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20 January 2023

Mapping Priority Areas for Connectivity of Yellow-Winged Darter (Sympetrum flaveolum, Linnaeus 1758) under Climate Change

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1
Faculty of Pharmacy, Department of Pharmacology, Complutense University of Madrid, Plaza de Ramón y Cajal, s/n, 28040 Madrid, Spain
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Department of Environment and Agroforestry, Faculty of Sciences and Arts, Catholic University of Ávila, 05005 Ávila, Spain
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Department of Landscape Architecture, Faculty of Agriculture, Aydın Adnan Menderes University, Aydın 09100, Turkey
4
Department of Landscape Architecture, Faculty of Forestry, Çankırı Karatekin University, Çankırı 18200, Turkey
Land2023, 12(2), 298;https://doi.org/10.3390/land12020298
This article belongs to the Special Issue Social, Ecological, and Landscape Resilience in the Face of Climate Change
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Abstract

The yellow-winged darter (Sympetrum flaveolum Linnaeus, 1758, Odonata), which is associated with high mountain areas, can be considered a flagship species. Due to climate change, its natural range will be negatively affected. In this study, we propose global potential distributions for this species up to the year 2100, considering four time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) and three shared socioeconomic pathways (optimistic—SSP245, middle of the road—SSP370, and worst—SSP585), by using an ecological niche model to produce two sets of distribution models (80% to 100% and 60% to 100%). It is foreseen that in the worst of the considered climate scenario (SSP585– 2100 year), the distribution of this species could be reduced by almost half, which could pose a risk for the species and provoke the shift from vulnerable to endangered. An analysis of connectivity has also been carried out for all the studied scenarios by applying the MSPA and PC indices, showing that the core habitat of this species will become more important, which is consistent with the decrease in the distribution range. Over time, the importance of the most valuable connectors will increase, implying a greater risk of some populations becoming isolated.

1. Introduction

Climate change is a major concern of the scientific community [,,]. The consequences of this global impact are manifold; among them, it is likely to have a significant impact on different levels of biodiversity [,,,,,] and, particularly, on freshwater biodiversity []. In the last decade of the 20th century and the first two decades of the 21st century, numerous and diverse studies have been carried out to predict climate change’s effects on ecological niches and biodiversity. Thus, changes in the phenology and life cycles of numerous species have been documented [,], as well as alterations in their range that may consist of expansions, reductions, or migratory shifts [,,]. In the most extreme situations, climate change can even lead to the extinction of the species [].
The order Odonata (dragonflies and damselflies) is a group of insects with ideal characteristics and life cycles to be used as bioindicators of ecosystem quality and, thus, in climate change research [,]. They are distributed in freshwater habitats with very specific conditions, and their populations are very sensitive to alterations in environmental conditions such as fluctuations in the water table or flow, air temperature, concentration of pollutants in the water, and water physicochemical characteristics such as electrical conductivity, pH, dissolved oxygen and temperature [,].
In this study, the global potential distribution of an odonate species, the yellow-winged darter (Sympetrum flaveolum L.), was investigated. It is a dragonfly belonging to the suborder Anisoptera, widely distributed throughout the Palearctic region from Japan to Portugal [,,]. In southern Europe, and particularly in the Iberian Peninsula, the southwestern limit of its natural range, populations are fragmented and generally associated with mountainous areas. Lowland populations are short-lived and, in most cases, die out after a few years (up to 5–6 years). This can be thought of as an “influx model pattern” followed by decline and disappearance. [,,,]. This species also occurs in the southern half of Fennoscandia [].
The habitat of S. flaveolum consists of shallow water areas with abundant vegetation that are usually dry in summer and are neither too eutrophic nor shaded [,,,,]. The aforementioned high mountain distribution and its habitat, associated with aquatic environments, make this species particularly susceptible to climate change. In this regard, Warren et al. [] suggest that a 2 °C increase in average temperature, the maximum limit set at the Paris Summit [], would make the current areas of distribution unsuitable. Paradoxically, however, a reduction in the depth of high mountain wetlands could improve the status of populations []. Therefore, it is foreseeable that the increase in mean temperature brought about by climate change and variations in water tables will lead to significant changes in the distribution of S. flaveolum.
Though globally listed as the Least Concern in the IUCN Red List, S. flaveolum is considered Vulnerable in some countries, such as Spain [] or Italy. This fact means that, at least in some peripheric areas of its natural range, it faces a high risk of shifting to Endangered status and, finally, becoming extinct. Likewise, its populations are severely fragmented, and a decrease in the area of distribution and the extent and/or quality of habitat has been observed or inferred [].
The stenosis of this species, its inclusion in the vulnerable category in the IUCN Red List for some countries, and the need to act on aquatic ecosystems to ensure its conservation justifies the interest in assessing the current status of S. flaveolum populations and predicting future scenarios under the pressure of climate change. Likewise, once the evolution of this species is known, it will be possible to infer that of other species with similar ecological values. In that sense, it is urgent to identify effective conservation strategies for protecting the biodiversity of freshwater ecosystems in the climate change scenario in which we are immersed [].
Thus, in order to predict future scenarios, it is necessary to have a better knowledge of, among other factors, the connectivity between populations, which, according to Bush et al. [], is a function of the dispersal capacity of the species and the availability of climatic refuge. In this regard, it is worth bearing in mind that S. flaveolum is a migratory species [,], although it does not present a high dispersal capacity [].
The present study aims to develop better knowledge of the future situation of S. flaveolum, particularly: (i) to predict the potential distribution area of this species in different future scenarios of climate change; and (ii) to study the connectivity within this potential distribution for all scenarios and with two probabilities of appearance (from 60% to 100% and from 80% to 100%) clustering by terrestrial ecoregions with similar connectivity, in order to inform potential conservation measures for this species, which will also contribute to the conservation of other species living in the same habitat.

2. Material and Method

2.1. Species

The yellow-winged darter [Sympetrum flaveolum (Linnaeus, 1758)] is distributed throughout most of Eurasia from Europe to mid and northern China [,]. It occasionally migrates to the United Kingdom []. The species is bred in a wide range of stagnant waters. It could be found in peat bogs, waterbodies, garden pools, wetland pools, oxbow lakes, quarry pools, even fishponds, and artificial canals. Adult dragonflies are found from late June to October and peak in August. The nymphs succeed in stagnant water, small, shallow, and rich in vegetation. They are usually found in peat bogs, flooded meadows, and marshy areas, often at higher altitudes [,]. The species is a prominent predator and has an important role in the food webs of high-altitude lakes. Therefore, its disappearance would lead to major changes in these food webs [,].

2.2. Study Area

The study area encompassed the present natural range of S. flaveolum and areas where the species could potentially live in the future in Europe and non-tropical Asia. For this study, we hypothesized that all this area is freely accessible to S. flaveolum currently and in the future.

2.3. Occurrence Data

Future predictions for S. flaveolum were made using all available data in the Global Biodiversity Information Facility, from which a total of 19,997 occurrence records were acquired (GBIF 2022: 19,901 records, www.gbif.org (accessed on 1 August 2022), and 96 capture data). These records were verified to be accurate using ArcGIS and georeferenced using the WGS84 coordinate system (v10.7, ESRI, Redlands, CA, USA). We used the tool spThin ver. 0.2.0 [] to draw a 5-km buffer area around each occurrence record to reduce sampling error that could overestimate the anticipated distribution [] and to reduce spatial autocorrelation [,]. We thinned a total of 19,997 occurrence records to 4837 to represent its presence for each grid cell. We simulated S. flaveolum’s potential range for both present and future situations []. We identified the research area where records of the species exist in order to predict the species’ future forecasts. The study region was shielded from climate influences.

Climate Data

The WorldClim v2.1 database ([]; www.worldclim.org), with a geographical resolution of 2.5’ (about 4.7 km), provided the climatic data used in this investigation. The various WorldClim variables were derived from monthly averages of precipitation and temperature for the years 1970 to 2000. The modeling process made use of fifteen recent bioclimatic variables. The removal of four variables (BIO8, BIO9, BIO18, and BIO19) where some spatial artifacts had been found in earlier studies (such as [,]) was done.
Using the ‘usdm’ package [], we removed the variance inflation factor higher than 5 and used a correlation threshold of 0.75 to lessen the potentially harmful effects that could arise from multicollinearity and high correlation (r>0.75 or −0.75) among the bioclimatic variables [,,,,]. The input variables used in this study were BIO2: mean diurnal range (mean of monthly [max temp—min temp]); BIO4: temperature seasonality (standard deviation ×100), BIO5: maximum temperature of warmest month; BIO13 = precipitation of wettest month; BIO14 = precipitation of driest month, and BIO15 = precipitation seasonality (coefficient of variation). For the analysis of the results, two distribution probability ranges (60–100 % and 80–100 %) were considered.
The fit models were projected to five different global circulation models (GCMs): BCC-CSM2-MR [], CNRM-CM6-1 [], CNRM-ESM2-1 [], CanESM5 [], and MIROC6 [] to account for an appropriate level of uncertainty in the climate model projections []. Future data from the 6th Climate Model Intercomparison Project (CMIP6, www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) with three shared socioeconomic pathways were acquired for the periods 2021–2040, 2041–2060, 2061–2080, and 2081–2100. (SSPs) (optimistic—SSP245, middle of the road—SSP370, and worst—SSP585).

2.4. Methodology

Phase 1. Sampling and data collection on the distribution of the target species
To know the global distribution of Sympetrum flaveolum, location points were compiled using the Global Biodiversity Information Facility database. For climatic data, the climate models of worldclim were used.
Phase 2. Ecological niche modeling
Using an ensemble method in the sdm package [] in the R v3.6.3 environment, we created ecological niche models to project the current and future habitat suitability of S. flaveolum. The generalized linear model (GLM; [], boosted regression trees (BRT) [], random forests (RF) [], and maximum entropy (MaxEnt) [] with 10,000 randomly selected pseudo-absences were five algorithms that we implemented using various approaches. According to Naimi and Araújo [], we used the sdm package’s usual parameterization to run all the algorithms. The small sample size necessitated the use of the subsample and bootstrapping resampling methods [], which were divided into subsets of 70-30% for model calibration and testing. A true skill statistic (TSS) and the area under the receiver operating characteristic (AUC) were calculated for each model, and each model was run ten times. To assess the species’ adaptability to its current environment at the time of analysis, we created 50 distinct models (5 algorithms × 1 resampling method × 10 replications). To create ensemble models for each scenario, we chose the models with TSS > 0.7 and AUC > 0.9 as the best. The best models were assembled using the mean of predicted presence-absence values technique, which involves converting the expected probability of occurrences to presence-absence using a threshold before averaging. Following that, the chosen models were projected into current and future circumstances. As a result, we implemented 60 projections (5 GCMs, 3 SSPs, and 4 time periods) and generated ensemble rasters for the GCM scenarios and periods. The outputs indicate habitat suitability on a scale of 0 (unsuitable) to 1 (suitable) (suitable). We converted the ensemble suitability models into binary maps of acceptable environmental conditions and used them by maximizing the sum of sensitivity and specificity (maxSSS), as Liu et al. [] proposed. The RasterVis package was used to illustrate the results [].
Phase 3. Comparison of distribution areas
In this phase, changes in the distribution area between the current situation and the expected situation for all the proposed scenarios were analyzed.
Phase 4. Calculation of connectivity using the MSPA and the PC index
This phase aims to compare connectivity by calculating the morphological spatial pattern analysis (MSPA) [], which measures structural connectivity through the number of connecting elements, and the probability of connectivity index (PC) [], which measures the importance of each of the connection elements previously analyzed, for all climate scenarios and with the two probability ranges, resulting in a total of 26 possible situations.

2.5. Statistical Analysis

In order to group the terrestrial ecoregions in clusters with similar connectivity (number of yellow-winged darter links), we used principal component analysis (PCA) []. First, we developed a PCA analysis with the number of links from terrestrial ecoregions obtained in the previous analysis according to the current situation and climate change scenarios: optimistic—SSP245, middle of the road—SSP370, and worst—SSP585 for 2040, 2060, 2080 and 2100. Then, we classified all terrestrial ecoregions (ordered by PCA) according to their similar connectivity across the different climate change scenarios. The hierarchical classification was performed using Ward’s criterion on the selected principal components [].
R. 4.2.1 (R Development Core Team 2022) with the packages “FactoMineR” [], “factoextra” [], and “vegan” [] were used for data processing and statistics.

3. Results

Overall, our ecological niche models (ENMs) have an average AUC of 0.936 (SD = 0.036) and an average TSS of 0.775 (SD = 0.115). Our ENM for present-day conditions indicates that the habitats suitable for S. flaveolum spread across most of Europe, western Siberia, northern Anatolia, the Caucasus, the Himalayas, northern Japan, Sakhalin, and Kamchatka Peninsula (Figure 1). According to the occurrence record, it is also quite frequent in other areas where our model indicates a low probability of occurrence, such as Korea or Mongolia.
Figure 1. Average prediction of climate habitat suitability maps for Sympetrum flaveolum projected to the present day. Red dots show occurrence records. The probability of occurrence ranges from 0 (dark purple, low probability) to 1 (yellow, highest probability).
Our results show that the habitat suitability of the species is explained by temperature seasonality (BIO4, 29%), precipitation of the driest month (BIO14, 22%), precipitation seasonality (BIO15, 19%), the maximum temperature of the warmest month (BIO5, 19%), mean diurnal range (BIO2, 9%), and precipitation of wettest month (BIO13, 8%). They also predict that the suitable habitats for this species will shift towards the north and will disappear up to 2100, under future climate scenarios, in southern Europe, Anatolia, the Caucasus, the southernmost area of western Siberia, and Japan, thus comprising all the southern limit of its present range (Figure 2). At the same time, some northern areas that are currently unsuitable for S. flaveolum will become suitable, as would be the case in northwestern Siberia and Chukotka.
Figure 2. Average prediction of climate habitat suitability maps for Sympetrum flaveolum under future climate scenarios. Average projections are presented for each of four time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) and three shared socioeconomic pathways (optimistic—SSP245, middle of the road—SSP370 and worst—SSP585). The probability of occurrence ranges from 0 (dark purple, low probability) to 1 (yellow, highest probability).
Table 1 and Table 2 show the changes in the potential distribution of Sympetrum flaveolum for the different study scenarios. For both distributions (80–100% and 60–100%), the potential area will decrease until it reaches 60.93% and 63.4%, respectively, for the year 2100 in the worst possible scenario. With scenario SSP245, affection would be initially much lower since its distribution would be reduced, in the year 2080, by 3% (80–100%) and just 0.7% (60–100%), though by the year 2100, affection will increase, with distribution been reduced to 91.81% (80–100%) and 93.88% (60–100%). In an intermediate position is scenario SSP370, the only scenario in which the area would decrease in all years and in which the area in the year 2100 would decrease by a quarter for both distributions.
Table 1. Evolution of the estimated potential distribution area of Sympetrum flaveolum in absolute and relative values (Present—2100). 80–100%.
Table 2. Evolution of the estimated potential distribution area of Sympetrum flaveolum in absolute and relative values (Present—2100). 60–100%.

3.1. MSPA Index

The distribution of the components of the MSPA, according to the results provided by Guidos software, is shown in Figure 3 for the current distribution and in Appendix A.1 and Appendix A.2 for the potential distribution under the different considered scenarios. Comparing these situations, it is found that, in all the cases, the area occupied by cores decreases notably, throughout the study period, especially in peripheral zones, except in the Russian Far East. Simultaneously, there is an increase in the number of islets in southern areas currently occupied by this species and a significantly higher number of bridges.
Figure 3. Results of the MSPA analysis for the current situation for distributions 80–100% (up) and 60–100% (down).
It should be noted that all possibilities show an increase in the number of bridges compared to the current distribution.
As these bridges link habitat patches and serve as vital functional dispersal corridors, their gradual loss until 2100 has a detrimental impact on the maintenance of functional connectedness [,].
The critical areas for both distributions have also been located and are shown in Appendix A.3 and Appendix A.4.
In addition, the PCA revealed an ordination of terrestrial ecoregions according to the different climate change scenarios considered in this study (Figure 4). The two first principal components (dimensions) explained more than 85% of the cumulative variance for both distributions, 60–100% and 80–100% (Figure 4i,ii, respectively). Finally, using the PCA hierarchical classification, we classified the ecoregions into four clusters with similar connectivity across the different scenarios of climate change and by distribution (60–100% Figure 4ii and 60–100% Figure 4iv). Most of the ecoregions were included in clusters 1 and 2 (Figure 4ii,iv; Appendix A.5 and Appendix A.6). However, Alps Conifer and mixed forests were exclusively included in cluster 4 for both distributions (Appendix A.5 and Appendix A.6), having different connectivity (number of links) that other terrestrial ecoregions by the different scenarios of climate change. Cluster 3 also included a few terrestrial ecoregions: Bering Tundra, Scandinavian Montane Birch Forest and Grasslands, and West Siberian Taiga. The tables with the calculation variables for the different clusters can be found in Appendix A.7 and Appendix A.8.
Figure 4. Connectivity terrestrial ecoregion ordination and cluster classification. Principal component analysis ((i) for 60–100% distribution and (iii) for 80–100% distribution) and hierarchical classification ((ii) for 60–100% distribution and (iv) for 80–100% distribution) focusing on link data numbers from the terrestrial ecoregions according to three shared socioeconomic pathways (optimistic—SSP245, middle of the road—SSP370, and worst—SSP585 in years 2040, 2060, 2080, and 2100. The symbols correspond to terrestrial ecoregions for points and thirteen climate change scenarios for grey arrows (i) for 60–100% distribution and (iii) for 80–100% distribution) and to terrestrial ecoregions grouped in four clusters (with different colors in the graph) with similar connectivity (ii) for 60–100% distribution and (iv) for 80–100% distribution). PCA1 and PCA2 explained 74.2% and 11.3% of the variance for 60–100% distribution (i), respectively, and PCA1 and PCA2 explained 78.6% and 7.3% of the variance for 80–100% distribution (iv), respectively.

3.2. PC Index

The study of the dPC index ranks the nodes and links on the map according to how much they contribute to connectivity []. As can be observed in Appendix A.1 and Appendix A.2, since core regions are usually so huge that we cannot use them to support management methods for boosting connectivity, we are focusing on corridors, which are also the most important structures in terms of connectivity [,,,].
Table 3 and Table 4 show the variation in the importance of connectors in the different considered scenarios and throughout the study period. According to these data, in the 80–100% distribution, the number of connectors increases in the first decades of the study period, reaching a maximum for SSP535 by the year 2040 (7370 connectors) and for SSP245 (6048 connectors) and SSP370 (6873 connectors) by the year 2060. Later on, the number of connectors decreases, reaching the minimum by the year 2080 for SSP245 (5808 connectors) and by the year 2100 for SSP370 (4281 connectors) and SSP535 (3499 connectors). In the 60–100% distribution, a similar pattern was present, with a maximum in the number of connectors by the year 2040 (SSP370 and SSP535) and 2060 (SSP245) and a minimum in 2100 for the three scenarios.
Table 3. Variation in the importance of connectors with the 80–100% distribution.
Table 4. Variation in the importance of connectors with the 60–100% distribution.
Usually, the maximum value of ecological corridors varied between 0.073731 (SSP245, the year 2040) and 2.307007 (SSP585, the year 2080) with the 80–100% distribution and between 0.143843 (SSP585, the year 2100) and 3.247807 (SSP585, the year 2080) with the 60–100% distribution. In the year 2100, however, a value of 10.48 is found for the maximum value of ecological corridors for the 80%–100% distribution for scenario SSP585, and a very similar value is found for the same year 2100 in the 60%–100% distribution for scenario SSP370 (10.45), suggesting that in 2100, with the reduction in surface area and potential isolation of the cores, the most crucial connectors assume a greater value to prevent fragmentation.
Analyzing the global set of connectors, in the 80–100% distribution, the value of importance is multiplied by almost four in scenario SSP245 for the year 2100. For the 60–100% distribution, this increase occurs for scenario SSP370, which corresponds to a 2100% increase compared to the importance of the current scenario. Such high increases indicate that the fragmentation that will occur in the different scenarios is maximum, so the importance of the connectors is increased to reduce the possible fragmentation caused by climate change.
In order to determine the PC index in smaller areas, PC values have been calculated for ecoregions in which there is potential distribution. Figure 5 shows, as an example, the evolution of the PC indices for the ecoregions and the different clusters previously obtained.
Figure 5. Evolution of pc values by ecoregion for scenario 585 in the distribution from 80% to 100%.
Maps of all the index values calculated for the different scenarios and distributions are shown in Appendix A.5 and Appendix A.6.

4. Discussion

While assessing connections between habitats in a landscape matrix, changes that may occur in land cover over time and how species may spread against bioclimatic variables are often ignored [,,,]. However, the responses of species to global climate change have been accepted as the most important environmental factor that determines the main characteristics of habitats and their distribution areas [,]. In this context, understanding the direction and magnitude of species responses is important for species conservation and sustainability [,]. Since climate change differentiates the bioclimatic demands of species under optimal conditions, it also causes changes in their geographical distribution [,,,,]. This change is widely linked to increased temperatures and decreased precipitation during the growing season []. Every 1 °C change in temperature moves ecological regions around the world about 160 km. Thus, for example, if the climate warms by 4 °C in the next century, species in the northern hemisphere may need to move 500 km north (or 500 m higher) []. Many studies that refer to the impact of climate change on species have investigated the effects of global temperature rise, confirming that species will migrate to the poles (higher latitudes) and higher altitudes as a result []. In addition, it has been predicted that the geographic ranges of species will expand, shift or contract []. While some studies indicate that certain species may become stronger against climate resistance in the future, it is predicted that some will experience habitat loss, which will negatively affect biodiversity [,,]. Furthermore, it has been reported that rapid climate change may put pressure on relict species and cause species extinction [,,]. Climate-related variables such as temperature and precipitation are important for the effects on species survival, distribution, and other characteristics, as well as for the species composition of natural ecosystems and the future of terrestrial ecosystems [,].
Our study is consistent with the aforementioned studies, suggesting that the natural range of S. flaveolum will reduce significantly in all the proposed scenarios, with this loss being particularly large (up to 40%) in the SSP585 scenario. Furthermore, as has been predicted in other species, the distribution area will also shift northward. Consequently, this species will virtually disappear from the Mediterranean Basin and other southern locations and will spread to northern areas of the Eurasian continent.
The results of the MSPA analysis and PC index showed a loss of connectivity in S. flaveolum patches, particularly in its southernmost range. Lack of landscape connectivity can isolate habitat patches that affect gene flow, among other ecological processes. Greater connectivity increases the ability of species to migrate to new regions in the face of climate change and reduces the likelihood of extinction. For this reason, greater connectivity may increase the chances of many organisms surviving under changing climatic conditions. Consequently, this loss of connectivity will negatively affect the populations of the yellow-winged darter and will pose a serious threat to the survival of this species in southern Eurasia.
Due to the strong dispersal capacity of dragonflies in general, changes in the current climate and resource availability primarily affect how they are distributed. This is because dragonflies can track changing climatic and environmental circumstances owing to their flying ability. Olsen et al. [] stated that dragonflies are often influenced by habitat specialization (species vulnerability to habitat loss and fragmentation [] or linked dispersal limitation. Previous studies confirm this statement and highlight that extreme habitat specialization can be more effective than dispersal ability, particularly for permanent running water species. The differences between nodes and links in this study can be a reason for either extreme habitat specialization or reduced dispersal ability.
Mountain chains in the European topography can act as barriers for odonate species; therefore, wide river plains can be regarded as corridors. This can be understood from the maps produced in this study. The northern side of the Iberian Peninsula or northern Europe is highly affected by climate change making these areas critical. Geostatistical analysis of the data from the critical detection areas supports this.
We discover that species in permanent water habitats, including both rushing and standing waters, move north to a far lesser extent than those that are adapted to seasonally dry habitats. This suggests that transient waters support the diversity of dragonflies and serve as stepping stones for the spread of generalist species []. In comparison to species suited to permanent flowing water environments, species adapted to permanent standing water or transient water habitats, which are less persistent in time and space, spread more effectively [].
From the point of view of potential distribution, and based on this study, it is more advisable to use the 60% to 100% range since the connectivity shown by this distribution is included within the 80% to 100% range, and the discontinuous zones show where the fragmentation risks really are.

5. Conclusions

Experimental studies that use ecological niche modeling predict significant changes in species distributions in response to climate change. As habitat fragmentation can hinder species range changes, maintaining wildlife corridors may be of increasing importance in enhancing climate resilience for species survival. Therefore, identifying degrees of connectivity between habitats play a vital role in adapting to changing climatic conditions.
In this study, current, potential, and future connectivity changes in S. flaveolum were predicted by combining an ecological niche model and an ecological connectivity approach. Besides determining suitable habitats for the species, we identified priority areas for connectivity relevant to the sustainability of S. flaveolum. Our approach provides a robust and practical tool to optimize biodiversity conservation objectively. Further study can integrate land use/land cover changes into our method and make a broader interpretation of the species distribution.

Author Contributions

Conceptualization, V.R., J.V., and D.G.; methodology, V.R., J.V., D.G., and A.L.-S.; validation D.G., A.U.Ö., and K.Ç.; formal analysis, J.V., K.Ç., and A.L.-S.; investigation, V.R., J.V., and D.G.; data curation, A.L.-S. and K.Ç.; writing—original draft preparation, C.J., J.C.L.-A., D.G. and J.V.; writing—review and editing, V.R., J.V., D.G. and A.L.-S.; visualization, A.U.Ö. and D.G.; supervision, J.V., T.S., and D.S.-M. project administration, J.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data of the current research is available from the corresponding author on request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Appendix A.1. MSPA Analysis under Different Climate Scenarios (60–100%)

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Appendix A.2. MSPA Analysis under Different Climate Scenarios (80–100%)

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Appendix A.3. Critical Areas for Connectivity under Different Climate Scenarios (60–100%)

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Appendix A.4. Critical Areas for Connectivity under Different Climate Scenarios (80–100%)

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Appendix A.5. MSPA Analysis under Different Climate Scenarios (60–100%)

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Appendix A.6. MSPA Analysis under Different Climate Scenarios (80–100%)

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Appendix A.7. Cluster Calculation (60–100%)

ECO_NAMETerrestrial Ecoregion ClustersC1_ 2020C245_2040C245_2060C245_2080C245_2100C370_2040C370_2060C370_2080C370_2100C585_2040C585_2060C585_2080C585_2100
Aegean And Western Turkey Sclerophyllous And Mixed Forests153350020002000
Alps Conifer And Mixed Forests40466601674836671747867832526812949810
Altai Alpine Meadow And Tundra216880112604115551279361838485
Altai Montane Forest And Forest Steppe21323380618871644860105784635
Altai Steppe And Semi-Desert140130131416231331007
Anatolian Conifer And Deciduous Mixed Forests112425248041029420
Appenine Deciduous Montane Forests1557394425602200472850
Atlantic Mixed Forests11038952629385877820751655
Azerbaijan Shrub Desert And Steppe10052000004000
Balkan Mixed Forests1781197759221135126012146100
Baltic Mixed Forests131433838363737385435353937
Baluchistan Xeric Woodlands13000000002200
Bering Tundra3158527526289174369584250376194465548
Caledon Conifer Forests1349001041190239125
Cantabrian Mixed Forests102000620289712812588883
Carpathian Montane Forests10616188411021571385917744
Caspian Hyrcanian Mixed Forests10602000000000
Caucasus Mixed Forests2183798710610153391593563467160
Celtic Broadleaf Forests27919514919917518775158609618611783
Central Anatolian Steppe And Woodlands123000000000000
Central European Mixed Forests202220022525211197146628821943
Cherskii-Kolyma Mountain Tundra13132135416012230090103017
Chukchi Peninsula Tundra200421011600224851621369420557
Corsican Montane Broadleaf And Mixed Forests103240353132313602831240
Crimean Submediterranean Forest Complex16519981390016430
Dinaric Mountains Mixed Forests2236112115816655190107010155520
East European Forest Steppe21892522791202292714015129128220
East Siberian Taiga215010715732921213760145974522294835
Eastern Anatolian Deciduous Forests1126000370000000
Eastern Anatolian Montane Steppe18434181264428814151340
Elburz Range Forest Steppe10600000000000
Emin Valley Steppe1039183955010110301418
Euxine-Colchic Broadleaf Forests186241694460996305664290
Gissaro-Alai Open Woodlands2278327263201175339237118923231997546
Himalayan Subtropical Pine Forests1198120089009000
Hindu Kush Alpine Meadow1000000150025131511
Hokkaido Deciduous Forests13642312525333721600158
Hokkaido Montane Conifer Forests12313251232647336008
Honshu Alpine Conifer Forests1749816000019300
Iberian Conifer Forests17210002200018000
Iberian Sclerophyllous And Semi-Deciduous Forests136000000000000
Illyrian Deciduous Forests1123112200210005000
Italian Sclerophyllous And Semi-Deciduous Forests1624351213361700501500
Junggar Basin Semi-Desert1230045800403200
Kamchatka Mountain Tundra And Forest Tundra212333946310359000441000
Kamchatka-Kurile Meadows And Sparse Forests1246432303424642102174381218
Kamchatka-Kurile Taiga17000050000000
Karakoram-West Tibetan Plateau Alpine Steppe211501341559942129156115529610378
Kazakh Forest Steppe21466846848696142172314445713
Kazakh Upland1523440022150048000
Kola Peninsula Tundra10000000006000
Kopet Dag Woodlands And Forest Steppe14000000000000
Lake: Palearctic18000000100600
Nihonkai Montane Deciduous Forests11104642522824100741400
North Atlantic Moist Mixed Forests100012000000000
Northeast Siberian Taiga221383118233610386701818904
Northeastern Spain And Southern France Mediterranean Forests125154741170801301405
Northern Anatolian Conifer And Deciduous Forests18250495347716553836461821
Northwest Iberian Montane Forests13214442104830012000
Northwest Russian-Novaya Zemlya Tundra135867413871958004191537
Northwestern Himalayan Alpine Shrub And Meadows22482361881268621722315410227114114296
Nujiang Langcang Gorge Alpine Conifer And Mixed Forests100000000001000
Okhotsk-Manchurian Taiga2441281089761131451039419922085165
Pamir Alpine Desert And Tundra210820224120519322524422727516921694188
Pannonian Mixed Forests1011012678155653006852181
Paropamisus Xeric Woodlands100000041127001128
Pindus Mountains Mixed Forests123606674077900513500
Po Basin Mixed Forests10100010000000
Pontic Steppe116393274130416012000
Pyrenees Conifer And Mixed Forests1404008200341490244855
Rock And Ice: Palearctic121016413710405111364551147
Rodope Montane Mixed Forests1023395010222700343700
Sakhalin Island Taiga136444444444544
Sarmatic Mixed Forests1331072911174216620313910493
Sayan Alpine Meadows And Tundra155353910395548114514484510
Sayan Montane Conifer Forests235011518711224517113120118867181127115
Scandinavian And Russian Taiga22865512559829310121419136711586224
Scandinavian Coastal Conifer Forests10405027237610135422527
Scandinavian Montane Birch Forest And Grasslands3106521460664495563538149240447491609226
South Appenine Mixed Montane Forests1227432512272760271402
South Sakhalin-Kurile Mixed Forests14800000000000
South Siberian Forest Steppe1378284119299313890509700
Southern Anatolian Montane Conifer And Deciduous Forests170000000000000
Southwest Iberian Mediterranean Sclerophyllous And Mixed Forests10000000003000
Sulaiman Range Alpine Meadows126210201104702105
Taiheiyo Evergreen Forests12000000000000
Taiheiyo Montane Deciduous Forests116054100000000
Taimyr-Central Siberian Tundra100000001151314706188
Tian Shan Foothill Arid Steppe192674149413624582387203816
Tian Shan Montane Conifer Forests152192438552640558264280
Tian Shan Montane Steppe And Meadows2538447145101891291395163889962
Trans-Baikal Bald Mountain Tundra1500000032603000
Tyrrhenian-Adriatic Sclerophyllous And Mixed Forests1134351004300041000
Ural Montane Forests And Tundra100100008477011366
Ussuri Broadleaf And Mixed Forests154567020666627302201528
West Siberian Taiga32941954483983911954513601092075479112
Western European Broadleaf Forests20901121781576612547017973221171155
Western Himalayan Alpine Shrub And Meadows173244321806400
Western Himalayan Broadleaf Forests21511008773611631048562177525242
Western Himalayan Subalpine Conifer Forests170305436244253424040443634
Western Siberian Hemiboreal Forests14401910178500018300
Yamal-Gydan Tundra121931121535601391163751143105
Kazakh Steppe14013261006400035000

Appendix A.8. Cluster Calculation (80–100%)

ECO_NAMETerrestrial Ecoregions ClustersC1_ 2020C245_2040C245_2060C245_2080C245_2100C370_2040C370_2060C370_2080C370_2100C585_2040C585_2060C585_2080C585_2100
Aegean And Western Turkey Sclerophyllous And Mixed Forests150240020002000
Alps Conifer And Mixed Forests45125856636477695488528228996389591042849
Altai Alpine Meadow And Tundra2174565211585725771129961148041
Altai Montane Forest And Forest Steppe1105415424483875716560484662
Altai Steppe And Semi-Desert12820417142414116181107
Anatolian Conifer And Deciduous Mixed Forests1100004808200460
Appenine Deciduous Montane Forests19514544357260059700
Atlantic Mixed Forests110472656434623438663205839
Azerbaijan Shrub Desert And Steppe10055020000000
Balkan Mixed Forests153102675331107342121012150
Baltic Mixed Forests113262513331515383927134716
Bering Tundra31363433062814652926561306449751421482
Caledon Conifer Forests11401056190150058
Cantabrian Mixed Forests1024258181825557010611339
Carpathian Montane Forests10135529268417818078910679
Caspian Hyrcanian Mixed Forests120000000000000
Caucasus Mixed Forests232914188531271018875271681464990
Celtic Broadleaf Forests290641318511716567871096471116166
Central European Mixed Forests215400151551937811323402329310
Cherskii-Kolyma Mountain Tundra1012606410176765218215212699
Chukchi Peninsula Tundra100391577001121192021614622845
Corsican Montane Broadleaf And Mixed Forests102836353328352602633230
Crimean Submediterranean Forest Complex1036969750023600
Dinaric Mountains Mixed Forests270311701481287715375025138770
East European Forest Steppe22014828426378138169715221125260
East Siberian Taiga27742137131159502331774930458729
Eastern Anatolian Deciduous Forests110320003000014000
Eastern Anatolian Montane Steppe1117389127291520261849
Emin Valley Steppe10008000000000
Euxine-Colchic Broadleaf Forests119795976587273430719850
Gissaro-Alai Open Woodlands2152184192226162220251111721901957976
Himalayan Subtropical Pine Forests122858001000000
Hokkaido Deciduous Forests12358120101212121205030100
Hokkaido Montane Conifer Forests120155441274910402300
Honshu Alpine Conifer Forests112124160000016000
Iberian Conifer Forests101900000000000
Iberian Sclerophyllous And Semi-Deciduous Forests149000000000000
Illyrian Deciduous Forests1450000600018000
Italian Sclerophyllous And Semi-Deciduous Forests1105221918017130053510
Junggar Basin Semi-Desert1140000954011500
Kamchatka Mountain Tundra And Forest Tundra21223732800875004943400
Kamchatka-Kurile Meadows And Sparse Forests21016958311431421151620260248
Kamchatka-Kurile Taiga10400000006000
Karakoram-West Tibetan Plateau Alpine Steppe1161122561053298948719506580
Kazakh Forest Steppe2112809070241737516913221161
Kazakh Upland12532170000009000
Kola Peninsula Tundra113151912000000000
Lake: Palearctic17000001000000
Nihonkai Montane Deciduous Forests110034214562060095800
Northeast Siberian Taiga21417514664101399233037310211556
Northeastern Spain And Southern France Mediterranean Forests1242434821064171324
Northern Anatolian Conifer And Deciduous Forests13546505652415639830551012
Northwest Iberian Montane Forests1626046001100022000
Northwest Russian-Novaya Zemlya Tundra10697781851012000000
Northwestern Himalayan Alpine Shrub And Meadows2214113174907427817311913322790103110
Okhotsk-Manchurian Taiga2552216566812311411222675123107138
Pamir Alpine Desert And Tundra293137238252226160272199225150251149174
Pannonian Mixed Forests101349381277231008928180
Paropamisus Xeric Woodlands100000000100017
Pindus Mountains Mixed Forests11675576706820095200
Po Basin Mixed Forests15200020000000
Pontic Steppe1119216142904061400
Pyrenees Conifer And Mixed Forests10099519502290026592
Rock And Ice: Palearctic10109264010203910502429138
Rodope Montane Mixed Forests11031404718323100353200
Sakhalin Island Taiga151543444444344
Sarmatic Mixed Forests144357545123162150294713721
Sayan Alpine Meadows And Tundra10101219461913354114214631
Sayan Montane Conifer Forests22518297137134651001631551331439659
Scandinavian And Russian Taiga239412715611073102248310116486142148169
Scandinavian Coastal Conifer Forests104743422263325352200
Scandinavian Montane Birch Forest And Grasslands384494518538496449420282255324400478242
South Appenine Mixed Montane Forests11751392316541200411700
South Sakhalin-Kurile Mixed Forests12000000000000
South Siberian Forest Steppe12414177282578200545200
Southern Anatolian Montane Conifer And Deciduous Forests124000000000000
Sulaiman Range Alpine Meadows150000011000050
Taiheiyo Evergreen Forests10000000002000
Taiheiyo Montane Deciduous Forests134005503000000
Taimyr-Central Siberian Tundra1000000088142006148
Tian Shan Foothill Arid Steppe10183418302332734241803
Tian Shan Montane Conifer Forests1105830120111100302000
Tian Shan Montane Steppe And Meadows1410473681184059769678784440
Trans-Baikal Bald Mountain Tundra10000000090000
Tyrrhenian-Adriatic Sclerophyllous And Mixed Forests1274310004100043000
Ural Montane Forests And Tundra10000700219910568426
Ussuri Broadleaf And Mixed Forests1728241332145914014230
West Siberian Taiga3263186227369347143341236354095771231
Western European Broadleaf Forests23011320314623414722625412938258288158
Western Himalayan Alpine Shrub And Meadows19770072001000
Western Himalayan Broadleaf Forests21691678511356139637070132663128
Western Himalayan Subalpine Conifer Forests169263240263939414133364029
Western Siberian Hemiboreal Forests170014060461670033500
Yamal-Gydan Tundra123427023273781533712953
Kazakh Steppe11213330015400012000

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