# Projecting spatiotemporal bioclimatic niche dynamics of endemic Pyrenean plant species under climate change: how much will we lose?

Noèmie Collette, Sébastien Pinel, Valérie Delorme-hinoux, Joris a M Bertrand

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Noèmie Collette, Sébastien Pinel, Valérie Delorme-hinoux, Joris a M Bertrand. Projecting spatiotemporal bioclimatic niche dynamics of endemic Pyrenean plant species under climate change: how much will we lose?. Ecography, 2025, 2026 (1), pp.e08067. 10.1002/ecog.08067. hal-05477516v2

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Research Article

Projecting spatiotemporal bioclimatic niche dynamics of endemic Pyrenean plant species under climate change: how much will we lose?

Noèmie Collette<fnref n="1" />, Sébastien Pinel<fnref n="2" />, Valérie Delorme-Hinoux<fnref n="1" /> and Joris A. M. Bertrand<fnref n="1" />

Correspondence: Noèmie Collette (noemie.collette@univ-perp.fr)

Ecography

2026: e08067

doi: 10.1002/ecog.08067

Subject Editor: Miguel Araújo

Editor-in-Chief: Miguel Araújo

Accepted 20 October 2025

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Species distributions are shifting under global change, with mountain ecosystems among the most vulnerable. In such landscapes, the ability to track changing conditions is limited, threatening narrowly distributed species. As a mountain biodiversity hotspot in southwestern Europe, the Pyrenees harbors many such species, making it a key case study for climate vulnerability assessments.

This study implements a bioclimatic niche modeling pipeline to evaluate the impact of climate change on endemic Pyrenean plant species by 2100. Its objectives are to: 1) map current bioclimatic niche suitability, 2) forecast future spatial dynamics, and 3) identify potential climate refugia for conservation. Species occurrences were combined with 19 bioclimatic variables (1 × 1 km resolution) to characterize bioclimatic niche suitability, using an ensemble modeling approach integrating five algorithms (maximum entropy, generalized linear model, generalized additive model, gradient boosting machine, and random forest). Their future spatiotemporal dynamics were projected under four climate scenarios (Shared Socioeconomic Pathways (SSP): 126, 245, 370, 585) for four successive periods spanning 2021–2100.

By 2100, 69% of endemic species are projected to lose over 75% of their bioclimatic niche, and half are expected to face complete losses under high-emission scenarios. Only two species may gain suitable areas, highlighting the need for species-specific conservation strategies. Bioclimatic niches are projected to shift by ~ 180 m upslope and ~ 3 km in latitude on average, with areas of highest multi-species suitability, referred to as bioclimatic hotspots, becoming restricted to elevations above 2000 m. These trends intensify after 2041–2060 period, reflecting escalating climate pressures as the century progresses.

Our findings highlight the profound threat climate change may pose to endemic Pyrenean flora, with widespread bioclimatic niche losses projected by the century's end and high-elevation refugia emerging as key conservation priorities. Anticipating

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<footnote n="1">¹Laboratoire Génome et Développement des Plantes (LGDP), Université de Perpignan Via Domitia (UPVD) – Centre National de la Recherche Scientifique (CNRS), Perpignan, France</footnote>

<footnote n="2">²Centre de Formation et de Recherche sur les Environnements Méditerranéens (CEFREM), Université de Perpignan Via Domitia (UPVD) – Centre National de la Recherche Scientifique (CNRS), Perpignan, France</footnote>
these shifts and integrating them into conservation planning will be crucial for mitigating high-elevation biodiversity loss in
a rapidly changing world.

Keywords: bioclimatic niche, climate change, endemic species, mountain, Pyrenees, species distribution models

**Introduction**

Climate change represents one of the greatest environmen-
tal challenges of our time, with a significant and far-reach-
ing impact on the world's ecosystems (Li et al. 2018, IPCC
2022). Among these ecosystems, mountain regions are of
special concern, as they are experiencing some of the highest
and fastest rates of warming, a phenomenon widely recog-
nized as elevation-dependent warming (Pepin et al. 2015).
This refers to the tendency for warming rates at higher alti-
tudes to surpass those observed in lowland regions, a pattern
identified across multiple mountainous regions worldwide
(Qixiang et al. 2018, Pepin et al. 2022). Such warming raises
significant concerns for mountain ecosystems, which are
particularly sensitive to these rapid changes (Knight 2022,
IPCC 2023). A cascade of ecological consequences follows
(Rogora et al. 2018, Dainese et al. 2024), including shifts in
species distributions, a well-documented response to climate
change (Pearse and Davies 2019, Rubenstein et al. 2023,
Lawlor et al. 2024).

Climate velocity describes the speed and direction in
which a species needs to move to stay within its climatic
niche as climate conditions change, which, in mountain-
ous contexts, forces species to occupy progressively higher
elevations (Chan et al. 2024), resulting in upward shifts
in their core distribution (Freeman et al. 2018). However,
these elevational shifts are unlikely to keep pace with the
rapidly changing climate (Carroll et al. 2015, Kellner et al.
2023, Goodwin et al. 2025). Mountain plants, in particu-
lar, face even greater challenges than other organisms due
to their limited dispersal capacity (Morgan and Venn 2017,
Di Musciano et al. 2020). Their migration is further con-
strained by the complex topography of mountainous land-
scapes and habitat fragmentation (Bueno de Mesquita et al.
2018, Elsen et al. 2020), which creates substantial barriers
to movement by breaking the spatial continuity of suitable
areas. Even when they successfully reach climatically suit-
able habitats, or are already located there, establishment and
persistence may be constrained by several factors. Biotic mis-
matches with pollinators (Marshall et al. 2020, Vitasse et al.
2021, Dainese et al. 2024, Trunschke et al. 2024), competi-
tion with resident or incoming species (Alexander et al. 2015,
Schuchardt et al. 2023) or the absence of key mutualists can
hindер recruitment. Abiotic constraints, including unsuitable
edaphic conditions (Eichel et al. 2023, Ni and Vellend 2024)
or microtopography, may also reduce habitat suitability.
Finally, intrinsic species traits such as germination require-
ments (Alexander et al. 2018) can further restrict success-
ful colonization. Moreover, if establishment occurs within
isolated microclimatic refugia, prolonged isolation of these

persistent populations may compromise their long-term sur-
vival by limiting genetic mixing, thereby reducing genetic
diversity (Pinto et al. 2024, Turnock et al. 2024). This, in
turn, limits their adaptability and resilience to environmen-
tal changes (Frankham 2005, Bijlsma and Loeschcke 2012,
Mathur et al. 2023). Such prolonged isolation mirrors pat-
terns observed on a broader scale among mountaintop spe-
cies, which persist in isolated high-elevation habitats often
referred to as 'sky islands'. Habitat fragmentation and pro-
gressive loss of physical space with elevation amplify extinc-
tion risk for these species (Elsen and Tingley 2015), as they
lack alternative areas to persist when facing unfavorable
conditions or competition with other species. This scenario,
referred to as 'mountaintop extinction', primarily threatens
species with narrow distributions, residing in the upper limits
of latitudinal gradients or restricted to the highest elevations.

The Pyrenees mountain chain, Europe's second-largest
biodiversity hotspot with 3652 indigenous vascular plants,
is particularly concerned due to the number of mountaintop
species, including a range of endemics and specialized flora
(Gómez et al. 2017). The region's complex climate and geo-
diversity have historically favored this exceptional diversity
(Rahbek et al. 2019a, Körner and Hiltbrunner 2021), and
have served as both a refuge and a corridor for organisms dur-
ing past climatic changes (Schmitt 2009, García et al. 2022).
However, intensifying global pressures such as climate, land
use, and socioeconomic changes, including tourism, urban
development and agricultural abandonment (OPCC 2018,
Zango-Palau et al. 2024), may compromise its capacity to
buffer future environmental shifts (Rahbek et al. 2019b). The
upslope shift of plants in the Pyrenees (Lenoir et al. 2008,
Pauli et al. 2012, Ameztegui et al. 2016) appears to contrib-
ute to increasing species richness on summits (Camarero et al.
2006, Steinbauer et al. 2018); yet this dynamic often comes
at the cost of endemic and range-restricted species.

Endemic species represent about 5% of the Pyrenean
flora, and species at their upper distribution limits represent
another 20% (Gómez et al. 2017). Although not all of them
are strictly confined to high elevations, their limited distri-
bution makes them particularly vulnerable to range contrac-
tion. Altogether, up to a quarter of the regional flora may face
a mountain-top extinction scenario in the coming decades.
Observations in the Alps, another European mountain
range, support the potential mountaintop extinction risk for
endemic and range-restricted plant species in the Pyrenees.
Red-listed plants have experienced rapid contractions of
their elevation margins, resulting in significant habitat loss
in lowland areas (Geppert et al. 2023), and endemic species
have undergone substantial range reductions, with projec-
tions suggesting that 40% of their current distribution could
become climatically unsuitable by the end of the 21st century (Dullinger et al. 2012). Similar dynamics in the Pyrenees could drive extinctions, altering summit biodiversity and ecosystems. Although alpine plants show traits promoting resilience, such as adaptation to harsh conditions, clonal growth, and access to diverse thermal microhabitats (Körner and Hiltbrunner 2021), these ongoing dynamics raise concerns about their ability to track or adapt to climatic changes, with potential consequences for mountain biodiversity and ecological communities. In this context, identifying potential responses of species to these changes is essential for anticipating and guiding conservation efforts.

To assess the impact of climate change on plant distributions, the use of modeling tools is a widely adopted approach. Species distribution models (SDMs), most often implemented as correlative approaches, relate species occurrences to environmental variables to predict and describe habitat suitability or the probability of a species' presence across spatial and temporal scales (Vasconcelos et al. 2024). The robust methodological foundations and predictive power of these models have led to their application across diverse fields, including agriculture, public health, and, notably, conservation biology (Franklin 2023). In the Pyrenees, SDMs have primarily been used for conservation purposes. Research has explored species distribution drivers (Kouba et al. 2011, Williams-Tripp et al. 2012, Dalibard et al. 2021), projected climate-driven shifts (Martínez et al. 2012, Pérez-García et al. 2013, de Pous et al. 2015, Glad and Mallard 2022, Salvado et al. 2022, 2025, Russo et al. 2023), and reconstructed distributions over geological time (Bidegaray-Batista et al. 2016, Charbonnel et al. 2016, Gutierrez et al. 2022). Together, these studies underscore the value of SDMs in informing management and conservation strategies for many species. By providing insights into species-environment relationships and distribution patterns for past, present and future, SDMs are relevant tools for anticipating and addressing climate change impacts, and, more generally, the impact of global change on biodiversity.

Open-access environmental and occurrence databases have expanded possibilities for studying species-environment relationships. Although various tools exist for each SDM stage (Kass et al. 2024), fully integrated pipelines (i.e. framework), especially those suited for rare or range-restricted species, such as endemics, remain scarce. Default settings in existing frameworks are often not fully suited for these taxa and may require manual adjustments that can become time-consuming when applied to many species. To address this, we propose a fully integrated and reproducible SDM pipeline specifically tailored to species with few occurrences. It automates algorithm-specific tuning, provides auxiliary scripts to guide key decisions (e.g. block size for spatial cross-validation) and uses evaluation metrics suited for low-prevalence data (e.g. Boyce index, AUC-PR). It also incorporates spatial visualization of uncertainties related to both algorithms and climate projections, addressing the frequent lack of uncertainty reporting in SDMs for rare and endemic species (Qazi et al. 2022). In addition to streamlining the modeling process, this pipeline produces harmonized outputs across species, enhancing

understanding of spatial and temporal biodiversity dynamics and enabling consistent comparisons of climate-driven range shifts, making it well suited for multi-species assessments. Ultimately, it provides natural area managers with practical tools to preserve floristic diversity and sustain the ecological resilience of mountain ecosystems in the face of accelerating climate change.

Endemic species, typically restricted to narrow geographical and climatic niches, serve as indicators of regional biodiversity (Lamoreux et al. 2006). Their concentration in mountainous areas highlights the key role these regions play in enhancing floristic richness and uniqueness. As irreplaceable components of the region's natural heritage, their conservation is therefore crucial. In this context, the proposed framework, which enables parallel analysis of SDM outputs across species, is particularly valuable for identifying both areas where several endemic species may overlap and areas that are most at risk, thereby supporting more effective and targeted management efforts. Consequently, using SDMs, this study aims to: 1) map current distribution of bioclimatic suitable areas for 59 endemic Pyrenean plant species, 2) forecast the future extent and dynamics of these areas by the end of the 21st century under multiple climate scenarios, and 3) identify where these species could find refuge by 2081-2100 period.

# Material and methods

## Study area and species occurrences data

This study follows the mountain delimitation of Körner et al. (2011), updated by Snethlage et al. (2022). Within this framework, the Pyrenees span ~ 40 000 km² along the France-Spain-Andorra border (Fig. 1), with over 150 peaks above 3000 m a.s.l. (Gómez et al. 2017). Their steep northern slopes contrast with broader southern valleys, featuring diverse landscapes. At the Atlantic-Mediterranean continental interface, this mountain range displays diverse climatic conditions. Mean annual temperatures range from > 10°C in lowlands to < 4°C on peaks, with precipitation varying from > 2000 mm (Atlantic slopes) to ~ 1000 mm (sheltered areas) (Cuadrat et al. 2024).

Among the 2682 species listed in the Atlas of Pyrenean Flora (Pironon et al. 2022), 84 were classified as endemic. Excluding endemic subspecies reduced the set to 78 species. Occurrence data were obtained from the Global Biodiversity Information Facility (GBIF 2025), supplemented with research-grade data from iNaturalist (www.inaturalist.org). Occurrences from 1970 within the study area and ≤ 1 km coordinate uncertainty were retained, fitting the resolution of environmental variables. Spatial thinning retained one occurrence per grid cell. Species with < 4 occurrences and/or highly restricted distributions were also removed, as the selected SDM assessment method requires presence of species in each spatial block during cross-validation and therefore requires sufficient spatial representation. Ultimately, 59 species were included.
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Figure 1. Geographic extent and topography of the Pyrenees. (a) The Pyrenees mountain range (red outline) spans the border regions of France, Spain, and Andorra. Its delimitation follows the definition set out by the Global Mountain Biodiversity Assessment (Körner et al. 2011, Snethlage et al. 2022). The inset map shows the location of the Pyrenees in Europe. Satellite imagery: Google Earth (© Google, Image Landsat/Copernicus, Data SIO, NOAA, US Navy, NGA, GEBCO). (b) Elevation gradient from a 90-m-resolution digital elevation model (CGIAR-CSI 2018), showing the region's topographic variation from lowlands (green) to high peaks (brown to white).

## Bioclimatic data

WorldClim 2.1 (Fick and Hijmans 2017) provided 19 rasterized bioclimatic variables (Supplemental information) for the current (1970–2000) and future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100, designated as 2030, 2050, 2070, and 2090 in this study). Future datasets based on CMIP6 General Circulation Models (GCMs) at 0.5 arc min⁻¹ resolution (~1 × 1 km at the equator) were combined into median raster to capture model variability (Song et al. 2024, Carroll and Mahony 2025). Projections follow four Shared Socioeconomic Pathways (SSPs: 126, 245, 370, 585; best- to worst-case, respectively), representing socioeconomic

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Figure 2. Projected changes in temperature and precipitation in the Pyrenees under Shared Socioeconomic Pathways (SSPs) by the 2081–2100 period from WorldClim 2.1. (a) Mean annual temperature (°C) and (b) annual precipitation (mm) projections for 2021–2100 under SSP126, SSP245, SSP370, and SSP585. Values (±) reflect differences from the 1970–2000 baseline (current period) for the 2081–2100 period. The 2021–2040, 2041–2060, 2061–2080, 2081–2100 periods are designated as 2030, 2050, 2070, and 2090, respectively.

trajectory and radiative forcing (W m⁻²) expected in 2100 (O'Neill et al. 2017, Welch 2024). Under these scenarios, mean annual temperature in the Pyrenees (9.4°C) is projected to rise by +18.69% (11.56°C) to +39.51% (15.54°C) in the 2081–2100 period, while current annual precipitation (975.41 mm) may decrease by -2.18% (971.85 mm) to -13.57% (843 mm) (Fig. 2).

To reduce redundancy, highly positively correlated variables ($r > 0.8$) at presence locations for each species were clustered. Within each cluster, the most relevant variable per species was retained based on a predefined ecological priority list (Supporting information), which favors variables that are easier to interpret ecologically (e.g. mean annual temperature), followed by variables describing climatic extremes during cold and warm seasons, and finally more derived or complex indices (e.g. temperature seasonality, isothermality). Negatively correlated variables, reflecting complementary environmental gradients, were not excluded. Depending on the species, models include between three and eight bioclimatic variables after the selection process. Selected variables for each species are listed in the Supporting information.
# Bioclimatic suitability modeling

## Model development

A presence/pseudo-absence approach was employed to improve SDM reliability when true absences are unavailable (Mateo et al. 2010). Following recommendations from Barbet-Massin et al. (2012) and the ‘biomod2’ team (Gueguen et al. 2025), we tested different pseudo-absence configurations they outlined and retained the one that, in our analyses, consistently improved model evaluation scores (Supporting information). The number of pseudo-absences was set equal to the number of presences and sampled within the environmental space, using environmental constraints to restrict sampling to unsuitable conditions (Broussin et al. 2024). In line with ‘biomod2’ guidelines, each algorithm was run 10 times with distinct pseudo-absence sets generated separately for each species, thereby reducing background dependence. Each set was spatially thinned to retain a single occurrence per environmental grid cell.

As modeling algorithms significantly impact SDM outcomes (Buisson et al. 2010, Thuiller et al. 2019) and predictive performance varies across approaches (Norberg et al. 2019, Valavi et al. 2022), bioclimatic suitability was modeled using five algorithms: MaxEnt (MXT), generalized linear models (GLM), generalized additive models (GAM), gradient boosting machines (GBM), and random forest (RF). To ensure comparability across algorithms, probability outputs from GLM, GAM, GBM, and RF were converted into favorability values using the *Fav*() function from the ‘fuzzySim’ package (Barbosa 2015), which corrects for sample prevalence (presence/absence ratio). This transformation was not applied to MXT outputs, as it generates suitability scores rather than probabilities.

## Parameters tuning, evaluation and ensemble modeling

An automated workflow tuned model parameters for each iteration and algorithm. For each case, a parameter combination was tested (Supporting information), and the combination yielding the highest performance score was retained. This score was calculated as a weighted average of three evaluation metrics: the Boyce index (weight 0.5), AUC-PR (0.25), and sensitivity (0.25). Greater weight was given to the Boyce index as it best captures how predicted suitability aligns with observed occurrences, ensuring more reliable projections from sparse data. Each metric captured distinct aspects of model performance: discrimination, classification, and calibration metrics (Sillero et al. 2021). Discrimination was assessed via area under the precision-recall curve (AUC-PR, 0–1 scale, with 0.5 indicating random performance), balancing precision and recall, making it particularly relevant for rare species, as it focuses on correctly predicting presences without being inflated by the abundance of absences like AUC (Sofaer et al. 2019, McDermott et al. 2025). Classification ability was estimated through sensitivity, measuring true positive rates (0 for no true positives, 1 for perfect classification), crucial given limited occurrences, where any omission of known occurrences would disproportionately affect model

reliability. Calibration was quantified using the Boyce index, which assesses alignment between predictions and observed presences (–1 for counter-prediction, 0 for random, and 1 for optimal prediction; Sillero et al. 2021, Liu et al. 2025). This metric relies solely on presence data, making it particularly advantageous in presence–pseudo-absence frameworks where absence information is artificially generated.

For each pseudo-absence set, block cross-validation was used to identify the best-performing parameter set, and the corresponding model was then run, and its evaluation retained for subsequent analyses. A four-fold (k=4) spatial cross-validation was used, training models on k – 1 blocks and testing on the remaining one. Spatial block size was derived from the range of environmental spatial autocorrelation, estimated using the *cv_spatial_autocor*() function from the ‘blockCV’ package (Valavi et al. 2019), based on 65 496 randomly sampled cells (subsampled by the function from the 1 244 424 cells available in the study area). Given limited occurrence data, final block size was set to two thirds of the median autocorrelation range. This configuration of k and block size balances environmental independence and data sufficiency, ensuring presence records in each fold. Larger blocks improve spatial independence but constrain k to avoid empty folds; conversely, higher k increases replication but may reduce independence. A script is available (Supporting information) to explore multiple k and block size combinations, aiming to retain as many species as possible. Once the optimal parameters were selected per species and algorithm, models were refitted, and only those with a Boyce index > 0.5 were retained for ensemble modeling. This threshold ensures minimal calibration quality.

Ensemble modeling enhances SDM robustness by combining multiple model outputs, effectively improving predictive accuracy by better capturing species distribution patterns (Marmion et al. 2009, Zhu and Peterson 2017). However, ensemble models do not always outperform individual ones in transferability (Zhu and Peterson 2017, Zhu et al. 2021). For this reason, ensemble modeling was applied at two distinct stages: 1) for current conditions, based on model runs calibrated and validated (Boyce index > 0.5; maximum 5 algorithms × 10 pseudo-absence sets=50 models) under present-day climate; and 2) for future projections, by projecting these same validated models to every combination of future periods and scenarios, and then creating an ensemble for each period–scenario combination. Consensus ensemble maps were computed as pixel-wise medians across all algorithms and replicates, minimizing the influence of outliers and aligning with model central tendencies (Marmion et al. 2009, Zhu et al. 2021). The entire pipeline is independently applied to each species, resulting in 17 continuous distribution maps per species (1 under current conditions, 16 under future). Ensemble models assessments were calculated by averaging metrics from models used to build the ensemble (Boyce index > 0.5). Algorithm performances were compared using Kruskal–Wallis, with Dunn’s post hoc test (Bonferroni correction) identifying pairwise differences between algorithms for each evaluation metric.
<figure><img src="image_6.png" /><figcaption>Figure 3. Overview of the species distribution modeling pipeline. The framework consists of three main stages: (1) Input data, (2) Modeling process, and (3) Output data.</figcaption></figure>

## Assessing uncertainty and spatio-temporal dynamics

Uncertainty was quantified using the standard deviation across predictions. Pixels with > 5% divergence, indicating inconsistent predictions across algorithms, were masked for visualization on the current distribution map to highlight the most reliable regions. To assess SDM reliability under novel conditions (i.e. conditions in future periods deviating from the calibration range), the multivariate environmental similarity surface (MESS) index was applied (Elith et al. 2010). Negative MESS values, indicating extrapolation, were masked for visualization on future distribution maps to highlight the most reliable projections.

To quantify extent changes in suitable bioclimatic areas, maps were binarized for each period and SSP using the maximum sum of sensitivity and specificity (MaxSSS) threshold, appropriate for presence-only data (Liu et al. 2013, 2016). Spatial overlap between current and future suitable areas was also computed on binarized maps, yielding the proportion of the present distribution projected to persist under each SSP. Spatial shifts between the current period and 2081–2100 were assessed by overlaying binarized projections with elevation data (CGIAR-CSI 2018; ~ 1 × 1 km at the equator) and latitude. Mean latitude and elevation of suitable pixels were calculated for the current period and for each SSP in the 2081–2100 period. Values were averaged across the four scenarios, and only species retaining suitable areas in all SSPs were used to estimate shifts between the present and future. Continuous suitability maps for the current and 2081–2100 periods and all SSPs were averaged across species, with persistent suitable pixels identified as bioclimatic hotspots (areas where the highest number of species could find suitable bioclimatic conditions). Results are measured on a continuous scale to emphasize relative hotspot importance.

Framework and model characteristics were documented following the ODMAP protocol (Zurell et al. 2020; Supporting information) and summarized in Fig. 3.

## Results

### Models performances

Between 19 and 50 models per species were retained for ensemble modeling based on the Boyce index criterion (Boyce index > 0.5). Significant differences in model performance across the five algorithms were detected for the Boyce index and sensitivity (Kruskal–Wallis, p < 0.001 and 0.01, respectively; see Fig. 4a for p-values), while no significant difference was found for AUC-PR (p = 0.07). Dunn's post hoc test revealed significant pairwise differences between algorithms.

including occurrence records, environmental data processing, and background sampling; (2) Modeling, encompassing model building, performance assessment, projection, and ensemble modeling; (3) Output processing, including manipulation of species projections to assess spatiotemporal dynamics of suitable bioclimatic niches.
<figure><img src="image_7.png" /><figcaption>Figure 4. Endemic Pyrenean plant species model performances. (a) Boxplot of three performance metrics, AUC-PR (discrimination), Boyce index (calibration), and sensitivity (classification), for the five modeling algorithms (GAM, GBM, GLM, MXT, RF) for all runs and species. Statistical significance is denoted as: \*p < 0.05, \*\*p <</figcaption></figure>

For the Boyce index, MXT and GBM outperformed GAM, GLM and RF. Regarding sensitivity, MXT outperformed GLM and GAM, while GBM and RF outperformed GLM. Other comparisons were not significant (Fig. 4a). This highlights variation in predictive performance, with GBM and MXT generally performing best, and RF consistently performing well in identifying suitable areas where species are present. GLM exhibited the lowest Boyce index values, falling under 0.5 for multiple species, indicating weaker prediction consistency.

Ensemble model performance ranged from 0.67–0.98 for AUC-PR, 0.62–0.92 for the Boyce index and 0.87–0.98 for sensitivity (Fig. 4b). Poor evaluation metrics were not consistently linked to species with fewer presence points, suggesting additional factors influencing performance.

## Projected bioclimatic niche loss and species vulnerability

The spatial extent of bioclimatic suitable areas for endemic Pyrenean plant species is projected to decline under climate change (Fig. 5). By the end of the century, on average across SSPs, 57 out of the 59 species are projected to lose a significant part of their suitable bioclimatic niche, with 41 species losing > 75%, 11 losing 50–75%, and 7 experiencing a more moderate loss of < 50%. These 57 species are expected to lose on average between -62.35% (min: +31.9%, max: -100%) and -95.21% (min: -19.8%, max: -100%) of their suitable bioclimatic niche by 2081–2100 period under the most optimistic (SSP126) and the worst-case (SSP585) climate scenario, respectively. The severity of loss varies across species. More than half (32 species) are projected to experience a complete loss (100%) of their suitable bioclimatic areas by the end of the century for at least one of the SSPs, putting them at elevated risk of extinction. *Delphinium montanum*, *Endressia pyrenaica*, and *Onobrychis pyrenaica* appear particularly vulnerable, as models project a total disappearance of their bioclimatic suitable areas as early as the 2021–2040 period under all SSPs, suggesting that these species are already present in unsuitable bioclimatic conditions. In contrast, *Pinguicula longifolia* and *Linaria bubanii* emerged as exceptions, showing a consistent increase in suitable areas with an average gain of +127.4% (min: +95.5%, max: +181.63%) by the 2081–2100 period. This suggests that shifting climatic conditions may benefit some species, including endemic and threatened ones. The analysis also highlights an accelerating trend in suitable area loss over time, particularly in more extreme scenarios such as SSP370 and SSP585. Most species experience sharp declines after the 2041–2060 period, reflecting the escalating impact of climate change as the century progresses (Fig. 5). All suitability maps, covering both current and future projections for all species, are available in the Supporting information.

<footnote n="0">0.01, \*\*\*p < 0.001. (b) Performance of ensemble models across species, computed by averaging evaluation metrics from the validated models (Boyce index > 0.5) across the five algorithms used to build the ensemble consensus. Darker shades indicate higher performance.</footnote>
<figure><img src="image_8.png" /><figcaption>Figure 5. Projected extent changes in bioclimatic suitability for endemic Pyrenean plant species under Shared Socioeconomic Pathways (SSPs) for the 2021–2100 period. Extent changes in suitable areas were calculated under SSP126, SSP245, SSP370, and SSP585, relative to the baseline period (1970–2000). Each box</figcaption></figure>

Spatial dynamics in bioclimatic niche suitability and upslope shift of bioclimatic hotspots

The spatial distribution of bioclimatic niche suitability for endemic Pyrenean plant species is projected to undergo significant changes by the 2081–2100 period under climate change (Fig. 6). Currently, bioclimatic hotspots, where most species could find suitable bioclimatic conditions, are mainly above 1000 m a.s.l., with a maximum favorability of 0.73 (Fig. 6a, top panel). By the 2081–2100 period, these hotspots are projected to shrink in size and shift upward, with the extent of this change strongly linked to the emission scenario. Climate scenarios such as SSP126 show a relatively moderate reduction, with some areas persisting near current locations. In contrast, more extreme scenarios project a pronounced contraction of suitable areas, becoming increasingly fragmented. Under SSP585, the maximum favorability value drops to 0.29, with suitable conditions restricted to areas above 2000 m in elevation (Fig. 6a, lower panels).

The projected mean elevation of bioclimatic suitable areas for endemic Pyrenean plant species shows a marked upward shift by the end of the century under climate change (Fig. 6b). Currently, the mean elevation of suitable bioclimatic areas ranges between 748 and 2269 m a.s.l., depending on the species. By 2081–2100, the average elevation across species is projected to increase by +182.58 m. While the lower margin remains relatively stable (671 m), the upper margin is expected to reach 2779 m. Species exhibit variable projected elevation trajectories. By the 2081–2100 period, 20 of the 27 species that still have suitable bioclimatic conditions across all SSPs display a clear upward trend, with mean increases ranging from +0.26 to +38.62%. Subsets of species that may experience a reduction in their mean elevation could see decreases from −8.93 to −41.51%. On average, species show a +19.49% increase in elevation, while the decrease is about −23.44%. In addition to these vertical shifts, species also display heterogeneous latitudinal responses by the 2081–2100 period. Among the 27 species included, 13 are projected to shift northward (+0.2 to +47.3 km, mean +11.1 km), while 14 shift southward (−11.3 to −0.6 km, mean −4.7 km), resulting in an overall average shift of +2.91 km northward. These results emphasize the significant influence of a warming climate on the spatial distribution of suitable bioclimatic areas, underscoring the increasing vulnerability of endemic Pyrenean flora to rising temperatures.

By 2081–2100, overlap between current and future suitable bioclimatic areas is generally low. Out of 236 species × scenario combinations, 177 (75%) fall in the lowest overlap class (0–25%). At the species level, 55 of the 59 endemics experience at least one scenario with less than 25% overlap. Notably, 38 species show complete loss of overlap (0%) under

represents the extent change in suitable area, with percentage values shown for the 2081–2100 period. Orange indicates a reduction in suitable areas, green an expansion, and black indicates a complete loss of suitability. The 2021–2040, 2041–2060, 2061–2080, 2081–2100 periods are designated as 2030, 2050, 2070, and 2090, respectively.
<figure><img src="image_9.png" /><figcaption>Figure 6. Projected distribution changes in bioclimatic suitability for endemic Pyrenean plant species between current and 2081–2100 periods. (a) Current and projected (2081–2100) bioclimatic hotspots, i.e. areas where the highest number of species could find suitable bioclimatic conditions, under Shared Socioeconomic</figcaption></figure>

the most severe scenario (SSP585). This means that while 32 species entirely disappear from the projections, six still retain suitable climatic conditions, but in areas completely distinct from their current distribution. In contrast, only seven species retain relatively high stability (≥ 75% overlap) under at least one scenario. These results highlight the drastic spatial reshuffling of suitable areas and the limited persistence of current bioclimatic ranges under future climates.

Discussion

Trends in bioclimatic niche suitability under climate
change

This study paints a concerning picture of the future for Pyrenean endemic flora under ongoing and projected climate change. By the 2081–2100 period, 57 out of the 59 studied species are projected to experience substantial reduction in suitable bioclimatic areas (on average, –62.35 to –95.21% for SSP126 and SSP585, respectively), ranging from moderate contraction in some species to complete losses in others. These findings underline the severe threat posed by a changing climate to narrowly distributed species and align with patterns projected in similar mountain regions, such as the Alps (Dirnböck et al. 2011, Dullinger et al. 2012, Hülber et al. 2016, Dagnino et al. 2020, Rota et al. 2022), where endemics are acutely sensitive to rapid warming. Parallels with other mountain systems (Manes et al. 2021) underscore the universal challenge that climate change poses to mountain endemism, which is moreover amplified by topography and human pressures (Elsen et al. 2020). Interestingly, not all species follow the same trajectory. While most of them show severe bioclimatic niche contractions, *Pinguicula longifolia* and *Linaria bubanii* are the only endemics projected to consistently gain suitable bioclimatic areas under all scenarios, mainly on the northern slopes and in the southeastern Pyrenees. For *P. longifolia*, this may reflect pre-adaptation to warmer climates, as suggested by the occurrence of a related subspecies in the drier Massif Central. For *L. bubanii*, a scree-dwelling species of high altitudes, the strong microclimatic heterogeneity of its habitat may facilitate persistence

Pathways (SSPs). The top panel shows the current distribution, while lower panels display projections for SSP126, SSP245, SSP370, and SSP585. Darker colors indicate higher bioclimatic suitability. Contour lines mark the 1000- and 2000-m elevation topography. (b) Projected latitudinal and elevational shifts in bioclimatic suitability for the 2081–2100 period, averaged across SSPs. Each dot represents one of the 27 species retaining suitable areas under all SSPs. Dot color reflects the relative change in suitable area (green = gain; red = loss). The black arrow indicates the overall mean displacement in latitude (Δ lat) and elevation (Δ elev) across species. (c) Projected spatial overlap (%) between current and future (2081–2100) distributions of bioclimatically suitable areas under SSP126, SSP245, SSP370, and SSP585. Each row represents one species, with colors indicating the proportion (%) of overlap with present-day suitable areas.
across diverse conditions and promote expansion. Despite
these projected gains, both species may remain vulnerable,
as establishment depends on strict ecological requirements
and interactions with other biotic components under climate
change. These outliers highlight that while some species face
heightened risks of extinction, others may find new opportu-
nities under climate change. This also underlines the need for
species-specific conservation strategies that account for this
ecological variability.

Beyond species-specific patterns, this study revealed nota-
ble upward shifts in mean elevations of suitable bioclimatic
niches, exceeding 180 m by the 2081–2100 period. If such
a trend had been consistent over the past century, it could
help explain part of the upward plant movement already
observed in the Pyrenees (Lenoir et al. 2008, Pauli et al.
2012, Ameztegui et al. 2016, Marshall et al. 2020, Aulló-
Maestro et al. 2023, Delpouve et al. 2025) and is consis-
tent with documented upward trends in other mountain
ecosystems (Iseli et al. 2023, Chan et al. 2024). In parallel,
for the same period, latitudinal dynamics indicate an over-
all northward displacement of about +3 km. Overlap with
current ranges is also very limited: 55 of the 59 endemic
species retain less than 25% of their present distribution
under at least one scenario, implying that many future suit-
able areas will occur entirely outside their current ranges.
Combined with the projected upward shift, it suggests a
substantial spatial displacement of endemic species under
ongoing warming. However, the pace at which species can
track these changes remains uncertain, as local-scale factors,
such as microclimatic buffering, habitat discontinuities, or
dispersal limitations, may slow or constrain their responses
(Maclean and Early 2023). Hotspots of suitable bioclimatic
niches, currently located above 1000 m, are projected to
shift above 2000 m. This shift drastically reduces the biocli-
matically suitable space available for endemic flora, as high
elevations cover a smaller geographic extent in the Pyrenees
(Elsen and Tingley 2015). Many species risk being squeezed
between warming lower slopes and shrinking refugia, exac-
erbate their risk of extinction. Bioclimatic hotspot maps also
revealed spatial patterns suggesting a future fragmentation of
suitable areas, with favorable habitats increasingly confined
to isolated mountaintops separated by valleys. This fragmen-
tation likely reduces connectivity across the landscape, limit-
ing the ability of many species to track climate change. In
the Pyrenees, this issue is compounded by regional warm-
ing and aridification, already documented at a rate of 30%
faster than the global average over the past 50 years (OPCC
2018). Combined with challenges already posed by disper-
sal barriers, topographic constraints, and competition from
upslope-moving species, these factors intensify pressure on
the native flora. The severe suitable bioclimatic area reduc-
tions projected under the SSP370 and SSP585 scenarios after
the 2041–2060 period are particularly alarming and suggests
that environmental pressures will intensify in the second half
of the century. The urgency of monitoring is reinforced by
the identification of high vulnerability of northern Spanish

flora, to the south of the Pyrenees, which is among the most
threatened in Europe (Engler et al. 2011).

Strengths and limitations of the modeling framework

The pipeline developed in this study represents a step forward
in addressing mountain biodiversity questions at a broad geo-
graphic scale, particularly for analyzing climate-driven trends
across multiple species. By integrating five algorithms (GLM,
GAM, GBM, RF, and MXT) into an ensemble modeling
framework, this approach minimizes model-specific biases
and improves the reliability of projected species distribu-
tions. Moreover, the agreement with previous projections for
an endemic species also analyzed here (e.g. *D. montanum*;
*Salvado et al. 2022*) suggests that our ensemble approach pro-
duces ecologically consistent outcomes. RF, MXT and GBM
emerged as particularly strong performers, likely due to their
capacity to handle small sample sizes and capture non-linear
relationships between species and environmental variables
(Valavi et al. 2022). The pipeline addresses challenges posed
by sparse data, providing consistent and interpretable results
for narrowly distributed species, as demonstrated by its abil-
ity to model 75% of the endemic species in a mountainous
region. It includes tools to adjust pseudo-absence sampling
to achieve stable predictions despite limited occurrence data.
Another component helps optimize spatial cross-validation
settings, ensuring the most reliable model evaluation while
maximizing the number of species that can be assessed and
thus studied. These features make the approach suitable for
rare or endemic species, whose restricted ranges and small
sample sizes could limit the applicability of standard SDM
procedures. To account for endemic Pyrenean plant species
excluded from this study, the pipeline could be applied on
a case-by-case basis at a smaller spatial scale (in regions of
recorded occurrences). As such, it provides a valuable and
adaptable framework for informing conservation strategies
across a diversity of endemic taxa. Moving forward, our
pipeline could be extended to species with broader distribu-
tions, adapted to biodiversity hotspots worldwide, and opti-
mized for a more extensive analysis across multiple species. It
would provide deeper insights into species' and communities'
response to climate change, focusing on richness patterns and
diversity dynamics.

Despite these strengths, several limitations of SDMs and
considerations warrant caution when interpreting the results
(Collette et al. unpubl.), particularly regarding methodologi-
cal biases and decision-making processes (Bryn et al. 2021,
Sillero et al. 2021, Leroy 2023). This study presents three
main considerations. First, in large-scale studies where fine-
resolution data are lacking, coarse-resolution bioclimatic
data can still capture general environmental trends, reduce
computational costs, and minimize biases from inaccuracies
in occurrence data. However, by failing to capture fine-scale
microhabitat heterogeneity in mountainous environments,
they may misestimate the extent of habitat suitability
(Trivedi et al. 2008, Randin et al. 2009, Gottfried et al. 2012,
Franklin et al. 2013, Nadeau et al. 2017), although the
overall trends remain consistent (Tsiftsis et al. 2024). Second, projections beyond the 2041–2060 period remain uncertain due to increasing divergence between climate models, particularly for the worst climate scenarios (SSP370, SSP585; Goberville et al. 2015, Tebaldi et al. 2021). Although the trajectory for most species aligns across scenarios, notable variation exists in whether the suitable bioclimatic niche disappears or persists to a lesser extent, depending on pseudo-absence sets. Long-term monitoring and high-resolution climate data are essential for validating projections and improve predictive tools. Third, the exclusion of biotic interactions (e.g. competition, mutualism, pollination), as well as important abiotic variables (e.g. soil, hydrology, radiation, land use), reduces the ecological realism of projections. Integrating such dynamics into SDMs would enhance the accuracy of projections and offer a more nuanced understanding of species' responses to climate change (Preuss and Padial 2021, Lee-Yaw et al. 2022, Cosentino et al. 2023).

Beyond these methodological considerations, SDMs also fail to capture complex ecological and evolutionary processes that shape species' responses to environmental changes (Williams et al. 2008). Expansion of bioclimatic suitability areas does not guarantee population growth, as dispersal barriers, establishment limitations, and biotic interactions may restrict species from fully occupying suitable areas. Likewise, suitability contraction does not imply imminent range collapse, as species can persist temporarily in suboptimal conditions before extinction, known as an 'extinction debt' (Cotto et al. 2017, Nomoto and Alexander 2021). From the adaptation perspective, rapid environmental changes may outpace the rate at which species can adapt, facing the disappearance of their suitable habitat (Jump and Peñuelas 2005, Shaw and Etterson 2012, Cotto et al. 2017). Moreover, fragmented populations may experience divergent selective pressures, fostering local adaptation, often overlooked in SDMs. Integrating local adaptation, phenotypic plasticity (Valladares et al. 2014, Benito-Garzon et al. 2019), and genomic data (Hällfors et al. 2016, Kort et al. 2024) could improve projections by better accounting for populations' adaptive capacity under environmental stress. Despite these limitations, correlative SDMs based on bioclimatic variables remain valuable tools for generating insights and understanding species' ecological niches, anticipating distributional changes, and informing conservation strategies.

## Conservation implications and policy recommendation

From the conservation and management perspectives, our findings underscore the urgent need for conservation strategies that address the specific challenges faced by mountain flora in the Pyrenees. High-altitude environments, where suitable bioclimatic conditions are expected to persist, represent critical bulwarks against climate change. These areas must be prioritized for legal protection as critical refugia for biodiversity preservation, since they are projected to concentrate the remaining suitable conditions for species by 2081–2100. To ensure the long-term relevance of protected

areas, conservation planning must integrate future suitability of species and regulate potentially harmful activities in these refugia, such as infrastructure development or unregulated tourism, particularly above 2000 m. However, traditional grazing should be promoted, as it maintains open habitats that support many endemic species adapted to these environments. In parallel, the consistent upward shift of bioclimatic hotspots and their fragmentation call for the maintenance of altitudinal corridors to support species migration and gene flow, thereby mitigating the adverse effects of isolation. Mapping these corridors based on projected trajectories could help identify priority areas for land acquisition or land-use planning. For species projected to experience a partial loss of their suitable bioclimatic niche, conservation interventions such as assisted migration and translocation may provide options to prevent extinction (Gallagher et al. 2014, Ferrarini et al. 2016). Evaluating candidate sites for reintroduction in newly suitable areas would facilitate the implementation of these actions. Moreover, integrating predictive modeling tools with field monitoring could enhance the precision of these interventions, ensuring that resources are allocated to areas with the greatest potential for success (Eyre et al. 2022, Elliott et al. 2024). Critically vulnerable taxa, such as *Delphinium montanum*, *Endressia pyrenaica*, and *Onobrychis pyrenaica*, for which models predict the disappearance of suitable conditions as early as 2021–2040 under all SSPs, should be urgently targeted. For these species, in situ strategies may need to be complemented by ex situ measures, such as seed banking or cultivation in botanical gardens, which represent a last-resort solution to preserve genetic material and avoid extinction. Developing emergency conservation plans for such high-risk taxa would improve coordination across institutions and ensure readiness for rapid intervention. In parallel, identifying microrefugia through fine-scale topographic analysis may uncover overlooked zones of persistence and identify new areas for assisted migration. Similarly, intensified demographic and phenological monitoring within residual populations would help assess whether they are viable or demographically declining, thereby informing prioritization. Re-evaluating conservation status in light of projected niche loss would better guide adaptive management priorities. Many endemic species are likely to face imminent extinction risks that are not yet recognized in current conservation assessments. Including climate-driven projections in Red List criteria could improve the alignment between legal protection and future vulnerability. National and regional conservation strategies should be updated to incorporate forward-looking criteria, ensuring that legal frameworks remain dynamic in the face of environmental change.

The transboundary distribution of Pyrenean species across France, Spain, and Andorra requires coordinated cross-border efforts to avoid fragmented conservation initiatives, such as inconsistent Natura 2000 habitat interpretation along this frontier (Prud'homme and Ninot 2018). Adaptive legal frameworks are essential to address species shifts, supported by collaborative efforts that align research, policy, and community engagement for enhanced conservation outcomes.
Establishing a transnational conservation platform for endemic mountain species could help harmonize monitoring, protection, and restoration efforts across borders, and reduce disparities in species management. Translating scientific evidence into clear and actionable policy briefs can bridge the gap between research and implementation, fostering informed and, consequently, more effective decision-making (Guisan et al. 2013). Understanding how climate change affects extinction risks and habitat availability is key to prioritizing conservation actions, especially as pressures intensify. This also requires combining ecological modeling, field experimentation, and long-term monitoring to refine conservation strategies and better address species redistribution and its effects on human well-being (Pecl et al. 2017, Bonebrake et al. 2018). Ultimately, preserving mountain biodiversity in an era of rapid change will require collaboration among scientists, resource managers, and local communities, supported by predictive tools to capture the complex realities of a rapidly changing world.

*Acknowledgements* – We thank Romain Bertrand (Toulouse III University) for valuable comments that contributed to shaping our methodology.

*Funding* – This study received financial support from the Occitanie Region (Emergence 2024) and the Commissariat du Massif des Pyrénées (FNADT 2023), as well as from the Fédération de Recherche 2043, Energie & Environnement (UPVD/CNRS). It also received technical and financial support from the InterregPOCTEFA program as part of the Floralab+ project (EFA024/01).

*Conflict of interest* – The authors declare no conflict of interest.

## Author contributions

**Noèmie Collette:** Conceptualization (supporting); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Software (lead); Validation (equal); Visualization (equal); Writing – original draft (lead); Writing – review and editing (lead). **Sébastien Pinel:** Funding acquisition (supporting); Methodology (equal); Project administration (equal); Supervision (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal). **Valérie Delorme-Hinoux:** Conceptualization (lead); Funding acquisition (lead); Methodology (equal); Project administration (lead); Supervision (equal); Validation (equal); Writing – original draft (equal); Writing – review and editing (equal). **Joris A. M. Bertrand:** Conceptualization (lead); Funding acquisition (lead); Methodology (equal); Project administration (lead); Supervision (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (lead).

## Transparent peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ecog.08067.

## Data availability statement

Data are available from the Zenodo Repository: https://doi.org/10.5281/zenodo.17412264 (Collette et al. 2025).

## Supporting information

The Supporting information associated with this article is available with the online version.

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