Between Grasslands and Mountains: Identification of valuable areas for landscape connectivity in the Tandilia Mountain System for carnivore species.

Between Grasslands and Mountains: Identification of valuable areas for landscape connectivity in the Tandilia Mountain System for carnivore species. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Between Grasslands and Mountains: Identification of valuable areas for landscape connectivity in the Tandilia Mountain System for carnivore species. María Florencia Aranguren, María Verónica Simoy, María Gimena Pizzarello, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4462760/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Habitat loss and fragmentation threaten biodiversity, particularly for carnivores whose dispersion and population viability are compromised by reduced available habitat and anthropic elements in the landscape, such as roads and crops. In Argentina, the Pampas grasslands have experienced considerable degradation and replacement by crops and are currently limited to natural patches scattered throughout the region. In the Tandilia mountains, these grassland remnants persist as crucial refuges for the species that inhabit them. Our objectives were to identify and map priority sites and areas that can act as ecological corridors between grassland patches to contribute to the connectivity knowledge of the Tandilia mountain system. We performed connectivity analyses using Least-cost Path Models and Circuit Theory. To do this, we generated habitat suitability models by combining environmental and anthropic variables, from which resistance surfaces were generated. We highlight areas of high habitat suitability for carnivores in the Pampas region, with particular emphasis on the mountain systems of Tandilia. We identified potential corridors and least-cost paths for five carnivore species, obtaining multi-species corridors highlighting the importance of landscape connectivity to maintain healthy populations. Centrality analyses revealed crucial grassland remnants and valuable corridors. These findings address the challenges associated with habitat fragmentation in the Pampas region and provide guidelines for future research and carnivore conservation actions. Connectivity Analysis grasslands carnivores corridor design Pampas Grassland Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Habitat loss and ecosystem fragmentation are considered the main threats to biodiversity today (Rands et al. 2010; Pereira et al. 2010; Yuan et al. 2024). As a result of these processes, native habitats are restricted to patches surrounded by a matrix of agricultural lands or other land uses that, to a greater or lesser extent, cause changes in both the physical environment and the populations that inhabit them(Saunders et al. 1991). In response to these threats, various strategies and tools have been proposed to help curb the loss and fragmentation of natural habitats, such as the creation of extensive protected natural areas or the implementation of policy mechanisms based on economic incentives (Sims 2014; Batáry et al. 2015; de Vries and Hanley 2016; Timmers et al. 2022). However, at present, a large portion of terrestrial ecosystems have already been degraded and fragmented by human activity (Elias et al. 2010; Venter et al. 2016), and as such, strategies aimed at maintaining, strengthening, or restoring connectivity in already affected ecosystems have become increasingly relevant in recent years (Grass et al. 2019, Hilti et al. 2020). Carnivores play a crucial role in ecosystem functioning by regulating community structure and dynamics through predation pressure and competition (Terborgh and James 2010). In fragmented environments, the configuration and characteristics of natural habitat patches determine the persistence of populations within them, and the effects this has on species can vary depending on the studied group of species. For carnivores, the size of natural patches may sometimes be too small to support stable populations with large spatial requirements (Buskirk and Gittleman 1990), and the degree of isolation within patches and the quality of the surrounding matrix may also have effects on their occupancy (Prugh et al. 2008). On the other hand, landscape alterations generated by human action create linear elements such as transition zones between natural habitats and crops, fences, and roads that can negatively affect carnivore species. Fences and roads can act as barriers to individual movement (Cozzi et al. 2013; Fahrig and Rytwinski 2009), affecting dispersal and gene flow within populations (Vaeokhaw et al. 2020), and roads, in particular, increase mortality through collisions and can generate stress to species due to noise pollution and visual stimuli (Červinka et al. 2015). The connectivity analysis and corridor design between natural habitat areas have become increasingly important in recent years, primarily in addressing conservation issues affecting carnivores (Castilho et al. 2015; Rio-Maior et al. 2019). In Argentina, Neotropical grasslands comprise approximately 398,966 km 2 of what is known as the Pampas ecoregion (Matteucci 2012). Over the last century, this region has witnessed significant agricultural development, leading to a simplified and homogenized landscape, with natural grasslands being reduced to scattered patches within a matrix consisting of various land uses (Soriano et al. 1992; Bilenca and Miñarro 2004). Bilenca and Miñarro (2004) identified around 30 Valuable Grassland Areas in the ecoregion, which are areas of considerable natural grassland surface area considered to be in good conservation status. Several of these Valuable Grassland Areas are located towards the southeast of the ecoregion, within the Tandilia mountain system, where grasslands persist as authentic relicts associated with peri-mountain plains, where rocky outcrops and shallow soils prevent cultivation (Herrera y Laterra 2008). These grasslands now cover only around 5% of their original surface area due to the high degree of agricultural transformation and serve as a last refuge for the different species that inhabit the area (Bilenca and Miñarro 2004; Valicenti et al. 2010; Velasco et al. 2013; Vera et al. 2021, 2023; Aranguren et al. 2023) Five species of carnivores are relatively common in the Pampas ecoregion: Pampas fox ( Lycalopex gymnocercus ), Geoffroy's cat ( Leopardus geoffroyi ), Molina's Hog-nosed Skunk ( Conepatus chinga ), Lesser grison ( Galictis cuja ), and Puma ( Puma concolor ). All these species are categorized as Least Concern (both nationally (Secretaría de Ambiente y Desarrollo Sustentable de la Nación y Sociedad Argentina para el Estudio de los Mamíferos 2019) and internationally (IUCN, 2024)) and their distribution ranges are wide. However, within the Tandilia mountain system, they could be experiencing different pressures at the local level that could endanger their persistence. Given the region´s fragmentation and degradation level and the relevance of grassland patches as persisting refuges for species, it is necessary to consider strategies that help conserve and promote connectivity between these natural areas. In this frame, our objectives were to identify and map priority sites and areas that can act as ecological corridors between grassland patches to contribute to the connectivity knowledge of the Tandilia mountain system. Methodology 1. Study Area The Tandilia mountain system is located in the southeastern Pampas ecoregion. It runs along a northwest-southeast diagonal, covering an extension of 350 km and a maximum width of 50-60 km. The region is characterized by a subhumid to humid temperate climate, with a marked seasonality in temperature and an average annual precipitation that ranges from 800 to 850 mm (Burgos and Vidal 1951; Valicenti et al. 2010; Falasca et al. 2000; Echeverría et al. 2017). Within the Tandilia mountain system, remnants of highland grasslands are mainly associated with hills and hillocks that rise above 50 and 500 meters over the Pampas plains (Cingolani 2011), and exist in areas where soil characteristics such as rocky outcrops and shallow substrate depth have hindered the advancement of agriculture (Herrera and Laterra 2008), allowing the persistence of native grassland. These highland grasslands are composed of several species of forage value that form tussocks and grasslands (Paspalum, Festuca, Poa, Stipa, among others), thistle species (Eryngium), as well as shrub species (Colletia paradoxa, Baccharis spp) (Herrera et al. 2019). Initially, grassland habitat also extended into the mountain valley areas, which currently have been replaced by pastures, forestation, and crops, forming a matrix that covers approximately 71% of the system's surface area, with soybeans, corn, sunflower, and winter cereals being the predominant crops (de Abelleyra 2023) Associated with this matrix, we also find a network of elements, such as roads and urban and rural centers, that help define the landscape along the mountain system. Throughout its extension are four main urban centers (Mar del Plata, Balcarce, Tandil, and Olavarría), and smaller rural areas, comprising a population of approximately 1,085,587 inhabitants (INDEC, 2022). These localities form a regional network where different productive activities emerge and diversify. The coastal area of Mar del Plata is linked to tertiary and productive activities related to tourism, fishing, and resource processing. The Balcarce area represents an essentially agricultural and livestock production environment, and the Tandil, Olavarría, and Azul areas comprise a regional network associated with the development of joint activities such as industrial activity, mining, exploitation, and tourism, in addition to agricultural and livestock activities (Mikkelsen et al. 2013). The entire area is connected by National Route 226, which, in its 297 km northeast-southeast route, passes through the main urban centers and, along with other provincial routes, forms a network of paved roads located within the mountain system (Fig. 1). 2. Data analysis We utilized a methodology consisting of several stages of analysis (summarized in Figure 2) to map the corridors and identify the contribution of landscape elements to connectivity. In the first stage, we selected and analyzed environmental variables (carnivore occurrence data, alongside bioclimatic variables, land cover) and anthropogenic variables (roads and urban centers) to elaborate the ambients factors, which resulted in the base inputs for developing suitability models to the Pampas ecoregion. Then, we obtained the resistance surfaces of the Tandilia mountain system, and finally, based on the obtained models, connectivity analyses were conducted using least-cost path models and circuit theory. 2.1 Variable analysis and environmental factor construction We can define the habitat of a species based on the biotic and abiotic elements of the landscape that individuals use (e.g., food, cover, refuge, etc.), assuming that these elements are what they require to survive, reproduce, and move through the landscape matrix (Beier et al. 2008). Using GIS tools, habitat suitability models relate suitability to raster layers representing elements of the available environment in this format (land cover, distance to roads, elevation, e.g.), and refer to these layers as environmental factors (Beier et al. 2007). We developed habitat models for each species based on occurrence data and followed part of the methodology proposed by González Saucedo et al. (2011) to define the factors we used. We constructed the models by combining the following: 1) A climate suitability model obtained from a Maximum Entropy (MaxEnt) analysis with 19 bioclimatic variables; 2) A habitat use model, which analyzes the use vs. availability of land cover; 3) Distance to roads and urban centers, two variables associated with anthropogenic disturbances that are important in the case of carnivores (Ordeñana et al. 2010). 2.1.1 Occurrence data of focal species When modeling corridors, it is essential to consider several focal species, especially those that ensure the functioning of ecological processes, both in patches of wild areas and within the matrix. Species that can act as umbrella species and safeguard many other species are also ideal for designing corridors (Beier et al. 2008). Taking this into account, we worked with the five native carnivore species for corridor modeling in the Tandilia mountain system: Lycalopex gymnocercus , Leopardus geoffroyi , Conepatus chinga , Galictis cuja , and Puma concolor . These species have a wide distribution and occur throughout the Pampas ecoregion, so we decided to work with occurrence data from the entire ecoregion to generate the modeling inputs. Therefore, we constructed an occurrence database for each species based on records obtained from camera trap surveys in the Tandilia mountain system and with data available in online databases for the rest of the Pampas ecoregion. We obtained camera trap records from a monitoring project for vertebrate species associated with remnants of highland grasslands. These surveys were conducted between September 2016 and May 2022 in 20 rural properties on remnants of grassland and surrounding areas, covering 50,738 hectares (31 remnants). We installed 248 camera trap stations, which remained active for an average period of 21 days, accumulating a sampling effort of 5,308 trap days. Regarding the data for the rest of the Pampas ecoregion, we used each species´ available records present in the Global Biodiversity Information Facility (GBIF) for the Pampas ecoregion. We obtained these records using the GBIF Occurrences plugin for QGIS software (https://doi.org/10.15468/dd.p8neyx). This tool allows downloading all available records in the online database with geographic coordinates, generating a point layer with the occurrences. This layer was subsequently processed, removing duplicate, invalid, and records whose coordinates coincide with urban centers. 2.1.2. Climate Suitability Models Using an Ecological Niche Modeling (ENM) approach, we constructed climate suitability models applying a maximum entropy modeling tool, MaxEnt. Based on presence data, this method estimates functions that relate environmental variables and habitat suitability to approximate each species’ niche and potential geographic distribution (Phillips et al. 2006). We worked with 19 raster layers corresponding to bioclimatic variables (Table 1, Supplementary Information 1), with a resolution of 30 arc-seconds, obtained from the WorldClim platform (Fick and Hijmans et al. 2017), which represent annual trends, seasonality, and extreme or limiting environmental factors. To avoid correlation between variables, we performed a Principal Component Analysis (PCA), thus reducing their dimensionality. We worked with the Principal Components (PC), which accumulated over 90% of the data variability. Since these are orthogonal, meaning independent of each other, they are not correlated, making them an excellent alternative to use as predictor variables in the ENM (Cruz-Cárdenas et al. 2014). Furthermore, to avoid spatial autocorrelation among occurrence records, we spatially filtered the data corresponding to each species based on their home range, using the spThin package in R (Aiello-Lammens et al. 2015). We filtered small and medium-sized species records at a distance of 5 km, while we filtered puma data at a distance of 15 km. We performed niche models using the kuenm package in R (Cobos et al. 2019), which utilizes MaxEnt (maximum entropy) as the modeling algorithm. The package allows the creation of several candidate models by testing different combinations of parameters and evaluating them. We combined six regularization multipliers (0.1, 0.5, 1, 2, 3, 4) and all possible combinations of the five features available in MaxEnt (linear, quadratic, product, threshold, and hinge). The models obtained were evaluated based on their statistical significance (partial ROC), prediction capacity (omission rate), and model complexity (AICc). To create the final model, we used 30 replicates per bootstrap, with raw output values of ROR (raw output). This MaxEnt modeling output directly measures habitat suitability without making assumptions about species prevalence or detection probabilities (Merow et al. 2013). Since the niche models obtained include all sites that meet the same bioclimatic conditions under which each species has been recorded, the geographic distribution of the species may be overrepresented (Illoldi-Rangel and Escalante 2008). To avoid this, we applied a threshold at the 10th percentile to the models using R software, defining the potential distribution area for each species. This threshold excludes all sites with habitat suitability values lower than the suitability values for the lowest 10% of occurrence records. It assumes that the 10% of occurrence records in the least suitable habitat do not occur in sites that are representative of the species' general habitat and, therefore, should be omitted (Babich Morrow 2019. Thus, we obtained a binary map for each species, representing their potential distribution in the Pampas ecoregion. From these binary maps, we constructed the final climate suitability maps using the distance to centroid niche (DCN) approach (González Saucedo 2011). Under this approach, it is assumed that the ideal ecological conditions for a species are found at the centroid of its multivariate niche in environmental space and that as conditions move away from this centroid, environmental suitability decreases (Yañez et al. 2020). We calculated the centroid of the climate niche as the mean of the values of each modeled component (Martínez-Meyer et al. 2013), in our case, the mean of each of the four PCs used for modeling. Then, using R software, we calculated the Euclidean distance between the niche centroid and the value of each pixel within the potential distribution area. The distance raster layers obtained were linearly rescaled between zero and one, assigning zero to the furthest value from the centroid of the climate niche (lower suitability) and one to the closest value to the centroid of said niche (higher suitability). 2.1.3. Analysis of use vs availability of land cover To include in the overall analysis the land cover characteristics that contribute to the presence of each species in the landscape, we conducted a habitat use vs availability analysis. For this, we obtained land cover information for the Pampas ecoregion from the MapBiomas Pampa Trinacional Collection 2.0 (https://pampa.mapbiomas.org/project ), with a resolution of 30x30 meters, which includes annual data on land cover and land use for the period from 1985 to 2021 of the South American Pampas biome. The original raster layer includes 11 land cover categories, which, for our analysis purposes, we summarized into seven categories using the QGIS raster calculator to reclassify the layer (Supplementary Information 1, Table 2). On the other hand, since the land cover category "Areas without vegetation" includes both urban areas and other vegetation-free areas (e.g., rocky areas), we decided to differentiate this category. Firstly, we obtained a vector layer containing polygons representing all urban areas in the Pampas ecoregion from the National Geographic Institute of the Argentine Republic (IGN, https://www.ign.gob.ar/ ). Then, we overlaid the raster layer of land cover using the Rasterize tool in QGIS, categorizing pixels coinciding with the polygons as Urban. Subsequently, we excluded this category from our analysis by assigning no data to these pixels. For corridor and connectivity analysis, we considered urban areas to be total barriers to movement. Once the land cover layer was processed, we conducted a Habitat Use vs Availability analysis for each species. Using all occurrence data and QGIS, we extracted the land cover information for each record and calculated the frequency of records for each type of land cover for each species. The frequencies of records in each category correspond to the habitat used by the species. Then, we created a buffer around each record based on the reported home range size for each species (Lucherini and Luengos Vidal 2008; Castillo 2010; Manfredi et al. 2012; Elbroch and Wittmer 2012; Luengos Vidal et al. 2016), clipped the land cover layer with these buffers, and calculated the proportion of each land cover type within the home ranges of each species. We calculated the expected frequency (availability) of use from these values for each category (Expected proportion of each category x total number of records). Then, with these observed and expected frequency values, we conducted a Chi-square goodness-of-fit test to see if there are significant differences between what the species use and what is available. In cases where expected frequencies were less than five, we performed a Yates' correction, and for the puma, whose records were low, we grouped the land cover type categories to perform the Chi-square test. To assess the use that species make of different land cover types, we created confidence intervals using the Bonferroni method with the HaviStat v2.4 program (Neu et al. 1974; Montenegro et al. 2014) to identify whether species prefer or avoid any particular land cover type or whether they use land cover based on availability. Once we determined each species’ use of different land cover types, we linearly rescaled the usage values of different categories between -1 (avoided cover) and 1 (selected cover). 2.1.4. Distance to road and urban centers Using the Distance Accumulation tool in ArcGIS Pro 3.1, we created a layer of distance to roads from a vector layer of paved roads provided by the IGN (IGN, https://www.ign.gob.ar/ )and a layer of distance to urban centers from the previously used vector layer of urban centers. Once we obtained the layers with distance values, we linearly scaled the values between 0 (corresponding to pixels with the shortest distance to roads) and 1 (corresponding to the cell with the farthest distance to roads). 2.2. Habitat suitability models for the Pampas ecoregion Using GIS tools, we built habitat suitability models for each species by summing the rescaled layers for each factor. The values obtained for each pixel encompass climatic suitability, land cover preference, and anthropogenic disturbances (distance to roads and urban centers). This procedure gives us maps where the highest possible habitat suitability value (4) reflects the best habitat conditions available, while the lowest possible value (-1) reflects the most unfavorable conditions for the species. 2.3. Modeling of connectivity in the Tandilia System We used circuit theory (McRae et al., 2008) and least-cost modeling (Adriaensen et al. 2003) to analyze connectivity. Both approaches have been employed in this analysis and have proven useful (LaRue and Nielsen 2008; Carroll et al. 2012; Etherington 2016; Belote et al. 2016). To apply them, we reduced the scale of analysis and focused on the Tandilia mountain system, where remnants of grassland persist within an agricultural matrix. We manually identified and delimited these remnants based on remote sensing imagery using GIS tools, selecting areas to connect and analyze grassland remnants with an area larger than 500 ha. Finally, we delimited the matrix area to be analyzed by applying a 15 km buffer around all mapped remnants. 2.3.1. Resistance surfaces Cost-distance and circuit theory models require a resistance surface representing the relative effort required for an organism to occupy a pixel on a map (Wade et al. 2015 ). We understand resistance as the magnitude with which a pixel facilitates or limits the movement of organisms through it (Spear et al. 2010). We also assume that suitability is synonymous with habitat permeability, that is to say, the degree to which the landscape allows the passage of organisms or ecological processes(Singleton et al. 2002) and is inversely related to resistance (Beier et al. 2007; Pullinger and Johnson 2010). If we think of these models as a set of scores, at the end of the scale reflecting low resistance, habitat quality is high, and at the other end, where resistance is high, habitat quality is low (Beier et al. 2008). With this in mind, we constructed a resistance layer by linearly scaling between 1 and 100 the suitability layer and applying an inverse linear function. This resulted in a resistance map for each species, where values of 1 in cells with lower resistance correspond to cells with higher values in the suitability layer (value of 4), and 100 in cells with the highest resistance correspond to the worst suitability values for the species (value of -1). 2.3.2 Corridors Identification We assessed connectivity in the Tandilia mountains using least-cost models (Adriaensen et al. 2003) to identify and map areas that could serve as ecological corridors between grassland remnants. We employed Linkage Mapper ver. 3.1 (McRae and Kavanagh 2011 ), which calculates cost-weighted distances between patches (core areas) from a resistance surface and then identifies and maps the least-cost paths between areas based on these values (McRae and Kavanagh 2011). Linkage Mapper allows limiting corridor identification based on a maximum geographic distance or a cost-weighted distance, among other criteria, to include species dispersal in the modeling. We chose not to limit and overestimate corridor mapping, as our goal is not to map dispersal pathways but to identify areas that may promote connectivity within the Tandilia mountains. We generated an integrated corridor map for all carnivore species from the five individual corridor models. To do this, we reclassified the values of each species model into deciles ranging from 1 to 10 and then summed them. We obtained an integrated corridor map with values ranging from 5 (low quality and higher travel costs for all the species together) to 50 (higher quality and lower travel costs) (Belote et al. 2016). 2.3.3. Centrality Analysis We estimated the centrality values for each species model. One way to analyze landscape connectivity is to assume that the landscape acts as an electrical circuit. Since a circuit's connectivity increases with the number of connections, distance metrics based on electrical connectivity apply to processes that respond positively to increased connections and redundancy. The relationship between current, voltage, and resistance with random walks in circuits makes it possible to link this approach to movement ecology, providing concrete ecological interpretations of parameters and predictions from circuit theory (McRae et al. 2008). Under this approach, the Centrality Mapper tool within Linkage Mapper (McRae 2012b), calculates the "current flow centrality" through a network of corridors and nodes (patches). The centrality values calculated for network components indicate how important a link or node is to maintaining overall connectivity. Results We compile an occurrence database (Supplementary Information 2) consisting of 391 records for L. gymnocercus , 140 for L. geoffroyi , 132 for C. chinga , 30 for P. concolor , and 95 for G. cuja . After spatial filtering, 162, 77, 66, 15, and 63 records remained, respectively, and these were used to generate the climatic niche models. As a result of the PCA performed with the bioclimatic variables, we constructed four raster layers corresponding to the first four principal components that accumulated more than 90% of the original variation of the dataset for modeling in MaxEnt. By modeling climatic ecological niches, we obtained 930 candidate niche models (186 for each species). For each species, we selected the best model, considering statistical significance, a low omission rate, and the Delta AICc (Supplementary Information 1, Table 3). We obtained habitat suitability models for the Pampas ecoregion (Fig. 3 ). The values obtained in each model indicated that the southeast region for the Pampas ecoregion (Fig. 3 ) contains good habitat suitability for the five studied carnivore species. We elaborated five resistance surfaces from each suitability model for the Tandilia mountain system, corresponding to each species (Fig. 4 ). In resistance surfaces, we observed areas with high resistance values in the intermountain areas and patches with low resistance values. The selection of areas to connect resulted in 46 remnants of highland grassland covering approximately 81,962 ha of grassland surface, meaning that we worked with 68% of the total area of grassland that persists in the Tandilia mountain system. From the connectivity analysis, we obtained a cost surface showing all corridor alternatives for Tandilia mountains and the least-cost paths (LCP) for all five species: 125 paths for L. gymnocercus , 122 paths for L. geoffroyi , 125 paths for C. chinga , 127 paths for G. cuja , and 111 paths for P. concolor (Fig. 5 ). The sum of the corridors resulted in a cost surface considering all five species at once (Fig. 6 ). The centrality analysis allowed us to identify those grassland remnants in the Tandilia mountain system that contributed most to the overall landscape connectivity (Fig. 5 ) and those LCPs that were most valuable for maintaining high connectivity (Fig. 5 ). Discussion In the habitat suitability models obtained for the Pampas ecoregion, extensive areas, mainly located towards the southeast region, which contain good habitat suitability for the five studied carnivore species, can be found. These areas with positive values correspond to the so-called Southern Pampas and the Depressed Pampas within what is generally known as the Pampas (Bilenca and Miñarro 2004 ). In both regions, the high values of habitat suitability observed could be associated with the persistence of natural or semi-natural grasslands, which have not been replaced by other uses, such as crops, given their soil conditions. On the one hand, the low, flood-prone soils of the Flooding Pampas, and on the other, the rocky soils of the Tandilia and Ventania mountain system in the Southern Pampas have limited the agriculturalization process in these regions (Bilenca and Miñarro 2004 ; Bilenca et al. 2008 ). However, it is essential to understand that despite persisting, these grasslands, like all or the vast majority of Pampean habitats, are subject to the pressures and modifications humans have exerted throughout the region in the last century (Bilenca et al. 2012 ). In the particular case of the Tandilia system, natural grasslands were replaced by crops, pastures, and forestations, mainly within inter-mountain and lowland areas (Bilenca and Miñarro 2004 ; Bilenca et al. 2008 ; Herrera et al. 2019 ). Taking this into account and observing in detail the northwest-southeast diagonal that comprises the mountain system of Tandilia, the habitat suitability values are low, except for the areas that include the hills and cerrilladas, which are distributed along the system as small patches. These patches include relict mountain grasslands that, despite being subject to a certain degree of grazing, still conserve much of the structure and composition of the native grasslands (Bilenca and Miñarro 2004 ; Valicenti et al. 2010 ; Herrera et al. 2019 ), which would explain the excellent habitat suitability values found in them. We can also observe this in the resistance surfaces generated for the Tandilia mountain system, where we observe the areas with the lowest resistance associated with the patches of highland grasslands and various edges of roads and streams. Another region where something similar to the Tandilia mountain system occurs is the Ventania mountain system. Here, we can observe areas with good habitat suitability associated with the areas that comprise the mountain system surrounded by extensive areas with low habitat quality. The habitat suitability of these mountain areas can be attributed to the relict grasslands present there. The outcomes of habitat suitability assessment in the Pampas ecoregion enable us to pinpoint areas for future research on a broader regional scale. We were able to generate a multi-species corridor map for the Tandilia system. Several authors agree that generating multi-species corridors is essential when planning strategies that promote landscape connectivity (Liu et al. 2018 ; Ersoy et al. 2019 ); in this way, it is guaranteed that the corridors not only function as areas that favor the movement of focal species but also allow the flow of the ecosystem processes that accompany them. In addition, the design of multi-species corridors can be thought of in terms of umbrella species, which have ample space and resource requirements, such as carnivores, and by protecting them or designing corridors for them, other species are indirectly favored (Caro 2010 ). Brodie et al. ( 2015 ) found that when seeking to generate multi-species corridors, selecting species whose ecological requirements are similar increases their efficiency and reduces costs compared to corridors designed for different ecological groups. In our case, being able to identify common corridors for carnivore species gives us not only information on valuable areas for their movement and dispersal but also allows us to identify priority sites where we can focus specific conservation actions for these particular species, such as mitigation of the conflict with livestock producers, given that in the Pampas ecoregion, species such as the puma and the gray fox are considered harmful to livestock activities (Caruso et al. 2017 ), and in the Tandilia system the situation is similar (Aranguren in prep.). Despite the advantages of thinking about multi-species corridors designed for carnivores, we must not ignore the fact that many of these are habitat generalists, with good plasticity and tolerance to degraded environments (Buskirk and Gittleman 1990 ), therefore generating corridors designed only for them can lead to a “negative umbrella effect” affecting those secondary, specialist species with more limited environmental requirements, which are expected to be protected under the umbrella (Beier et al., 2008 ). We observed that some main corridors (those that coincide with the lowest-cost roads) run either entirely or in some sections along paved roads or routes, such as in the case of a corridor accompanying National Route 226. These results could be attributed to broad shoulders and abundant vegetation, coupled with the fact that roads offer the shortest distance between two areas. In landscapes dominated by agricultural matrix, road edges or shoulders can serve as habitat for species, both for plant communities and for arthropods and small mammals, including some carnivores (Tikka et al. 2001 ; Ruiz-Capillas et al. 2013 ; Rotholz and Mandelik 2013 ; Carmona et al. 2024 ). Depending on their characteristics, road edges, and shoulders can also act as corridors that allow for the dispersal of some species between patches of natural habitat (Eversham and Telfer 1994 ; Vermeulen and Opdam 1995 ; Le Viol et al. 2008 ; Redon (de) et al. 2015; Galantinho et al. 2020 ). Considering this and the results obtained, some of the shoulders of the roads that run through the Tandilia system could act as corridors for the studied species, with many of them being associated with roads in various sections (Fig. 7 ). Nonetheless, the disturbances generated by these routes usually awaken behavioral changes in species, such as avoidance behaviors (McClure et al. 2013 ; Navarro-Castilla et al. 2014 ; Grilo et al. 2015 ), which lead carnivore species not to take advantage of these environments as runners. However, the proximity to the routes exposes the carnivores to risk of being struck by vehicles. Death due to being run over on roads is one of the threats that can compromise local populations (Schwab and Zandbergen 2011 ; Gregory et al. 2021 ). This forces us to reevaluate these environments as potential corridors and invest in mitigation or management efforts. In any case, since they can act as available habitats and corridors for other groups of species in a landscape as intervened and fragmented as the Pampas, it is crucial to aim for adequate management of them, such as implementing regulations that regulate agricultural activities on shoulders (e.g., Rimoldi and Chimento, 2020 ), a common practice in the Pampas region. The centrality analysis of the grassland relicts allowed us to identify that the patches that contribute most to the general connectivity are located in the central zone of the system (area of the city of Tandil). Taking into account the spatial configuration of the mountain range on a diagonal and that centrality reflects the contribution of the patches to the general connectivity of the network (McRae et al. 2008 ), these central habitat patches would be acting as a bridge between those that are distributed at the NW and SE ends of the system. On the other hand, we can also find numerous patches of various sizes in this central region, which have many connections to nearby patches. For Jaimes et al. (2019), this central area of the mountain system counts as an important landscape unit to conserve, given that mountainous environments predominate, present as large patches, and the anthropic elements of a matrix are few. On the other hand, their results also highlight the importance of other groups of patches within the system, such as some in the Balcarce and Mar del Plata areas. In our analysis, these groups of patches presented average centrality values. That is, they contribute less to the general connectivity of the system than others. However, these patches that stand out in the study by Jaimes et al. (2019) make up groups of large patches, with smaller patches as satellites, which can be of great value at a local scale within the mountain system. Although the analysis approaches in both works differed, both represent sufficient evidence to conserve these areas with important average centrality values. From the centrality values and the mapped corridors, we can identify priority corridors and areas to maintain the connectivity of the focal species. Looking at the mapped corridors, we can find several smaller pass-over natural grassland patches that were not included in the analysis. These results could be due to small habitat patches that act as stepping stones between larger patches. Herrera et al. ( 2017 ) found that small habitat patches for the Tandilia system function as stepping stones, particularly to the connectivity of species that move long distances. Considering this evidence, searching for conservation and management strategies for these areas is advisable. Also, we identified several corridors that maintain a considerable width along their route and connect valuable areas for landscape connectivity (greater centrality), which we could consider a priority in the mountain system. However, some of these corridors are interrupted by barriers such as paved routes, which could be an important barrier to the movement of species, not only because of the risk of mortality but because some of them avoid them, thus promoting the fragmentation of their populations (Grilo et al. 2015 ). Therefore, it would be necessary to mitigate these effects by resorting to underground passageways or sewers, which have proven helpful in facilitating the flow of small and medium-sized carnivores through the routes (Grilo et al. 2008 ). In conclusion, our study shows us that despite the Pampas ecoregion's history of transforming and replacing grasslands, there are still areas with good environmental suitability for carnivore species. Mountain systems would be essential conservation sites towards the south of the region. We also considered that several priority areas to conserve connectivity within the Tandilia system for the carnivore species studied, as well as corridors that could keep them connected, exist. A study like this in the region can be used as a frame of reference for future studies or the implementation of conservation actions in the area that favor the use of space by carnivore species. Declarations Acknowledgments We want to thank field assistants and collaborators (Carmela Marin, Manuela Santiago, Celena Sarasola, Estefania Paz, Maximiliano Calcagno, Ailen Chuchuy, M. Eva Cabanellas, Claudio Santiago, Estefania Marisol Avalo, Felisa La Pescadora) for their kind assistance and support during fieldwork activities with trap cameras. We also appreciate the collaboration of owners and managers of highland grassland remnants for allowing us to visit their properties: Federico Juana (Estancia Las Mercedes), Paulo Mosca (Estancia Nilonil), Reina Feldman (Estancia Sanmalucon), Mario Bustillo (Estancia La Asunción), Alfonso (Valle de Los Ciervos), Emilio Milanessi (Estancia Chapaleofu), Manuel Castelar (La Argentina), Raul Eyheramendy (Sierra Alta) and Tomas Pérez Marino (Estancia El Bonete). We thank Claudio Barletta (Reserva Natural Sierra del Tigre) for his attention and collaboration. Also, we would like to thank Estefanía Paz for her support and contributions during the development of this work. We want to thank the assistance of the Scouts de la Ciencia program for their cooperation in fieldwork with camera traps. Also, we would like to thank M. Ignacio Simoy, Santiago Linares, and David Vera for their collaboration with the analysis in R and GIS. Funding This study was partially supported by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET PIP. 1220150100598CO), Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT, PICT 2015-2281), Neotropical Grassland Conservancy (Student Grant Program 2021), IDEA WILD (IDEA WILD Equipment Support 2021). M.F. Aranguren, M.G. Pizzarello, V. Leber, D. Franzoia Moss, and J. Dopazo were supported by fellowships from the Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET). C. Trofino-Falasco is a support technician of Comisión de Investigaciones Cientificas de la Provincia de Buenos Aires (CICPBA), V. Simoy, M.A. Velasco, and I. Berkunsky are CONICET Research Fellows. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study's conception and design. M. Florencia Aranguren and M. 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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-4462760","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309430739,"identity":"a7ffbaa7-855f-43f2-bb96-a1fd679becdc","order_by":0,"name":"María Florencia Aranguren","email":"data:image/png;base64,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","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":true,"prefix":"","firstName":"María","middleName":"Florencia","lastName":"Aranguren","suffix":""},{"id":309430740,"identity":"1f7e5630-f70f-43d1-9a76-bc8829741237","order_by":1,"name":"María Verónica Simoy","email":"","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Verónica","lastName":"Simoy","suffix":""},{"id":309430741,"identity":"44b66036-8144-4c6b-9aa2-62ba85a12e40","order_by":2,"name":"María Gimena Pizzarello","email":"","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Gimena","lastName":"Pizzarello","suffix":""},{"id":309430742,"identity":"39a0cfca-b313-4563-8538-620bee120c60","order_by":3,"name":"Clara Trofino-Falasco","email":"","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Trofino-Falasco","suffix":""},{"id":309430743,"identity":"239fb5b2-5c7e-448c-a11d-b42f8c1311ce","order_by":4,"name":"Melina Alicia Velasco","email":"","orcid":"","institution":"Sección Herpetología, División Zoología Vertebrados, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata- CONICET","correspondingAuthor":false,"prefix":"","firstName":"Melina","middleName":"Alicia","lastName":"Velasco","suffix":""},{"id":309430744,"identity":"92686f8b-4366-4cd0-9ba2-2ed9268d6652","order_by":5,"name":"Virginia Leber","email":"","orcid":"","institution":"Centro de Investigaciones y Estudios Ambientales. Facultad de Ciencias Humanas. Universidad Nacional del Centro de la Provincia de Buenos Aires - CONICET","correspondingAuthor":false,"prefix":"","firstName":"Virginia","middleName":"","lastName":"Leber","suffix":""},{"id":309430745,"identity":"23f7c408-2e68-4bc7-8efa-8a3a9929e396","order_by":6,"name":"Daniella Franzoia Moss","email":"","orcid":"","institution":"Instituto de Geografía, Historia y Ciencias Sociales (IGEHCS), Centro de Investigaciones Geográficas (CIG) –UNICEN/CONICET.","correspondingAuthor":false,"prefix":"","firstName":"Daniella","middleName":"Franzoia","lastName":"Moss","suffix":""},{"id":309430746,"identity":"59c2023d-4d25-4a44-86f8-e177892e9d7f","order_by":7,"name":"Judit Dopazo","email":"","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Judit","middleName":"","lastName":"Dopazo","suffix":""},{"id":309430747,"identity":"2897709e-db5a-463b-b88e-474c635253b3","order_by":8,"name":"Igor Berkunsky","email":"","orcid":"","institution":"Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable-CICPBA, Universidad Nacional del Centro de la Provincia de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Igor","middleName":"","lastName":"Berkunsky","suffix":""}],"badges":[],"createdAt":"2024-05-22 19:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4462760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4462760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57816535,"identity":"43b711ec-28a0-43e4-b9a0-2ba920489ea1","added_by":"auto","created_at":"2024-06-06 04:44:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1873500,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003cstrong\u003e.\u003c/strong\u003e Tandilia Mountain System. a) Map of the study area indicating mapped mountain grassland patches, main urban centers, and roads (RN: National Route and RP: Provincial Route). b) Panoramic photographs of the mountain system and relicts of highland grasslands.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/95f55f517c6679ee43131866.png"},{"id":57815830,"identity":"85f13d14-bfc9-4ec2-a3d3-3a69db3bd8f8","added_by":"auto","created_at":"2024-06-06 04:28:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539308,"visible":true,"origin":"","legend":"\u003cp\u003eWork diagram\u003cstrong\u003e. \u003c/strong\u003eSummary of the work diagram used in this research, including inputs, analysis, programs used, and obtained results.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/7aa780ded6e1056b3f33c07e.png"},{"id":57817091,"identity":"f84b19d8-b606-4e35-bea8-2c33e360acbd","added_by":"auto","created_at":"2024-06-06 04:52:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3284176,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat suitability models for the Pampas ecoregion.\u003cstrong\u003e \u003c/strong\u003eRepresentation of the habitat suitability values for the Pampas ecoregion for the five studied carnivore species.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/a9d3b94fe30398bc9bd721c4.png"},{"id":57816123,"identity":"c2d4de37-2e74-4e7e-b07d-768435ea42a8","added_by":"auto","created_at":"2024-06-06 04:36:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2136295,"visible":true,"origin":"","legend":"\u003cp\u003eResistance surfaces for the Tandilia mountain system.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/f06b01e28315ab6a0bb5392a.png"},{"id":57817487,"identity":"ebb49859-caeb-46f4-87e2-98d4c240b167","added_by":"auto","created_at":"2024-06-06 05:00:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1516810,"visible":true,"origin":"","legend":"\u003cp\u003eLeast cost paths between relicts for each species. The least cost paths (black lines) are represented for each species, superimposed on the integral corridors on a blue/green scale.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/8545270fd06822c740987fbf.png"},{"id":57815832,"identity":"031918d9-bbc6-4bb6-a86d-41742329638b","added_by":"auto","created_at":"2024-06-06 04:28:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1255805,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-species ecological corridor for the Tandilia mountain system and the results of the centrality analysis.\u003cstrong\u003e \u003c/strong\u003eWe show the mapped corridors based on the rescaled cost values on a green scale. The analyzed grassland relicts are represented according to their centrality value, ranging from lowest (light color) to highest (dark color).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/c16b89e88a82167962aadbc4.png"},{"id":57815838,"identity":"9d35290b-39a5-4b0c-8e0d-4521c1901acf","added_by":"auto","created_at":"2024-06-06 04:28:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1545751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhotographs of road shoulders in the Tandilia mountain system.\u003c/strong\u003e a) Adult Molina's Hog-nosed Skunk (\u003cem\u003eConepatus chinga\u003c/em\u003e) with chick, crossing a road; b) Shoulders of a route within the mountain system.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/42f98f8daca3e2b5dbcba1a0.png"},{"id":59753327,"identity":"301004a6-94fd-41dd-a996-816977149053","added_by":"auto","created_at":"2024-07-06 00:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14725840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/cc9a6376-c20f-4822-ac27-13f7d25f602a.pdf"},{"id":57816124,"identity":"04fc87d9-4e19-4b88-a0d7-c8c560991f95","added_by":"auto","created_at":"2024-06-06 04:36:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":100808,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/e6320ea197a5b28964888e47.pdf"},{"id":57815837,"identity":"6a1822be-7f8c-4ae3-a430-019a9207a31d","added_by":"auto","created_at":"2024-06-06 04:28:25","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34131,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4462760/v1/fef2558ab45105a032f4a0f2.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Between Grasslands and Mountains: Identification of valuable areas for landscape connectivity in the Tandilia Mountain System for carnivore species.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHabitat loss and ecosystem fragmentation are considered the main threats to biodiversity today (Rands et al. 2010; Pereira et al. 2010; Yuan et al. 2024). As a result of these processes, native habitats are restricted to patches surrounded by a matrix of agricultural lands or other land uses that, to a greater or lesser extent, cause changes in both the physical environment and the populations that inhabit them(Saunders et al. 1991). In response to these threats, various strategies and tools have been proposed to help curb the loss and fragmentation of natural habitats, such as the creation of extensive protected natural areas or the implementation of policy mechanisms based on economic incentives (Sims 2014; Bat\u0026aacute;ry et al. 2015; de Vries and Hanley 2016; Timmers et al. 2022). However, at present, a large portion of terrestrial ecosystems have already been degraded and fragmented by human activity (Elias et al. 2010; Venter et al. 2016), and as such, strategies aimed at maintaining, strengthening, or restoring connectivity in already affected ecosystems have become increasingly relevant in recent years (Grass et al. 2019, Hilti et al. 2020). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarnivores play a crucial role in ecosystem functioning by regulating community structure and dynamics through predation pressure and competition (Terborgh and James 2010). In fragmented environments, the configuration and characteristics of natural habitat patches determine the persistence of populations within them, and the effects this has on species can vary depending on the studied group of species. For carnivores, the size of natural patches may sometimes be too small to support stable populations with large spatial requirements (Buskirk and Gittleman 1990), and the degree of isolation within patches and the quality of the surrounding matrix may also have effects on their occupancy (Prugh et al. 2008). On the other hand, landscape alterations generated by human action create linear elements such as transition zones between natural habitats and crops, fences, and roads that can negatively affect carnivore species. Fences and roads can act as barriers to individual movement (Cozzi et al. 2013; Fahrig and Rytwinski 2009), affecting dispersal and gene flow within populations (Vaeokhaw et al. 2020), and roads, in particular, increase mortality through collisions and can generate stress to species due to noise pollution and visual stimuli (Červinka et al. 2015). The connectivity analysis and corridor design between natural habitat areas have become increasingly important in recent years, primarily in addressing conservation issues affecting carnivores (Castilho et al. 2015; Rio-Maior et al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Argentina, Neotropical grasslands comprise approximately 398,966 km\u003csup\u003e2\u003c/sup\u003e of what is known as the Pampas ecoregion (Matteucci 2012). Over the last century, this region has witnessed significant agricultural development, leading to a simplified and homogenized landscape, with natural grasslands being reduced to scattered patches within a matrix consisting of various land uses (Soriano et al. 1992; Bilenca and Mi\u0026ntilde;arro 2004). Bilenca and Mi\u0026ntilde;arro (2004) identified around 30 Valuable Grassland Areas in the ecoregion, which are areas of considerable natural grassland surface area considered to be in good conservation status. Several of these Valuable Grassland Areas are located towards the southeast of the ecoregion, within the Tandilia mountain system, where grasslands persist as authentic relicts associated with peri-mountain plains, where rocky outcrops and shallow soils prevent cultivation (Herrera y Laterra 2008). These grasslands now cover only around 5% of their original surface area due to the high degree of agricultural transformation and serve as a last refuge for the different species that inhabit the area \u0026nbsp;(Bilenca and Mi\u0026ntilde;arro 2004; Valicenti et al. 2010; Velasco et al. 2013; Vera et al. 2021, 2023; Aranguren et al. 2023)\u003c/p\u003e\n\u003cp\u003eFive species of carnivores are relatively common in the Pampas ecoregion: Pampas fox (\u003cem\u003eLycalopex gymnocercus\u003c/em\u003e), Geoffroy\u0026apos;s cat (\u003cem\u003eLeopardus geoffroyi\u003c/em\u003e), Molina\u0026apos;s Hog-nosed Skunk (\u003cem\u003eConepatus chinga\u003c/em\u003e), \u0026nbsp;Lesser grison (\u003cem\u003eGalictis cuja\u003c/em\u003e), and Puma (\u003cem\u003ePuma concolor\u003c/em\u003e). All these species are categorized as Least Concern (both nationally (Secretar\u0026iacute;a de Ambiente y Desarrollo Sustentable de la Naci\u0026oacute;n y Sociedad Argentina para el Estudio de los Mam\u0026iacute;feros 2019) and internationally (IUCN, 2024)) and their distribution ranges are wide. However, within the Tandilia mountain system, they could be experiencing different pressures at the local level that could endanger their persistence. Given the region\u0026acute;s fragmentation and degradation level and the relevance of grassland patches as persisting refuges for species, it is necessary to consider strategies that help conserve and promote connectivity between these natural areas. In this frame, our objectives were to identify and map priority sites and areas that can act as ecological corridors between grassland patches to contribute to the connectivity knowledge of the Tandilia mountain system.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003e1. Study Area\u003c/h2\u003e\n\u003cp\u003eThe Tandilia mountain system is located in the southeastern Pampas ecoregion. It runs along a northwest-southeast diagonal, covering an extension of 350 km and a maximum width of 50-60 km. The region is characterized by a subhumid to humid temperate climate, with a marked seasonality in temperature and an average annual precipitation that ranges from 800 to 850 mm (Burgos and Vidal 1951; Valicenti et al. 2010; Falasca et al. 2000; Echeverr\u0026iacute;a et al. 2017).\u003c/p\u003e\n\u003cp\u003eWithin the Tandilia mountain system, remnants of highland grasslands are mainly associated with hills and hillocks that rise above 50 and 500 meters over the Pampas plains (Cingolani 2011), and exist in areas where soil characteristics such as rocky outcrops and shallow substrate depth have hindered the advancement of agriculture (Herrera and \u0026nbsp;Laterra 2008), allowing the persistence of native grassland. These highland grasslands are composed of several species of forage value that form tussocks and grasslands (Paspalum, Festuca, Poa, Stipa, among others), thistle species (Eryngium), as well as shrub species (Colletia paradoxa, Baccharis spp) (Herrera et al. 2019). Initially, grassland habitat also extended into the mountain valley areas, which currently have been replaced by pastures, forestation, and crops, forming a matrix that covers approximately 71% of the system\u0026apos;s surface area, with soybeans, corn, sunflower, and winter cereals being the predominant crops (de Abelleyra 2023)\u003c/p\u003e\n\u003cp\u003eAssociated with this matrix, we also find a network of elements, such as roads and urban and rural centers, that help define the landscape along the mountain system. Throughout its extension are four main urban centers (Mar del Plata, Balcarce, Tandil, and Olavarr\u0026iacute;a), and smaller rural areas, comprising a population of approximately 1,085,587 inhabitants (INDEC, 2022). These localities form a regional network where different productive activities emerge and diversify. The coastal area of Mar del Plata is linked to tertiary and productive activities related to tourism, fishing, and resource processing. The Balcarce area represents an essentially agricultural and livestock production environment, and the Tandil, Olavarr\u0026iacute;a, and Azul areas comprise a regional network associated with the development of joint activities such as industrial activity, mining, exploitation, and tourism, in addition to agricultural and livestock activities (Mikkelsen et al. 2013). The entire area is connected by National Route 226, which, in its 297 km northeast-southeast route, passes through the main urban centers and, along with other provincial routes, forms a network of paved roads located within the mountain system (Fig. 1).\u003c/p\u003e\n\u003ch2\u003e2. Data analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe utilized a methodology consisting of several stages of analysis (summarized in Figure 2) to map the corridors and identify the contribution of landscape elements to connectivity. In the first stage, we selected and analyzed environmental variables (carnivore occurrence data, alongside bioclimatic variables, land cover) and anthropogenic variables (roads and urban centers) to elaborate the ambients factors, which resulted in the base inputs for developing suitability models to the Pampas ecoregion. Then, we obtained the resistance surfaces of the Tandilia mountain system, and finally, based on the obtained models, connectivity analyses were conducted using least-cost path models and circuit theory.\u003c/p\u003e\n\u003ch2\u003e2.1 Variable analysis and environmental factor construction\u003c/h2\u003e\n\u003cp\u003eWe can define the habitat of a species based on the biotic and abiotic elements of the landscape that individuals use (e.g., food, cover, refuge, etc.), assuming that these elements are what they require to survive, reproduce, and move through the landscape matrix (Beier et al. 2008). Using GIS tools, habitat suitability models relate suitability to raster layers representing elements of the available environment in this format (land cover, distance to roads, elevation, e.g.), and refer to these layers as environmental factors (Beier et al. 2007).\u003c/p\u003e\n\u003cp\u003eWe developed habitat models for each species based on occurrence data and followed part of the methodology proposed by Gonz\u0026aacute;lez Saucedo et al. (2011) to define the factors we used. We constructed the models by combining the following: 1) A climate suitability model obtained from a Maximum Entropy (MaxEnt) analysis with 19 bioclimatic variables; 2) A habitat use model, which analyzes the use vs. availability of land cover; 3) Distance to roads and urban centers, two variables associated with anthropogenic disturbances that are important in the case of carnivores (Orde\u0026ntilde;ana et al. 2010).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1.1 Occurrence data of focal species\u003c/h3\u003e\n\u003cp\u003eWhen modeling corridors, it is essential to consider several focal species, especially those that ensure the functioning of ecological processes, both in patches of wild areas and within the matrix. Species that can act as umbrella species and safeguard many other species are also ideal for designing corridors (Beier et al. 2008). Taking this into account, we worked with the five native carnivore species for corridor modeling in the Tandilia mountain system: \u003cem\u003eLycalopex gymnocercus\u003c/em\u003e, \u003cem\u003eLeopardus geoffroyi\u003c/em\u003e, \u003cem\u003eConepatus chinga\u003c/em\u003e, \u003cem\u003eGalictis cuja\u003c/em\u003e, and \u003cem\u003ePuma concolor\u003c/em\u003e. These species have a wide distribution and occur throughout the Pampas ecoregion, so we decided to work with occurrence data from the entire ecoregion to generate the modeling inputs. Therefore, we constructed an occurrence database for each species based on records obtained from camera trap surveys in the Tandilia mountain system and with data available in online databases for the rest of the Pampas ecoregion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe obtained camera trap records from a monitoring project for vertebrate species associated with remnants of highland grasslands. These surveys were conducted between September 2016 and May 2022 in 20 rural properties on remnants of grassland and surrounding areas, covering 50,738 hectares (31 remnants). We installed 248 camera trap stations, which remained active for an average period of 21 days, accumulating a sampling effort of 5,308 trap days.\u003c/p\u003e\n\u003cp\u003eRegarding the data for the rest of the Pampas ecoregion, we used each species\u0026acute; available records present in the Global Biodiversity Information Facility (GBIF) for the Pampas ecoregion. We obtained these records using the GBIF Occurrences plugin for QGIS software (https://doi.org/10.15468/dd.p8neyx). This tool allows downloading all available records in the online database with geographic coordinates, generating a point layer with the occurrences. This layer was subsequently processed, removing duplicate, invalid, and records whose coordinates coincide with urban centers.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1.2. Climate Suitability Models\u003c/h3\u003e\n\u003cp\u003eUsing an Ecological Niche Modeling (ENM) approach, we constructed climate suitability models applying a maximum entropy modeling tool, MaxEnt. Based on presence data, this method estimates functions that relate environmental variables and habitat suitability to approximate each species\u0026rsquo; niche and potential geographic distribution \u0026nbsp;(Phillips et al. 2006). We worked with 19 raster layers corresponding to bioclimatic variables (Table 1, Supplementary Information 1), with a resolution of 30 arc-seconds, obtained from the WorldClim platform (Fick and Hijmans et al. 2017), which represent annual trends, seasonality, and extreme or limiting environmental factors. To avoid correlation between variables, we performed a Principal Component Analysis (PCA), thus reducing their dimensionality. We worked with the Principal Components (PC), which accumulated over 90% of the data variability. Since these are orthogonal, meaning independent of each other, they are not correlated, making them an excellent alternative to use as predictor variables in the ENM (Cruz-C\u0026aacute;rdenas et al. 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, to avoid spatial autocorrelation among occurrence records, we spatially filtered the data corresponding to each species based on their home range, using the \u003cem\u003espThin\u003c/em\u003e package in R (Aiello-Lammens et al. 2015). We filtered small and medium-sized species records at a distance of 5 km, while we filtered puma data at a distance of 15 km.\u003c/p\u003e\n\u003cp\u003eWe performed niche models using the\u003cem\u003e\u0026nbsp;kuenm\u003c/em\u003e package in R (Cobos et al. 2019), which utilizes MaxEnt (maximum entropy) as the modeling algorithm. The package allows the creation of several candidate models by testing different combinations of parameters and evaluating them. We combined six regularization multipliers (0.1, 0.5, 1, 2, 3, 4) and all possible combinations of the five features available in MaxEnt (linear, quadratic, product, threshold, and hinge). The models obtained were evaluated based on their statistical significance (partial ROC), prediction capacity (omission rate), and model complexity (AICc). To create the final model, we used 30 replicates per bootstrap, with raw output values of ROR (raw output). This MaxEnt modeling output directly measures habitat suitability without making assumptions about species prevalence or detection probabilities \u0026nbsp;(Merow et al. 2013).\u003c/p\u003e\n\u003cp\u003eSince the niche models obtained include all sites that meet the same bioclimatic conditions under which each species has been recorded, the geographic distribution of the species may be overrepresented (Illoldi-Rangel and Escalante 2008). To avoid this, we applied a threshold at the 10th percentile to the models using R software, defining the potential distribution area for each species. This threshold excludes all sites with habitat suitability values lower than the suitability values for the lowest 10% of occurrence records. It assumes that the 10% of occurrence records in the least suitable habitat do not occur in sites that are representative of the species\u0026apos; general habitat and, therefore, should be omitted (Babich Morrow 2019. Thus, we obtained a binary map for each species, representing their potential distribution in the Pampas ecoregion.\u003c/p\u003e\n\u003cp\u003eFrom these binary maps, we constructed the final climate suitability maps using the distance to centroid niche (DCN) approach (Gonz\u0026aacute;lez Saucedo 2011). Under this approach, it is assumed that the ideal ecological conditions for a species are found at the centroid of its multivariate niche in environmental space and that as conditions move away from this centroid, environmental suitability decreases (Ya\u0026ntilde;ez et al. 2020). We calculated the centroid of the climate niche as the mean of the values of each modeled component (Mart\u0026iacute;nez-Meyer et al. 2013), in our case, the mean of each of the four PCs used for modeling. Then, using R software, we calculated the Euclidean distance between the niche centroid and the value of each pixel within the potential distribution area. The distance raster layers obtained were linearly rescaled between zero and one, assigning zero to the furthest value from the centroid of the climate niche (lower suitability) and one to the closest value to the centroid of said niche (higher suitability).\u003c/p\u003e\n\u003ch3\u003e2.1.3. Analysis of use vs availability of land cover\u003c/h3\u003e\n\u003cp\u003eTo include in the overall analysis the land cover characteristics that contribute to the presence of each species in the landscape, we conducted a habitat use vs availability analysis. For this, we obtained land cover information for the Pampas ecoregion from the MapBiomas Pampa Trinacional Collection 2.0 (https://pampa.mapbiomas.org/project ), with a resolution of 30x30 meters, which includes annual data on land cover and land use for the period from 1985 to 2021 of the South American Pampas biome. The original raster layer includes 11 land cover categories, which, for our analysis purposes, we summarized into seven categories using the QGIS raster calculator to reclassify the layer (Supplementary Information 1, Table 2). On the other hand, since the land cover category \u0026quot;Areas without vegetation\u0026quot; includes both urban areas and other vegetation-free areas (e.g., rocky areas), we decided to differentiate this category. Firstly, we obtained a vector layer containing polygons representing all urban areas in the Pampas ecoregion from the National Geographic Institute of the Argentine Republic (IGN, https://www.ign.gob.ar/ ). Then, we overlaid the raster layer of land cover using the Rasterize tool in QGIS, categorizing pixels coinciding with the polygons as Urban. Subsequently, we excluded this category from our analysis by assigning no data to these pixels. For corridor and connectivity analysis, we considered urban areas to be total barriers to movement.\u003c/p\u003e\n\u003cp\u003eOnce the land cover layer was processed, we conducted a Habitat Use vs Availability analysis for each species. Using all occurrence data and QGIS, we extracted the land cover information for each record and calculated the frequency of records for each type of land cover for each species. The frequencies of records in each category correspond to the habitat used by the species. Then, we created a buffer around each record based on the reported home range size for each species (Lucherini and Luengos Vidal 2008; Castillo 2010; Manfredi et al. 2012; Elbroch and Wittmer 2012; Luengos Vidal et al. 2016), clipped the land cover layer with these buffers, and calculated the proportion of each land cover type within the home ranges of each species. We calculated the expected frequency (availability) of use from these values for each category (Expected proportion of each category x total number of records). Then, with these observed and expected frequency values, we conducted a Chi-square goodness-of-fit test to see if there are significant differences between what the species use and what is available. In cases where expected frequencies were less than five, we performed a Yates\u0026apos; correction, and for the puma, whose records were low, we grouped the land cover type categories to perform the Chi-square test.\u003c/p\u003e\n\u003cp\u003eTo assess the use that species make of different land cover types, we created confidence intervals using the Bonferroni method with the HaviStat v2.4 program (Neu et al. 1974; Montenegro et al. 2014) to identify whether species prefer or avoid any particular land cover type or whether they use land cover based on availability. Once we determined each species\u0026rsquo; use of different land cover types, we linearly rescaled the usage values of different categories between -1 (avoided cover) and 1 (selected cover).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1.4. Distance to road and urban centers\u003c/h3\u003e\n\u003cp\u003eUsing the Distance Accumulation tool in ArcGIS Pro 3.1, we created a layer of distance to roads from a vector layer of paved roads provided by the IGN (IGN, https://www.ign.gob.ar/ )and a layer of distance to urban centers from the previously used vector layer of urban centers. Once we obtained the layers with distance values, we linearly scaled the values between 0 (corresponding to pixels with the shortest distance to roads) and 1 (corresponding to the cell with the farthest distance to roads).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2. Habitat suitability models for the Pampas ecoregion\u003c/h2\u003e\n\u003cp\u003eUsing GIS tools, we built habitat suitability models for each species by summing the rescaled layers for each factor. The values obtained for each pixel encompass climatic suitability, land cover preference, and anthropogenic disturbances (distance to roads and urban centers). This procedure gives us maps where the highest possible habitat suitability value (4) reflects the best habitat conditions available, while the lowest possible value (-1) reflects the most unfavorable conditions for the species. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.3. Modeling of connectivity in the Tandilia System\u003c/h2\u003e\n\u003cp\u003eWe used circuit theory (McRae et al., 2008) and least-cost modeling (Adriaensen et al. 2003) to analyze connectivity. Both approaches have been employed in this analysis and have proven useful (LaRue and Nielsen 2008; Carroll et al. 2012; Etherington 2016; Belote et al. 2016). To apply them, we reduced the scale of analysis and focused on the Tandilia mountain system, where remnants of grassland persist within an agricultural matrix. We manually identified and delimited these remnants based on remote sensing imagery using GIS tools, selecting areas to connect and analyze grassland remnants with an area larger than 500 ha. Finally, we delimited the matrix area to be analyzed by applying a 15 km buffer around all mapped remnants.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.3.1. Resistance surfaces\u003c/h3\u003e\n\u003cp\u003eCost-distance and circuit theory models require a resistance surface representing the relative effort required for an organism to occupy a pixel on a map (Wade et al. 2015 ). We understand resistance as the magnitude with which a pixel facilitates or limits the movement of organisms through it (Spear et al. 2010). We also assume that suitability is synonymous with habitat permeability, that is to say, the degree to which the landscape allows the passage of organisms or ecological processes(Singleton et al. 2002) and is inversely related to resistance (Beier et al. 2007; Pullinger and \u0026nbsp;Johnson 2010). If we think of these models as a set of scores, at the end of the scale reflecting low resistance, habitat quality is high, and at the other end, where resistance is high, habitat quality is low (Beier et al. 2008). With this in mind, we constructed a resistance layer by linearly scaling between 1 and 100 the suitability layer and applying an inverse linear function. This resulted in a resistance map for each species, where values of 1 in cells with lower resistance correspond to cells with higher values in the suitability layer (value of 4), and 100 in cells with the highest resistance correspond to the worst suitability values for the species (value of -1).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.3.2 Corridors Identification\u003c/h3\u003e\n\u003cp\u003eWe assessed connectivity in the Tandilia mountains using least-cost models (Adriaensen et al. 2003) to identify and map areas that could serve as ecological corridors between grassland remnants. We employed Linkage Mapper ver. 3.1 \u0026nbsp;(McRae and Kavanagh 2011 ), which calculates cost-weighted distances between patches (core areas) from a resistance surface and then identifies and maps the least-cost paths between areas based on these values (McRae and Kavanagh 2011). Linkage Mapper allows limiting corridor identification based on a maximum geographic distance or a cost-weighted distance, among other criteria, to include species dispersal in the modeling. We chose not to limit and overestimate corridor mapping, as our goal is not to map dispersal pathways but to identify areas that may promote connectivity within the Tandilia mountains. We generated an integrated corridor map for all carnivore species from the five individual corridor models. To do this, we reclassified the values of each species model into deciles ranging from 1 to 10 and then summed them. We obtained an integrated corridor map with values ranging from 5 (low quality and higher travel costs for all the species together) to 50 (higher quality and lower travel costs) (Belote et al. 2016).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.3.3. Centrality Analysis\u003c/h3\u003e\n\u003cp\u003eWe estimated the centrality values for each species model. One way to analyze landscape connectivity is to assume that the landscape acts as an electrical circuit. Since a circuit\u0026apos;s connectivity increases with the number of connections, distance metrics based on electrical connectivity apply to processes that respond positively to increased connections and redundancy. The relationship between current, voltage, and resistance with random walks in circuits makes it possible to link this approach to movement ecology, providing concrete ecological interpretations of parameters and predictions from circuit theory \u0026nbsp;(McRae et al. 2008). Under this approach, the Centrality Mapper tool within Linkage Mapper \u0026nbsp;(McRae 2012b), calculates the \u0026quot;current flow centrality\u0026quot; through a network of corridors and nodes (patches). The centrality values calculated for network components indicate how important a link or node is to maintaining overall connectivity.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe compile an occurrence database (Supplementary Information 2) consisting of 391 records for \u003cem\u003eL. gymnocercus\u003c/em\u003e, 140 for \u003cem\u003eL. geoffroyi\u003c/em\u003e, 132 for \u003cem\u003eC. chinga\u003c/em\u003e, 30 for \u003cem\u003eP. concolor\u003c/em\u003e, and 95 for \u003cem\u003eG. cuja\u003c/em\u003e. After spatial filtering, 162, 77, 66, 15, and 63 records remained, respectively, and these were used to generate the climatic niche models. As a result of the PCA performed with the bioclimatic variables, we constructed four raster layers corresponding to the first four principal components that accumulated more than 90% of the original variation of the dataset for modeling in MaxEnt.\u003c/p\u003e \u003cp\u003eBy modeling climatic ecological niches, we obtained 930 candidate niche models (186 for each species). For each species, we selected the best model, considering statistical significance, a low omission rate, and the Delta AICc (Supplementary Information 1, Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eWe obtained habitat suitability models for the Pampas ecoregion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The values obtained in each model indicated that the southeast region for the Pampas ecoregion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) contains good habitat suitability for the five studied carnivore species. We elaborated five resistance surfaces from each suitability model for the Tandilia mountain system, corresponding to each species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In resistance surfaces, we observed areas with high resistance values in the intermountain areas and patches with low resistance values.\u003c/p\u003e \u003cp\u003eThe selection of areas to connect resulted in 46 remnants of highland grassland covering approximately 81,962 ha of grassland surface, meaning that we worked with 68% of the total area of grassland that persists in the Tandilia mountain system.\u003c/p\u003e \u003cp\u003eFrom the connectivity analysis, we obtained a cost surface showing all corridor alternatives for Tandilia mountains and the least-cost paths (LCP) for all five species: 125 paths for \u003cem\u003eL. gymnocercus\u003c/em\u003e, 122 paths for \u003cem\u003eL. geoffroyi\u003c/em\u003e, 125 paths for \u003cem\u003eC. chinga\u003c/em\u003e, 127 paths for \u003cem\u003eG. cuja\u003c/em\u003e, and 111 paths for \u003cem\u003eP. concolor\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The sum of the corridors resulted in a cost surface considering all five species at once (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The centrality analysis allowed us to identify those grassland remnants in the Tandilia mountain system that contributed most to the overall landscape connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and those LCPs that were most valuable for maintaining high connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the habitat suitability models obtained for the Pampas ecoregion, extensive areas, mainly located towards the southeast region, which contain good habitat suitability for the five studied carnivore species, can be found. These areas with positive values correspond to the so-called Southern Pampas and the Depressed Pampas within what is generally known as the Pampas (Bilenca and Mi\u0026ntilde;arro \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In both regions, the high values of habitat suitability observed could be associated with the persistence of natural or semi-natural grasslands, which have not been replaced by other uses, such as crops, given their soil conditions. On the one hand, the low, flood-prone soils of the Flooding Pampas, and on the other, the rocky soils of the Tandilia and Ventania mountain system in the Southern Pampas have limited the agriculturalization process in these regions (Bilenca and Mi\u0026ntilde;arro \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bilenca et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, it is essential to understand that despite persisting, these grasslands, like all or the vast majority of Pampean habitats, are subject to the pressures and modifications humans have exerted throughout the region in the last century (Bilenca et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the particular case of the Tandilia system, natural grasslands were replaced by crops, pastures, and forestations, mainly within inter-mountain and lowland areas (Bilenca and Mi\u0026ntilde;arro \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bilenca et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Herrera et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taking this into account and observing in detail the northwest-southeast diagonal that comprises the mountain system of Tandilia, the habitat suitability values are low, except for the areas that include the hills and cerrilladas, which are distributed along the system as small patches. These patches include relict mountain grasslands that, despite being subject to a certain degree of grazing, still conserve much of the structure and composition of the native grasslands (Bilenca and Mi\u0026ntilde;arro \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Valicenti et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Herrera et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which would explain the excellent habitat suitability values found in them. We can also observe this in the resistance surfaces generated for the Tandilia mountain system, where we observe the areas with the lowest resistance associated with the patches of highland grasslands and various edges of roads and streams. Another region where something similar to the Tandilia mountain system occurs is the Ventania mountain system. Here, we can observe areas with good habitat suitability associated with the areas that comprise the mountain system surrounded by extensive areas with low habitat quality. The habitat suitability of these mountain areas can be attributed to the relict grasslands present there. The outcomes of habitat suitability assessment in the Pampas ecoregion enable us to pinpoint areas for future research on a broader regional scale.\u003c/p\u003e \u003cp\u003eWe were able to generate a multi-species corridor map for the Tandilia system. Several authors agree that generating multi-species corridors is essential when planning strategies that promote landscape connectivity (Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ersoy et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); in this way, it is guaranteed that the corridors not only function as areas that favor the movement of focal species but also allow the flow of the ecosystem processes that accompany them. In addition, the design of multi-species corridors can be thought of in terms of umbrella species, which have ample space and resource requirements, such as carnivores, and by protecting them or designing corridors for them, other species are indirectly favored (Caro \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Brodie et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that when seeking to generate multi-species corridors, selecting species whose ecological requirements are similar increases their efficiency and reduces costs compared to corridors designed for different ecological groups. In our case, being able to identify common corridors for carnivore species gives us not only information on valuable areas for their movement and dispersal but also allows us to identify priority sites where we can focus specific conservation actions for these particular species, such as mitigation of the conflict with livestock producers, given that in the Pampas ecoregion, species such as the puma and the gray fox are considered harmful to livestock activities (Caruso et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and in the Tandilia system the situation is similar (Aranguren in prep.). Despite the advantages of thinking about multi-species corridors designed for carnivores, we must not ignore the fact that many of these are habitat generalists, with good plasticity and tolerance to degraded environments (Buskirk and Gittleman \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), therefore generating corridors designed only for them can lead to a \u0026ldquo;negative umbrella effect\u0026rdquo; affecting those secondary, specialist species with more limited environmental requirements, which are expected to be protected under the umbrella (Beier et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe observed that some main corridors (those that coincide with the lowest-cost roads) run either entirely or in some sections along paved roads or routes, such as in the case of a corridor accompanying National Route 226. These results could be attributed to broad shoulders and abundant vegetation, coupled with the fact that roads offer the shortest distance between two areas. In landscapes dominated by agricultural matrix, road edges or shoulders can serve as habitat for species, both for plant communities and for arthropods and small mammals, including some carnivores (Tikka et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ruiz-Capillas et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rotholz and Mandelik \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Carmona et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Depending on their characteristics, road edges, and shoulders can also act as corridors that allow for the dispersal of some species between patches of natural habitat (Eversham and Telfer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Vermeulen and Opdam \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Le Viol et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Redon (de) et al. 2015; Galantinho et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Considering this and the results obtained, some of the shoulders of the roads that run through the Tandilia system could act as corridors for the studied species, with many of them being associated with roads in various sections (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNonetheless, the disturbances generated by these routes usually awaken behavioral changes in species, such as avoidance behaviors (McClure et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Navarro-Castilla et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Grilo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which lead carnivore species not to take advantage of these environments as runners. However, the proximity to the routes exposes the carnivores to risk of being struck by vehicles. Death due to being run over on roads is one of the threats that can compromise local populations (Schwab and Zandbergen \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gregory et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This forces us to reevaluate these environments as potential corridors and invest in mitigation or management efforts. In any case, since they can act as available habitats and corridors for other groups of species in a landscape as intervened and fragmented as the Pampas, it is crucial to aim for adequate management of them, such as implementing regulations that regulate agricultural activities on shoulders (e.g., Rimoldi and Chimento, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a common practice in the Pampas region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe centrality analysis of the grassland relicts allowed us to identify that the patches that contribute most to the general connectivity are located in the central zone of the system (area of the city of Tandil). Taking into account the spatial configuration of the mountain range on a diagonal and that centrality reflects the contribution of the patches to the general connectivity of the network (McRae et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), these central habitat patches would be acting as a bridge between those that are distributed at the NW and SE ends of the system. On the other hand, we can also find numerous patches of various sizes in this central region, which have many connections to nearby patches. For Jaimes et al. (2019), this central area of the mountain system counts as an important landscape unit to conserve, given that mountainous environments predominate, present as large patches, and the anthropic elements of a matrix are few. On the other hand, their results also highlight the importance of other groups of patches within the system, such as some in the Balcarce and Mar del Plata areas. In our analysis, these groups of patches presented average centrality values. That is, they contribute less to the general connectivity of the system than others. However, these patches that stand out in the study by Jaimes et al. (2019) make up groups of large patches, with smaller patches as satellites, which can be of great value at a local scale within the mountain system. Although the analysis approaches in both works differed, both represent sufficient evidence to conserve these areas with important average centrality values.\u003c/p\u003e \u003cp\u003eFrom the centrality values and the mapped corridors, we can identify priority corridors and areas to maintain the connectivity of the focal species. Looking at the mapped corridors, we can find several smaller pass-over natural grassland patches that were not included in the analysis. These results could be due to small habitat patches that act as stepping stones between larger patches. Herrera et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that small habitat patches for the Tandilia system function as stepping stones, particularly to the connectivity of species that move long distances. Considering this evidence, searching for conservation and management strategies for these areas is advisable.\u003c/p\u003e \u003cp\u003eAlso, we identified several corridors that maintain a considerable width along their route and connect valuable areas for landscape connectivity (greater centrality), which we could consider a priority in the mountain system. However, some of these corridors are interrupted by barriers such as paved routes, which could be an important barrier to the movement of species, not only because of the risk of mortality but because some of them avoid them, thus promoting the fragmentation of their populations (Grilo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, it would be necessary to mitigate these effects by resorting to underground passageways or sewers, which have proven helpful in facilitating the flow of small and medium-sized carnivores through the routes (Grilo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, our study shows us that despite the Pampas ecoregion's history of transforming and replacing grasslands, there are still areas with good environmental suitability for carnivore species. Mountain systems would be essential conservation sites towards the south of the region. We also considered that several priority areas to conserve connectivity within the Tandilia system for the carnivore species studied, as well as corridors that could keep them connected, exist. A study like this in the region can be used as a frame of reference for future studies or the implementation of conservation actions in the area that favor the use of space by carnivore species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to thank field assistants and collaborators (Carmela Marin, Manuela Santiago, Celena Sarasola, Estefania Paz, Maximiliano Calcagno, Ailen Chuchuy, M. Eva Cabanellas, Claudio Santiago, Estefania Marisol Avalo, Felisa La Pescadora) for their kind assistance and support during fieldwork activities with trap cameras. We also appreciate the collaboration of owners and managers of highland grassland remnants for allowing us to visit their properties: Federico Juana (Estancia Las Mercedes), Paulo Mosca (Estancia Nilonil), Reina Feldman (Estancia Sanmalucon), Mario Bustillo (Estancia La Asunci\u0026oacute;n), Alfonso (Valle de Los Ciervos), Emilio Milanessi (Estancia Chapaleofu), Manuel Castelar (La Argentina), Raul Eyheramendy (Sierra Alta) and Tomas Pérez Marino (Estancia El Bonete). We thank Claudio Barletta (Reserva Natural Sierra del Tigre) for his attention and collaboration. Also, \u0026nbsp;we would like to thank Estefan\u0026iacute;a Paz for her support and contributions during the development of this work. We want to thank the assistance of the Scouts de la Ciencia program for their cooperation in fieldwork with camera traps. Also, we would like to thank M. Ignacio Simoy, Santiago Linares, and David Vera for their collaboration with the analysis in R and GIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially supported by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET PIP. 1220150100598CO), Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT, PICT 2015-2281), Neotropical Grassland Conservancy (Student Grant Program 2021), IDEA WILD (IDEA WILD Equipment Support 2021). M.F. Aranguren, M.G. Pizzarello, V. Leber, D. Franzoia Moss, and J. Dopazo were supported by fellowships from the Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET). C. Trofino-Falasco is a support technician of Comisi\u0026oacute;n de Investigaciones Cientificas de la Provincia de Buenos Aires (CICPBA), V. Simoy, M.A. Velasco, and I. Berkunsky are CONICET Research Fellows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026apos;s conception and design. M. Florencia Aranguren and M. Veronica Simoy performed material preparation, data collection, and analysis. Clara Trofino Falasco and Gimena Pizzarello collaborated on the camera trap data collection. M. Florencia Aranguren wrote the manuscript\u0026apos;s first draft, and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA part of the data set of occurrences filtered and generated during the current study is available in the GBIF repository: https://doi.org/10.15468/dd.p8neyx. The other part corresponds to camera trap data, available in Supplementary Information 2.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdriaensen F, Chardon JP, De Blust G, et al (2003) The application of \u0026lsquo;least-cost\u0026rsquo; modelling as a functional landscape model. Landsc Urban Plan 64:233\u0026ndash;247. https://doi.org/10.1016/S0169-2046(02)00242-6 \u003c/li\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. 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United States Department of Agriculture, Forest Service, Rocky Mountain Research Station.\u003c/li\u003e\n\u003cli\u003eYa\u0026ntilde;ez C, Mart\u0026iacute;n G, Osorio-Olvera L, Escobar-Luj\u0026aacute;n J, Casta\u0026ntilde;o-Quintero S, Chiappa-Carrara X, and Mart\u0026iacute;nez-Meyer E (2020) \u0026ldquo;The Abundant Niche-Centroid Hypothesis: Key Points About Unfilled Niches and the Potential Use of Supraspecfic Modeling Units\u0026rdquo;. Biodivers Inform 15 (3): 92-102. https://doi.org/10.17161/bi.v15i2.13218. \u003c/li\u003e\n\u003cli\u003eYuan R, Zhang N, \u0026amp; Zhang Q (2024). The impact of habitat loss and fragmentation on biodiversity in global protected areas. Science of The Total Environment, 931, 173004. https://doi.org/10.1016/j.scitotenv.2024.173004 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Connectivity Analysis, grasslands, carnivores, corridor design, Pampas Grassland","lastPublishedDoi":"10.21203/rs.3.rs-4462760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4462760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHabitat loss and fragmentation threaten biodiversity, particularly for carnivores whose dispersion and population viability are compromised by reduced available habitat and anthropic elements in the landscape, such as roads and crops. In Argentina, the Pampas grasslands have experienced considerable degradation and replacement by crops and are currently limited to natural patches scattered throughout the region. In the Tandilia mountains, these grassland remnants persist as crucial refuges for the species that inhabit them. Our objectives were to identify and map priority sites and areas that can act as ecological corridors between grassland patches to contribute to the connectivity knowledge of the Tandilia mountain system. We performed connectivity analyses using Least-cost Path Models and Circuit Theory. To do this, we generated habitat suitability models by combining environmental and anthropic variables, from which resistance surfaces were generated. We highlight areas of high habitat suitability for carnivores in the Pampas region, with particular emphasis on the mountain systems of Tandilia. We identified potential corridors and least-cost paths for five carnivore species, obtaining multi-species corridors highlighting the importance of landscape connectivity to maintain healthy populations. Centrality analyses revealed crucial grassland remnants and valuable corridors. These findings address the challenges associated with habitat fragmentation in the Pampas region and provide guidelines for future research and carnivore conservation actions.\u003c/p\u003e","manuscriptTitle":"Between Grasslands and Mountains: Identification of valuable areas for landscape connectivity in the Tandilia Mountain System for carnivore species.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 04:28:20","doi":"10.21203/rs.3.rs-4462760/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c2bd8a7-f187-43a0-9215-e0ebd415849e","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-09T05:42:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 04:28:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4462760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4462760","identity":"rs-4462760","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}