Priority conservation areas for protected saproxylic beetles in Romania under current and future climate scenarios

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Brodie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3969647/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Climate change poses an increasing risk to biodiversity and habitats important for saproxylic beetles are likely to experience severe pressure and threats. The diversity of saproxylic beetles is an indicator of healthy forest ecosystems, and thus, the conservation of beetles is now a priority for EU Member States. We developed ensemble species distribution models for five saproxylic beetles for current and three-time future horizons under two emission scenarios and two GCMs. We then used a systematic conservation planning approach to assess the effectiveness and resilience to climate change of Romanian Natura 2000 network for saproxylic beetles while identifying future areas for protected area expansion to meet EU conservation targets. Our study revealed that under all scenarios and time horizons, the saproxylic beetles will lose over 80% of their suitable habitat and restrict their distribution to higher elevations. According to the prioritization analysis, we found that when considering 30% of the landscape as protected, an average of 85% of species distribution is retained with priority areas overlapping the Carpathian Mountains, while for the current conditions (18% of Romania’s terrestrial surface), the existing Natura 2000 network does not perform well, with almost ~30% of the saproxylic species distributions falling inside. Our results support the idea that the distribution of saproxylic beetles could change as a result of climate change, and the effectiveness of the current Natura 2000 network is put into question as it may be insufficient in protecting these species. To achieve the goals of the EU Biodiversity Strategy 2030 of protecting at least 30% of the EU’s land, we urge the expansion of the Natura 2000 sites. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Human-induced climate change contributes to changing the landscape on an unprecedented scale, endangering habitats and species (Mantyka-Pringle et al. 2012; Harvey et al. 2023). The most visible impact is the increase in temperature when compared to pre-industrial levels, with Europe being the fastest-warming continent in the world (Kjellström et al. 2018). Under these conditions, habitats important for saproxylic beetles, such as old-growth forests, are highly likely to experience severe pressure and threats (La Porta et al. 2008; European Commission 2021). Because of the importance of forests for human well-being and biodiversity, the European Union (EU) not only set an overall goal of protecting at least 30% of the EU’s land area under an effective management regime, of which one-third should be strictly protected but considered old-growth forests as a priority for including under strict protection (European Commission 2021). Saproxylic beetles are deadwood specialists and keystone species in maintaining forest ecosystems. The diversity of saproxylic beetles is an indicator of healthy forest ecosystems (Jansson et al. 2009; Mazzei et al. 2018), and thus, the conservation of beetles is now a priority for EU Member States. Yet, insects are declining at an alarming rate in both modified and intact landscapes (Seibold et al. 2015; Wagner 2020). In Europe, species associated with deadwood habitats are among the most threatened taxa by intensive forest management practices and habitat fragmentation (Nieto and Alexander 2010). Due to saproxylic beetles’ affinity for dead and dying hardwood, their diversity is positively correlated with the amount and diversity of deadwood (Lassauce et al. 2011; Lachat et al. 2012; Seibold et al. 2018), and therefore a decline in old-growth forest habitats has led to reducing the ranges of several species. In Europe, there are 21 saproxylic beetle species listed in the EU Habitats Directive, including species such as Rosalia alpina , Cerambyx cerdo , and Lucanus cervus (Cálix et al. 2018). The EU has developed strategies for the recovery of saproxylic beetles, yet range shifts and forest structure changes have not yet been accounted for in conservation planning. Among the European countries, Romania harbors the most continuously forested areas, which includes the Carpathian Mountains, a biodiversity hotspot for saproxylic beetles (Munteanu et al. 2022; Stanciu et al. 2023). These forests shelter several of the most iconic saproxylic insects, which are listed in Annex II of Habitats Directive (Directive/92/43/EEC 1992): Rosalia alpina, Lucanus cervus, Cerambyx cerdo, Osmoderma eremita and Morimus funereus , and their populations are deemed to be viable. Past forestry management practices in the Carpathians, which include selective logging and removal of deadwood and old trees, degraded the forest structure and led to local decrease of species abundance (Prunar et al. 2013; Olenici and Fodor 2021). Additionally, Romanian forest managers are faced with conflicting mandates with regard to these species, i.e., either consider them as pest species and apply lethal methods to lower population and damage or protect them as threatened and endangered species per the EU environmental mandates (Brodie et al. 2019). As a result, forest managers neither have the incentives nor the scientific information on the saproxylic beetle community to promote concrete conservation actions. Thus, the most important tool in protecting these species is the inclusion of their habitats in Natura 2000 network, one of the most extensive networks of conservation areas in the world, which has been created to operationalize EU Birds (Directive 2009/147/EC 2009) and Habitats Directives (Directive/92/43/EEC 2013). However, designation of Natura 2000 sites often lacks clear, quantifiable conservation objectives or extensive spatial planning, as highlighted in previous studies (Iojă et al. 2010; Kukkala et al. 2016a; Miu et al. 2020; Cazzolla Gatti et al. 2023). Frequently, these designations result from a pursuit of area-based targets established by the European Union for country-level protection, with a specific goal of protecting 30% of each EU country by 2030 (European Commission 2020). An effective approach for establishing a network of protected areas that aligns with EU targets is the application of systematic conservation planning (Margules and Pressey 2000). This framework aims to optimize conservation benefits while minimizing adverse impacts on other resources. Spatial conservation prioritization, as part of systematic conservation planning, typically employs algorithms that account for complementarity and representativeness of species and communities to identify areas that complement each other to prevent redundant conservation efforts (Mikusiński et al. 2007; Kukkala et al. 2016b; Kujala et al. 2018). This strategy is widely recognized as an efficient instrument for identifying spatial priorities and to effectively achieve conservation objectives (Wintle et al. 2019). In Europe, few studies have assessed the distribution of saproxylic insects and the effectiveness and representativity of Natura 2000 in protecting these species, and the conclusion tended to highlight suboptimal planning (D’Amen et al. 2013; Zehetmair et al. 2015; Bosso et al. 2018). For example, Bosso et al., 2013 pointed out that Natura 2000 network protects less than 56% of Rosalia alpina ’s suitable habitat in Italy, while Lachat et al., 2013 found that only 11% of its suitable habitat is protected in Switzerland. In their study, Bosso et al., 2018 found that only 25% of Rosalia alpina ’s potential distribution is covered by Natura 2000 sites from France, and only 35% of its suitable areas are protected in Austria. For Romania, few attempts have been made to assess the coverage and the effectiveness of Natura 2000 in protecting species listed in Annex II of Habitats Directive (Iojă et al. 2010; Popescu et al. 2013; Miu et al. 2018, 2020), due to limitations associated with the availability of occurrence data. Specifically for the saproxylic insects, most studies have been aimed at evaluating insects diversity in small areas (Stan and Nitzu 2013; Bărbuceanu et al. 2015; Manu et al. 2016, 2017, 2019; Stan et al. 2016; Maican et al. 2019; Brodie et al. 2019). Thus, there is an urgent need for systematic conservation approaches at the national level to identify the representation of these taxa in the current protected area network and to highlight gaps and additional areas for protection to achieve national, European, and global protection targets. Such approaches would also benefit forest biodiversity conservation in general, as saproxylic beetles can serve as surrogates for other taxa (Ranius 2002; Holland 2007; Foit et al. 2016). The aim of our study is to identify conservation priorities for five listed saproxylic beetle species ( Rosalia alpina, Lucans cervus, Cerambyx cerdo, Osmoderma eremita , and Morimus funereus ) in Romania, under current and future climate change scenarios. First, we developed species distribution models for current and three-time future horizons (2021–2040, 2041–2060, and 2061–2080). We then used a systematic conservation planning approach in combination with forest cover information to identify the representation of these species in the current protected area system and to identify additional priorities to meet EU conservation targets. Specifically, our objectives are: 1) to map the distribution of five target species by using an ensemble modeling approach; 2) to assess the changes in the distribution of saproxylic beetles due to climate change; 3) to assess the effectiveness and resilience to climate change of Romanian Natura 2000 network for saproxylic beetles, and 4) identify future areas for protected area expansion. Overall, this research aims to aid current efforts from Romanian and EU decision-makers to identify optimal areas for protected area expansion and to develop management plans that include forest conservation strategies for saproxylic beetles. Materials and methods Study area, species, and occurrence data Romania is a hotspot of biodiversity in Europe, overlapping five European biogeographical regions, i.e., Alpine, Continental, Pannonian, Steppic, and Black Sea (Rozylowicz et al. 2019). Over 27% of its territory is covered by forest habitats (Munteanu et al. 2016), with more than 3.5% represented by old-growth forests (Knorn et al. 2013). Forest habitats are dominated by broadleaved species (70% of the forest habitats, mostly Quercus ssp. and Fagus sylvatica as dominant species) and deciduous species (30% of the forest habitats, mainly Picea abies and Abies alba as dominant species) (Veen et al. 2010). Due to the large extent of old-growth and less intensively managed forests, Romania is also considered a hotspot of saproxylic beetle biodiversity (Nieto and Alexander 2010). Of over 200 saproxylic beetles’ reported from Romania, over 20 are protected by Habitats Directive (Gîdei and Popescu 2012, 2014; Directive/92/43/EEC 2013; Fusu et al. 2015). Natura 2000 protected areas created for Habitats Directive species and habitats include 425 Sites of Community Interest, covering 40500 km 2 , i.e., 17% of Romania (European Environment Agency 2021). For this study, we selected five saproxylic species listed in Annex II of EU Habitat Directive (Directive 92/43/EEC, 1992), i.e., the alpine longicorn Rosalia alpina Linnaeus, 1758, the Morimus longicorn Morimus funereus Mulsant, 1863 and the great capricorn beetle Cerambyx cerdo Linnaeus, 1758 of family Cerambycidae, the hermit beetle Osmoderma barnabita Motschulsky 1845, part of Habitats Directive Osmoderma eremita complex (family Scarabaeidae) and the stag beetle Lucanus cervus (Linnaeus 1758) (family Lucanidae). These species were selected because they have a relatively extensive range in Romania when compared to other saproxylic species listed in Annex II of EU Habitat Directive (e.g., Cucujus cinnaberinus, Buprestis splendens, Pseudogaurotina excellens, Rhysodes sulcatus ). Saproxylic beetle species occurrences were retrieved (i) public biodiversity databases (Global Biodiversity Information Facility (GBIF.org 2023), (ii) peer review articles and technical reports (supplementary file S1), and (iii) citizen science data from social media (Facebook entomology groups, e.g., Insects of Romania and Europe). Following Marcer et al. (2022), we discarded occurrence data with more than 5 km uncertainty in the GBIF database. To further minimize sampling and spatial bias, we initially applied a thinning process ensuring only one record per grid cell was retained, followed by spatial thinning using spThin R package in order to remove clustered occurrence records within a 2 km radius (Boria et al. 2014). The spThin package uses a randomization approach and returns a dataset with the maximum number of records for a given thinning distance (Aiello-Lammens et al. 2015). The final saproxylic beetle’s occurrence database contains 530 occurrence records: 60 occurrence records for Cerambyx cerdo , 190 for Lucanus cervus , 118 for Morimus funereus , 38 for Osmoderma eremita , and 124 for Rosalia alpina . The grid used in the thinning process was created at the same resolution as the WorldClim database, i.e., ~ 1 km 2 , and was used to resample all data sets used in the study, i.e., occurrence records, environmental data, and protected areas data set. Environmental data for species distribution modelling To model the current distribution of the five saproxylic species in Romania, we used 1970–2000 WorldClim 2.1 database at 30 seconds spatial resolution, i.e., ~ 1 km 2 (Hijmans et al. 2005; Fick and Hijmans 2017). We extracted 19 bioclimatic variables and, to avoid overfitting, we selected for analysis only the variables with a Pearson pairwise correlation <|0.75| (Dormann et al. 2013). The final set of variables used as environmental predictors includes six variables: Isothermality (BIO3) in %, Temperature Annual Range (BIO7) in °C, Mean Temperature of Driest Quarter (BIO9) in °C, and Precipitation Seasonality (BIO15), Precipitation of Warmest Quarter (BIO18), and Precipitation of Coldest Quarter (BIO19), each measured in mm. To infer about future changes in the distribution of the selected species due to climate changes, we used two IPCC emission scenarios (SSP1-2.6 sustainability pathway, i.e., best-case scenario, and SSP5-8.5 fossil-fueled development, i.e., worst-case scenario) from two general circulation models (HadGEM3-GC31-LL and MIROC6) (Hideo et al. 2019; Ridley et al. 2019). HadGEM3 and MIROC6 were selected because they offer diverse climate projections and help cover a wide range of possible future climates that reduce uncertainty in predictions (Thuiller et al. 2019). For each general circulation model under an emission scenario, we selected for modelling three time-horizons, 2021–2040, 2041–2060, and 2061–2080, resulting in 12 modeling cases (Table 1 ). Table 1 Future and current climate data used for modeling species distribution Time frame SSP GCM Acronym Current Baseline Baseline Current range 2021–2040 SSP1-2.6 HadGEM3 Had best-case 2021–2040 SSP5-8.5 HadGEM3 Had worst-case 2021–2040 SSP1-2.6 MIROC6 Miroc best-case 2021–2040 SSP5-8.5 MIROC6 Miroc worst-case 2021–2040 2041–2060 SSP1-2.6 HadGEM3 Had best-case 2041–2060 SSP5-8.5 HadGEM3 Had worst-case 2041–2060 SSP1-2.6 MIROC6 Miroc best-case 2041–2060 SSP5-8.5 MIROC6 Miroc worst-case 2041–2060 2061–2080 SSP1-2.6 HadGEM3 Had best-case 2061–2080 SSP5-8.5 HadGEM3 Had worst-case 2061–2080 SSP1-2.6 MIROC6 Miroc best-case 2061–2080 SSP5-8.5 MIROC6 Miroc worst-case 2061–2080 Saproxylic beetles distribution modeling Current potential distribution of the five saproxylic beetles and predicted future changes were modeled using BIOMOD2 R package for ensemble modeling approach, which consists of running a group of algorithms simultaneously (Thuiller et al. 2014). For each species, we fitted an ensemble SDM based on five modeling techniques, i.e., two regression methods, generalized linear model (GLM) and generalized additive model (GAM), two machine learning methods Random forests (RF) and Generalized boosting model (GBM), and maximum entropy (MAXENT). The use of five algorithms allows for a comprehensive approach that can capture different aspects and a robust approach to predicting species distributions (Thuiller et al. 2014). Because we lacked true absence data for each species, we created pseudo-absences datasets. For this, for each species, we generated ten sets of random pseudo-absence records outside a buffer of 20 km from the presence points. The ratio between pseudo-absences and presence records was 3:1. This ratio was chosen to ensure robust modeling, as a balanced or slightly biased dataset towards absences can improve model performance by reducing overfitting and increasing the model’s ability to discriminate between presence and absence locations (Barbet-Massin et al. 2012). Species distribution models were calibrated using 80% random samples from occurrence data, while the model performance was assessed using the remaining 20% of data (Gholamy et al. 2018). We evaluated the results of SDMs ensemble models using the area under the curve (AUC) of the receiver operating characteristic (ROC) and the true skill statistic (TSS) (Allouche et al. 2006). We also evaluated the contribution of dependent variables in predicting the species range for individual models and ensemble models. We also transformed the probability of occurrence for the models to a binary present/absent using the TSS cut-off value calculated by BIOMOD2 (Hao et al. 2019). For each species, we created 13 ensemble species distribution models, one with current climate data and 12 predictions for 2021–2040, 2041–2060, and 2061–2080 horizons (general circulation model × emission scenario × time horizon, see Table 1 ). In order to develop the ensemble models, we implemented the mean ensemble modeling algorithm (Hao et al. 2019), incorporating only individual models with a TSS > 0.4. Priority areas for conservation of saproxylic beetles Current and 2041–2060 future species distribution data were further used to select priority areas for conservation of saproxylic beetles. Spatial conservation prioritization was performed using Zonation 5 software, a decision-support tool for spatial conservation planning (Moilanen 2022; Moilanen et al. 2022). Zonation produces a priority ranking by iteratively removing grid cells with the lowest total marginal loss of conservation value while accounting for total and remaining distributions of protected saproxylic beetles. It produces a uniform distribution hierarchical ranking of the landscape from highest (1) to lowest (0) conservation value (Kujala et al. 2013; Moilanen 2022). Spatial conservation prioritization was produced using the mean ensemble occurrence probability for each species. We accounted for uncertainty in ensemble model predictions by subtracting the standard deviation of the ensemble model predictions from the mean ensemble values (Moilanen 2022). We included species distribution under the current climate scenario and four species distribution predictions for 2041–2060 timeline (two GCMs × two emission scenarios, see Table 1 ). We use only one time horizon because the earlier (2021–2040) time horizon incorporates our baseline data, and the later (2061–2080) time horizon is associated with higher levels of uncertainty and unpredictability in climate forecasts (Lee et al. 2023). For each scenario, we ran two prioritization analyses: (1) considering Natura 2000 network (Sites of Community Interest and Special Areas for Conservation) as de facto with the highest conservation value (constrained prioritization using protected areas as a hierarchical mask) and (2) selecting a priority area for conservation irrespective of Natura 2000 network (unconstrained prioritization). The former identifies conservation priorities that complement the current protected area network (i.e., best areas to expand the network to accommodate the highest values area for beetle conservation). The latter identifies the highest conservation value areas irrespective of their protection status; this prioritization can then be used to identify representation of beetle species in the current protected area network and highlight gaps in protection. As a prioritization algorithm, we used the Core Area Zonation type marginal loss rule, CAZ2 algorithm (Moilanen et al. 2022). CAZ2 maintains a relatively high average coverage of features while not significantly compromising the performance of the worst-performing features, making it a more suitable approach for achieving the actual conservation goals (Moilanen 2022; Moilanen et al. 2022). We also used condition with renormalization, an additional analysis option that represents information about local habitat condition and their influence on biodiversity features. As a raster layer for habitat condition, we used Corine Land Cover 2018 data (European Environment Agency 2019) to extract two main types of habitats: (1) all types of forests, and (2) all non-forested areas, such as places where agriculture and forestry mix, natural grasslands, and shrubs. The reasoning behind using forest as a condition layer is twofold: (1) our focal species are forest specialists, and (2) the amount of forest cover is not predicted to change over the four decades (Kucsicsa et al. 2020), despite potential changes in forest composition. We calculated the proportion of forest using a 1000 m moving window via function 'focal' in the package 'raster’ for program R. We used the proportion of forest within 1000 m for each map cell as the condition value, with higher values denoting good habitat condition and values of 0 (i.e., no forest) denoting non-habitat. We used a 1000 m moving window based on estimates of movement distance (Drag et al. 2011; Dodelin et al. 2017; Drag and Cizek 2018) and to fully include heterogeneous habitats, for example, traditional wood-pastures in Central Romania (Hartel et al. 2014; Plieninger et al. 2015), which harbor a rich saproxylic beetle fauna. We considered as top spatial conservation priorities all grid cells falling in the top 30% of the predicted priority ranks (rank values = 0.7–1), which maximizes saproxylic beetles representation at the national level and corresponds to the European Union Biodiversity Strategy to achieve 30% protected lands by 2030 (European Commission 2020). Natura 2000 gap analysis We evaluated whether the most suitable habitats, as predicted from ensemble species distribution modelling binary outputs overlap with the Natura 2000 protected areas (hereafter PAs) sites in Romania. We did not consider other Special Protection Areas Natura 2000 sites in our analysis (i.e., SPAs), as they are specifically aimed at protecting bird species under Birds Directive. We extracted the potential numbers of Natura 2000 PAs where each species has a potential presence based on our ensemble projections. We further calculated the percentage of area coverage inside the Natura 2000 PA from the binary distributions. The evaluation was implemented by quantifying the spatial extent of each species’ distribution area within the current Natura 2000 network and calculating the percentage of the total projected distribution area for each species from the total Natura 2000 Sites of Community Importance area. The area of terrestrial Natura 2000 Sites of Community Importance is approximately 40,500 km 2 or ~ 17% of Romania (~ 18% when converting to 1 km 2 raster). Results Beetle species distributions Species distribution models performed well according to TSS and AUC evaluation values for all species under the ensemble models (Table 2 ). The most accurate model is for Cerambyx cerdo , followed by Osmoderma eremita , Lucanus cervus , Morimus funereus , and Rosalia alpina . Table 2 Evaluation of TSS and AUC for the ensemble models by species for current and future scenarios Species TSS AUC Sensitivity Specificity Score Sensitivity Specificity Score Cerambyx cerdo 90.00 84.34 0.75 90.00 84.78 0.95 Lucanus cervus 83.68 81.66 0.66 84.74 81.30 0.91 Morimus funereus 84.75 84.77 0.70 84.75 85.22 0.92 Osmoderma eremita 97.37 76.89 0.74 97.37 77.86 0.94 Rosalia alpina 88.71 75.82 0.65 89.52 75.58 0.90 The distribution of the saproxylic insects showed a diverse response to climate change under the two IPCCs scenarios (supplementary file S2). Cerambyx cerdo and Morimus funereus distributions are best explained by the mean temperature of the driest quarter (BIO9). Lucanus cervus distribution is influenced by the precipitation seasonality (BIO 15) and the precipitation of the warmest quarter (BIO18), while Rosalia alpina distribution is influenced by temperature annual range (BIO7) and by the precipitation of the warmest quarter (BIO18). The distribution of the species Osmoderma eremita is the precipitation of the coldest quarter (BIO19) (Fig. 1 ). Under the current climate, for Cerambyx cerdo our models identified suitable habitats in the western, southern, and eastern parts of the country, where most of the oak forest are present (Fig. 2 ). For Lucanus cervus , substantially uninterrupted suitable habitats were identified in the western part of the country. Most of the suitable habitats for Morimus funereus are found in the west, with some hotspots in the southeastern part of Romania. A more uniform pattern of suitable habitats for Osmoderma eremita is found in the western half of the country, with emphasis on the southwestern and western Carpathian. For Rosalia alpina , the suitable habitats are restricted to the mountainous area in the Carpathians, as well as in the central part of Romania. The binary distribution (presence/absence) also showed larger suitable areas for the saproxylic beetles than indicated from known presences. Osmoderma eremita has the largest distribution area of all saproxylic beetles, covering over 62,000 km 2 , followed by Rosalia alpina with a distribution of 57,000 km 2 , Lucanus cervus with 45,000 km 2 , Cerambyx cerdo with 42,000 km 2 , and Morimus funereus with the smallest area of 37,000 km 2 . Changes in species distributions The most significant decline in suitable habitat is observed for Cerambyx cerdo , showing an average area loss of 75% for all projections, with a maximum loss of 100% in the case of Had best-case 2061–2080 scenario, and a minimum loss of 35% for Had best-case 2021–2040 scenario. Lucanus cervus is expected to experience a 99% decrease in suitable habitat according to Had worst-case 2061–2080 scenario and an increase of 3.5% in suitable habitat under the Had best-case 2021–2040 scenario. In comparison, Osmoderma eremita will lose a maximum of 84% under Had worst-case 2061–2080 scenario and a minimum loss of 18% under Had best-case 2021–2040 scenario. Rosalia alpina will lose a maximum of 88% under Had worst-case 2061–2080 scenario and a gain of 15% in suitable habitat under Had best-case 2021–2040 scenario. Furthermore, Morimus funereus will likely suffer the smallest reduction in range, with an average decrease of 25% of the suitable habitat (Table 3 ). Table 3 Range change (%) under future climate conditions (two general circulation models × two emission scenarios × three time horizons) Species distribution model (Ensemble mean) Cerambyx cerdo Lucanus cervus Morimus funereus Osmoderma eremita Rosalia alpina Had best-case 2021–2040 -35.9 + 3.5 + 13.6 -18.6 + 15.7 Had worst-case 2021–2040 -89.2 -69.6 -27.1 -45.2 -33.9 Miroc best-case 2021–2040 -51.8 -37.2 -2.2 -61.6 -20.1 Miroc worst-case 2021–2040 -70.8 -35.1 + 4.8 -51.8 -8.3 Had best-case 2041–2060 -70.4 -91.4 -35.6 -27.1 -31.4 Had worst-case 2041–2060 -98.6 -83.9 -45.4 -49.3 -45.5 Miroc best-case 2041–2060 -66.4 -35.3 -9.4 -63.2 -22.1 Miroc worst-case 2041–2060 -69.1 -41.6 -14.8 -65.5 -24.8 Had best-case 2061–2080 -100 -94 -45.7 -42 -39.4 Had worst-case 2061–2080 -99.9 -99.6 -91.5 -84 -88 Miroc best-case 2061–2080 -56.1 -39.4 -17.9 -64.4 -30.1 Miroc worst-case 2061–2080 -92 -58.1 -42.4 -69.8 -43.6 The HadGEM3-GC31-LL and MIROC6 climate models differ significantly. The Had best-case from 2021–2040 shows habitat increases for some species; however, species are likely to lose over 50% of their habitats, with some losing up to 100%. MIROC6 also forecasts substantial habitat loss, with potential losses between 50–90% for some species. Spatially, future distribution pattern of Cerambyx cerdo and Lucanus cervus is characterized by a relatively widespread distribution across Romania. In contrast, Osmoderma eremita and Rosalia alpina show a more localized presence, predominantly in mountainous regions. Lastly, Morimus funereus presents a unique distribution pattern, blending aspects of the other four species. HadGEM3-GC31-LL model predicts a more drastic change, characterized by a more dispersed distribution of species and a bigger loss of suitable area for the species. On the other hand, MIROC6 model indicates a more concentrated distribution of species, primarily within and around the Carpathians Mountains, where their habitats are currently located (Fig. 3 ). Conservation priorities for protected saproxylic beetles Based on the results from the Zonation analysis, when integrating the species distribution models for unconstrained prioritization (i.e., without considering Natura 2000 sites as high priority), we found that top 30% spatial conservation priorities for saproxylic beetles shifted across the current and future climate change scenarios. For the current unconstrained prioritization, the top priority areas for saproxylic beetles overlap southern, southeastern, and western Carpathians, sub-Carpathians, and the central part of Romania, and several hotspots in the eastern and southeastern parts of the country, with 85% of the species distribution retained (Fig. 4 a). When comparing the current unconstrained top priority areas with the top priority areas for the Had best-case 2041–2060 unconstrained scenario, the analysis revealed a shifting of the areas to the eastern Carpathian Mountains, while the Had worst-case 2041–2060 unconstrained scenario showed a relatively aggregated pattern of the top priority areas in the Carpathians and a drastic reduction of the priority areas in sub-Carpathians and the southeastern region. Regarding the Miroc best-case 2041–2060 scenarios, the top priority areas were distributed in the Carpathians and the eastern part of the country. For Miroc worst-case 2041–2060 scenario, the analysis revealed more aggregated areas in the Carpathians. For both best-case and worst-case scenarios, an average of 90% of species distribution is retained. Regarding the species, top priority areas maintained a higher representation (almost 95%) for Osmoderma eremita , under Had worst-case 2041–2060 and Miroc worst-case 2041–2060 (Fig. 5 ). In the current constrained prioritization, the actual Natura 2000 network (Sites of Community Interest) encompasses almost 18% of the highest conservation value, retaining an average species representation of about 35% for all the species (supplementary file S3). By keeping Natura 2000 sites as areas with highest priority ranks, the next 12% of the priority conservation areas to identify the optimal locations needed to expand the network up to 30% revealed potential areas for the Natura 2000 sites in the western Carpathians, southern and southeastern sub-Carpathians and central part of the country (Fig. 4 b), with an average of 85% of the saproxylic beetles' species representation retained (supplementary file S3). When comparing the current constrained top priority areas with the top priority areas for the Had best-case 2041–2060 and the Had worst-case 2041–2060 constrained scenarios, the 12% priority areas for conservation revealed a shift of the priority habitats to the higher elevation areas of the Carpathians (Fig. 5 ). The 12% top priority areas for Miroc best-case 2041–2060 scenario revealed slightly more areas extended to the eastern Carpathians, while for Miroc worst-case 2041–2060 scenario, the top priority areas revealed a more aggregated pattern to the eastern Carpathians and a reduction of areas in the southwestern Carpathians (Fig. 5 ). The constrained prioritization analysis for current and future scenarios revealed that the species with the best representation is Osmoderma eremita , which, when considering expanding to 30% of the landscape as protected, will retain 90% of species representation (supplementary file S3). Natura 2000 gap evaluation The Natura 2000 network covers only a small part of the saproxylic beetle distributions. Specifically, only 13.5% of the Cerambyx cerdo current distribution is protected by the network. Similarly, the Lucanus cervus habitat is only 20% covered, while for Morimus funereus , 25% of its habitat falls within the network. Lastly, the habitats of both the Osmoderma eremita and Rosalia alpina are better represented, with 40% included in the Natura 2000 network. The trend is also present in the number of PAs that currently include these species, compared to the number of possible PAs that the species can be present in. For example, Cerambyx cerdo is currently listed in only 52 Natura 2000 Standard Data forms, but the current projection overlaps more than 196 PAs from the current Natura 2000 network. In comparison, Lucanus cervus is currently included in 86 Natura 2000 PAs, while the current projection overlaps more than 190 PAs. Similarly, Morimus funereus is included in 40 PAs, with potential to be in more than 130 sites. Out of all the species, the representation of the species Osmoderma eremita is noticeably insufficient within PAs, while is included in only 16 sites, the species has the potential distribution in over 180. Lastly, Rosalia alpina is included in only 42 sites, with potential presence in 194 sites for the current projection. The future projection also shows a drastic reduction in species distribution coverage of the Natura 2000 network. Cerambyx cerdo shows the biggest loss of suitable habitats, with reductions in both the number of protected areas available for its conservation (supplementary file S4) and the extent of which the current Natura 2000 network can cover the species distribution in the future. In most cases, the existing Natura 2000 network inadequately represents this species, covering only 10% of its distribution. In contrast, Rosalia alpina has the best-represented distribution by the current Natura 2000 network in the future projection. The majority of the projection indicates that, in all future projections, the current protected area network can cover about 40% of the species distribution. Discussion Our study revealed that the forecasted climate change would induce significant shifts in saproxylic beetles’ range, with most analysed saproxylic beetles losing over 80% of their suitable habitat under most future projections. As a general pattern, in the future, the saproxylic beetles will restrict their distribution to the Carpathians due to favorable climatic and environmental conditions and reduce their distribution in the lowland areas. We used a systematic conservation planning approach to identify the conservation priority areas for saproxylic beetles, and we found that for both unconstrained (i.e., current PAs network not considered) and constrained prioritization (i.e., current PAs network not retained as high priority areas) scenarios the top 30% spatial conservation priorities will shift across the current and future climate change scenarios with top priority areas overlapping the Carpathian Mountains with an average of 85% of species distribution retained. Under current conditions, the existing Natura 2000 PAs does not perform well for saproxylic beetles, as only a small percent of their distribution is covered by the current network. Contrary to Bosso et al. (2018), for certain saproxylic beetles (e.g., Rosalia alpina ), we found that for future projection, a large area of suitable habitats will be lost, and Cerambyx cerdo is going to be the species with the most potential area lost, followed by Lucanus cervus . Future distribution changes Future projections for the analysed saproxilic beetles' distributions showed that a large area of suitable habitats will be lost by 2041-60. We found out that Cerambyx cerdo will lose the most suitable area, with over 90% of its suitable habitat under Had best-case 2061–2080 projection, while Lucanus cervus experienced over 80% decrease under Had worst-case 2061–2080 projection. Morimus funereus may lose up to 90% of suitable habitats under Had worst-case 2061–2080 projection and Rosalia alpina around 88% under Had worst-case 2061–2080 projection. The HadGEM3-GC31-LL model predicts a more drastic change in the suitable habitats with a bigger loss of suitable areas for the species. Under the MIROC6 model, the Rosalia alpina loses the most suitable habitat, almost 43%, at the same time indicating a more concentrated distribution for all species around mountainous regions of the Carpathians (Fig. 3 ). These responses support previous findings in Europe, indicating that under future climate scenarios, there will be a reduction of saproxylic beetles distribution, and the species habitat will be restricted to higher altitudinal areas (Poloni et al. 2022). We found that Cerambyx cerdo is likely to lose almost 75% of its suitable areas due to the species requirements for old-growth oak forested habitats (Redolfi De Zan et al. 2017; Manu et al. 2017), which can be found at lower elevations and are the one most affected by future climate change and fragmentation (Parisi et al. 2018), as well as the decline in the number of old growth trees found in wood-pasture and semi-open habitats (Hartel et al. 2013; Redolfi De Zan et al. 2017; Torres-Vila 2017). For Morimus funereus and Rosalia alpina , the average for all the losses or gains for suitable areas are ~ 26% and 30%, respectively. Osmoderma eremita and Lucanus cervus are likely to lose over 50% of their habitats across all projections, a pattern also highlighted by other studies in Europe (Della Rocca and Milanesi 2020) (Table 3 ). While in the future some species may lose up to 90% of their suitable areas, some of the projections show a slight increase in some models (Had best-case 2021–2040 – Lucanus cervus, Morimus funereus, Rosalia alpina ), findings also stated by Della Rocca and Milanesi, (2020), which found that climate change can have a positive effect on species distribution. Most of the projections show a decrease in distribution of suitable areas across the country. The most stable species under both GCMs and SSPs are Morimus funereus and Rosalia alpina . While the species are losing suitable area, compared to the other species with dramatic losses, such as Lucanus cervus and Cerambyx cerdo , these species show a more stable pattern. Our results showed the importance of Carpathians mountains as suitable habitats for the saproxylic beetles under future climate conditions. The saproxylic beetles will tend to concentrate in the Carpathian region due to favorable climatic conditions, while isolated lowland forested patches will disappear due to extreme climatic conditions and land use change (Mikolāš et al. 2023). These findings corroborate with other studies in Europe, which highlighted that future emission scenarios show a general reduction in suitable habitats for saproxylic beetles and a shift towards higher altitudes (Della Rocca et al. 2019). Priority areas for saproxylic beetles The current unconstrained spatial prioritization analysis showed that Carpathians Mountains and the western part of the country had consistently high conservation value for the saproxylic beetles and represent a refuge for these species under both current and future climate conditions, being a region which harbors most of the remaining old-growth forests in Romania (Veen et al. 2010; Knorn et al. 2013), (Fig. 4 , 5 ). Furthermore, based on the two HadGEM3-GC31-LL future scenarios, climate change is anticipated to lead to a decline in priority areas for saproxylic beetles in the lowland areas, especially in the central and eastern regions. In comparison, for the two MIROC6 future scenarios, climate change is more conservative, preserving priority areas in the Carpathians and some priority hotspots in the eastern part of the country and western Transylvania (Fig. 5 ). One of the factors that lead to the restriction of the habitat towards higher elevation areas is represented by climate conditions in the current habitat, which are expected to adversely impact the saproxylic beetles due to increased droughts and higher temperatures, as well as a possible reduction in the lowland forested habitats threatened more by agricultural intensification and development (Seibold et al. 2015). Hence, our findings align with the conclusions drawn by other researchers (Bosso et al. 2018; Della Rocca et al. 2019), that higher altitudinal sites will offer favorable climate conditions for saproxylic beetles. For the current unconstrained scenario, our results from the spatial prioritization analysis showed that when considering 30% of the landscape as protected, about 80% of the species distribution is retained and did not overlap completely with Natura 2000 sites. Similarly, Miu et al., (2020) demonstrated that a high proportion of invertebrate species are not covered by Natura 2000 sites. Highest conservation value was found to be outside the protected areas in the western and southwestern parts of the country, as well as in the southern and eastern sub-Carpathians, with small hotspots in the central and eastern parts of the country, with the most vulnerable areas also located in the southern and eastern Romania, with a huge reduction in the species suitable habitats in future scenarios. Although Natura 2000 PAs encompass only 18% of Romania’s landmass (Miu et al. 2020), ~ 30% of the saproxylic species distributions fall inside protected areas, with a high number of potentially suitable PAs for saproxylic beetles uncovered by their distribution (supplementary file S3). For the current constrained scenario, the prioritization analysis retained an average species representation of about 40% for all saproxylic species for current and future scenarios, less than half of their distribution in Romania (supplementary file S3). The performance of the Natura 2000 coverage in protecting saproxylic beetles was also questioned by several authors who highlighted that a similar percentage or less of their distribution is protected (Bense and Bussler 2003; Viñolas and Vives 2012; Bosso et al. 2013, 2018; Lachat et al. 2013). Regarding the species Rosalia alpina , one of the most charismatic in Europe (Campanaro et al. 2017), we found out that only 35% of species distribution is retained by Natura 2000 PAs, values close to the ones stated in the previous studies (supplementary file S3). The species with the least retained distribution in Natura 2000 PAs (32%) is Lucanus cervus , a species inhabiting mature deciduous forests from lowland and oak forests having rotten dead wood at ground level (Bardiani et al. 2017; Méndez and Thomaes 2021). This situation occurs in other EU countries; Thomaes et al. (2008) highlighted that 11% of the species distribution is covered by Natura 2000 network in Belgium. Osmoderma eremita was the only species whose coverage in PAs increased slightly from 30% under current to 45% under Had worst-case 2041–2060 and Miroc worst-case 2041–2060 scenarios. Lastly, under the constrained scenario, when considering the additional 12% of the landscape for the optimal expansion of Natura 2000, an average of 80% of the saproxylic beetles species representation is retained, with the best representation for Osmoderma eremita (90% of distribution protected). Under the constrained scenario, most of the priority areas will be restricted to the Carpathian region, which harbors continuously forested habitats suitable for saproxylic insect development. Therefore, the Carpathians will represent a refuge for the saproxylic beetles in the future. Effectiveness of Romanian Natura 2000 PAs network for protecting saproxylic beetles The current Natura 2000 PAs network does not achieve the EU conservation targets for the five listed saproxylic beetle species (Rosalia alpina, Lucanus cervus, Cerambyx cerdo, Osmoderma eremita , and Morimus funereus ) in Romania, and its effectiveness is put into question, as may not be sufficient to protect and conserve the saproxylic insects. However, it is anticipated that under some future emission scenarios, the saproxylic beetles will experience increased levels of representation. The decrease in species distributions as a result of climate changes leads to a change in the distribution of species, pushing the distribution of species to higher elevation areas, such as the Alpine biogeographic region, which includes a high number of Natura 2000 PAs (Popescu et al. 2013). As such, we predict a significant increase in the representation of saproxylic insect species by the Natura 2000 network under Had best-case 2021–2040, Had worst-case 2021–2040, and all Miroc best-case and worst-case scenarios. The evaluation of Natura 2000 current PAs network covers only a small part of the saproxylic beetles’ distributions, with the gap analysis revealing insufficient representation of the beetles. The gap analysis results revealed that the current representation of the saproxylic beetles is noticeably insufficient within PAs, with most of the species reported in fewer protected Natura 2000 sites than their current potential distribution (supplementary file S4). For example, Osmoderma eremita is reported in only 16 sites, while the species has the potential distribution in over 180 sites. Unfortunately, in Romania, there is a knowledge gap regarding this elusive species, which requires certain habitat types, such as hollow-bearing old trees with dead wood and specific climatic factors, to develop (Chiari et al. 2013; Maurizi et al. 2017). A recent study by Cazzolla Gatti et al., (2023) regarding the distribution of strictly protected areas under EU 2030 strategy revealed that for Romania, the Steppic and the Continental biogeographic regions offer very limited protection to biodiversity and rare species; these findings corroborate, studies by Miu et al. (2020) and Popescu et al. (2013), which found an urgent need for expanding the protected areas from these regions, as some of the protected saproxylic beetles are dependent of old trees in open or semi-open landscapes from these regions (Stan and Nitzu 2013; Manu et al. 2017, 2019). These findings underscore the limitations of relying solely on presence records for conservation gap assessments. Our results emphasize the significance of incorporating species distribution modeling and spatial planning techniques to estimate the probability of presence of saproxylic species and the coverage of protected areas, enabling more effective planning of protection measures and enhancing species management strategies. Our study represents the first comprehensive evaluation of the priority suitable habitats of saproxylic species in Romania, providing valuable resources for future investigations, including the identification of connectivity corridors utilized by the species and the prediction of suitable habitats in response to climate change. 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Annu Rev Entomol 65:457–480. https://doi.org/10.1146/annurev-ento-011019-025151 Wintle BA, Kujala H, Whitehead A, et al (2019) Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc Natl Acad Sci USA 116:909–914. https://doi.org/10.1073/pnas.1813051115 Zehetmair T, Müller J, Zharov A, Gruppe A (2015) Effects of Natura 2000 and habitat variables used for habitat assessment on beetle assemblages in European beech forests. Insect Conserv Diversity 8:193–204. https://doi.org/10.1111/icad.12101 Additional Declarations No competing interests reported. Supplementary Files SupplementaryS1occreferences.docx SupplementaryS2variablecurves.docx SupplementaryS3zonationcurves.docx SupplementaryS4speciesPA.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 20 Feb, 2024 Submission checks completed at journal 19 Feb, 2024 First submitted to journal 19 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3969647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273825945,"identity":"7e537df1-4eab-4e1f-b325-edf72dd80304","order_by":0,"name":"Marian Dumitru Mirea","email":"","orcid":"","institution":"University of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marian","middleName":"Dumitru","lastName":"Mirea","suffix":""},{"id":273825946,"identity":"92f6d0ae-5cf3-4c44-89f8-79f6419c42a1","order_by":1,"name":"Iulia Viorica Miu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNgbmhgNAmoeBvcEASAJFJAhqYYRq4TlApBYGoBYILZEA0cJASAufRGLjgR8M22QMbj7e9uENw+E8Pukeww+Me2xwO0wiseFgD8NtHoPbacUz5zAcLmaTOWMswfAsDa+WAzxgLTnGzDwMhxPbJHLMGBgOHMZvyx+QlptnSNByGGzLDR5itfA8bDgsY3CbR/JMWjHjHIP0xDaZY8USCQdw+0W+PfnwxzcVt+35jh/ezPCmwjpx/uzmjR8+HMAdYhBggM5IIKBhFIyCUTAKRgF+AAD/Ek++EneY9gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bucharest","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Iulia","middleName":"Viorica","lastName":"Miu","suffix":""},{"id":273825947,"identity":"7daf5e09-b6ae-43d7-9a32-988654e94f3e","order_by":2,"name":"Viorel Dan Popescu","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Viorel","middleName":"Dan","lastName":"Popescu","suffix":""},{"id":273825948,"identity":"0457e040-d22a-4147-a2c6-9c0f386f02c4","order_by":3,"name":"Bekka S. Brodie","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bekka","middleName":"S.","lastName":"Brodie","suffix":""},{"id":273825949,"identity":"d809954f-bf4c-4015-8eda-5c21fa2671e3","order_by":4,"name":"Silviu Chiriac","email":"","orcid":"","institution":"Vrancea Environmental Protection Agency","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Silviu","middleName":"","lastName":"Chiriac","suffix":""},{"id":273825950,"identity":"f8687d6b-e493-4f48-bbe7-fb08022b26b4","order_by":5,"name":"Laurentiu Rozylowicz","email":"","orcid":"","institution":"University of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laurentiu","middleName":"","lastName":"Rozylowicz","suffix":""}],"badges":[],"createdAt":"2024-02-19 09:46:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3969647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3969647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51425419,"identity":"da9fa4df-756a-4274-9e30-f56e14d8bc19","added_by":"auto","created_at":"2024-02-21 11:23:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36559,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative importance of environmental variables used to model saproxylic beetles’ distribution. Box plot = IQR of relative importance of individual models, vertical line = median, whiskers = outliers; black diamond = median importance ensemble model.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/10eaae139ba143ba9d897702.png"},{"id":51425420,"identity":"dc866497-1bba-4592-bc77-7187c5a0736b","added_by":"auto","created_at":"2024-02-21 11:23:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5016121,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies distribution models for the base line in Romania (A – Cerambyx cerdo; B –Lucanus cervus; C – Morimus funereus; D – Osmoderma eremita; E – Rosalia alpina)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/ec6a354b3f3d3c672bba7638.png"},{"id":51425429,"identity":"c99012df-2395-4670-be79-2d01e8f297d9","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4799033,"visible":true,"origin":"","legend":"\u003cp\u003eProjection distribution patterns of saproxylic beetles for time horizon 2041-2060 under two emission scenarios: (SSP1-2.6 – best-case scenario \u0026amp; SSP5-8.5- worst-case scenario)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/4d44259ca698696217c65d25.png"},{"id":51426042,"identity":"fbc5611a-6406-4aa7-ba92-4d432155d12c","added_by":"auto","created_at":"2024-02-21 11:31:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4215618,"visible":true,"origin":"","legend":"\u003cp\u003ePriority rank maps for the current climate, with unconstrained prioritization (A) and with constrained prioritization (B). Areas have been graded according to their priority rank, with highest priorities in red (top 30%, ranks 0.7 - 1). Natura 2000 protected areas (SCIs – Sites of Community Interest) are represented by the grey color.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/50c8fa8f8e496d7daf57e3fb.png"},{"id":51425424,"identity":"074a35da-602c-4aa6-83a8-d5e72a255042","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7474403,"visible":true,"origin":"","legend":"\u003cp\u003ePriority rank maps with unconstrained prioritization (A) and with constrained prioritization (B) for Had best-case 2041-2060, Had worst-case 2041-2060, Miroc best-case 2041-2060 and Miroc worst-case 2041-2060 scenarios. Areas have been graded according to their priority rank, with highest priorities in red (top 30%, ranks 0.7 - 1). The Natura 2000 protected areas (SCIs – Sites of Community Interest) are represented by the grey color.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/44ff8b4fb9f66125406aecf6.png"},{"id":51425427,"identity":"449175ce-1f14-4daf-8377-82a1e580dc90","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":61464,"visible":true,"origin":"","legend":"\u003cp\u003eThe proportion of saproxylic beetles’ distributions within the protected area network (Natura 2000 SCIs) under current and future projections.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/77ad8ac1c956675267c2010e.png"},{"id":51426378,"identity":"9692da49-4b11-4864-988f-4ec28121b143","added_by":"auto","created_at":"2024-02-21 11:39:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5843500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/a5c06494-c1b7-4934-8023-c80e8d35f037.pdf"},{"id":51425422,"identity":"a3b1e4c8-a5f7-4d1a-a546-4252dd0fa6d7","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17931,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS1occreferences.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/1cd133bc1619b8f50f071d8e.docx"},{"id":51425425,"identity":"8f50ffbc-2c8f-4ea7-83ab-4be02962f4ca","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44996,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS2variablecurves.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/1a3960fe5039efbef242a1e9.docx"},{"id":51425421,"identity":"686205ef-0803-4e9d-9d72-5fb9971b0593","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":178116,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS3zonationcurves.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/3ee1a09b91126209f5be385b.docx"},{"id":51425423,"identity":"93aa2936-4d80-416e-9400-b896e693fc97","added_by":"auto","created_at":"2024-02-21 11:23:43","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15984,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryS4speciesPA.docx","url":"https://assets-eu.researchsquare.com/files/rs-3969647/v1/fb089ac97585d391522a7a81.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Priority conservation areas for protected saproxylic beetles in Romania under current and future climate scenarios","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman-induced climate change contributes to changing the landscape on an unprecedented scale, endangering habitats and species (Mantyka-Pringle et al. 2012; Harvey et al. 2023). The most visible impact is the increase in temperature when compared to pre-industrial levels, with Europe being the fastest-warming continent in the world (Kjellstr\u0026ouml;m et al. 2018). Under these conditions, habitats important for saproxylic beetles, such as old-growth forests, are highly likely to experience severe pressure and threats (La Porta et al. 2008; European Commission 2021). Because of the importance of forests for human well-being and biodiversity, the European Union (EU) not only set an overall goal of protecting at least 30% of the EU\u0026rsquo;s land area under an effective management regime, of which one-third should be strictly protected but considered old-growth forests as a priority for including under strict protection (European Commission 2021).\u003c/p\u003e \u003cp\u003eSaproxylic beetles are deadwood specialists and keystone species in maintaining forest ecosystems. The diversity of saproxylic beetles is an indicator of healthy forest ecosystems (Jansson et al. 2009; Mazzei et al. 2018), and thus, the conservation of beetles is now a priority for EU Member States. Yet, insects are declining at an alarming rate in both modified and intact landscapes (Seibold et al. 2015; Wagner 2020). In Europe, species associated with deadwood habitats are among the most threatened taxa by intensive forest management practices and habitat fragmentation (Nieto and Alexander 2010). Due to saproxylic beetles\u0026rsquo; affinity for dead and dying hardwood, their diversity is positively correlated with the amount and diversity of deadwood (Lassauce et al. 2011; Lachat et al. 2012; Seibold et al. 2018), and therefore a decline in old-growth forest habitats has led to reducing the ranges of several species. In Europe, there are 21 saproxylic beetle species listed in the EU Habitats Directive, including species such as \u003cem\u003eRosalia alpina\u003c/em\u003e, \u003cem\u003eCerambyx cerdo\u003c/em\u003e, and \u003cem\u003eLucanus cervus\u003c/em\u003e (C\u0026aacute;lix et al. 2018). The EU has developed strategies for the recovery of saproxylic beetles, yet range shifts and forest structure changes have not yet been accounted for in conservation planning.\u003c/p\u003e \u003cp\u003eAmong the European countries, Romania harbors the most continuously forested areas, which includes the Carpathian Mountains, a biodiversity hotspot for saproxylic beetles (Munteanu et al. 2022; Stanciu et al. 2023). These forests shelter several of the most iconic saproxylic insects, which are listed in Annex II of Habitats Directive (Directive/92/43/EEC 1992): \u003cem\u003eRosalia alpina, Lucanus cervus, Cerambyx cerdo, Osmoderma eremita\u003c/em\u003e and \u003cem\u003eMorimus funereus\u003c/em\u003e, and their populations are deemed to be viable. Past forestry management practices in the Carpathians, which include selective logging and removal of deadwood and old trees, degraded the forest structure and led to local decrease of species abundance (Prunar et al. 2013; Olenici and Fodor 2021). Additionally, Romanian forest managers are faced with conflicting mandates with regard to these species, i.e., either consider them as pest species and apply lethal methods to lower population and damage or protect them as threatened and endangered species per the EU environmental mandates (Brodie et al. 2019). As a result, forest managers neither have the incentives nor the scientific information on the saproxylic beetle community to promote concrete conservation actions. Thus, the most important tool in protecting these species is the inclusion of their habitats in Natura 2000 network, one of the most extensive networks of conservation areas in the world, which has been created to operationalize EU Birds (Directive 2009/147/EC 2009) and Habitats Directives (Directive/92/43/EEC 2013). However, designation of Natura 2000 sites often lacks clear, quantifiable conservation objectives or extensive spatial planning, as highlighted in previous studies (Iojă et al. 2010; Kukkala et al. 2016a; Miu et al. 2020; Cazzolla Gatti et al. 2023). Frequently, these designations result from a pursuit of area-based targets established by the European Union for country-level protection, with a specific goal of protecting 30% of each EU country by 2030 (European Commission 2020).\u003c/p\u003e \u003cp\u003eAn effective approach for establishing a network of protected areas that aligns with EU targets is the application of systematic conservation planning (Margules and Pressey 2000). This framework aims to optimize conservation benefits while minimizing adverse impacts on other resources. Spatial conservation prioritization, as part of systematic conservation planning, typically employs algorithms that account for complementarity and representativeness of species and communities to identify areas that complement each other to prevent redundant conservation efforts (Mikusiński et al. 2007; Kukkala et al. 2016b; Kujala et al. 2018). This strategy is widely recognized as an efficient instrument for identifying spatial priorities and to effectively achieve conservation objectives (Wintle et al. 2019). In Europe, few studies have assessed the distribution of saproxylic insects and the effectiveness and representativity of Natura 2000 in protecting these species, and the conclusion tended to highlight suboptimal planning (D\u0026rsquo;Amen et al. 2013; Zehetmair et al. 2015; Bosso et al. 2018). For example, Bosso et al., 2013 pointed out that Natura 2000 network protects less than 56% of \u003cem\u003eRosalia alpina\u003c/em\u003e\u0026rsquo;s suitable habitat in Italy, while Lachat et al., 2013 found that only 11% of its suitable habitat is protected in Switzerland. In their study, Bosso et al., 2018 found that only 25% of \u003cem\u003eRosalia alpina\u003c/em\u003e\u0026rsquo;s potential distribution is covered by Natura 2000 sites from France, and only 35% of its suitable areas are protected in Austria. For Romania, few attempts have been made to assess the coverage and the effectiveness of Natura 2000 in protecting species listed in Annex II of Habitats Directive (Iojă et al. 2010; Popescu et al. 2013; Miu et al. 2018, 2020), due to limitations associated with the availability of occurrence data. Specifically for the saproxylic insects, most studies have been aimed at evaluating insects diversity in small areas (Stan and Nitzu 2013; Bărbuceanu et al. 2015; Manu et al. 2016, 2017, 2019; Stan et al. 2016; Maican et al. 2019; Brodie et al. 2019). Thus, there is an urgent need for systematic conservation approaches at the national level to identify the representation of these taxa in the current protected area network and to highlight gaps and additional areas for protection to achieve national, European, and global protection targets. Such approaches would also benefit forest biodiversity conservation in general, as saproxylic beetles can serve as surrogates for other taxa (Ranius 2002; Holland 2007; Foit et al. 2016).\u003c/p\u003e \u003cp\u003eThe aim of our study is to identify conservation priorities for five listed saproxylic beetle species (\u003cem\u003eRosalia alpina, Lucans cervus, Cerambyx cerdo, Osmoderma eremita\u003c/em\u003e, and \u003cem\u003eMorimus funereus\u003c/em\u003e) in Romania, under current and future climate change scenarios. First, we developed species distribution models for current and three-time future horizons (2021\u0026ndash;2040, 2041\u0026ndash;2060, and 2061\u0026ndash;2080). We then used a systematic conservation planning approach in combination with forest cover information to identify the representation of these species in the current protected area system and to identify additional priorities to meet EU conservation targets. Specifically, our objectives are: 1) to map the distribution of five target species by using an ensemble modeling approach; 2) to assess the changes in the distribution of saproxylic beetles due to climate change; 3) to assess the effectiveness and resilience to climate change of Romanian Natura 2000 network for saproxylic beetles, and 4) identify future areas for protected area expansion. Overall, this research aims to aid current efforts from Romanian and EU decision-makers to identify optimal areas for protected area expansion and to develop management plans that include forest conservation strategies for saproxylic beetles.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area, species, and occurrence data\u003c/h2\u003e \u003cp\u003eRomania is a hotspot of biodiversity in Europe, overlapping five European biogeographical regions, i.e., Alpine, Continental, Pannonian, Steppic, and Black Sea (Rozylowicz et al. 2019). Over 27% of its territory is covered by forest habitats (Munteanu et al. 2016), with more than 3.5% represented by old-growth forests (Knorn et al. 2013). Forest habitats are dominated by broadleaved species (70% of the forest habitats, mostly \u003cem\u003eQuercus\u003c/em\u003e ssp. and \u003cem\u003eFagus sylvatica\u003c/em\u003e as dominant species) and deciduous species (30% of the forest habitats, mainly \u003cem\u003ePicea abies\u003c/em\u003e and \u003cem\u003eAbies alba\u003c/em\u003e as dominant species) (Veen et al. 2010). Due to the large extent of old-growth and less intensively managed forests, Romania is also considered a hotspot of saproxylic beetle biodiversity (Nieto and Alexander 2010). Of over 200 saproxylic beetles\u0026rsquo; reported from Romania, over 20 are protected by Habitats Directive (G\u0026icirc;dei and Popescu 2012, 2014; Directive/92/43/EEC 2013; Fusu et al. 2015). Natura 2000 protected areas created for Habitats Directive species and habitats include 425 Sites of Community Interest, covering 40500 km\u003csup\u003e2\u003c/sup\u003e, i.e., 17% of Romania (European Environment Agency 2021).\u003c/p\u003e \u003cp\u003eFor this study, we selected five saproxylic species listed in Annex II of EU Habitat Directive (Directive 92/43/EEC, 1992), i.e., the alpine longicorn \u003cem\u003eRosalia alpina\u003c/em\u003e Linnaeus, 1758, the Morimus longicorn \u003cem\u003eMorimus funereus\u003c/em\u003e Mulsant, 1863 and the great capricorn beetle \u003cem\u003eCerambyx cerdo\u003c/em\u003e Linnaeus, 1758 of family Cerambycidae, the hermit beetle \u003cem\u003eOsmoderma barnabita\u003c/em\u003e Motschulsky 1845, part of Habitats Directive \u003cem\u003eOsmoderma eremita\u003c/em\u003e complex (family Scarabaeidae) and the stag beetle \u003cem\u003eLucanus cervus\u003c/em\u003e (Linnaeus 1758) (family Lucanidae). These species were selected because they have a relatively extensive range in Romania when compared to other saproxylic species listed in Annex II of EU Habitat Directive (e.g., \u003cem\u003eCucujus cinnaberinus, Buprestis splendens, Pseudogaurotina excellens, Rhysodes sulcatus\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eSaproxylic beetle species occurrences were retrieved (i) public biodiversity databases (Global Biodiversity Information Facility (GBIF.org 2023), (ii) peer review articles and technical reports (supplementary file S1), and (iii) citizen science data from social media (Facebook entomology groups, e.g., Insects of Romania and Europe). Following Marcer et al. (2022), we discarded occurrence data with more than 5 km uncertainty in the GBIF database. To further minimize sampling and spatial bias, we initially applied a thinning process ensuring only one record per grid cell was retained, followed by spatial thinning using spThin R package in order to remove clustered occurrence records within a 2 km radius (Boria et al. 2014). The spThin package uses a randomization approach and returns a dataset with the maximum number of records for a given thinning distance (Aiello-Lammens et al. 2015). The final saproxylic beetle\u0026rsquo;s occurrence database contains 530 occurrence records: 60 occurrence records for \u003cem\u003eCerambyx cerdo\u003c/em\u003e, 190 for \u003cem\u003eLucanus cervus\u003c/em\u003e, 118 for \u003cem\u003eMorimus funereus\u003c/em\u003e, 38 for \u003cem\u003eOsmoderma eremita\u003c/em\u003e, and 124 for \u003cem\u003eRosalia alpina\u003c/em\u003e. The grid used in the thinning process was created at the same resolution as the WorldClim database, i.e., ~\u0026thinsp;1 km\u003csup\u003e2\u003c/sup\u003e, and was used to resample all data sets used in the study, i.e., occurrence records, environmental data, and protected areas data set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental data for species distribution modelling\u003c/h2\u003e \u003cp\u003eTo model the current distribution of the five saproxylic species in Romania, we used 1970\u0026ndash;2000 WorldClim 2.1 database at 30 seconds spatial resolution, i.e., ~\u0026thinsp;1 km\u003csup\u003e2\u003c/sup\u003e (Hijmans et al. 2005; Fick and Hijmans 2017). We extracted 19 bioclimatic variables and, to avoid overfitting, we selected for analysis only the variables with a Pearson pairwise correlation \u0026lt;|0.75| (Dormann et al. 2013). The final set of variables used as environmental predictors includes six variables: Isothermality (BIO3) in %, Temperature Annual Range (BIO7) in \u0026deg;C, Mean Temperature of Driest Quarter (BIO9) in \u0026deg;C, and Precipitation Seasonality (BIO15), Precipitation of Warmest Quarter (BIO18), and Precipitation of Coldest Quarter (BIO19), each measured in mm.\u003c/p\u003e \u003cp\u003eTo infer about future changes in the distribution of the selected species due to climate changes, we used two IPCC emission scenarios (SSP1-2.6 sustainability pathway, i.e., best-case scenario, and SSP5-8.5 fossil-fueled development, i.e., worst-case scenario) from two general circulation models (HadGEM3-GC31-LL and MIROC6) (Hideo et al. 2019; Ridley et al. 2019). HadGEM3 and MIROC6 were selected because they offer diverse climate projections and help cover a wide range of possible future climates that reduce uncertainty in predictions (Thuiller et al. 2019). For each general circulation model under an emission scenario, we selected for modelling three time-horizons, 2021\u0026ndash;2040, 2041\u0026ndash;2060, and 2061\u0026ndash;2080, resulting in 12 modeling cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuture and current climate data used for modeling species distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime frame\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcronym\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCurrent range\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad best-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad worst-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc best-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc worst-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad best-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad worst-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc best-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc worst-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad best-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadGEM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHad worst-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc best-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiroc worst-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSaproxylic beetles distribution modeling\u003c/h2\u003e \u003cp\u003eCurrent potential distribution of the five saproxylic beetles and predicted future changes were modeled using BIOMOD2 R package for ensemble modeling approach, which consists of running a group of algorithms simultaneously (Thuiller et al. 2014). For each species, we fitted an ensemble SDM based on five modeling techniques, i.e., two regression methods, generalized linear model (GLM) and generalized additive model (GAM), two machine learning methods Random forests (RF) and Generalized boosting model (GBM), and maximum entropy (MAXENT). The use of five algorithms allows for a comprehensive approach that can capture different aspects and a robust approach to predicting species distributions (Thuiller et al. 2014). Because we lacked true absence data for each species, we created pseudo-absences datasets. For this, for each species, we generated ten sets of random pseudo-absence records outside a buffer of 20 km from the presence points. The ratio between pseudo-absences and presence records was 3:1. This ratio was chosen to ensure robust modeling, as a balanced or slightly biased dataset towards absences can improve model performance by reducing overfitting and increasing the model\u0026rsquo;s ability to discriminate between presence and absence locations (Barbet-Massin et al. 2012).\u003c/p\u003e \u003cp\u003eSpecies distribution models were calibrated using 80% random samples from occurrence data, while the model performance was assessed using the remaining 20% of data (Gholamy et al. 2018). We evaluated the results of SDMs ensemble models using the area under the curve (AUC) of the receiver operating characteristic (ROC) and the true skill statistic (TSS) (Allouche et al. 2006). We also evaluated the contribution of dependent variables in predicting the species range for individual models and ensemble models. We also transformed the probability of occurrence for the models to a binary present/absent using the TSS cut-off value calculated by BIOMOD2 (Hao et al. 2019).\u003c/p\u003e \u003cp\u003eFor each species, we created 13 ensemble species distribution models, one with current climate data and 12 predictions for 2021\u0026ndash;2040, 2041\u0026ndash;2060, and 2061\u0026ndash;2080 horizons (general circulation model \u0026times; emission scenario \u0026times; time horizon, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In order to develop the ensemble models, we implemented the mean ensemble modeling algorithm (Hao et al. 2019), incorporating only individual models with a TSS\u0026thinsp;\u0026gt;\u0026thinsp;0.4.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003ePriority areas for conservation of saproxylic beetles\u003c/h2\u003e \u003cp\u003eCurrent and 2041\u0026ndash;2060 future species distribution data were further used to select priority areas for conservation of saproxylic beetles. Spatial conservation prioritization was performed using Zonation 5 software, a decision-support tool for spatial conservation planning (Moilanen 2022; Moilanen et al. 2022). Zonation produces a priority ranking by iteratively removing grid cells with the lowest total marginal loss of conservation value while accounting for total and remaining distributions of protected saproxylic beetles. It produces a uniform distribution hierarchical ranking of the landscape from highest (1) to lowest (0) conservation value (Kujala et al. 2013; Moilanen 2022).\u003c/p\u003e \u003cp\u003eSpatial conservation prioritization was produced using the mean ensemble occurrence probability for each species. We accounted for uncertainty in ensemble model predictions by subtracting the standard deviation of the ensemble model predictions from the mean ensemble values (Moilanen 2022). We included species distribution under the current climate scenario and four species distribution predictions for 2041\u0026ndash;2060 timeline (two GCMs \u0026times; two emission scenarios, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We use only one time horizon because the earlier (2021\u0026ndash;2040) time horizon incorporates our baseline data, and the later (2061\u0026ndash;2080) time horizon is associated with higher levels of uncertainty and unpredictability in climate forecasts (Lee et al. 2023). For each scenario, we ran two prioritization analyses: (1) considering Natura 2000 network (Sites of Community Interest and Special Areas for Conservation) as de facto with the highest conservation value (constrained prioritization using protected areas as a hierarchical mask) and (2) selecting a priority area for conservation irrespective of Natura 2000 network (unconstrained prioritization). The former identifies conservation priorities that complement the current protected area network (i.e., best areas to expand the network to accommodate the highest values area for beetle conservation). The latter identifies the highest conservation value areas irrespective of their protection status; this prioritization can then be used to identify representation of beetle species in the current protected area network and highlight gaps in protection.\u003c/p\u003e \u003cp\u003eAs a prioritization algorithm, we used the Core Area Zonation type marginal loss rule, CAZ2 algorithm (Moilanen et al. 2022). CAZ2 maintains a relatively high average coverage of features while not significantly compromising the performance of the worst-performing features, making it a more suitable approach for achieving the actual conservation goals (Moilanen 2022; Moilanen et al. 2022). We also used condition with renormalization, an additional analysis option that represents information about local habitat condition and their influence on biodiversity features. As a raster layer for habitat condition, we used Corine Land Cover 2018 data (European Environment Agency 2019) to extract two main types of habitats: (1) all types of forests, and (2) all non-forested areas, such as places where agriculture and forestry mix, natural grasslands, and shrubs. The reasoning behind using forest as a condition layer is twofold: (1) our focal species are forest specialists, and (2) the amount of forest cover is not predicted to change over the four decades (Kucsicsa et al. 2020), despite potential changes in forest composition. We calculated the proportion of forest using a 1000 m moving window via function 'focal' in the package 'raster\u0026rsquo; for program R. We used the proportion of forest within 1000 m for each map cell as the condition value, with higher values denoting good habitat condition and values of 0 (i.e., no forest) denoting non-habitat. We used a 1000 m moving window based on estimates of movement distance (Drag et al. 2011; Dodelin et al. 2017; Drag and Cizek 2018) and to fully include heterogeneous habitats, for example, traditional wood-pastures in Central Romania (Hartel et al. 2014; Plieninger et al. 2015), which harbor a rich saproxylic beetle fauna.\u003c/p\u003e \u003cp\u003eWe considered as top spatial conservation priorities all grid cells falling in the top 30% of the predicted priority ranks (rank values\u0026thinsp;=\u0026thinsp;0.7\u0026ndash;1), which maximizes saproxylic beetles representation at the national level and corresponds to the European Union Biodiversity Strategy to achieve 30% protected lands by 2030 (European Commission 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eNatura 2000 gap analysis\u003c/h2\u003e \u003cp\u003eWe evaluated whether the most suitable habitats, as predicted from ensemble species distribution modelling binary outputs overlap with the Natura 2000 protected areas (hereafter PAs) sites in Romania. We did not consider other Special Protection Areas Natura 2000 sites in our analysis (i.e., SPAs), as they are specifically aimed at protecting bird species under Birds Directive. We extracted the potential numbers of Natura 2000 PAs where each species has a potential presence based on our ensemble projections. We further calculated the percentage of area coverage inside the Natura 2000 PA from the binary distributions. The evaluation was implemented by quantifying the spatial extent of each species\u0026rsquo; distribution area within the current Natura 2000 network and calculating the percentage of the total projected distribution area for each species from the total Natura 2000 Sites of Community Importance area. The area of terrestrial Natura 2000 Sites of Community Importance is approximately 40,500 km\u003csup\u003e2\u003c/sup\u003e or ~\u0026thinsp;17% of Romania (~\u0026thinsp;18% when converting to 1 km\u003csup\u003e2\u003c/sup\u003e raster).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBeetle species distributions\u003c/h2\u003e \u003cp\u003eSpecies distribution models performed well according to TSS and AUC evaluation values for all species under the ensemble models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most accurate model is for \u003cem\u003eCerambyx cerdo\u003c/em\u003e, followed by \u003cem\u003eOsmoderma eremita\u003c/em\u003e, \u003cem\u003eLucanus cervus\u003c/em\u003e, \u003cem\u003eMorimus funereus\u003c/em\u003e, and \u003cem\u003eRosalia alpina\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of TSS and AUC for the ensemble models by species for current and future scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSensitivity\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSpecificity\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eScore\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSensitivity\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSpecificity\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eScore\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCerambyx cerdo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLucanus cervus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMorimus funereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOsmoderma eremita\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRosalia alpina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of the saproxylic insects showed a diverse response to climate change under the two IPCCs scenarios (supplementary file S2). \u003cem\u003eCerambyx cerdo\u003c/em\u003e and \u003cem\u003eMorimus funereus\u003c/em\u003e distributions are best explained by the mean temperature of the driest quarter (BIO9). \u003cem\u003eLucanus cervus\u003c/em\u003e distribution is influenced by the precipitation seasonality (BIO 15) and the precipitation of the warmest quarter (BIO18), while \u003cem\u003eRosalia alpina\u003c/em\u003e distribution is influenced by temperature annual range (BIO7) and by the precipitation of the warmest quarter (BIO18). The distribution of the species \u003cem\u003eOsmoderma eremita\u003c/em\u003e is the precipitation of the coldest quarter (BIO19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder the current climate, for \u003cem\u003eCerambyx cerdo\u003c/em\u003e our models identified suitable habitats in the western, southern, and eastern parts of the country, where most of the oak forest are present (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For \u003cem\u003eLucanus cervus\u003c/em\u003e, substantially uninterrupted suitable habitats were identified in the western part of the country. Most of the suitable habitats for \u003cem\u003eMorimus funereus\u003c/em\u003e are found in the west, with some hotspots in the southeastern part of Romania. A more uniform pattern of suitable habitats for \u003cem\u003eOsmoderma eremita\u003c/em\u003e is found in the western half of the country, with emphasis on the southwestern and western Carpathian. For \u003cem\u003eRosalia alpina\u003c/em\u003e, the suitable habitats are restricted to the mountainous area in the Carpathians, as well as in the central part of Romania.\u003c/p\u003e \u003cp\u003eThe binary distribution (presence/absence) also showed larger suitable areas for the saproxylic beetles than indicated from known presences. \u003cem\u003eOsmoderma eremita\u003c/em\u003e has the largest distribution area of all saproxylic beetles, covering over 62,000 km\u003csup\u003e2\u003c/sup\u003e, followed by \u003cem\u003eRosalia alpina\u003c/em\u003e with a distribution of 57,000 km\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eLucanus cervus\u003c/em\u003e with 45,000 km\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eCerambyx\u003c/em\u003e cerdo with 42,000 km\u003csup\u003e2\u003c/sup\u003e, and \u003cem\u003eMorimus funereus\u003c/em\u003e with the smallest area of 37,000 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChanges in species distributions\u003c/h3\u003e\n\u003cp\u003eThe most significant decline in suitable habitat is observed for \u003cem\u003eCerambyx cerdo\u003c/em\u003e, showing an average area loss of 75% for all projections, with a maximum loss of 100% in the case of Had best-case 2061\u0026ndash;2080 scenario, and a minimum loss of 35% for Had best-case 2021\u0026ndash;2040 scenario. \u003cem\u003eLucanus cervus\u003c/em\u003e is expected to experience a 99% decrease in suitable habitat according to Had worst-case 2061\u0026ndash;2080 scenario and an increase of 3.5% in suitable habitat under the Had best-case 2021\u0026ndash;2040 scenario. In comparison, \u003cem\u003eOsmoderma eremita\u003c/em\u003e will lose a maximum of 84% under Had worst-case 2061\u0026ndash;2080 scenario and a minimum loss of 18% under Had best-case 2021\u0026ndash;2040 scenario. \u003cem\u003eRosalia alpina\u003c/em\u003e will lose a maximum of 88% under Had worst-case 2061\u0026ndash;2080 scenario and a gain of 15% in suitable habitat under Had best-case 2021\u0026ndash;2040 scenario. Furthermore, \u003cem\u003eMorimus funereus\u003c/em\u003e will likely suffer the smallest reduction in range, with an average decrease of 25% of the suitable habitat (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRange change (%) under future climate conditions (two general circulation models \u0026times; two emission scenarios \u0026times; three time horizons)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies distribution model\u003c/p\u003e \u003cp\u003e(Ensemble mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCerambyx cerdo\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLucanus cervus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMorimus funereus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOsmoderma eremita\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eRosalia alpina\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad best-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad worst-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-69.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-33.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc best-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-61.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc worst-case 2021\u0026ndash;2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad best-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-91.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad worst-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-83.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc best-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc worst-case 2041\u0026ndash;2060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad best-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-39.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad worst-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-99.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc best-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiroc worst-case 2061\u0026ndash;2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-43.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe HadGEM3-GC31-LL and MIROC6 climate models differ significantly. The Had best-case from 2021\u0026ndash;2040 shows habitat increases for some species; however, species are likely to lose over 50% of their habitats, with some losing up to 100%. MIROC6 also forecasts substantial habitat loss, with potential losses between 50\u0026ndash;90% for some species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpatially, future distribution pattern of \u003cem\u003eCerambyx cerdo\u003c/em\u003e and \u003cem\u003eLucanus cervus\u003c/em\u003e is characterized by a relatively widespread distribution across Romania. In contrast, \u003cem\u003eOsmoderma eremita\u003c/em\u003e and \u003cem\u003eRosalia alpina\u003c/em\u003e show a more localized presence, predominantly in mountainous regions. Lastly, \u003cem\u003eMorimus funereus\u003c/em\u003e presents a unique distribution pattern, blending aspects of the other four species. HadGEM3-GC31-LL model predicts a more drastic change, characterized by a more dispersed distribution of species and a bigger loss of suitable area for the species. On the other hand, MIROC6 model indicates a more concentrated distribution of species, primarily within and around the Carpathians Mountains, where their habitats are currently located (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConservation priorities for protected saproxylic beetles\u003c/h2\u003e \u003cp\u003eBased on the results from the Zonation analysis, when integrating the species distribution models for unconstrained prioritization (i.e., without considering Natura 2000 sites as high priority), we found that top 30% spatial conservation priorities for saproxylic beetles shifted across the current and future climate change scenarios. For the current unconstrained prioritization, the top priority areas for saproxylic beetles overlap southern, southeastern, and western Carpathians, sub-Carpathians, and the central part of Romania, and several hotspots in the eastern and southeastern parts of the country, with 85% of the species distribution retained (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing the current unconstrained top priority areas with the top priority areas for the Had best-case 2041\u0026ndash;2060 unconstrained scenario, the analysis revealed a shifting of the areas to the eastern Carpathian Mountains, while the Had worst-case 2041\u0026ndash;2060 unconstrained scenario showed a relatively aggregated pattern of the top priority areas in the Carpathians and a drastic reduction of the priority areas in sub-Carpathians and the southeastern region. Regarding the Miroc best-case 2041\u0026ndash;2060 scenarios, the top priority areas were distributed in the Carpathians and the eastern part of the country. For Miroc worst-case 2041\u0026ndash;2060 scenario, the analysis revealed more aggregated areas in the Carpathians. For both best-case and worst-case scenarios, an average of 90% of species distribution is retained. Regarding the species, top priority areas maintained a higher representation (almost 95%) for \u003cem\u003eOsmoderma eremita\u003c/em\u003e, under Had worst-case 2041\u0026ndash;2060 and Miroc worst-case 2041\u0026ndash;2060 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current constrained prioritization, the actual Natura 2000 network (Sites of Community Interest) encompasses almost 18% of the highest conservation value, retaining an average species representation of about 35% for all the species (supplementary file S3). By keeping Natura 2000 sites as areas with highest priority ranks, the next 12% of the priority conservation areas to identify the optimal locations needed to expand the network up to 30% revealed potential areas for the Natura 2000 sites in the western Carpathians, southern and southeastern sub-Carpathians and central part of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), with an average of 85% of the saproxylic beetles' species representation retained (supplementary file S3).\u003c/p\u003e \u003cp\u003eWhen comparing the current constrained top priority areas with the top priority areas for the Had best-case 2041\u0026ndash;2060 and the Had worst-case 2041\u0026ndash;2060 constrained scenarios, the 12% priority areas for conservation revealed a shift of the priority habitats to the higher elevation areas of the Carpathians (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The 12% top priority areas for Miroc best-case 2041\u0026ndash;2060 scenario revealed slightly more areas extended to the eastern Carpathians, while for Miroc worst-case 2041\u0026ndash;2060 scenario, the top priority areas revealed a more aggregated pattern to the eastern Carpathians and a reduction of areas in the southwestern Carpathians (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe constrained prioritization analysis for current and future scenarios revealed that the species with the best representation is \u003cem\u003eOsmoderma eremita\u003c/em\u003e, which, when considering expanding to 30% of the landscape as protected, will retain 90% of species representation (supplementary file S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNatura 2000 gap evaluation\u003c/h2\u003e \u003cp\u003eThe Natura 2000 network covers only a small part of the saproxylic beetle distributions. Specifically, only 13.5% of the \u003cem\u003eCerambyx cerdo\u003c/em\u003e current distribution is protected by the network. Similarly, the \u003cem\u003eLucanus cervus\u003c/em\u003e habitat is only 20% covered, while for \u003cem\u003eMorimus funereus\u003c/em\u003e, 25% of its habitat falls within the network. Lastly, the habitats of both the \u003cem\u003eOsmoderma eremita\u003c/em\u003e and \u003cem\u003eRosalia alpina\u003c/em\u003e are better represented, with 40% included in the Natura 2000 network.\u003c/p\u003e \u003cp\u003eThe trend is also present in the number of PAs that currently include these species, compared to the number of possible PAs that the species can be present in. For example, \u003cem\u003eCerambyx cerdo\u003c/em\u003e is currently listed in only 52 Natura 2000 Standard Data forms, but the current projection overlaps more than 196 PAs from the current Natura 2000 network. In comparison, \u003cem\u003eLucanus cervus\u003c/em\u003e is currently included in 86 Natura 2000 PAs, while the current projection overlaps more than 190 PAs. Similarly, \u003cem\u003eMorimus funereus\u003c/em\u003e is included in 40 PAs, with potential to be in more than 130 sites. Out of all the species, the representation of the species \u003cem\u003eOsmoderma eremita\u003c/em\u003e is noticeably insufficient within PAs, while is included in only 16 sites, the species has the potential distribution in over 180. Lastly, \u003cem\u003eRosalia alpina\u003c/em\u003e is included in only 42 sites, with potential presence in 194 sites for the current projection.\u003c/p\u003e \u003cp\u003eThe future projection also shows a drastic reduction in species distribution coverage of the Natura 2000 network. \u003cem\u003eCerambyx cerdo\u003c/em\u003e shows the biggest loss of suitable habitats, with reductions in both the number of protected areas available for its conservation (supplementary file S4) and the extent of which the current Natura 2000 network can cover the species distribution in the future. In most cases, the existing Natura 2000 network inadequately represents this species, covering only 10% of its distribution. In contrast, \u003cem\u003eRosalia alpina\u003c/em\u003e has the best-represented distribution by the current Natura 2000 network in the future projection. The majority of the projection indicates that, in all future projections, the current protected area network can cover about 40% of the species distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed that the forecasted climate change would induce significant shifts in saproxylic beetles\u0026rsquo; range, with most analysed saproxylic beetles losing over 80% of their suitable habitat under most future projections. As a general pattern, in the future, the saproxylic beetles will restrict their distribution to the Carpathians due to favorable climatic and environmental conditions and reduce their distribution in the lowland areas. We used a systematic conservation planning approach to identify the conservation priority areas for saproxylic beetles, and we found that for both unconstrained (i.e., current PAs network not considered) and constrained prioritization (i.e., current PAs network not retained as high priority areas) scenarios the top 30% spatial conservation priorities will shift across the current and future climate change scenarios with top priority areas overlapping the Carpathian Mountains with an average of 85% of species distribution retained. Under current conditions, the existing Natura 2000 PAs does not perform well for saproxylic beetles, as only a small percent of their distribution is covered by the current network. Contrary to Bosso et al. (2018), for certain saproxylic beetles (e.g., \u003cem\u003eRosalia alpina\u003c/em\u003e), we found that for future projection, a large area of suitable habitats will be lost, and \u003cem\u003eCerambyx cerdo\u003c/em\u003e is going to be the species with the most potential area lost, followed by \u003cem\u003eLucanus cervus\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFuture distribution changes\u003c/h2\u003e \u003cp\u003eFuture projections for the analysed saproxilic beetles' distributions showed that a large area of suitable habitats will be lost by 2041-60. We found out that \u003cem\u003eCerambyx cerdo\u003c/em\u003e will lose the most suitable area, with over 90% of its suitable habitat under Had best-case 2061\u0026ndash;2080 projection, while \u003cem\u003eLucanus cervus\u003c/em\u003e experienced over 80% decrease under Had worst-case 2061\u0026ndash;2080 projection. \u003cem\u003eMorimus funereus\u003c/em\u003e may lose up to 90% of suitable habitats under Had worst-case 2061\u0026ndash;2080 projection and \u003cem\u003eRosalia alpina\u003c/em\u003e around 88% under Had worst-case 2061\u0026ndash;2080 projection. The HadGEM3-GC31-LL model predicts a more drastic change in the suitable habitats with a bigger loss of suitable areas for the species. Under the MIROC6 model, the \u003cem\u003eRosalia alpina\u003c/em\u003e loses the most suitable habitat, almost 43%, at the same time indicating a more concentrated distribution for all species around mountainous regions of the Carpathians (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These responses support previous findings in Europe, indicating that under future climate scenarios, there will be a reduction of saproxylic beetles distribution, and the species habitat will be restricted to higher altitudinal areas (Poloni et al. 2022).\u003c/p\u003e \u003cp\u003eWe found that \u003cem\u003eCerambyx cerdo\u003c/em\u003e is likely to lose almost 75% of its suitable areas due to the species requirements for old-growth oak forested habitats (Redolfi De Zan et al. 2017; Manu et al. 2017), which can be found at lower elevations and are the one most affected by future climate change and fragmentation (Parisi et al. 2018), as well as the decline in the number of old growth trees found in wood-pasture and semi-open habitats (Hartel et al. 2013; Redolfi De Zan et al. 2017; Torres-Vila 2017). For \u003cem\u003eMorimus funereus\u003c/em\u003e and \u003cem\u003eRosalia alpina\u003c/em\u003e, the average for all the losses or gains for suitable areas are ~\u0026thinsp;26% and 30%, respectively. \u003cem\u003eOsmoderma eremita\u003c/em\u003e and \u003cem\u003eLucanus cervus\u003c/em\u003e are likely to lose over 50% of their habitats across all projections, a pattern also highlighted by other studies in Europe (Della Rocca and Milanesi 2020) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile in the future some species may lose up to 90% of their suitable areas, some of the projections show a slight increase in some models (Had best-case 2021\u0026ndash;2040 \u0026ndash; \u003cem\u003eLucanus cervus, Morimus funereus, Rosalia alpina\u003c/em\u003e), findings also stated by Della Rocca and Milanesi, (2020), which found that climate change can have a positive effect on species distribution. Most of the projections show a decrease in distribution of suitable areas across the country. The most stable species under both GCMs and SSPs are \u003cem\u003eMorimus funereus\u003c/em\u003e and \u003cem\u003eRosalia alpina\u003c/em\u003e. While the species are losing suitable area, compared to the other species with dramatic losses, such as \u003cem\u003eLucanus cervus\u003c/em\u003e and \u003cem\u003eCerambyx cerdo\u003c/em\u003e, these species show a more stable pattern.\u003c/p\u003e \u003cp\u003eOur results showed the importance of Carpathians mountains as suitable habitats for the saproxylic beetles under future climate conditions. The saproxylic beetles will tend to concentrate in the Carpathian region due to favorable climatic conditions, while isolated lowland forested patches will disappear due to extreme climatic conditions and land use change (Mikolāš et al. 2023). These findings corroborate with other studies in Europe, which highlighted that future emission scenarios show a general reduction in suitable habitats for saproxylic beetles and a shift towards higher altitudes (Della Rocca et al. 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePriority areas for saproxylic beetles\u003c/h2\u003e \u003cp\u003eThe current unconstrained spatial prioritization analysis showed that Carpathians Mountains and the western part of the country had consistently high conservation value for the saproxylic beetles and represent a refuge for these species under both current and future climate conditions, being a region which harbors most of the remaining old-growth forests in Romania (Veen et al. 2010; Knorn et al. 2013), (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, based on the two HadGEM3-GC31-LL future scenarios, climate change is anticipated to lead to a decline in priority areas for saproxylic beetles in the lowland areas, especially in the central and eastern regions. In comparison, for the two MIROC6 future scenarios, climate change is more conservative, preserving priority areas in the Carpathians and some priority hotspots in the eastern part of the country and western Transylvania (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). One of the factors that lead to the restriction of the habitat towards higher elevation areas is represented by climate conditions in the current habitat, which are expected to adversely impact the saproxylic beetles due to increased droughts and higher temperatures, as well as a possible reduction in the lowland forested habitats threatened more by agricultural intensification and development (Seibold et al. 2015). Hence, our findings align with the conclusions drawn by other researchers (Bosso et al. 2018; Della Rocca et al. 2019), that higher altitudinal sites will offer favorable climate conditions for saproxylic beetles.\u003c/p\u003e \u003cp\u003eFor the current unconstrained scenario, our results from the spatial prioritization analysis showed that when considering 30% of the landscape as protected, about 80% of the species distribution is retained and did not overlap completely with Natura 2000 sites. Similarly, Miu et al., (2020) demonstrated that a high proportion of invertebrate species are not covered by Natura 2000 sites. Highest conservation value was found to be outside the protected areas in the western and southwestern parts of the country, as well as in the southern and eastern sub-Carpathians, with small hotspots in the central and eastern parts of the country, with the most vulnerable areas also located in the southern and eastern Romania, with a huge reduction in the species suitable habitats in future scenarios. Although Natura 2000 PAs encompass only 18% of Romania\u0026rsquo;s landmass (Miu et al. 2020), ~\u0026thinsp;30% of the saproxylic species distributions fall inside protected areas, with a high number of potentially suitable PAs for saproxylic beetles uncovered by their distribution (supplementary file S3).\u003c/p\u003e \u003cp\u003eFor the current constrained scenario, the prioritization analysis retained an average species representation of about 40% for all saproxylic species for current and future scenarios, less than half of their distribution in Romania (supplementary file S3). The performance of the Natura 2000 coverage in protecting saproxylic beetles was also questioned by several authors who highlighted that a similar percentage or less of their distribution is protected (Bense and Bussler 2003; Vi\u0026ntilde;olas and Vives 2012; Bosso et al. 2013, 2018; Lachat et al. 2013). Regarding the species \u003cem\u003eRosalia alpina\u003c/em\u003e, one of the most charismatic in Europe (Campanaro et al. 2017), we found out that only 35% of species distribution is retained by Natura 2000 PAs, values close to the ones stated in the previous studies (supplementary file S3). The species with the least retained distribution in Natura 2000 PAs (32%) is \u003cem\u003eLucanus cervus\u003c/em\u003e, a species inhabiting mature deciduous forests from lowland and oak forests having rotten dead wood at ground level (Bardiani et al. 2017; M\u0026eacute;ndez and Thomaes 2021). This situation occurs in other EU countries; Thomaes et al. (2008) highlighted that 11% of the species distribution is covered by Natura 2000 network in Belgium. \u003cem\u003eOsmoderma eremita\u003c/em\u003e was the only species whose coverage in PAs increased slightly from 30% under current to 45% under Had worst-case 2041\u0026ndash;2060 and Miroc worst-case 2041\u0026ndash;2060 scenarios.\u003c/p\u003e \u003cp\u003eLastly, under the constrained scenario, when considering the additional 12% of the landscape for the optimal expansion of Natura 2000, an average of 80% of the saproxylic beetles species representation is retained, with the best representation for \u003cem\u003eOsmoderma eremita\u003c/em\u003e (90% of distribution protected). Under the constrained scenario, most of the priority areas will be restricted to the Carpathian region, which harbors continuously forested habitats suitable for saproxylic insect development. Therefore, the Carpathians will represent a refuge for the saproxylic beetles in the future.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffectiveness of Romanian Natura 2000 PAs network for protecting saproxylic beetles\u003c/h2\u003e \u003cp\u003eThe current Natura 2000 PAs network does not achieve the EU conservation targets for the five listed saproxylic beetle species \u003cem\u003e(Rosalia alpina, Lucanus cervus, Cerambyx cerdo, Osmoderma eremita\u003c/em\u003e, and \u003cem\u003eMorimus funereus\u003c/em\u003e) in Romania, and its effectiveness is put into question, as may not be sufficient to protect and conserve the saproxylic insects. However, it is anticipated that under some future emission scenarios, the saproxylic beetles will experience increased levels of representation. The decrease in species distributions as a result of climate changes leads to a change in the distribution of species, pushing the distribution of species to higher elevation areas, such as the Alpine biogeographic region, which includes a high number of Natura 2000 PAs (Popescu et al. 2013). As such, we predict a significant increase in the representation of saproxylic insect species by the Natura 2000 network under Had best-case 2021\u0026ndash;2040, Had worst-case 2021\u0026ndash;2040, and all Miroc best-case and worst-case scenarios.\u003c/p\u003e \u003cp\u003eThe evaluation of Natura 2000 current PAs network covers only a small part of the saproxylic beetles\u0026rsquo; distributions, with the gap analysis revealing insufficient representation of the beetles. The gap analysis results revealed that the current representation of the saproxylic beetles is noticeably insufficient within PAs, with most of the species reported in fewer protected Natura 2000 sites than their current potential distribution (supplementary file S4). For example, \u003cem\u003eOsmoderma eremita\u003c/em\u003e is reported in only 16 sites, while the species has the potential distribution in over 180 sites. Unfortunately, in Romania, there is a knowledge gap regarding this elusive species, which requires certain habitat types, such as hollow-bearing old trees with dead wood and specific climatic factors, to develop (Chiari et al. 2013; Maurizi et al. 2017).\u003c/p\u003e \u003cp\u003eA recent study by Cazzolla Gatti et al., (2023) regarding the distribution of strictly protected areas under EU 2030 strategy revealed that for Romania, the Steppic and the Continental biogeographic regions offer very limited protection to biodiversity and rare species; these findings corroborate, studies by Miu et al. (2020) and Popescu et al. (2013), which found an urgent need for expanding the protected areas from these regions, as some of the protected saproxylic beetles are dependent of old trees in open or semi-open landscapes from these regions (Stan and Nitzu 2013; Manu et al. 2017, 2019). These findings underscore the limitations of relying solely on presence records for conservation gap assessments. Our results emphasize the significance of incorporating species distribution modeling and spatial planning techniques to estimate the probability of presence of saproxylic species and the coverage of protected areas, enabling more effective planning of protection measures and enhancing species management strategies. Our study represents the first comprehensive evaluation of the priority suitable habitats of saproxylic species in Romania, providing valuable resources for future investigations, including the identification of connectivity corridors utilized by the species and the prediction of suitable habitats in response to climate change.\u003c/p\u003e \u003cp\u003eTo best achieve the goals of the EU Strategy 2030 of protecting at least 30% of the EU\u0026rsquo;s land, we urge the expansion of the Natura 2000 sites or establishing new protected areas covering the suitable habitats of protected saproxylic beetles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLR and MDM conceived the study; LR, MDM and IVM designed the methodology; MDM and IVM collected the data; MDM, IVM and VDP analyzed the data; LR, MDM and IVM led the writing of the manuscript; VDP, BSB, and SC contributed to the writing of the manuscript. All authors contributed to the drafts and approved the final version for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAiello‐Lammens ME, Boria RA, Radosavljevic A, et al (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541\u0026ndash;545. https://doi.org/10.1111/ecog.01132\u003c/li\u003e\n \u003cli\u003eAllouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43:1223\u0026ndash;1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x\u003c/li\u003e\n \u003cli\u003eBarbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? 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Insect Conserv Diversity 8:193\u0026ndash;204. https://doi.org/10.1111/icad.12101\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3969647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change poses an increasing risk to biodiversity and habitats important for saproxylic beetles are likely to experience severe pressure and threats. The diversity of saproxylic beetles is an indicator of healthy forest ecosystems, and thus, the conservation of beetles is now a priority for EU Member States. We developed ensemble species distribution models for five saproxylic beetles for current and three-time future horizons under two emission scenarios and two GCMs. We then used a systematic conservation planning approach to assess the effectiveness and resilience to climate change of Romanian Natura 2000 network for saproxylic beetles while identifying future areas for protected area expansion to meet EU conservation targets. Our study revealed that under all scenarios and time horizons, the saproxylic beetles will lose over 80% of their suitable habitat and restrict their distribution to higher elevations. According to the prioritization analysis, we found that when considering 30% of the landscape as protected, an average of 85% of species distribution is retained with priority areas overlapping the Carpathian Mountains, while for the current conditions (18% of Romania’s terrestrial surface), the existing Natura 2000 network does not perform well, with almost ~30% of the saproxylic species distributions falling inside. Our results support the idea that the distribution of saproxylic beetles could change as a result of climate change, and the effectiveness of the current Natura 2000 network is put into question as it may be insufficient in protecting these species. To achieve the goals of the EU Biodiversity Strategy 2030 of protecting at least 30% of the EU’s land, we urge the expansion of the Natura 2000 sites.","manuscriptTitle":"Priority conservation areas for protected saproxylic beetles in Romania under current and future climate scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 11:23:38","doi":"10.21203/rs.3.rs-3969647/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-02-20T07:38:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-20T01:51:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2024-02-19T09:30:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6a0d79d1-5779-48e4-b9c0-8d2586b9fd54","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T00:41:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 11:23:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3969647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3969647","identity":"rs-3969647","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}