Application of the Continuum Suitability Index (CSI) model to display the permeability of the Alpine-Dinaric landscape and to define intervention priorities for ecological linkages

doi:10.21203/rs.3.rs-2470399/v1

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Application of the Continuum Suitability Index (CSI) model to display the permeability of the Alpine-Dinaric landscape and to define intervention priorities for ecological linkages

https://doi.org/10.21203/rs.3.rs-2470399/v1

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The Alps and the Dinaric Mountains are biodiversity hubs and, although they are subjected to heavy human-related pressures, they still show one of the lowest levels of landscape fragmentation in Europe. The DINALPCONNECT project has the aim of identifying the most important geographical areas for ensuring the biological processes and natural movements and the gene flow of wildlife species between the Alps and the Dinaric Mountains.

The current paper presents the results of the implementation of the Continuum Suitability Index (CSI) model in combination with the least-cost path (LCP) – GIS model in the DINALPCONNECT project area in order to detect the macro-regional ecological connectivity network stretching from the Alps to the Dinaric Mountains. The aim is to support regional decision-makers in defining the actions that will ensure both the ecological network and local human-wildlife coexistence as regards the most important linkages.

The results show that the main core areas (SACA1) of the overall ecological network represent 20% of the total project area. The calculated macro-regional LCP connects 51% of the surface of these areas and reveals great differences in legislation on protected areas between EU and Non-EU countries.

The results show that non-EU countries have the highest shares of SACA1 areas outside protected areas, contributing to important macro-regional connections. The CSI is proving to be a highly adaptable model on the macro-regional level, which can include current and future local issues concerning ecological connections. Therefore, the presented GIS analysis could be a valuable tool enabling regional decision-makers to better manage human and wildlife presence and to identify the best actions for the development of the ecological network.

Alps

Dinaric

ecological connectivity

CSI

least-cost path

SACA areas

Ecological connectivity (EC) represents the backbone of biodiversity conservation (Beier 1995, Bennett 1998, Taylor et al., 2006; Favilli et al., 2015) by ensuring the flow of organisms and ecological processes across landscapes (Taylor et al. 1993) and counteracting the negative effects of habitat fragmentation and climate change (Heller and Zavaleta, 2009). The Alps and the Dinaric Mountains are exceptionally rich in biodiversity and, although they still show one of the lowest levels of landscape fragmentation in Europe and feature the largest Natura2000 sites as measured by area (EEA, 2011; Lawrence et al., 2021), they are threatened by climate change and habitat loss (Laner et al., 2020). Several projects implemented in the Alpine area have clearly shown that the Alps are a sort of island embedded in a matrix of human presence and urban areas, underlying the need for the enhancement of the ecological network (i.e. Plassmann et al., 2019).

The main reason to enhance ecological connectivity, especially between different mountain ranges, is that this should facilitate the ability of species to move into new regions as the climate changes (Krosby et al., 2010), thereby decreasing the probability of extirpation or extinction. A network of connected areas has the potential to allow populations to persist following different spatial dynamics (e.g. classic metapopulations, source-sink dynamics, or patchy populations, as in Harrison, 1991) even in the absence of vast intact natural areas. This is possible through long-distance animal movements such as juvenile dispersal, seasonal migrations (DeFries et al., 2007), and geographical range shifts in response to habitat turnover (Van Teeffelen et al., 2012), environmental stochasticity, and climate change (Hannah, 2008; Heller and Zavaleta, 2009; Verboom et al., 2010), if the distance or landscape resistance to movement between habitat fragments is not excessive.

The DINALPCONNECT project (https://dinalpconnect.adrioninterreg.eu/) is aimed at strengthening transnational and sectoral cooperation to improve the EC throughout the Dinaric Mountains, connecting them with the Alps, thus enabling the long-term protection of biodiversity in view of current and future climatic changes. DINALPCONNECT is focused on terrestrial habitats and operates at different administrative levels to achieve this goal. The first step was to assess the current state of the EC and barriers at the macro-regional level. This would serve as a foundation for defining the actions on the ground that might facilitate both the dispersal of individuals within a species among natural areas and human-wildlife coexistence, thus identifying the potential socioeconomic benefits for the given territories and local inhabitants. We selected a structural connectivity approach in the project area, and avoided concentrating on functional connectivity (as in Taylor et al., 1993, 2006; Favilli et al., 2015), which describes an organism’s behavioural response to both the landscape structure and the landscape matrix. The structural connectivity analysis has a more holistic approach and describes the physical relationships and permeability between habitat patches (Hilty et al., 2020). The Geographic Information System (GIS) habitat suitability model applied in the DINALPCONNECT project was adapted from the AlpBioNet2030 project (Plassmann et al., 2019), which did not concentrate on specific species, but analysed the overall Alpine connectivity and barriers using the structural approach of the Continuum Suitability Index (CSI). The CSI aims at detecting the general permeability of habitats and landscapes based on the physical features and arrangements of habitat patches, as well as the presence or absence of human pressure (SNP, 2019). This structural connectivity model prioritises areas with a low degree of human disturbance, which are assumed to be permeable for terrestrial species (Hilty et al., 2020). Least-cost-path (LCP) modelling is one of the methods used in landscape ecology to measure ecological connectivity by representing the landscape as an energy-cost surface. LCPs represent the route of maximum efficiency between two locations as a function of the distance travelled and the costs expended (Douglas 1994, Adriansen et al., 2003, Affolter et al., 2011, Etherington and Holland, 2013, Favilli et al., 2015).

This methodology enabled the identification of the main Strategic Alpine Connectivity Areas (SACAs), demonstrating that the CSI is suitable for a large-scale analysis, although it requires the potential to adapt to major disturbing and connecting elements (https://www.jecami.eu/; SNP, 2019).

DINALPCONNECT is the first EU project attempting to analyse the current situation regarding the EC in the Dinaric Mountains and the Alps-Dinaric connection, and to identify priority areas, barriers, and the most important linkages related to biodiversity conservation and EC (as already stated by Gazoulis et al., 2022). The aim of the present paper is to analyse and display the general permeability of the Alpine-Dinaric landscape, highlighting the Strategic Connectivity Areas (SACAs), and the LCPs connecting them. The present paper will also attempt to highlight this methodology in order to help local decision-makers identify the most valuable areas to be protected to enhance EC and develop the best strategies to promote local human-wildlife coexistence.

Project area

The total DINALPCONNECT project area included in the analysis was 274,981 km², extending along approximately 1,300 km from Greece in the south to Italy in the north. This comprises eight countries, of which the entire state territories of Albania, Bosnia and Herzegovina, Croatia, Montenegro, and Slovenia were included. The project area also includes the north-eastern part of Italy (the NUTS 2 regions Friuli-Venezia Giulia, Trentino-Alto Adige, and Veneto), the southern part of Austria (the NUTS 2 regions Carinthia and Styria, and the NUTS 3 Eastern Tyrol) and the central and western mainland of Greece (the NUTS 2 regions Western Macedonia, Epirus, Central Greece, and the NUTS 3 region Aetolia-Acarnania) (Fig. 1).

The Continuum Suitability Index (CSI) modelling approach

The CSI is a spatial suitability analysis of landscape permeability, using a structural connectivity approach, focussing on three main ecological objectives in accordance with Ferretti and Pomarico (2013): the minimization of human pressure, the current biotic factors, and the physical environment. The CSI approach was developed and improved in several EU projects on Alpine ecological connectivity between 2008 and 2020 (please see Affolter, 2010; Haller, 2016; and Plassmann 2019).

The CSI is composed of five indicators that cover all of the above-mentioned issues:

Land use/land cover (LAN): defines landscape permeability based on land use, with landscape permeability values assigned to different land-use classes by experts. Data source: Corine Land Cover 2019, 100 x 100m raster.
Population (POP): describes the pressure of the human population in and around settlements, with higher population reducing landscape permeability. Data source: GEOSTAT Population Grid 2011, updated with municipal data for 2018, 1 x 1 km grid.
Environment (ENV): highlights the level of environmental protection of a specific site, representing a positive contribution to ecological integrity. It considers protection at different levels, from regional and national to the Natura 2000 EU network. For areas with several levels of environmental protection, the highest level of protection was noted. Data sources: World Database on Protected Areas, national repositories, and Shapefiles.
Fragmentation (FRA): describes the rate of landscape fragmentation by human infrastructure, which reduces landscape permeability. Data sources: Roads and railways from the European Global Map 2019 and national repositories for non-EU countries. The effective mesh density was calculated for a 1 x 1 km grid.
Topography (TOP): combines the absolute altitude of the terrain and the slope as landscape permeability factors. The assumption is that general movement decreases with increasing altitude and slope. Data source: EU-DEM v.1.1 (2017), 25 x 25m raster.

For each indicator, an indicator value from 0 (lowest permeability) to 10 (highest permeability) is assigned according to the landscape characteristic. The values for each indicator were defined using the AlpBioNet2030 values (Affolter, 2011; SNP, 2019), which were revised by DINALPCONNECT’s experts in each project country (Laner and Favilli, 2022a). These values were transposed onto a 100 x 100 m raster file of the project area and the overall CSI value for each cell was calculated as a weighted mean of all five indicators: CSI = (2*LAN + 2*POP + ENV + FRA + TOP)/7. For the CSI model details and calculations, please see: Affolter, 2010, 2011; Swiss National Park, 2019; Laner and Favilli, 2022a. The aim of the CSI model is to detect the most important areas for sustaining the macro-regional ecological network, named Strategic Alpine Connectivity Areas (SACAs) (Swiss National Park, 2019; Laner and Favilli, 2022a, b).

The SACAs are based on the CSI calculation results and classified into three categories:

SACA1 (Ecological Conservation Areas): CSI ≥ 8 AND Area ≥ 100 ha;
SACA2 (Ecological Intervention Areas): 4 < CSI < 8; and
SACA3 (Ecological Restoration Areas/Barriers): CSI ≤ 4.

The first category, SACA1, comprises Ecological Conservation Areas where the landscape features a high degree of permeability and ecological linkages are functioning (cf. Swiss National Park, 2019). They include all contiguous areas with a high CSI (≥ 8) and a size of at least 100 ha – which was estimated as a sufficient size for ensuring the relevant biological processes (cf. Affolter et al., 2011). The second category, SACA2, comprises Ecological Intervention Areas. These are the areas where concrete actions could be implemented on the ground to form stepping stones and ecological corridors connecting larger SACA1 areas. The third category, SACA3, comprises so-called Ecological Restoration Areas, meaning specific sites that currently act as barriers to ecological connectivity. They are mainly built-up areas, roads, and industrial zones, which form important barriers to wildlife movement, and therefore have a low CSI. For the calculation of these areas, all cells with CSI values between 1–4 were selected.

Definition of ecological linkages and the identification of best paths

The identification of ecological linkages was conducted on two levels: firstly, to investigate the best connections of SACA1 areas to define the south-north ecological corridor from Greece to Italy. Secondly, to identify the most important regional linkages within SACA2 areas. The identification of the linkages was based on the LCP approach. It was conducted with the Linkage Mapper Toolset and its Linkage Pathways Tool (LinkageMapper.org). The LCP represents the route along which the least resistance – imposed by the landscape matrix on the general wildlife movements – occurs (Favilli et al., 2015). The LCP identifies the route of least cumulative resistance for a species moving between two SACA1 areas (McRae and Kavanagh 2012; Jones, 2015; Beier, 2018; Cayton 2019). To build a resistance raster for the corridor design, the values of the CSI were transposed to resistance values ranging from 0 to 100 by a data transformation (Table 1). Linear resistances were selected for the CSI values 5–10, but higher resistance values for the CSI values 1–5 (Table 1) were chosen because a test run in Slovenia, which showed several LCPs passing through SACA3 areas. For the macro-regional linkage, several test runs were carried out using the southernmost and most important SACA1 areas in Greece and the northernmost ones in Italy and Austria. For the regional linkages, it would not have been possible to consider each single SACA1 area due to the limited time and computing capacity, as well as the excessive number of resulting ecological linkages. Therefore, a selection of the most important SACA1 areas was conducted based on an expert evaluation, considering size, the importance for potential connections to other countries and the south-north direction, and the general biological diversity. This approach allowed a reduction in the number of SACA1 areas from 2,208 to 159. The calculation of regional linkages was done with “bounding circles” of 60 km, since this limits the cost-weighted distance calculations from each source core to including only the portion of the landscape likely to be relevant to the connectivity between the sources and target cores, thus reducing processing time (McRae and Kavanagh, 2012). The width of the corridors in the LCP approach is the deviation from the LCP and is determined by the resistance of the surrounding cells; therefore, the corridor is narrow if the resistance of the surrounding landscape is high. Instead of defining the maximum width of the corridors at any point of the corridor, we defined the maximum deviation of the corridor from the LCP along the entire length of the corridor network. The maximum deviation was truncated at 40,000 m for regional linkages and at 400,000 m for the macro-regional corridor. To build a resistance raster for the corridor design, the values of the continuum suitability index were transferred to resistance values ranging from 0 to 100 by a data transformation to obtain linear resistances (Res_lin): to build a resistance raster for the corridor design, the values of the continuum suitability index were transferred to resistance values ranging from 0 to 100 by a data transformation to get linear resistances: Res_lin=(10 − CSI) ×10.

Table 1

The transformation of CSI values into linear resistance values for corridor design.
CSI Value	Resistance Value
1	100
2	95
3	80
4	65
5	52
6	40
7	30
8	20
9	10
10	0

To remove linkages produced as artifacts along the border of the project area and the coast of the Adriatic Sea, all the regional linkages with a Euclidean distance between two core areas longer than 140 km were removed. In addition, 38 linkages were deleted by hand as they were long-distance connections between core areas which had alternative short-distance connections via a series of intermediate core areas.

Implementation of the Continuum Suitability Index (CSI) model in the DINALPCONNECT project area

The land cover indicator showed low values due to intensive agricultural land use mostly in lowlands (Fig. 2). The project area is relatively sparsely populated except for the Po River valley in Italy and the surroundings of larger cities in all countries; therefore, the human population pressure in most of the area was low (Fig. 3). The environmental protection indicator showed considerable differences among EU and non-EU countries, with a larger overall area under protection in EU countries, mostly due to the Natura 2000 network (Fig. 4). The fragmentation indicator showed higher connectivity in Albania, Bosnia and Herzegovina, and Montenegro, and at higher altitudes in the Alps, reflecting less developed traffic infrastructure (Fig. 5). The topography indicator showed that connectivity in the area is hindered by high altitude and steep slopes only in the Alps (Fig. 6). Although the average indicator values do not differ greatly between countries, they give some indication of the main causes of low or high ecological permeability in different countries (Table 2).

SACA areas in the DINALPCONNECT project area

The identification of SACA areas using the CSI approach in the DINALPCONNECT project area revealed that the structure of the landscape is as follows (Fig. 7):

21.2% of the total project area is classified as SACA1 areas. They represent the main ecological conservation and core areas of the overall ecological network of the investigated area. 82.0% of these areas are part of an already protected site.
75.4% of the area falls inside the SACA2 areas. This result shows that there exists a high potential for establishing measures to enhance the connectivity between Ecological Conservation Areas (SACA1).
The remaining 3.4% of the total area is classified as SACA3 areas, which represent the most visible barriers, such as major cities and agglomerations, as well as important linear transport infrastructure. Although it is a small percentage, the barrier effect of these areas can have a big impact on connectivity. SACA3 areas have the lowest fragmentation, land-use, and population index values.

A total of 2,208 SACA1 areas covering 58,235 km² with an average size of 26.4 km² were identified in our analysis. The largest cumulative SACA1 area was detected in Croatia and Greece (13,958 km² and 12,547 km², respectively), which correlates to the large area of these countries included in our analysis (Table 2). The SACA1 areas in these two countries are the largest in size, with an average area of 38.2 km² in Croatia and 37.2 km² in Greece (Table 2). However, the proportion of the national territory in the SACA1 category among different countries varied considerably, with 31.3% of Albania’s territory assigned to SACA1, followed by 27.7% in Slovenia (Table 2). Bosnia and Herzegovina had the lowest percentage of territory designated as SACA1 areas (7.2%).

The results show that a highly permeable landscape can also be achieved without protection. There was a wide gap between EU and non-EU countries, with Bosnia and Herzegovina, Montenegro, and Albania having the lowest proportions of SACA1 areas within protected areas and the highest outside protected areas (Table 2). This difference is clearly visible at the national borders. For example, on the border between Croatia and Bosnia and Herzegovina there are SACA1 areas on the Croatian side, with a sharp cut-off on the Bosnian side (Fig. 7). On the contrary, in EU countries the proportion of SACA1 areas under protection was high, between 85.4 and 99.5% (Table 2). The high proportion of nationally designated protected areas which fall outside SACA1 can be found in Slovenia (52.9%), Croatia (47.0%), and Greece (36.1%). Those countries also have the most fragmented landscapes, with low average fragmentation indicator values (Table 2).

Table 2

Characteristics of SACA1 (Ecological Conservation Areas) in project countries
Indicator	IT	AT	SI	HR	BA	ME	AL	GR
Area of the country included in the analysis [km²]	39,069	11,564	20,272	56,516	51,207	13,882	28,800	53,671
Total size of SACA1 areas in each country [km²]	7,906	2,271	5,611	13,958	3,685	3,238	9,004	12,547
Percentage of SACA1 areas in each country	20.2%	19.6%	27.7%	24.7%	7.2%	23.3%	31.3%	23.4%
% of protected areas in the project area	23.4%	16.8%	58.5%	46.0%	3.5%	14.3%	17.9%	35.4%
SACA1 areas – average size [km²]	32.5	16.1	28.9	38.2	8.7	12.6	26.9	37.2
Average CSI value*	6.43	6.96	6.77	6.67	6.56	7.15	7.05	6.71
Average land-use indicator value*	5.70	6.64	5.99	6.00	6.41	7.34	6.76	6.34
Average population indicator value*	9.52	9.89	9.79	9.85	9.85	9.91	9.81	9.92
Average fragmentation indicator value*	6.47	8.20	5.19	4.96	6.26	7.70	8.14	4.97
Average environmental protection indicator value*	1.70	1.43	3.74	2.91	0.27	0.90	1.29	2.39
Average topography indicator value*	9.30	9.13	9.88	9.99	9.94	9.79	9.82	9.90
Total size of SACA1 areas within protected areas [km²]	7,269	1,940	5,583	13,771	1,669	1,749	4,56	12,139
% of SACA1 areas within protected areas	91.9%	85.4%	99.5%	98.7%	45.3%	54.0%	50.6%	96.7%
Total size of SACA1 areas outside protected areas [km²]	637	331	286	187	2,016	1,489	4,444	407
% of SACA1 areas outside protected areas	8.1%	14.6%	0.5%	1.3%	54.7%	46.0%	49.3%	3.2%
Total size of protected areas not defined as SACA1 areas [km²]	1,858	6.8	6,281	12,203	134	241	610	6,853
% of protected areas not defined as SACA1 areas	20.4%	0.3%	52.9%	47.0%	7.4%	12.1%	11.8%	36.1%

* Low values indicate low permeability; high values indicate good permeability.

Approximately half of all SACA1 areas had a transboundary characteristic (they extend on both sides of a national border), or they reached a national border. A comparison between national and transboundary SACA1 areas showed a similar total area in both categories, with a considerable difference in the average size, at 14.3 km² for national and 184.6 km² for transboundary SACA1 areas (Table 3).

Table 3

Characteristics of National and Transboundary SACA1 areas.
Indicator	Total area [km²]	Number	Average size [km²]
National SACA1 areas	29,376	2,052	14.3
Transboundary SACA1 areas	28,797	156	184.6

Macro-regional connection

The CSI model test was applied for the identification of the LCP between northern and southern SACA1 areas and produced the same results from Slovenia to Albania regardless of the SACA1 area starting and ending points in Italy and Greece.

The total area covered by the south-north connection was 105,669 km². It interlinked 108 of the total of 2,208 SACA1 areas; however, these 108 SACA1 covered 30,572 km², which represents 52% of the total SACA1 surface. The 11 biggest SACA1 areas were larger than 1,000 km², and among them only one was not connected to the identified south-north LCP. These results show that the LCP tends to select larger SACA1 areas and those with a higher environmental protection index value (Fig. 8).

The largest width of the macro-regional connection was approximately 100 km near the border of Montenegro and Bosnia and Herzegovina. The narrowest width of the south-north connection was 9 km and was in Albania between Lake Prespa and Lake Ohrid. Beside this bottleneck, two other bottlenecks are visible by a cartographic interpretation: in Slovenia between Triglav National Park and the more southern protected areas, and in the south of Croatia near the city of Sinj (Fig. 8).

The south-north connection covered the largest area in Greece (25,210 km²), followed by Italy (16,650 km²) and Albania (15,990 km²). The smallest area of the south-north connection was in Slovenia and Austria (Table 4). The highest proportion of the identified south-north connection is under environmental protection in Slovenia (79.4%), Croatia (63.7%), and Greece (43.5%). The relatively low number of protected areas in Bosnia and Herzegovina, Montenegro, and Albania was reflected again when considering only the south-north connection. In these countries protection was also lacking on the identified south-north connection (Table 4).

Table 4

Characteristics of the south-north connection.
Indicator	IT	AT	SI	HR	BA	ME	AL	GR	Sum
Number of times the south-north corridor is crossed by a highway on the level of the project area	1	0	1	4	0	0	0	5	11
Area of the south-north corridor [km²]	16,650	4,804	4,935	13,880	13,652	10,548	15,990	25,210	105,669
Area of the south-north corridor under ENV protection [km²]	6,090	1,682	3,918	8,847	1,373	1,967	3,827	10,975	38,679
Percentage of the south-north corridor under ENV protection per country	36.6%	35.0%	79.4%	63.7%	10.1%	18.6%	23.9%	43.5%	36.6%
Area of the south-north corridor in intensive agriculture [km²]	668	115	313	835	954	228	1.569	3896	8.577
Percentage of the south-north corridor in intensive agriculture	4.0%	2.4%	6.3%	6.0%	7.0%	2.2%	9.8%	15.4%	8.1%

Considering some of the potential threats to ecological connectivity, we found that 8.1% of the south-north connection was used for intensive agriculture (Table 4), with the highest proportion in Greece (15.5%) and the lowest in Austria (2.4%) and Montenegro (2.2%). The most important agricultural barriers were the Adige Valley and the Non Valley in Italy, the surroundings of Postojna in Slovenia, the surroundings of the Croatian cities of Sinj and Ploče, and the surroundings of the city of Lezha in Albania (Fig. 9). Furthermore, we found 11 intersections between the south-north connection and motorways in total, of which five in Greece, four in Croatia, one in Slovenia, and one in Italy, with no apparent connectivity mitigation measures.

The aim of the DINALPCONNECT project is to identify actions on the ground in order to enhance the macro-regional ecological network to respond to the political and economic circumstances that have created transboundary barriers between associated countries, which thus pose a major challenge for the conservation of EC and biodiversity (Gazoulis et al., 2022). In addition thereto, the area between Italy, Slovenia, and Austria is part of the European Green Belt, as is the area between Greece, Montenegro, and Albania – making the project area of great importance for the overall Trans-European Nature Network (TEN-N). According to that, non-EU countries should be better supported by EU neighbouring countries in order to enhance and maintain their national ecological network, by improving the presence and extension of protected areas and the measures to connect them. A substantial part of the Balkan peninsula in the DINALPCONNECT project area has been affected by turbulent political conflicts in the recent past, which have weakened transboundary cooperation in biodiversity conservation. However, the importance of transboundary connectivity in this area has been advocated for the conservation of flagship species, such as the Balkan lynx (Schwaderer, 2012). Furthermore, a study on the long-distance dispersal of grey wolf from the Dinaric-Balkan population in Slovenia into the Alps, and the consequent establishment of a mixed Alpine and Dinaric-Balkan pack in Italy, highlighted the importance of maintaining the transboundary connectivity between the Alps and the Dinaric Mountains (Ražen et al., 2016).

The transboundary approach for enhancing ecological connectivity is a priority for biodiversity conservation to respond to habitat fragmentation and ecosystems due to artificial geopolitical country borders and linear infrastructures, and can be summed up to the problems of weak governance (Liu et al., 2020).

In addition, the Balkan peninsula is one of the three major European areas which has acted as glacial refugia for almost all European species of Mediterranean origin, from where these species re-colonised Central and Northern Europe (Schmitt, 2007). In view of the current and future climatic changes, connectivity in this area will be crucial for enabling range shifts of species to the north, following the spatial distribution of well-preserved natural areas with lower human pressures, as identified by the CSI macro-regional model based on a structural connectivity approach (Hilty et al., 2020). Structural connectivity is easier to measure and visualize compared to functional connectivity (Crooks and Sanjayan, 2006) and it was therefore useful in a large study area spanning eight countries with very different availability and accessibility of spatial data. The assessment of the human pressures on landscape naturalness and permeability was based on expert opinions, which were harmonised among countries, taking into account that land-use management can differ considerably within the same land-use category. Similarly, expert opinion-based harmonization was necessary in order to understand the levels of environmental protection in the different countries, which necessarily introduced a certain degree of subjectivity. Although the expert opinions introduced some bias, they were successfully used in biodiversity conservation studies where sufficient data was not available (Di Febbraro et al., 2018; Broome et al., 2019). With 21.2% of the surface assigned to SACA1 category, the DINALPCONNECT study area shows a higher degree of naturalness compared to the EUSALP macro-region (8%, as in Plassmann et al., 2019). This difference can be attributed to relatively lower population pressure, lower fragmentation by transport infrastructure, and lower terrain elevation, as well as to the presence of large areas of well-preserved natural ecosystems in the Dinaric-Alpine area. However, caution must be taken when considering average low population pressure on large scales, because regional or local ecological linkages can in any case be threatened by urbanization.

There were considerable differences among the countries in the proportion of the national territories assigned to SACA1, ranging between 7.2% in Bosnia and Herzegovina and 31.3% in Albania. A higher proportion of SACA1 areas was included in established protected areas in the EU compared to non-EU countries. This partly reflects the high percentage of land under environmental protection in Slovenia, Croatia, and Greece compared to the global average (15.3% of land and freshwater environments, see Maxwell et al., 2020), but also the EU-27 average (26.4% of land; EC, 2021). The non-EU countries have in general less land designated as protected areas mostly due to the lack of Natura 2000 sites, although the CSI demonstrated that environmental protection is not always decisive for the identification of SACA1 areas, as seen for Bosnia and Herzegovina, Montenegro, and Albania, where approximately half of the SACA1 areas fall outside of protected areas. These results highlight the opportunities of non-EU countries to protect the identified SACA1 areas, as part of the European TEN-N.

SACA1 areas represent the backbone of biodiversity conservation in the area and the main management goal should be to preserve and promote connectivity between them. Although the LCP method tended to favour the largest SACA1s with the highest environmental protection index value, the macro-regional south-north connection linked approximately half of the area of all SACA1 areas. Along the south-north connection, several bottlenecks were identified, which are in need of special attention in order to designate the area as an ecological corridor and manage it over the long term to maintain or restore effective ecological connectivity (Hilty et al., 2020). The narrowest point, between Lake Prespa and Lake Ohrid in Albania, should be interpreted with caution as it is very close to the edge of the study area (the border between Albania and North Macedonia), and since the CSI model lacks information on the North Macedonia side. In eleven locations, the south-north connection was traversed by a motorway without any mitigation measures for EC. In addition to these threats to connectivity due to linear infrastructure, the model showed that approximately 8% of the south-north connection is currently used for intensive agriculture. In the 2020, State of Nature in the EU report, agriculture was the most frequently reported pressure on habitats and species (EEA, 2020). Agricultural intensification between 1990–2007 was more pronounced in the Western and Central European countries (Levers et al., 2016), but now this trend is spreading more to Eastern European countries.

For the above-mentioned reasons, it is therefore important to develop a methodology that allows both the identification and prioritization of the most important areas and linkages regarding EC, and of the main barriers that could hinder the enhancement thereof. The involvement of regional and transnational stakeholders is considered a fundamental step in the discussion on the results of the spatial analysis and the development of scenarios that could take into consideration the needs of the local people and the effects of the current climate crisis (i.e. Sahraoui et al., 2021). Due to the large extent of the Dinaric Mountain range and to the national differences, it is almost impossible to give the derived results the same priority in each Dinaric country; the same topic/problem is tackled in different ways in different locations. That is evident due to historical reasons: the socio-economic environment, the national/local laws, the conformity of the landscape, the species present and their interactions with human society, and the personal relation of the people with their natural capital.

The conservation of EC is a major challenge for the whole of Europe (Perrin et al., 2022), which aims at developing and connecting a larger EU-wide network of protected areas on land and at sea, legally protecting a minimum of 30% of the EU’s land area, and “integrating ecological corridors as part of a true Trans-European Nature Network” (European Commission, 2021). The analysis proposed herein confirms the importance of transnational cooperation in biodiversity conservation, considering that approximately half of the SACA1 areas in the study area span or traverse national borders. The CSI model results provide information for policymakers and managers on the macro-regional level and can be explored online at https://maps.eurac.edu/maps/1140/view. However, at the macro-regional level, a lot of valuable detailed information is not available, or it can be misinterpreted. Local data on the current and future presence of human-related infrastructure are needed to detect potential barriers to wildlife dispersal and to define the best countermeasures. In many cases, depending on the investigated region, this data can be obsolete and incomplete. Land cover maps, some landscape features, and urban sprawl cannot be projected in the actual way and some results can be badly interpreted or overestimated. Therefore, a GIS analysis of the identified linkages must be confirmed by field verification and validation. The CSI GIS model applied in the DINALPCONNECT project is a powerful tool that requires only publicly accessible data to create reliable maps of the macro-regional ecological connectivity. To perform a valuable analysis, it needs to receive input from local experts regarding the values to give to each indicator, according to each country’s situation. If the input data, factors, values, and weights used are consistent with the actual situation, the model can visualize the general connectivity of the studied areas, and detect the paths that may provide safe and alternative routes for wildlife in view of infrastructures development and climate change. The topography indicator has little influence on the connections’ width, considering that also high and steep areas in the macro-regional area are located within the south-north connection. It is visible that the width is a combination of various factors. In EU countries, where the level of protection is high, the width of the connection is influenced by protected areas. The connection of protected areas by the macro-regional LCP can be explained by the main approach we apply because the protection of an area implies low human disturbance and the absence of major artificial infrastructure.

Landscape corridors are important features for wildlife, and ensure the flexibility needed to counteract habitat fragmentation. The connection between SACA1 areas should be developed to ensure gene flow. That is why, after the macro-regional analysis, it is fundamental to move to the Pilot Region level, including the behavioural ecology and landscape structure of target species when evaluating the connection type (“functional connectivity”). The reason is that the characteristics of the species are considered to be the most important factors in the evaluation of landscape resistance. Resistance maps should be carefully explored in order to create corridors as well as to provide biological conservation measures. The presented results have identified major barriers in corridors and in corridors between the core areas. Although the SACA1 areas were mapped on the basis of general landscape permeability, rather than on the needs of species, SACA1 areas are expected to meet the needs of most species in each region. Nevertheless, the actual adaptability of the SACA1 network to local and transnational wildlife movements is uncertain and could vary tremendously among species and locations. Consequently, future work should focus on assessing the functionality of the network for diverse wildlife species at the regional level and refining the Ecological Connectivity Map and the following recommendations based on the results.

In developing a regional connectivity analysis, it is important to involve end-users early in the design process in order to collectively agree on what types of areas they want to connect, which areas need connectivity, and which areas merit the highest priority. The entire process should be transparent and repeatable in order to build trust and allow the updating of new or better data as it becomes available.

For macro-regional strategy development, the authors recommend a macro-regional connectivity analysis to identify SACA1 areas and LCPs. An assessment of the ecological value of linkages, coupled with an analysis of the threats and opportunities, is fundamental before moving to the regional scale, which will involve local decision-makers and stakeholders. The authors hope that this study will be regarded as a guide for researchers and planners to identify ecological network features, and for policymakers to adopt policies helping biological conservation and eco-regional landscape planning.

FUNDING

This work was supported by the ADRION INTERREG Project DINALPCONNECT (2020-2023) and Slovenia Research Agency (grant P4-20431 for I.B.). The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

COMPETING INTERESTS

The authors have no relevant financial or non-financial interests to disclose.

AUTHOR CONTRIBUTION

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Peter Laner, Filippo Favilli, and Irena Bertoncelj. The first draft of the manuscript was written by Filippo Favilli and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

DATA AVAILABILITY

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Application of the Continuum Suitability Index (CSI) model to display the permeability of the Alpine-Dinaric landscape and to define intervention priorities for ecological linkages

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Abstract

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Introduction

Methods

Project area

The Continuum Suitability Index (CSI) modelling approach

Definition of ecological linkages and the identification of best paths

Results

Implementation of the Continuum Suitability Index (CSI) model in the DINALPCONNECT project area

SACA areas in the DINALPCONNECT project area

Macro-regional connection

Discussion

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References

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