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Article

Land-Use Changes Largely Determine the Trajectory of Plant Species Distributions Under Climatic Uncertainty in a Mediterranean Landscape

by
Spyros Tsiftsis
1,
Anna Mastrogianni
2,
Diogenis A. Kiziridis
2,
Fotios Xystrakis
3,
Magdalini Pleniou
3 and
Ioannis Tsiripidis
2,*
1
School of Agricultural and Forestry Sciences, Democritus University of Thrace, 66132 Drama, Greece
2
School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Forest Research Institute, Hellenic Agricultural Organization “Dimitra”, 57006 Vasilika, Greece
*
Author to whom correspondence should be addressed.
Land 2025, 14(7), 1438; https://doi.org/10.3390/land14071438
Submission received: 19 May 2025 / Revised: 1 July 2025 / Accepted: 8 July 2025 / Published: 9 July 2025

Abstract

We investigated the combined effects of climate and land-use change on plant diversity in northwestern Greece, a region representative of broader European trends in land abandonment. We based our study on comprehensive field data on plants’ distribution and modelling of land-use changes based on socio-economic trends. We build distribution models for 358 taxa based on current (2015) and future (2055) conditions according to the combinations of three climate and three land-use change scenarios. We compared species distribution changes between current and future conditions for each scenario, and we investigated species trends concerning their ecological indicator values and strategies. Additionally, by analyzing the distribution changes in aggregated differential taxa representing the various plant communities in the study area, we identified patterns of distribution shifts at the community level. Our results indicated more pronounced differences between land-use scenarios than between climate ones, which was attributed to the local scale of the study area, its climatic and physiographic characteristics, and its complex land-use legacy. Both climate and land-use changes drastically reduced the distribution of some species, with species distribution loss exceeding 80% under certain combinations of socioeconomic and climate change scenarios. Species ecological indicator values and strategies showed a buffering effect of forest microclimate against climate change, which, however, may favor only species of forest communities. At the community level, land-use change had again a stronger impact than climate change, with consistent patterns within major vegetation types (forests and open habitats) but contrasting trends between them. Our results highlight the need for appropriate conservation plans to counteract the negative impacts of land abandonment and to take advantage of its positive impacts.

1. Introduction

We live in a rapidly changing world, where native species are being threatened by several factors. According to the global assessment report on biodiversity of IPBES, the factors with the greatest negative influence on biodiversity, listed in descending order of importance, are habitat loss or change (due to land-use changes), direct exploitation of organisms, climate change, pollution, and invasive species [1]. Climate change drastically threatens biodiversity by affecting species distributions [2,3], while it is expected that habitat loss caused indirectly by climate may exacerbate the negative impacts of other biodiversity threats [1].
Land-use change has posed the highest negative impact on biodiversity loss to date, mainly due to loss or degradation of species habitats [1,4]. The importance of habitat loss because of land-use change is highlighted when considering that wildland covers around 22% of the ice-free land on earth, with only about 3.3% being retained in biomes other than the cold and dry ones [5,6]. The impact of land-use change that has been covered in the bibliography so far mainly concerns habitat loss or degradation due to the intensification of land uses. However, the growing trend of farmland and pastureland abandonment worldwide, especially in mountainous areas and areas of low productivity, results in significant habitat loss for many species [7].
In Europe, land abandonment is currently the most prominent cause of landscape changes [8], with 20% of European agricultural land being under a high or very high risk of abandonment until 2030 [9]. On the one hand, land abandonment is considered an opportunity for the natural restoration of ecosystems and recovery or re-introduction of many species (ref. [10] and many articles therein). On the other hand, it has been considered a biodiversity threat, having a negative impact on species of open or seminatural habitats that have been formed by traditional human use of ecosystems for millennia [11,12,13]. Specifically, natural succession, which follows land abandonment, causes replacement of open and seminatural habitats with scrubs and, finally, forests [14].
Although climate change and land-use change are probably the most widely studied biodiversity threats globally [4], it is only recently that an assessment of their combined impact has been thoroughly studied, as has been shown by the significantly more studies appearing in the bibliography (search in Scopus database on 20 January 2024). Nevertheless, there is still a lot of research to be undertaken towards a better understanding of the combined effect of these two threats on biodiversity, for different biomes or ecosystems, environmental conditions, groups of taxa, etc. [15,16,17].
Indicators of species’ ecological or habitat preferences can significantly enhance our understanding of how climate and land-use change affect species diversity by revealing distributional shifts among groups of species with distinct ecological characteristics or life strategies. On one hand, plant species’ ecological characteristics are available through the concept of ecological indicator values, which represent their preferences in relation to light availability, temperature, continentality, soil moisture, soil reaction, soil fertility, and salinity [18]. On the other hand, species’ habitat preferences can be captured by incorporating functional diversity metrics, such as trait syndromes, including Grime’s Competitor, Stress-tolerator, Ruderal (CSR) model of plant life strategies [19,20]. Competitive species may thrive under reduced disturbance regimes linked to land abandonment, whereas ruderal species depend on frequent disturbances and may decline as traditional land uses are abandoned. Stress-tolerant species often exhibit greater resistance to climatic extremes but may be constrained by changing land cover. Identification of groups of species with distinct ecological optima or life strategies and investigation of their distribution patterns under future climate and land-use conditions can significantly assist biodiversity conservation and efforts towards the monitoring of climate and habitat change.
In Greece, and especially towards the northern and central parts of the country, high landscape changes have been recorded due to land abandonment, especially in (sub)mountainous areas, concerning a high decrease in farmlands and seminatural grasslands and forest increase (e.g., [21,22,23,24]). In the present study, in such a sub-mountainous area, representative of the landscape changes caused by land abandonment in northern and central Greece, we assessed the combined effects of land abandonment and climate change on vascular plant species distribution at a local scale. For this area, Kiziridis et al. [22] found an increase in forests from 22% to 63% in the period from 1945 to 2015, and a decrease in farmland from 30 to 3%. For assessing the impact of climate and land-use change in this area, we incorporated combinations of three land cover and three climate scenarios referring to the year 2055. For current conditions, as well as for the nine combinations of the land cover and climate scenarios, we predicted and compared species’ potential distribution. Through the above-described rationale, we addressed the following questions: (a) What are the patterns of losses and gains of species per future climate and land-use change scenarios? (b) Do these patterns vary among the plant communities found in the study area? (c) Are there any “winners” and “losers” among taxa with different ecological preferences and life strategies in relation to future scenarios?

2. Materials and Methods

2.1. Study Area

The study area comprised five circular sites with a total area of 141.4 km2, located at the north-western sub-mountainous region of the Pindus Mountains in Greece (Figure 1). Historically, this area had extensive farmlands and grasslands traditionally exploited via low-intensity farming and transhumance, but land abandonment after the 1940s has led to significant landscape changes [22]. Elevation ranged from 248 to 1203 m, while the area is characterized by gentle slopes (0–10°), reaching a maximum of 55°. The geological substrate of the study area is constituted by 50% limestone, 25.4% deposits, 18.9% silicate, and 5.7% flysch [25]. According to Köppen–Geiger climatic classification, the area belongs to the Csa type [26], since it has a temperate climate, with hot and dry summers, as well as mild and wet winters. Additionally, the study area belongs to the vegetation formation of thermophilous deciduous oaks [27], which represents the climax vegetation of the study area.

2.2. Vegetation Sampling and Plant Communities

The field data used in the present study to determine species occurrences across the study area are described in detail in Mastrogianni et al. [28]. In brief, vegetation sampling of 250 plots was conducted in 2020, with 125 plots located in grassland or shrubland habitats (50 m2 each) and 125 in forest habitats (200 m2 each). Within each plot, all vascular plant taxa and their cover were recorded by using the 9-grade Braun–Blanquet scale [29,30]. Following vegetation classification of these plots, 11 floristically and ecologically distinct vegetation communities were identified in the study area [28]. These communities represented different stages of a secondary vegetation succession gradient. The grassland communities along the successional stages included (a) two communities at the early successional stage, namely, hay meadows with Alopecurus rendlei and old-fields with Hordeum bulbosum; (b) one community at the advanced and ongoing successional stage, namely, wet meadows with Cynosurus cristatus; (c) two communities at the advanced but stable successional stage, namely, semi-natural grasslands with Chrysopogon gryllus and semi-natural grassland with Phlomis fruticosa; and (d) one retrogressive community of Pteridium aquilinum stands. Regarding forests, the five identified communities included (a) two advanced and stable communities, namely, Carpinus orientalis forests and xero-thermophytic oak forests (Quercus pubescens–Q. trojana); and (b) three late forest communities, Quercus frainetto forests, Quercus cerris–Q. frainetto mixed forests and riparian forests. For more details on these communities, please see Mastrogianni et al. [28].

2.3. Land Cover Mapping

For the determination of the distribution of land-use classes in the study area, we mapped land cover into five land types, namely, farmland, grassland, open-scrub, closed-scrub, and forest, by using natural-color orthophotos for 2015, obtained from the Hellenic Cadastre. From the total study area, we excluded settlements and water bodies as unsuitable for species distribution modelling within the context of the present study, leading to a final study area of 138.4 km2. Farmlands represented areas with evidence of agriculture and land management practices. Grasslands represented natural habitats with cover of trees and shrubs ranging between 0 and 10%, open-scrubs with respective cover of trees and shrubs ranging between 10 and 40%, closed-scrubs with cover of trees and shrubs ranging between 40 and 70% and, finally, forests with cover of trees and shrubs ranging between 70 and 100%. Mapping was achieved by the visual interpretation of orthophotos, vectorization of the land-type boundaries, and subsequent rasterization of the maps at 25 m resolution [22]. The land-use change projections and associated validation data employed in this study were derived from Kiziridis et al. [31], where the full methodology and model performance metrics are described in detail. In brief, the future land cover was predicted for 2055 according to three socioeconomic scenarios: business-as-usual (USU), preservation of intensive farming (INT), and extensification of farming (EXT) [31]. The three scenarios were developed after taking into account the study area’s land-use cover in past years (1945, 1996, and 2005), combined with the changes in the trends of socioeconomic variables in our study area among these years, as well as climate change. More specifically, in the USU scenario, the trend in demand for land cover is the same as the trend observed for the period 1996–2015. The INT scenario is similar to the USU scenario but preserves the farmland of low elevation and slope, whereas the EXT scenario is characterized by an inverse transitioning to the demands of 1970. More details on the demand scenarios can be found in Kiziridis et al. [31]. The total area of the different land-use types under each scenario is given in Table 1, while the patterns of substitution among these land-use types can be found in Kiziridis et al. [31].
The floristic character of each land-use type can be inferred by matching them with the plant communities identified within the study area. Specifically, grasslands include areas occupied by semi-natural grasslands with Chrysopogon gryllus, semi-natural grasslands with Phlomis fruticosa, old fields with Hordeum bulbosum, hay meadows with Alopecurus rendlei, wet meadows with Cynosurus cristatus, and stands of Pteridium aquilinum. Open and closed scrubs encompass areas occupied by all plant communities identified in the study area, differentiated primarily by tree cover density. Finally, forests correspond to areas occupied by Quercus frainetto forests, Carpinus orientalis forests, xero-thermophytic oak forests (Quercus pubescens–Q. trojana), Quercus cerris–Q. frainetto mixed forests, and riparian forests.

2.4. Species Distribution Modelling

For the evaluation of the potential effects of climate and land-use changes on species’ distribution across the study area, we applied the species distribution modelling (SDM) technique for 358 out of the 629 plant taxa recorded in the study area. Specifically, we applied modelling only for taxa recorded in at least seven localities (n = 7–129 occurrences) within the study area [32], to allow reliable modelling results. Occurrence data of these species were collected through the vegetation sampling in 250 plots, representative of the different plant communities, ecological conditions, and land cover types found in the study area (see Section 2.2). Current and future potential distribution of species were predicted by applying the MaxEnt algorithm [33,34], which is known to have high predictive performance, even in the case of very small sample sizes [35,36].
The employed predictive variables were representative of the area’s climate, physiography, geological substrate, soil attributes of the topsoil, as well as land cover, at a spatial resolution of 25 m. CHELSAcruts and CHELSAfuture V2.1 datasets were used for the construction of 19 bioclimatic variables, for current conditions and for three future climate scenarios, namely, the Shared Socioeconomic Pathways of sustainability (SSP126), regional rivalry (SSP370), and fossil-fueled development (SSP585) from the CMIP6 framework [37] (see Kiziridis et al. [31] for more details). We checked for multicollinearity between them, by using Pearson’s correlation coefficients for all pairwise combinations, and kept those bioclimatic variables with a higher percent contribution and training gain among any pair with a correlation coefficient |r| > 0.70: annual precipitation, precipitation of the warmest quarter, mean temperature of the coldest quarter, and mean temperature of the warmest. The geological substrate was obtained from the map of Nakos [25], and the soil variables (pH, nitrogen, proportion of silt and clay, soil organic carbon) were obtained from the global gridded soil information, SoilGrids [38]. For altitude, we used the Digital Elevation Model over Europe at a 25 m resolution [39], based on which the ground’s slope raster was also generated by the R package “raster” [40]. Geological substrate and soil variable data were assumed to remain unchanged and were applied identically across current (2015) conditions and all nine future socioeconomic scenarios.
To make more accurate predictions for models produced with the MaxEnt algorithm based on presence-only distribution data, and to capture the variability of the study area’s environmental conditions, we incorporated 10,000 random background points [36]. This is the default number of background points and was selected because it ensures a broad and representative sampling of the environmental space (e.g., different land uses and environmental gradients), as well as stable and reliable estimates of the environmental distribution across the study area. MaxEnt models were run using 5-fold cross-validation, and default settings as suggested by Phillips and Dudík [33]. This process was repeated ten times, and the average prediction was then calculated for each plant taxon. We ran MaxEnt models under current conditions (2015) and projected them to future conditions (2055), using nine combined scenarios of climate and land-use change. This resulted in a total of ten potential distribution maps for each taxon. We used the Maximum Sensitivity plus Specificity (MaxSSS) to convert habitat suitability rasters into presence/absence maps [41]. We selected this threshold as it is known to provide better results independently of the type of the employed data (presence/absence or presence-only) [41], leading to resulting binary maps characterized by balanced commission and omission errors. All SDM analyses were conducted with the R package “dismo” [42].

2.5. CSR Life Strategies and Ecological Indicator Values of Species

We used taxa ecological indicator values [43] and their ecological strategies according to Grime’s CSR theory [44] to explore the distribution changes in taxa with different ecological characteristics, under future climate and land-use scenarios. Ecological indicator values of light, temperature, soil moisture, nitrogen, and reaction were obtained from Dengler et al. [45]. For 11 taxa not included in Dengler et al. [45], we calculated indicator values by averaging the indicator values of the five taxa with the highest percentage of co-occurrence with the taxon under question in the vegetation dataset. The taxa ternary coordinates in Grime’s triangle of ecological strategies were calculated based on extensive sampling of plant traits in the study area [28,32] and the methodology of Pierce et al. [46]. Three coordinates were produced for each taxon, each ranging from 1 to 100, representing the three main dimensions in Grime’s triangle, namely, competition (C), stress-tolerance (S), and ruderality (R). Four taxa were omitted from the dataset for the analysis of ecological strategies because of a lack of data. Finally, we calculated linear regressions between the percentage of change in the distribution extent of the modelled plant taxa among current conditions and future scenarios, and their indicator values as well as taxa ternary coordinates in Grime’s triangle of ecological strategies.

2.6. Vegetation Communities’ Distribution

In order to identify any differences in the patterns of distribution changes among species that occur preferentially in specific plant communities, we investigated the trends of distribution changes in differential taxa per community under the future climate and land-use scenarios. Specifically, we determined the differential taxa for each plant community by applying the indicator species analysis proposed by De Cáceres et al. [47], though the package “indicspecies” in R (ver. 1.7.12) [48]. Then, we selected all taxa having a statistically preferential occurrence in any of the 11 plant communities, leading to a subset of 233 out of the 358 studied taxa, and determined the minimum number of indicator taxa per plant community according to the vegetation plot data (Table S1). Based on the predicted current and future potential distribution of the 233 differential taxa, we calculated the number of cells with at least the minimum number of differential taxa per community. The number of cells per plant community with an equal or higher number of indicator species than the minimum number per plot within each community is herein used as a surrogate for the investigation of a possible differentiation in species distribution changes between the plant communities.

3. Results

The SDMs built for the 358 taxa had Area Under Curve values always higher than 0.75, indicating good predictive accuracy. The results concerning the distribution changes in all investigated taxa among the current (2015) and the different future scenarios are presented in Table S2 (Supplementary Material). Based on models’ predictions, no taxon is expected to disappear from the study area. Nevertheless, the overall distribution extent of the great majority of taxa is going to be subjected to significant changes (Figure 2; Table 2). Specifically, for 13–16% of taxa, a severe reduction (>80%) in their current distribution was predicted (Figure 2) for the INT and USU scenarios and the two worst climate scenarios. The corresponding percentages for the above land-use scenarios and the mildest climate scenario were reduced by approximately 50%. Many taxa (27 to 44% of total examined taxa) were predicted to have a reduction in their distribution extent by 40 to 80% under the INT and USU land-use scenarios, independently of the climate scenario. The EXT scenario presented the lowest numbers of taxa with a significant reduction in their distribution extent (e.g., >40%), especially for the case of the SSP126 scenario. The same land-use scenario also resulted in the highest number (i.e., increased by 43 to 71%) of taxa with a significant gain (40 to >100%) in their distribution compared to the remaining two land-use scenarios and for all the climate scenarios.
The coefficients and statistical significance of the regression analyses are presented in Table S3 (Supplementary Material). Statistically significant association (p < 0.05) was found concerning the percentage change in taxa distribution between current and future conditions and their indicator values for light, temperature, and soil moisture (Figure 3a–c), but not for soil reaction and soil nitrogen content (Table S2). The type of association between percentage change in taxa distribution and indicator values was similar between climate scenarios, while it followed an opposite trend among the EXT scenario and the other two land-use scenarios (Figure 3a–c). Regarding the light indicator values, a high increase was found in the distribution of the shade-loving taxa compared to current conditions, as well as a decrease in a similar magnitude of the light-loving taxa, for the INT and USU scenarios. For the EXT scenario, a very high decrease in the distribution of shade-loving taxa and a moderate increase in light-loving taxa were found. Regarding the temperature indicator values, no change in distribution extent (or very small increase) was predicted for the cold-adapted, while a high decrease was found for the most thermophilous taxa (heat-loving plant species), for the INT and USU scenarios. For the EXT scenario, this trend was opposite, showing a small decrease for the cold-adapted taxa, and no changes for the most thermophilous taxa. Finally, for the INT and USU scenarios, there was a high decrease in the distribution of drought-tolerant taxa and a moderate increase for taxa preferring moister soil conditions. For the EXT scenario, the drought-tolerant taxa had a stable distribution, while the more hydrophilous taxa had a small decrease.
The relation between the percentage change in taxa distribution extent under current and future conditions and the three plant ecological strategies was found statistically significant (p < 0.05) for competition and ruderality (except for the combinations of SSP370-INT and SSP585-INT scenarios), but not for stress tolerance (Table S2). Similar to the indicator values, the type of association between taxa distribution and ecological strategies was the same between climate scenarios but differed between land-use scenarios (Figure 3d,e). The current distribution extent of taxa of low competitiveness was predicted to decrease, while the most competitive taxa had almost unchanged distribution, under the INT and USU scenarios. Under the EXT scenario, the distribution extent of taxa of low competitiveness remained almost unchanged in relation to current conditions, but competitive taxa showed a high decrease (Figure 3d). Regarding ruderality, a slightly decreasing trend in the current distribution extent of ruderals under the INT and USU scenarios was observed. In contrast, the current distribution extent of non-ruderal taxa was predicted to decrease, while the current distribution extent of ruderal species was expected to slightly increase under the EXT scenario (Figure 3e).
Regarding the distribution changes in taxa in relation to their preferential occurrence in plant communities, for all the communities, the differences in change trends between the climate scenarios were minor, while a striking difference in this trend was found between the EXT scenario and the other two land-use scenarios (Figure 4). The trends concerning land-use change were similar within the species of the two main groups of plant communities, namely, open habitats and forests, and opposite among them. Specifically, the distribution of the species differential of all open habitat communities was decreased according to the USU and INT scenarios and relatively to the EXT scenario, while those of the forest communities showed the opposite trends between the above-mentioned scenarios. Species distribution of all the forest communities was slightly increased (from 5 to around 25% in comparison with the current conditions) on the basis of the USU and INT scenarios, but was more sharply decreased (around 50%) according to the EXT scenario. The species of the riparian forest community (Figure 4K) showed an exceptionally high decrease (around 80%) for the EXT scenario. Concerning the species of the open-habitat communities, their distribution in four out of six communities showed a moderate increase (from around 25 to 50% in comparison with the current conditions) for the EXT scenario and a more pronounced decrease (about 50%) for the other two scenarios. As an exception, the species of the Phlomis fruticosa community (Figure 4B) showed a small decrease in the EXT scenario compared to current conditions and a high decrease (around 75%) for the other two scenarios. Species of the Pteridium aquilinum community (Figure 4F) were predicted with a high to very high decrease in their distribution for the extensive farming scenario and the remaining two land-use scenarios, respectively.

4. Discussion

4.1. Characteristics of Our Case Study

In this study, we explored the combined effects of climate change and land-use change due to the abandonment of traditional agricultural activities. Ecosystems with particularly high levels of species diversity and endemism [49], complex historical legacies of human management, as well as biogeographical and ecological identity [7], such as those occurring in the wider Mediterranean biogeographic region, provide great opportunities for exploring such an effect. Our study area, namely, a sub-mountainous region in northern Pindus, is highly representative of such ecosystems, mainly concerning the land abandonment trends, despite its relatively small geographical extent. The spatial scale of studies exploring the combined effects of climate and land-use changes is occasionally considered a limitation, since these two biodiversity threats operate on different scales, whereas land-use changes are difficult to quantify on wide or global scales [4,17,50]. Our small-scale study area included only part of the total environmental variation, and it mainly allowed inferences about local-scale extinctions, since it did not include the full distribution of species. Nevertheless, we consider that such limitations are far outweighed by the following advantages of this study area. Firstly, this small scale allowed more precise quantification of the ecological and socioeconomic factors affecting nature and its management. Secondly, the local scale of the study area allowed the application of SDMs that would allow comparable inferences of impacts of climate change and land-use change. Although SDMs constitute the most common approach for studying the impacts of climate change on biodiversity [3], this is also not the case for the impacts of land-use change, which are usually studied through the space-for-time substitution approach [4], potentially obscuring the investigation of their combined impacts [15,51]. Finally, local-scale studies, which allow inferences on local-scale biodiversity, ecosystem functions, and services, have been found to be particularly important for management and conservation purposes [52,53].
Newbold et al. [53] argued that, in changing environments, local biodiversity determines many ecosystems’ functions and services, and management at that scale may have a strong impact on biodiversity at higher scales. Furthermore, conservation targets should be set also at the local scale (i.e., protected area level), and conservation measures as well as the monitoring of their effectiveness should be implemented starting from this scale towards an effective conservation policy [52], whereas according to Sodhi et al. [54], “habitat and species preservation epitomize microscale conservation projects”.

4.2. Impact of Climate and Land-Use Change on Species Distribution

During the last decade, the debate concerning whether climate or land-use change exerts a higher impact on biodiversity and how such impact may be differentiated across spatial or temporal scales and among different biogeographical regions or categories of organisms has been investigated in an increasing number of studies. On the one hand, recent reviews of such studies show that most of them resulted in a similar effect size of climate and land-use change [17,55]. On the other hand, in studies on a global scale, in which the combined effect of land-use and climate changes was predicted for the future, results showed that climate change effects surpass by far those of land-use, especially after the year 2050 or 2070 [50,56].
Here, we found that a significant fraction of taxa (13–16%) will have a more than 80% reduction in their current distribution under some climate and land-use change scenarios, with approximately 9% of taxa having an even more than 90% reduction. According to the literature, such percentages of distribution reduction are used as thresholds to consider a species “committed to extinction” [2,3]. In our case, such reductions cannot be interpreted as extinctions, since our study area does not correspond with overall species distributions, and because newly occurring habitats (e.g., because of natural disturbances) may have been recolonized from neighboring areas through direct seed dispersal [57]. Limitations related to seed dispersal are unlikely to influence our results, given the high spatial resolution of the study area. Nevertheless, taking into account the substantial loss of species’ habitats and that the herein recorded land-use change trends are similar across a much greater geographical extent, it would be reasonable to consider that such species will be locally represented by hardly viable sub-populations (i.e., locally extinct).
Our results indicated that land-use change had a more pronounced impact on species distribution than climate change in the investigated spatial and time scale. The impact of land-use change within our study area has already been highlighted at the landscape level over the past years, and significant changes have also been predicted for future conditions [22,31]. Specifically, a reduction in land cover diversity and an increase in habitat fragmentation were observed for the period 1945 to 2015, with these trends projected to continue under the USU and INT scenarios. The accompanying significant forest expansion, along with a reduction in farmland and open or semi-open habitats, is predicted and likely explains the high or moderate distribution decrease observed for a large number of species under these scenarios. In contrast, under the EXT scenario, open and semi-open habitats were predicted to expand and cover almost half of the landscape [31], justifying the percentage of species with significantly increased distributions under future conditions. On the other hand, the intensity of climate change, as imposed by the different climate change scenarios, was also found to affect changes in species distributions. This was indicated mainly by the high increase in the number of taxa that were predicted to lose more than 80% of their current distribution extent, under the two more extreme climate change scenarios, independently of the consideration of the land-use change scenario. Rathore et al. [58] also found a declining extent of species bioclimatic envelopes in the western Himalaya because of climate change, while Newbold et al. [53] noted that climate change may exacerbate losses caused by land-use changes. Our results suggest that both climate and land-use changes drastically affect the distribution of some species, with the latter also affecting a much higher number of species at a moderate level. Here, we assess the relative impact of climate and land-use change by comparing the differences in the impact of land-use change scenarios within the same climate scenario and vice versa. However, as the interactions between these two threats may be very complex [59], further investigation is needed to quantify their unique and joint impact, which is planned to be conducted on the basis of the data of this study.
Recent research has highlighted that the impact of each threat alone, as well as their combined effect, varies significantly according to location, biome, latitude, altitude, taxonomic group, land-use history, climate type, and spatial and temporal scales [2,4,16,17,58,60]. For instance, Newbold et al. [16] found that land-use changes will have a more severe impact on tropical areas than temperate ones (Mediterranean) and on areas with only a recent history of human management disturbances, but climate change will have a higher impact on areas with low climatic seasonality and on species in areas near their warmest distribution limit. Santos et al. [17] reported that climate and land-use changes were found to affect different biodiversity attributes and to operate at different spatial and temporal scales, with climate change being more important in longer-term and broader-scale cases. A lot of studies report different relative effects of climate and land-use changes among categories of taxa, as well as among taxa [50,61,62]. According to Bellard et al. [60], the relative effect of different biodiversity threats depends on the local context (i.e., location, habitats, taxa) and methods and metrics used for its assessment, and thus the ranking of biodiversity threats is not always applicable for conservation prioritization purposes. Therefore, we can attribute the predominance of land-use effects in our results to the following reasons: (a) the small scale of the study area, which renders the effect of land-use changes as more important due to its higher variability compared to climate change [17]; (b) the temperate climate of the study area (Csa type according to Köppen–Geiger climatic classification) in which land-use change impacts usually prevail [16,60]; (c) the long history of traditional land-use of the study area, which has favored the adaptation of species in such use [16]; (d) the relatively short altitudinal gradient of the study area, which does not include high-altitude alpine areas, where climate change has a stronger influence on species appearing in the borders of their geographical and/or ecological space [58,60]; and (e) the highly significant changes in the landscape from 1945 to the predicted ones in 2055, according to the USU and INT scenarios [16,31], which render some land cover types as very rare, thus leading to a great habitat loss, especially for the species of open habitats. The last reason is expected to be the most influential, and it also explains the great difference in the results between the EXT and the other two scenarios (see also below).

4.3. Ecological Characteristics of Species Distribution Changes

Ecological indicator values of species are semi-quantitative estimates of environmental conditions and comprise a very useful tool for monitoring purposes [63,64,65]. The pattern of decrease in thermophilous species and the increase in the more soil moisture-demanding species for all climate change scenarios under the USU and INT scenarios indicated an opposite response of species in relation to what would be expected because of temperature rise and precipitation reduction, predicted especially for the SSP370 and SSP585 scenarios [37]. Specifically, it showed an antagonistic effect of land-use changes because of abandonment in relation to that of climate change, caused by the progressive establishment of shrub and tree species due to the reduction or cessation of disturbances in the landscape. Therefore, for as long as secondary succession proceeds, the micro-environment (microclimate) within the vegetation stands becomes more shaded and thus cooler and moister, as it is also supported by the simultaneous significant increase in shade-tolerant species and the corresponding decrease in light-loving species for the USU and INT scenarios.
Microclimate can differ greatly from climatic averages [66], since vegetation canopy and, especially, the canopy of tree species affect photosynthetically active radiation, soil water content, air humidity, and soil and air temperature in the understory layer [67]. It has been found that forest habitats function as a “thermal insulator” against global warming due to anthropogenic climate change, as well as extreme climate events [68,69]. Our results indicate indirectly, through the species indicator values, that land-use changes because of land abandonment (increase in forests and decrease in open habitats) under the USU and INT scenarios will have a higher impact than climate warming, at least until 2055. On the other hand, this is not the case for the EXT scenario, for which almost opposite trends were found compared to the two other land-use scenarios, indicating more of a synergetic interaction between land-use and climate change, rather than an antagonistic one. This is in agreement with the findings of Zellweger et al. [69], who showed that the reduction in forest cover may lead to the exacerbation of climate change’s negative impacts on species distribution. Perring et al. [63] and Saatkamp et al. [64] also found an antagonistic effect of the microclimate against the “thermophilization” and “xerophytization” of plant communities in vegetation stands with higher tree canopy, which was based on species indicator values.
Ecological strategies showed a corresponding pattern with that of indicator species values. Ecological strategies reflect the functional response of plant species and their communities to different intensities of stress and disturbance [19,20], and thus constitute an appropriate tool for assessing the impacts of environmental changes [70,71,72]. Here, the competitive dimension of plants’ life strategy was found to increase for the USU and INT scenarios in the future, while ruderality decreased. This may be attributed, as in the case of ecological indicator values, to the increase in forest cover and the decrease in open habitats in these scenarios. Mastrogianni et al. [28] indeed found that the highest level of functional differentiation of vegetation communities in the study area was observed between forest and grassland communities, with forests hosting more competitive taxa, and grasslands more ruderal ones. For the EXT scenario, the opposite trends were found, and with a steeper slope of the trend. This indicates that the historical land-use changes already made until 2015 have already changed the landscape significantly, and the future (predicted) changes will almost complete these changes in terms of species ecological strategies by further minimizing non-competitive and ruderal species. Dalle Fratte et al. [71] in a regional scale study (northern Italy), also found the predominant impact of land-use change on plant traits, attributed to differential effects of land-use and climate change, with the former causing more abrupt differentiation of traits and the latter acting more gradually on the results of land-use changes. Their results, especially for the sub-mountainous part of their study gradient, agree with ours, indicating that future land-use circumstances will favor more competitive and/or shade-tolerant species. Furthermore, Chai et al. [73] found the predominance of ruderal species at the pioneer stage of vegetation succession, their replacement by a higher variance of intermediate plant ecological strategies in the intermediate stages of succession, and finally the dominance of competitive or competitive/stress-tolerant species in the last successional stages represented by forests. From a conservation management perspective, these findings underline the importance of retaining at least part of the historical disturbance regimes, such as low-intensity grazing or mowing, to preserve the full spectrum of ecological strategies across the landscape. Such interventions can maintain structurally diverse habitats and prevent the competitive exclusion of stress-tolerant or ruderal species, which often support unique ecological functions and contribute disproportionately to regional plant diversity.
The results concerning species distribution changes in relation to their preferential occurrence in plant communities align with the overall results. Land-use change was again found to have a higher effect on the observed patterns compared to climate change. These patterns were found to be common among the plant communities of each major vegetation type (open habitats or forests) but different between them, highlighting the decisive role of the distribution pattern of the two vegetation types. The comparatively small percentage increase in the species distribution of forest communities in relation to the at least double decrease in distribution of species of open habitats for the USU and INT scenarios probably is due to the land cover types trends described by Kiziridis et al. [31] for the study area, and indicates that the changes in the distribution of species and their communities because of land abandonment are reaching towards a saturation stage. Similarly, Chontos and Tsiripidis [21] found that land abandonment and the corresponding landscape changes in a region of southern Pindos (central Greece) have also reached their semi-final stage in the last decade.
Additionally, these observed patterns of distribution changes enable further inferences with important implications for conservation. This is particularly the case for one community of open habitats and one forest habitat, namely, the Phlomis fruticosa community and the riparian forests, respectively. As outlined below, our study highlights two examples of high conservation value communities, from which one will be critically endangered in the future due to land abandonment, while the second has been naturally restored and will continue to be restored in the future for the same reason.
On the one hand, riparian forests are expected to be positively affected by land abandonment. Specifically, the high suitability of the sites of these forests for the expansion of farmlands under the EXT scenario is possibly the reason for the very high decrease in their species distribution under this scenario. On the other hand, the Phlomis fruticosa community is expected to be negatively affected by land abandonment. It constitutes the most thermophytic and xeophytic community in the study area but occurs at higher altitudes compared to the other communities of open habitats according to the results of Mastrogianni et al. [28]. It is also the richest community in terms of taxonomic and functional diversity, albeit with a small difference compared to the other semi-natural grassland community of an advanced but stable successional stage, the Chrysopogon gryllus community. These two communities of semi-natural grasslands comprise typical examples of high conservation value vegetation types that have been formed by traditional human management practices (especially grazing) and are highly threatened by land abandonment [11,13]. The small distribution reduction in the species of the Phlomis fruticosa community, even for the EXT scenario, which favors significantly the cover of open habitats, as well as its very high decrease for the remaining land-use change scenarios, contradicts the trends found for the other open habitat communities. This peculiar trend of the distribution of the species of Phlomis fruticosa community may be attributed to the fact that this community has been formed mainly by grazing, which, however, shows decreasing trends with time in the study area [22] and is predicted to further reduce until 2055 [31]. Furthermore, its occurrence in higher altitudes and steeper slopes than other open habitat communities renders this community more prone to replacement by forests [31], as these conditions favor their establishment in the study area [31]. The same ecological characteristics of the Phlomis fruticosa community make its area less suitable for farmland re-establishment under the EXT scenario, which explains the fact that its differential species area does not increase under this scenario.
Therefore, the results of species distribution changes in relation to their preferential occurrence in plant communities highlighted special case habitats and species that may be more threatened in the future under certain scenarios of land-use policy and/or the expected environmental changes.

4.4. Concluding Remarks and Conservation Implications

Mainly, we found that (a) land-use changes exert a much higher impact on species distribution than climate change, and (b) both changes may drive a significant part of plant taxa (around 10 to 15%) to be threatened with very high (>80%) distribution reduction. These findings were interpreted on the basis of the spatial (local) and the temporal (year 2055) scale of the study, its temperate macro-climate, the altitudinal gradient of the study area, its long history of traditional land-use by humans and significant changes in land cover types during the past 77 years (1945–2015), and saturation of the landscape changes according the past trends in the next 40 years (2015–2055).
Such a pronounced impact of land-use change over climate change on plant species distributions echoes patterns reported also in other Mediterranean and European sub-mountainous landscapes [16,74]. However, we acknowledge limitations in direct generalization. Local factors such as landscape configuration, microclimatic heterogeneity, and the intensity and legacy of traditional land uses can substantially modulate species responses. Therefore, extrapolation of the herein identified trends should consider site-specific ecological and socioeconomic contexts. Future studies at regional and continental scales would be essential to confirm the consistency of these patterns across gradients of climate, land-use, and biodiversity.
Land abandonment may have already progressed significantly in many places of the world, as in our study area [22,31], and in such areas, a small time window remains to conserve species and habitats before they are represented by non-viable/persistent populations/patches at the landscape level. Retaining traditional anthropogenic disturbances, such as grazing or re-introducing naturally caused disturbances, is crucial for the conservation of biodiversity at the landscape level [75,76].
Land abandonment leads to an increase in the extent of high-canopy vegetation, which may buffer the climate change-induced temperature rise and precipitation reduction, through their influence on the microclimate [68], but such a positive impact mainly concerns species of forest habitats. Considering the high taxonomic diversity of the grasslands of the study area and their very high taxonomic turnover with forests [28], it is shown that a very high proportion of the total species diversity (γ) at the landscape level will not be favored by the aforementioned buffering effect of forests. Therefore, it is becoming prevalent that conservation plans at the landscape level, which are detailed and based on appropriate data, are needed to counteract the negative impacts of land abandonment. Such plans will also take advantage of the natural restoration of habitats they enhance, to minimize losses and maximize gains in terms of biodiversity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14071438/s1, Table S1: Minimum number of indicator taxa per plant community within the general study area; Table S2: Percentage change of distribution extent per species, across the three climate (SSP126, SSP370 and SSP585) and three land-use scenarios compared to their current (2015) distribution; Table S3: Coefficients and statistical significance of the regression analyses between the percentage of change in the distribution extent of the modelled plant taxa and their indicator values, as well as ecological strategies.

Author Contributions

Conceptualization, S.T., D.A.K., and I.T.; methodology, S.T.; formal analysis, S.T., A.M., and D.A.K.; investigation, A.M., F.X., M.P., and I.T.; writing—original draft preparation, S.T. and I.T.; writing—review and editing, S.T., A.M., D.A.K., F.X., M.P., and I.T.; visualization, A.M.; funding acquisition, I.T. All authors have read and agreed to the published version of the manuscript.

Funding

The present study was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “1st Call for H.F.R.I. Research Projects to support Faculty Members & Researchers and the Procurement of High-cost Research Equipment Grant” [Project Number: 2333].

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. IPBES. Global Assessment Report on Biodiversity and Ecosystem Services | IPBES Secretariat. Available online: https://www.ipbes.net/node/35274 (accessed on 27 January 2024).
  2. Urban, M.C. Accelerating extinction risk from climate change. Science 2015, 348, 571–573. [Google Scholar] [CrossRef] [PubMed]
  3. Wiens, J.J.; Zelinka, J. How many species will earth lose to climate change? Glob. Change Biol. 2024, 30, e17125. [Google Scholar] [CrossRef]
  4. Davison, C.W.; Rahbek, C.; Morueta-Holme, N. Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature. Glob. Change Biol. 2021, 27, 5414–5429. [Google Scholar] [CrossRef]
  5. Ellis, E.C.; Klein Goldewijk, K.; Siebert, S.; Lightman, D.; Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 2010, 19, 589–606. [Google Scholar] [CrossRef]
  6. Watson, J.E.M.; Shanahan, D.F.; Di Marco, M.; Allan, J.; Laurance, W.F.; Sanderson, E.W.; Mackey, B.; Venter, O. Catastrophic declines in wilderness areas undermine global environment targets. Curr. Biol. 2016, 26, 2929–2934. [Google Scholar] [CrossRef]
  7. Plieninger, T.; Hui, C.; Gaertner, M.; Huntsinger, L. The impact of land abandonment on species richness and abundance in the mediterranean basin: A meta-analysis. PLoS ONE 2014, 9, e98355. [Google Scholar] [CrossRef]
  8. Plieninger, T.; Draux, H.; Fagerholm, N.; Bieling, C.; Bürgi, M.; Kizos, T.; Kuemmerle, T.; Primdahl, J.; Verburg, P.H. The driving forces of landscape change in europe: A systematic review of the evidence. Land Use Policy 2016, 57, 204–214. [Google Scholar] [CrossRef]
  9. Perpiña Castillo, C.; Coll Aliaga, E.; Lavalle, C.; Martínez Llario, J.C. An assessment and spatial modelling of agricultural land abandonment in Spain (2015–2030). Sustainability 2020, 12, 560. [Google Scholar] [CrossRef]
  10. Pereira, H.M.; Navarro, L.M. (Eds.) Rewilding European Landscapes; Springer International Publishing: Cham, Switzerland, 2015; ISBN 978-3-319-12038-6. [Google Scholar]
  11. Janssen, J. European Red List of Habitats: Part 2. Terrestrial and Freshwater Habitats; Publications Office of the European Union: Luxembourg, 2016. [Google Scholar]
  12. Normile, D. Nature from nurture. Science 2016, 351, 908–910. [Google Scholar] [CrossRef]
  13. Prangel, E.; Kasari-Toussaint, L.; Neuenkamp, L.; Noreika, N.; Karise, R.; Marja, R.; Ingerpuu, N.; Kupper, T.; Keerberg, L.; Oja, E.; et al. Afforestation and abandonment of semi-natural grasslands lead to biodiversity loss and a decline in ecosystem services and functions. J. Appl. Ecol. 2023, 60, 825–836. [Google Scholar] [CrossRef]
  14. Prévosto, B.; Kuiters, L.; Bernhardt-Römermann, M.; Dölle, M.; Schmidt, W.; Hoffmann, M.; Van Uytvanck, J.; Bohner, A.; Kreiner, D.; Stadler, J.; et al. Impacts of land abandonment on vegetation: Successional pathways in European habitats. Folia Geobot. 2011, 46, 303–325. [Google Scholar] [CrossRef]
  15. Williams, J.J.; Newbold, T. Local climatic changes affect biodiversity responses to land use: A review. Divers. Distrib. 2020, 26, 76–92. [Google Scholar] [CrossRef]
  16. Newbold, T.; Oppenheimer, P.; Etard, A.; Williams, J.J. Tropical and mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 2020, 4, 1630–1638. [Google Scholar] [CrossRef]
  17. Santos, M.J.; Smith, A.B.; Dekker, S.C.; Eppinga, M.B.; Leitão, P.J.; Moreno-Mateos, D.; Morueta-Holme, N.; Ruggeri, M. The role of land use and land cover change in climate change vulnerability assessments of biodiversity: A systematic review. Landsc. Ecol. 2021, 36, 3367–3382. [Google Scholar] [CrossRef]
  18. Zolotova, E.; Ivanova, N.; Ivanova, S. Global overview of modern research based on Ellenberg indicator values. Diversity 2023, 15, 14. [Google Scholar] [CrossRef]
  19. Grime, J. Plant Strategies, Vegetation Processes, and Ecosystem Properties, 2nd ed.; John Wiley & Sons: New York, NY, USA, 2001. [Google Scholar]
  20. Grime, J. Vegetation classification by reference to strategies. Nature 1974, 250, 26–31. [Google Scholar] [CrossRef]
  21. Chontos, K.; Tsiripidis, I. Open habitats under threat in mountainous, mediterranean landscapes: Land abandonment consequences in the vegetation cover of the thessalian part of Mt Agrafa (central Greece). Land 2023, 12, 846. [Google Scholar] [CrossRef]
  22. Kiziridis, D.A.; Mastrogianni, A.; Pleniou, M.; Karadimou, E.; Tsiftsis, S.; Xystrakis, F.; Tsiripidis, I. Acceleration and relocation of abandonment in a mediterranean mountainous landscape: Drivers, consequences, and management implications. Land 2022, 11, 406. [Google Scholar] [CrossRef]
  23. Sidiropoulou, A.; Chouvardas, D.; Mantzanas, K.; Stefanidis, S.; Karatassiou, M. Impact of transhumant livestock grazing abandonment on pseudo-alpine grasslands in Greece in the context of climatic change. Land 2022, 11, 2126. [Google Scholar] [CrossRef]
  24. Xystrakis, F.; Psarras, T.; Koutsias, N. A process-based land use/land cover change assessment on a mountainous area of Greece during 1945–2009: Signs of socio-economic drivers. Sci. Total Environ. 2017, 587–588, 360–370. [Google Scholar] [CrossRef]
  25. Nakos, G. Classification, Mapping and Evaluation of Soils; Institute of Mediterranean Forest Ecosystems and Forest Products Technology, Ministry of Agriculture: Athens, Greece, 1991. (In Greek) [Google Scholar]
  26. Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the Koppen-geiger climate classification. Hydrol. Earth Syst. Sci. 2007, 11, 1633–1644. [Google Scholar]
  27. Bohn, U.; Neuhäusl, R.; Gollub, G.; Hettwer, C.; Neuhäuslová, Z.; Raus, T.; Schlüter, H.; Weber, H. (2000/2003): Karte der natürlichen vegetation Europas/Map of the Natural Vegetation of Europe. Maßstab/Scale 1:2 500 000; Münster (Landwirtschaftsverlag), Bundesamt für Naturschutz: Bonn, Germany, 2004. [Google Scholar]
  28. Mastrogianni, A.; Kiziridis, D.A.; Karadimou, E.; Pleniou, M.; Xystrakis, F.; Tsiftsis, S.; Tsiripidis, I. Community-level differentiation of grime’s csr strategies along a post-abandonment secondary successional gradient. Flora 2023, 308, 152399. [Google Scholar] [CrossRef]
  29. Braun-Blanquet, J. Pflanzensoziologie, 3rd ed.; Springer: Berlin, Germany; New York, NY, USA, 1964; ISBN 086054928. [Google Scholar]
  30. Wilmanns, O. Ökologische Pflanzensoziologie, 4th ed.; Aufl. Quelle & Meyer: Heidelberg, Germany, 1989. [Google Scholar]
  31. Kiziridis, D.A.; Mastrogianni, A.; Pleniou, M.; Tsiftsis, S.; Xystrakis, F.; Tsiripidis, I. Simulating future land use and cover of a mediterranean mountainous area: The effect of socioeconomic demands and climatic changes. Land 2023, 12, 253. [Google Scholar] [CrossRef]
  32. Mastrogianni, A.; Kiziridis, D.A.; Eleftheriadou, A.; Paradisiotis, M.; Pleniou, M.; Xystrakis, F.; Tsiftsis, S.; Tsiripidis, I. Contribution to the functional flora of Greece: A case study in the northwestern Pindus mountains. Willdenowia 2024, 53, 269–295. [Google Scholar] [CrossRef]
  33. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New Extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  34. Phillips, S.J.; Anderson, R.P.; Dudík, M.; Schapire, R.E.; Blair, M.E. Opening the black box: An open-source release of Maxent. Ecography 2017, 40, 887–893. [Google Scholar] [CrossRef]
  35. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 2007, 34, 102–117. [Google Scholar] [CrossRef]
  36. Elith, J.; Leathwick, J. The contribution of species distribution modelling to conservation prioritization. In Spatial Conservation Prioritization: Quantitative Methods and Computational Tools; Moilanen, A., Wilson, K.A., Possingham, H.P., Eds.; Oxford University Press: Oxford, UK, 2009; ISBN 978-0-19-954776-0. [Google Scholar]
  37. O’Neill, B.C.; Tebaldi, C.; van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, R.; Kriegler, E.; Lamarque, J.-F.; Lowe, J.; et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2016, 9, 3461–3482. [Google Scholar] [CrossRef]
  38. Poggio, L.; de Sousa, L.M.; Batjes, N.H.; Heuvelink, G.B.M.; Kempen, B.; Ribeiro, E.; Rossiter, D. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil 2021, 7, 217–240. [Google Scholar] [CrossRef]
  39. European Environment Agency. Copernicus Land Monitoring Service -EU-DEM (European Digital Elevation Model). Version 1.1. 2016. Available online: http://Land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1/ (accessed on 10 January 2022).
  40. Hijmans, R.J.; Van Etten, J.; Cheng, J.; Mattiuzzi, M.; Sumner, M.; Greenberg, J.A.; Lamigueiro, O.P.; Bevan, A.; Racine, E.B.; Shortridge, A. Package ‘Raster’. 2015. Available online: https://cran.r-project.org/web/packages/raster/raster.pdf (accessed on 7 July 2025).
  41. Liu, C.; Newell, G.; White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 2016, 6, 337–348. [Google Scholar] [CrossRef]
  42. Hijmans, R.J.; Phillips, S.; Leathwick, J.; Elith, J. Dismo: Species distribution modeling. 2017. Available online: https://cran.r-project.org/web/packages/dismo/dismo.pdf (accessed on 7 July 2025).
  43. Ellenberg, H.; Weber, H.E.; Düll, R.; Wirth, V.; Werner, W.; Paulissen, D. Zeigerwerte von Pflanzen in Mitteleuropa, 2nd ed.; Scripta Geobotanica; Verlag Wrich Goltze: Gottingen, Germany, 1992; Volume 18, ISBN 978-3-88452-518-0. [Google Scholar]
  44. Grime, J. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 1977, 111, 1169–1194. [Google Scholar] [CrossRef]
  45. Dengler, J.; Jansen, F.; Chusova, O.; Hüllbusch, E.; Nobis, M.P.; Meerbeek, K.V.; Axmanová, I.; Bruun, H.H.; Chytrý, M.; Guarino, R.; et al. Ecological Indicator Values for Europe (EIVE) 1.0. Veg. Classif. Surv. 2023, 4, 7–29. [Google Scholar] [CrossRef]
  46. Pierce, S.; Negreiros, D.; Cerabolini, B.E.L.; Kattge, J.; Díaz, S.; Kleyer, M.; Shipley, B.; Wright, S.J.; Soudzilovskaia, N.A.; Onipchenko, V.G.; et al. A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct. Ecol. 2017, 31, 444–457. [Google Scholar] [CrossRef]
  47. De Cáceres, M.; Legendre, P.; Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 2010, 119, 1674–1684. [Google Scholar] [CrossRef]
  48. De Cáceres, M.; Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 2009, 90, 3566–3574. [Google Scholar] [CrossRef]
  49. Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; Da Fonseca, G.A.B.; Kent, J. Biodiversity hotspots for conservation priorities. Nature 2000, 403, 853–858. [Google Scholar] [CrossRef]
  50. Newbold, T. Future Effects of Climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 2018, 285, 20180792. [Google Scholar] [CrossRef] [PubMed]
  51. Oliver, T.H.; Morecroft, M.D. Interactions between climate change and land use change on biodiversity: Attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 2014, 5, 317–335. [Google Scholar] [CrossRef]
  52. Margules, C.R.; Pressey, R.L. Systematic Conservation Planning. Nature 2000, 405, 243–253. [Google Scholar] [CrossRef]
  53. Newbold, T.; Hudson, L.N.; Hill, S.L.L.; Contu, S.; Lysenko, I.; Senior, R.A.; Börger, L.; Bennett, D.J.; Choimes, A.; Collen, B.; et al. Global effects of land use on local terrestrial biodiversity. Nature 2015, 520, 45–50. [Google Scholar] [CrossRef]
  54. Sodhi, N.S.; Butler, R.; Laurance, W.F.; Gibson, L. Conservation successes at micro-, meso- and macroscales. Trends Ecol. Evol. 2011, 26, 585–594. [Google Scholar] [CrossRef] [PubMed]
  55. Selwood, K.E.; McGeoch, M.A.; Mac Nally, R. The effects of climate change and land-use change on demographic rates and population viability. Biol. Rev. 2015, 90, 837–853. [Google Scholar] [CrossRef] [PubMed]
  56. Di Marco, M.; Harwood, T.D.; Hoskins, A.J.; Ware, C.; Hill, S.L.L.; Ferrier, S. Projecting impacts of global climate and land-use scenarios on plant biodiversity using compositional-turnover modelling. Glob. Change Biol. 2019, 25, 2763–2778. [Google Scholar] [CrossRef]
  57. Piqueray, J.; Cristofoli, S.; Bisteau, E.; Palm, R.; Mahy, G. Testing coexistence of extinction debt and colonization credit in fragmented calcareous grasslands with complex historical dynamics. Landsc. Ecol. 2011, 26, 823–836. [Google Scholar] [CrossRef]
  58. Rathore, P.; Roy, A.; Karnatak, H. Predicting the future of species assemblages under climate and land use land cover changes in Himalaya: A geospatial modelling approach. Clim. Change Ecol. 2022, 3, 100048. [Google Scholar] [CrossRef]
  59. Schulte To Bühne, H.; Tobias, J.A.; Durant, S.M.; Pettorelli, N. Improving predictions of climate change–land use change interactions. Trends Ecol. Evol. 2021, 36, 29–38. [Google Scholar] [CrossRef] [PubMed]
  60. Bellard, C.; Marino, C.; Courchamp, F. Ranking threats to biodiversity and why it doesn’t matter. Nat. Commun. 2022, 13, 2616. [Google Scholar] [CrossRef]
  61. Tuan, L.Q.; Thong, V.D.; Son, N.T.; Tu, V.T.; Tuan, T.A.; Luong, N.T.; Vy, N.T.; Thanh, H.T.; Huang, J.C.-C.; Csorba, G.; et al. Potential individual and interactive effects of climate and land-cover changes on bats and implications for conservation planning: A case study in Vietnam. Biodivers. Conserv. 2023, 32, 4481–4508. [Google Scholar] [CrossRef]
  62. Ah Koo, K.; Uk Park, S. The effect of interplays among climate change, land-use change, and dispersal capacity on plant redistribution. Ecol. Indic. 2022, 142, 109192. [Google Scholar] [CrossRef]
  63. Perring, M.P.; Bernhardt-Römermann, M.; Baeten, L.; Midolo, G.; Blondeel, H.; Depauw, L.; Landuyt, D.; Maes, S.L.; De Lombaerde, E.; Carón, M.M.; et al. Global environmental change effects on plant community composition trajectories depend upon management legacies. Glob. Change Biol. 2018, 24, 1722–1740. [Google Scholar] [CrossRef]
  64. Saatkamp, A.; Argagnon, O.; Noble, V.; Finocchiaro, M.; Meineri, E. Climate change impacts on mediterranean vegetation are amplified at low altitudes. Glob. Ecol. Biogeogr. 2023, 32, 1113–1126. [Google Scholar] [CrossRef]
  65. Scherrer, D.; Guisan, A. Ecological indicator values reveal missing predictors of species distributions. Sci. Rep. 2019, 9, 3061. [Google Scholar] [CrossRef]
  66. Zellweger, F.; De Frenne, P.; Lenoir, J.; Rocchini, D.; Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 2019, 34, 327–341. [Google Scholar] [CrossRef]
  67. Sanna, F.; Campesi, G.; Deligios, P.; Ledda, L.; Piluzza, G.; Sulas, L.; Re, G.A. Combined effects of microenvironment and land use on C fluxes in a mediterranean agro-silvopastoral system. Eur. J. Agron. 2021, 130, 126348. [Google Scholar] [CrossRef]
  68. De Frenne, P.; Zellweger, F.; Rodríguez-Sánchez, F.; Scheffers, B.R.; Hylander, K.; Luoto, M.; Vellend, M.; Verheyen, K.; Lenoir, J. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 2019, 3, 744–749. [Google Scholar] [CrossRef]
  69. Zellweger, F.; De Frenne, P.; Lenoir, J.; Vangansbeke, P.; Verheyen, K.; Bernhardt-Römermann, M.; Baeten, L.; Hédl, R.; Berki, I.; Brunet, J.; et al. Forest microclimate dynamics drive plant responses to warming. Science 2020, 368, 772–775. [Google Scholar] [CrossRef] [PubMed]
  70. Bricca, A.; Tardella, F.M.; Ferrara, A.; Panichella, T.; Catorci, A. Exploring assembly trajectories of abandoned grasslands in response to 10 years of mowing in sub-mediterranean context. Land 2021, 10, 1158. [Google Scholar] [CrossRef]
  71. Dalle Fratte, M.; Brusa, G.; Pierce, S.; Zanzottera, M.; Cerabolini, B.E.L. Plant trait variation along environmental indicators to infer global change impacts. Flora 2019, 254, 113–121. [Google Scholar] [CrossRef]
  72. Zanzottera, M.; Dalle Fratte, M.; Caccianiga, M.; Pierce, S.; Cerabolini, B.E.L. Community-level variation in plant functional traits and ecological strategies shapes habitat structure along succession gradients in alpine environment. Community Ecol. 2020, 21, 55–65. [Google Scholar] [CrossRef]
  73. Chai, Y.; Yue, M.; Wang, M.; Xu, J.; Liu, X.; Zhang, R.; Wan, P. Plant functional traits suggest a change in novel ecological strategies for dominant species in the stages of forest succession. Oecologia 2016, 180, 771–783. [Google Scholar] [CrossRef]
  74. Sirami, C.; Caplat, P.; Popy, S.; Clamens, A.; Arlettaz, R.; Jiguet, F.; Brotons, L.; Martin, J. Impacts of global change on species distributions: Obstacles and solutions to integrate climate and land use. Glob. Ecol. Biogeogr. 2017, 26, 385–394. [Google Scholar] [CrossRef]
  75. Navarro, L.; Proença, V.; Kaplan, J.; Pereira, H. Maintaining disturbance-dependent habitats. In Rewilding European Landscapes; Springer: Berlin/Heidelberg, Germany, 2015; pp. 143–167. ISBN 978-3-319-12038-6. [Google Scholar]
  76. Viljur, M.-L.; Abella, S.R.; Adámek, M.; Alencar, J.B.R.; Barber, N.A.; Beudert, B.; Burkle, L.A.; Cagnolo, L.; Campos, B.R.; Chao, A.; et al. The effect of natural disturbances on forest biodiversity: An ecological synthesis. Biol. Rev. 2022, 97, 1930–1947. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Map of the study area, depicting the five circular collection sites and their location in Greece depicted as a black-filled rectangle (top right). Numbers (1–5) outside the circular collection sites refer to the code names of each collection site.
Figure 1. Map of the study area, depicting the five circular collection sites and their location in Greece depicted as a black-filled rectangle (top right). Numbers (1–5) outside the circular collection sites refer to the code names of each collection site.
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Figure 2. Number of species per category of percentage changes in their distribution extents in comparison to present conditions and the nine scenarios employed. Τhe terms “minus” and “plus” indicate reduction and increase in species distribution (e.g., minus 80–100 means reduction in a species from 80 to 100% in terms of distribution extent). Percentages are presented for the combinations of the three climatic scenarios, namely, SSP126, SSP370, and SSP585, as well as the three socioeconomic scenarios, namely, extensive farming (EXT), intensive farming (INT), and business-as-usual (USU).
Figure 2. Number of species per category of percentage changes in their distribution extents in comparison to present conditions and the nine scenarios employed. Τhe terms “minus” and “plus” indicate reduction and increase in species distribution (e.g., minus 80–100 means reduction in a species from 80 to 100% in terms of distribution extent). Percentages are presented for the combinations of the three climatic scenarios, namely, SSP126, SSP370, and SSP585, as well as the three socioeconomic scenarios, namely, extensive farming (EXT), intensive farming (INT), and business-as-usual (USU).
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Figure 3. Associations between the percentage of change in the distribution extent of the modelled plant taxa and their indicator value for (a) light, (b) temperature, and (c) soil moisture, as well as their ecological strategies (d) competition, and (e) ruderality among the different scenarios and the current conditions (2015). For the abbreviations of climate and land-use scenarios, see Figure 1. Violin plots at the bottom of each plot illustrate the distribution of species values for each respective indicator.
Figure 3. Associations between the percentage of change in the distribution extent of the modelled plant taxa and their indicator value for (a) light, (b) temperature, and (c) soil moisture, as well as their ecological strategies (d) competition, and (e) ruderality among the different scenarios and the current conditions (2015). For the abbreviations of climate and land-use scenarios, see Figure 1. Violin plots at the bottom of each plot illustrate the distribution of species values for each respective indicator.
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Figure 4. Percentage of change in the distribution of the species of the 11 communities, distinguished in the study area, for between the different scenarios (socioeconomic and future climate) and that of 2015 in relation to the current conditions. The different communities are indicated with capital letters ((A): Chrysopogon gryllus community; (B): Phlomis fruticosa community; (C): Hordeum bulbosum community; (D): Alopecurus rendlei community; (E): Cynosurus cristatus community; (F): Pteridium aquilinum community; (G): Quercus frainetto community; (H): Carpinus orientalis community; (I): Xero-thermo-phytic oak forests; (J): Quercus cerris–Q. frainetto community; (K): Riparian forests). The terms “ext”, “int”, and “usu” presented on the X-axes of the graphs indicate the three socioeconomic land-use scenarios (extensive farming, intensive farming, and business-as-usual, respectively).
Figure 4. Percentage of change in the distribution of the species of the 11 communities, distinguished in the study area, for between the different scenarios (socioeconomic and future climate) and that of 2015 in relation to the current conditions. The different communities are indicated with capital letters ((A): Chrysopogon gryllus community; (B): Phlomis fruticosa community; (C): Hordeum bulbosum community; (D): Alopecurus rendlei community; (E): Cynosurus cristatus community; (F): Pteridium aquilinum community; (G): Quercus frainetto community; (H): Carpinus orientalis community; (I): Xero-thermo-phytic oak forests; (J): Quercus cerris–Q. frainetto community; (K): Riparian forests). The terms “ext”, “int”, and “usu” presented on the X-axes of the graphs indicate the three socioeconomic land-use scenarios (extensive farming, intensive farming, and business-as-usual, respectively).
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Table 1. Total area (in km2) of each land-use type (farmlands, grasslands, open scrubs, closed scrubs, and forests) across the ten land-use and climate change scenarios, namely, SSP126, SSP370, and SSP585, as well as the three socioeconomic scenarios, namely, extensive farming (EXT), intensive farming (INT), and business-as-usual (USU). Data are derived from Kiziridis et al. [31].
Table 1. Total area (in km2) of each land-use type (farmlands, grasslands, open scrubs, closed scrubs, and forests) across the ten land-use and climate change scenarios, namely, SSP126, SSP370, and SSP585, as well as the three socioeconomic scenarios, namely, extensive farming (EXT), intensive farming (INT), and business-as-usual (USU). Data are derived from Kiziridis et al. [31].
ScenarioFarmlandsGrasslandsOpen ScrubsClosed ScrubsForests
20154.4921.2411.2614.7586.66
SS126-EXT30.0223.5817.4921.5845.72
SS126-INT3.897.886.9310.86108.84
SS126-USU0.589.47.6311.26109.53
SS370-EXT30.0223.5817.4921.5845.72
SS370-INT3.897.886.9310.86108.84
SS370-USU0.589.47.6311.26109.53
SS585-EXT30.0223.5817.4921.5845.72
SS585-INT3.897.886.9310.86108.84
SS585-USU0.589.47.6311.26109.53
Table 2. Number of species per category of percentage change in distribution extent across the three climate (SSP126, SSP370, and SSP585) and three land-use (business-as-usual; USU, intensive farming; INT, and extensive farming; EXT) scenarios.
Table 2. Number of species per category of percentage change in distribution extent across the three climate (SSP126, SSP370, and SSP585) and three land-use (business-as-usual; USU, intensive farming; INT, and extensive farming; EXT) scenarios.
SSP126 EXTSSP126 INTSSP126 USUSSP370 EXTSSP370 INTSSP370 USUSSP58 EXTSSP585 INTSSP585 USU
minus 80–100112929335249345653
minus 60–80366249364739374237
minus 40–60499578497867457461
minus 20–40593152494255524254
minus 0–20604441483536444040
plus 0–20513645423134442428
plus 20–40423029303032303838
plus 40–60311012281213261013
plus 60–80158918452156
plus 80–10028711761154
plus >100257142022142224
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Tsiftsis, S.; Mastrogianni, A.; Kiziridis, D.A.; Xystrakis, F.; Pleniou, M.; Tsiripidis, I. Land-Use Changes Largely Determine the Trajectory of Plant Species Distributions Under Climatic Uncertainty in a Mediterranean Landscape. Land 2025, 14, 1438. https://doi.org/10.3390/land14071438

AMA Style

Tsiftsis S, Mastrogianni A, Kiziridis DA, Xystrakis F, Pleniou M, Tsiripidis I. Land-Use Changes Largely Determine the Trajectory of Plant Species Distributions Under Climatic Uncertainty in a Mediterranean Landscape. Land. 2025; 14(7):1438. https://doi.org/10.3390/land14071438

Chicago/Turabian Style

Tsiftsis, Spyros, Anna Mastrogianni, Diogenis A. Kiziridis, Fotios Xystrakis, Magdalini Pleniou, and Ioannis Tsiripidis. 2025. "Land-Use Changes Largely Determine the Trajectory of Plant Species Distributions Under Climatic Uncertainty in a Mediterranean Landscape" Land 14, no. 7: 1438. https://doi.org/10.3390/land14071438

APA Style

Tsiftsis, S., Mastrogianni, A., Kiziridis, D. A., Xystrakis, F., Pleniou, M., & Tsiripidis, I. (2025). Land-Use Changes Largely Determine the Trajectory of Plant Species Distributions Under Climatic Uncertainty in a Mediterranean Landscape. Land, 14(7), 1438. https://doi.org/10.3390/land14071438

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