Hsu et al. 2023).

In our work, we leveraged citizen science observations on presences of *Prunus* species across Europe, which generally represent an enormous resource for scientific research and in particular for planetary-scale challenges such as global warming (Vanalli et al. 2024). However, the use of citizen science data presents several biases (Kosmala et al. 2016): i) observations can be related to ornamental cultivars of the considered species rather than actual agricultural crops, ii) spatial and temporal distribution of records depend on many factors including the popularity of these platforms, iii) species absences are rarely available and have not been considered in the current work. The latter represents an important drawback for the entire research field of species distribution modeling, that is often overcome by artificially generating ‘pseudoabsences’. However, the method used to select these points can influence the overall species distribution predictions (Whitford et al. 2024). Here, we did not generate absence data, but only used presence data to validate existing models which have been previously calibrated using field and/or experimental phenological observations. For this reason, the selected phenological models could potentially overestimate the true phenological niches, as we highlighted for UK and the Baltic countries, and indeed bias the future estimates of niche shifts at higher latitudes. In the future, observations regarding
true absences, in addition to specific plant phenological stages, would be beneficial to study climatic impacts on plant phenology and redistribution.

Our study is based on process-based phenological modeling frameworks from the literature (Vanalli et al. 2021; Chuine et al. 2016; Parker and Abatzoglou 2018; 2017; Guerrero et al. 2024; Orjuela-Angulo et al. 2022; Ruml et al. 2010; Fadón et al. 2021; Chozinski et al. 2005) that provided crucial knowledge on the climatic response of the different plant phenological stages. Plant phenology and its climatic dynamics are rarely addressed in ecological niche modeling, which usually disregards the link between specific phenological stages and environmental variables, potentially producing unreliable future projections under rapid climatic changes (Tang et al. 2016; Zurell et al. 2024). In addition, the existing phenological works usually focus on one specific phenological stage of the plant annual cycle (but see Vanalli et al. 2021; Parker and Abatzoglou 2018). The evaluation of the entire annual plant phenological cycle is instead needed to achieve a more complete understanding of climate change impacts across the multiple plant climatic requirements. In our phenological framework, for each species and phenological stage, we selected the highest scoring model based on its robustness and completeness. A different approach could have been to ensemble the multiple models available in the literature (Yun et al. 2017; McCormick et al. 2021). An additional level of model complexity is represented by cultivar-specific parametrization of the selected models. In this work, we took into account the range of phenological requirements for different cultivars when producing niche estimates, running the same phenological model using different cultivar-specific parameters, when available. From the multiple niche outputs, we then assumed a cell to be suitable if at least one cultivar could successfully achieve ripening. The choice of this permissive hypothesis is mainly due to the lack of cultivar-level observations to validate the cultivar-specific outputs. We believe that in the next years, the power of cultivar identification algorithms, from images gathered via citizen science sources, would allow not only the classification of cultivars, but also the evaluation of their phenological stage at the moment when the image has been taken. This additional information would be crucial not only to apply our framework at the cultivar level, but also to validate the spatial suitability of each phenological stage. Differences in plant phenological requirements between different cultivars represent one potential adaptation strategy thanks to the selection of specific cultivars with a low chilling requirement for dormancy break (Salama et al. 2021). Ultimately, the present work is based on the phenological thermal requirements of *Prunus* species and maps the potential ecological niche of the examined species, neglecting additional biotic interactions that could play a relevant role in the realized niche (Hutchinson 1957). For instance, pollinators are essential to complete the plant phenological cycle and are undermined by climate change (Renner and Zohner 2018). To effectively design crop migration strategies to adapt to future climatic conditions, our considerations based on phenological climatic suitability need to be paired with targeted studies on economic feasibility and territorial characteristics.

We considered temperature as the main driver of plant phenology, but we do not exclude that the integration of additional bioclimatic factors could further improve our niche estimates (e.g., frost events and heat waves). Specifically, photosensitivity has been shown to interact with phenological thermal triggers, with a delayed budburst at shorter photoperiods (Laube et al. 2014; Basler and Körner 2012). Careful consideration is needed regarding our predicted *Prunus* niche expansion at northern latitudes: higher temperatures would allow the fulfillment of plant heat requirements for blooming, but the great seasonal fluctuations of
photoperiod could limit the suitability of these locations. Ultimately, future research should focus on developing photo-thermal models to better predict the combined effects of light and temperature on plant phenology. This work would not benefit only from the inclusion of additional climatic variables but also from the evaluation of their projections from multiple global circulation models to fully capture the range of uncertainty of climatic inputs of the phenological models. Here, we used temperature outputs provided by the French National Center for Meteorological Research (CNRM) circulation model, that is part of the coordinated effort for the Coupled Model Intercomparison Projects of the IPCC sixth assessment report (CMIP6) and available on the Copernicus Data Store (Cds) 2021). When the performance of the climatic model has been evaluated against historical data (1981–2000), temperate latitudes belonging to the northern hemisphere, such as Europe, present slightly underestimated winter and summer temperatures in southern Europe and overestimated summer temperatures in northern Europe (Voldoire et al. 2019). The highlighted biases in the temperature dataset could have implications on our results. Notably, these biases could have resulted in predicting less chilling failures and consequently an overestimate of the suitability niche in southern areas. In contrast, a bias in overestimated summer temperatures in northern Europe could produce too optimistic range expansion at high latitudes. Given that projections from different climatic models often diverge substantially in spatial patterns and magnitude, future work should be focused on examining multiple climatic datasets generated from different circulation models and provide spatially explicit statistical measures of mean and variability of the suitability niche of fruit crops in a multi-circulation model manner (Ma et al. 2021). In addition, our work neglects potential effects of plant phenotypic plasticity, i.e., the plant’s ability to modify its phenological traits in response to changing environmental conditions (Nicotra et al. 2010). Such flexibility can represent an evolutionary adaptation strategy to maintain fitness and productivity under shifting climatic conditions. However, the extent and limits of plant phenotypic plasticity under accelerating climate change ask for further investigation. For these reasons, we would like to reinforce that our findings should be interpreted as potential suitable areas where thermal conditions might be suitable for *Prunus* phenology, but might not be suitable for other cultivation requirements, such as slope, irrigation and soil properties.

To tackle the great challenges that climate change is posing to agriculture and food security, our study can be used to minimize the negative impacts of the changing climate, with an optimal migration of crop cultivation areas towards new suitable areas where the plant phenological requirements will still be fulfilled in the future.

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1007/s10584-025-04103-2.

**Acknowledgements** We would like to acknowledge Dr. Devis Tuia for his valuable support.

**Author contributions** The original idea was developed by Chiara Vanalli and Daniele Bevacqua. Chiara Vanalli performed the literature analysis, modeling work, result visualization and wrote the first draft of the manuscript. Daniele Bevacqua, Marino Gatto and Renato Casagrandi guided the modeling development advised on the analysis and results interpretation. All authors reviewed the manuscript.

**Funding** Open access funding provided by EPFL Lausanne. This work was partially funded by the SNSF (deepHSM, grant number 204057).
**Data availability** The datasets used in the current study are publicly available on GBIF (https://www.gbif.org/) and on Copernicus (https://cds.climate.copernicus.eu/datasets). Data and modeling scripts to produce our findings are accessible at https://figshare.com/s/951b026e6092af3a2b0c.

**Declarations**

**Ethics approval and consent to participate** This article does not contain any studies with human or animal participants performed by any of the authors.

**Competing interests** The authors have no conflicts of interest to declare.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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